This study investigates the impact of water stress on grapevines, specifically examining the role of rootstocks and aquaporins. Two experiments on potted plants were conducted in central Chile during the summer, under conditions of high water demand, involving various rootstock genotypes and combinations of Cabernet Sauvignon (CS) grafted onto rootstocks. Significant differences were observed among plants in terms of stem water potential, stomatal conductance, and growth rate. Notably, the CS/CS combination consistently displayed the slowest growth rate, regardless of the irrigation treatment. The study also analyzed the expression levels of plasma membrane intrinsic proteins (PIPs) and tonoplast intrinsic proteins (TIPs) aquaporins in the leaves of grafted plants. Specifically, VvPIP2;2 aquaporins showed reduced expression after 14 days without irrigation, whereas VvTIP1;1 and VvTIP2;1 expression levels correlated positively with gs responses in grafted plants, suggesting their role in modulating water content in leaves under water stress. TIP aquaporins likely play a significant role in the differential responses of CS plants towards nearly isohydric or anisohydric behaviour. The CS/CS combination exhibited near isohydric behavior, correlating with lower TIP aquaporin expression, while the combination of CS onto 1103P and 101-14 showed higher expression, indicating anisohydric behavior. The findings suggest that grafted plants are more resilient to water stress, supporting the idea that rootstocks can mitigate the effects of water stress on the scion.

Breast cancer is currently the second leading cause of death worldwide, accounting for 15% of all deaths (8.2 million deaths). Approximately 20% of breast cancer diagnoses are triple-negative breast cancer (TNBC), a type of cancer that lacks receptors for estrogen (ER negative), progesterone (PR negative), and human epidermal growth factor receptor 2 (HER2/neu negative). Consequently, TNBC doesn’t respond to hormonal or targeted therapies, making it challenging to treat due to its rapid growth, metastasis, and high recurrence rate within the first three years of therapy. Alternative chemotherapies are needed to address this problem. Pharmacological dose of vitamin C (high-dose VC) has been identified as a potential treatment for some cancer cells. The present study aimed to evaluate whether VC has a therapeutic effect on TNBC using MDA-MB-231 cells as the model. MTT assay, Hoechst 33342 staining, Nuclear ID Red/Green staining, Rhodamine 123 staining, and Western blot analysis were employed to test the hypothesis that pharmacological dose of VC can kill TNBC cells. Upregulation of Apaf-1, caspases -3, -6, -7, -8, and -9, inhibition of matrix metalloproteinases (MMP-2 and MMP-9), reduction in cell cycle protein expression, and enhancement of tumor suppressor proteins such as p53 and p21 indicated that pharmacological dose of VC has promising anti-cancer properties in the treatment of TNBC.

The rapid pace of urbanization, coupled with the growing need for environmental sustainability and technological innovation, has brought smart city initiatives to the forefront of global urban development. This paper provides a comparative analysis of smart city policies from leading cities such as Oslo, Zurich, and Canberra, and derives strategic insights for Qatar. By examining the strategies, priorities, outcomes, and challenges associated with implementing smart city initiatives in these cities, the study identifies areas of alignment and divergence with Qatar's own smart city policy framework. The research highlights potential opportunities for cross-learning and suggests how Qatar can leverage lessons from international best practices to enhance its smart city initiatives. Key areas of focus include fostering strategic partnerships, promoting innovation, and ensuring effective governance and data management systems. The findings provide actionable insights to improve the effectiveness of Qatar's smart city projects, aligning them with the Qatar National Vision 2030.

Recent studies with ApoE knockout (ApoE-/-) mice deficient for PACAP (pituitary adenylate cyclase-activating polypeptide) or its receptor PAC1 provide evidence for a possible involve-ment of immune- and vasoregulatoy PACAP signaling at the PAC1 receptor in atherogenesis and plaque associated vascular inflammation. Therefore, we tested the PAC1 receptor agonist Maxadilan and the PAC1 selective antagonist M65 on plaque development and lumen stenosis in the ApoE-/- atherosclerosis model for possible anti-atherogenic effects. Adult male ApoE-/- mice were fed a cholesterol-enriched diet (CED) or standard chow (SC) treat-ed with Maxadilan, M65 or Sham. Effects of treatment on atherosclerotic plaques, lumen stenosis, apoptosis and pro-inflammatory signatures were analyzed in the brachiocephalic trunk (BT). The percentage of Maxadilan treated mice exhibiting plaques under SC and CED was lower than that of Sham or M65 treatment indicating opposite effects of Maxadilan and M65. Maxadilan ap-plication inhibited lumen stenosis under SC and CED compared to Sham. In spite of significantly increased cholesterol levels, lumen stenosis of Maxadilan-treated mice was similar under CED and SC. In contrast, M65 under SC or CED did not reveal a significant influence on lumen steno-sis. Maxadilan significantly reduced the TNF-α+ area in the plaques under CED, but not under SC. In contrast, the IL-1β+ area was reduced after Maxadilan treatment under SC, but remained unchanged under CED compared to Sham. Maxadilan reduced the caspase-3+ area in the media under both SC and CED without affecting lipid content in plaques. Despite persisting hypercholesterolemia, Maxadilan reduces lumen stenosis, apoptosis and TNF-α driven inflammation. Our data suggest that Maxadilan provides atheroprotection by act-ing downstream of cholesterol-induced vascular inflammation. This implicates the potential of PAC1-specific agonist drugs against atherosclerosis even beyond statins and PCSK-9.

Background: Urinary tract infections (UTI) are a significant health concern, particularly in women, with almost half of them experiencing at least one UTI in their lifetime. This problem is further compounded by the high recurrence rate within 6-12 months. Postmenopausal women are especially vulnerable to recurrent UTI due to the natural decline in estrogen levels, which alters the urogenital epithelium and subsequently affects the urogenital microbiome. Methods: This study included 674 female patients that were admited at "Dr. Carol Davila" Central Military Emergency University Hospital in Bucharest over a period of 3 years. Results: Of the 674 women with urinary tract infection, 435 (79.3%) had more than one positive culture, and 239 (35.4%) experienced at least one recurrent urinary tract infection 6-12 months after the initial diagnosis. The mean age of women with urinary tract infection was 63 (± 15.61 years). Recurrent urinary tract infections were most prevalent in those aged 65-80 years (52%). Escherichia coli was detected in 71% of the positive cultures. Conclusions: The management of recurrent urinary tract infections in women presents a complex challenge that necessitates a multifaceted approach. The emerging understanding of the urobiome and its role in bladder health offers promising avenues for developing targeted interventions. As research in this field advances, it is imperative to integrate these new insights into clinical practice to balance traditional antibiotic treatments with novel strategies that consider the intricate microbial ecosystem of the urinary tract.

Disruptions in power quality have a negative impact on many energy consumers. These include lighting, where interference manifests itself, among others, in the form of light flickering. The article presents phenomena accompanying the operation of modern light sources against the background of exemplary results of studies on the flicker of conventional light sources, such as incandescent or fluorescent lamps. To assess the effects of these phenomena, it is necessary to use measures other than those traditionally used. The results of measurements carried out in accordance with different recommendations take into account both power supply disturbances and the properties of modern light sources.

Clinical supervision is an essential element in trainees, preventing burnout and ensuring the effectiveness of their intervention. AI technologies offer increasing possibilities for the development of clinical practices, among it supervision appears to possess the ideal characteristics for automation processes. In this study we test the capabilities of ChatGPT-4 to provide supervisory feedback by then placing them in comparison with feedback produced by a qualified supervisor. Two ChatGPT-4-generated feedbacks (the first one produced by a naïve identity and the second one by a trained identity) and one human-produced feedback were evaluated by means of a liking questionnaire, which was filled out to a group of gestalt psychotherapy trainees. Principal Component Analysis (PCA) highlighted 4 components of the questionnaire: relational and emotional dimensions (C1), didactic and technical quality (C2), treatment support and development (C3), and professional orientation and adaptability (C4). The ratings of satisfaction, obtained from the three supervisory feedbacks, were compared by applying 1-way analysis of variance (ANOVA). Statistical evaluations were performed using the statistical package SPSS version 25. The feedback generated by pre-trained AI (f2) was rated significantly higher than the other two (untrained AI feedback (f1) and human feedback (f3) in C4; in C1 the superiority of f2 over f1 but not over f3 appears significant. These results suggest that the use of pre-trained AI may be an appreciable option for increasing the effectiveness of clinical supervision in some specific areas, including especially career guidance.

The next generation network requires not only ultra high data rate, global coverage and connectivity, but also reducing network deployment costs and energy consumption. The emergence of reconfigurable intelligent surface (RIS) technology provides an effective way to improve efficiency and reduce cost, while the passive elements bring new challenges of channel estimation (CE) and beam tracking. For a RIS-aided multiple-input and single-output (MISO) system, in this paper, to achieve the channel state information (CSI), we propose a principle component analysis (PCA) based staged channel estimation method. Based on the estimated channel, we propose a deep learning (DL) based beam tracking scheme to realize low-complexity RIS reflection coefficient design, which effectively improves the signal-to-noise ratio (SNR) at the user side. Simulation results verify our proposed channel estimation scheme based on PCA and beam tracking scheme based on DNN for the semi-active RIS-aided MISO systems can obtain approximate performances with much lower computational complexity.

This study delves into the impact of artificial intelligence on students’ learning.The paper pioneers the use of kmeans classification to reduce the dimensionality of features before the classification process, which minimizes redundant information and computational overhead. This step is not commonly found in prior research.Firstly, a random forest model is established with “whether AI assistance was used to complete the paper” as the output, and various features as inputs. The analysis of the predictive accuracy when changing input features reveals the degree of influence of each feature on using AI to complete the paper. After feature selection, the remaining 6 features can still effectively predict the use of AI to complete the paper, indicating the representativeness of the extracted indicators. Subsequently, the PCA-K-means algorithm is employed to classify the use of artificial intelligence. PCA algorithm is used for dimensionality reduction of the extracted features to better characterize students’ use of AI. Three representative variables are selected, and the contribution of each feature is computed based on the factor loading matrix. Finally, the K-means algorithm is used to classify students based on the three dimensional features, showing that students can be effectively classified into 2 categories. The results indicate that the majority of students support the use of AI technology to enhance their abilities, although some students may only intend to use AI to cope with exams and similar tasks.

An empirical investigation was carried out to study the relationship between the soil characteristics, including the Nitrogen (N), Phosphorus (P), Potassium (K), potential of Hydrogen (pH), Electrical conductivity (EC), Sulfur (S,), Zinc (Zn), Iron (Fe), Copper (Cu), Manganese (Mn) and Boron (B) using principal component analysis (PCA), Factor Analysis (FA), and Canonical Correlation analysis (CCA) for data reduction multivariate techniques. The first five main components accounted for 24.22%, 18.52%, 16.1%, 9.97%, and 9.27% of sample variances, respectively. All five principal components (PCs) accounted for 78.07% of the sample variance. The soil characteristics viz., P and K have highly dominated the first PC; the soil characteristics N and pH have dominated the second PC; K, EC, and Cu have dominated the third PC; Fe, Mn, and pH have dominated in the fourth PC; whereas the parameter Cu dominated the fifth PC. The first factor showed a strong negative loading on S and a strong favorable loading on Cu; P, K, and EC have substantial positive loadings in the second factor. Significant positive loadings on Mn, Fe, Cu, pH, N, and K are present in the third component. The fourth factor had a significantly positive pH, Fe, N, P, K, and B loadings. Fe, P, and B have much weight in the fifth factor. The soil characteristics viz., N, Zn, pH, K, Fe, and Mn have dominated the first PC; the soil characteristics B, P, S, Zn, and Fe have dominated the second PC; B and P have dominated the third PC whereas the only character Cu dominated the fourth PC. Cu and OC have much weight in the fourth factor. The canonical redundancies for dependent and independent sets are 12% and 17%, respectively. The Stewart-Love canonical redundancy index is 70.58%, which means that the first linear combination of the X-set explains 70.58% of the total variance in the Y-set.

Psoriasis is a chronic, immune-mediated skin disease significantly impacting women, with disease severity often modulated by hormonal fluctuations. This review examines the influence of hormonal changes on the course of psoriasis in women, focusing on key life stages—including the menstrual cycle, pregnancy, postpartum, and menopause—and their impact on disease progression and symptomatology. Estrogen, the principal female sex hormone, plays a critical role in immune modulation. Variations in estrogen levels, which occur naturally throughout a woman's life, are associated with fluctuations in psoriasis severity. Low estrogen levels, as seen during menstruation or menopause, are linked to symptom exacerbation, while elevated levels during pregnancy may reduce symptoms in some women. However, responses are variable, with others experiencing no change or worsening during pregnancy. Postpartum, the rapid decline in estrogen often triggers severe flare-ups, while menopause, marked by a sustained estrogen reduction, frequently correlates with increased disease severity and flare frequency. The review also addresses the profound impact of psoriasis on women’s quality of life, including physical discomfort, psychological distress, and social stigma. Additionally, fertility concerns are discussed, as severe psoriasis and associated treatments may increase the risk of adverse pregnancy outcomes. Consideration is given to hormonal therapies, lifestyle modifications, and their effects on psoriasis, underscoring the need for personalized treatment approaches that account for hormonal influences. Understanding these hormonal dynamics is essential for developing targeted, effective management strategies that enhance quality of life for women affected by psoriasis.

introduction In the professional practice of nursing, differences have been described between the habitus shared in the academic training process and the professional practice developed in the field of health systems; in this scenario, nurses tend to be affected by how their professional habitus has been established, which, in some way, alters the cultural capital established in their academic training process. The research objective was to identify aspects that facilitate and limit the development of the theoretical component in nursing practice.Method: The research was based on a qualitative hermeneutic study addressed from a critical approach that linked Bourdieu's field theory. The study included 17 students, 11 nurses active in health services, and six university professors. Data were collected from a sociodemographic questionnaire, episodic interviews, and focus groups. The information collected was analyzed using the hermeneutic understanding process from the grounded theory.Results: The research revealed a central thematic core called clarification of the role of the nurse (professional habitus) and three secondary thematic cores related to the need to strengthen the nursing essence (identity habitus), the adjustment of training processes (optimization habitus) and, finally, the strengthening of the nurse's credibility (validation habitus).Conclusion: The professional habitus of nursing professionals is shaped by different elements of the social field of the health system. This field is repressed by hierarchical structures that succeed in disconnecting theory from praxis. Aspects such as subordination to the hegemonic biological discourse and the medical profession, the limited appreciation of humanized care, restrictions on the recognition of the nursing process by health institutions, and limited solidarity and leadership end up conditioning the accumulation of symbolic capital and social capital of nursing, which leads to the loss of autonomy and the advancement of professional development.

The present paper tackles the important issue of tensile ultimate strength of ceramic matrix composites, using a multiscale approach. The ultimate strength is investigated at the successive increasing length scales inherent to 2D woven SiC/SiC composites, i.e. single filaments, fiber tow, minicomposites reinforced with a single tow, and 2D woven composite. First experimental results on tensile behavior under strain-controlled conditions are summarized for tows, minicomposites and composites. Then, models of tow ultimate failure under controlled force and strain are presented. A criterion of tow failure is developed for filament fracture initiation and then propagation based on applied stress and on filament strength gradient. The model of ultimate failure of minicomposite under strain-controlled condition is based on the strength of filaments in the presence of matrix cracks and the overstress induced by interactions of broken filaments and the matrix. The variability of ultimate strengths of filaments, minicomposites and composites at various gauge lengths is described by linear p-quantile diagrams which indicates that the data follow normal distribution function. The contribution of structural effects to variability of composite and minicomposite strength under strain-controlled loading is analyzed. Their dependence on specimen size is related to reproducibility of critical flaw population and structural effects.

The proliferation of cloud computing has given rise to challenges in security that established perimeter-based models cannot easily adapt to. The new buzzword Zero Trust, which is based on the principle of "never trust, always verify," has been looked up as one of the promising solutions. Is Zero Trust a certainty or pie in the sky for security in the cloud? This is discussed further in this paper. This philosophical approach leads us through the earlier work and scientific literature available on the subject, presenting Zero Trust's capabilities, advantages when introduced into cloud security architecture, and obstacles about its incorporation. The study finds that Zero Trust holds the potential to enhance cloud security significantly, but it is no silver bullet and demands careful implementation and further studies to understand its intricacies as well as pitfalls.

Insurance companies need to calculate solvency capital requirements in order to ensure that they can meet their future obligations to policyholders and beneficiaries. The solvency capital requirement is a risk management tool essential for, when extreme catastrophic events occur, resulting in a high number of possibly interdependent claims. This paper studies the problem of aggregating the risks coming from several insurance business lines and analyses the effect of reinsurance in the level of risk. Our starting point is to use a Hierarchical Risk Aggregation method, which was initially based on 2-dimensional elliptical copulas. We then propose the use of copulas from the Archimedean family and a mixture of different copulas. Our results show that a mixture of copulas can provide a better fit to the data than an individual copula and consequently avoid over or underestimating of the capital requirement of an insurance company. We also investigate the significance of reinsurance in reducing the insurance company’s business risk and its effect on diversification. The results show that reinsurance does not always reduce the level of risk, but can also reduce the effect of diversification for insurance companies with multiple business lines.

The primary goal of Theory of Everything (ToE) is to unify General Relativity and Quantum Mechanics, establishing a theoretical framework that explains all natural phenomena and fundamental laws of the universe. The rapid development of artificial intelligence in the 21st century presents new opportunities for the establishment of TOE. Research has revealed that the observer and the agent, as key concepts in physics and intelligence science respectively, share intrinsic consistency. Using this as a breakthrough point, we first proposed Standard Agent Model, which, from the perspective of information processing, establishes a unified structure for agents. This model posits that any agent possesses five essential capabilities: input, output, storage, creation, and control of these four capabilities.By setting the values of these five essential capabilities in Standard Agent Model to either zero or infinity, we identify two extreme states of agents: Absolute Zero Agent (α Point) and Omniscient and Omnipotent Agent (Ω Point). Based on the evolution of agents towards these two extreme states, we derive two driving forces for agent evolution: α Gravity and Ω Gravity. These elements collectively form General Model of Agent Evolution.Building on the first two models, we further derive Intelligent Universe Evolution Model, proposing that Universe, as an agent, evolves dynamically between α Point and Ω Point under the influence of α Field and Ω Field. These three models together constitute "Theory of Universal Intelligent Evolution," abbreviated as Ω Theory. According to Ω Theory, we demonstrate that the observer in physics and a wide range of research objects can be unified within the framework of Standard Agent Model. Additionally, we argue that the four fundamental forces have the potential to be unified into α Gravity and Ω Gravity.Within the framework of Ω Theory, through theoretical analysis and experimental validation, it has been demonstrated that the differences between classical mechanics, relativity, and quantum mechanics stem from the varying intelligence levels assigned to observers. Furthermore, it has been discovered that the differences in intelligent characteristics between the theoretical Universe proposed by these three major physical theories and Universe in which humanity currently resides are the root cause of the varying precision and limitations of these theories.

As the global economy pays increasing attention to sustainable development and environmental protection, the role of green finance in promoting regional technological innovation is becoming more and more prominent. This study aims to analyse the impact of green finance on regional technological innovation and its mechanism of action, with special attention to the mediating effect of R&D inputs and the moderating role of regional characteristics. By analysing the panel data of 30 provinces in China from 2008 to 2021 with fixed and mediated effect models, the results show that green finance significantly promotes regional technological innovation. R&D investment intensity partially mediates this effect, while regional innovation and entrepreneurship capacity plays a significant moderating role in this relationship. Specifically, regional innovation and entrepreneurship capacity reinforces the positive impact of green finance on R&D investment, but the marginal effect of R&D investment is reduced in regions with stronger innovation and entrepreneurship capacity. The findings provide a theoretical basis for formulating relevant policies to promote green finance development and sustainable innovation in regional economies.

Here, we describe the bicomplex (p,q)- Fibonacci numbers and the bicomplex (p,q)- Fibonacci quaternions that are based on these numbers and give some of their equations, including the Binet formula, generating function, Catalan, Cassini, d’Ocagne’s identities, and some summation formulas for both of them. Finally, we create a matrix for bicomplex (p,q)- Fibonacci quaternions, and we obtain a determinant of a special matrix that gives the terms of that quaternion.

In this paper, some convexity properties and some inequalities for the (p,k)-analogue of the Gamma function, Г_p,k(x) are given. In particular, a (p,k)-analogue of the celebrated Bohr-Mollerup theorem is given. Furthermore, a (p,k)-analogue of the Riemann zeta function, ζ_p,k(x) is introduced and some associated inequalities are derived. The established results provide the (p,k)-generalizations of some known results concerning the classical Gamma function.

We introduce the notion of p-adic equiangular lines and derive the first fundamental relation between common angle, dimension of the space and the number of lines. More precisely, we show that if {τj} n j=1 is p-adic γ-equiangular lines in Qd p , then (1) |n| 2 ≤ |d| max{|n|, γ2 }. We call Inequality (1) as the p-adic van Lint-Seidel relative bound. We believe that this complements fundamental van Lint-Seidel [Indag. Math., 1966] relative bound for equiangular lines in the p-adic case. Keywords: Equiangular lines, p-adic Hilbert space.

This article presents a proposed system that enables energy transfer from the dc grid to a single-phase onboard grid, operating at an hi frequency. In addition to the energy transfer function, it additionally enables compensation of reactive power in the ac grid when the converter operates at less than rated power. To operate in compensator mode, it is necessary to decouple the current from the grid into active and reactive components and control them independently. To achieve good dynamics, synchronizers that operate in the dq system are used. In the case of a single-phase grid, it requires such a synchronizer to generate virtual quadrature signals. The used systems based on second-order generalized integrator are good, but to improve dynamics in the work a quadrature generator based on trigonometric calculations was proposed. The developed system was implemented in a proportional resonant current control system. Tests performed in steady state and in dynamic states related to typical grid disturbances showed better dynamic properties than the standard integrator-based system.Keywords: power transfer; compensator; P+R controller

Arc sensors have been used in seam tracking and widely studied since the 80s; commercial arc sensing products for T and V shaped grooves have been developed. However, it is difficult to use these arc sensors in narrow gap welding because arc stability and sensing accuracy are not satisfactory. Pulse gas melting arc welding (P-GMAW) has been successfully applied in narrow gap welding and all position welding processes, so it is worthwhile to research P-GMAW arc sensing technology. In this paper, we derived a linear mathematical P-GMAW model for arc sensing, and the assumptions for the model are verified through experiments and finite element methods. Finally, the linear characteristics of the mathematical model were investigated. In torch height changing experiments, uphill experiments, and groove angle changing experiments the P-GMAW arc signals all satisfied the linear rules. In addition, the faster the welding speed, the higher the arc signal sensitivities; the smaller the groove angle, the greater the arc sensitivities. The arc signal variation rate needs to be modified according to the welding power, groove angles, and swing or rotate speed.

Pardosa pseudoannulata is one of the most common wandering spiders in agricultural fields and a potentially good bioindicator for heavy metal contamination. However, little is known about the mechanism by which spiders respond to heavy metals at the molecular level. In this study, high-throughput transcriptome sequencing was employed to characterize the de novo transcriptome of the spiders and to identify differentially expressed genes (DEGs) after cadmium exposure. We obtained 60,489 assembled unigenes, 18,773 of which were annotated in the public databases. Ultimately, 3450 cDNA simple sequence repeats were identified and validated as potential molecular markers in the unigenes. A total of 2939, 2491 and 3759 DEGs were detected among the three libraries of two Cd-treated groups and the control. Functional enrichment analysis revealed that metabolism processes and digestive system function were predominately enriched in response to Cd stress. At the cellular and molecular levels, significantly enriched pathways in lysosomes and phagosomes as well as replication, recombination and repair demonstrated that oxidative damage resulted from Cd exposure. Based on the selected DEGs, certain critical genes involved in defence and detoxification were analysed. These results may elucidate the molecular mechanism underlying spiders' responses to heavy metal stress.

Bacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. In order to promote the development of platforms enabling to the discovery of compounds with anti-bacterial activity, Fourier Transformed Mid-Infrared (FT-MIR) spectroscopy associated with the machine learning algorithms were used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli cells. According to the plant tissue (seeds, dry and fresh leaves, and flowers) and the solvents (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning, phenol content, antioxidant, and antimicrobial activity were obtained. Principal component analysis of spectra, allowed to discriminate compounds that inhibit the E. coli growth, according to the conventional assay. The supervised classification models enabled the prediction of the compound's impact on the E. coli growth, with the accuracy for the test data: Partial least squares-discriminant analysis of 94%; Support vector machines of 89%; k-Nearest neighbor of 72%; and a Backpropagation network of 100%. According to the promising results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of Gaussian Mixture Model (GMM) and Long-Short Term Memory (LSTM) autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states—two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis (PCA) to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of GMM and LSTM in fault detection. GMMs are deployed for initial fault classification, leveraging their clustering capabilities, while LSTM autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the GMM and the reconstruction error distribution of the LSTM autoencoder model.

Climate change is a phenomenon that makes the climate system of a given region to be more unpredictable and increases the risk of water-related problems. GCMs under the new CMIP6 framework holds several climate models with many improvements as compared to past similar efforts. The improvements are mainly in the number of scenarios formulated, setup, parametrization, and resolution. In this study, 10 downscaled climate models from CMIP6 are evaluated by applying statistical and data mining tools and are ranked based on their capability to describe the historical observed series. The result of the analysis showed that the outputs of the MPI-ESM1-2-HR model have a good overall ranking among those 10 models. The output of this top-ranked model is used to understand future climate over UASB after properly bias-corrected using the QM method. Results of the bias correction step show that average annual precipitation has shown an increment of 6.5\% in the middle (SSP2-4.5) and 10.3\% in the worst (SSP5-8.5) case scenarios for the mid-century (2040 - 2069). Similarly, for the end of the century (2070 - 2099) an increment of 4.7\% and 17.5\% was predicted for the two scenarios respectively. Whereas average annual maximum temperature series showed an increment of 1.5 $\degree C$ for middle and 2.6 $\degree C$ for the worst case in the mid-century. At the same time, an increment of 2.2 $\degree C$ and 3.5 $\degree C$ were predicted for the end of the century similarly for those scenarios. Furthermore, it was predicted that the average annual minimum temperature series will have an increment of 2.6 $\degree C$ and 3.1 $\degree C$ for mid-century and 3.1 $\degree C$ and 4.7 $\degree C$ for the end century for the two scenarios respectively. An increase in precipitation with increased land degradation problems in the sub-basin increases the risk of flood events in the future.

The visualization of hyperspectral images in display devices, having RGB colour composition channels is quite difficult due to the high dimensionality of these images. Thus, principal component analysis has been used as a dimensionality reduction algorithm to reduce information loss, by creating uncorrelated features. To classify regions in the hyperspectral images, K-means clustering has been used to form clusters/regions. These two algorithms have been implemented on the three datasets imaged by AVIRIS and ROSIS sensors.

In regenerative medicine, mesenchymal stem cells (MSCs) have shown their importance and potential in tissue reconstruction and immune system modification. However, such cells’ potential is often diminished by factors such as oxidative stress, immune rejection, and inadequate engraftment. This review highlights the role of molecular hydrogen (H₂) and cold atmospheric plasma (CAP) as adjunct therapies to improve the effectiveness of MSC therapy has a strong antioxidative and anti-inflammatory action as it quenches reactive oxygen species and positively stimulates the Nrf2 pathway that promotes MSC survival and life. CAP, being a modulated source of ROS and RNS, also assists MSCs by altering the cellular redox balance, thus facilitating cellular adaptation, migration and differentiation. H₂ and CAP in conjunction with each other assist in establishing an ambience favorable for promoting MSC survival and growth ability and reducing the healing time in various pathways such as wound, neuroprotection, and ischemia. Besides these concerns, this review also covers the best administration routes and doses of H₂ and CAP together with MSCs in therapy. This study informs on a novel dual method aimed at improving the outcome of MSC therapy while adding several molecular targets and relevant clinical use concerning these therapies. Research of the future has to deal with bettering these protocols so that the therapeutic benefits can be maximized without long-term implications for clinical applications.In regenerative medicine, mesenchymal stem cells (MSCs) have shown their importance and potential in tissue reconstruction and immune system modification. However, such cells’ potential is often diminished by factors such as oxidative stress, immune rejection, and inadequate engraftment. This review highlights the role of molecular hydrogen (H₂) and cold atmospheric plasma (CAP) as adjunct therapies to improve the effectiveness of MSC therapy has a strong antioxidative and anti-inflammatory action as it quenches reactive oxygen species and positively stimulates the Nrf2 pathway that promotes MSC survival and life. CAP, being a modulated source of ROS and RNS, also assists MSCs by altering the cellular redox balance, thus facilitating cellular adaptation, migration and differentiation. H₂ and CAP in conjunction with each other assist in establishing an ambience favorable for promoting MSC survival and growth ability and reducing the healing time in various pathways such as wound, neuroprotection, and ischemia. Besides these concerns, this review also covers the best administration routes and doses of H₂ and CAP together with MSCs in therapy. This study informs on a novel dual method aimed at improving the outcome of MSC therapy while adding several molecular targets and relevant clinical use concerning these therapies. Research of the future has to deal with bettering these protocols so that the therapeutic benefits can be maximized without long-term implications for clinical applications.

The emergence of mRNA vaccines represents a significant advancement in public health by conferring protection against a variety of infectious diseases. However, concerns regarding the potential risk of inducing or exacerbating autoimmune disorders, particularly connective tissue diseases (CTDs), have arisen. This paper explores the mechanisms by which mRNA vaccines may lead to the onset or worsening of CTDs, focusing on the immune responses induced by Pathogen-Associated Molecular Patterns (PAMPs), the interactions between Toll-like receptors 7/8 and 9 (TLR7/8 and TLR9), and the activation of self-reactive B cells. Furthermore, guidelines are suggested for administering vaccines to individuals at elevated risk, such as those with a positive antinuclear antibody (ANA) test or specific rheumatic disease genes. Our findings underscore the necessity for targeted vaccination strategies that mitigate risks while maintaining public health efficacy.

Tick-borne encephalitis (TBE) is the most significant arboviral infection in Croatia. The aim of the study was to analyse and correlate 17-year TBE seroprevalence data with winter temperature, precipitation and wildlife abundance to identify possible patterns that can be predictive of TBE incidence. TBE diagnosis was based on IgM/IgG anti-TBE antibodies and individual clinical interpretation of the results. Of the 19,094 analysed patients, 4.2% had acute TBE, significantly more often in older age (p<0.001) and in men (p<0.001). Overall seroprevalence of TBE among the tested population was 5.8%, and varied annually from 2.8% to 10.7%. The mean acute TBE incidence rate was 1.1 per 100.000 population with significant regional differences: 1.7 in the continental vs. 0.2 and 0.5 in the Mediterranean and Alpine regions, respectively. Particularly high incidence of 3.1 was recorded in northern Croatia. TBE displayed a seasonal pattern, peaking in June and July. Moderate negative correlations were observed between TBE acute cases and winter temperatures from December to February (r=-0.461; p=0.062), relative rodent abundance (r=-0.414; p=0.098) and yearly precipitation from one year before (r=-0.401; p=0.123). Considerable efforts are needed to understand the impact on TBE incidence to improve disease prevention.

Understanding the factors that contribute to slope failures, such as soil saturation, is essential for mitigating rainfall-induced landslides. Cost-effective capacitive soil moisture sensors have the po-tential to be widely implemented across multiple sites for landslide early warning systems. How-ever, these sensors need to be calibrated for specific applications to ensure high accuracy in readings. In this study, a soil-specific calibration was performed in a laboratory setting to integrate the soil moisture sensor with an automatic monitoring system using the Internet of Things (IoT). This re-search aims to evaluate a low-cost soil moisture sensor (SKU:SEN0193) and develop calibration equations for the purpose of slope model experiment under artificial rainfall condition using silica sand. The results indicate that a polynomial function is the best fit, with a coefficient of determi-nation (R²) ranging from 0.92 to 0.98 and a root mean square error (RMSE) ranging from 1.17 to 2.67. The calibration equation was validated through slope model experiments, with soil samples taken from the model after the experiment finished. Overall, the moisture content readings from the sensors showed approximately a 10% deviation from the actual moisture content. The findings suggest that the cost-effective capacitive soil moisture sensors has the potential to be used for the development of landslide early warning system.

In the present study, we introduce the two dimensional Chlodovsky type Bernstein operators based on (p,q)−integer. We examine approximation properties of our new operator by the help of Korovkin-type theorem. Further, we present the local approximation properties and establish the rates of convergence by means of the modulus of continuity and the Lipschitz type maximal function. Also, we give a Voronovskaja type theorem for this operators. And, we investigate weighted approximation properties of these operators and estimate rate of convergence in the same space. Finally, with the help of Maple, illustrative graphics show the rate of convergence of these operators to certain functions . The optimization of approximation speeds by operators during system control provides significant improvements in stability and performance. As a result, the control and modeling of dynamic systems become more efficient and effective through innovative methods. These advancements in the fields of modeling fractional differential equations and control theory offer substantial benefits to both modeling and optimization processes, expanding the range of applications in these areas.

This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann's Limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies.

This article explores how Artificial Intelligence (AI) is drastically changing the logistics industry by transforming operations and significantly improving customer satisfaction. Through meticulous analysis of real-life cases and extensive examination of numerical data, this comprehensive research thoroughly investigates the profound impact of AI on supply chain management. The findings clearly reveal the multifaceted role of AI in significantly reducing costs, greatly improving delivery accuracy, and significantly enhancing inventory control. AI is not just an incremental advancement, but rather a revolutionary leap forward for the future logistics industry, firmly paving the way for unparalleled automation, digital transformation, and operational effectiveness.

Pancreatic cancer, one of the deadliest cancers worldwide, necessitates improved therapeutics due to its low survival rate (12% in 5 years) and typically late-stage diagnosis [1]. This malignancy tends to present asymptomatically until advanced stages, leading to high rates of metastasis and limiting surgical options [2]. Genetic mutations in key genes like KRAS, CDKN2A, TP53, and SMAD4 are central to pancreatic cancer pathogenesis, driving uncontrolled cell proliferation and making treatment difficult [3]. Current treatment modalities include surgery, chemotherapy, radiation, and emerging immunotherapies. Surgery, particularly the Whipple procedure, offers the only potential cure but is applicable in just 15-20% of cases due to the late diagnosis [4]. Chemotherapy, though commonly used, offers limited long-term survival benefits [5]. Radiation therapy has seen advancements with techniques like stereotactic body radiation therapy (SBRT), which shows promise in reducing toxicity and improving outcomes [6] . Immunotherapy, particularly mRNA vaccines, has demonstrated potential in prolonging recurrence-free survival (>18 months), especially when combined with traditional therapies (13.4 months with surgical resection alone) [7] . Combinations of different treatments have been found to be effective. The challenge of early pancreatic cancer diagnosis mirrors the difficulty of disease management. Prophylactic immunotherapies, like vaccines, could offer a crucial breakthrough for prevention and recurrence reduction. As pancreatic cancer remains difficult to treat effectively, continued research and development in preventative and therapeutic strategies are essential to improving outcomes.

In an outstanding article published in 2008, Suzuki obtained a nice generalization of the Banach contraction principle from which derived a characterization of metric completeness. Although Suzuki’s theorem has been successfully generalized and extended in several directions and contexts, we here show by means of a simple example that the problem of achieving, in an obvious way, its full extension to the framework of w-distances does not have an emphatic response. Motivated by this fact we introduce the concept of presymmetric w-distance on metric spaces, we give some properties and examples of this new structure and show that it provides a reasonable setting to obtain a real and hardly forced w-distance generalization of Suzuki’s theorem. This is realized in our main result, which is a fixed point theorem that involves presymmetric w-distances and certain contractions of Suzuki-type. We also discuss the relationship between our main result and the well-known w-distance full generalization of the Banach contraction principle, due to Suzuki and Takahashi. Connected to this approach we prove another fixed point result that compare with our main result through some examples. Finally, we state a characterization of metric completeness by using our fixed point results.

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzo-p-furans (PCDD/Fs) are environmental pollutants with a great persistence, capacity of bioaccumulation, and well known important toxic effects in humans and animals. Incinerators of hazardous, municipal and medical waste, chlorine bleaching of paper pulp, cement plants, and the traffic of motor vehicles are the most frequent emission sources of these compounds. The diet, followed at a great distance by inhalation, is generally the main way of human exposure to PCDD/Fs. Human biomonitoring is of a great importance to prevent potential adverse effects derived from exposure to chemicals such as PCDD/Fs. In relation to this, blood is among the most used biological monitors. In the current review, we have summarized the recent information (2000-2009) published in the scientific literature (databases: Scopus and PubMed) on the concentrations of PCDD/Fs in blood samples of non-occupationally exposed populations, as well as in some groups of occupationally exposed individuals. We have revised a number of studies conducted in various African American, Asian and European countries, and Australia. Unfortunately, the information is quite limited. No data are available for most countries over the world. Based on the results here reviewed –where available- the current health risks for the general populations do not seem to be of concern. Moreover, taking into account the important reductions observed in the levels of PCDD/Fs in foodstuffs, new decreases in the concentrations of PCDD/Fs in blood -and other biological tissues- are very probable in the immediate years.

Tuberculosis (TB) continues to pose a global health challenge, exacerbated by the rise of drug-resistant strains. The development of new TB therapies is an arduous and time-consuming process. To expedite the discovery of effective treatments, computational structure-based drug re-purposing has emerged as a promising strategy. From this perspective, conditionally essential targets present a valuable opportunity, and the mycobactin biosynthesis pathway stands out as a prime example highlighting the intricate response of Mycobacterium tuberculosis (Mtb) to changes in iron availability. This study focuses on the re-purposing and revival of FDA-approved drugs (library) as potential inhibitors of MbtA, a crucial enzyme in mycobactin biosynthesis in Mtb conserved among all species of mycobacteria. Literature suggests this pathway to be associated with drug efflux pumps, which potentially contribute to drug resistance. This makes it a potential target for antitubercular drug discovery. Herein we utilized cheminformatics and structure-based drug repurposing approaches, viz., molecular docking, dynamics, and PCA analysis, to decode the intermolecular interactions and binding affinity of the FDA-reported molecules against MbtA. The virtual screening revealed ten molecules with significant binding affinities and interactions with MbtA. These drugs, originally designed for different therapeutic indications (4: antiviral, 3: anticancer, 1: CYP450 inhibitor, 1: ACE inhibitor, and 1: leukotriene antagonist), are repurposed as potential MbtA inhibitors. Furthermore, our study explores the binding modes and interactions between these drugs and MbtA, shedding light on the structural basis of their inhibitory potential. Principal component analysis highlighted significant motions in MbtA-bound ligands, emphasizing the stability of the top Protein-Ligand Complexes (PLCs). This computational approach provides a swift and cost-effective method for identifying new MbtA inhibitors, which can subsequently undergo validation through experimental assays. This streamlined process is facilitated by the fact that these compounds are already FDA-approved and have established safety and efficacy profiles. This study has the potential to lay the groundwork for addressing the urgent global health challenge at hand, specifically in the context of combating Antimicrobial Resistance (AMR) and Tuberculosis (TB).

In this study, our primary objective was to analyze the tradeoff between accuracy and complexity in machine learning models, with a specific focus on the impact of reducing complexity and entropy on the production of landslide susceptibility maps. We aimed to investigate how simplifying the model and reducing entropy can affect the capture of complex patterns in the susceptibility maps. To achieve this, we conducted a comprehensive evaluation of various machine-learning algorithms for classification tasks. We compared the performance of these algorithms in terms of accuracy and complexity, considering both "before" and "after" scenarios of dimensionality reduction using Principal Component Analysis (PCA). Our findings revealed that reducing complexity and lowering entropy can lead to an increase in model accuracy. However, we also observed that this reduction in complexity comes at the cost of losing important complex patterns in the produced landslide susceptibility maps. By simplifying the model and reducing entropy, certain intricate relationships and uncertain patterns may be overlooked, resulting in a loss of information and potentially compromising the accuracy of the susceptibility maps. The analysis encompassed a diverse range of machine learning algorithms, including Random Forest (RF), Extra Trees (EXT), XGboost, LightGBM, Catboost, Naive Bayes (NB), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), and Decision Trees (DT). Each algorithm was evaluated for its strengths and limitations, considering the tradeoff between accuracy and complexity. Before dimensionality reduction, the algorithms demonstrated promising results, with RF exhibiting excellent AUC/ROC scores and average accuracy. However, computational costs were noted as a potential drawback for RF, especially when dealing with large datasets. EXT showcased robust performance and good accuracy, while XGboost demonstrated its ability to handle complex relationships within large datasets, albeit requiring careful hyperparameter tuning. The efficiency and scalability of LightGBM made it a suitable choice for large datasets, although it displayed sensitivity to class imbalance. Catboost excelled in handling categorical features, but longer training times were observed for larger datasets. NB showcased simplicity and computational efficiency but assumed independence among features. KNN, known for its capability to capture local patterns and spatial relationships, was found to be sensitive to the choice of distance metric. GBM, while capturing complex relationships effectively, was prone to overfitting without proper regularization. DT, with its interpretability and ease of understanding, faced limitations in terms of overfitting and limited generalization. After dimensionality reduction, certain algorithms exhibited improvements in their AUC/ROC scores and average accuracy, including RF, EXT, XGboost, and LightGBM. However, for a few algorithms, such as NB and DT, a decrease in performance was observed. This study provides valuable insights into the performance characteristics, strengths, and limitations of various machine learning algorithms in classification tasks. Researchers and practitioners can utilize these findings to make informed decisions when selecting algorithms for their specific datasets and requirements. We also aim to identify the potential factors contributing to the high accuracy rates obtained from these ensembled algorithms and explore possible shortcomings of non-ensembled algorithms that may result in lower accuracy rates. By conducting a comprehensive analysis of these algorithms, we seek to provide valuable insights into the benefits and limitations of ensembled approaches for landslide susceptibility mapping. Our study sheds light on the challenges faced when balancing accuracy and complexity in machine learning models for landslide susceptibility mapping. It emphasizes the importance of carefully considering the level of complexity and entropy reduction in relation to the specific patterns and uncertainties present in the data. By providing insights into this tradeoff, our research aims to assist researchers and practitioners in making informed decisions regarding model complexity and entropy reduction, ultimately improving the quality and interpretability of landslide susceptibility maps.

Hyperspectral imaging provides vital information about the objects and elements present inside the image. That’s why they are very useful in satellite imagery as well as image forensics. Hyperspectral document analysis (HSDI) can be used for document authentication using ink analysis which can provide sufficient information about the composition and type of ink. In this project, we have implemented HSDI based ink classification technique using Principle Component Analysis for dimensionality reduction and K-means clustering for ink classification. This is unsupervised learning approach and it is very simple and efficient in order to classify limited number of bands. We have used this technique to classify 33 different bands of ink.

Floods and droughts are driven, in part, by spatial patterns of extreme rainfall. Heat waves are driven by spatial patterns of extreme temperature. It is therefore of interest to design statistical methodologies that allow the identification of likely patterns of extreme rain or temperature from observed historical data. The standard work-horse for identifying patterns of climate variability in historical data is Principal Component Analysis (PCA) and its variants. But PCA optimizes for variance not spatial extremes, and so there is no particular reason why the first PCA spatial pattern should identify, or even approximate, the types of patterns that may drive these phenomena, even if the linear assumptions underlying PCA are correct. We present an alternative pattern identification algorithm that makes the same linear assumptions as PCA, but which can be used to explicitly optimize for spatial extremes. We call the method Directional Component Analysis (DCA), since it involves introducing a preferred direction, or metric, such as `sum of all points in the spatial field'. We compare the first PCA and DCA spatial patterns for US rainfall anomalies on a 6 month timescale, using the sum metric for the definition of DCA in order to focus on total rainfall anomaly over the domain, and find that they are somewhat different. The definitions of PCA and DCA result in the first PCA spatial pattern having the larger explained variance of the two patterns, while the first DCA spatial pattern, when scaled appropriately, has a higher likelihood and greater total rainfall anomaly, and indeed is the pattern with the highest total rainfall anomaly for any given likelihood. In combination these two patterns yield more insight into rainfall variability and extremes than either pattern on its own.

Dayaoshan, as an important metal ore producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing method is used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference bas been removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information has been extracted based on principal component analysis (PCA) and support vector machine (SVM) method; Thirdly, the favorable areas of gold mining in Gulong has been delineated by using ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; Lastly, field survey verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing.

Associating the most influential parameters with the product distribution is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). These correlations can lead to the information-driven catalyst screening, kinetic modeling and reactor design. In this work, a dataset of 104 uncorrelated experiments, with 64 variables, has been obtained in an FCC simulator using 6 types of feedstock (vacuum gasoil, polyethylene pyrolysis waxes, scrap tire pyrolysis oil, dissolved polyethylene and blends of the previous), 36 possible sets of conditions (varying contact time, temperature and catalyst/oil ratio) and 3 industrial catalysts. Principal component analysis (PCA) has been applied over the dataset, showing that the main components are associated with feed composition (27.41% variance); operational conditions (19.09%) and catalyst properties (12.72%). The variables of each component have been correlated with the indexes and yields of the products: conversion, octane number, aromatics, olefins (propylene) or coke, among others.

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.

The earthquake alert of the early warning systems is based on the delay that the S waves have in reference to the P waves and on the interpretation of the P waves’ specific parameters. One of the most used parameters for estimating the moment magnitude of an earthquake is the characteristic period measured in the first 3 seconds of the appearance of the P wave. The typical method determines the characteristic period in the time domain, based on the velocity wave and the displacement wave. In the present work, we present a method for estimating the frequency of the characteristic period. This method includes zero padding of the P wave sequence, conversion of the extended sequence from the time domain to the frequency domain, the identification of local frequency maxima and the calculation of the weighted average of the frequency based on the identified maxima. Tests conducted on synthetic signals, as well as standard deviation evaluation tests for simultaneous recordings on several stations, reveal better performance to the usual method.

Cell migration crucial contributor to metastasis, a critical process associated with the mortality of cancer patients. The initiation of metastasis is triggered by epithelial-mesenchymal transition (EMT), along with the changes of the expression of EMT marker proteins. Inflammation plays a significant role in carcinogenesis and metastasis. Lipopolysaccharide (LPS), a typical inflammatory agent, promoted the generation of superoxide through the activation of p-Tyr42 RhoA, Rho-dependent kinase 2 (ROCK2) and the phosphorylation of p47phox. In addition, p-Tyr42 RhoA activated phospholipase D1 (PLD1), with PLD1 and phosphatidic acid (PA) being involved in superoxide production. PA also regulated the expression of EMT maker proteins. Consequently, we have identified MHY9 (Miosin IIA, NMIIA) as a PA-binding protein in response to LPS. MYH9 also contributed cell migration and the alteration of the expression of EMT marker proteins. P-Tyr42 RhoA, PLD1 and MYH9 associated to form a complex, which was distributed in both the cytosol and nucleus. In addition, we have found that p-Tyr42 RhoA and PLD1 associated with the ZEB1 promoter. Taken together, we propose that p-Tyr42 RhoA and PLD1, responsible for producing PA, and PA-bound MYH9 are involved in the regulation of ZEB1 expression, thereby promoting cell migration.

Abstract: (1) Background: Surgery is the best curative option for Gastric Cancer (GC). However, morbimortality associated with surgery still a significant problem. Thus, it is essential to determine the preoperative surgical risk, to optimize outcomes in higher risk patients. (2) Methods: Patients who underwent curative gastrectomy between January 2010 and December 2013 were included, obtaining a sample of 131 patients, and the acuity of the scales understudy was evaluated. The predictive power of the scales was evaluated through calibration and discrimination. (3) Results and Conclusions: Regarding mortality, the scales demonstrated good calibration, with POSSUM and P-POSSUM obtaining the best O:E ratio. The scales presented good discrimination, with POSSUM showing the best result. Considering morbidity, the scales showed good calibration and discrimination. POSSUM and P-POSSUM seem to have potential in predicting morbidity and mortality when applied to the studied population, but there is a need for further studies in this area.

Emerging evidence has shown that microRNAs play pivotal roles in wound healing. MicroRNA-21 (miR-21) was previously found to upregulate in order to fulfill an anti-inflammation role for wounds. Exosomal miRNAs have been identified and explored as essential markers for diagnostic medicine. However, the role of exosomal miR-21 in wounds has yet to be well studied. In order to facilitate the early management of poorly healing wounds, we developed an easy-to-use, rapid, paper-based microfluidic-exosomal miR-21 extraction device to determine wound prognosis in a timely manner. We isolated and then quantitatively examined exosomal miR-21 in wound fluids from normal tissues, acute and chronic wounds. Eight improving wounds displayed lower levels of exosomal miR-21 expression after wound debridement. However, four instances of increased exosomal miR-21 expression levels were notably associated with patients with poor healing wounds despite aggressive wound debridement, indicating a predictive role of tissue exosomal miR-21 for wound outcome. Paper-based nucleic acid extraction device provides a rapid and user-friendly approach for evaluating exosomal miR-21 in wound fluids as a means of monitoring wounds. Our data suggest that tissue exosomal miR-21 is a reliable marker for determining current wound status.

If an algebra of type $A$ contains a subalgebra which is also an algebra of type $B$, then it is a Smarandache $B$-type $A$-algebra provided the subalgebra of type $B$ contains at least two elements. This generalizes the notion of Smarandache group, where the group of order $\geq 2$ is a subsemigroup of a semigroup. In this paper we investigate a number of such pairings and we deduce a number of conclusions as a consequence.

This study explores the impact and significance of implementing a comprehensive education system for children with special needs. The research aims to understand how inclusive practices influence the academic, social, and emotional development of these students. Using a mixed-methods approach that includes case study analysis, reviews of global education models, and theoretical frameworks, the study identifies key strategies for fostering inclusivity. The findings reveal that inclusive education not only enhances academic performance but also promotes social integration and emotional well-being. The analysis compares inclusive practices with traditional special education models and highlights the advantages of equity-driven, accessible learning environments. The study also addresses the challenges faced, such as inadequate resources and the need for continuous teacher training. Recommendations include policy reforms, increased funding, and the adoption of assistive technologies to support diverse learning needs. The research underscores the importance of a collaborative approach involving educators, parents, policymakers, and the community in creating an education system where every child has the opportunity to thrive.

The Median Based Unit Weibull is a new 2 parameter unit Weibull distribution defined on the unit interval (0,1). Estimation of the parameters using MLE encountered some problems like large variance. Using generalized method of moments (GMMs) and percentile method may ameliorate this condition. This paper introduces GMMs and the percentile methods for estimating the parameters of the new distribution with illustrative real data analysis.

Interspecific competition between mosquito larvae may affects adult vectorial capacity, potentially reducing disease transmission. It also influences population dynamics, cannibalistic and predatory behaviors. However, knowledge of interspecific competition between Ae. aegypti and Anopheles species is limited. The study examined interspecific competition between Ae. aegypti larvae and either An. arabiensis, An. gambiae, or An. funestus on individual fitness in semi-field settings. The experiments involved density combinations of 100:100, 200:0, and 0:200 (Ae. aegypti: Anopheles), reared with and without food, in small habitat (8.5 cm height × 15 cm diameter) with 0.5 litre and medium habitats (15 cm height × 35 cm diameter) with 1 litre of water. The first group received Tetramin® fish food (0.02 g), while the second group was unfed to assess cannibalism and predation. While, interspecific competition affected both genera, Anopheles species experienced greater effect, with reduced survival and delayed development, compared to Ae. aegypti. The mean wing lengths of all species were significantly small in small habitats in mixed population (p &lt; 0.001). The presence of food reduced cannibalism and predation compared to its absence. These interactions have implications for diseases transmission dynamics and can serve as biological indicators to signal the impacts of vector control interventions.

In response to a perceived epidemic of coronary heart disease, Ancel Keys introduced the lipid-heart hypothesis in 1953 which asserted that high intakes of total fat, saturated fat, and cholesterol lead to atherosclerosis and that consuming less fat and cholesterol, and replacing saturated fat with polyunsaturated fat, would reduce serum cholesterol and consequently the risk of heart disease. Keys proposed an equation that would predict the concentration of serum cholesterol (DChol.) from consumption of saturated fat (DS), polyunsaturated fat (DP), and cholesterol (DZ): ΔChol. = 1.2(2ΔS − ΔP) + 1.5ΔZ. However, the Keys equation conflated natural saturated fat and industrial trans-fat into a single parameter and considered only linoleic acid as the polyunsaturated fat. This ignored the widespread consumption of trans-fat and its effects on serum cholesterol and promoted an imbalance of omega-6 to omega-3 fatty acids in the diet. Numerous observational, epidemiological, interventional, and autopsy studies have failed to validate the Keys equation and the lipid-heart hypothesis. Nevertheless, these have been the cornerstone of national and international dietary guidelines which have focused disproportionately on heart disease and much less so on cancer and metabolic disorders, which have steadily increased since the adoption of this hypothesis.

In this paper, we define and investigate modified p-Leonardo, p-Leonardo-Lucas and p-Leonardo sequences as special cases of the generalized Leonardo sequence. We present Binet's formulas, generating functions, Simson formulas, and the summation formulas for these sequences. Moreover, we give some identities and matrices related with these sequences. Furthermore, we show that there are close relations between modified p-Leonardo, p-Leonardo-Lucas, p-Leonardo numbers and Fibonacci, Lucas numbers.

For evaluating probabilities of arbitrary random events with respect to a given multivariate probability distribution, specific techniques are of great interest. An important two-dimensional high risk limit law is the Gauss-exponential distribution whose probabilities can be dealt with based upon the Gauss-Laplace law. The latter will be considered here as an element of the newly introduced family of (p,q)-spherical distributions. Based upon a suitably defined non-Euclidean arc-length measure on (p,q)-circles we prove geometric and stochastic representations of these distributions and correspondingly distributed random vectors, respectively. These representations allow dealing with the new probability measures similarly like with elliptically contoured distributions and more general homogeneous star-shaped ones. This is demonstrated at hand of a generalization of the Box-Muller simulation method. En passant, we prove an extension of the sector and circle number functions.

In this research paper, we implement the theory of the primorial function, to develop the Supremum and Infimum bounds for the sum of (log(p))². There are, however, considerable computational difficulties related to these bounds. Therefore, from a pragmatic point of view, a set of Upper and Lower bounds had been developed to bypass this issue. Despite the increased estimation error, the Upper and Lower bounds are still considered sufficiently accurate, while facilitating an easy and fast computation of the estimate of the sum.

Polyhedral cages (p-cages) describe the geometry of some families of artificial protein cages. We identify the p-cages made out of families of equivalent polygonal faces such that the faces of 1 family has 5 neighbours and P_1 edges, while those of the other family have 6 neighbours and P_2 edges. We restrict ourselves to polyhedral cages where the holes are adjacent to at most 4 faces. We characterise all the p-cages with a deformation of the faces, compared to regular polygons, not exceeding 10%.

Mevcut çalışmada, (p, q)−tam sayıya dayalı iki boyutlu Chlodovsky tipi Bernstein operatörlerini tanıtıyoruz. Yeni operatörümüzün yaklaşıklık özelliklerini Korovkin tipi teorem yardımıyla inceliyoruz. Ayrıca, yerel yaklaşıklık özelliklerini sunuyoruz ve süreklilik modülü ve Lipschitz tipi maksimal fonksiyon vasıtasıyla yakınsama oranlarını belirliyoruz. Ayrıca, bu operatörler için bir Voronovskaja tipi teorem veriyoruz. Ve, bu operatörlerin ağırlıklı yaklaşıklık özelliklerini inceliyoruz ve aynı uzayda yakınsama oranını tahmin ediyoruz. Son olarak, Maple'ın yardımıyla, açıklayıcı grafikler bu operatörlerin belirli fonksiyonlara yakınsama oranlarını göstermektedir. Sistem kontrolü sırasında operatörler tarafından yaklaşıklık hızlarının optimizasyonu, kararlılık ve performansta önemli iyileştirmeler sağlar. Sonuç olarak, dinamik sistemlerin kontrolü ve modellenmesi yenilikçi yöntemlerle daha verimli ve etkili hale gelir. Kesirli diferansiyel denklemlerin modellenmesi ve kontrol teorisi alanındaki bu gelişmeler, hem modelleme hem de optimizasyon süreçlerine önemli faydalar sunarak bu alanlardaki uygulama yelpazesini genişletmektedir.

In our previous study of 291 cancer patients, we showed that 20% did not respond to two doses of COVID-19 vaccine administered six weeks apart. Here we investigated if psychological factors (distress, anxiety and depressive symptoms) affected antibody response and markers of vaccine activation (D-dimer) after 6 months from initial vaccination. Overall, 31 subjects (14.2%) had no antibody response at 6 months. Our analysis revealed significant predictors of vaccine failure, including stage of metastatic disease and high stress levels (OR=2.46, 95% CI, 1.05-5.77, p=0.04). Notably, nonresponders showed twice the prevalence of distress than responders (21% vs. 10%, p=0.04). Longitudinal measurements of IgG levels indicated that participants with high depressive symptoms at baseline maintained lower antibody levels over six months (p=0.003). In addition, women with high anxiety showed reduced levels of D-dimer at 6 month time (p=0.03). The data also showed that smokers and former smokers had significantly lower antibody levels than their nonsmoking counterparts (p=0.0004). At baseline, the high discomfort rate (≥5) was 34.4% in women and 23.8% in men; only men experienced an increase in median discomfort during the observation period. Moreover, higher educational level was related to increased distress among women (p=0.046). These findings underscore a critical association between elevated psychological distress and reduced immune responses to the COVID-19 vaccine, emphasizing the urgent need for targeted psychological and behavioral support within this vulnerable population.

CO₂-soluble surfactant foam systems have garnered significant attention in the fields of enhanced oil and gas recovery, environmental protection, and greenhouse gas emission reduction. This review provides a comprehensive explanation of the basic theory of CO₂-soluble surfactant foam, the mechanism of enhanced oil recovery, and the classification and application of various CO₂-soluble surfactants.The mechanism of enhanced oil recovery by CO₂-soluble surfactant foam involves the effective reduction of CO₂ fluidity, the decrease in oil-air flow ratio, and the stabilization of the displacement front. Foam plays a vital role in mitigating the issues of channeling and gravity separation often caused by simple CO₂ injection. The reduction in gas fluidity can be attributed to the increase in apparent viscosity and trapped gas fraction.Future research should prioritize the development of more efficient and environmentally-friendly CO₂-soluble surfactants. It is essential to further explore the advantages and challenges associated with their practical applications in order to maximize their potential impact.

Objectives: Cardiogenic shock [CS] is associated with a high-mortality. Suitable patients maybe successfully bridged using newer intravascular micro-axial left-ventricular assist devices [M-LVAD] for recovery or determination of definitive therapy. Methods: Between January-2020 and July-2024, 107 patients underwent placement of M-LVAD for CS. The cohort was divided into 4 groups based on their destination; group-1: 34 patients [32%] receiving transplant; group-2: 25 patients [23%] receiving durable LVAD; group-3: 42 patients [39%] bridging from post-cardiotomy CS [PCCS]; and group-4: 6 patients [5.6%] bridging decision/recovery [these were excluded from analysis]. Multivariable logistic-regression [MLR] and Cox-regression [MCR] models identified predictors of early-hospital and late mortality, with data reported as odds ratios [ORs], and hazard ratios [HRs], respectively. P<0.05 was statistically significant. Results: Complications included device-malfunction [n=6, 6%], gastrointestinal bleed [n=9, 9%], stroke [n=11, 11%]; long-term hemodialysis [n=21, 21%]. Early-hospital mortality included 13 patients [13%]: 2 in group-1, 1 in group-2 and 10 in group-3 [p=0.02]. In the MLR model, the category of cardiogenic shock requiring M-LVAD placement was statistically significant [OR=4.7 (0.9-24), P=0.05]. Patients were followed for up to 4.5-years, and 23 deaths occurred; group-1: 3 patients, group-2: 5 patients, and group-3: 15 patients [p=0.019]. At 4.5-years, actuarial survival was 90.7±5.1% in group-1, 79.2±8.3% in group-2, and 62.8%±7.7% in group-3 [P=0.01]. In the MCR model, M-LVAD category [HR=3.63 (1.03-12.9) P=0.04], and long-term postoperative dialysis [HR=3.9 (1.6-9) P=0.002], emerged as statistically significant predictors of long-term mortality. Conclusions: In cardiogenic shock, our mid-term outcomes demonstrate good survival with M-LVADs as bridge to transplant and durable LVAD, and reasonable survival as bridge to recovery following cardiotomy, reduced ECMO usage and early ambulation/rehabilitation.

Antimalarial drug resistance has become a real public health problem despite WHO measures. New sequencing technologies make it possible to investigate genomic variations associated with resistant phenotypes at the genome-wide scale. Based on the use of hemisynthetic nanopores, the PromethION technology from Oxford Nanopore Technologies can produce long-read sequences, in contrast to previous short-read technologies used as gold-standard to sequence Plasmodium. Two clones of P.falciparum (Pf3D7 and PfW2) were sequenced in long-read using the PromethION sequencer from Oxford Nanopore Technologies without genomic amplification. This made it possible to create a processing analysis pipeline for human Plasmodium with ONT Fastq only. De novo assembly revealed N50 lengths of 18,488 kb and 17,502 kb for the Pf3D7 and PfW2, respectively. The genome size was estimated at 23,235,407 base pairs for the Pf3D7 clone and 21,712,038 base pairs for the PfW2 clone. The average genome coverage depth was estimated at 787X and 653X for the Pf3D7 and PfW2 clones, respectively. This study proposes an assembly processing pipeline for the human Plasmodium genome using software’s adapted to large ONT data and the high AT percentage of Plasmodium. This search provides all the parameters, which have been optimized, for use with the software selected in the pipeline.

Prostate cancer (PCa) pathogenesis relies on intercellular communication, which can involve tunneling nanotubes (TNTs) and extracellular vesicles (EVs). TNTs and EVs have been reported to transfer critical cargo involved in cellular function and signalling, prompting us to investigate the extent of organelle and protein transfer in PCa cells and the potential involvement of the androgen receptor. Using live cell imaging microscopy, we observed extensive formation of TNTs and EVs operating between PCa, non-malignant and immune cells. PCa cells were capable of transferring lysosomes, mitochondria, lipids and endoplasmic reticulum as well as syndecan-1, sortilin, Glut1 and Glut4. In mechanistic studies, androgen sensitive PCa cells exhibited changes in cell morphology when stimulated by R1881 treatment. Overexpression assays of a newly designed androgen receptor (AR) plasmid revealed its novel localization in PCa cellular vesicles, which were also transferred to neighbouring cells. Selected molecular machinery, thought to be involved in intercellular communication, was investigated by knockdown studies and Western blotting/immunofluorescence/scanning electron microscopy (SEM). PCa TNTs and EVs transported proteins and organelles, which may contain specialist signalling, programming and energy requirements that support cancer growth and progression. This makes these important intercellular communication systems ideal potential targets for therapeutic intervention.

Soil potentially toxic elements (PTEs) pollution in black soil is of significant concern in China. However, research is scarce regarding the current status, ecological risk, and human health risk of PTEs in the black soil area of northeast China. In our study, 304 soil samples (0-20 cm) were collected in Gonghe Town, Hailun City. The pollution degree and spatial distribution patterns of soil PTEs were analyzed by using the single-factor pollution index (PI) and the Nemerow pollution index (NPI). The source apportionment of PTEs was carried out by combining correlation analysis, principal component analysis (PCA), and positive matrix factorization (PMF) methods. Meanwhile, the potential ecological risk (RI) and the human health risk (HI) associated with soil contamination from various sources were evaluated through the RI and the HI. The results demonstrated that the average content (mean ± standard deviation) of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were 11.16 ± 1.32 mg/kg, 0.11 ± 0.04 mg/kg, 65.29 ± 3.46 mg/kg, 22.56 ± 1.32 mg/kg, 0.03 ± 0.01 mg/kg, 27.07 ± 1.46 mg/kg, 26.09 ± 2.84 mg/kg, and 66.01 ± 4.52 mg/kg, respectively. The overall PTEs in the study area exhibited slight pollution levels. The PCA and PMF model source apportionment was validated against each other to yield four sources, which were natural source (33.2%), irrigation source (29.5%), traffic source (23.4%), and fertilizer source (13.2%). The overall RI in the study area was determined to be at the level of slight ecological risk. The non-carcinogenic risk of PTEs to children and adults was ignored, and the carcinogenic risk was at an acceptable level. The comprehensive analysis of PTEs, pollutant sources, RI, and HI concluded that the fertilizer source should be the primary control source, with Cd identified as the first control PTE. The irrigation source and the traffic source were identified as the secondary control sources, with As, Pb, and Hg identified as the secondary control PTEs. This study revealed the status, risks, and sources of PTEs in black soils and provided a scientific basis of PTEs control for policymakers.

In a context of increasingly necessary energy transition, precise modelling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSO) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modelling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow the estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

There is voluminous literature on Food Security in Africa. This study explicitly considers the spatio-temporal factors in addition to the usual FAO-based metrics in modeling and understanding the dynamics of food security and nutrition across the African continent. To better understand the complex trajectory and burden of food insecurity and nutrition in Africa, it is crucial to consider space-time factors when modeling and interpreting food security. The spatio-temporal anova model was found to be superior(employing statistical criteria) to the other there models from the spatio-temporal interaction domain models. The results of the study suggest that dietary supply adequacy, food stability, and consumption status are positively associated with severe food security, while average food supply and environmental factors have negative effects on Food Security and Nutrition. The findings also indicate that severe food insecurity and malnutrition are spatially and temporally correlated across the African continent. Spatio-temporal modeling and spatial mapping are essential components of a comprehensive practice to reduce the burden of severe food insecurity. likewise, any planning and intervention to improve the average food supply and environment to promote sustainable development should be regional instead of one size fit all.

Traditional Filuferru is an ancient spirit from Sardinia, Italy, usually obtained from the distillation of wine or grape marc. In this contribution, the results of the first chemical characterization of a wide number of craft Filuferru samples has been accomplished in terms of evaluation of the alcoholic strength, qualitative and quantitative GC-MS analysis of the volatile composition of the distillate, and its trace element composition by means a ICP-MS method. Both instrumental methods have been validated and applied on 21 craft samples of Filuferru, whereas one sample of commercial distillate has been analyzed for comparison purposes. Alcoholic strength ranged between 41.0 and 62.4% (v/v). Sixty volatile compounds were identified and ten of them have been quantified. Analogies and differences with Grappa (i.e. the Italian distilled spirit most close to Filuferru) have been highlighted in the qualitative and quantitative profile of this matrix. Often meaningful amounts of acetaldehyde, ethyl acetate, dietyl acetal and acetic acid were measured. Elemental analysis, performed on toxic, non-toxic elements and oligoelements, 18 in total, revealed a wide variability of concentrations in both analytes and samples. High concentrations of Cu are sometimes evidenced, likely caused by losses from the distillation apparatus. The principal components analysis (PCA) allowed the differentiation of the ten volatile compounds quantified in two groups: the former, described mainly by PC1, constituted by acetic acid, ethyl acetate, dietyl acetal and acetaldehyde, and the second, described by PC2, constituted by 1-propanol, 2-methyl-1-propanol, the two coeluiting isomers 2-methyl-1-butanol and 3-methyl-1-butanol,1-hexanol, 2-phenylethanol and 2,3-butanediol. Data obtained may be useful in order to establish a regulation for the production of high-quality traditional Filuferru from Sardinia.

This study aims to explore methods for automating the replication of correlated portfolios across shares using various algorithms. The goal is to select one algorithm for implementation in a visualization tool that will facilitate portfolio building. The dynamics of the stock market provide a comprehensive view of the global economy, acting as a catalyst for stakeholders to participate and benefit various sectors financially, primarily through investments in companies that yield returns and contribute to the economy. The evaluation and comparison of these algorithms are crucial for making well-informed decisions regarding the core objectives of the study. Ultimately, a determination will be made as to whether the program can effectively analyse data from Yahoo finance to provide a quick overview of the financial market state of companies in the S&P 500 US stock prices.

Porphyromonas gingivalis is the major pathogenic bacteria found in the subgingival plaque of patients with periodontitis, leading to neuroinflammation. The bacteria destroy periodontal tissue through its virulence factors which are retained in the bacteria’s outer membrane vesicles (OMV). The purpose of this study was to determine the real time effect of intraperitoneal in-jection of P. gingivalis OMV on the production and expression of inflammatory markers as well as histopathological changes in the central nervous system (CNS) of adult zebrafish. Fol-lowing the LD50 (21 µg of OMV), the zebrafish were injected intraperitoneally with 18 µg of OMV and control group with normal saline at 7 different time points. Brains of experimental zebrafish were dissected at desired time points for colorimetric assays, ELISA and histology. This study discovered that nitric oxide and PGE2 were significantly increased at 45 minutes while IL-1β and IL-6 were expressed at subsequent 12- and 24-hours. respectively. Histopatho-logical changes such as blood coagulation, astrocytosis, oedema, spongiosis, and necrosis were also observed between 6- and 24-hours. The two apoptotic enzymes, caspases 3 and 9 were not expressed at any time point. In summary, the OMV induced neuroinflammatory responses and histopathological changes in adult zebrafish were time point dependent

Aluminum (Al) toxicity and low phosphorus availability (LP) are the top two co-existing edaphic constraints limiting agriculture productivity in acid soils. Plants have evolved versatile mechanisms to cope with the two stresses alone or simultaneously. However, the specific and common molecular mechanisms, especially those on flavonoids and carbohydrate metabolism, are still unclear. Laboratory studies were conducted on two wheat genotypes, Fielder (Al-tolerant and P-efficient) and Ardito (Al-sensitive and P-inefficient), exposed to 50 μM Al and 2 μM Pi (LP) in hydroponic solutions. After 4 days of stress, wheat roots were analyzed via transcriptomic and targeted metabolomic techniques. A total of 2296 differentially expressed genes (DEGs, 1535 up and 761 down) under Al and 3029 DEGs (1591 up and 1438 down) under LP were identified in Fielder. Similarly, 4404 DEGs (3191 up and 1213 down) under Al and 1430 DEGs (1176 up and 254 down) under LP were identified in Ardito. The GO annotation analysis results that 4079 DEGs annotated to the metabolic processes term. These DEGs were significantly enriched in the phenylpropanoid, flavonoid, flavone and flavonol biosynthesis, and carbohydrate metabolism pathways by performing the KEGG enrichment analysis. The targeted metabolome analysis detected 19 flavonoids and 15 carbohydrate components in Fielder and Ardito under Al and LP. More responsive genes and metabolites were involved in flavonoid metabolism under LP than Al in the Fielder, while an opposite trend in the Ardito. In the carbohydrate metabolism pathway, the genes and metabolites were higher in Fielder than in Ardito. The combined transcriptome and metabolome analysis revealed that the flavonoid and carbohydrate genes and metabolites differed between Fielder and Ardito under Al and LP, which may be an important reason for Fielder’s high Al and LP resistance. The results of this study lay a foundation for pyramiding genes and breeding multi-resistance varieties.

Flexible capacity applications demands to large energy storage density and high breakdown electric field strength of flexible films. Here, P(VDF-HFP) with ultra-thin Al2O3 nanosheets composite films were designed and fabricated through an electrospinning process followed by hot-pressing in sandwich structured. The results show that the insulating ultra-thin Al2O3 nanosheets and the sandwich structure can enhance the composites' breakdown field (by 24.8%) and energy density (by 30.6%) remarkedly to P(VDF-HFP) polymer matrix. An energy density of 23.5 J/cm3 at the ultrahigh breakdown field of 740kV/mm can be therefore realized. Insulating test and phase-field simulation results reveal that ultra-thin nanosheets insulating buffer layers can reduce the leakage current in composites, thus it effects the electric field spatial distribution to enhance breakdown strength. Our research provides a feasible method to increase the breakdown strength of ferroelectric polymers, which is comparable to those of non-ferroelectric polymers.

The role of biomarkers in the diagnosis of inflammatory bowel disease is not fully characterized. C-reactive protein has a short half-life and elevates quickly after the onset of an inflammatory process, the performance is better in Crohn’s disease than in ulcerative colitis. Erythrocyte sedimentation rate is easy to determine, widely available, and cheap but the long half-life the influence of age, anemia, smoking, and drugs limit its usefulness. Fecal markers have good specificity, but suboptimal accuracy. Microbial antibodies and novel immunological markers show promise but need further evidence before entering clinical practice. Proteomic methods could represent the dawn of a new era of stool protein/peptide biomarker panels able to select patients at risk of inflammatory bowel disease.

Background: Hypovitaminosis D is a public health problem due to its implications for various diseases. Vitamin D has numerous functions, such as modulating the metabolism of some cellular tissues, and is expressed through the VDR gene that may influence gene expression modulation, which plays an important role in vitamin D metabolism. Objective: To evaluate the effect of the genotypes of single nucleotide polymorphism (SNP) BsmI of the vitamin D receptor (VDR) gene on VDR, SOD2, and CYP24A1 gene expression in individuals with low serum vitamin D levels. Methods: This was a cross-sectional analytical study. Individuals were invited to participate and, after signing the informed consent form, answered a structured questionnaire with identification data. Blood was collected for biochemical analysis, and vitamin D was measured by chemiluminescence; BsmI polymorphism was determined using real-time polymerase chain reaction (PCR) assays with TaqMan allelic discrimination, and gene expression was conducted by qRT-PCR using QuantiFast SYBR® Green PCR Master Mix. Data were analyzed using the SPSS 20.0 software, and differences were considered significant at p&lt;0.05. Results: 98 individuals with vitamin D ≤20 ng/dL were evaluated, and the SNP BsmI of the VDR gene showed CYP24A1 overexpression and low SOD2 expression. Conclusion: SNP Bsm I of the VDR gene can modulate the expression of the genes evaluated without interfering with serum levels.

The multidrug efflux transporter ABCB1 is clinically important for drug absorption and distribution and can be a determinant of chemotherapy failure. Recent structure data shows that three glutamines donate hydrogen bonds to co-ordinate taxol in the drug binding pocket. This is consistent with earlier drug structure-activity relationships that implicated the importance of hydrogen bonds in drug recognition by ABCB1. By replacing the glutamines with alanines we have tested whether any or all of Q347, Q725 and Q990 are important for the transport of three different drug classes. Flow cytometric transport assays show that Q347A and Q990A act synergistically to reduce transport of Calcein-AM, BODIPY-verapamil and OREGON GREEN-taxol bisacetate but the magnitude of the effect was dependent on the test drug and no combination of mutations completely abrogated function. Surprisingly, Q725A mutants generally improved transport of Calcein-AM and BODIPY-verapamil, suggesting that engagement of the wild-type Q725 in a hydrogen bond is inhibitory for the transport mechanism. To test transport of unmodified taxol, stable expression of Q347/725A and the triple mutant was engineered and shown to confer equivalent resistance to the drug as the wild-type transporter, further indicating that none of these potential hydrogen bonds between transporter and transport substrate are critical for function of ABCB1. The implications of the data for plasticity of the drug binding pocket are discussed.

During their complex life cycles, the Apicomplexan parasites, Plasmodium falciparum and Toxoplasma gondii employ several genetic switches to regulate their gene expression. One such switch is mediated at the level of translation through upstream Open Reading Frames (uORFs). As uORFs are found in the upstream regions of a majority of genes in both the parasites, it is essential that their roles in translational regulation be appreciated to a greater extent. This review provides a comprehensive summary of studies that show uORF-mediated gene regulation in these parasites and highlights examples of clinically and physiologically relevant proteins that exhibit uORF-mediated regulation. In addition to these examples, several studies that use bioinformatics, transcriptomics, proteomics, and ribosome profiling also indicate the possibility of widespread translational regulation by uORFs. Further analysis of genome-wide datasets will reveal novel genes involved in key biological pathways such as cell-cycle progression, stress-response, and pathogenicity. The cumulative evidence from studies presented in this review suggests that uORFs will play crucial roles in regulating gene expression during clinical disease caused by these important human pathogens.

It was once thought that high QoS (Quality of Service) performance solves recurrent problems of low-quality multimedia services. Since then, solutions have been proposed to ensure a high level of QoE (Quality of Experience). In this document, the authors attempt to outline their understanding of an accurate meaning of quality of multimedia services. Starting from QoS and passing through generalised QoE, the authors focus on aspects of subjective and objective quality modelling and optimisation of visual performance for TRV (Target Recognition Video) applications (such as video surveillance), outlining the path of ITU-T standardisation in this area. The authors revised the ITU-T Recommendation P.912 to reflect improved subjective test techniques developed since this Recommendation was approved. The authors also attempt to predict at least some existing errors of reasoning, which are likely to become evident for the industry in the next decade.

Prediction and early detection of atrial fibrillation (AF) remain a permanent challenge in everyday practice. Timely identification of an increased risk for AF episodes (which are frequently asymptomatic) is essential in the primary and secondary prevention of cardioembolic events. One of the noninvasive modalities of AF prediction is represented by the electrocardiographic P-wave analysis. This includes the study and diagnosis of interatrial conduction block (Bachmann`s bundle block). Bayés’ Syndrome (named after its first descriptor) denotes the association between interatrial conduction defect and supraventricular arrhythmias (mainly AF) predisposing to cardioembolic events. Our short review presents an update of the most important data concerning this syndrome: brief history, main ECG features, pathophysiological background and clinical implications.

SARS-CoV-2 infections underlie the current Coronavirus disease (COVID-19) pandemic and are causative for a high death toll particularly among elderly subjects and those with comorbidities. Selenium (Se) is an essential trace element of high importance for human health and particularly for a well-balanced immune response. Mortality risk from severe disease like sepsis or polytrauma is inversely related to Se status. We hypothesized that this relation also applies to COVID-19. Serum samples (n=166) from COVID-19 patients (n=33) were collected consecutively and analysed for total Se by X-ray fluorescence and selenoprotein P (SELENOP) by a validated ELISA. Both biomarkers showed the expected strong correlation (r=0.7758, p<0.001), pointing to an insufficient Se status for optimal selenoprotein expression. In comparison to reference data from a European cross sectional analysis (EPIC, n=1915), the patients showed a pronounced deficit in total serum Se (mean±SD, 50.8±15.7 vs. 84.4±23.4 µg/L) and SELENOP (3.0±1.4 vs. 4.3±1.0 mg/L). A Se status below the 2.5th percentile of the reference population, i.e., [Se] < 45.7 µg/L and [SELENOP] < 2.56 mg/L was present in 43.4% and 39.2% of COVID samples, respectively. The Se status was significantly higher in samples from surviving COVID patients as compared to non-survivors (Se; 53.3±16.2 vs. 40.8±8.1 µg/L, SELENOP; 3.3±1.3 vs. 2.1±0.9 mg/L). We conclude that Se status analysis in COVID patients provides diagnostic information. However, causality remains unknown due to the observational nature of this study. Nevertheless, the findings strengthen the notion on a relevant role of Se for COVID convalescence, and support the discussion on adjuvant Se supplementation in severely diseased and Se-deficient patients.

P-glycoprotein (Pgp) determines resistance to a broad spectrum of drugs in glioblastoma multiforme (GB) because it is highly expressed in GB stem cells and in brain-blood barrier (BBB), the peculiar endothelium surrounding brain. Inhibiting Pgp activity in BBB and GB is still an open challenge. Here, we tested the efficacy of a small library of tetrahydroisoquinoline derivatives with an EC₅₀ for Pgp < 50 nM, in primary human BBB cells and in patients-derived GB, from which we isolated differentiated/adherent cells (AC, i.e. Pgp-negative/doxorubicin-sensitive cells) and stem cells (neurospheres, NS, i.e. Pgp-positive/doxorubicin-resistant cells). At 1 nM, 3 compounds increased the delivery of doxorubicin, a typical substrate of Pgp, across BBB monolayer, without altering expression and activity of other transporters. The compounds increased the drug accumulation within NS, restoring necrosis, apoptosis and reduction in cell viability induced by doxorubicin. In co-culture systems, the compounds added to the luminal face of BBB increased the delivery of doxorubicin to NS growing under BBB and rescued the drug’s cytotoxicity. Our work identified new ligands of Pgp active at low nanomolar concentrations, that effectively reduce Pgp activity in BBB and GB, and can improve chemotherapy efficacy in this tumor.

Possibilities of utilization of two frequencies synthetic aperture radar (SAR) operating simultaneously in P-band and S or L-band are considered. Advantages of such system are shown. They are caused by improvement of data interpretation and by decrease of ionosphere influence. Tasks of surface and subsurface sensing by the complex SAR were under investigation for vegetation, soil covers are given. As an example, the investigation results of coniferous forest areas backscattering by the spaceborne P-band SAR together with L-band SAR in the September-February period are presented. Studies have shown that the joint use of the results in L and P bands offer unique opportunities to explore the effects of changes in the backscattering properties of forest surfaces.

In winter 2008-2009, Lake Occhito, a strategic multiple-uses reservoir in South Italy, was affected by an extraordinary Planktothrix rubescens bloom. P. rubescens is a filamentous potentially toxic cyanobacterium which has recently colonized many environments in Europe. A number of studies is currently available on the use of remote sensing techniques to monitor different fresh water cyanobacteria species. By contrast no specific applications are available on the remote sensing monitoring of P. rubescens. In this paper we present a specific algorithm, based on Water Leaving Reflectances (WLR) from MERIS data, atmospherically corrected using the Aerosol Optical Thickness (AOT) retrieved by MODIS data, to detect P. rubescens blooms. The high accuracy in AOT data, provided by MOD09 surface reflectance product, at 1km spatial resolution, allowed obtaining a good correlation between the WLR and the P. rubescens chlorophyll-a concentrations measured in the field, through multiple stations fluorometric profiles. A modified Normalized Difference Chlorophyll index (NDCI) algorithm is presented. The performance of the proposed algorithm has been successfully compared with other specific algorithms for turbid productive waters. We demonstrated how important is to verify the spectral behaviour of bio-optical parameters in order to develop an ad hoc algorithm that better performs with respect to standard algorithms.

In this paper, we establish some inequalities bounding the ratio Г_p,k(x)/Г_p,k(y), where Г_p,k(.) is the (p,k)-analogue of the Gamma function. Consequently, some previous results are recovered from the obtained results.

In this data deposit, I describe a dataset that is the result of content mining 167,318 published articles for statistical test results. As a result of this content mining, 688,112 results from 50,845 articles were extracted. In order to provide a comprehensive set of data, the statistical results are supplemented with metadata from the article they originate from. The dataset is provided in a comma separated file (CSV) in long-format. For each of the 688,112 results, 20 variables are included, of which seven are article metadata and 13 pertain to the individual statistical results (e.g., reported and recalculated p-value). A five-pronged approach was taken to generate the dataset: (i) collect journal lists, (ii) spider journal pages for articles, (iii) download articles, (iv) add article metadata, and (v) mine articles for statistical results.

Transcriptome assembly and functional annotation are essential for understanding gene expression and biological function. Nevertheless, many existing tools lack the flexibility to integrate both short- and long-read sequencing data or fail to provide a complete, customizable workflow for transcriptome analysis. Here, we present TrAnnoScope, a comprehensive transcriptome analysis pipeline that provides a complete, customizable workflow capable of efficiently processing and integrating short- and long-read sequencing data to generate high-quality, full-length transcripts with detailed functional annotation. The pipeline encompasses steps from quality control to functional annotation, employing a range of tools and established databases, such as SwissProt, Pfam, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes. As a case study, TrAnnoScope was applied to RNA-Seq data from zebra finch brain, ovary, and testis tissues. TrAnnoScope produced comprehensive transcriptome, demonstrating strong alignment with the reference genome (99.63%) and capturing a significant proportion of nearly complete protein sequences (92.7%). The functional annotation process yielded extensive matches to known protein databases and successfully assigned relevant functional terms to majority of the transcripts. As such, TrAnnoScope successfully integrates multiple sequencing technologies to generate comprehensive transcriptomes with minimal user input. Its modular design, flexibility, and ease of use make it a valuable tool for researchers working with complex datasets, particularly for non-model organisms.

Triple negative breast cancer (TNBC) is associated with a generally poor prognosis due to its highly aggressive and metastatic nature, lack of targetable receptors, as well as the frequent development of resistance to chemotherapy. We previously reported that AU1, a small molecule developed as an inhibitor of BPTF (bromodomain PHD finger containing transcription factor), was capable of sensitizing preclinical models of triple negative breast cancer to chemotherapy, in part via the promotion of autophagy. In studies reported here, we identify an additional property of this compound, specifically that sensitization is associated with inhibition of the P-glycoprotein efflux pump. In silico molecular docking studies indicate that AU1 binds to active regions of the efflux pump, in a manner consistent with inhibition of pump function. This work identifies a novel chemical structure that can influence multidrug efflux, an established mechanism of drug resistance in triple negative breast cancer, that has not yet been successfully addressed by clinical efforts.

We prove the continuous non-Archimedean (resp. p-adic) Banach space and Hilbert space versions of non-Archimedean (resp. p-adic) Welch bounds proved by M. Krishna. We formulate continuous non-Archimedean and p-adic functional Zauner conjectures.

We prove the non-Archimedean (resp. p-adic) Banach space version of non-Archimedean (resp. p-adic) Welch bounds recently obtained by M. Krishna. More precisely, we prove following results. \begin{enumerate}[\upshape(i)] \item Let $\mathbb{K}$ be a non-Archimedean (complete) valued field satisfying $\left|\sum_{j=1}^{n}\lambda_j^2\right|=\max_{1\leq j \leq n}|\lambda_j|^2$ for all $ \lambda_j \in \mathbb{K}, 1\leq j \leq n$, for all $n \in \mathbb{N}.$ Let $\mathcal{X}$ be a $d$-dimensional non-Archimedean Banach space over $\mathbb{K}$. If $\{\tau_j\}_{j=1}^n$ is any collection in $\mathcal{X}$ and $\{f_j\}_{j=1}^n$ is any collection in $\mathcal{X}^*$ (dual of $\mathcal{X}$) satisfying $f_j(\tau_j) =1$ for all $1\leq j \leq n$ and the operator $S_{f, \tau} : \text{Sym}^m(\mathcal{X})\ni x \mapsto \sum_{j=1}^nf_j^{\otimes m}(x)\tau_j^{\otimes m} \in \text{Sym}^m(\mathcal{X})$, is diagonalizable, then \begin{align}\label{NONFUNCTIONALWELCH} \max_{1\leq j,k \leq n, j \neq k}\{|n|, |f_j(\tau_k)f_k(\tau_j)|^{m} \}\geq \frac{|n|^2}{\left|{d+m-1 \choose m}\right| }. \end{align} We call Inequality (\ref{NONFUNCTIONALWELCH}) as non-Archimedean functional Welch bounds. \item For a prime $p$, let $\mathbb{Q}_p$ be the p-adic number field. Let $\mathcal{X}$ be a $d$-dimensional p-adic Banach space over $\mathbb{Q}_p$. If $\{\tau_j\}_{j=1}^n$ is any collection in $\mathcal{X}$ and $\{f_j\}_{j=1}^n$ is any collection in $\mathcal{X}^*$ (dual of $\mathcal{X}$) satisfying $f_j(\tau_j) =1$ for all $1\leq j \leq n$ and there exists $b \in \mathbb{Q}_p$ such that $ \sum_{j=1}^{n}f_j^{\otimes m}(x) \tau_j^{\otimes m} =bx$ for all $ x \in \text{Sym}^m(\mathcal{X}),$ then \begin{align}\label{PADICFUNCTIONALWELCH} \max_{1\leq j,k \leq n, j \neq k}\{|n|, |f_j(\tau_k)f_k(\tau_j)|^{m} \}\geq \frac{|n|^2}{\left|{d+m-1 \choose m}\right| }. \end{align} We call Inequality (\ref{PADICFUNCTIONALWELCH}) as p-adic functional Welch bounds. \end{enumerate} We formulate non-Archimedean functional and p-adic functional Zauner conjectures.

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with PCA (OCC-PCA) model. Such results have confirmed the increased performance achieved by the proposed model.

China's implementation of public-private partnerships projects has been quite effective, involving infrastructure and other livelihood projects, a total of 19 industries, and an investment of nearly 1.5 trillion yuan. The characteristics of PPP projects such as long construction period and large investment amount determine the risks of PPP projects are also great, and the PPP projects of comprehensive environmental governance are also the same. The government and social capital use the PPP model to cooperate, and use the principal component analysis method to assess the risks of the entire life cycle of the comprehensive environmental governance PPP project. Therefore, it plays an important role in ensuring the smooth implementation of projects and reducing the losses caused by risks. According to the risk factors of the whole life cycle of the comprehensive environmental governance PPP project, an indicator system of 5 first-level indicators, 18 second-level indicators, and 43 third-level indicators has been established. Principal component analysis is used to analyze the influence weight of risk factors at each stage. The analysis shows that among the four stages, environmental pollution risk, project approval delay risk, completion risk, interest rate and financial fluctuation risk, and franchise life risk are the most influential risks in the implementation of PPP projects. Therefore, suggestions are made through the risk factors of each stage in the comprehensive environmental governance PPP project. For example, strengthen the response to the external environment risks of the comprehensive environmental governance PPP project, standardize the bidding and procurement of the comprehensive environmental governance PPP project, and strengthen the subsequent management of the transfer of the comprehensive environmental governance PPP project. In this way, the ability to resist risks of comprehensive environmental governance PPP projects is improved; the smooth implementation of the project is guaranteed, and the long-term development of comprehensive environmental governance PPP projects is promoted.

The COVID-19 pandemic situation, disease intensity, weak healthcare facilities, unawareness, and misinformation led people to fear and anxiety in Bangladesh. This study intended to get peoples’ perception on psychosocial, socio-economic and environmental crisis amidst the pandemi. An online questionnaire was surveyed nationwide (respondents no.1066). Datasets were analyzed through the Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the psychosocial, socio-economic and environmental parameters. A significant association between fear of COVID-19 with struggling healthcare system (p<0.05) was found. Also, negative association between fragile health system and government’s ability to deal with the pandemic (p<0.05) revealing poor governance. Again, a positive association of shutdown and social distancing with fear of losing life, and due to lack of health treatment (p<0.05) reveals that shut down hampers normal activities which may lead to mental and economic stress. However, a positive association of socio-economic impact of the shutdown with poor people’s suffering, the price hike of basic need, hamper of formal education (p<0.05) may lead to severe socio-economic and health crisis. There is a possibility of climate-induced disaster during/after the pandemic, which will create severe food insecurity (p<0.01). Daily wage earners and poors will suffer most by food and nutritional deficiency, and the country may face huge economic burden. Proper risk assessment and communications is needed to alleviate fear and anxiety. Thus, financial support and mental boosting is required.

The main objective of the present study is to demonstrate the possibility of actively influencing the position of the bow shock wave and the main parameters of supersonic flow over a blunt body by organizing a gas discharge near the front surface, in the region between the body and the bow shock wave. The research is carried out using both experimental and numerical methods. The working gas was xenon. It is shown that the steady bow shock wave stand-off distance along with the current and power of the discharge, is associated with the change in the adiabatic index of the plasma created by the discharge, which, in turn, is determined by the plasma parameters, such as the degrees of nonequilibrium and the degree of ionization. It is shown that the adiabatic index with the power supplied to the impact zone in the range of 30-120 kW can both increase and de-crease in the range of 1.25-1.288. The study of the discharge created plasma zone was conducted and the correspondence between the discharge current and power, and the average parameters in the plasma zone created by the discharge is presented. A good agreement between the numerical and experimental data was shown. The results may be useful in developing control systems for high-speed civil aircrafts.

Human immunodeficiency virus (HIV) and malaria, caused by infection with Plasmodium spp., are endemic in similar geographical locations. As a result, there is high potential for HIV/Plasmodium co-infection, which increases the pathology of both diseases. However, the immunological mechanisms underlying the exacerbated disease pathology observed in co-infected individuals are poorly understood. Here, we used the rhesus macaque (RM) model to characterize the immunopathogenic impact of Plasmodium fragile co-infection during antiretroviral therapy (ART)-treated simian immunodeficiency virus (SIV)-infection. We observed that P. fragile co-infection resulted in parasitemia and anemia, as well as persistently detectable viral loads (VL) and decreased absolute CD4+ T-cell counts despite daily ART treatment. Notably, P. fragile co-infection was associated with increased levels of inflammatory cytokines linked with neutrophil function, including monocyte chemoattractant protein 1 (MCP-1). P. fragile co-infection was associated with increased levels of neutrophil elastase, a plasma marker of neutrophil extracellular trap (NET) formation, but significant decreases in markers of neutrophil degranulation, potentially indicating a shift in neutrophil functionality during co-infection. Finally, we characterized levels of plasma markers of gastrointestinal (GI) barrier permeability and microbial translocation and observed significant correlations between indicators of GI dysfunction, clinical markers of SIV and Plasmodium infection, and neutrophil frequency and function. Taken together, these data indicate that neutrophil-driven inflammation and GI dysfunction may underlie heightened SIV/P. fragile co-infection pathogenesis.

Opuntia ficus-indica has always interacted with many phytophagous insects at its center of origin; two of them are Dactylopius coccus and D. opuntiae. D. coccus, or true cochineal, is produced to extract carminic acid, and D. opuntiae or wild cochineal is a key invasive pest of O. ficus-indica in more than 20 countries around the world. Despite the economic and environmental relevance of this cactus and, D. opuntiae and D. coccus, there are few studies that have explored volatile organic compounds (VOCs) derived from the plant-insect interaction. The aim of this work was to determine VOCs of D. coccus and D. opuntiae feeding on cultivars suitable for their development and to identify different VOCs in cladodes infested by each Dactylopius species. Volatiles were identified through their essential oils, which were obtained by hydrodistillation. A total of 66 VOCs from both Dactylopius species were identified, and 125 from the Esmeralda and Rojo Pelón cultivars infested by D. coccus and D. opuntiae, respectively, were determined. Differential VOCs production due to infestation by each Dactylopius species was also found. Some changes in methyl salicylate, terpenes such as linalool or the alcohol p-vinylguaiacol were related to Dactylopius feeding on cladodes of their respective cultivars. Changes in these VOCs and their probable role in plant defense mechanisms should receive more attention because this knowledge could improve D. coccus rearing, or to its inclusion in breeding programs for D. opuntiae control in regions where it is a key pest of O. ficus-indica.

In the recent past, the number of invasive species around the world has increased significantly due to increased globalization and climate change. Understanding biological invasions under climate change is crucial for sustainable conservation of biodiversity. The insect pest Pseudococcus viburni (Signoret) can cause damages to plants, resulting in leaf yellowing, leaf defoliation, and reduced plant growth. A serious attack by this insect post can even lead to the entire plant withering and dying, bringing huge economic and ecological losses. Despite these damages, the potential distribution range and management strategies for this insect pest are poorly understood. Based on historical distribution data and environmental factors, the present study predicted the potentially suitable areas for P. viburni spread under different climate change scenarios using MaxEnt (maximum entropy). The results showed that precipitation of the coldest quarter (Bio19), precipitation seasonality (Bio15), and mean temperature of the wettest quarter (Bio8) were the most important environmental factors determining the distribution of P. viburni. Under the current climate conditions, its potential suitable areas are southern China, the whole of Japan, North America (especially the eastern part of the United States), the southwestern part of South America, the Mediterranean coast and most of Europe, the central part of Africa i.e., the south of the Sahara Desert and most of the southern coast of Australia. The total area of habitats suitable for this insect pest is predicted to be increased in the future. In order to prevent P. viburni transmission and spread, there is a need to strengthen the monitoring and quarantine measures against this pest at the Southern ports.

Lignin, and its by-products in the effluent of most agrochemical-based industries like pulp and paper, increase environmental pollution. There is, therefore, an increasing demand to degrade lignin and its derivatives using bacteria-produced ligninolytic enzymes. This study focused on optimizing growth and lignin peroxidase production of ligninolytic potential bacterial strains (E. coli accession no: LR0250096.1 and P. aeruginosa accession no: CP031449.2) for the first time. A submerged fermentation process was used to simultaneously optimize growth and crude lignin peroxidase production using kraft lignin as the substrate. The E. coli and P. aeruginosa were separately cultured under different pH, temperature, lignin (substrate) concentration and incubation periods. It was observed that the enzyme production was growth dependent. The optimum conditions for E. coli were pH 7.5, temperature 30 °C and lignin concentration 1 g/L. For P. aeruginosa the best conditions were pH 6.5, temperature 40 °C and lignin concentration 1 g/L. The enzyme production peaked after 7 days of incubation for both organisms were 21.53 ± 3.11 mg/ml and 18.13 ± 2.1 mg/ml respectively. It is apparent that both E. coli (LR0250096.1) and P. aeruginosa (CP031449.2) are mesophiles bacteria. E. coli function best in alkaline pH while P. aeruginosa prefers a slightly acidic environment. Hence, they are promising candidates in the reduction of lignocellulosic wastes.

The development of good conductivity p-type β-Ga2O3 is critical to realize its devices and applications. The nitrogen-doped p-type β-Ga2O3 films with enhanced conductivity characteristics were prepared through the thermal oxidation of GaN in the N2O atmosphere. Further measurements were performed on the oxidized films at 1000, 1050, and 1100 °C to have insight into the underlying mechanism of the thermally activated transformation process. The room temperature photoluminescence (PL) spectra demonstrated a moderate ultraviolet emission peak at 246 nm, confirming the generation of gallium oxide with a band gap of ~5.0 eV. The normalized X-ray diffraction (XRD), the high-resolution transmission electron microscopy (HRTEM), and the selected area electron diffraction (SAED) patterns were used to confirm the characteristic of polycrystalline and anisotropic growth. Then, the effects of the oxidation temperature on the amount of incorporated nitrogen were analyzed using secondary ion mass spectrometry (SIMS). Moreover, the ionization energy of the acceptor of films oxidized at 1000, 1050, and 1100 °C was calculated and analyzed using the temperature-dependent Hall test results. The results showed that nitrogen doping was the main contributor to p-type electrical properties. The activation energy of the polycrystalline β-Ga2O3 prepared through thermal oxidation of GaN in the N2O atmosphere was estimated to be 147.175 kJ·mol-1 using the Arrhenius plot, which was considerably lower than that of both dry and wet oxidations of GaN in O2 ambient, thus confirming the efficiency of thermal oxidation of GaN in N2O.

The Mediterranean region offers good weather conditions for outdoor pig production (OPP), which is considered more environmentally friendly than intensive indoor production. However, the continuous input of food and pigs' excreta increases the soil organic matter (SOM) and phosphorus (P), increasing the risk of waterbodies eutrophication. This work aimed at evaluating in OPP areas soil P dynamics and the role of SOM on P sorption and P release. The experiment was done for two years, at an area of 2.8 ha with an animal charge of 9 adults ha-1. Georeferenced soil samples were taken at 0.20 m depth, and a soil P sorption experiment was carried out. At the end of the experiment, for the background value, the levels of SOM increased between 85–376%, and Olsen P values ranged between -82–884%. SOM levels above 2% caused a decrease in the binding energy of P sorption according to the linear model b=-15.541SOM+115.20 (p <0.01) as well as a decrease of the soil P sorption capacity Qmax=-41.272SOM+298.37 (p <0.01). To avoid the accumulation of SOM and P preventing hotspots for waterbodies eutrophication, an adequate animal charge together with soil cultivation for pig grazing can be a cost-effective practice.

Antisense technologies consist of the utilization of oligonucleotides or oligonucleotide analogs to interfere with undesirable biological processes, commonly through inhibition of expression of selected genes. This field holds a lot of promise for the treatment of a very diverse group of diseases including viral and bacterial infections, genetic disorders, and cancer. To date, drugs approved for utilization in clinics or in clinical trials target diseases other than bacterial infections. Although several groups and companies are working on different strategies, the application of antisense technologies to prokaryotes still lags with respect to those that target other human diseases. In those cases where the focus is on bacterial pathogens, a subset of the research is dedicated to produce antisense compounds that silence or reduce expression of antibiotic resistance genes. Therefore, these compounds will be adjuvants administered with the antibiotic to which they reduce resistance levels. A varied group of oligonucleotide analogs like phosphorothioate or phosphorodiamidate morpholino residues, as well as peptide nucleic acids, locked nucleic acids and bridge nucleic acids, the latter two in gapmer configuration, have been utilized to reduce resistance levels. The major mechanisms of inhibition include eliciting cleavage of the target mRNA by the host’s RNase H or RNase P, and steric hindrance. The different approaches targeted resistance to β-lactams including carbapenems, aminoglycosides, chloramphenicol, macrolides, and fluoroquinolones.

Adsorption is an effective strategy for the removal of pollutants from the wastewater. Herein, a 2-hydroxyterephthalic acid (HTC) modified hypercrosslinked polymer (HTC-HCP) is successfully synthesized via Friedel-Crafts reactions, and used as an adsorbent for the different types of pollutants including organic contaminants and heavy metal ions from wastewater. Excellent adsorption capacities are observed for amines (aniline, p-methylaniline (p-MA), p-chloroaniline (p-CA), and p-aminobenzoic acid (p-ABA)), phenols (phenol, p-chlorophenol (4-CP) Bisphenol A (BPA), 1-Naphthol (1-NP)), dyes (rhodamine B (RhB) and methyl orange (MO)), and metal ions (Pb2+, Hg2+, and Cd2+). The resulting polymers exhibited excellent adsorption performance towards these pollutants. Especially, the removal rate of aniline is above 95% in the concentration of 2.5 mg/L in 40 min at 25 °C. The interaction mechanism has been investigated, and confirmed by FTIR and the theoretical calculation results. It is due to surface complexation and chemisorption between adsorbent and adsorbate. The polymer exhibits good performance such as high adsorption capacity, high separation efficiency, biodegradable properties, and easy regeneration, suggesting that its potential technological applications for the removal of organic compounds and heavy metal ions from actual industrial effluent.

In these times electricity is big trouble of problem facing by the world. Therefore, there is necessitating for the property basis of energy that is employed for as bio-electricity. Many of the scientists and researchers are trying to find out the sustainable energy generates with the help of plant microbial fuel cell. Plant microbial fuel cell (P-MFCs) could be feasibility technology approach of bio-electricity generation which is mutualism interaction of the plants along with their rhizospheric bacteria. In the present study, Plant-microbial fuel cell was observed in grass e-table evaluated in term of bioelectricity generation from Cynodon dactylon (Dooba Ghas). This e-table was connected with electrode and different condition (physical and chemical) to detect the change in bioelectric potential. It was found that maximum voltage generated among all the conditions was 4.24 ±2V at 15 days by using Cynodon dactylon through P-MFCs. The potential difference generated through P-MFCs was measured using a multimeter. The generation of bioelectricity was observed under different conditions like exposure to light and shade condition measured for voltage was found to be significantly different parameters. The maximum recorded under light and shade conditions were 3.82 ±2 V and 4.25 ±2 V respectively at 15 days of incubation.