Paratuberculosis, or Johne's disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a chronic granulomatous enteritis affecting both domestic and wild ruminants. The agent was also found in wild mammals such as wild boar (Sus scrofa), however, the role of wild mammals in the epidemiology of MAP is unclear. During the research period, 941 free-ranging wild boar (S. scrofa) legally hunted in two locations in the Centre-eastern region of Portugal were examined. Ninety-seven wild boar exhibited one or more gross lesions and were tested for the presence of Mycobacterium avium subsp. paratuberculosis using acid-fast staining, mycobacterial culture, poly-merase chain reaction (PCR), and histopathological examination. Forty-five animals (46.4%, 95% CI: 36.5-56.3%) were identified as infected, as indicated by positive results in culture and/or PCR. The findings revealed that the most significant risk factor was being a juvenile compared to year-lings and adults (OR = 10.2, 95% CI: 2.2-48.0). Based on our results, 37.9% (n = 11) of the infected animals were considered suitable for human consumption. Our findings offer novel insights into mycobacterial infections in wild boar populations in Portugal and suggest that wild boar could be a source of human infection if zoonotic potential is considered.

Objective. To evaluate serum neuronal and inflammatory biomarkers in asphyctic newborns treated with hypothermia alone or hypothermia plus melatonin, and whether biomarkers correlate with neurodevelopmental outcomes. Design. This is a pilot multicentre, randomised, controlled, double blind clinical trial. Neuronal glial fibrillary acidic protein (GFAP), granulocyte-macrophage colony-stimulating factor (GM-CSF), and inflammatory cytokines (interleukin (IL)-1, IL-2, IL-7 and IL-13) were measured in serum samples at hospital admission (T0), 24 hours (T1), 72 hours (T2) and 7-10 days of age (T3). Neurodevelopmental outcomes (Bayley Scales of Infant and Toddler Development-III scales, Gross Motor Function Classification System (GMFCS) and the Tardieu scale) were performed at 6 and 18 months. Setting: Level 3 neonatal intensive care unit. Patients and interventions: 25 newborns were recruited. The treated patients received a daily dose of intravenous melatonin, 5 mg per kg body weight, for 3 days. Results. After adjusting the data for severity, in the group treated with melatonin, there is a decrease in plasma levels of GM-CSF, IL-2 and IL-13 at T1 compared to placebo, as well as, at T2, in GM-CSF concentrations, as well as IL-7 and IL-13 at T3. Evolutionarily, we found a significant decrease in GM-CSF concentrations in the treatment group compared to placebo, with no differences for the rest of the biomarkers. It is noteworthy that the sustained decrease in GM-CSF and inflammatory cytokines IL-2, IL-7 and IL-13 correlated with better neurodevelopmental outcomes at 6 and 18 months. Conclusions. In neonates with hypoxic-ischemic encephalopathy, the addition of iv melatonin to hypothermia therapy affects the plasma concentration of biomarkers in the first week of life, with a high correlation with long-term neurological prognosis.

Hazelnut, pistachio and cashew are tree nuts with health benefits but also with allergenic properties being prevalent food allergens in Europe. The allergic characteristics of these tree nuts after processing combining heat, pressure and enzymatic digestion were analyzed through in vitro (Western blot and ELISA) and in vivo test (Prick-Prick). In the analyzed population, the patients sensitized to Cor a 8 (nsLTP) were predominant over those sensitized against hazelnut seed storage proteins (Sprot, Cor a 9 and 14), which displayed higher IgE reactivity. The protease E5 effectively hydrolyzed proteins from hazelnut and pistachio, while E7 was efficient for cashew protein hydrolysis. When combined with pressured heating (autoclave and Controlled Instantaneous Depressurization (DIC)), these proteases notably reduced the allergenic reactivity. The combination of DIC treatment before enzymatic digestion resulted in the most effective methodology to drastically reduce or indeed eliminate the allergenic capacity of tree nuts.

The therapeutic, attenuative, and morphological effects of proprietary neural stem cell-derived extracellular vesicles (NSC EVs, AB126) produced by Aruna Bio, Inc. were assessed in a range of in vitro and in vivo models of stroke. We emphasize that modulation of neuroinflammation is a key target and suggest further studies to advance our understanding of the mechanisms of action and ultimately how best to dose and monitor the benefits of AB126 in stroke patients.

Permafrost covers about 23% ~ 25% of the land in the northern hemisphere, mainly in Russia, Canada, Alaska, Northeast and Qinghai Tibet Plateau (QTP) in China. Many engineering especially the transportation has been in the permafrost regions above. Under the action of highways construction and global warming, permafrost under infrastructures is degraded and causes serious damage. Two phases closed thermosyphon (TPCT) is a widely accepted green countermeasure against the problem in permafrost regions. Although it has been applied to prevent permafrost degradation, their application presents significant challenges on account of the stronger endothermic action of asphalt pavement. This paper focused on the thermosyphon technology and application in the permafrost. Moreover, the article highlighted the excellent working performance of the TPCT that improving the stability of the infrastructures and prevent it degrading due its excellent efficiency of heat transfer. The industrial applications of the TPCT were also summarized, along with their limitations. Finally, the results reported in this paper can provide a valuable guidance for the design of the TPCT and construction of the permafrost regions in the future.

In "genome editing," a type of genetic engineering, DNA is added, taken away, or changed in an organism's genome. Biomedicine, biotechnology, and synthetic biology, just to name a few, have all gained a lot from how often this method is used. Before the editing process can start, a Double-Strand Breaks (DSB) must be made in the DNA at a specific gene. Researchers have made nucleases, which are sometimes called "molecular scissors," to fix this DSB. Transcription activator-like effector nucleases (TALENs), Zinc-finger nucleases (ZFNs), and homing endonucleases are all examples of protein targets that have been studied and changed. The last part gave an overview of these study projects and explained how to make Cas12a for specific uses. The applications of Cas12a have been extensively studied in recent years, but the protein still holds a lot of promise as a treatment and screening tool. In order to give a quick review of CRISPR-Cas12a and its uses, we will briefly talk about the structure and function of the different parts of the reaction pathway that lead up to the catalysis of the target DNA. Cas12a uses a multistep process to make sure that it is selecting the right DNA. This is a good trait for a device that changes the DNA because it makes it less likely that something bad will happen. Even though data shows that a new CRISPR RNAs (crRNA) molecule can stop Cas12a from randomly destroying ssDNA, it may still hurt the host cell while trying to change the genome. The action of Cas12a catalysis, which is a powerful tool for changing the genome, needs to be changed so that it can be more easily controlled and managed. Using what we know about how Cas12a works, we have made mutants that are less active on ssDNA and more active on dsDNA. So far, only Cas9 and Cas12a, which are both part of the CRISPR family, have been used to change the genome. Because of how similar and different these two endonucleases are, CRISPR can now be used for multiple scientific purposes. Cas12a is better than Cas9 because it makes double-strand breaks (DSBs) that favor Homology-Directed Repair (HDR) over Non-homologous end joining (NHEJ) and can change more than one copy of the genome at the same time. Both Cas9 and Cas12a are being changed to make them better at detecting Protospacer Adjacent Motif (PAM) than they are in their natural state. This will make it possible to start focusing on more genes.

This article describes a novel topical composition constituted by gamma-amino butyric acid (GABA) and microbial chondroitin sulfate. This composition exhibits potential for non-invasive intervention in anxiety management, sleep quality enhancement, and promotion of brain-related functions like meditation and nonlocal consciousness. The core component comprises GABA, a key inhibitory neurotransmitter within the central nervous system, complexed with a suitable carrier molecule, microbial chondroitin sulfate. This complexation aims to enhance transdermal absorption and optimize delivery of GABA to target tissues. The proposed mechanism of action involves modulation of GABAergic signal transduction pathways. Application of the topical composition allows GABA penetration into the skin and potentially deeper tissues, where it interacts with GABA receptors. This interaction leads to mental calmness and relaxation as assessed by electroencephalography. The influence of GABAergic signaling on brain circuits demonstrated in this article, when associated with meditation and nonlocal consciousness, suggests potential for the composition to support these practices. The advantage of such an approach consists in avoiding the potential systemic side effects associated with oral or injectable administration of GABA, anxiolytics or hypnotics. Furthermore, the combination of GABA and microbial chondroitin sulfate may offer synergistic benefits, potentially enhancing the overall biological effects. In conclusion, this article presents a novel topical approach for managing anxiety, enhancing sleep quality, and potentially facilitating meditation and nonlocal consciousness through modulation of GABAergic signaling pathways. While further research is necessary, the potential for a non-invasive and potentially side-effect-free intervention in these areas is promising.

Nanoindentation load-depth (P-h) plots is the basis for determination of mechanical properties of the samples and is especially vital for thin coatings deposited on substrates of various mechanical makeup from amorphous-brittle (borosilicate glass) to Silicon (having different crystallographic texture) to ductile substrates like stainless steel as well other aspects like indenter tip shape. The elastic-plastic response by simulated P-h plots as per modified empirical relations have been presented. The outcomes of this computational studies enlighten the database on the P-h plots for various coating/substrate systems and shall aid in the analysis of experimental P-h plots obtained,

Among other fossil fuels, natural gas is a major energy source. It is generally created saturated with water vapor. Dehydration of natural gas is vital in the gas business to remove water vapor from the gas supply, which may cause hydrate development in pipes. Gas dehydration employs Tri ethylene glycol (TEG) to remove water vapor from natural gas flow. The wet gas is dehydrated with lean glycol in an absorber, and the rich glycol is recovered and reused. This work investigates the use of dry natural gas instead of nitrogen in the glycol dehydration re-generator, and compares the results using HYSYS modeling and simulation. The two techniques were compared for capital and utility expenses, while maintaining the identical glycol purity criteria. The wet gas from the stripping mechanisms may also be utilized to run steam pumps and compressors or recycled. The model is based on the real mechanism flow diagram. Finally, the findings of this model might be used to design a new heat and material balance for the plant.

Solid-state transformer (SST) is a promising technology for smart grids due to its flexible power control (better reliability) and high efficacy (by decreasing losses) compared with traditional transformers. The SST is designed in tandem with three stages, i.e., the input, isolation, and output stages. As three converter stages are used to design the SST to make a more reliable device in practice, it is highly prone to instabilities. Moreover, the key objective of SST design is to implement a modern power distribution system to make it more intelligent and reliable. The stability issue can be even more complicated by the presence of distributed energy resources in the distribution system. Thus, the stability of SST must be measured prior to /during the design, and to determine, a state-space model is used, which is developed in this paper. Each stage of SST is modeled with its controller, and the system's stability is measured through the controllability and observability test. Further, the conversion from state-space to transfer function model is done, and the stability of SST is shown using frequency and time-domain diagrams. With these, the different plots, namely, the Bode plot, Nyquist plot, Nichols chart, Root locus, pole-zero plot, and Eigen plot, are used to ascertain the relative and absolute stability. Finally, the SST Simulink model is tested and validated through hardwate-in-the-loop simulation, i.e., OPALRT, to show its effectiveness and applicability.

Traditional methods of non-metallic materials joining for industrial uses are limited to lasers, or induction or conduction heating. The precision and efficiency for large-scale applications are difficult to be achieved at the same time. Here in this paper, we report a precise microwave welding method induced by localized surface plasmon of metallic macro-gaps under microwave excitation. The proposed method utilizes energy from well-developed commercial microwaver, and benefits from the precision of localized surface plasmon, an oscillating electron cloud excited by electromagnetic waves, which is currently under hot investigation only at micro- or nano-scales. The method is applied to the welding of polymers to solve the problems of complex processing equipment and high processing energy consumption in the traditional welding process, so as to cope with the growing energy and environmental crisis. Finite element numerical simulations are used to study the distribution characteristics of electric fields and temperature fields. The results show that the process is a high quality, efficient, low consumption and environmentally friendly green process compared to traditional polymer welding methods.

The cycling of soil phosphorus (P) is inherently linked with soil organic carbon-iron (C-Fe) cycling, yet empirical integration of these processes within paddy soils remains scarce. In this study, we conducted a microcosm experiment using paddy soils subjected to six distinct fertilization regimes involving varying P inputs for five years. In addition to evaluating P activation under reflooding conditions, we assessed the Fe reduction process and characterized the properties of dissolved organic matter (DOM) at the molecular level using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), alongside profiling the composition of soil microbial communities with high-throughput sequencing. Our findings revealed that after 25 days of reflooding, soil Olsen-P content increased by an average of 73% compared to its initial state, showing a strong correlation with the Fe reduction process. Specifically, treatments involving pig manure application exhibited higher Fe reduction rates and enhanced P activation, highlighting the role of organic matter in facilitating Fe reduction. Investigations on the relative abundance of typical iron-reducing microbes further supported their importance in P activation, but the rate of iron reduction is limited by soil organic matter content. Delving deeper into DOM properties, soil DOM composition profiling and network analysis suggested that high-molecular-weight DOM, particularly lignins, served as the primary resources driving Fe reduction by iron-reducing microbes, consequently promoting Fe reduction and P release. Taken together, our study assembled the C-Fe-P cycling dynamics in paddy soils, emphasizing the pivotal role of microbial-driven Fe reduction facilitated by soil DOM in P availability and subsequently sustainable agricultural practices.

This study investigated how broader parental factors including parental happiness, parental play engagement, and parenting stress are related to Korean children’s happiness and weight status across three years via indirect pathways through the children’s energy-related behaviors of healthy and unhealthy food intake, physical activity, and screen time. Data from 1551 Korean parent pairs and 7-yaer-old children in the Panel Study on Korean Children were analyzed. Path analysis and gender-based structural equation modeling were conducted. Maternal happiness was negatively related to child screen time. Maternal play engagement showed positive concurrent associations with child healthy food intake and physical activity and negative associations with screen time. Maternal parenting stress was negatively related to child healthy eating. There was one significant finding related to fathers’ role on children’s energy-related behaviors, happiness, and weight status: the positive association between parental happiness and boys’ unhealthy food intake. Child screen time was positively related to child weight status and negatively to child happiness at each age. Broader maternal parenting factors can serve as a protective factor for childhood happiness and weight status in 7-to 9-year-olds through being associated with a reduction in child screen time.

The decade of research concentrating on the area of Easter Slavonia revealed abundance of large on complex late Neolithic sites. It changed profoundly how we perceive late Neolithic settlement and space size and organization. As s first step in particular site detection we mostly conducted aerial reconnaissance and satellite image analysis but also data from the field such as excavation. On detected potential sites we conducted intensive field surveys and confirmed the attribution to the late Neolithic by surface finds. On those confirmed sites in the vicinity of Đakovo, Croatia, magnetic survey was conducted on 5 sites so far and the results confirmed presence of large scale late Neolithic settlements with complex spatial organization and enclosure(s). Most complex remains so far is site Gorjani Kremenjača and Topole which we present in this paper where a remains of one or two settlements covers the area of 70 hectares and is currently in the process of formal protection as a cultural landscape by the Ministry of Culture and Media of the Republic of Croatia. Special focus of the paper is the application of remote sensing in detection, archaeological confirmation and protection of the position Gorjani Topole.

Background: Poisoning is a growing public problem that has shown increased prevalence in many countries worldwide, contributing to increased costs and mortalities. A lack of or insufficient information regarding the circumstances, chemicals, drugs and people at risk due to imperfect reporting system is regarded as a barrier to successful poisoning prevention and intervention efforts. In Saudi Arabia, poisoning cases are monitored at the central level in the Ministry of Health, with meticulous follow-up of cases across country areas through public health departments. The current study's objective was to describe the poisoning notification and reporting system in Makkah.Subjects and methods: All reports of chemical and drug overdose poisoning delivered to the environmental health and occupational safety department in Makkah Almukarramah over two years (2018-2019) were reviewed, together with verification of all systems, rules and guidelines organizing the performance of the department.Results: The total number of cases reported in 2018 was 209 cases, while it reached 42 cases in 2019. The overwhelming majority of the cases were Saudi (91.6%), with a slight predominance of males (57.4%) over females (42.6%). Generally, almost two-thirds of the cases occurred in children below 13 years (61.4%), most of the poisoning cases resulted from drug overdose (63.3%), while 34.7% were chemical poisoning. The most common drug poisoning was attributed to analgesics followed by antiepileptics, antihypertensive drugs, antipsychotics and antimicrobials, while cleaning and detergent agents were the most commonly poisoned chemicals. The majority of these agents were taken by the oral route. Children's chemical poisoning was significantly higher than that in older age groups (p<0.05). All intentional poisoning occurred in adults; the great majority of them (88.9%) used drug overdosing rather than chemical substances.Conclusion and recommendations: The current study results are consistent with most of the previous studies carried out in different regions in Saudi Arabia. Reviewing the performance and adherence of the “Environmental Health and Occupational Safety Department” to the guidelines and instruction regulating monitoring, notification and reporting of poison cases are satisfactory and the poisoning surveillance system is effective. Further in-depth studies are needed to elaborate other socioeconomic and demographic factors associated with chemical and drug overdose poisoning. Public health plans, policies and legislations should be implemented to reduce these factors, including prohibition over-the-counter pharmaceutical sales, selling the possibly harmful substances in kid-proof bottles and additional stringent rules governing chemical sale and storage. Intensive supervision of children is needed. Suicidal poisoning patients should seek psychiatric help to limit the possibility of attempting suicide again in the future. Furthermore, efforts are needed to plan and implement health education programs and campaigns about the factors proven in the current study to be significantly related to chemical and drug overdose poisoning to raise public knowledge about exposure to chemicals and its consequences. Popular communication media, commercial malls, road ads, learning institutions and workplaces can be used to deliver health education campaigns and programs. Advice on how to store chemicals safely and how to keep kids safe should be focused on during such programs.

The different systems in the nuclear power plants (NPP) are critical and complex which requires a continuous rigorous monitoring for both normal and abnormal conditions. However, due to the nonlinearity of the dynamic behavior of these systems, implementation of artificial intelligence within the NPP components is crucial to enhance the monitoring and the predictability of the key operating parameters trend. On the other hand, lessons learned from large nuclear accident proves that a remote real-time coordination between the different stakeholders involved in the safety of the nuclear reactor is needed. This remote feature can be implemented in the existing power plants by embedding a mobile computing networks within the plant components. This network will send early warning and transmit the NPP data on real-time to different authorities such as the regulatory authority and the decision-making committees, to enhance the interpretability of the nuclear event and deliver a collaborative decision which in return mitigate the risk associated with these events and increase the overall safety of the plant. The integration of AI and mobile computing in NPP would be most of interest in countries where nuclear reactors data are challenged to reach during an accident. For instance, during the meltdown of Chernobyl, the Soviet government covered the accident from the news in an attempt to contain the consequences but there was a spread of radioactive contamination to some other Europe countries. Therefore, in the case of such scenario the implementation of mobile computing and AI would result in different outcomes as the European countries would be notified through the mobile networking and the spread of contamination would be forecasted by the AI algorithms. In this paper, we examine the different modern AI and mobile computing technologies, the different potential application, the associated features and challenges, and future work direction.

Reducing fertilizer-N rate, applying nitrification inhibitor (NI), and incorporating straw are widely recommended to improve N use efficiency of crops and decrease N losses. However, how these practices affect crop N uptake by regulating the fertilizer-N fate during the growing season is not well understood. We conducted a field 15N tracer study to explore the effectiveness of reduced N application, NI, and straw incorporation on fertilizer-N fates for maize cultivation in Northeast China. The following six treatments were used: 1) no N fertilization (control); 2) 200 kg urea-N ha−1 (100%N); 3) 200 kg urea-N ha−1 and 2400 kg dry-straw ha−1 (100%N+S); 4) 160 kg urea-N ha−1 (80%N, i.e., 20% reduction of N fertilization); 5) 160 kg urea-N ha−1 and nitrification inhibitor (Nitrapyrin) (80%N+NI); 6) 160 kg urea-N ha−1, nitrification inhibitor and 2400 kg dry-straw ha−1 (80%N+NI+S). Our results showed that the five N fertilization treatments yielded 16%–25% more grain and 39%–60% more crop N uptake than the control receiving no N fertilizer, but the differences among the five treatments were not statistically significant. In contrast to plant growth, changes in fertilizer-N fates in soil and plant pools were more pronounced. Compared with the 100%N treatment, 20% fertilizer-N reduction (80%N) decreased 15N concentration in topsoil and plant pools but increased the proportion of plant 15N recovery at harvesting (N use efficiency, NUE15N, 60% vs. 50%) (P &lt; 0.05). Nitrapyrin co-application (80%N+NI) delayed soil nitrification and increased soil 15N retention at harvesting (52% vs. 36%), thereby decreasing NUE15N significantly (P &lt; 0.05). Straw incorporation (100%N+S) decreased fertilizer-N retention compared with NI co-application because it promoted NUE15N significantly (P &lt; 0.05). The combined application of NI and straw (80%N+NI+S) was more conducive to fertilizer-N retention in soil. Our findings demonstrate that NI and straw additions are efficient strategies for stabilizing fertilizer-N in soils and potentially minimizing N loss; however, their effects on NUE15N vary and the related mechanism must be further clarified in long-term trials.

a Special Issue of Microorganisms devoted to ‘Microbial Biocatalysis and Biodegradation’ would be incomplete without some form of acknowledgement of the many important roles that dioxygen-dependent enzymes (principally mono- and dioxygenases) play in relevant aspects of biooxygenation. This is reflected by the multiple strategic roles that dioxygen -dependent microbial enzymes play both in generating valuable synthons for chemoenzymatic synthesis, and in facilitating key reactions that drive the global geochemical Carbon Cycle. A useful insight into this relationship can be gained by reviewing the evolution of the current status of 2,5-diketocamphane 1,2-monooxygenase (EC 1.14.14.108) from camphor-grown Pseudomonas putida ATCC 17453. Over the last sixty years, the perceived nature of this monooxygenase has transmogrified significantly. Commencing in the 1960s, extensive initial studies consistently reported that the enzyme was a monomeric true flavoprotein dependent on both FMNH₂ and nonheme iron as bound cofactors. However, over the last decade all those criteria have changed absolutely, and the enzyme is currently acknowledged to be a metal ion-independent homodimeric flavin-dependent two-component monooxygenase deploying FMNH₂ as a cosubstrate. That transition is a paradigm of the ever evolving nature of scientific knowledge.

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.

Psychotherapy is a comprehensive biological treatment modifying complex underlying cognitive, emotional, behavioral, and regulatory responses in the brain, leading patients with mental illness to a new interpretation of the sense of self and others. Psychotherapy is an art of science integrated with psychology and/or philosophy. Neurological science studies the neurological basis of cognition, memory, and behavior as well as the impact of neurological damage and disease on the functions, and their treatment. Both psychotherapy and neurological science deal with the brain; nevertheless, they continue to stay polarized far. Existential phenomenological psychotherapy (EPP) has been in the forefront of meaning-centered counseling for almost a century. The phenomenological approach in psychotherapy originated in the works of Martin Heidegger, Ludwig Binswanger, Medard Boss and Viktor Frankl, and it has been committed to account for the existential possibilities and limitations of one’s life. EPP provides philosophically rich interpretations and empowers counseling techniques to assist mentally suffering individuals by finding meaning and purpose of life. The approach has proven to be effective in treating mood and anxiety disorders. This narrative review article demonstrates the development of EPP, the therapeutic methodology, evidence-based accounts of its curative techniques, current understanding of mood and anxiety disorders in neurological science, and a possible converging path to translate and integrate meaning-centered psychotherapy and neuroscience, concluding that the existential phenomenological psychotherapy potently plays a synergistic role with the currently prevailing medication-based approaches for the treatment of mood and anxiety disorders.

Radiation therapy remains to be one of the main treatment modalities for the management of breast cancer. Several reports have asserted that low dose photon irradiation, which is the most available radiation modality, can promote migration and invasion of malignant cells. Also, some approved chemotherapy drugs used in the management of breast cancer are implicated in promoting metastasis. Therefore, it has become critical to unravel novel therapies that are effective on tumour cells and can reduce the metastatic potential. Methods: Malignant (MCF-7), triple-negative (MDA-MB-231) and spontaneously immortalized MCF-10A human breast cancer cell lines were pre-treated with pan-HDACi SAHA or multi-target inhibitor CUDC-101 and exposed to 2Gy and 6Gy 148 MeV mid-SOBP protons or 250KeV X-rays. Wound healing and trans-well invasion assays were performed. Imaging was done at 4-hour intervals for wound healing assays, and at 24-hours post-irradiation for trans-well migration assays. Results: Significant reduction in migration was observed after treatment with proton alone at lower doses (2Gy) and at higher (6Gy) doses in the MCF-7 cell line. In the triple negative cell line MDA-MB-231 cell line, reduction in migration was evident at higher doses(6Gy) protons. Combination therapy of CUDC-101 or SAHA and protons also inhibited migration and invasion in the MDA-MB-231 cell line. Treatment with CUDC-101 monotherapy showed benefit in reducing migration and invasion in the MDA-MB-231 cell line. Treatment with SAHA, either as monotherapy or in combination with X-rays, promoted migration in the MCF-7 cell line. Conclusions: CUDC-101 or proton monotherapies, as well as combination therapy with radiation, inhibits migration as monotherapy particularly in the MDA-MB-231 which has high metastatic potential.

Classical Optimal Power Flow (OPF) is one of the complex and challenging problem in power system that includes non-convex, nonlinear and large-scale structure. With the use of incorporation of uncertain and intermittent wind energy sources in the OPF problem, the complexity of the problem escalates. In a power system, Flexible Alternating Current Transmission Systems (FACTS) devices can mitigate most of the problems associated with power quality and overload in the network. However, determining the placement and sizing of facts devices is an additional problem to minimize the total cost of production of the power system. As a result, in order to solve OPF problem which includes all of these conditions, an artificial intelligence based optimization algorithm needs to have an unusual exploration ability as well as exploitation–exploration balance. Weighted mean of vectors (INFO) is a new heuristic optimizer, which can help finding a more effective solutions in engineering design optimization problems. In this study, firstly, INFO algorithm was improved by using the Fitness–Distance Balance (FDB) method with its abilities. Then, the algorithm developed with a hyper-heuristic method to create the beginning optimal population by using Linear Population Reduction Success-History based Adaptive Differential Evolution (LSHADE). Finally, the developed algorithm has been applied for solving optimal placement and sizing of facts devices for optimal power flow problem incorporating wind energy source. The obtained results showed that the proposed algorithm is more effective solver for the problems cases while compared to the literature.

The article addresses the development and evaluation of a novel environmentally-friendly cutting fluid prepared using sunflower oil and ZnO nanoparticles, specifically developed for use in metal machining processes. An experimental investigation was conducted to examine the impact of three adjustable input process factors: spindle speed, feed rate, and depth of cut on Mild steel. The investigation was carried out under two conditions: one with a 1%(weight) ZnO nano cutting fluid and the other with a 1.5%(weight) ZnO nano cutting fluid. The study focused on two factors: surface roughness and material removal rate (MRR), which were measured during the process of face-milling mild steel. The research aims to optimize the milling machine settings to improve the efficiency of machining mild steel as the work material. This will be achieved by using Response Surface Methodology (RSM). The ZnO nano-cutting fluid was created using a meticulous technique and its stability was assessed by several investigations. An investigation indicates that a ZnO nano-cutting fluid with a weight concentration of 1.5% is more stable. The process was enhanced by the use of a newly created integrated approach grounded on response surface methodology (RSM). The suitability of the model was confirmed by an analysis of variance (ANOVA). The analysis revealed that, when considering equal weights of responses, the ideal values for the input parameters of spindle speed, feed rate, and depth of cut were determined to be 408.081 rpm, 146.667 m/min, and 0.4878 mm accordingly, under the circumstances of using 1.5% weight of ZnO nano cutting fluid. The projected output responses were as follows: 777.920 for the MRR (Material Removal Rate) and 3.5297 for surface roughness. The composite attractiveness was calculated to be 0.61433. The suggested model in this research shows a fitting of over 80% with our studied values. This model may be used to discover the ideal states and enhance the efficiency of the milling process. The enhanced milling parameters exhibit the potential to enhance machining performance, offering useful insights for firms seeking environmentally sustainable alternatives for metal-cutting operations. This work contributes to the advancement of sustainable machining practices by developing a novel formulation for cutting fluid and providing a systematic approach to improving machining parameters.

Introduction: The COVID-19 pandemic prompted a global shift to distance learning, profoundly affecting children's mental health. In Saudi Arabia, remote education was implemented on March 8, 2020, impacting students across various factors, such as age, vaccination status, and school system. This shift lasted between 1.5 to 2.5 years, influencing students’ experiences differently. This study explores the psychological impact of distance learning on children and adolescents, with the goal of informing mental health advocacy and guiding educational strategies in scenarios where distance learning becomes essential. Methods: We conducted a cross-sectional study using an online survey distributed to parents of children aged 6 to 18 in the major metropolitan areas of Jeddah and Riyadh. The survey collected demographic information and included the validated Arabic version of the Vanderbilt ADHD Diagnostic Rating Scale. This tool assesses behavioral challenges, anxiety, and symptoms of attention deficit hyperactivity disorder (ADHD). Results: The study found that 71.6% of families reported a positive experience with distance learning. A significant correlation was identified between parents' marital status and children's ability to cope with remote education. Interestingly, students without ADHD symptoms experienced three times more negative outcomes than those with ADHD. However, despite reporting fewer negative experiences, children with ADHD symptomatology faced heightened symptom severity and academic difficulties. The results further indicated that 5.4% of students were classified with ADHD predominantly inattentive presentation, 1.8% with predominantly hyperactive/impulsive presentation, and 3.9% with combined ADHD presentation. Additionally, 7.2% of students screened positive for oppositional defiant disorder, 1.5% for conduct disorder, and 6.6% for anxiety or depression. Conclusion: This study is the first comprehensive, multi-city investigation in Saudi Arabia to analyze the relationship between distance learning experiences, sociodemographic factors, and mental health symptoms in children. The findings provide essential insights for future research and offer a foundation for developing targeted interventions to support children’s mental health and education in the region.

Combining genomics with digital healthcare information is set to transform per- sonalized medicine. However, this integration is challenging due to the differing nature of the data modalities. The large size of the genome makes it impossible to store it as part of the standard electronic health record (EHR) system. Rep- resenting the genome as a condensed representation containing biomarkers and usable features is required to make the genome interoperable with EHR data. This systematic review examines both conventional and state-of-the-art methods for genome language modeling (GLM), which involves representing and extract- ing features from genomic sequences. Feature extraction is an essential step for applying machine learning (ML) models to large genomic datasets, especially within integrated workflows. We first provide a step-by-step guide to various genomic sequence pre-processing and representation techniques. Then we explore feature extraction methods including tokenization, and transformation of tokens using frequency, embedding, and neural network-based approaches. In the end, we discuss ML applications in genomics, focusing on classification, prediction, and language processing algorithms. Additionally, we explore the role of GLM in func- tional annotation, emphasizing how advanced ML models, such as Bidirectional encoder representations from transformers (BERT), enhance the interpretation of genomic data. To the best of our knowledge, we compile the first end-to-end analytic guide to convert complex genomic data into biologically interpretable information using GLM, thereby facilitating the development of novel data-driven hypotheses.

Our findings illustrate that CS should be employed as a means of popularizing scientific output, facilitating the promotion of political decision-making processes, and acting as a knowledge broker to reverse consumer behavior towards more environmentally friendly and conducive to SUP usage.

Flexible surgical robots are emerging as advanced tools for minimally invasive surgeries, offering greater versatility compared to traditional rigid robots. Unlike commercial endoscopes, the overtube should remain fixed to maintain stability, ensure a clear field of view, and allow surgical tools to perform tasks efficiently. While constant curvature bending of the overtube is sufficient for some lesions, certain lesions require the overtube to bend into specific shapes to achieve appropriate positioning. Various methods for creating different bending shapes have been proposed in previous research, typically involving connecting multiple segments. However, this approach complicates control and reduces both space and cost efficiency. This study proposed a conceptual method for adding a shaping tendon to control the bending shape and mathematically analyzed the effect of this shaping tendon, inserted along an arbitrary path in addition to the main driving tendons for constant curvature bending, on the bending shape of the hyper-redundant manipulator with rolling joints. The overall system was modeled and analyzed from an energy perspective, and the validity of the proposed mathematical modeling was verified through comparison with results obtained from physical experiments. In addition, it was identified that the design parameter determining the tendon path is significant element in defining bending shape of the overtube.

As modern technology enables instant access to virtually limitless information, students may perceive memorization of information as lacking in practical importance. The current study investigated the relationship between attitudes toward remembering and metamemory as well as objective memory performance. University students completed the Importance of Remembering questionnaire (IORQ) as a measure of attitudes toward remembering. Subjective components of memory were measured by immediate and delayed judgements of learning (JOLs), global judgements of learning (global JOLs), retroactive confidence judgements (RCJs), as well as subjective mental workload. Objective memory performance was measured using a cued recall test. The IORQ was only significantly correlated with absolute accuracy of delayed judgements of learning for words and pictures such that higher IORQ ratings were associated with less accurate judgments about how well they learned the items. No other correlations were significant. This suggests that a student’s lack of belief in the importance of remembering, at least as conceptualized on the IORQ, may not affect most aspects of memory performance, including those related to academic outcomes.

Background. One of the most important prognostic factors in periampullary and pancreatic cancers is perineural infiltration, whose preoperative detection could be decisive in selecting which patients should not undergo upfront surgery. We evaluated CA 19.9 and the Neutrophil-to-lymphocyte ratio (NLR) as preoperative predictors of perineural invasion (PNI). Methods: Retrospective analysis including patients with periampullary and pancreatic cancers who underwent curative resection from January 2013 to August 2023 in our institution. A univariate analysis and multivariate analysis were performed to analyze the association between the CA 19.9 and NLR with the existence of PNI. Results: A total of 136 patients were included in the study. PNI was observed in 95 (69.9%) patients. The selected cut-off points were NLR: 2.2 and CA 19.9: 37 U/mL. In univariate analysis, NLR (p=0.001) and CA 19.9 (p=0.006) were statistically significantly associated with PNI. In multivariate analysis, baseline NLR levels (p=0.012; OR: 1.95; 95%CI: 1.16-3.29) as well as CA 19.9 levels (p=0.026; OR: 1.67; 95%CI: 1.06-2.64) remained independent prognostic factors for PNI. The area under de ROC curves was 0.67 for CA 19.9 (p=0.004) and 0.72 for NLR (p<0.001). A sensitivity of 65.9% with a positive predictive value of 81.7% were obtained for CA 19.9, and a sensitivity of 62.1% with a positive predictive value of 84.3% were achieved for NLR. Conclusion: Preoperative CA 19.9 and NLR levels seem to be good predictors of PNI. In patients with normal levels of CA 19.9, preoperative NLR above of 2.2 should be an indication for neoadjuvant therapy.

.Citizen or community science (CS) projects in the marine environment rarely consider carbon footprint and sustainability. In this case study, we assessed the effectiveness of ten CS methods used by tourists in the Great Barrier Reef Marine Park (GBRMP) and Coral Sea Marine Park (CSMP). We also assessed baseline coral reef knowledge of the tourists, observations of marine species and the communication of our results to the public. Specifically, the tourists utilised up to ten methods: iNaturalist, CoralWatch, Great Barrier Reef Census, Eye on the Reef (EoR), environ-mental DNA (eDNA) testing kits, photogrammetry, social surveys, and Red Map, as well as ma-rine debris and marine vegetation collections. A total of 10,421 data points were collected across 14 days, including 5,390 records (52 % of the total) uploaded to iNaturalist, comprising 640 plant animal species. Public awareness of the CS expedition reached over 700,000 people based on estimates from advertising, media, social media, family and friends, and conference presenta-tions. We estimated the total carbon footprint for the expedition as 268.7 tonnes of CO2 or 4.47 tonnes of CO2 per person, equivalent to $112 AUD needed to offset this input. Based on these re-sults, our recommendations to leverage CS methods include governmental review strategies, temporal replication to allow the measurement of changes through time, integrating sustainabil-ity into CS ecotourism platforms, and encouraging broad participation.

Pastırma is an ethnic meat product defined as dry-curing, drying, pressing the whole muscles of cattle and buffalo, and coating them with a special paste containing fenugreek seed flour, garlic, milled red capia pepper, and water. In this narrative literature review, historical aspects of pastırma, its definition and classification, detailed production steps, composition and yield, chemical and microbiological properties, pastırma fraud and customer concerns are mentioned. In the shape of the narrative review, relevant studies were identified by searching Scopus, Science Direct, Web of Science, Trdizin, and Google Scholar Search included English- or Turkish-based articles, online reports, books, and electronic books. The keywords "pastırma, pastirma, çemen, cemen, cemening, cemen paste, Fenugreek" were used. The results of this review indicate that future studies on pastırma may focus on related cultural aspects, elimination of unpleasant odour from fenugreek, histological and chemical effects of pressing meat parts, kinetics of drying, osmotic dehydration, and developing new starter combinations.

A new 2 parameter unit Weibull distribution is defined on the unit interval (0,1). The methodology of deducing its PDF, some of its properties and related functions are discussed. The paper is supplied by many figures illustrating the new distribution and how this can make it illegible to fit a wide range of skewed data. The new distribution holds a name (Attia) as a nickname.

The basic helix-loop-helix (bHLH) transcription factor family, is the second-largest superfamily in plants after MYB, which plays a significant role in the physiological processes of plant growth, tissue development, and environmental variation. However, the systematized study of the bHLH transcription factor family has not yet been conducted in A. catechu. Herein, we conducted genome genome-wide investigation of AcbHLH genes located on 16 chromosomes of A. catechu. A phylogenic tree was constructed to adjudge their homology of genes, and 24 subgroups were identified. Finally, we analyzed the dynamic changes of gene-expression levels of nine AcbHLH genes in response to drought and salt in leaves and roots. The expression patterns of 9 AcbHLH genes show differences in leaves and roots. Under stress conditions induced by salt and drought, AcbHLH22, AcbHLH62 and AcbHLH45 are significantly upregulated in both leaves and roots. In conclusion, this study will substantially contribute to the foundation for exploring the role of the bHLH superfamily in A.catechu in dealing with abiotic stresses.

This study presents a novel investigation into the use of Light Emitting Diodes (LEDs) for the in vitro rooting of 'Marubakaido' and 'M9' apple tree rootstocks, marking the first time this approach has been reported in the literature. The research compares four different light sources: blue LED (450 nm), red LED (660 nm), a combination of red and blue LEDs, and traditional fluorescent lamps as a control. Mini-cuttings were inoculated in Murashige &amp; Skoog (MS) medium with reduced nutrient concentrations and supplemented with indoleacetic acid (IAA) and sucrose. The explants were incubated under controlled conditions for 30 days, allowing for a thorough evaluation of the effects of different light sources on various growth metric. The study found that blue LEDs significantly increased dry mass accumulation in 'Marubakaido' seedlings compared to both red LEDs and fluorescent lamps, demonstrating the effectiveness of LEDs in promoting plant growth. Utilizing LEDs not only enhances seedling development but also offers economic benefits over fluorescent lamps. LEDs are known for their high luminous efficiency, low energy consumption, and long lifespan, which can lead to reduced operational costs in plant production systems. This research not only contributes to the understanding of light effects on plant tissue culture but also suggests that the combination of blue and red LEDs can serve as a viable alternative to fluorescent lamps, potentially transforming practices in the field of horticulture and plant propagation.

This study explores the potential of Federated Learning (FL) to facilitate the sharing and collaboration of medical data in drug development under the premise of privacy protection. While traditional centralized data processing methods limit effective collaboration across agencies due to data privacy and compliance concerns, federated learning avoids the risk of privacy breaches through a distributed architecture that allows participants to train artificial intelligence (AI) models together without sharing raw data. This paper systematically describes the core mechanism of federated learning, including the key technologies such as model parameter updating, differential privacy and homomorphic encryption, and their applications in drug development and medical data processing. Examples, such as NVIDIA Clara's Federated learning application and COVID-19 resource prediction, show that federated learning improves the efficiency of multi-party collaboration and model performance while ensuring data privacy. In addition, this study explores the scalability and generality of federated learning in the medical field, and points out that the technology is not only suitable for drug development, but also has broad cross-industry application potential, especially in areas such as finance and insurance, where data privacy is critical.

Parental willingness is a prerequisite for childhood immunization. This study evaluated whether parental vaccination readiness and willingness, measured using the modified Japanese 7C Vaccination Readiness Scale, predicted vaccination behavior. In December 2021, 2 months before the approval of COVID-19 vaccination for children aged 5–11 years in Japan, we asked 1,190 parents of children in this age groups about their intention regarding vaccinating their children against COVID-19 using an online modified 7C Vaccination Readiness Scale, consisting of seven components, namely, Confidence, Complacency, Constraints, Calculation, Collective responsibility, Compliance, and Conspiracy. The modified 7C components were analyzed for model fit and association with parental intention using structural equation modeling. The model fit of the modified 7C scale was good (goodness-of-fit index: 0.98), and the factor loadings and model fit for parental readiness were high (standardized factor loadings: 0.84). In January 2024, we asked the same parents about their children's COVID-19 vaccination status (valid responses: 536). The components of the modified 7C scale were compared with actual vaccination behavior. Parents with a low readiness to vaccinate their children against COVID-19 had a high actual non-vaccination rate. The use of the modified 7C scale may assist in the development of childhood vaccination interventions.

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WT), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-time scale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ES, and distributed WT are analyzed. Based on this analysis, then a multi-time scale voltage optimization scheme for distribution networks using MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of proposed method, simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results that are more realistic compared to the day-ahead optimization method.

The learning system is primarily based on teaching, and teaching through digital resources is referred to as e-learning. As the world progresses towards success day by day, the use of technology, digital devices, and modern techniques is becoming increasingly prevalent. The global shift from traditional paperwork to digital learning is one of the most significant changes in the education sector. However, many factors have created numerous hurdles during the online setup. The Covid-19 pandemic has led to serious problems all over the world, with the education sector being one of the most affected. One of the greatest challenges students face during online education is the lack of digital infrastructure, especially in remote areas of Pakistan. The biggest challenge is connecting students with teachers due to limited internet access. Without proper internet connectivity, both students and teachers cannot access educational materials. In this context, the government plays a crucial role in providing adequate facilities to students, particularly in primary schools. If proper infrastructure is not made available during this pandemic, students will be deprived of a good quality education. Therefore, good infrastructure is essential for effective online education. With the proper resources in place, students will be able to receive a quality education.

Chitosan spherical scaffolds, derived from the deacetylation of chitin, are natural polymers with significant potential, though challenges remain in optimizing their properties. The use of chitosan as a carrier for the controlled release of iron chloride is not yet fully established, despite iron’s critical role in various essential biological processes, including as a component of metalloenzymes and in the maintenance of life. Methods: This study aimed to develop a novel protocol for synthesing chitosan scaffolds and to characterize their properties using analytical techniques. Additionally, the release profile of iron was modeled using two primary controlled-release mathematical models. Results: The results demonstrated successful synthesis and physicochemical characterization of the chitosan sphere scaffolds. These scaffolds exhibited appropriate topography for iron loading and cellular interaction, as confirmed by SEM analysis, and contained functional groups as identified by FTIR. The scaffolds were also semi-crystalline (XRD), showed controlled degradation, and were confirmed to be non-toxic biomaterials. Furthermore, the modeling of the controlled release of iron chloride adsorbed on the scaffold surface, as well as the modeling of iron chloride specimens using the MEDUZA software, was successfully conducted. A theoretical curve was generated from the experimental dose-response data collected over time, considering variations in pH and medium concentration. Conclusions: In conclusion, a novel protocol for chitosan-based carriers was successfully developed, enabling thorough physicochemical characterization and modeling of both absorption and controlled release.

The modern world is gradually moving towards a global civilisation that is automated and propelled by technology. Artificial intelligence (AI) is a highly advanced technology that has been acknowledged as a major innovation. The incorporation of this technology into the banking sector has enhanced operational procedures in relation to customer satisfaction, virtual assistance, and risk reduction. This machine is employed to streamline activities and improve how decisions are made in this sector. This study investigates the potential benefits and challenges of implementing artificial intelligence in the banking sector. The methodology ensures a comprehensive understanding of the implementation of artificial intelligence in banking by showcasing its effectiveness, precision, and efficiency, as well as the opportunities it presents. The study data were collected from secondary sources, such as recent publications that demonstrate applications of artificial intelligence from five different banks, as well as the examination of industry reports. The main findings of this study demonstrate the substantial improvement of administrative operations through AI-driven technology, which effectively automates activities that include inputting data and handling information. As a result, the frequency of mistakes caused by people has reduced, leading to a 30% decrease in operational expenditures for all evaluated institutions. However, the banking industry admits the challenges it encounters, such as issues with confidentiality and a scarcity of skilled employees. In the financial services industry, the integration of artificial intelligence into the banking sector is perceived as an advantageous chance instead of a harmful danger, with a greater potential for beneficial impact.

Urbanization degrades natural habitats and creates new urban ecosystems, like domestic gardens. The plant composition of these gardens varies with socio-economic factors and urban planning levels. However, the diversity and impact of introduced species are often poorly assessed, causing potential ecological imbalances, particularly in Lubumbashi (DR Congo). The objective was to analyze the spatial structure, plant diversity, propagation strategies, and ecological functions of domestic gardens. Three distinct neighborhoods were selected: planned, unplanned and residential neighborhood. 20 avenues (with 5 plots per avenue) were chosen to represent the diversity within each neighborhood, and stratified random sampling of plots was conducted to analyze gardening practices. Gardens were classified into types, and their vegetation was evaluated based on species origin and ecological impact. The analysis of domestic gardens in Lubumbashi reveals significant variations across different neighborhood types. Residential neighborhoods exhibit larger average garden sizes (315.1 m²), higher species richness (22 species), and larger plot sizes (1032 m²) compared to unplanned and planned neighborhoods, where garden areas and species richness are notably lower. Rectangular gardens dominate in unplanned areas, while planned neighborhoods feature more intentional landscaping elements, such as flowerbeds and hedges. The use of gardens for food production is prominent in planned areas (40.7%), whereas residential neighborhoods prioritize ornamentation (51.4%). The study identifies 232 species across 68 families, with a predominance of exotic species (80%) in all neighborhoods, particularly in unplanned areas (82.25%). The data reveals that Mangifera indica and Persea americana are abundant in all neighborhoods, illustrating their adaptability to different urban contexts. Herbaceous species are most common, followed by woody plants, with vines being sparse. Species dispersal is primarily driven by human activities (anthropochory), accounting for over 85% in all neighborhoods. These findings highlight the strong human influence on the composition and structure of domestic gardens in Lubumbashi, emphasizing the dominance of exotic species and the importance of anthropogenic factors in shaping urban green spaces. Urban policies should incorporate strategies to minimize the negative impacts of exotic species on native flora.

Recent developments in metallic additive manufacturing (AM) processes for the production of high-performance industrial pieces have been hampered by the limited availability of reliably processable or printable alloys. To date, most of the alloys used in AM are commercial grades that have been previously optimized for different manufacturing techniques. This study aims to design new alloys specifically tailored for AM processes, to minimize defects in the final products and optimize their properties. A computational approach is proposed to design novel and optimized austenitic alloy compositions. This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. Within this framework, several material-dependent criteria are examined and their impact on properties and on the occurrence of defects is identified. To validate our approach, experimental tests are performed on a selected alloy composition: powder is produced by gas atomization and samples are fabricated by laser powder bed fusion. The microstructure and mechanical properties of the alloys are evaluated and its printability is compared to a commercial 316L stainless steel taken as a reference.

Aortic transection, a near-complete tear through the layers of the aorta, is a critical condition often resulting from trauma such as motor vehicle collisions. The urgency of managing aortic transection underscores the critical need for effective interventions. We report the case of a male in his early 50’s with no significant medical history who presented to the emergency department following a motor vehicle collision, sustaining multiple injuries including a descending thoracic aortic transection. Rapid diagnostic assessment confirmed the severity of the injury, necessitating immediate intervention. Endovascular aortic repair (EVAR) was successfully employed, highlighting its efficacy in managing acute aortic injuries. The patient responded well to treatment, underscoring the importance of timely intervention in improving patient outcomes. This case emphasizes the critical role of rapid diagnostic assessment and endovascular intervention in managing life-threatening thoracic aortic injuries, particularly in the acute setting.

The fundamental divergence in foundation of modern physics lies in the incompatibility between quantum theory and relativity. This study created a relative continuum, found a mathematical tool for the cosmic continuum; proposed dark particle hypothesis, filled gap on minimum existence quantity particle; discovered new equivalence principle, bridged differences in foundation of physics; and established an ideal model, revealed deep essence of cosmic and material structure. In this model, any cosmic system is a continuum composed of existence continuum relative to the wavelength of bosons energy waves and its dimension continuum. This model provides new perspectives on the fundamental problems of physics and cosmology. Firstly, it elucidated the physical mechanism of bosons in fundamental interactions. Secondly, it reconstructed the understanding of the basic unit of the cosmic and material structure. Thirdly, it updated the inherent concepts about the existence form and existence dimension. Fourthly, it restored the causality truth of wave function collapse in quantum mechanics.

This study examines the aggregate consumption function of Saudi Arabia from 2000 to 2022, focusing on identifying key determinants of household consumption and evaluating the impacts of disposable income, household wealth, government expenditure, interest rates, and oil revenues. The research uses advanced econometric methods, including the Autoregressive Distributed Lag (ARDL) model and Johansen cointegration test, to analyze the relationships among these variables. The findings reveal that disposable income, household wealth, and government expenditure significantly and positively influence consumption, whereas interest rates show a negative correlation. Oil revenues also play a critical role, reflecting the country's economic reliance on oil. The study highlights the necessity for economic diversification to reduce the impact of oil price volatility on household income and consumption stability. The results offer crucial insights for policymakers, emphasizing the need for strategies that enhance household income and wealth, maintain robust public sector spending, and effectively manage interest rates. These findings also support the importance of consistent and predictable income sources for sustaining consumption. Additionally, this study suggests directions for future research, including developing sophisticated forecasting models to predict consumption trends and exploring other influencing factors such as demographic shifts and technological progress.

The distribution of zakat, which is still carried out directly through mosques or zakat amil bodies, can hinder the payment process due to inaccurate calculations and difficulties in finding zakat amil institutions. In the digital era, information technology is very important in the management of zakat, alms, and infaq. Therefore, a zakat information management system was created. The e-Zakat program, developed using the waterfall method, plays an important role in five main aspects of zakat management: muzaki and mustahiq data collection, zakat collection, zakat distribution, mustahiq development, and reporting. The use of information technology in this program is expected to increase efficiency, accountability, transparency, as well as the reach of zakat and muzaki involvement.

The normative and safeguarding role of ethics and law in the development of biomedical science and technology, as well as their importance in promoting the progress of the industry, analyzed the key role of ethics and law in the field of biomedical science and technology, by restricting scientific research behavior, protecting the rights and safety of subjects, providing a solid guarantee for scientific research activities, and promoting the healthy development of biomedical science and technology. At the same time, the positive role of ethics and law in promoting international cooperation and exchange of biomedical science and technology is also discussed, which will inject strong impetus into the global development of biomedical science and technology. In addition, through case analysis, the paper demonstrates the key role of ethical review in the field of biomedical science and technology, as well as the importance of law in maintaining market order, protecting subjects' rights and interests, and fairness and justice. The successful practice of ethics and law in the development of biomedical science and technology is emphasized, which proves that following ethical principles and legal framework is the key factor to promote the progress of biomedical science and technology. This paper explores the important role of ethics and law in the development of biomedical science and technology, and provides valuable reference and guidance for practitioners in the industry. By following ethical principles and legal norms, we will jointly promote the prosperity and development of biomedical science and technology, and contribute to the common progress of human health.

This bibliometric study explores the cryptocurrency accounting (CA) literature and the connections between authors, institutions, and countries where cryptocurrency activity involves transactions that must be legally recognized in accounting, assure accuracy and reliability for auditing, and adhere to tax compliance. The design involves data selection from Web of Science Core Collection (WoS) and Scopus, published between 2007 - 2023. The technique helps identify influential publications, collaboration networks, thematic clusters, and trends in research on CA using tools VOSviewer, Biblioshiny, and MS Excel. The originality of the study lies in its dual role as a support for accounting professionals and academics to develop innovative solutions for the challenges posed by crypto technology across core accounting areas: financial and managerial accounting, taxation, and auditing. The findings offer insights into the themes mentioned, and even if the collaboration between the authors is not very developed, the innovation and public recognition of the subject could raise researchers' interest. The limitation of the dataset is that it does not cover all relevant publications in a different period from the one in which the data was retrieved, 9-11 May 2024. This review might need periodic updates because the CA landscape is constantly changing.

Evidence is emerging on the role of maternal diet, gut microbiota, and other lifestyle factors in establishing lifelong health and disease, which are determined by trans-generationally inherited epigenetic modifications. Understanding epigenetic mechanisms may help identify novel biomarkers for pregnancy-related exposure, burden, or disease risk. Such biomarkers are essential for developing tools for the early detection of risk factors and exposure levels. It is necessary to establish the exposure threshold to the fetus due to nutrient deficiencies or other environmental factors that can lead to clinically relevant epigenetic changes that modulate disease risks. This narrative review summarizes the latest updates on the roles of maternal nutrients (n-3 fatty acids, polyphenols, vitamins) and gut microbiota on the placental epigenome and its impacts on fetal brain development.

The dynamic landscape of AI adoption and its impact on employment in Malaysia demands a multifaceted approach to navigate the challenges and harness the opportunities presented. While the government's commitment to fostering AI adoption through initiatives like the National AI Framework and the Malaysia Digital Economy Blueprint is commendable, it is imperative to address concerns regarding job displacement and ensure inclusive growth. This study identifies the pressing research problem of workforce readiness for the AI-driven future, emphasizing the need to explore strategies for reskilling and upskilling affected workers. By employing critical lenses such as Marxist, feminist, and postcolonial perspectives, scholars highlight the power dynamics and societal implications inherent in AI deployment, guiding the development of policies and interventions that prioritize workers' interests and address social inequalities. Moving forward, a comprehensive approach combining policy interventions, educational reforms, and inclusive strategies is essential to maximize the benefits of AI adoption while mitigating its potential risks. Longitudinal studies, interdisciplinary research, and comparative analyses will further advance our understanding and inform evidence-based policymaking in this evolving field. Ultimately, by prioritizing workforce preparedness, social equity, and sustainable development, Malaysia can position itself as a leader in the AI-driven global economy, fostering a resilient, inclusive, and prosperous society for all.

Individuals with BPD grapple with unstable relationships, marked by extreme emotional swings and interpersonal conflicts. Emotional instability, a distorted self-image, and impulsivity further complicate matters, often leading to self-harm and suicide attempts as desperate coping mechanisms. Genetics and adverse childhood experiences contribute to BPD's development, shaping brain function and emotional regulation. Diagnosis involves meticulous assessments, paving the way for personalized treatments like dialectical behavior therapy and, in severe cases, hospitalization. Early intervention, professional support, and societal understanding are pivotal for positive outcomes, and challenging mental health stigma.

We show that one of the two important uncertainty principles derived by Maccone and Pati \textit{[Phys. Rev. Lett., 2014]} can be derived for arbitrary maps defined on subsets of $\mathcal{L}^p$ spaces for $1< p<\infty$. Our main tool is the Clarkson inequalities. We also derive a nonlinear uncertainty principle for weak parallelogram spaces and Type-p Banach spaces.

Keywords: Innovation ecosystems, Economic sustainability, Hydrocarbon-based economies, Economic diversification, Qualitative approach

Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.

Regular weight measurement is important in fattening geese to assess their health status. Failure to gain weight may indicate a potential illness. Standard weight gain analysis involves direct contact with the animal, which can cause stress to the animal, resulting in overall negative impacts on animal welfare. The focus of this study was to design a smart solution for monitoring weight changes in the breeding of farm animals. The proposed IoT system with a weighing device equipped with RFID technology for animal registration aimed to minimise the negative aspects associated with measuring in contact with humans. The designed system aims to incorporate modern approaches in animal husbandry and utilise the acquired data for the potential development of breeding approaches for various animal breeds. The system consisted of three main components: a data acquisition system, a weighing system with RFID, and an environmental monitoring system. In this study, the RFID system accuracy for detecting geese in the weighing system environment was assessed. The entire system evaluation yielded a sensitivity of 93.87%, specificity of 99.94%, accuracy of 99.85%, and precision of 95.09%. Regression analysis revealed a good correlation between observed feeding and RFID registrations with a determination coefficient of R2 = 0.9813.

Albert Einstein, shortly after publishing his General Relativity Theory (GRT) applied it to the entire universe in a paper of 1917. Einstein assumed that such a universe was static in time and possessed a uniform distribution of matter on the largest scales, obtaining a static and finite universe of spherical spatial curvature of radius R. Although this model was refuted by Edwin Hubble's discovery of the expansion of the universe, it is still often studied as a theoretical framework for some problems, such as the twin paradox. This paper will show that Einstein's static universe model leads to a privileged frame of reference, so that any observer moving at the speed v will be able to measure speeds of light other than c depending on the direction of beam transmission. The above will refute the validity of the application to this closed and static universe of the Special Relativity Theory (SRT) proposed by Einstein in 1905 in his article . In addition, a new synchronization convention and new coordinate transformations will be deduced, the "generic" transformations, which will replace those of Lorentz. Finally, the twin paradox in this universe will be solved by making use of them.

The assessment of Histone deacetylases in conjunction with Curcumin, conducted through the Mcule Database employing the Autodock Vina algorithm, involves an examination of molecular interactions. This analysis includes the calculation of binding energies, revealing the strength and stability of the interactions between Curcumin and various Histone deacetylase targets. The comparison encompasses diverse aspects such as binding energies, specific binding sites, interactions across different targets, structural details, and potential implications for biological activity. The results imply that Curcumin shows heightened affinity for Histone deacetylase 4 in comparison to other investigated histone deacetylases.

Background: Hepatic erythrophagocytosis is augmented in NASH and amplifies inflammation and fibrosis. Although various pro-phagocytic signals have been identified on erythrocytes of NASH patients, the role of bound thrombospondin-1 (TSP-1), which acts as an “eat-me” signal, arginase-1, which regulates the levels of nitric oxide in erythrocytes, and phosphatidylethano-lamine (PE) which can amplify erythrophagocytosis and hepatic inflammation have not been explored. Hence, we sought to investigate the levels of arginase-1 and TSP-1 in erythrocyte lysate and PE in erythrocyte membranes of NASH patients. Methods: Twenty-four patients and 14 healthy controls participated in our study. The levels of TSP-1 and arginase were quantified by ELISA in erythrocyte lysates, and the levels of PE in erythrocyte membranes by thin layer chro-matography. Results: Erythrocytes of NAFLD patients exhibit lower levels of arginase-1 and TSP-1 (p<0.01). Erythrocyte-bound TSP-1 levels correlated with the levels of erythrocyte surface CD47. Phosphatidylethanolamine was increased in erythrocytes of NASH patients and was accompanied by increased release, indicating exposure. Conclusion: Our results imply reduced TSP-1 binding by erythrocytes which could allow free TSP-1 molecules to act on macrophages, enhancing erythrophagocytosis. Increased PE which could amplify inflammation after efferocytosis, while downregulation of arginase-1 could lead to defective efferocytosis.

Recently, Gohar et al. introduced a novel, local, and well-behaved fractional calculus. It possesses all the classical properties, and Its locality imposes simplicity and accuracy in modeling fractional order systems. In this article, we further develop the definitions and extend the classical properties of Gohar fractional calculus to address some of the open problems in Calculus. The fractional Gronwall's integral inequality, Taylor power series expansion, and Laplace transform are defined and applied to overcome some of the limitations in the classical integer-order calculus. The fractional Laplace transform is applied to solve Bernoulli-type logistic and Bertalanffy nonlinear fractional differential equations, and the criteria under which it can be applied to solve linear differential equations are investigated.

Social Anxiety Disorder (SAD) is a common mental health condition characterized by an intense fear of social situations, leading to significant impairments in daily functioning. Traditional exposure therapies, including Cognitive-Behavioral Therapy (CBT), have shown efficacy in treating SAD but are often limited by logistical challenges and patient reluctance. Virtual Reality Exposure Therapy (VRET) has emerged as a promising alternative, providing a controlled and immersive environment for exposure to anxiety-provoking scenarios. This paper explores the integration of Generative Adversarial Networks (GANs) into VRET to enhance the personalization, realism, and adaptability of therapeutic scenarios. GANs, which consist of a generator and a discriminator network, allow for the creation of highly realistic virtual environments tailored to the specific fears and triggers of individual patients. By offering real-time adjustments and personalized exposure scenarios, GAN-enhanced VRET addresses key limitations of traditional therapies, increasing patient engagement and treatment adherence. This review synthesizes findings from key studies on GAN-enhanced VRET, evaluating its efficacy in reducing anxiety symptoms and discussing the potential for broader application across various anxiety disorders. The results indicate that GAN-enhanced VRET offers a significant advancement in the treatment of SAD, providing a more accessible, engaging, and effective approach to exposure therapy. Future directions include expanding accessibility through cost reduction, integrating additional AI techniques, and exploring applications beyond SAD to improve mental health outcomes across diverse patient populations.

This study investigates the use of two nanoclays, Laponite (Lap) and Montmorillonite (Mt), as eugenol slow release carriers for enhancing the functionality of low-density polyethylene (LDPE) active packaging films. Utilizing a vacuum-assisted adsorption method, eugenol was successfully intercalated into Lap and Mt nanoclays, forming EG@Lap and EG@Mt nanohybrids. The nanohybrids were subsequently incorporated into LDPE films at varying concentrations (5, 10, and 15 wt.%). Extensive characterization by a number of analytical techniques confirmed effective integration and dispersion of the nanohybrids within the LDPE matrix. EG@Lap and EG@Mt nanohybrids enhanced the films’ antioxidant and barrier properties, with EG@Lap demonstrating higher eugenol release rates, antioxidant activity, and barrier effectiveness. Packaging tests on minced pork revealed that the LDPE/15EG@Lap films extended shelf life by approximately two days more compared to the LDPE/15EG@Mt films, due to the higher eugenol release. These findings suggest that EG@Lap-based films offer improved active packaging capabilities, with potential applications in a number of areas where extending the freshness of perishable foods is needed.

Introduction: Previous research has suggested, that primary breast cancer (PBC) and ovarian cancer (OC) risks can be modified by mutation location in BRCA1 gene. In this study we assessed and compared the risks of contralateral breast cancer (CBC) and OC after the event of PBC in carriers of regionally frequent BRCA1 founder pathogenic variants (PV) c.5266dup and c.4035del. Subjects and Methods: In the analysis of CBC and OC risk, 1364 cases with BRCA1 PV and PBC were included. The control group consisted of 11350 consequent unselected and unscreened cases with PBC. The follow-up started at the time of PBC diagnosis and continued till the event of CBC or OC had occurred. The cumulative risks of CBC and OC were calculated using the Kaplan-Meier analysis. Risk factor for developing CBC/OC (age < 40 years) was calculated using cox proportional hazards model. Results: Cumulative 10 years risk of developing CBC was 3.0% in control group, and 20.1% in study group of BRCA1 carriers (log-rank p < 0.001). Cumulative risk of CBC at 10,15 and 20 years was 25.0%, 37.0% and 51.4% in PV c.5266dup subgroup, as opposed to 14.1%, 27.2% and 44.5% in PV c.4035del subgroup (log-rank p = 0.045). Age younger than 40 years at the time of PBC was risk factor for CBC (hazard ratio 2.06, 95% CI 1.83-2.29, p < 0.001). Cumulative risk of developing OC at 10 and 15 years was 1.0% and 1.2% in control group, 10.8% and 16.8% for PV c.5266dup, 13.5% and 30.8% for PV c.4035del. There was difference in OC risks between control group and BRCA1 carriers (log-rank p< 0.001). PV c.4035del showed higher risk of OC, compared to PV c.5266dup, however, this was of borderline statistical significance (log-rank p = 0.057). Age younger than 40 years at the time of PBC was not associated with any difference in future OC risks (hazard ratio 1.03, 95% CI 0.77-1.29, p = 0.23). Conclusion: The data of this study confirms previously established evidence that in cases of PBC the risk of future CBC and OC is increased significantly for BRCA1 carriers in comparison to general unscreened population. Moreover, BRCA1 PV c.5266dup is associated with higher CBC risk and possibly lower OC risk in comparison to BRCA1 PV c.4035del. Age younger than 40 years at diagnosis of PBC is risk factor for CBC, but does not seem to alter the risk of OC.

Age is an influential factor in the Second Language Acquisition (SLA). The debate on whether young learners are more successful than older learners has been going on for more than half of a century, starting from the Critical Period Hypothesis, which is based on brain structure. This study examines a range of studies revolving around the Critical Period Hypothesis in the past several decades to analyze the common SLA belief ‘Younger learners are more skillful than older learners in acquiring second language.’ The study disagrees that all younger learners are more advantageous than older learners in all areas of SLA. In fact, learners from different age categories have unique advantages and disadvantages in SLA. Moreover, the study approves a more holistic perspective on a wider range of non-biological factors that impact the success of SLA. At the end, the study puts forward a diversity of instructional methods that suit children and adolescents specifically in Vietnamese English Education context.

Camphora longepaniculata is an important economic crop renowned for its rich volatile terpene compounds. Terpene synthases (TPS) are key enzymes in the biosynthesis of these compounds, playing significant roles in plant growth, development, and secondary metabolism. In this study, a total of 86 TPS genes were identified in Camphora longepaniculata, which were classified into five groups based on their evolutionary relationships. Analysis of cis-regulatory elements revealed associations between TPS genes and processes related to plant growth, development, and environmental stress responses. Gene Ontology (GO) enrichment analysis indicated that these TPS genes are predominantly linked to various enzymatic activities. Furthermore, analysis of duplication events revealed that tandem duplications (TD) and whole genome duplications (WGD) are major driving forces in the evolution of the TPS gene family. Notably, 18 TPS genes were found to be upregulated in high essential oil content varieties of Camphora longepaniculata. These findings lay a solid foundation for further exploration of the functions of TPS genes in Camphora longepaniculata.

Generating executable code from natural language instructions using Large Language Models (LLMs) poses challenges such as semantic ambiguity and understanding task- specific contexts. To address these issues, we propose a system called DemoCraft, which enhances code generation by leveraging in-context learning and demonstration selection, combined with latent concept learning. Latent concept learning introduces additional concept tokens, which are trainable embeddings that capture task-specific knowledge. We then test our system on two major datasets: MBPP and HumanEval. Our experimental results demonstrate that the proposed system achieves an approximate 2x increase in the pass@k metric compared to baseline models. Furthermore, we introduce two novel evaluation metrics: correctness@k and similarity@k. Our empirical studies indicate that our system attains nearly a 3x improvement in these metrics as well.

In this paper, we model uncertainty in both the objective function and the constraints for the robust semi-infinite interval equilibrium problem involving data uncertainty. We particularize these conditions for the robust semi-infinite mathematical programming problem with interval-valued functions by extending results from the literature. We introduce the dual robust version of the above problem, prove the Mond-Weir type weak and strong duality theorems, and illustrate with an example.

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.

Objective: The aim of this review was to identify newly discovered bacteria from individuals with periodontal/peri-implant diseases and organize them in new clusters (GF-MoR complexes) to update Socransky’s complexes (1998). Methods: The focus question was developed based on the PCC (Population, Concept, Context) strategy: “In patients with periodontal and/or peri-implant disease, what bacteria (microorganisms) were detected through laboratory assays?” The search strategy was applied to PubMed/MEDLINE, PubMed Central, and Embase. The search keyterms, combined with Boolean markers, were: (1) bacteria, (2) microbiome, (3) microorganisms, (4) biofilm, (5) niche, (6) native bacteria, (7) gingivitis), (8) periodontitis, (9) peri-implant mucositis, and (10) peri-implantitis. The search was restricted to 1998–2024 and English language. The bacteria groups in the oral cavity obtained/found were retrieved and were included in the GF-MoR complexes which were based on the disease/condition, presenting six groups: (1) Health, (2) Gingivitis, (3) Peri-implant mucositis, (4) Periodontitis, (5) Peri-implantitis, and (6) Necrotizing and Molar-Incisor (M-O) pattern Periodontitis. The percentual found per group refers to the number of times a specific bacterium was found associated with a particular disease. Results: A total of 381 articles were found; 162 articles were eligible for full-text reading (k=0.92); 9 articles were excluded with justification, and 153 were included in this review (k=0.98). Most of the studies reported results for Health condition, Periodontitis, and Peri-implantitis (3 out of 6 GF-MoR clusters), limiting the number of bacteria found in the other groups. Therefore, it is essential to understand that bacterial colonization is a dynamic process, and the bacteria present in one group can also be present in other one or others, such as observed with the bacteria found in all groups (Porphyromonas gingivalis, Tannarela forsythia, Treponema denticola, and Aggregatibacter actinomycetemcomitans) (GF-MoR’s red Triangle); the second most observed bacteria were grouped at GF-MoR’s blue Triangle: Porphyromonas spp., Prevotela spp., and Treponema spp., present in 5 of the six groups; and the third most detected bacteria were clustered in the grey Polygon (GF-MoR’s grey Polygon): Fusobacterium nucleatum, Prevotella intermedia, Campylobacter rectus, and Eikenella corrodens. These three geometric shapes had the most relevant bacteria for periodontal and peri-implant diseases. Specifically, per group, GF-MoR’s Health had 58 species; GF-MoR’s Gingivitis presented 16 bacteria; the GF-MoR’s Peri-implant mucositis included 17 bacteria; GF-MoR’s Periodontitis group had 101 different bacteria; GF-MoR’s Peri-implantitis presented 61 bacteria; and the last group was a combination of Necrotizing diseases and Molar-Incisor (M-I) pattern Periodontitis, with seven bacteria. Observing the top seven bacteria of all groups, all were gram-negative; Groups 4 and 5 (Periodontitis and Peri-implantitis) presented the same top seven bacteria. Conclusion: For the first time in the literature, GF-MoR’s complexes are presented, gathering the bacteria according to the condition found and including more bacteria than Socransky’s complexes. On this understanding, this study can drive future research into treatment options for periodontal and peri-implant diseases, guiding future studies and collaborating to prevent and worsen systemic conditions. Moreover, it permits the debate about the evolution of the bacterial clusters correlated to periodontal and peri-implant diseases and conditions.

The current developments in the theory of quantum static triplet correlations and their associated structures (real r-space and Fourier k-space) in monatomic fluids are reviewed. The main framework utilized is Feynman’s path integral formalism (PI) and the issues addressed cover quantum diffraction effects and zero-spin bosonic exchange. The structures are associated with the external weak fields that reveal their nature, and due attention is paid to the underlying pair level structures. Without the pair level one cannot fully grasp the triplet extensions in the hierarchical ladder of structures, as both the pair and the triplet structures are essential ingredients in the triplet response functions. Three general classes of PI structures do arise: centroid, total continuous linear response, and instantaneous. Use of functional differentiation techniques is widely made and, as a bonus, this leads to identify an exact extension of the “classical isomorphism” when the centroid structures are considered. In this connection, the direct correlation functions, as borrowed from classical statistical mechanics, play a key role (either exact or approximate) in the corresponding quantum applications. Additionally, as an auxiliary framework, the traditional closure schemes for triplets are also discussed, owing to their potential usefulness for rationalizing PI triplet results. To illustrate some basic concepts, new numerical calculations (path integral Monte Carlo PIMC and closures) are reported. They are focused on the purely diffraction regime and deal with supercritical helium-3 and the quantum hard-sphere fluid.

Objectives: the aim of this study was the assessment and monitoring of the safety and quality of the existing water supplies in Ibb and Taiz governorates from the microbial side and testing the quality of drinking water in the Ibb and Taiz governments and comparing it with the World Health Organization. Methods: the water samples (99 samples) taken from some water sources, distribution reservoirs, and different regions were studied for bacteriological analysis, in which they were inoculated into culture media using two methods: the presumptive test for the most probable number and the confirmatory test for bacterial identification. Results: the results indicated the non-existence of indicator bacteria that were used to indicate the possible presence of fecal bacteria among 24 (24.24%) water samples taken from Taiz governorate, while the presence of an unacceptable number of Escherichia coli in house tanks (30%) was indicated among 75 (74.25%) water samples taken from Ibb governorate. Conclusions: the results of the study in Taiz governorate show that the water is fit for human consumption, as well as the samples taken from water sources and distribution networks in Ibb governorate, but the results of the samples taken from house tanks showed that the water is not fit for human consumption and needs to be treated.

Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: This study aimed to assess the accuracy level of arti-ficial intelligence as a Clinical Decision Support System (CDSS) called Therapa compared to MRI in predicting lumbar disc herniated patients. Methods: One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in 3 stages, at first, a case se-ries was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Lastly, to determine the ac-curacy of the newly developed software, a cross-sectional study was undertaken, involving 100 patients. Results: The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%. Conclusion: The study’s findings revealed that CDSS using Therapha has a reasonable level of ef-ficacy, and this can be utilized clinically to acquire faster and more accurate screening of patients with lumbar disc herniation.

Milk consumption is crucial for a balanced diet, yet recent trends indicate a decline, especially in Italy. A significant factor in this decline is the altered perception of milk quality among consumers, creating a communication gap between them and other stakeholders. This study aimed to explore the discourse on social media and sentiment towards the concept of milk quality among consumers, farmers, and processors. The research adopted a Social Media Analysis to examine online community messages. A sample of 19,906 Italian comments and posts mentioning keywords Milk, Quality, Cow, and Vaccine was collected and categorized using term frequency analysis, correspondence analysis, and sentiment analysis. Results highlighted gaps in the perceptions of milk quality: farmers focused on economic issues, consumers on animal welfare and health, and processors on lactose content. For farmers, almost all comments were negative, while for processors, nearly all comments were positive. Consumers presented a more mixed picture. This work contributes to the literature by expanding research on milk quality using social media as a source of information. The findings suggest that enhancing communication and understanding among these groups could lead to more effective strategies for addressing consumer concerns, potentially reversing the declining of milk consumption.

Multi-threshold image segmentation is a relevant research field in further higher-level image preprocessing and computer vision. This paper adopts the Bayesian Forecasting Evolutionary Algorithm (BFEA) for natural scenery image segmentation using multilevel thresholding. An update method for the prediction vector is designed for image segmentation. The design idea of the BFEA algorithm for multilevel thresholding is based on the basic principle of Evolutionary Computation, the probability distribution of promising solutions, and the Bayesian theorem. BFEA could effectively solve the curse of dimensionality. Extensive experiments have demonstrated that the proposed algorithm dominated the state-of-the-art population-based thresholding method in term of the quality of image, function fitness value, optimal threshold value, SSIM value, PSNR value, computation time, dimensionality issue and convergence rate. The proposed method is very effective for color image segmentation.

This study is based on the BP neural network regression model optimised by genetic algorithm and dedicated to predicting the amount of car purchase, this study is important for all parties in the automotive industry. By plotting the violins of six indicators, including gender, age, annual salary, credit card debt, net worth, and car purchase amount, we found that, except for gender, the other data showed a trend of dense in the middle gradually decreasing to the upper and lower sides, with an overall normal distribution. The correlation analysis shows that the amount of car purchase has a high positive correlation with age (0.63), followed by annual income (0.62), the correlation between net worth and the amount of car purchase is 0.49, while the correlation between credit card debt and the amount of car purchase is low (0.03). In the experimental part, we analyse the predicted-actual values of the training and test sets in comparison and find that the model predicted values are very close to the actual values. The RMSE of the training set is 11.559 and the RMSE of the test set is 11.4835, which is not much different from each other, indicating that the model has good generalisation ability. In addition, the R2 of both the training and test sets is 1, indicating that the model fits well. The MAE of the training set is 8.2512 and the MAE of the test set is 9.578; the MBE of the training set is -4.3035 and the MBE of the test set is -2.4547.These metrics indicate that the model performs well in predicting the amount of car purchase. In conclusion, this study provides an effective and reliable method to predict the amount of car purchase, creating a useful bridge between car manufacturers, dealers and consumers.

Ten new Schiff bases, including thiocarbohydrazone derivatives (1–10), were synthesized using an efficient, uncomplicated, and environmentally friendly approach using the reaction of alkyl-substituted aldehydes. The thiocarbohydrazones that were obtained were derived from o-phthalaldehyde. The chemical structures of the compounds were identified by the utilization of UV-Vis spectroscopy, Fourier transform infrared (FT-IR), ¹H nuclear magnetic resonance (¹H NMR), ¹³C nuclear magnetic resonance (¹³C NMR), and elemental analysis methods. The thermal stability of thiocarbohydrazones was examined using thermogravimetric analysis (TGA-DSC). Investigations were conducted on urease inhibition tests, pharmacokinetic property research, and molecular docking. The collection of samples with the urease enzyme revealed significant molecular docking findings, with MolDock scores ranging from -153 to -189. The compounds have the potential to be effective drug candidates for treating certain diseases, as indicated by the consistent results of molecular docking, urease inhibition tests, and pharmacokinetic characteristics investigations.

This study investigates the efficiency of BEAM (Biologically Enhanced Agriculture Management) inoculant, derived from compost produced under Dr. David Johnson-Su's composting technique, in promoting plant growth. A winter mix of pasture comprising Oats, Tetila, unhulled Couch, and Haifa was used as the test crop. Seeds were soaked in BEAM inoculant, water (control), and a competitor product 'Companion.' Results revealed significant enhancements in germination rates, foliage growth, and root development in the BEAM-treated group compared to controls. Further research aims to identify the microbial components of BEAM and explore its potential as a suppressant for fusarium species in orchards.

Retroviruses perpetuate their survival by incorporating a copy of their genome into the host cell, a critical step catalyzed by the virally encoded integrase. The viral capsid plays an important role during the viral life-cycle, including nuclear importation in case of lentiviruses and integration targeting events, hence, targeting the integrase and the viral capsid is a favorable therapeutic strategy. While integrase strand transfer inhibitors (INSTIs) are recommended as first-line regimens given their high efficacy and tolerability, lenacapavir is the first capsid inhibitor and the newest addition to the HIV treatment arsenal. These inhibitors are however designed for treatment of HIV-1 infection, and their efficacy against HIV-2 remains widely understudied and inconclusive, supported only by few limited phenotypic susceptibility studies. We therefore carried out inhibition profiling of a panel of second-generation INSTIs and lenacapavir against HIV-2 in cell culture, utilizing pseudovirion inhibition profiling assays. Our results show that the tested INSTIs and lenacapavir exerted excellent efficacy against ROD-based HIV-2 integrase, and interestingly, raltegravir also showed modest antiviral activity against the SARS-CoV-2 Omicron variant in antiviral assays carried out in Vero E6 cells.

Our study investigated the innate immune response to Toxoplasma gondii infection by assessing microglial phenotypic changes and sickness behavior as inflammatory response markers post-ocular tachyzoite instillation. Disease progression in Swiss albino mice was compared with documented outcomes in BALB/c mice using identical ocular route and parasite burden (2x105 tachyzoites), with saline as control. Contrary to expectations, Swiss albino mice displayed rapid, lethal disease progression, marked by pronounced sickness behaviors and mortality within 11-12 days post-infection, while survivors showed no apparent signs of infection. Comparative analysis revealed T. gondii-infected BALB/c mice exhibited reduced avoidance of feline odors, while in-fected Swiss albino mice showed enhanced avoidance responses. There was a significant increase in microglial cells in the dentate gyrus molecular layer of infected Swiss albino mice compared to BALB/c mice and their respective controls. Hierarchical cluster and discriminant analyses iden-tified three microglial morphological clusters, differentially affected by T. gondii infection across strains. BALB/c mice exhibited increased microglial branching and complexity, while Swiss al-bino mice showed reduced shrunk microglial arbors, diminishing morphological complexity. These findings highlight strain-specific differences in disease progression and inflammatory reg-ulation, indicating lineage-specific mechanisms in inflammatory responses, tolerance, and re-sistance. These elements are critical in devising control measures for toxoplasmosis.

Background: Fine-needle aspiration cytology (FNAC) is widely used to diagnose and monitor thyroid nodules. The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) is the standard for interpreting FNAC specimens. The risk of malignancy in Bethesda III nodules also known as Atypia of Undetermined Significance (AUS) varies significantly throughout several studies published worldwide. This retrospective study examines the risk of cancer in thyroid FNAC categorized as Bethesda III, as identified in the final histopathology of thyroidectomy specimens at a single endocrine surgery center. Methods: This retrospective cohort analysis included 1038 consecutive patients who underwent elective thyroid surgery with complete follow up data between January 2020 and March 2024. Preoperative data on clinical and pathological characteristics have been collected. The final histopathology report from the thyroidectomy specimen was compared to the results of the preoperative FNAC on nodules that were judged to be Bethesda category III. Statistical methods were performed using SPSS version 29. Results: A total of 670 ultrasound-guided FNACs (64.5%) performed during the study period were included in the final analysis. The study population was predominantly female, represented by 79.6% of patients with a mean age of 42.5 (SD 12.1), while 20.4% were male and significantly older with mean age 45.13 years (p = 0.02). The FNAC inadequacy rate was 5.1%, which was associated with a high risk of malignancy (6 out of 34, 17.6%). Out of the total sample size of 170 patients classified as group III, 57 were found to have malignancies in final surgical histopathology, representing 33.5% of the cases within this category. The secondary gender-related outcome analysis showed that female patients classified under the Bethesda II category had a significantly higher risk of malignancy, with a rate of 21.2%, compared to males who had a malignancy rate of 3.4% in the same Bethesda category (p = 0.001, chi square test). However, female had more better prognostic non-invasive tumors than male (p = 0.02, chi square test). Conclusion: The study's results indicate that Bethesda categories II and III are associated with a higher risk of malignancy in comparison to the reports of the first and third editions of the TBSRTC, particularly for female patients classified under category II.

During the coronavirus disease 2019 (COVID-19) pandemic, individuals with immune-mediated inflammatory diseases (IMIDs), such as systemic lupus erythematosus and rheumatoid arthritis, face a heightened risk of infection and severe outcomes due to immunological alterations resulting from their underlying conditions and immunosuppressive treatments. Even as the pandemic has transitioned to an endemic state, it remains crucial to recognize that these patients continue to be at risk. In this narrative review, we analyzed existing literature to explore the impact of IMIDs, clinical risk factors, and the influence of immunosuppressive therapies on COVID-19-related risks and outcomes. Notably, certain disease-modifying antirheumatic drugs (DMARDs), like rituximab, are associated with worse COVID-19 outcomes, and rituximab-treated patients show impaired immune responses to COVID-19 vaccination. Additionally, we outline the diverse effects of glucocorticoids on COVID-19 outcomes and management. To highlight real-life challenges faced by clinicians caring for patients with IMIDs, we present an illustrative scenario that underscores the importance of effective vaccination, timely boosting, and additional mitigation strategies against COVID-19. Given the clinical heterogeneity and diverse disease states within IMIDs, it is crucial to understand the ongoing implications and risks associated with COVID-19 in these patients, to guide the implementation of appropriate measures and optimize care and outcomes in the current endemic era.

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

Smoking is a pervasive global health concern associated with a myriad of diseases, but its impact on cellular aging, particularly in relation to telomeres, is increasingly recognized. Telomeres, protective caps at the ends of chromosomes, play a crucial role in maintaining genomic integrity. This review critically examines the existing evidence on the association between smoking and telomere atrophy, exploring the molecular mechanisms involved and the implications for accelerated aging and heightened disease risk. The comprehensive analysis encompasses both cross-sectional and longitudinal studies, elucidating potential gender and age disparities. Insights into the molecular pathways linking smoking to telomere shortening are discussed, along with the broader implications for public health. The review concludes by outlining potential interventions and suggesting future research directions in this critical area.

The only cases where exact distributions of estimates are known is for samples from exponential families, and then only for special functions of the parameters. So statistical inference was traditionally based on the asymptotic normality of estimates. To improve on this we need the {\it Edgeworth expansion} for the distribution of the standardized estimate. This is an expansion in $n^{-1/2}$ about the normal distribution, where $n$ is typically the sample size. The 1st few terms of this expansion were originally given for the special case of a sample mean. In earlier work we derived it for {\it any } standard estimate, hugely expanding its application. We call an estimate $\hat{w}$ of an unknown vector $w\in R^p$, a {\it standard estimate}, if $E\ \hat{w}\rightarrow w$ as $n\rightarrow \infty$, and for $r\geq 1$ the $r$th order cumulants of $\hat{w}$ have magnitude $n^{1-r}$ and can be expanded in $n^{-1}.$ Here we give another huge extension. We give the expansion of the distribution of {\it any smooth function} of $\hat{w}$, say $t(\hat{w})\in R^q,$ giving its distribution to $n^{-5/2}$. We do this by showing that $t(\hat{w})$, is a standard estimate of $t(w)$. This provides far more accurate approximations for the distribution of $t(\hat{w})$ than its asymptotic normality. %NOT USED: The building blocks of the Edgeworth expansions are the {\it cumulant coefficients } of the estimate. These cumulant coefficients are also needed for bias reduction, Bayes estimates and confidence regions. We give {\it chain rules} for the cumulant coefficients of $t(\hat{w})$ in terms of those of $\hat{w}$ and the derivatives of $t(w)$, up to those needed for 5th order Edgeworth expansions for $t(\hat{w})$ and its {\it tilted expansion}, useful for the tail of the distribution.

Simple Summary: The distribution of typical bacterial redox enzymes such as pyruvate-ferredoxin oxidoreductase (PFOR) in protozoa remains interestingly puzzling. Previous studies have demonstrated diverse cellular localizations of PFOR in some amitochondriate anaerobic protozoa. PFOR is of particular pharmacological importance because it catalyzes the reductive bio-activation of nitro-based prodrugs to cytotoxic radical metabolites. Metronidazole was developed primarily as an antiprotozoal agent against infections caused by Trichomonas vaginalis. However, its antimicrobial spectrum was subsequently expanded to cover anaerobic bacterial infections. It has been shown that mutations in the genes encoding PFOR result in inherent resistance of PFOR-possessing anaerobic protozoa and bacteria to nitro-based prodrugs. Deciphering the evolutionary history of PFOR is crucial in deepening our understanding of the evolution of anaerobic pathogens and unfold new approaches for drug discovery and targeting in pathogen chemotherapy. Abstract: The present frontrunners in the chemotherapy of infections caused by protozoa are nitro-based prodrugs that are selectively activated by PFOR-mediated redox reactions. This study seeks to analyze the distribution of PFOR in selected protozoa and bacteria by applying comparative bioinformatics approaches to test the hypothesis that PFOR was most likely acquired through horizontal gene transfer (HGT) from bacteria. Furthermore, to identify other genes that were putatively acquired using proteome-wide and gene expression analyses. A plausible explanation for the patchy occurrence of PFOR in protozoa is based on the hypothesis that bacteria are potential sources of genes that enhance optimal adaptation of protozoa in hostile environments. The exclusively expressed proteins obtained from Entamoeba histolytica and the putative gene donor, Desulfovibrio vulgaris, showed an over-expression of eleven genes involved in intermediary metabolism. If these results can be reproduced in other PFOR-possessing protozoa, it would provide a more validated evidence to support the horizontal transfer of pfor from bacteria.

This paper provides a new discovery that adds to the contemporary theory of modelling of non-stationary queues, by capturing analytically the behavior of the root parameter, for the G1/M/1 PSFFA model of non-stationary Ek/M/1 queue. Additionally, it has been also revealed that the resulting root parameter reduces to the PSFFA model of non-stationary queue. Potential applications of PSFFA to Computer engineering are provided. Some challenging open problems are addressed combined with closing remarks and future research directions.

Green economy has emerged as a significant pillar of sustainable development in the current global context, and the unpredictability of economic policy has progressively garnered the interest of businesses and government officials as one of the obstacles they must confront. This paper examines how uncertainty in the green economy impacts the green economy's efficacy. The research concentrates on how changes in a particular region can result in geospatial diffusion (spatial spillover), which is a form of spatial spillover wherein the uncertainty of the green economy can impact its efficacy. This paper examines the policy formulation and current state of the green economy across various regions, as well as the diffusion of economic policy uncertainty across geographical areas and the subsequent impact on the green economy's status quo. There is a negative correlation between economic policy uncertainty and green economy efficacy, according to research. There is a spatial spillover effect between economic policy uncertainty and the efficacy of the green economy. Regional variations exist in the impact of economic policy uncertainty on the efficacy of the green economy. The findings indicate that the implementation of AI has a more substantial beneficial effect on carbon emissions. An increase in carbon dioxide emissions poses a growing challenge for the industrial sector in its pursuit of carbon neutrality.

Using the h-index to assess individual researchers is not only unethical and unfair but also inaccurate and misleading. This index fails to offer a reliable measure of a researcher's impact based on their citation scores. Beyond statistical and conceptual considerations, authorship practices, especially in the case of multi-authored publications, give rise to significant problems that often go unnoticed. While some modifications of the h-index have been proposed to mitigate these weaknesses, the fundamental deficiencies persist. Most of these flaws have been effectively addressed by the c-score, a composite citation index. The c-score excludes self-citations, normalizes the number of citations by considering the number of authors in each paper, and takes into account first, single, and last authorship. This approach provides a more realistic measure of the impact of each individual researcher based on raw citations.

Foodborne illnesses can be infectious and dangerous and most of them are caused by bacteria. Some types of common food-related bacteria exist widely in nature and pose a serious threat to both humans and animals, and can cause poisoning, diseases, disabilities and even death. Rapid, reliable and cost-effective methods for bacteria detection are of paramount importance in food-safety and environmental monitoring. Polymerase chain reaction (PCR), lateral flow immunochromatographic assay (LFIA) and electrochemical methods have been widely used in food-safety and environmental monitoring. In this paper, the recent developments (2013-2023) covering PCR, LFIA and electrochemical methods for various bacteria detection (Salmonella, Listeria, Campylobacter, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli)) considering different foods types, analytical performances and the reported limit of detection (LOD) are discussed. It is found that the bacteria type and food sample type contribute significantly to the analytical per-formance and LOD. Detection by LFIA has higher average LOD (24 CFU/ml) than detection by electrochemical methods (12 CFU/ml) and PCR (6 CFU/ml). Salmonella and E. coli in the Pseudomonadota domain usually have low LODs. LODs are usually lower for detection in fish and eggs. LFIA with gold and iron nanoparticles are prominent in the majority of articles of 26 CFU/ml and 12 CFU/ml respectively. Electrochemical methods reveal that the average LOD is highest for cyclic voltammetry (CV) of 18 CFU/ml), followed by electrochemical impedance spectroscopy (EIS) of 12 CFU/ml and differential pulse voltammetry (DPV) of 8 CFU/ml. Finally, the review discusses the challenges and future perspectives (including the role of nanomaterials/advanced materials) to improve the analytical performance for bacterial detection.

Conventional text style transfer (TST) methodologies primarily utilize style classifiers to segregate the content and stylistic elements of text for effective style transformation. Despite the pivotal role of these classifiers, their influence on TST techniques remains largely unexplored. This study embarks on a detailed exploration of the limitations inherent in style classifiers within current TST frameworks. We reveal that these classifiers often inadequately comprehend sentence syntax, leading to diminished performance in TST models. In response, we introduce the Syntax-Enhanced Style Transfer (SEST) model, a groundbreaking approach incorporating a syntax-sensitive style classifier. This classifier ensures that the extracted style representations robustly encapsulate syntax nuances, enhancing TST effectiveness. Rigorous evaluations across diverse TST benchmarks demonstrate that SEST significantly surpasses contemporary models in performance. Additionally, our case studies highlight SEST's proficiency in producing syntactically coherent sentences that aptly retain original content.

Background: During esophagogastroduodenoscopy performed with colonoscopy, gastric and duodenal erythema, erosions, and ulcerations are often observed. This investigation was designed to review the prevalence of gastroduodenal lesions in patients who underwent wireless capsule endoscopy using standard bowel cleansing preparations, but no endoscopy or sedation. Methods: A retrospective analysis was conducted on patients referred for capsule endoscopy. Records and capsule reports were reviewed for patient demographics, preparation prescribed, procedural indications, and gastroduodenal findings. Preparations studied included polyethylene glycol lavage (PEG), PEG plus bisacodyl (PEG+bis), bisacodyl (bis), oral sulfate solution (OSS) and no prep. Results: Among the 1236 records, 498 (40.3%) were men and 738 (59.7%) were women. The mean age was 57 years +/- 17.8 years SD. The percentage of patients with lesions after any bowel preparation was 52.7% for gastric lesions and 23.6% for duodenal lesions. The percentage of patients with gastroduodenal lesions was 58.3% with prep, compared to 38.2% without prep. These findings were statistically significant with RR 1.53 [1.19-1.94] (p-value=0.00004). This difference was more pronounced in the OSS group RR 1.65 [1.29-2.1] and bisacodyl groups RR 1.64 [1.25-2.15] compared to the PEG group RR 0.95 [0.7-1.3]. Conclusion: This study showed that patients undergoing wireless capsule endoscopy who received bowel preparations had a significant increase in gastric and duodenal lesions. Of the preparations studied, OSS was associated with the greater number of gastroduodenal lesions, while PEG was the least associated with lesions with an occurrence similar to the non-prep group. The clinical significance of these lesions remains undetermined.

Cloud computing has become a widespread technology due to ICT advancements. It allows businesses to move and take advantage of the pay-as-you-go price model. Enterprises pay for what they use and don't have to buy, setup, or maintain resources. However, outsourcing core resources such as storage, servers, operating systems, and software applications from a third party needs careful decisions to take advantage of cloud facilities. Choice of appropriate service provider, deployment and service model must be aligned with business needs and goals. In this paper, we will present cloud computing benefits, cloud deployment methods, services, state-of-the-art migration methods, tools and techniques and will conclude with the role of SDN in cloud computing.

Fragile X syndrome (FXS), the most common monogenic cause of inherited intellectual disability and autism spectrum disorder, is caused by a full mutation (&gt;200 CGG repeats) in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene. Individuals with FXS experience a wide range of challenges related to social interaction (SI). Animal models, such as Drosophila melanogaster model for FXS where the only ortholog of human FMR1 (dFMR1) is mutated, have played a crucial role in understanding of FXS. The aim of this study was to investigate SI in the dFMR1B55 mutants using the novel Drosophila Shallow Chamber and a Python data processing pipeline based on social network analysis (SNA). In comparison with wild-type flies (w1118), SNA analysis in dFMR1B55 mutants revealed hypoactivity, fewer connections in their networks, a lower ability to efficiently transmit information, fewer alternative pathways for information transmission, a higher variability in the number of interactions they achieved, and tended to stay near the boundaries of the testing chamber. These observed alterations indicate the presence of characteristic strain-dependent social networks in dFMR1B55 flies, commonly referred to as the group phenotype. Finally, a combination of novel research tools is a valuable method for SI research in fruit flies.

Prostate cancer (PCa) is the most prevalent malignancy and the second leading cause of can-cer-related death in men. Although current therapies can effectively manage the primary tumor, most patients with late-stage disease manifest with metastasis in different organs. From surgery to treatment intensification (TI), several combinations of therapies are administered to improve prognosis of patients with metastatic PCa. Due to the high frequency of mutation during the metastatic phase, the Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 genetic engineering tool can accelerate the effects of TI by enhancing targeted gene therapy or immunotherapy. This review describes the genetic backgrounds of metastatic PCa and how CRISPR/Cas9 technology can contribute to the field of PCa treatment development. It also dis-cusses current limitations of conventional PCa therapy and the potential of CRISPR-based-PCa therapy.

Abstract: New paradigms have emerged in mapping since the 20th century due to theoretical and practical advances in computer technology, communications, and cartography. For the design of the Expert-Topographic Map Service in Mexico, we adopted a theoretical framework of Cybercartography and expanded to incorporate expert systems and eventually other artificial intelligence resources. Using a transdisciplinary approach, our efforts could focus on the interconnection between knowledge and geotechnology to offer solutions to societal complex problems. This paper explores the theoretical pillars supporting the design of an Expert-Topographic Map Service, including cybercartography, knowledge-based systems and surface mathematical modeling. For the first time, cybercartography is applied in the realm of topographic mapping. As shown, a map conceived solely as an information system has been transformed, in the digital era, into a container of knowledge through the abstract representation of features like relief via the Triangular Irregular Network Model. Thereby, we can bridge from digital topographic maps to other knowledge-based models such as expert systems. The societal demand and empirical context for this research demonstrate that topographic maps and services are still at the core of cartographic initiatives in the 21st century.

Gold nanoparticles (NPs) are among the most commonly employed metal NPs in biological applications, with their distinctive physicochemical features. Their extraordinary optical properties, stemming from the strong localized surface plasmon resonance (LSPR), contribute to the devel-opment of novel approaches in the areas of bioimaging, biosensing, and cancer research, especially for photothermal and photodynamic therapy. The ease of functionalization with various lig-ands provides a novel approach to the precise delivery of these molecules to targeted areas. Gold NPs ability to transfer heat and electricity positions them as valuable materials for advancing thermal management and electronic systems. Moreover, their inherent characteristics, such as in-ertness, give rise to the synthesis of novel antibacterial and antioxidant agents as they provide a biocompatible and low-toxic approach. Chemical and physical synthesis methods are utilized to produce gold NPs. The pursuit of more ecologically sustainable and economically viable large-scale technologies, such as environmentally benign biological processes referred to as green/biological synthesis, has garnered increasing interest among global researchers. Green synthesis methods are favorable among other synthesis techniques as they minimize the necessity for hazardous chemicals in the reduction process due to their simplicity, cost-effectiveness, energy efficiency, and biocompatibility. This article discusses the importance of gold NPs, their optical and conductivity properties, antibacterial, antioxidant, and anticancer properties, synthesis methods, contemporary uses, and biosafety, emphasizing the need to understand toxicology principles and green commercialization strategies.

Polo-Like Kinase-1 (PLK1) is a key regulator of the G2/M cell cycle and has been linked to treatment resistance in prostate cancer (PCa). This study aimed to explore the correlation between PLK1 expression, determined through RNA-seq analysis, and clinical outcomes in PCa patients. We analyzed tumor samples from 452 patients diagnosed with prostate adenocarcinoma between 2014 and 2020, sourced from nine institutions affiliated with the Oncology Research Information Exchange Network (ORIEN). PLK1 expression levels were determined using RNA-seq data and stratified into high (≥75th percentile) vs. low (<75th percentile) expression. Univariate and multivariate Cox proportional hazard models compared overall survival between patients with low vs. high PLK1 expression, adjusting for age, race, PSA, and stage. Of the 452 tumors, 241 (53.3%) had high PLK1 and 211 (46.7%) had low PLK1 expression. Median follow-up was 2.74 years for low PLK1 and 2.71 years for high PLK1. No significant difference in overall survival was observed between low and high PLK1 groups in univariate (p=0.86; HR 1.09) or multivariate (p=1.0; HR 0.99) analyses. This study found no association between RNA-seq-based PLK1 expression and prognosis in PCa. PLK1 expression is not a prognostic marker for prostate cancer.

Cisplatin (CDDP) remains a key drug for patients with advanced bladder cancer (BC), despite the emergence of new therapeutic agents. Identifying factors contributing to treatment resistance to CDDP is crucial. The acidity of the tumor microenvironment has been reported to be associated with treatment resistance and poor prognosis across various cancer types. Our objective was to investigate the effects of an acidic environment on BC cells and elucidate the mechanisms behind CDDP resistance. Our findings show that BC cells cultured in acidic conditions developed resistance to cisplatin as acidity increased. Notably, CDDP administered to BC cells in a pH 6.0 environment required twice the concentration compared to pH 7.5 to achieve equivalent toxicity. Using chloroquine and navitoclax, we identified the involvement of Bcl-2 and LC3B pathways in the acquisition of CDDP resistance under acidic conditions. Western blot analysis revealed that the activation of Bcl-2 and XIAP expression appears to inhibit both apoptotic and autophagic cell death. These results suggest that alleviating the acidity of the tumor microenvironment in clinical settings might enhance BC sensitivity to CDDP.

Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. The study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, entailing several motor-related symptoms that contribute to a reduced quality of life in affected subjects. Recent advances in wearable technologies and computing resources have shown great potential for the assessment of PD-related symptoms. However, the potential applications (e.g., early diagnosis, prognosis and monitoring) and key features of digital biomarkers for motor symptoms of PD (DB-MS-PD) have not been comprehensively studied. This study aims to provide a state-of-the-art review of current digital biomarker definitions for PD, focusing on the use of wearable devices. This review systematically examines research articles from 2012 to 2024, focusing on key features and recent technologies in PD research. A total of 22 studies were included and thoroughly analyzed. Results indicate that DB-MS-PD can accurately distinguish patients with PD (PwPD) from healthy controls (HC), assess disease severity or treatment response, and detect motor symptoms. Large sample sizes, proper validation, non-invasive devices, and ecological monitoring make DB-MS-PD promising for improving PD management. Challenges include sample and method heterogeneity and lack of public datasets. Future studies can leverage evidence of the current literature to provide more effective and ready-to-use digital tools for monitoring PD.

Multifunctional coatings based on Sn-Ni materials with and without titanium oxide nanoparticles (TiO2NPs) incorporation were prepared using the electrochemical deposition technique at 70°C. TiO2NPs were dispersed in the electrolyte bath, and their influence on both surface texture, crystalline phase, and properties was investigated. Various techniques such as X-ray phase analysis (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray microanalysis (EDX) were used to characterize the prepared coatings. The anti-corrosion effectiveness of the coatings was assessed using potentiodynamic polarization curves and electrochemical impedance spectroscopy in an artificial sweat medium, while the bactericidal activity of the composite coatings (the ability to induce cell death) was evaluated in accordance with ISO 27447:2009 test. The influence of the TiO2NPs on the composition, structure and properties of the deposited coatings was established. Particular attention was paid to the relationship between the anticorrosive and bactericidal properties of the coatings and their structure and composition. The synergistic effect of the various properties was investigated and discussed.