Brine-dependent recovery process has seen much global research efforts in the past two decades because of their benefits over other oil recovery methods. The process involves the tweaking of the ionic composition and strength of the injected water to improve oil production. In recent years, several studies ranging from laboratory coreflood experiments by many researchers to field trials by several companies admit to the potential of recovering additional oil in sandstone and carbonate reservoirs. Sandstone and carbonate rocks are composed of completely different minerals, with varying degree of complexity and heterogeneity, but wettability alteration has been widely considered as the consequence rather than the cause of brine-dependent recovery. However, there is no consensus on the cause as several mechanisms have been proposed to relate the wettability changes to the improved recovery. This review paper aims to provide a state-of-the-art development in published research and various efforts of the industry. This review outlines an overview of laboratory and field observations, descriptions of underlying mechanisms and their validity, the complexity of the oil-brine-rock interactions, modelling works, and comparison between sandstone and carbonate rocks. The provided information is intended to provide the reader with up-to-date information, point to relevant studies for those who are new and those implementing either laboratory- or field-scale projects to speed up the process of further investigations in this research area. Overall, the outcome of this review would potentially be of immense benefit to the oil industry.

Prediabetes is a significant risk factor for the development of Type 2 diabetes, cardiovascular diseases, chronic kidney disease, and other complications. Early diagnosis of prediabetes, coupled with education on lifestyle changes that support blood glucose management, are crucial for the prevention or delay of type 2 diabetes and related complications. This study aimed to examine the effects of individualized nutrition therapy (INT) combined with continuous glucose monitoring (CGM) on blood glucose control in individuals with prediabetes who are overweight or obesese. Participants (mean age ± SD: 55 ± 6 years; BMI: 31.1 ± 4.1 kg/m²) were randomly assigned to either the treatment group (n = 15) or the control group (n = 15). Both groups received INT and CGM, but the control group was blinded to the CGM data until the end of the study. Participants were followed for 30 days and visited the lab every 10 days for CGM replacement, study measurements, and dietary consultations. The treatment group showed a significant increase in the percent of time in the target blood glucose range (p=0.02) and a significant decrease in mean blood glucose concentration (p<0.05), glucose management indicator (p=0.02), percent coefficient of variation for blood glucose (p=0.01), and the percent time spent in the high or very high blood glucose ranges (p= 0.04). These changes were not statistically significant for the control group. We conclude that adding CGM feedback to INT resulted in better management of blood glucose levels in overweight or obese individuals with prediabetes.

We investigate some information properties of consecutive k-out-of-n:G systems in light of fractional generalized cumulative residual entropy. We firstly derive a formula to compute fractional generalized cumulative residual entropy related to the system's lifetime and explore its preservation properties in terms of established stochastic orders. Additionally, we obtain useful bounds. To aid practical applications, we propose two non parametric estimators for the fractional generalized cumulative residual entropy in these systems. The efficiency and performance of these estimators are illustrated using simulated and real datasets.

The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or Volunteered Geographic Information (VGI). Key challenges with such ‘opportunistic’ data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area, or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results already provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.

Precipitation patterns are critical for understanding the hydrological and climatological dynamics of any region. Sicily, the largest island of the Mediterranean sea, with its diverse topography and climatic conditions, serves as an ideal case study for analyzing precipitation data to gain insights into regional water resources, agricultural productivity, and climate change impacts. This paper employs advanced statistical physics methods, particularly Tsallis q-statistics, to analyze sub-hourly precipitation data from 2002 to 2023, provided by the Sicilian Agrometeorological Informative System (SIAS). We investigate several critical variables related to rainfall events, including duration, depth, maximum record, and inter-event time. The study spans two decades (2002-2012 and 2013-2023), analyzing the distributions of relevant variables. Additionally, we examine the simple returns of these variables to identify significant temporal changes, fitting these returns with q-Gaussian distributions. Our findings reveal the scale-invariant nature of precipitation events, the presence of long-range interactions and memory effects, characteristic of complex environmental processes.

A series of novel substituted isoxazolidine derivatives was synthesized through 1,3-dipolar cycloaddition reactions of α-aryl-N-methyl nitrones with various ethylene moieties, resulting in the formation of isoxazolidine derivatives. The structures of the synthesized compounds were characterized using spectroscopic methods. The [3+2] cycloaddition (32CA) reactions of α-aryl-N-methyl nitrones with diverse ethylene substituents were computed employing Density Functional Theory (DFT) at the B3LYP/6–31G(d) level, upgrading bond parameters. An investigation of the electron localization function (ELF) unveiled the zwitterionic nature of α-aryl-N-methyl nitrones, devoid of pseudoradical or carbenoid centers. Utilization of CDFT indices enabled the prediction of global electronic flux from the strongly nucleophilic α-aryl-N-methyl nitrones to the varied electrophilic ethylene functionalities. Molecular docking and molecular dynamics simulations were conducted on the isoxazolidine derivatives against the EGFR receptor (PDB: 4ZAU). In silico assessment of the synthesized isoxazolidines involved molecular docking, wherein the compound with the highest docking score was subjected to molecular dynamics simulations. Compound 11a, out of the thirty isoxazolidine derivatives, displayed the most substantial binding energy, yielding a g score of -6.57 kcal/mol. These observations highlighted the functional role of amide NH2 and NO2 in the binding process with the target protein, influencing its activity. Furthermore, prediction of ADME drug-likeness properties was performed to identify a suitable candidate.

Background The coronavirus pandemic has led to the creation of clinical guidelines by a large number of professional medical communities. However, the quality and methodology of development of Russian clinical guidelines has been little studied. The continued relevance of studying the use of DOACs in patients with COVID-19 was the basis for conducting this study. Aim The objective of this study was to assess DOACs consumption and expenditures in Russian Federation during COVID-19 pandemic and to analyse domestic evidence base for the use of DOACs in COVID-19 patients through identifying all publicly available Russian-produced CPGs for the treatment of COVID-19 and assessing their quality as the source of recommendations for the use of oral anticoagulants for the prevention of thrombotic complications in COVID-19 patients. Methods We searched Russian databases for CPGs, published between 2020 and 2023. We identified 7 relevant documents that met our inclusion criteria. Three authors analyzed Russian clinical guidelines using a AGREE II questionnaire. We calculated DOACs DDD consumption according to Russian clinical guidelines and DDD consumption in patients with COVID-19 for the period 2020-2022. Results 7 clinical CPGs were analyzed with the AGREE II tool, it was revealed that experts gave the highest scores for the sections scope and purpose (from 62.98% to 100%), clarity of presentation (from 96.30% to 100%). The lowest scores were given for the sections stakenholder involment (33.33% to 64.81%), rigour of development (from 0% to 49.31%), applicability (from 23.61% to 50%), editory independence ( from 0% to 50%). When comparing the total score, it was found that two clinical guidelines received the highest scores - ROPNIZ (Livzan), ROPNIZ (Drapkina). The minimum score is registered with the NIIOZMM (Khripun) clinical guideline. No guideline received a total score of more than 70%. According to clinical recommendations, the consumption of apixaban and rivaroxaban is 15 DDD (30-day course of therapy), 22.5 DDD (45-day course of therapy). Consumption of apixaban in the Russian Federation in 2020 and 2021 corresponds to the indicators presented in clinical recommendations (in 2020 – 26.59 DDD per patient with COVID-19; in 2021 – 15.75 DDD per patient with COVID-19), and in 2022 – 10.67 DDD, which is below the recommended values. In 2020, consumption of rivaroxaban in the Russian Federation was 26.59 which corresponds to data from clinical recommendations; in 2021, consumption decreased to 7.87 DDD; in 2022 – 5.48 DDD, which is 2.74 times less than recommended.

Plant and animal agriculture is a part of a larger system where the environment, soil, water, nutrient management interact. Biochar (a pyrolyzed biomass) has been shown to affect the single components of this complex system positively. Biochar is a soil amendment, which has been documented for its benefits as soil enhancer particularly to increase soil carbon, improve soil fertility, and better nutrient retention. These effects have been documented in the literature. Still, there is a need for a broader examination of these single components and effects that aims at the complementarity and synergy attainable with biochar and the animal and crop production system. Thus, we report a comprehensive dataset documenting the interactions of biochar with manure, soil, and plants. We evaluated three biochars mixed with manure alongside both manure and soil controls for improvement in soil quality, reduction in nutrient movement, and increase in plant nutrient availability. We explain the experiments and the dataset which contains the physicochemical properties of each biochar-manure mixture, the physicochemical properties of soil amended with each biochar-manure mixture, and the biomass and nutrient information of plants grown in biochar-manure mixture amended soil. This dataset is useful for continued research examining both the short and long-term effects of biochar-manure mixtures on both plant and soil systems. In addition, these data will be beneficial to extent the findings to field settings for practical and realized gains.

While microcredits are, by the development practitioners, repeatedly hailed as a useful measure of development policy, this empirical research in Bangladesh arrives at different results: the governing and disciplinary measures are necessary to trigger the best possible repayment success. Based on interviews with the microfinance beneficiaries, loan officers, and microfinance experts in Bangladesh, I chart some of the most frequently used governing and disciplinary techniques including fictitious proximity of loan officers, their fields of visibility and surveillance processes. The “success” of credit delivery is a neoliberal stance that ensures the ceaseless debt repayment process using the intentional and planned conduct, capitalizing social capital and credit honour reputation, surveillance over business enterprises, use of equivocal language and different forms of deception. The development strategy through microfinance in Bangladesh is a development “dispositif” with a changing shift in its declared social missions.

There is consensus that climate variability and change is impacting food security in Eastern Africa, and that conventional extension approaches, based on top-down model of information dissemination and technology transfer, are too inadequate to help smallholder farmers tackle increasingly complex agro-climatic adversities. Innovative service delivery options exist but are mostly operated in silos with little effort to explore and blend them. There are efforts to develop a blended Climate-Resilient Farmers Field School methodology to address the gaps, with objective to improve participants’ knowledge, skills and attitude to apply the blended approach and to sensitize actors on what needs to be advocated at the policy level. Some 661 local trainers/facilitators (ToT/ToFs), 32% of them women and 54% youth, were trained across Kenya, Tanzania, and Uganda, with additional 76 Master Trainers (MToTs) trained to backstop the ToT/ToFs. Through the implementation, the process reached 36 agribusinesses covering some 237,250 smallholder farmers trained across Kenya, Tanzania, and Uganda on CSA technologies, practices, and innovations by the end of 2020. The blended approach offers lessons to transform extension to help farmers improve food security and resilience. Preliminary findings indicate that the process is rapidly shaping individual adaptive behavior and group adaptive thinking. Lessons also show a strong need for agronomists to work more closely with agro-meteorologists to ensure that farmers are properly guided to participate appropriately in the co-generation and application of climate information and agro-weather advisories, which they can interpret easily and utilize for their agricultural production purposes. Experience from this initiative can be leveraged to develop scalable participatory extension and training models

The Vaal Dam catchment, which is the source of potable water for Gauteng province, holds various human activities including mining, urbanization, agricultural and industrial activities coupled with high fluxes of nutrients from different areas within the catchment. This situation led to an enhancement of Harmful Algal Blooms (HABs) in the dam reservoir. Phosphorus and Nitrogen are the limiting nutrients for HABs in many eutrophic aquatic systems when their concentrations are raised in such waters. In this study, multi-time-series plot types and scutter plots were analysed to reveal the relationship between HABs proxy chlorophyll-a (Chl-a) and the potential nutrients. Also (Chl-a) data extracted from landsat-8 satellite images were visualised to show the spatial distribution of HABs in the reservoir. The results showed that the Vaal Dam HABs productivity is a function of the total phosphorus (TP) and the organic nitrogen (KJEL_N) levels, which were positively correlated with chlorophyll-a (Chl-a) concentration. The long-term analysis of Chl-a in-situ data (1986 – 2022) measured at one point within the reservoir showed an average of 11.25μg/l but at some stochastic dates, the Chl-a concentration jumped to very high values reaching the maximum value of 452.8μg/l. Such high values are associated with high records of TP and KJEL_N concentrations at the same dates which clearly reveals their effect on the productivity levels. The decadal time series and its trend analysis showed that the average productivity level of Chl-a increased during the studded period from 4.75μg/l in the first decade (1990 – 2000) to 10.51μg/l in the second decade (2000 – 2010) reaching 16.7 during the last decade (2010 – 2020). The increasing averages of the decadal values are associated with the increasing decadal average of its driving factors, TP from 0.1043 to 0.1096 to 0.1119mg/l for the three decades, respectively and KJEL_N from 0.80mg/l in the first decade to 1.14mg/l in the last decade. The decadal average of temperature showed no significant increase, 17.9 °C in the first decade and 18 °C in the last decade which suggests that temperature is not the only enhancing factor of the HABs productivity in the Vaal Dam. The satellite data analysis during the last decade revealed that the HABs spatial dynamics is a function of the dam geometry and the levels of the discharges from its two feeding rivers. Higher concentrations were observed where the reservoir is meandering and within the areas of restricted water circulation.

Biliary tract cancers (BTCs) are aggressive in nature, often presenting asymptomatically until they are diagnosed at an advanced stage. Surgical resection or liver transplantation are potential curative options. However, a large proportion of patients present with incurable locally advanced or metastatic disease and most of these patients are only eligible for palliative chemotherapy or best supportive care. More recently, targeted therapies have proven beneficial in a molecularly selected subgroup of patients with cholangiocarcinoma who have progressed on previous lines of systemic treatment. However, only a minority of patients with BTCs whose tumours harbour specific molecular alterations can access these therapies. Antibody drug conjugates (ADCs) are novel anti-cancer therapies designed to minimise challenges of systemic drug distribution and are currently being investigated in multiple types of malignancies, after failure of standard therapeutic options. This review outlines the mechanism of action of ADCs and the current research in this field in various cancer types, including BTCs.

Coastal wave modeling and forecasting are essential in oceanography, sustainable marine energy, and ocean engineering. Precise forecasting of wave speed and direction are crucial for offshore operations, marine energy, risk management, environmental management, coastal and sustainable maritime management. The coastline of Brunei Darussalam can generate between 15 and 126 Giga Watt of wave energy. In this experimental research, we used two numerical approaches, the finite difference, and spectral element methods, to model and simulate wave speed and direction. We calculated the mean error between numerical and analytical solutions. We proposed a novel, promising, univariate time series forecasting model by combining the Long Short-Term (LSTM) with KerasTuner hyperparameters tuning and optimization techniques. This method helps us to improve the accuracy and efficiency of time-series forecasting.. The experimental data was computed from high-precision Acoustic Doppler Current Profiler (ADCP) sensor data. This research is part of the preliminary feasibility analysis of wave energy production in Brunei Darussalam and net zero commitment for a sustainable environment. Seven independent forecast experiments were performed for wave speed and direction in degree and radian units for 1, 3, 6, 8, 10, 12, and 24 hours. Mean squared error (MSE) was adopted as a metric for both training and testing. The experimental results reveal that the wave speed forecast has the lowest MSE compared to direction, regardless of the unit of measure, but has a longer duration. In addition, the direction forecast in the degree unit has the lowest errors compared to the unit of radians; the latter has a longer running time than the former. The model has delivered optimal results throughout the experiments with minor training and test errors. We conducted a thorough evaluations on two benchmark time series datasets, which include the study dataset and air quality index dataset, to validate the performance of the proposed model with other models. The proposed model outperforms cutting-edge forecasting models, such as the conventional LSTM, ARIMA, and Prophet. The model has the slightest forecast error compared to the existing literature’s result.

Low-cost monitors make it possible now for the first time to collect long-term (months to years) measurements of potential indoor exposure to fine particles. Indoor exposure is due to two sources: particles infiltrating from outdoors and those generated by indoor activities. Calculating the relative contribution of each source requires identifying an infiltration factor. We develop a method of identifying periods when the infiltration factor is not constant, and searching for periods when it is relatively constant. From an initial regression of indoor on outdoor particle concentrations, a Forbidden Zone can be defined with an upper boundary below which no observations should appear. If many observations appear in the Forbidden Zone, they falsify the assumption of a single constant infiltration factor. This is a useful quality assurance feature, since investigators may then search for subsets of the data in which few observations appear in the Forbidden Zone. The usefulness of this approach is illustrated using examples drawn from the PurpleAir network of optical particle monitors. An improved algorithm is applied with reduced bias, improved precision, and a lower limit of detection than either of the two proprietary algorithms offered by the manufacturer of the sensors used in PurpleAir monitors.

Introduction: COVID-19 is an unprecedented global public health emergency currently impacting India in an unprecedented manner. Aim of this study was to assess knowledge, awareness, perceptions and related factors of Indian dentists on COVID-19. Methodology: A cross-sectional, on-line questionnaire-based study was conducted over one week between 3rd May, 2020 to 10th May, 2020. The sample comprised 403 Indian dentists in solo, group practices and in the academic arena. The self-administered questionnaire assessed 1) knowledge/awareness on factors related to COVID-19 patient identification and symptomatology, 2) knowledge/awareness of COVID-19 transmission and 3) perceptions of COVID-19 history taking procedure. Statistical analyses were conducted using Statistical Package for Social Sciences for Windows, version 21.0 (IBM Corp., Armonk, NY., USA). Frequency distributions and logistic regression analyses were used. Results: Indian dentists demonstrated an overall modest level of knowledge on identification of patients with COVID-19. Moreover, they had a high level of awareness of the COVID-19 transmission means, and the generally accepted procedural perceptions on patient history taking. However, there were some gaps in specific aspects of knowledge and perceptions. Those who were aged ≥ 30-years had a significantly higher level of knowledge of patient identification means than those who were < 30-years (OR=1.78:1.12-2.83); p=0.01. Moreover, specialized dentists were significantly knowledgeable of COVID-19 transmission means than the general dentists (OR=1.89:1.22-2.93; p=0.004). Conclusions: Our findings demonstrate identifiable gaps in knowledge/awareness and perceptions of COVID-19 in Indian dental professionals. These gaps should be fulfilled, at the earliest, due to the rising burden of COVID-19 in India, to ensure safe dental care delivery.

The intravenous cocaine self-administration model is widely used to characterize the neurobiology of cocaine seeking. When studies are aimed at understanding relapse to cocaine-seeking, a post-cocaine abstinence period is imposed, followed by “relapse” tests to assess the ability of drug-related stimuli (“primes”) to evoke the resumption of the instrumental response previously made to obtain cocaine. Here we review this literature on the impact of post-cocaine abstinence procedures on neurobiology, finding that the prelimbic and infralimbic regions of the prefrontal cortex are recruited by extinction training, and are not part of the relapse circuitry when extinction training does not occur. Pairing cocaine infusions with discrete cues recruits the involvement of the NA which, together with the dorsal striatum, is a key part of the relapse circuit regardless of abstinence procedures. Differences in molecular adaptations in the NA core include increased expression of GluN1 and glutamate receptor signaling partners after extinction training. AMPA receptors and glutamate transporters are similarly affected by abstinence and extinction. Glutamate receptor antagonists show efficacy at reducing relapse following extinction and abstinence, with a modest increase in efficacy of compounds which restore glutamate homeostasis after extinction training. Imaging studies in humans reveal cocaine-induced adaptations that are similar to those produced after extinction training. Thus, while instrumental extinction training does not have face validity, its use does not produce adaptations distinct from human cocaine users.

Flexible electrochemical sensors can adhere to any bendable surface with conformal contact, enabling continuous data monitoring without compromising the surface's dynamics. Among various materials that have been explored for flexible electronics, metal-organic frameworks (MOFs) exhibit dynamic responses to physical and chemical signals, offering new opportunities for flexible electrochemical sensing technologies. This review aims to explore the role of electrocatalysis in MOF films specifically designed for flexible electrochemical sensing applications, with a focus on their design, fabrication techniques, and applications. We systematically categorize the design and fabrication techniques used in preparing MOF films, including in-situ growth, layer-by-layer assembly, and polymer-assisted strategies. The implications of MOF-based flexible electrochemical sensors are examined in the context of wearable devices, environmental monitoring, and healthcare diagnostics. Future research is anticipated to shift from traditional microcrystalline powder synthesis to MOF thin-film deposition, which is expected to not only enhance the performance of MOFs in flexible electronics but also improve sensing efficiency and reliability, paving the way for more robust and versatile sensor technologies.

Endurance triathlons impose substantial physiological stress, yet effects of short-course formats remain relatively unexplored. In this preliminary study, we assessed proteinuria and hydration levels in 27 well-trained triathletes (41.9 [7.4] years) who completed a sprint triathlon consisting of a 1500-m swim, 26-km cycle, and 8-km run. Urine samples were collected before and after the race. The results showed an increase in cases of post-race proteinuria from four to nine (p=0.03). Also, one case of urobilinuria and ketoacidosis was observed after the race. Pre-race glucosuria, which was present in nine cases, decreased to three cases post-race. Although hematuria cases decreased from six to two (p=0.13), pre-race leukocyturia (found in two cases) resolved post-race. There was a significant increase in urine specific gravity (from 1.018 to 1.023, p=0.03), indicating dehydration. In conclusion, short-course triathlons were found to induce post-race proteinuria, urobilinuria, and dehydration without exacerbating pre-race glucosuria, hematuria, or leukocyturia. These preliminary findings contribute to our understanding of medical guidelines for sprint triathlons, an area of endurance sports that has received limited research attention. Further investigation, both mechanistic and longitudinal, is warranted in this regard.

The article suggests a Value Proposition (VP) framework that enables the analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP development activities. To develop such a framework, we examined existing management research publications to identify and extract assertions that could be used as a source of actionable insights for new growth-oriented companies. The extracted assertions were assembled into a corpus of texts that were subjected to topic modelling analysis – a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modeling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modeling results led to the identification of seven topics: 1) Value created; 2) Stakeholder value propositions; 3) Foreign market entry; 4) Customer base; 5) Continuous improvement; 6) cross-border operations; and 7) Company image. The uniqueness of the adopted topic modeling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e. in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance emerging the four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, Foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the VP development of new growth-oriented companies.

Understanding the structural intricacies of RNA molecules is essential for deciphering numerous biological processes. Traditionally, scientists have relied on experimental methods to gain insights and draw conclusions. However, the recent advent of advanced computational techniques has significantly accelerated and refined the accuracy of research results in several areas. A particularly challenging aspect of RNA analysis is the prediction of its secondary structure, which is crucial for elucidating its functional role in biological systems. This paper deals with the prediction of pseudoknots in RNA, focusing on two types of pseudoknots: K-type and M-type pseudoknots. Pseudoknots are complex RNA formations in which nucleotides in a loop form base pairs with nucleotides outside the loop and thus contribute to essential biological functions. Accurate prediction of these structures is crucial for understanding RNA dynamics and interactions. Building on our previous work in which we developed a framework for the recognition of H- and L-type pseudoknots, an extended grammar-based framework tailored to the prediction of K- and M-type pseudoknots is proposed. This approach uses syntactic pattern recognition techniques and provides a systematic method to identify and characterize these complex RNA structures. Our framework uses context-free grammars (CFGs) to model RNA sequences and predict the occurrence of pseudoknots. By formulating specific grammatical rules for type K and type M pseudoknots, we enable efficient parsing of RNA sequences to recognize potential pseudoknot configurations. This method ensures an exhaustive exploration of possible pseudoknot structures within a reasonable time frame. In addition, the proposed method incorporates essential concepts of biology, such as base pairing optimization and free energy reduction, to improve the accuracy of pseudoknot prediction. These principles are crucial to ensure that the predicted structures are biologically plausible. By embedding these principles into our grammar-based framework, we aim to predict RNA conformations that are both theoretically sound and biologically relevant.

(1) Background: We aim to construct a machine learning (ML) algorithm to predict the risk of distant metastasis (DM) of T1 and T2gallbladder cancer (GBC); (2) Demographic and clinical pathological data of T1 and T2 GBC patients were extracted from the National Institutes of Health (NIH)’s Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 to develop seven ML algorithm models. Models were evaluated based on accuracy, precision, recall rate, F1- score, and area under the receiver operating characteristic curve (AUC); (3) Results:A total of 4371 patients were included in the study, of whom 764 (17.4%) developed DM. Multivariate logistic regression showed that age, histology, tumor size, T and N stages were independent factors in GBC with DM. A novel nomogram was established to predict distant metastasis in early T stage GBC patients. Evaluation indicators of the best model Random Forest (RF) were as follows: accuracy (0.828), recall rate (0.862), precision (0.811), F1- score (0.836), and AUC value (0.913); (4) Con-clusions: The RF model constructed in this study could accurately predict distant metastasis in GBC patients, which may provide clinicians with more personalized clinical decision-making recom-mendations.

(1) Background: Mindfulness-based interventions (MBIs) have shown interesting preliminary ef-fects over self-regulation processes in early adolescence. However, interventions usually integrate various types of practices with no clarity about the specific effect of each of them over attentional, emotional, and behavioral regulation. The objective of this research was to evaluate the effect of two MBIs, one focused on classic attentional practices and another one focused on the recognition and expression of emotions, over attentional, emotional, and behavioral self-regulation in early adolescents. (2) Method: An experimental method was carried out. A sample of 70 children be-tween 8 and 12 years old were randomized and assigned to three experimental conditions: 1) MBI with a focus on attentional practices, 2) MBI with a focus on recognition and expression of emotions, 3) control group, waiting list. The programs lasted 8 weeks, with a weekly 1-hour online synchronic session, plus home practices. Children were evaluated before starting the in-tervention and at the end of the 8-week program. The assessed variables were: 1) Mindfulness; 2) Emotional Regulation; 3) Attentional Regulation and 4) Behavioral regulation. (3) Results: Chil-dren that participated in both programs, increased their mindfulness and emotional and be-havioral regulation scores. Only children who participated in the program with a focus on at-tention showed significant changes in their ability to self-regulate attention. (4) Conclusions: online MBIs, with attention to external and internal stimuli practices, can be a good strategy to strengthen self-regulation skills of attention, emotions, and behavior in early adolescence.

We report a simple and high-performance flexible strain sensor based on liquid metal nanoparticles (LMNPs) on polyimide substrate by laser induced deposition. The LMNPs were prepared by ultrasonic method, and then the femtosecond laser direct writing technology was used to induce the assembly and deposition of the LMNPs to form liquid metal microwires. Laser local sintering enhances the connection between particles, and a wearable strain sensor was fabricated with the high sensitivity as high as 76.18, the good linearity (a correlation coefficient of 0.999) in a wide sensing rage, the fast response/recovery time of 159 ms/120 ms. Attributed to these extraordinary strain sensing performances, the sensor can sense various dynamic strains in real time, monitor both subtle physiological activities and large human motions. It can be adhered to human skin, and well reflect the change of facial expression and realize real-time monitoring of facial expression. And the vocal cord vibration can be detected for speech recognition while the sensor attached to the outside of the throat.

Conducting computational stress-strain analysis using finite element methods (FEM) is a common approach when dealing with the complex geometries of atherosclerosis, which is a leading cause of global mortality and complex cardiovascular disease. The considerable expense linked to FEM analysis encourages the substitution of FEM with a considerably faster data-driven machine learning (ML) approach. This study investigated the potential of end-to-end deep learning tools as a more effective substitute for FEM in predicting stress-strain fields within 2D cross sections of arterial wall. We first proposed a U-Net based fully convolutional neural network (CNN) to predict the von Mises stress and strain distribution based on the spatial arrangement of calcification within arterial wall cross-sections. Further, we developed a conditional generative adversarial network (cGAN) to enhance, particularly from the perceptual perspective, the prediction accuracy of stress and strain field maps for arterial walls with various calcification quantities and spatial configurations. On top of U-Net and cGAN, we also proposed their ensemble approaches to further improve the prediction accuracy of field maps. Our dataset, consisting of input and output images, was generated by implementing boundary conditions and extracting stress-strain field maps. The trained U-Net models can accurately predict von Mises stress and strain fields, with structural similarity index scores (SSIM) of 0.854 and 0.830 and mean squared errors of 0.017 and 0.018 for stress and strain, respectively, on a reserved test set. Meanwhile, the cGAN models in a combination of ensemble and transfer learning techniques demonstrate high accuracy in predicting von Mises stress and strain fields, as evidenced by SSIM scores of 0.890 for stress and 0.803 for strain. Additionally, mean squared errors of 0.008 for stress and 0.017 for strain further support the model's performance on a designated test set. Overall, this study developed a surrogate model for finite element analysis, which can accurately and efficiently predict stress-strain fields of arterial walls regardless of complex geometries and boundary conditions.

Mitochondrial hypochlorite (ClO-) plays important and often contradictory roles in maintaining the redox balance of mitochondria. Abnormal ClO- levels can induce mitochondrial inactivation and further cause cell apoptosis. Herein. we have developed an anthracene carboxyimide-based fluorescent probe mito-ACS for imaging mitochondrial ClO- in living cells. This probe exhibits some distinctive features as excellent resistance to photobleaching, high selectivity and sensitivity, as well as good water solubility. Mito-ACS showed a noticeable fluorescence response toward ClO-with a fast response (within 6 s) and a low detection limit (23 nM). Moreover, the introduction of triphenylphosphonium makes the probe soluble in water and selectively localizes to mitochondria. Furthermore, mito-ACS was successfully applied to image mitochondria ClO- in living cells with low toxicity. Remarkably. the less used fluorophore anthracene carboxyimide exhibiting excellent photostability and desirable optical properties provides a promising application prospect in biological systems.

Real-time fluorescent quantitative PCR (RT-qPCR) is the most classic and widely used technology for evaluating the expression level of target gene. In order to select the proper internal reference genes for RT-qPCR analysis in Amorphophallus konjac (Araceae), eight candidate internal reference genes, including 25S ribosomal RNA gene (25S rRNA), 18S ribosomal RNA gene (18S rRNA), actin gene (ACT), glyceraldehyde-3-phosphate dehydrogenase gene (GAPDH), Ubiquitin gene (UBQ), β-tubulin gene (β-TUB), eukaryotic elongation factor 1-αgene(eEF-1α), and eukaryotic translation initiation factor 4α-1 gene (eIF-4α) were selected and tested the corresponding expression level in different tissues at different growing stages. The results showed that 25S rRNA, 18S rRNA, and ACT at the reproductive periods, eEF-1α and eIF-4α at the nutritional periods, and eEF-1α, UBQ, and ACT at different leaf developmental periods had a stable level of gene expression, respectively. These results might be useful for the study of gene function in A. konjac.

Chitosan and its derivatives as a biocompatible, biodegradable polymer that has been well-established in biotechnology and medicine as the nanogels and nanofilms forming material, has a number of practical applications in the field of the supply of medicines, food colloids, agrotechnology, as well. In this paper, the focus of attention is directed to the study of the physico-chemical principles of the formation of gels and nano-particles using fluorescence spectroscopy methods. We have obtained chitosan (Chit5) and pegylated chitosan (Chit5-PEG) particles with an average size of 170 or 400 nm, respectively by counterflow extrusion method. The paper presents three based fluorescent probes sensitive to the formation of chitosan particles: congo red, malachite green as well as covalently attached pyrene-label. Dyes fluoresce about an order of magnitude brighter in the composition of chitosan nanogel particles or in mscrogels. With these fluorescence probes, the pH- and temperature-dependent behavior of polymers was studied, and the influence of the genipin -based crosslinking agent on the gel formation parameters, such as kinetics and concentration dependence of the nanoparticles formation accompanied by sharply increases in the fluorescence intensity of dyes was evaluated. A sensitive approach for tracking gelation and including of small drug molecules and protein by FRET using a pyrene-tryptophan donor-acceptor pair is proposed using tryptophan or ovalbumin as fluorophores-donors. Upon nanogel formation FRET effect is observed manifested in sharp increase if the fluorescence intensity of the pyrene (acceptor). Thus, the application of the fluorescent probes with FRET function for monitoring the gelation and nanoparticles formation as well as drug encapsulation in chitosan-based parts, which have broad prospects in biomedical practice and food industry.

In this study, an extensive data set was based on existing literature records in order to enable the suitability of several predictive models, from Multiple Linear Regression (MLR) to Neural Networks (NN). The main objective was to, through regression analyses, generate model computations to correlate tensile properties (UTS- Ultimate Tensile Strength, YTS – Yield Tensile Strength and EF – Elongation-to-Fracture) to a given alloy composition and microstructural spacing. This investigation led to positive results, as the highest accuracies of the trained modules (in 80% of the database) were found to be above ~82% (UTS and EF) and a maximum of ~98% (YTS), when analyzing the results to a test data set. Later, these models were used to define trends for possible next solder alloy commercial compositions. Overall, using the standard model’s setup, the Random Forest and Decision Tree models showed the highest accuracy results, with 0.958 for YTS as opposed to 0.907 for MLR. Moreover, Multilayer Perceptron (MLP)-optimized models yielded the best results for each variable, with the highest increases in accuracy associated with the YTS and EF. The present contribution might imply an important milestone towards alloy design research based on data science guidelines to unlock the full potential of former experiments and their extensive set of results.

One of the key issues in sustainable tourism research is the gap between tourists' expressed friendly attitudes towards sustainable behaviors and their actual behaviors. Although many "low-carbon" themed restaurants have emerged during the low-carbon transformation of the Chinese tourism industry, low-carbon food services have not been significantly improved. This study takes food as the entry point to explore tourists' behavior and attitudes towards low-carbon tourism in relation to food. We conducted two interviews. The first interview was a semi-structured contextual interview with 120 tourists who had experiences in food streets, aim-ing to identify the core user group: low-carbon attitude-friendly tourists with high-carbon food behaviors. The second interviews was an in-depth interview based on grounded theory with 29 core users, analyzing the four main reasons for their high-carbon food behaviors and their re-quirements for low-carbon food services in tourism. Based on this, we extracted four design ele-ments for low-carbon tourism food services: low-carbon information show service, low-carbon service product attractiveness improvement, low-carbon food environment atmosphere creation, and service providers' low-carbon behaviors. Through these four service elements, we constructed a low-carbon tourism food service design framework based on the core users' needs, discussed the mechanism of service elements, and provided service design suggestions accordingly. The re-search results serve tourism providers, low-carbon tourism researchers, and designers.

Education is one of the most important tools available to policymakers and non-governmental bodies to promote a change in the behavior of society and to address the climate crisis, in line with the 4^th and 13^th Sustainable Development Goals of the United Nations 2030 Agenda. Project-based learning (PBL) addresses real and global challenges and allows the academic and professional formation of students. As part of the reflection stage of the educational action research method using the Business Canvas Model (BMC), pedagogic deficiencies were identified in the PBL and a didactic proposal was elaborated to emulate the interaction of students with the ecosystems of society. A prototype for improving the management of organic manures as soil amendments was initially developed, to provide the students with a quick start on the first steps of the proposed project. In the 10 sessions designed for the PBL (Make it happen!), the students elaborate a more sophisticated artifact in response to the demands of potential clients, as per the outcomes of the primary market research (5^th session). Active teaching methods and tools, such as a modified template of the BMC with the Rumsfeld’s matrix, aid the metacognition of students and their competences development. In a double session of the PBL, the primary market research is organized with key stakeholders of the agroindustry to enquire about the feasibility of the implementation of the technological solution and the logistics at farm level. Finally, the evaluation relies on the suitability of the upgraded prototype to respond customers’ demands.

Both SARS-CoV-2 and SARS-CoV initially appeared in China and spread to other parts of the world. SARS-CoV-2 has generated a COVID-19 pandemic causing more than 6 million human deaths worldwide while the SARS outbreak quickly ended in six months with a global total of 774 reported deaths. One of the factors contributing to this stunning difference in the outcome between these two outbreaks is the inaccuracy of the RT-qPCR tests for SARS-CoV-2, which generated a large number of false-negative and false-positive test results that have misled patient management and public health policy-makers. This article presented Sanger sequencing evidence to show that the RT-PCR diagnostic protocol established in 2003 for SARS-CoV can in fact detect SARS-CoV-2 accurately due to the well-known nonspecific PCR amplification of DNAs with similar sequences. Using nested RT-PCR followed by Sanger sequencing to retest 50 patient samples collected in January, 2022 and sold as RT-qPCR positive reference confirmed 21 (42%) were false-positive. Although the other 29 positive isolates were categorized as Omicron variant by partial sequencing of the N gene, and the RBD and the NTD of the S gene, 9 (31%) showed focal to complete sequencing failure in the S gene segments due to multi-allelic SNPs. During the course of the study, an Omicron variant isolate containing a BA.1 NTD and a BA.2 RBD in its S gene was also detected. Routine partial S gene sequencing of all PCR-positive samples can timely discover multi-allelic SNPs and viral recombination in the circulating variants for investigation of their impacts on vaccine efficacies, therapeutics and diagnostics.

Cerebrospinal fluid (CSF)-contacting neurons (CSF-N) located in the surface of both brain ventricles and the central canal (cc) in the spinal cord. The cc and CSF maintain a proliferative niche for neural progenitor cells and play a vital role in development of the brain. The CSF circulates in the ventricles and the subarachnoid spaces with the CSF rhythmic flow: cardiac pulsation and respiratory fluctuation. A new concept of CSF motion may be contrary to the classical one that the direction of CSF motion may vary in direction and may be dynamic in its location. The CSF pressure may also depend on the body position. Moderate music-making has been considered a potential approach for rehabilitative and restorative therapy of brain dysfunctions. Recently, we find that the CSF-Ns are present in both the interior CFS in the cc and also exterior CSF around the surface of the spinal cord. We hypothesize that CSF-N as mechanical sensors in the spinal cord could sense motion of the spinal cord. The myodural bridge is a ligament connecting a pair of deep, upper-neck muscles to the dura mater, which envelops the arachnoid mater and contains the CSF surrounding the brain and the spinal cord. We figure out the term “CSF-static compartment” and classify CSF storage location as rostral pool and caudal pool to demonstrate our hypothesis. We presume that the somatic body movement with music-making and rehabilitation-based interventions would orchestrate the CSF motion with head movement, myodural bridge stretching and puling as well as spinal bending.

A common task in the immunodetection of structurally close compounds is to analyze the selectivity of immune recognition: it is required to understand the regularities of immune recognition and to elucidate the basic structural elements which provide it. Triazines are compounds of particular interest for such a research due to their high variability and the necessity of their monitoring to provide safety of agricultural products and foodstuffs. We evaluated the binding of 20 triazines with polyclonal (pAb) and monoclonal (mAb) antibodies obtained using atrazine as the immunogenic hapten. A total of >3000 descriptors was used in QSAR analysis of binding activities (pIC50). Comparison of the two enzyme immunoassay systems showed that the system with pAb is much easier to describe using 2D QSAR methodology, while the system with mAb can be described using the 3D QSAR COMFA. Thus, for the 3D QSAR model of the polyclonal antibodies, the main statistical parameter q² (‘leave-many-out’) is equal 0.498, and for monoclonal antibodies q² is equal 0.566. Obviously, in the case of pAb, we deal with several targets, while in the case of mAb the target is one, and therefore it is easier to describe it using specific fields of molecular interactions distributed in space.

Human footprint is having a unique set of ridges unmatched by any other human being, and therefore it can be used in different identity documents for example birth certificate, Indian biometric identification system AADHAR card, driving license, PAN card, and passport. There are many instances of the crime scene where an accused must walk around and left the footwear impressions as well as barefoot prints and therefore it is very crucial to recovering the footprints to identify the criminals. Footprint-based biometric is a considerably newer technique for personal identification. Fingerprints, retina, iris and face recognition are the methods most useful for attendance record of the person. This time world is facing the problem of global terrorism. It is challenging to identify the terrorist because they are living as regular as the citizens do. Their soft target includes the industries of special interests such as defense, silicon and nanotechnology chip manufacturing units, pharmacy sectors. They pretend themselves as religious persons, so temples and other holy places, even in markets is in their targets. These are the places where one can obtain their footprints easily. The gait itself is sufficient to predict the behaviour of the suspects. The present research is driven to identify the usefulness of footprint and gait as an alternative to personal identification.

Background: Diet is a risk factor for gestational diabetes mellitus (GDM). There are few studies on women´s diet and glucose tolerance test (GTT) results. Objective: To evaluate the relationship between previous diet and the number of abnormal values on the diagnostic GTT in women with GDM. We hypothesized there would be an inverse relation between antioxidant micronutrient consumption and the number of abnormal GTT values. Methods: This cross-sectional study included 60 women diagnosed with GDM (2-hours, 75g-GTT). Shortly after diagnosis, participants answered a validated food frequency questionnaire to assess food consumption in the last 6 months. Mann-Whitney´s test was used to compare the dietary intake of participants with one (group 1) versus two-three (group 2) abnormal GTT values. Results: Participant characteristics were similar. There were no significant differences between groups in median intake of total calories, carbohydrates, lipids and proteins. Participants in group 1 had significantly higher intakes of fiber (11.9 vs. 11.0 g/day p= 0.049), vitamin D (40.6 vs. 40.4 mcg/day p= 0.049) and vitamin C (180.0 vs. 151.0 mg/day p= 0.008) than those in group 2. Conclusion: Results suggest a possible association between consumption of fiber and some antioxidant micronutrients and GDM severity at the time of diagnosis.

Techniques of stripping voltammetry (SV) determination of silver and gold in pyrites and carbonaceous matter were developed. The problem of quantitative transfer of the sample into the solution was solved. For this purpose, the ore matrix of carbonaceous shales was decomposed by mineral acids in autoclaves at high pressures. The element to be determined from the sample matrix was separated by extraction. Ag(I) ions from the solutions were extracted in the form of dithizonate complex in CCl₄. Au(III) ions were extracted by diethyl ether. The extracts were decomposed thermally. The dry residue was dissolved in the background electrolyte, and the element was determined by the SV method. The graphite electrode (GE) impregnated with polyethylene was used as a working electrode in SV–determination of silver. SV–determination of gold was carried out using GE modified by bismuth. The limits of detection (LOD) of Ag(I) and Au(III) contents were equal to 0.016 mg L^-1 and 0.0086 mg L^-1, respectively. The results of SV-determination of gold and silver in standard samples, pyrites and carbonaceous shales were presented. The silver content in the pyrite was 13.6 g t^-1, in carbon shale—0.34 g t^-1. The concentration of gold in the pyrite ore zone "Kirovsko–Kryklinskaya" was 1.15 g t^-1; in carbonaceous shales—2.66 g t^-1. The obtained data were consistent with the data of atomic emission spectroscopy (AES) and inductively coupled plasma mass spectrometry (ICP—MS). The error of determination of elements by stripping voltammetry was calculated when determining the silver content of 10...6 g t^-1 in pyrite and carbonaceous material, which was less than 12%, and when determining the gold content of 1...3 g t^{- 1} in pyrite and carbonaceous matter, which was less than 23%.

Here we investigated the longer-term effect of Ncald-ASOs by additional i.c.v. bolus injection at PND28. Two weeks after injection of 500 µg Ncald-ASO in wild-type mice, NCALD was significantly reduced in brain and spinal cord and well tolerated. Next, we performed a double-blinded preclinical study combining low-dose SMN-ASO (PND1) with 2x i.c.v. Ncald-ASO or CTRL-ASO (100 µg at PND2, 500 µg at PND28). Ncald-ASO re-injection significantly ameliorated electrophysiological defects and NMJ denervation at 2 months. Moreover, we developed and identified a nontoxic and highly efficient human NCALD-ASO that significantly reduced NCALD in hiPSCs-derived MNs. This improved both neuronal activity and growth cone maturation of SMA MNs, emphasizing the additional protective effect of NCALD-ASO treatment.

Systemic lupus erythematosus (SLE) is a clinically heterogeneous autoimmune disease, and organ manifestations, such as lupus nephritis (LN) or skin disease, may be refractory to standard treatment. Therefore, new agents are required to allow for a more personalized therapeutic approach. Recently, several new therapies have been approved internationally, including voclosporine for LN and anifrolumab for moderately to severely active SLE. Here, we report a case of SLE with a predominant and refractory cutaneous manifestation despite treatment with glucocorticoids, hydroxychloroquine, mycophenolate mofetil, and belimumab. Belimumab was switched to anifrolumab, and the patient responded quickly after two infusions (eight weeks) with a reduction of the Cutaneous Lupus Assessment and Severity Index (CLASI) from 17 to 7. In addition, we review the available clinical trial data for anifrolumab with a focus on cutaneous outcomes. Based on phase II and III clinical trials investigating the intravenous administration, a consistent CLASI improvement was observed at 12 weeks. Interestingly, in a phase II trial of subcutaneous anifrolumab application, CLASI response was not different from placebo at 12 weeks but numerically different at 24 and 52 weeks, respectively. Thus, anifrolumab emerges as an attractive new therapeutic option suggesting a possible domain-based approach.

This study contributes to the Sybil nodes detecting algorithm in Online Social Networks (OSNs). As major communication platforms, online social networks get significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. Additional exploration of distinct reputation systems and their practical application led to the study’s discovery of machine learning algorithms, i.e., the KNN, Support Vector Machines, and Random Forest algorithms. The study details the data cleansing process for the employed dataset in its process for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to explaining and analysis of conducting experiments and comparing their results. Such experiments demonstrate the algorithm’s ability to detect Sybil nodes in OSNs (100% accuracy in SVM, 99.6% in Random Forest, and 97% in KNN algorithms); thus concluding by proposing future research opportunities.

Choanoflagellates are single-celled eukaryotes with complex signaling pathways. They are considered the closest non-metazoan ancestors to mammals and other metazoans, and form multicellular-like states called rosettes. The choanoflagellate Monosiga brevicollis contains over 150 PDZ domains, an important peptide-binding domain in all three domains of life (Archaea, Bacteria, and Eukarya). Therefore, an understanding of PDZ domain signaling pathways in choanoflagellates may provide insight into the origins of multicellularity. PDZ domains recognize the C-terminus of target proteins and regulate signaling and trafficking pathways, as well as cellular adhesion. Here, we developed a computational program, Domain Analysis and Motif Matcher (DAMM), that predicts target specificity in choanoflagellate PDZ domains by analyzing peptide-binding cleft sequence identity as compared to human PDZ domains. We used this program, protein biochemistry, fluorescence polarization, and structural analyses to characterize the specificity of A9UPE9_MONBE, a M. brevicollis PDZ domain-containing protein with no homology to any metazoan protein, finding that its PDZ domain is most similar to those of the DLG family. We then identified two endogenous sequences that bind A9UPE9 PDZ with &lt;100 M affinity, a value commonly considered the threshold for cellular PDZ-peptide interactions. Taken together, this approach can be used to predict cellular targets of previously uncharacterized PDZ domains in choanoflagellates and other organisms. Our data contributes to investigations into choanoflagellate signaling and how it informs metazoan evolution.

Background: Papillomaviruses (PVs) are double-stranded DNA viruses and are more common in skin of ruminants in Iraq. A P53 (tumor suppressor protein) reveals an essential role in cell cycle control. This study aimed to describe the clinical, histopathological and immunohistochemical aspects of naturally occurring cutaneous ruminant’s papillomatosis. Methods: Samples were collected from totally, 10 animals (3 cattle, 3 goats and 4 sheep) with multiple papillomatosis lesions. Results: Clinically, exophytic multiple, cauliflower-like growths (warts) of varying sizes (0.5-11 cm) were found in different areas of the animal’s bodies. Histopathological features were various degrees of koilocytosis, ortho and parakeratotic, hyperkeratosis, hypergranulosis in granular layer and acanthosis. Immunohistochemical (IHC) investigations revealed some nuclei in the granular and basal layers of the epidermis with intense positivity for papillomavirus antigen. All tumor samples were positive for p53 expression that appeared as a strong cytoplasmic and perinuclear staining mainly in the basal and parabasal layers. Conclusion: this study described the papillomavirus lesions in bovine, ovine and caprine, that located in different anatomical areas with minor variations in histopathological features. The tumor samples showed positive results for PV antigen and P53 expression that considered as the useful markers in the diagnosis of cutaneous papilloma.

A combination of Poloxamer 407 (P407) and hydroxypropyl methylcellulose (HPMC) hydrosols is proposed as an in situ thermo-gelling vehicle for the nasal drug delivery of chlorhexidine-silver nanoparticles conjugates (SN-CX). Varying ratios of P407 and HPMC were applied in the presence and absence of SN-CX in order to optimize the gelation temperature (Tg) to values close to the physiological nasal such (30–34°C). Mechanisms for the observed gelation phenomena were suggested based on viscosimetry, texture analysis, and dynamic light scattering. Tests were carried out for sprayability, washout time, in vitro drug release, ex vivo permeation, and antimicrobial activity. When applied separately, HPMC was found to lower the P407 gelation temperature, whereas SN-CX increased it. However, in the presence of HPMC, SN-CX interfered with the P407 micellar organization in a principally contrasting way, while leading to an even further decrease in Tg. SN-CX-loaded nasal formulation composed of P407 16% and HPMC 0.1% demonstrated a desired gelation at 31.9 °C, good sprayability (52.95% coverage of the anterior nasal cavity), mucoadhesion for 70 min under simulated nasal clearance, expedient release and permeation, and preserved anti-infective activity against seasonal Influenza virus and beta-coronavirus, Pseudomonas aeruginosa, Klebsiella pneumoniae, Staphylococcus aureus and other pathogens. Our findings suggest that the current development could be considered a potential formulation of a protective nasal spray against respiratory infections.

The worldwide competition to vaccinate against COVID-19 has highlighted the importance of immunization measures in containing the virus. Saudi Arabia, like the rest of the globe, must assess socio-demographic subtleties, chronic illnesses, vaccination effects, and public health policy implications as it navigates this complicated environment. This cross-sectional study examined Saudi COVID-19 vaccine in many ways. It examined the socio-demographic makeup of vaccine recipients, the prevalence of chronic conditions in this population, vaccine administration patterns, post-vaccination side effects, and vaccine-infection rates. A questionnaire-based online survey of 401 participants ensured gender, nationality, age, weight, and educational diversity. We analyzed vaccine manufacturer choice for each dosage, side effects after the first and second doses, and chronic diseases in the sample. Pfizer/BioNTech vaccines are preferred for all three doses, reflecting worldwide vaccination acceptance patterns. Side effects were not significantly different between vaccination kinds. Interestingly, immunization reduced infection rates, demonstrating the efficacy of COVID-19 vaccines. The data fills important information gaps and illuminates Saudi COVID-19 vaccination kinetics. These data support the global preference for mRNA vaccines, demonstrating the faith in this breakthrough technology. They also sponsor COVID-19 vaccine safety and effectiveness studies. The dramatic drop in infection rates among vaccinated people shows how vaccinations suppress the pandemic. These findings inform Saudi Arabia's immunization efforts. As booster doses become more common, long-term vaccination research is crucial. Vaccine reluctance, especially in certain demographics, must be addressed. Due to vaccines' protective potential, equitable vaccine distribution and large immunization programs are essential for herd immunity and pandemic management. This thorough cross-sectional study helps policymakers and healthcare practitioners build successful and inclusive COVID-19 immunization regimens in Saudi Arabia.

β-Lactams are pharmacologically important compounds because of their various biological uses, including antibiotic and so on. β-Lactams were synthesized from benzylidene-inden derivatives and acetoxyacetyl chloride. The inhibitory effect of these compounds was also examined for human carbonic anhydrase I and II (hCA I, and II) and acetylcholinesterase (AChE). The results reveal that β-lactams are inhibitors of hCA I, II and AChE. The K_i values of β-lactams (2a-k) were 0.44-6.29 nM against hCA I, 0.93-8.34 nM against hCA II, and 0.25-1.13 nM against AChE. Our findings indicate that β-lactams (2a-k) inhibit both CA isoenzymes and AChE at low nanomolar concentrations.

Coronavirus Disease 2019 (COVID-19) exhibits a significant variation in presentation of symptoms ranging from mild to severe life-threatening symptoms, including death. COVID-19 infection susceptibility has been linked with various covariates, but studies in Qatar are limited. Data from Qatar Biobank (QBB) (n = 10,000; 18- to 79-year-old adults) were analyzed for associations between sociodemographic and lifestyle factors and susceptibility to COVID-19. Logistic regression was used to assess the associations between COVID-19 and age, gender, body mass index (BMI), smoking status, education level, dietary patterns, supplement use, physical activity, history of bariatric surgery, diabetes, and hypertension. In total, 10.5% (n = 1045) of the participants had COVID-19. Compared to non-smokers, current and ex-smokers had lower odds of having COVID-19 (odds ratio [OR]= 0.55; 95% CI: 0.44-0.68 and OR= 0.70; 95% CI: 0.57-0.86, respectively). Vitamin D supplement use was associated with an 18% reduction of the likelihood of contracting COVID-19 (OR = 0.82; 95% CI: 0.69-0.97). Obesity (BMI ≥30 kg/m2), history of bariatric surgery and higher adherence to the modern dietary pattern– characterized by consumption of foods high in saturated fat and refined carbohydrates– were positively associated with COVID-19. Our findings indicate that adopting a healthy lifestyle may be helpful in the prevention of COVID-19 infection.

Mediterranean diet has been widely suggested to exert significant beneficial effects on endothelial oxida-tive status and cardiometabolic health. Greek Orthodox monasteries, due to their specific nutritionaland sartorial habits, comprise a population which strictly adheres to nutritional patterns with restricted eating and a plant-based subset of the Mediter-ranean diet, often accompanied by profound hypovitaminosis D. Time –restricted eating is also adopted from a large part of general lay Greek population for health promoting reasons, without restrictions on an-imal product consumption, as imposed by Orthodox religious fasting. However, comparative effects of these nutritional patterns on oxidative stress markers remain scarce. The present study attempted to evaluate the effects of Christian Orthodox fasting (COF) in a group of vitamin D –deficient and overweight Orthodox nuns from Central and Northern Greece, compared to the implementation of TRE, 16:8 dietary regimen in a cohort of adult women from the general population from the same region, with regard to markers of endothelial oxidative status. A group of 50 women from two Orthodox monasteries in Northern Greece and one group of 50 healthy lay women were included. During enrollment a detailed recording of dietary habits was performed, along with a scientific registry of demographic and anthropometric characteristics (via bioimped-ance).Orthodox nuns followed a typical Orthodox fasting regimen[daily feeding window (8 am–4 pm)] whereas lay women followed a TRE 16:8 regimen with the same feeding time-window with recommenda-tion to follow a low-fat diet, without characteristics of the Mediterranean diet. We included a complete biochemical analysis, as well as calciotropic profiles [Calcium-Ca, Albumin, Parathyroid hormone-PTH, 25-hydroxyvitamin D- 25(OH)D] as well as and markers of TAC (trichloroacetic acid), (glutathione) GSH and thiobarbituric acid reactive substances (TBARS) concentrations, as markers of oxidative status. All groups were comparable at baseline for calcium, PTH and 25(OH)D concentrations, with no significant differences between groups. Orthodox nuns manifested a lower median GSH compared to con-trols (6.0 vs. 7.2, p 0.04) and a higher median TAC (0.92 vs. 0.77, p <0.001). TBARS comparisons showed no significant difference between the two groups. No significant associations of oxidative status with 25(OH)D, PTH and markers of glucose homeostasis were evident. Results of this small pilot study indicate that both dietary regimens have advantages over oxidative markers compared to each other, with increased TAC in the groups of Orthodox Nuns after a 16th week period of COF compared to a 16:8 TRE and increased GSH concentrations in the lay women group. Future randomized trials are required to investigate superiority or non-inferiority between these dietary patterns, in the daily clinical setting.

SET protein carries out multiple functions including those for protein phosphatase 2A (PP2A) inhibition, histone modification, DNA repair and gene regulation. SET overexpression has been detected in brain neurons of Alzheimer's disease patients, follicle theca cells of Polycystic Ovary Syndrome (PCOS) patients, and ovarian cancer cells, indicating that SET may play a pathological role for these disorders. SET transcript 2, produced by a specific promoter, represents a major transcript variant in different cell types. In this study, we characterized the transcriptional activation of human SET transcript 2 promoter in HeLa cells. Promoter deletion experiments and co-transfection assays indicated that ZFX, the Zinc finger and X-linked transcription factor, was able to transactivate the SET promoter. A proximal promoter region containing four ZFX-binding sites was found to be critical for the ZFX-mediated transactivation. Mutagenesis study indicated that the site located closest to the transcription start site accounted for most of the ZFX-mediated transactivity. Manipulation of ZFX levels by overexpression or siRNA knockdown confirmed the significance and specificity of the ZFX-mediated SET promoter activation. Chromatin immunoprecipitation results verified the binding of ZFX to its cognate site in the SET promoter. These findings have led to identification of ZFX as an upstream factor regulating SET gene expression. More studies are required to define the in vivo significance of this mechanism, and specifically, its implication for several benign and malignant diseases related to SET dysregulation.

Several cities are experiencing an increase in flood hazard and exposure, and consequentially in flood risk, attributable to the coupled effect of climate change and urbanization. Non-structural protection strategies demonstrate significant potential in the short term for mitigating hydraulic risk in urban areas, particularly where the implementation of structural measures is impeded by a complex urban environment. This work proposes an Early Warning System (EWS) for fluvial floods developed for the city of Palermo (Italy). It is based on the preliminary definition of po-tential Flood Event Scenarios (FESs) as a function of typical potential precursors. Each FES pre-dicts first points of flooding, flood areas and water depths, also providing specific hazard maps for people, vehicles, and buildings. The EWS uses rainfall depth-duration thresholds to assess the expected hydrograph as a function of rainfall forecasts and the water stage observed in real time at an upstream section of the river crossing the city. This last also allows for estimating the ante-cedent wetness conditions. Based on this information, the EWS retrieves an expected FES from a pre-built library with a preannouncement time up to 36 h. The EWS is here successfully tested on a historical flood event.

The severe acquired respiratory coronavirus–2 (SARS–CoV-2) infection has initiated both acute and chronic COVID–19 disease between 2020 and 2023, currently evolving with other homologous prior coronavirus strains of the Nidoviridae order, which encompasses other prevalent alpha/ beta coronaviruses, but also the Middle East Respiratory Syndrome (MERS-CoV) and SARS-CoV-1, with recent SARS–CoV–2 variants, increasing demands for effective immunogens and therapeutic approaches that will reduce global disease burden and further infection from SARS–CoV-2 affected individuals that may experience post acute sequelae (PASC) or “Long COVID”. Following a worldwide programme of prophylactic vaccination, there is still a dilemma in the efforts to find prophylactic and early therapeutic approaches that would treat novel SARS-CoV-2 variants and prevent future epidemics or pandemics within host human and animal populations, where zoonotic or cross species transfer naturally occurs. Concerns about viral immune escape intersect at a specific point; a gained evolutionary ability of several viruses to co–infect and compete against previous scientific advances since 1796 that remain undetected or asymptomatic during the early stages of infection progressing to symptomatic and severe disease via the double methylation of the 5' end of eukaryotic DNA or RNA-based viral genomes, the 7-MeGpppA2’-O-Me cap, and its double methylation capping process is performed by the activated viral 2’ - O - Methyltransferase (MTase) enzyme, a complex of two viral non-structural proteins (NSPs) joined together through an activation process (NSP10/16) and by N7-Methyltransferase (N7-MTase/NSP14), respectively. Moreover, it was discovered that polymorphic viruses translate NSP1, which prevents the activation of various Pattern Recognition Receptors (PRRs), and consequently, detection of Pathogen-Associated Molecular Patterns (PAMPs) and Damage-Associated Molecular Patterns (DAMPs) alike. NSP1 also silences important interferon-encoding genes (INGs) and interferon-stimulated genes (ISGs), is signalled in a paracrine manner to neighbouring cells, and that induces the apoptosis of host cells, inducing an effect of “trace erase” effect and making the viral infection as immunologically “invisible” as possible during the initial, key stages of viral replication and distribution, all such mechanisms occurring independently of the viruses in cause. Another important viral NSP is NSP14, as it plays two functional roles that are independent of each other; to produce new viral genetic material for the purpose of maintaining the validity of the viral genome as well, and not just transfer a methyl group to the 5’ end of the viral genome. Other viral NSPs share a role with NSP1, 10, 14 and 16 in directly suppressing the activation of PRRs and ISGs, and all such viral proteins help the virus in its process of self-camouflaging against first- and second-line immunity, thereby often severely impacting the quality of the produced adaptive immune responses. The outcome of all such phenomena is the sharp decrease in the host Type I and Type III interferons' (IFNs) rate of synthesis by the host cells, that would usually occur and affect homeostatic cellular pathways, resulting in further viral replication and induced apoptosis. Nonetheless, effects of microbial immune evasion during the development of other viral or carcinogenic pathologies are not widely known. In short, polymorphic viruses developed a proportionate evolutionary response against developed adaptive immune responses, by currently relying on gaps mostly situated in the natural immune system in their process of molecular self-camouflaging. Scientists developed numerous approaches of early treatment that generally showed good success rates and fewer risks of adverse events, and the still early present stages of COVID-19 research should also be taken into consideration whilst filtering for the most appropriate solutions. For example, the administration of recombinant human interferons I and III into the nasal mucosa cellular layer, as key mediators of anti–viral activity, can simulate intracellular infection and stimulate cellular activity in a timely manner, training the innate and adaptive immune system cells to develop and appropriately stimulate an adequate immune response through B and T cells. Another example could involve the treatment of natural and adaptive lymphocytes with a low dose of IFNs I and possibly III, prior to their insertion into the host lymphatic system, possibly alongside additional recruitment of plasmacytoid dendritic cells (pDCs) as further interferon “factories”, all with the purpose of early infection management. It might be that focusing on directly offering the immune system the information about the genetics and protein structure of the pathogen, rather than training its first-line mechanisms to develop faster, excessively increases its specificity, making it reach a level that brings the virus the opportunity to evolve and escape previously-developed host immune mechanisms. It is until the scientific community realises this potentially crucial aspect that large proportions of the world population will probably continue to face serious epidemics and pandemics of respiratory diseases over the coming several decades, evidenced with dengue fever and more recently, monkeypox and possibly avian flu. Of note, it has been indicated that IFN I and / or III display significant immunising, early therapeutic and clinical disease onset-attenuating effects for many other microbial evoked diseases, as well as for a number of oncological diseases.

This paper aims to explore the potential application of Extreme Learning Machines (ELM) in stock price prediction and optimise the ELM model by combining the Crown Porcupine Optimisation Algorithm and the Adaptive Bandwidth Kernel Function Density Estimation Algorithm. Specifically, the model's root mean square error (RMSE) on the test set is 0.018, which shows its good prediction accuracy. Meanwhile, the correlation coefficient R reaches 0.968, proving the model's effectiveness in predicting stock prices. In addition, the coefficient of determination R² of the training set and test set are 0.987 and 0.954, respectively, which are not much different, indicating that the model has good generalisation ability and is not prone to overfitting. This is crucial for practical application, as it implies that the model fits the existing data well and makes accurate predictions for future data. In terms of other evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), the performance is excellent in both the training and test sets. Together, these results indicate that after optimising the ELM by introducing the crown porcupine optimisation algorithm and the adaptive bandwidth kernel function density estimation algorithm, the model can effectively capture the changing stock price pattern, thus achieving efficient and accurate stock price prediction. In summary, this study not only demonstrates the great potential of ELM in stock price prediction but also highlights the importance of improving the model performance through advanced algorithm optimisation, which provides a new way of thinking and methodology for financial market analysis. This strongly supports investors' decision-making and lays the foundation for related research.

Research has shown that the consumption of herbal teas is linked with positive effects on the regulation of postprandial blood glucose level. In this study, a comparative analysis of aqueous and ethanol extractions is performed on four herbal teas (Mallotus, Cyclocarya, Rubus, Vine) to evaluate their phytochemical profiles and functional activities. Investigations of phytochemical contents, antioxidant activities, α-glucosidase inhibitory activities and chemical compositions are carried out through colorimetric analyses and UPLC-Q-Orbitrap HRMS/MS, respectively. Results showed that among the studied herbal teas, Vine exhibited the highest glucose re-gulating abilities under both aqueous and ethanol extractions. Overall, ethanol extracted samples yielded higher phytochemicals content, but the compositions of the phytochemicals varied. Meanwhile, aqueous extracts showed surprisingly strong antioxidant activities and comparable α-glucosidase inhibitory activities with ethanol extracts. Phytochemicals contents presented a positive correlation with antioxidant activities and α-glucosidase inhibitory activities. Nevertheless, antioxidant activities displayed a weak positive correlation with α-glucosidase inhibitory activities. Results from this study provided evidence that aqueous extracts from herbal teas possessed valuable phytochemicals composition for antioxidants and the hyperglycemic populations, and are promising candidates for potential applications as functional ingredients with health promoting effects and as natural additives to boost the nutritional value of herbal food products.

The oral mucosa represents a defense between the external environment and the rest of the body and encompasses several antimicrobial and antiviral mechanisms. Oral mucosal cells are constantly bathed in hypotonic saliva (normally one-third tonicity compared to plasma) and are repeatedly exposed to environmental stresses of tonicity, temperature and pH by the drinks we imbibe (e.g. hypotonic: water, tea, coffee; hypertonic: assorted fruit juices, red wines), as well as of diverse microbial and viral flora. In the mouth, the broad-spectrum antiviral mediator MxA (a dynamin-family large GTPase) is constitutively expressed in healthy periodontal tissues, and also induced by Type III interferons (e.g. IFN-λ1). Endogenously induced human MxA and exogenously expressed human GFP-MxA formed membraneless biomolecular condensates in the cytoplasm of oral carcinoma cells (OECM1 cell line). These condensates likely represent storage granules in equilibrium with antivirally active dispersed MxA. Remarkably, cytoplasmic MxA condensates were exquisitely sensitive sensors of hypotonicity – the condensates in oral epithelium disassembled within 1-2 min of exposure of cells to saliva-like one-third hypotonicity, and spontaneously reassembled in the next 4-7 min. OECM1 cells preloaded with calcein-AM (a reporter of cytosolic “macromolecular crowding”) confirmed unquenching and then requenching of calcein fluorescence during this process. Hypertonicity had little effect on MxA condensates. The spontaneous reassembly of GFP-MxA condensates in oral epithelial cells even under continuous saliva-like hypotonicity was slowed by the protein phosphatase inhibitor cyclosporin A (CsA) and by the K-channel blocker tetraethylammonium chloride (TEA) suggestive of the involvement of the volume-sensitive WNK kinase-protein phosphatase (PTP)-K-Cl cotransporter (KCC) pathway in the regulated volume decrease (RVD) during condensate reassembly in oral cells. Thus, physiologically, hypotonic drinks (water, tea, coffee) trigger a rapid deployment of antiviral MxA throughout the cytoplasm of oral epithelial cells, with subsequent salvage of MxA back into storage granules/condensates.

Woodland strawberry (Fragaria vesca L.) is an underutilized plant species, appreciated by consumers for its qualitative features. Utilizing technical means of production, such as plant biostimulants, that can increase crop productivity and quality could be highly important in woodland strawberry cultivation. The scope of the present study was to appraise the influence of two biostimulants (Trichoderma atroviride and plant protein hydrolysate) - used either alone or combined - on plant performance and economic profitability of two woodland strawberry genotypes (‘Alpine’ and ‘Regina delle Valli’). Overall, data showed that ‘Alpine’ had the highest productive performances, whereas ‘Regina delle Valli’ revealed the highest fruit qualitative traits. T. atroviride inoculation and V-PH application significantly boosted plant marketable yield (+20.5% for T. atroviride and +12.9% for V-PH), total sugars (+1.9% for Trichoderma and +1.4% for V-PH) and anthocyanins (+14.1% for T. atroviride and +9.8% for V-PH) compared to non-treated plants. Plants supplied with both biostimulants had higher marketable yield (+34.8%), mean fruit weight (+6.0%), fruit dry matter (+13.8%), total sugars (+3.5%), ascorbic acid (+12.7%), flavonoid (+26.3%) and anthocyanins (+29.9%) compared to non-treated plants. Furthermore, our study revealed that the highest fruit polyphenols concentration was recorded in both genotypes treated with the combination of biostimulants and in ‘Regina delle Valli’ sprayed with V-PH, whereas the highest antioxidant activity was found in ‘Regina delle Valli’ fruit when plants were supplied with both biostimulants. Our study pointed out that the application of microbial and non-microbial biostimulants, especially in combination, might be an useful strategy for improving performances of underutilized species and, therefore, encourage its cultivation, valorisation and economic profitability (+10195.3 €/ha when plants were exposed to both biostimulants).

Open Educational Resources (OER), educational materials permit unrestricted use and adaption, have proven beneficial for cost savings and curriculum flexibility. However, OER are not frequently used in college courses. An OER initiative was designed to understand the factors contribute to increasing access to STEM course materials. This study employed a survey to investigate faculty awareness and usage of OER and explore support needed to increase the use of OER in STEM. Results indicated that one-sixth of the respondents were unaware of OER, and 53% had never used OER. The motivating factors for OER usage included cost-free licenses, day-one availability to students, and increased access. The support strategies for new OER creation included OER workshops, grants, and recognition for OER authoring. In conclusion, the current study fills a gap in the existing literature by documenting motivating factors for faculty to integrate OER into STEM for the first time.

Lessons learned and risk-based outcomes from modernising the Australian Standard for post-mortem inspection and disposition judgement of beef, sheep, goat and pig carcases are described. Initial Risk Profiles identified priorities for quantitative assessments. The main difficulty encountered was the paucity of quantified performance of current inspection. Resolving this involved acquiring gross abnormality data representing regional production/proportional abattoir volumes, the range of gross abnormalities appearing nationally, proportional occurrence at carcase sites, and seasonality to enable comparisons. Evidence and discussion are provided for associated challenges experienced including preventing contamination, use of food chain information to support amendment, inspection as a part of industry Quality Assurance and opportunities to improve inspector training. Criteria considered by the Competent Authority for determination of equivalence of alternative post-mortem inspection techniques included comparisons of public health risk, non-detection rates for gross abnormalities, and microbial contamination resulting from inspection activities, as appropriate. Most gross abnormalities detected arose from animal health and welfare conditions affecting wholesomeness and did not present as food safety hazards. Non-detection rates between current and alternative inspection (observation) were negligible. A Quantitative Risk Assessment for Cysticercus bovis was conducted. Carcases with multiple gross abnormalities predominantly reflected historic infections (prior septicaemia) where trimming achieved wholesomeness unless cachexic.

Biodiesel has several drawbacks, such as being prone to oxidation, having reduced stability, and having limited storage time. Antioxidants compatible with biodiesel are being used to address its drawbacks. Utilizing antioxidants effectively improves the quality of biodiesel. Enhancing the quality of biodiesel for use as a clean energy source benefits both the global economy and ecology. Therefore, we believe that our work will contribute to the advancement of the worldwide industry. The study used blends consisting of 20% biodiesel and 80% diesel fuel. Isatin-thiosemicarbazones were tested as additives in blends at a concentration of 3000 parts per million (ppm) using an oxifast device. Comparing them to the chemical antioxidant Trolox. FT-IR, DSC, and TGA were used to characterize these samples. DSC measured sample crystallization temperatures (Tc). Samples with antioxidants showed decreased values compared to the non-antioxidant diesel sample D100. Several DSC tests were conducted to determine the antioxidant strengths of various samples. The results showed that the FT-IR spectrum's antioxidant effect regions grow clearer with antioxidants. The extra antioxidant is effective. Biodiesel's oxidative stability improves with isatin-thiosemicarbazones at varying concentrations. The kinetics of thermal decomposition of isatin-thiosemicarbazones under non-isothermal conditions have been determined using the Kissinger, Ozawa, and Boswell techniques. The activation energies of compounds 1 and 2 were calculated 137–147 kJ mol^-1 and 173–183 kJ mol^-1, respectively.

Chemoradiation-based bladder preservation therapy (BPT) is currently a curative option for non-metastatic muscle-invasive bladder cancer (MIBC) patients at favorable risk or an alternative to radical cystectomy (RC) for those who are unfit for RC. In BPT, only patients who achieve complete response (CR) after chemoradiation have favorable prognosis and quality of life with preserved functional bladder. Thus, predicting CR and favorable prognosis is important for optimal patient selection for BPT. We reviewed biomarkers for predicting clinical outcomes of chemoradiation-based BPT. Biomarkers studied were categorized into those related to apoptosis, cell proliferation, receptor tyrosine kinases, DNA damage response genes, hypoxia, molecular subtype, and others. Among these biomarkers, Ki-67 labeling index (Ki-67 LI) and meiotic recombination 11 may be used for selecting BPT or RC. Ki-67 LI and erythroblastic leukemia viral oncogene homolog 2 (erbB2) may be used for predicting both chemoradiation response and prognosis of patients on BPT. Concurrent use of trastuzumab and a combination of carbogen and nicotinamide can overcome chemoradiation resistance conferred by erbB2 overexpression and tumor hypoxia. Further studies are needed to confirm the practical utility of these biomarkers for progress on biomarker-directed personalized management of MIBC patients.

Atopic dermatitis (AD) is a chronic eczematous inflammatory disease that may arise from environmental, genetic and immunological factors. Despite the efficacy of current treatment options like corticosteroids, such approaches are mainly focused on symptoms relief, and may present certain undesirable side effects. In recent years, investigation regarding isolated natural compounds, oils, mixtures and/or extracts, have gained scientific attention because of their high efficiency and moderate to low toxicity. Despite their promising therapeutic effects, the applicability of such natural health care solutions is somehow limited by their instability, poor solubility, and low bioavailability. Therefore, novel nanoformulation-based systems have been designed to overpass these limitations, thus enhancing the therapeutic potential, by promoting the capacity of these natural drugs to properly exert their action in AD-like skin lesions. For the best of our knowledge, this is the first literature review that focused on summarizing the last nanoformulation-based solutions loaded with natural ingredients, and specifically for the management of AD. We suggest that future studies should focus on robust clinical trials that may confirm the security and effectiveness of such natural-based nanosystems, thus paving the way for more reliable AD treatments.

Among the various methods for collecting oil spills and oil products, including from the water surface, one of the most effective is the use of sorbents. In this work, three-component bio-based composite granular adsorbents were produced and studied for oil products pollution collection. A bio-based binder made of peat, devulcanised crumb rubber from used tyres, and part fly ash as cenospheres were used for absorbent production. The structure, surface morphology, porosity, mechanical properties, and sorption kinetics of the obtained samples were studied. Composite hydrophobicity and sorption capacity to oil products such as diesel fuel (DF) and motor oil (MO) were determined. The obtained pellets are characterised by a sufficiently pronounced ability to absorb oil products such as DF. As the amount of CR in the granules increases, the diesel absorption capacity increases significantly. The case of 30-70-0 is almost 3 times higher than the granules from homogenised peat. The increase in q is due to two factors: pronounced surface hydrophobicity of the samples (Θ = 152°) and a heterogeneous porous granule structure. The presence of the cenosphere in the biocomposite reduces its surface hydrophobicity while increasing the diesel absorption capacity. Relatively rapid realisation of the maximum saturation by the MO was noted. In common, the designed absorbent shows up to 0.7 g·g^-1 sorption capacity for MO and up to 1.55 g·g^-1 sorption capacity for diesel. A possible mechanism of DF absorption and the limiting stages of the process approximated for different kinetic models are discussed. The Weber-Morris diffusion model is used to primarily distinguish the limiting effect of external and internal diffusion of adsorbate on the absorption process

Hydraulic conductivity is the key and one of the most uncertain parameters in groundwater modeling. The grid based numerical simulation require spatial distribution of sampled hydraulic conductivity at un-sampled locations in the study area. This spatial interpolation has been routinely performed using variogram based models (two-point geostatistics methods). These traditional techniques fail to capture the complex geological structures, provides smoothing effects and ignore the higher order moments of subsurface heterogeneities. In this work, a multiple-point geostatistics (MPS) method is applied to interpolate hydraulic conductivity data which will be further used in WASH123D numerical groundwater simulation model for regional smart groundwater management. To do this, MPS need ‘training images (TIs) as a key input. TI is a conceptual model of subsurface geological heterogeneity which was developed by using concept of ages, topographic slope as an index criteria and knowledge of geologist. After considerations of full physics of study area, an example shows the advantages of using multiple-point geostatistics compared with the traditional two-point geostatistics methods (such as Kriging) for the interpolation of hydraulic conductivity data in a complex geological formation.

The performance evaluation of the Internet of Things (IoT) based supply chain is challenging due to the involvement of multiple decision makers, the multi-dimensional nature of the evaluation process, and the existence of uncertainty and imprecision in the decision making process. To ensure effective decisions are made, this paper presents a fuzzy multicriteria analysis model for evaluating the performance of IoT based supply chain. The inherent uncertainty and imprecision of the performance evaluation process is adequately handled by using intuitionistic fuzzy numbers. A new algorithm is developed for determining the overall performance index for each alternative across all criteria. The development of the fuzzy multicriteria group decision making model provides organizations with the ability to effectively evaluate the performance of their IoT based supply chains for improving their competitiveness. An example is presented for demonstrating the applicability of the model for dealing with real world IoT-based performance evaluation problems.

The study investigates the role of NWP filtering for the remote sensing of Cumulonimbus Clouds (Cbs) by implementation of 14 different experiments, covering Central Europe. These experiments compiles different stability filter settings as well as the use of different channels for the InfraRed (IR) brightness temperatures. As stability filter parameters from Numerical Weather Prediction (NWP) are used. The brightness temperature information results from the IR SEVIRI instrument on-board of Meteosat Second Generation satellite and enables the detection of very cold and high clouds close to the tropopause. The satellite only approaches (no NWP filtering) result in the detection of Cbs with a relative high probability of detection, but unfortunately combined with a large False Alarm Rate (FAR), leading to a Critical Success Index (CSI) below 60 %. The false alarms results from other types of very cold and high clouds. It is shown that the false alarms can be significantly decreased by application of an appropriate NWP stability filter, leading to the increase of CSI to about 70 % . A brief review and reflection of the literature clarifies that the this function of the NWP filter can not be replaced by MSG IR spectroscopy. Thus, NWP filtering is strongly recommended to increase the quality of satellite based Cb detection. Further, it has been shown that the well established convective available potential energy (CAPE) and the convection index (KO) works well as stability filter.

A drug repositioning effort supported the possible use of the anti-HIV drug etravirine as a treatment for Friedreich Ataxia (FRDA). Etravirine increases frataxin protein and corrects the biochemical defects in cells derived from FRDA patients. Because of these findings, and since etravirine displays a favorable safety profile, we conducted this pilot open-label phase 2 clinical trial assessing the safety and potential efficacy of etravirine in FRDA patients. Thirty-five patients were stratified into 3 severity groups and randomized to etravirine 200 mg/day or 400 mg/day. They were treated for 4 months. Efficacy endpoints were represented by changes in peak oxygen uptake and workload as measured by incremental exercise test, SARA score, cardiac measures, measures of QoL and disability. Data were collected 4 months before the start of the treatment (T-4), at the start (T0), at the end (T4) and 4 months after the termination of the treatment (T+4). Etravirine was well tolerated. Etravirine completely stopped the progression of the SARA score during the 4-months treatment period, compared to the 4 months pre and post treatment. It increased peak workload, while the improvement of peak oxygen uptake was not statistically significant. No changes in the cardiac measures were observed. Health and QoL measures showed a worsening at the suspension of the drug. In this open trial etravirine significantly improved neurological function and was generally safe and reasonably tolerated. This suggests that etravirine represents a potential therapeutic agent in FRDA deserving testing in a randomized placebo controlled clinical trial.

There are four subtypes of adenosine receptors (ARs), named A1, A2A, A2B and A3, all of which are G protein-coupled receptors. The A2BAR, coupled to both Gαi and Gαq G proteins, is one of the several G-protein-coupled receptors that are expressed in a significantly higher level in some cancer tissues in comparison to adjacent normal tissues. There is growing evidence that the A2BAR plays an important role in tumor cell proliferation, angiogenesis, metastasis, and immune suppression. Thus, A2BAR antagonists are potentially novel attractive anticancer agents. Several antagonists targeting at the A2BAR are currently in clinical trials for various types of cancers. In this review, we first describe the signaling, agonists, and antagonists of the A2BAR. We further discuss the role of the A2BAR in the progression of various types cancers, and the rationale of using A2BAR antagonists in cancer therapy

Abstract: Abnormal growth of oligodendrocyte precursor cells (OPCs) importantly contributes to the progression of glioblastoma tumors. Hence, molecules that block OPC growth may be of therapeutic importance in treating gliomas. 2-methoxyestradiol (2ME), an endogenous tubulin interacting metabolite of estradiol is effective against multiple proliferative disorders. Based on its anti-carcinogenic and anti-angiogenic actions it is undergoing phase II clinical trials. We hy-pothesize that 2ME may prevent glioma growth by targeting OPC growth. Here we tested this hypothesis by assessing the impact of 2ME on growth of OPC cell line “Oli-neu” and dissected the underlying mechanism(s). Treatment with 2ME inhibited OPC growth in a concentra-tion-dependent manner and accompanied with significant upregulation in expression of p21 and p27, negative cell cycle regulators. Moreover, treatment with 2ME altered OPC morphology from multi-arm processes to rounded cells. At concentrations of 1uM and greater 2ME induced apoptosis with increased expression of caspase 3, PARP and caspase-7 fragments, externalized phosphatidylserine staining / apopercentage and increased mitochondrial activity. Flow-cytometry and microscopic analysis demonstrated that 2ME triggers endoreduplication in a concentration-dependent fashion. Importantly, 2ME induced cyclin E, JNK1/2 and p53 expres-sion as well as OPC cell fusion, key mechanisms driving endoreduplication or whole-genome duplication. Importantly, inhibition of p53 with pfithrin-α rescued 2ME-induced endoreduplica-tion. The pro-apoptotic and endoreduplication actions of 2ME were accompanied with upregula-tion of survivin, cyclin A, Cyclin B, Cyclin D2, ppRB. Taken together our findings provide evi-dence that 2ME not only inhibits OPC growth and triggers apoptosis, but also activates OPCs in a survival - fight or flight mode leading to endoreduplication. This inherent survival character-istic of OPCs may, in part be responsible for drug resistance in gliomas as observed for many tubulin interacting drugs. Importantly, the fate of OPCs after 2ME treatment may depend on cell-cycle status of the individual cells. Combining tubulin-interfering molecules with drugs, such as pfithrin-α, that inhibit endoreduplication may help inhibit OPC/glioma growth and lim-it drug resistance.

Colonoscopy, essential for diagnosing and treating colorectal cancer, relies on effective bowel preparation to inspect the colonic mucosa fully. Traditional education methods include printed instructions and verbal explanations, but these may not ensure clear patient understanding. Poor bowel preparation can obscure mucosal visibility, delaying cancer diagnosis and treatment. Using the outpatient’s Android mobile device, the mobile health care model is provided as a nursing guide to assist in bowel preparation for colonoscopy. This single-blinded, randomized trial en-rolled patients for colonoscopy at a Northern Taiwan gastroenterology center. Eligible patients were physically and mentally healthy and could communicate in Chinese. The study tested the ColonClean app during bowel preparation. After the procedure, endoscopists rated the prepara-tion quality using the Aronchick Scale. Data analysis with SPSS software (version 25.0) evaluated if the app significantly improved bowel preparation compared to those who did not use it. Forty patients were recruited for both the control (conventional method) and experimental (conven-tional method plus ColonClean app) groups. In the experimental group, endoscopists assessed bowel preparation quality; all achieved at least "Fair," with 75% receiving "Excellent" or "Good" ratings. The ColonClean App enhanced bowel preparation quality more effectively than tradi-tional nursing guidance.

It was previously reported that the peptide derived from LfcinB, R-1-R exhibited anti-Candida activity and this activity was enhanced in combination with an extract from the Bidens pilosa plant. However, the mechanism of action has not been studied. In the current study, a proteomic study was carried out, followed by a bioinformatic analysis and biological assays in both SC5314 strain and a fluconazole-resistant isolate of Candida albicans, after incubation with R-1-R. The proteomic data showed that after treatment with R-1-R in the two study strains, most of the differentially expressed proteins in comparison to controls were up-regulated. These proteins were mainly involved in membrane and cell wall biosynthesis, membrane transporters, oxidative stress, the mitochondrial respiratory chain, and response to DNA damage. Besides, the proteomic analysis of C. albicans parental strain SC5314 treated with R-1-R in combination with an ethanolic extract of B. pilosa was also undertaken. The proteins differentially expressed after this treatment were involved in similar functional processes as the proteins differentially expressed after treatment with R-1-R peptide alone, but at different levels and were mostly down-regulated. The biological assays allowed validation of the proteomic results, evidencing damage to the cell surface, generation of reactive oxygen species and decrease in mitochondrial membrane potential. The results provide information to begin to unlock the understanding of the complex antifungal mode of action of the R-1-R peptide, its combination with the extract, and possibly other derivatives of natural products.

The integration of Artificial Intelligence (AI) in predicting and managing risks for coastal port developments significantly enhances planning and operational efficiency. This paper presents a theoretical framework utilizing AI to predict future scenarios based on geological, hydrodynamic, and oceanographic factors. Focusing on the effective implementation of AI in ports, with a specific emphasis on Bulgarian Black Sea coastal areas such as Nesebur, Sarafovo, Sozopol, St. Anastasia Island, and the region of Karantinata in Varna, the study explores AI applications for site selection, risk assessment, and operational optimization to ensure resilient, efficient, and sustainable port infrastructure.

In reinforced concrete (RC) structures, splicing is required owing to the length limitations of rebars, insufficient lengths, and transportation issues. In particular, splices connect the rebar of RC structures such as walls, columns, beams, slabs, and joints. Lap splices are the most commonly used method worldwide because they do not require specific equipment or skilled workers. However, lap splices incur high construction costs because of the long splice lengths required for large-diameter rebars in megastructures, as well as issues pertaining to material supply, labor costs, constructability, and project duration. Additionally, approximately 15% more rebar is required because of the overlap. Energy saving for a sustainable built environment is possible if the disadvantage of lap splices, which generate high CO2 emissions due to the excessive use of rebar, are resolved. Hence, mechanical rebar couplers (MRCs) have been developed. However, despite their advantages, they have not been widely applied in construction sites owing to concerns regarding safety, quality, and constructability. Although various MRCs have been developed, most studies focus only on their structural performance. Therefore, a data-driven approach for selecting MRCs based on the reinforcing bar shape and structural characteristics is proposed in this study. Using a data-driven MRC selection algorithm, using the T-threaded coupler for one rebar over two floors resulted in 56% more efficient labor productivity, 15% shorter assembly time, 17% lower costs, and 26% lower CO2 emission. Using a developed algorithm, the appropriate MRC can easily and rapidly be selected for frequent design changes.

Forecasting electricity demand is of utmost importance for ensuring the stability of the entire energy sector. However, predicting the future electricity demand and its value poses a formidable challenge due to the intricate nature of the processes influenced by renewable energy sources. Within this piece, we have meticulously explored the efficacy of fundamental deep-learning models designed for electricity forecasting. Among the deep learning models, we have innovatively crafted recursive neural networks (RNNs) predominantly based on LSTM and combined architectures. The data-set employed was procured from a SolarEdge designer. The data-set encompasses daily records spanning the past year, encompassing an exhaustive collection of parameters extracted from solar farm (based on location in Central Europe (Poland Swietokrzyskie Voivodeship)). The experimental findings unequivocally demonstrated the exceptional superiority of the LSTM models over other counterparts concerning forecasting accuracy. Consequently, we compared multilayer DNN architectures with results provided by the simulator.

This study investigates the concentrations of physicochemical and heavy metal contaminants in the groundwater of the Middle Awash Basin, Ethiopia, to inform targeted water management strategies. A total of 32 groundwater samples were collected from 16 stations via piezometers and boreholes at the end of both the dry (June 2021) and wet (October 2021) seasons. Utilizing Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Atomic Absorption Spectroscopy (AAS), and in situ metering, 22 physicochemical and 24 heavy metal parameters were analyzed. The data revealed significant levels of contamination; notably, sample GW11 had extraordinarily high concentrations of total dissolved solids (10,826 mg/L), strontium (908 µg/L), molybdenum (802.4 µg/L), zinc (6060 µg/L), and electrical conductivity (15,645 µS/cm), while GW12 exhibited elevated levels of aluminum (2615 µg/L), zinc (4446 µg/L), and arsenic (117.2 µg/L). Contaminants such as arsenic, vanadium, gallium, lithium, rubidium, chromium, manganese, copper, and zinc were found enriched in groundwater near Lake Beseka, influenced by agricultural runoff from sugarcane plantations and agro-industry activities. Over half of the groundwater sources were unsuitable for drinking, posing significant health risks to local communities that rely heavily on these sources due to limited access to clean surface water. The findings emphasize the urgent need for comprehensive groundwater management and remediation plans in the Middle Awash region to ensure safe and sustainable water use, particularly addressing the variation in contamination levels influenced by Lake Beseka. These measures are critical to protect public health and support local development in the face of ongoing environmental and anthropogenic pressures.

It is vital to analyze ship collision risk for preventing collisions and improving safety at sea. This paper takes Ningbo-Zhoushan Port, a typical complex navigable water, as the research object, propose a probabilistic conflict detection method to estimate the potential collision risk by using a dynamic domain model driven by AIS data. Combined with the algorithm of fast modularity optimization and spectral clustering, a ship group extraction method from the perspective of water navigation management was proposed. Aiming at ship traffic characteristics in complex navigable waters, conduct dynamic calculation of ship collision risk from individual, regional and local multi-scale perspectives. The results show that the proposed method can detect the collision risk in a timely, reliable and effective manner under complex dynamic conditions. As such, they provide valuable insights for ship collision risk prediction and the development of risk mitigation measures.

In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach integrating vehicle and Unmanned Aerial Vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework that employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm's tendency towards early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach that successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system's logistics during critical situations.

This review article delves into the multifaceted nature of primary ovarian insufficiency (POI), a condition marked by diminished ovarian function before age 40, underscoring the complexity of its aetiology encompassing genetic, autoimmune, infectious, and environmental factors. A significant focus is placed on the potential role of single-gene mutations, such as those in FMR1, BMP15, and GDF9, in the pathogenesis of POI, highlighting the intricate interplay between genetics and ovarian health. The article also explores innovative therapeutic avenues, including the activation of dormant follicles in vitro, offering hope for fertility restoration in affected individuals. Despite the prevalence of traditional treatments, such as Chinese medicine and hormone therapy, in China, these approaches often need to meet therapeutic needs, underscoring the urgency for advanced research and tailored treatment strategies. The discovery of a pituitary tumour-associated POI case further complicates the diagnostic landscape, suggesting a broader diagnostic lens and more in-depth research to unravel the complex underpinnings of POI and enhance patient care.

Taking into consideration the arising resistance towards the currently available antimycobacterial, there is still an unmet need for the development of new chemotherapeutic agents to combat the infectious agent. This study presents the X-ray single-crystal analysis to verify the structure of leading sulfonyl hydrazone 3b, which have proven their potent antimycobacterial activity against Mycobacterium tuberculosis H37Rv with MIC value of 0.0716 μM, respectively, low cytotoxicity and very high selectivity indexes (SI=2216) and fully characterized by NMR and HRMS methods. Furthermore, this study assessed the ex vivo antioxidant activity, acute and subacute toxicity, and in vitro inhibition capacity against enoyl-ACP reductase of hydrazones 3a and 3b, as 3a was identified as the second leading compound in our previous research. Compared to isoniazid, compounds 3a and 3b demonstrated lower acute toxicity for intraperitoneal administration, with LD50 values of 866 and 1224.7 mg/kg, respectively. Subacute toxicity tests, involving the repeated administration of a single dose of the test samples per day, revealed no significant deviations in hematological and biochemical parameters or pathomorphological tissues. The compounds exhibited potent antioxidant capabilities, reducing malondialdehyde (MDA) levels and increasing reduced glutathione (GSH). Enzyme inhibition assays of the sulfonyl hydrazones 3a and 3b with IC50 values of 18.2 µM and 10.7 µM, respectively and molecular docking studies revealed enoyl acyl carrier protein reductase (InhA) as the possible target enzyme of the compounds to show their antitubercular activities. In conclusion, the investigated sulfonyl hydrazones display promising antitubercular drug-like properties and warrant further investigation.

In areas with a deep floor plan, the distribution of natural light is not uniform. Consequently, relying solely on daylight may not suffice to meet the space's lighting requirements, necessitating the use of artificial lighting in darker areas. Therefore, a lighting system is needed that not only controls the glare near the windows but also increases the light at the end of the room and provides uniform daylight. One of the widely used systems is the “light shelf“which has three main functions: shading, increasing the depth of light penetration and reducing glare. Review articles about light shelves have been done in 2015 and 2017, while more than 80% of the studies have been carried out since 2016 and light shelves with more diverse forms and dynamic elements and many consolidations were proposed. Therefore, there is a need for a more comprehensive review. The main question of this research is how different parameters (including climate, material, ceiling and integrated systems) can help to increase the efficiency of light shelves. By using a systematic review, studies in the past three decades were classified in order to determine the effect of these parameters on improving lighting performance and controlling solar heat gain.

In this paper, we consider a class of bilevel programming problems (abbreviated as, BLPP). Exploiting the generalized approximate convexity assumptions, we investigate the relations among the solutions of approximate Minty (respectively, Stampacchia) type variational inequalities (abbreviated as, AMTVI (respectively, ASTVI)), and the local ϵ-quasi solutions of the BLPP. Moreover, by employing the generalized Knaster–Kuratowski–Mazurkiewicz (abbreviated as, KKM)-Fan’s lemma, we derive some existence results for the solutions of approximate variational inequalities (abbreviated as, AVI), namely, AMTVI and ASTVI. A non-trivial example is given to highlight the importance of the established results. To the best of our knowledge, there is no research paper available in the literature that establishes relationships between the AVI and the BLPP under the assumptions of generalized approximate convexity in terms of limiting subdifferentials.

The article presents a model for optimizing biomass supply chains using the linear programming framework integrated with a Geographic Information System. Based on the distance from a given type of biomass resource, its calorific value, price, and transportation costs, the model identifies the optimal source of biomass, allowing it to cover the demand for the required total energy value with the lowest possible costs. The case study includes the Połaniec power plant in southeastern Poland and potential sources of forest biomass and agricultural straw within 100 km of the plant. Unit costs of biomass varied depending on biomass availability and energy demands. For energy demand on the level of 1 PJ yearly, the lowest unit costs of biomass (4.08 €/MJ) were when all kinds of biomass were available, and the highest (5.47 €/MJ) was when only stacked wood was available. As energy demand increased, unit costs increased, and the ability to meet this demand with just one type of biomass decreased. The energy biomass sector can utilize the model to benefit both biomass producers and their final buyers.

The Arabian Peninsula with its rugged mountains, wadis, alluvial plains, sand dune deserts, and diverse coastlines span over 3 million square kilometres. The Peninsula is situated at the crossroads of Africa and Asia and is a meeting point for diverse biogeographic realms, including the Palearctic, Afrotropical, and In-domalayan regions. This convergence of biogeographic zones has resulted in a remarkably diverse flora and fauna, which is adapted to the harsh and varied climates found throughout the Peninsula. Each of the coun-tries of the Arabian Peninsula are biologically diverse and unique in their own rite, but Yemen, Saudi Arabia and Oman are the most diverse in terms of their landforms and biological diversity. The mountainous regions support a cooler and more moderate climate compared to the surrounding lowlands, thus forming unique ecosystems that function as refugia for plant and animal species and have a high endemism of plant species. The desert ecosystems support a variety of life that are specially adapted to an extreme arid climate. Due to its long history of human habitation and subsistence agriculture, particularly in the mountainous areas, the Arabian Peninsula possesses unique crop varieties adapted to extreme arid climates, making them important genetic resources for the future in the face of climate change. The Arabian Peninsula, though rich and diverse in its biological diversity, has been greatly affected by human activities especially in the last 50 years, including urbanization, habitat destruction, overgrazing, and climate change; these pose significant threats to the biodiversity of the region.

An exposition of entropy applications to highlight some potential entropic applications to artificial intelligence and knowledge transfer. Entropy is a basic idea in artificial intelligence that is applied to many different tasks, such as reinforcement learning, data compression, and decision-making. By quantifying uncertainty and information content, it helps artificial intelligence models provide predictions and decisions that are well-informed. Therefore, this paper shines on spotlighting the significance of entropy to attract the attention of artificial intelligence research community to entropy as a powerful tool to advance artificial intelligence.The significance of knowledge transfer (KT), particularly intergenerational knowledge transfer (IGT), in knowledge management is also covered in this paper. To quantify the complexity of knowledge distribution inside an organisation and assess the efficacy of knowledge transfer (KT) initiatives, the notion of knowledge entropy (KE) is presented. Additionally, the KT model—which is based on the conceptions of tacitness and information content—is introduced. It combines personalisation and codification methodologies. Future research directions are offered alongside a few difficult open problems.

This paper discusses how mathematical models are used to predict environmental conditions. While these models are successful for larger scale phenomena, it becomes more complex for local weather or wave processes. Several approaches have been proposed to address this problem, including neural networks and statistical and mathematical models. The novelty in this approach is the use of statistical manifolds, which provide a more accurate way to estimate distances and cost functions. This work focuses on advancements in differential and information geometry for creating different types of models that can be used for environmental simulation and forecasting. These fields are vital for many institutes and centres worldwide, as they help solve challenges in areas such as meteorology, renewable energy, and aviation safety. Some potential IG applications to advance energy were given. Closing remarks with new open problems combined with the next research phase are provided.

This study investigates the efficiency of a biogas-powered cooling system through the utilization of energy and exergy calculations. Biogas, which can be generated and stored in small-scale plants as needed, serves as a viable fuel source for absorption cooling systems. The present research focuses on the biogas consumption of a triple-effect absorption cooling system, specifically designed to supply a fixed cooling load of 100 kW under varying operational conditions. The study highlights the COP (Coefficient of Performance) and ECOP (Exergetic Coefficient of Performance) values of the system, along with the exergy destruction rates of its individual components, at the optimal temperatures of operation. Furthermore, to determine the necessary biogas consumption, the study explores the establishment of dedicated farms for various animal species, ensuring an adequate number of animals for biogas production. The findings reveal a COP of 1.78 and an ECOP of 35.4% at the optimized operating temperatures. The minimum mass flow rate of biogas is determined to be 0.0034 kg/s, facilitating the operation of the boiler with a methane content of 65%. The study concludes that a total of 290 head of cattle is required to generate the annual biogas consumption necessary for the cooling system. Also, number of the cattle is enough to establish 284 biogas plants in Bursa province in Türkiye.

Life Cycle Assessment (LCA) is a widely used tool to measure the environmental sustainability of products or processes. Integrating LCA into the assessment of waste diversion strategies recognizes current waste diversion strategies are insufficient to effectively stem the global impacts of waste particularly with increased pressure to recover organic and inorganic materials to reduce landfill impacts and promote the circular economy. Historically, waste diversion efforts in municipalities and industries focused on higher-profile inorganic wastes, such as plastics and other recyclables. However, organic waste is increasingly identified as a key waste fraction that must be effectively managed and regulated. This research surveys published LCA studies from 2019 to 2023 focusing on the anaerobic digestion (AD) of organic waste. Notable conclusions include the lack of studies comparing AD with the latest treatment options such as co-gasification; the insufficient attention to the LCAs on biogas upgrading methods; and the monetization of LCA results using carbon credits. In addition, more than 50% of reviewed LCA studies concluded the results with a sensitivity analysis which was not a common practice before 2019 in LCA studies in anaerobic digestion. This signifies the increasing need to understand uncertainty in the circumstances governing applying AD to wastes. Finally, neglecting the combined effect of several parameters in sensitivity analysis might have reduced the accuracy of the sensitivity analyses in the reviewed LCAs. Overall, LCAs conducted on AD-related applications vary widely in terms of scope and consistency, implying that the outcomes may not be as applicable as intended. The identified challenges, issues, and other findings related to this research are expected to help standardize LCA procedures as applied to AD to promote greater comparability.

In this article, we consider a class of nonsmooth interval-valued multiobjective semi-infinite programming problems with vanishing constraints (in short, NIMSIPVC). We introduce the VC-Abadie constraint qualification (in short, VC-ACQ) for NIMSIPVC and employ it to establish Karush-Kuhn-Tucker (in short, KKT)-type necessary optimality conditions. Related to NIMSIPVC, we formulate interval-valued vector Lagrange type dual and scalarized Lagrange type dual problems. Subsequently, we establish weak, strong, and converse duality results relating NIMSIPVC and corresponding dual problems. In addition, we introduce the notions of saddle points for interval-valued vector Lagrangian and scalarized Lagrangian of NIMSIPVC. Moreover, we establish the saddle point optimality criteria for NIMSIPVC. Various non-trivial examples are provided to demonstrate the validity of established results. To the best of our knowledge, optimality conditions, Lagrange type duality, and saddle point optimality criteria for NIMSIPVC have not been investigated yet via Clarke subdifferentials.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

Aim: To investigate the effects of intensive physical exercise on retinal microvascular regulation in healthy volunteers by the adaptive optics retinal camera measurement. Methods: We included healthy volunteers (11 men and 14 women) aged 20.6± 0.9. Heart rate (HR) and systolic and diastolic blood pressure (SBP, DBP) were recorded before and after a submaximal physical exertion of continuously riding a training ergometer. The superior temporal retinal artery measurements were captured using the adaptive optics (AO) camera rtx1TM (Imagine Eyes, Orsay, France) without pupil dilatation. We compared measures of vessel diameter (VD), lumen diameter (LD), two walls (Wall 1, 2), wall-to-lumen ratio (WLR), and wall cross-sectional analysis (WCSA) before and immediately after the cessation of exercise. Results: Cardiovascular parameters: After exercise, SBP, DBP and HR changed significantly from 130.2±13.2 to 159.7±15.6 mm Hg, 81.2±6.3 to 77.1±8.2 mm Hg and 80.8±16.1 to 175.0±6.2 bpm, respectively (p < 0.002). Retinal microcirculation analysis: Before the exercise, the mean LD was 96.0±6.8, VD 118.5±8.3, WLR 0.234±0.02, WCSA 3802.8±577.6. The mean LD and Wall 1 did not change significantly after exercise but slightly decreased. The VD, Wall 2, WLR and WCSA decreased significantly (p

Balsam fir trees are a specialty agricultural species that are largely used as Christmas trees. Soil or tissue sampling is seldom performed to assess balsam fir nutrition needs, which can contribute to over or under fertilization and subsequent environmental challenges. Remote sensing to determine the normalized difference vegetation index (NDVI) has had strong relationships with nutrition in other plants. This research aimed to determine whether there is a relationship between NDVI and nutrition in balsam fir. A random sample of 45 trees was selected in autumn of 2021 and then another 70 trees selected in spring of 2022. Soil and needle tissue samples were analyzed for N, P, K, Ca, Mg, S, Fe, Zn, Mn, B, and Cu. An uncrewed aerial vehicle equipped with a multispectral camera was flown over the orchard to determine the NDVI for each tree. Nutrition and NDVI were analyzed for correlations through all sampled trees. Separate correlation analyses were repeated for mature trees (greater than 5 years old) and young trees (less than 5 years old). Soil nutrients N, P, K had a significant relationship with NDVI in autumn sampling. Only N was correlated consistently in both autumn and spring, with R2 of 37% and 33% respectively. Relationships were much stronger when only mature trees were considered, increasing to 60% and 51% respectively. NDVI was found to offer predictive power for tissue N that can be useful to producers to help optimize fertilizer input. However, further work is needed to refine these relationships.

Exposure to vaccine lipid nanoparticles, mRNA, adenoviral DNA, and or Spike protein from one of the approved Covid-19 vaccines, or through secondary exposure, as through blood transfusion, is a potential source of harm. Blood reactions are an acknowledged side-effect of Covid-19 vaccination, not limited to hemolysis, paroxysmal nocturnal hemoglobinuria, chronic cold agglutinin disease, immune thrombocytopenia, haemophagocytosis, hemophagocytic lymphohistiocytosis, and many other blood related conditions. The observation of adverse events has motivated investigation into the cardiovascular mechanisms of harm by Covid-19 vaccines, and the biodistribution of vaccine contents. Biodistribution may not be limited to the body of the vaccine recipient, as a growing body of evidence demonstrates the possibility of secondary exposure to vaccine particles. These can be via bodily fluids and include the following routes of exposure: blood transfusion, organ transplantation, breastfeeding, and possibly other means. As covid-19 vaccines are associated with an increased risk of stroke, the persistence of vaccine artifacts in the blood presents a possible threat to a recipient of a blood donation from a vaccinated donor who suffered from vaccine induced thrombosis or thrombocytopenia. (VITT) We assess the feasibility and significance of these risks through an overview of the case report literature of blood disorders in vaccinated individuals, pharmacovigilance reports from the US Vaccine Adverse Events Reporting System (VAERS) and a meta-analysis of the available literature on organ transplants from vaccinated organ donors. Our analysis establishes biological mechanistic plausibility, a coherent safety signal in pharmacovigilance databases for secondary vaccine contents exposure (for the cases of blood transfusion and breastfeeding) and also an elevated level of adverse events in organ transplants from VITT-deceased donors, echoing increases in organ transplantation related complications seen in national statistics for some countries. Secondary exposure to vaccine artifacts is a potential explanation for some of the cases put forth, and requires a deeper investigation.

During recent years there is an on-going deployment of 5G base stations for radiofrequency (RF) communication in Sweden as well in many other countries. This is made without investigations on risks to human health and the environment. Since 2016 we have made several measurements of environmental exposure to RF radiation in Stockholm, Sweden, including previous generations and now also 5G. In the current study, performed in October 2023, the broadband meter Narda-550 with the probe EF-1891 was used. It gives results in the root mean square (RMS) mode, thus not peak levels. Both the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the US Federal Communications Commission (FCC) base their guidelines on RMS levels of RF radiation. Measurements were made in similar city areas as in our previous studies. Results show that the RF radiation has increased substantially. High maximum levels were found at e.g. the Central Railway Station (3 637 191 µW/m2), and popular walking streets such as Mäster Samuelsgatan (11 613 976 µW/m2), and Drottninggatan (5 271 555 µW/m2). The peak values would be much higher. These levels are in the same magnitude or even higher than those measured in homes of persons that rapidly developed symptoms of the microwave syndrome after installation of 5G, usually in combination with 4G+, in the neighborhood. They are also substantially higher than levels that have previously been linked to cancer and the microwave syndrome in studies of people living near base stations and masts from previous generations of telecommunications.

Modern automotive systems should must satisfy strict reliability requirements. Most of the real vehicle systems and safety-critical networks have complex interconnections. Sensitivities and probabilistic uncertainties of reliability of System with Complex Interconnections (SwCI) can be investigated by the Monte-Carlo Simulation (MCS). The paper focuses on sensitivities and parametrical uncertainties of SwCIs reliability. The proposed method can be used forimplemented in the investigation of uncertainties of SwCIs reliability – in other wordsi.e., for in the determination of critical system elements and to estimateion of the Required Number of Spare Parts (RNSP) of the system, which depends on the probability of allowable spare equipment shortage.

Background: The last-mile delivery phase, the final stage where goods move from a distribution center to customers, is pivotal but faces significant inefficiencies and high costs due to its complexity. Recent advancements in Edge AI or Edge Intelligence (EI) offer promising solutions to these challenges. Methods: This study explores how AI-driven technologies and real-time data processing, combined with EI, can enhance last-mile delivery operations. A thorough literature review was conducted to assess technological advancements, and a Delphi method was used to systematically and empirically assess the impact of EI solutions on both operational efficiency and customer satisfaction. Results: Although EI technologies offer substantial benefits, EU companies are hesitant to adopt these innovations due to high implementation costs. However, firms that have embraced these technologies report significant improvements, including better route optimization, reduced delivery times, and enhanced service reliability. These findings highlight the need for a culture of innovation and the recruitment of experts with advanced qualifications to drive technological advancement in last-mile logistics. Conclusions: The integration of EI represents a significant step towards more efficient, cost-effective, and customer-focused last-mile delivery solutions. Future research should aim to refine these technologies and explore their long-term impacts on the logistics industry.

Climate change and the increasing frequency and severity of drought events pose significant challenges for sustainable agriculture worldwide. Soil microorganisms, both beneficial and pathogenic, play a crucial role in mediating plant-environment interactions and shaping the overall functioning of agroecosystems. This review synthesizes the current knowledge on the contrasting adaptive mechanisms utilized by different groups of plant-soil microorganisms focusing on beneficial and pathogenic bacterial and fungal communities in response to drought and desiccation stresses. The review examines the common survival strategies employed by microbes specifically rhizobacteria and arbuscular mycorrhizal fungi, such as the production of osmoprotectants, altered gene expression, and biofilm formation. It also highlights the distinct adaptive mechanisms of pathogenic versus mutualistic microbes, with pathogens tending to prioritize virulence factors and suppress plant growth, while beneficial microbes enhance plant growth and stress tolerance. Genetic exchange such as horizontal gene transfer (HGT) is identified as a key adaptive mechanism, allowing both pathogenic and non-pathogenic microbes to acquire beneficial traits like stress tolerance and virulence factors. Environmental stressors like drought can promote increased genetic exchange and the spread of pathogenic traits within the soil microbiome. The complex interplay between drought-adapted microbes and their interactions with plants is discussed, emphasizing the need for a deeper understanding of soil microbiome dynamics under climate change. This knowledge can be utilized in sustainable agricultural practices to mitigate the impacts of drought on plant health and productivity. This review provides insights into the divergent survival strategies of soil microorganisms in response to drought and desiccation, for managing the resilience of agroecosystems to climate change.

Agricultural activities are the second largest source of greenhouse gas emissions, and carbon emissions from agricultural land use (CEALU) have become a hot issue across the world. However, few scholars explored the impact of agricultural land policies on carbon emissions, such as the High-standard farmland construction(HSFC) in China.Thus，by relying on provincial panel data for China for the period 2005-2017, the effect of the high-standard basic farmland construction policy on carbon emissions from agricultural land use per unit area and its regional differences were quantitatively analyzed using the difference-in-difference (DID) model. The results showed that: 1) China's CEALU per unit area presented a fluctuating upward trend during the period 2005-2017, from 392.58 kg/ha to 457.72 kg/ha, with an average annual growth rate of 1.31%. 2) The high-standard farmland construction(HSFC) policy produced a significant carbon emission reduction effect in agricultural land use, and reduced the CEALU per unit area by 10.80% on average. With the promotion of this policy, its carbon emission reduction effect in agricultural land use presented an overall increasing trend. 3) The carbon emission reduction effect of the high-standard farmland construction policy in agricultural land use was significant in central China, but non-significant in eastern China and western China.

In this work, satellite data from the Clouds and Earth’s Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments is analyzed to determine how the global absorbed sunlight and global entropy generation rate have changed during the time period from 2002 to 2023. The data is used to test hypotheses derived from the Maximum Power Principle (MPP) and Maximum Entropy Production Principle (MEP) about the evolution of earth’s surface and atmosphere. The results indicate that the amount of absorbed sunlight has increased over the last 20 years but rising surface temperatures have caused the entropy generation rate to remain approximately constant despite that decrease in albedo. The results are simpler to explain using the MPP but do not completely rule out the MEP. Given the acceptance of the MPP or MEP, some extensions and nuances are discussed.

This investigation mainly focused on the microstructural analysis of Al based hybrid metal matrix composites. Aluminium as base metal and reinforced with 2% B4 C, ZrO2 (2%,4%,6%), SiC (4%,8%,12%). Specimens were manufactured by using conventional stir casting technique. Hybrid metal matrix composites were examined by using Scanning electron microscope. This research explained XRD, EDS and Fractography analysis. This investigation shows the reinforced material has given better results in all aspects than pure Al6061 alloy and homogeneous dispersion of particles inside the matrix. Porosity, delamination, cracks, particle distributions were observed through microstructural analysis. Additionally determined the crystal size of pure Aluminium 6061 alloy and hybrid composites.

In the paper the verification of forecasts of precipitation conditions measured by the standardized precipitation index SPI is presented. For the verification of categorical forecasts a contingency table was used. Standard verification measures were used for the SPI value forecast. The 30 day SPI moved every 10 days by 10 days was calculated in 2013-2015 from April to September on the basis of precipitation data from 35 meteorological stations in Poland. Predictions of the 30 day SPI were created in which precipitation was forecasted in the next 10 days (the SPI 10-day forecast) and 20 days (the SPI 20-day forecast). Both for the 10 and 20 days, the forecasts were skewed towards drier categories at the expense of wet categories. There was a good agreement between observed and 10-day forecast categories of precipitation. Less agreement is obtained for 20-day forecasts – these forecasts evidently “over-dry” the assessment of precipitation anomalies. The 10-day SPI value forecast accuracy is acceptable, whereas for the 20-day forecast is unsatisfactory. Both for the SPI categorical and the SPI value forecast, the 10-day SPI forecast is reliable and the 20-day forecast should be accepted with reservation and used with caution.

Finding a good MIMO system model also major issue in Wireless Communication system. It is facing with so many problem, one of the major problem is finding good system model in terms of capacity and transmitting antenna system. In this paper, we analyze the channel capacity of various MIMO system model with some constant SNR level and outage probability. We establish a novel idea for MIMO system models as consider as 2^N- MIMO system model and find-out change in channel capacity when different transmitting antennas with constant SNR and outage probability. The channel capacity ratio CC_R is presented here on the basis of 2^N- MIMO channel capacity model. Number of transmitted antenna presented in MIMO system is increases is well-known however paper shows change in capacity in simple form.

The study aims to utilize the convergence-confinement method (CCM) by considering non-hydrostatic stress assumptions in the analysis of the surrounding rock in a circular tunnel. The rock mass properties should adhere to the criteria of the non-linear Hoek-Brown failure criterion. Through a thorough theoretical analysis approach, an analytical solution is derived to determine the stress and displacement induced by tunnel excavation, particularly in the elastic and plastic zones. This solution, applicable under anisotropic stress conditions, involves accounting for confinement loss incrementally for computational feasibility. The implementation of this analytical solution, facilitated by a straightforward spreadsheet, was validated against existing data to evaluate the impact of non-linear failure criteria on ground reaction behavior. The study scrutinizes the mechanical response at the tunnel's inner curve and assesses stress-displacement distribution across the tunnel cross-section. A comparison between the proposed solution and published results demonstrates a consistent and promising correlation.

This work deals with a novel porous TiNi-based material for ophthalmic orbital implants that was produced by high-temperature synthesis (SHS) and possesses a specific microstructure of its surface pores, which makes it attractive for biomedical use in implants. After preparation via SHS, the obtained porous TiNi material was etched in acidic environment, to get rid of its surface Ti2Ni secondary-phase particles. This was found to improve the material’s surface morphology, adding micro-roughness to its macro-rough pores. As a result, cell growth tests conducted on the material demonstrated improved cell adhesion and growth kinetics on such a porous material with improved roughness. Finally, the material was tested in vivo as an ophthalmic orbital implant, demonstrating good biocompatibility, good degree of biointegration with surrounding eyeball tissues, and no signs of rejection after as long as 180 days. Thus, the novel porous TiNi-based material shows promise for its use in ophthalmic implantology, for instanse for manufacturing musculoskeletal stumps of the eyeball after evisceration, as it is biocompatible, has a high tissue-implant integration potential and demonstrates reduced risks of exposure and rejection of the implant.

Background: Media coverage of Lyme disease (LD) has led to an increase in consultations for presumed LD in Europe. However, LD is confirmed in only 10-20% of patients, with a significant number remaining in a diagnostic dead-end. Objectives: To reach a deeper understanding of how patients themselves contribute to the diagnostic process. To describe the genesis of the LD hypothesis in care pathways. Methods: In 2019, 30 patients from a prospective cohort consulting in the infectious diseases department at University Hospital in Marseille for presumed LD were recruited for semi-structured interviews. The inclusion criteria were : suffering from subjective symptoms for 6 months, no clinical or paraclinical argument suggesting current LD. The patients’ medical trajectories were collected using a biographical approach. Results: The diagnosis of Lyme disease was primarily triggered by identification with personal testimonies found on the internet. Most of patients were leading their own diagnostic investigation. The majority of participants were convinced they had LD despite the lack of medical evidence and the scepticism of their referring GP. Conclusion: GPs should first systematically explore patients’ etiologic representations in order to improve adherence to the diagnosis especially in the management of medically unexplained symptoms. Long COVID-19 syndrome challenge offers an opportunity to promote active patient involvement in diagnosis.