Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data.Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluatedtheir applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

Background: The control of energy metabolism is fundamental for cell growth and function, and anomalies are implicated in complex diseases and ageing. It is important for cells to carefully tune metabolic pathways to optimize their function in response to different nutrient or physiological conditions. Metabolism in yeast cells can be easily manipulated by supplying different carbon sources: on glucose yeast rapidly proliferates by fermentation, analogous to tumour cells growing by aerobic glycolysis, whereas on non-fermentable carbon sources metabolism shifts towards respiration. Results: We screened deletion libraries of fission yeast to identify over 200 genes required for respiratory growth. The growth medium and auxotrophic mutants strongly influenced respiratory metabolism. Most genes uncovered in the mutant screens have not been implicated in respiration in budding yeast. We applied gene expression profiling approaches to compare steady-state fermentative and respiratory growth and to analyse the dynamic adaptation to respiratory growth. The transcript levels of most genes functioning in key energy metabolism pathways were coherently tuned, reflecting anticipated differences in metabolic flows between fermenting and respiring cells. We show that the acetyl-CoA synthase, rather than the citrate lyase, is essential for acetyl-CoA synthesis in fission yeast. We also investigated the transcriptional response to mitochondrial damage by genetic or chemical perturbations, defining a retrograde response that involves the concerted regulation of distinct groups of nuclear genes that may avert harm from mitochondrial malfunction. Conclusions: These systematic and targeted analyses provide a rich framework of the genetic and regulatory basis of fundamental metabolic states to guide future studies on energy metabolism in fission yeast and beyond. Our study pinpoints weaknesses of commonly used auxotroph mutants for investigating energy metabolism. As a model for cellular energy regulation, fission yeast provides an attractive and complementary system to budding yeast.

Background/Objectives: Chemerin, which is a multifunctional cytokine and adipokine, has been implicated in inflammatory and metabolic processes and might play a role in upper gastrointes-tinal (GI) malignancies, particularly gastric and esophageal cancer. The aim of this review is to explore the role of chemerin in the pathophysiology of upper GI cancers, as well as its potential as a biomarker for early detection and as a therapeutic target. Methods: A comprehensive review of recent studies about chemerin's biochemical properties and interaction with its receptors, as well as its effects on inflammatory responses, immune regulation and metabolic processes was con-ducted. Clinical implications of chemerin on gastric and esophageal cancer were analyzed, whereas the potential therapeutic strategies targeting chemerin were discussed. Results: Elevated chemerin levels are associated with poor prognosis in gastric cancer and promote invasiveness and metastasis in esophageal cancer. Chemerin receptor antagonists show promising results in inhibiting cancer cell migration, invasion and progression. Conclusions: Chemerin could repre-sent a valuable prognostic biomarker and therapeutic target for upper GI cancers. Future observa-tional studies should validate its clinical applications and investigate the efficacy of chemerin in-hibitors as potential therapeutic targets.

The Aspergillus genus comprises fungi that are widely distributed in nature. The Terrei section is particularly important due to its functions in recycling organic matter. Aspergillus hortae is a species from this section that has been isolated from clinical samples, but its role as a pathogenic agent is unclear. In this study, three clinical A. hortae isolates from Colombia, initially identified as A. terreus, were characterised by micro and macro-morphological analysis at 26°C and 37°C. A bioinformatic pipeline was used for molecular typing, which included analysis of whole genome sequences for accurate species identification. Susceptibility assays were also conducted using the microdilution broth method (EUCAST). It was confirmed that this species is intrinsically resistant to Amphotericin B with a minimum inhibitory concentration (MIC) of 2-4 mg/L, and is also thermotolerant. We obtained the in vitro susceptibility profiles for three azole drugs (MIC, 0.25-1 mg/L). The three isolates showed phylogenetic divergence from the reference genome strain. A mutation (M769K) in the MelA ortholog of a hypopigmented isolate, which had the lowest MIC value for AmB (2 mg/L), was identified. This study presents the morphological characterization, molecular typing through whole-genome analysis and identification of susceptibility profiles to azoles and amphotericin B of three clinical isolates of A. hortae from Colombia.

Within the European Union (EU), new plant varieties, to be included at the Common catalog of a member state, has to be registered at the national list, after plant variety testing processes to establish whether the candidate variety is distinguishable, uniform and stable (DUS) and meets the requirement of the cultivation or use value requirement (VCU). Technical development, climate change and changing consumer needs, including the detection of GMOs, necessitate the innovation of plant variety testing methods. Within the EU-funded "InnoVar" project, it has been sent out questionnaires to several countries, which were aimed at analyzing strengths, weaknesses, opportunities and threats (SWOT) of the current methods and with their help it is possible to have look for a new direction, opportunity and strategy, which can incorporate together with the innovation results into the development of the new methods. Our study confirmed that the SWOT analysis could be used to achieve the set goals; according to the answers to the questionnaires, it became necessary to introduce new characters (e.g. cold tolerance) and new test methods (molecular markers, precision techniques, organic farming) into the variety testing procedures. The development of high-yielding, disease and/or pest-resistant plant varieties with good adaptability and the accurate evaluation of genotypes play a crucial role in ensuring that farmers can access high-performing plant varieties and contribute to sustainable food production.

The evolution of the H5 highly pathogenic avian influenza (HPAI) viruses has led to the emergence of distinct groups with genetically similar clusters of hemagglutinin (HA) sequences. In this study, a consensus H5 HA sequence was cloned into the baculovirus expression system. The HA protein was expressed in baculovirus-infected insect cells and utilized as the antigen for the production of an oil emulsion-based H5 avian influenza vaccine (rBacH5Con5Mut). 21-day old SPF chickens were immunized with this vaccine and then challenged at 21 days post-vaccination (dpv) with clade 2.3.2.1, clade 2.3.4.4 and clade 7.2 of H5 HPAI virus, respectively. The chicken sera of vaccinated chickens exhibited high hemagglutination inhibition (HI) titers against the rBacH5 vaccine antigen, while lower HI titers were observed against the different challenge virus H5 hemagglutinins. Furthermore, the rBacH5Con5Mut vaccine provided 100% protection from mortality and clinical signs. Virus isolation results showed that oropharyngeal and cloacal shedding was prevented in 100% of the vaccinated chickens when challenged with clade 2.3.2.1 and clade 2.3.4.4 H5 viruses. When the rBacH5Con5Mut vaccine candidate was administrated at one day of age, 100% protection was demonstrated against challenge of clade 2.3.4.4 virus at 3 weeks of age, indicating the potential of this vaccine for hatchery vaccination. Overall, the rBacH5Con5Mut vaccine candidate with a consensus HA antigen and a single dose can protect chickens against different clades of H5 HPAI viruses throughout the rearing period of broiler chickens without a boost, thus fulfilling the criteria for an efficacious broad-spectrum H5 avian influenza vaccine.

Metallo-β-lactamases (MBLs) are members of the structurally conserved but functionally diverse MBL-fold superfamily of metallohydrolases. MBLs are a major concern for global health care as they efficiently inactivate β-lactam antibiotics, including the “last-resort” carbapenems, and no clinically suitable inhibitors are currently available. Increasingly, promiscuous β-lactamase activity is also observed in other members of the superfamily, including from viruses, which represents an underexplored reservoir for future pathways to antibiotic resistance. Here, two such MBL-fold enzymes from Bacillus phages, the cyclic mononucleotide-degrading proteins ApycGoe3 and ApycGrass, are shown to efficiently degrade β-lactam substrates in vitro. In particular, ApycGrass displays a distinct preference for carbapenem substrates with a catalytic efficiency that is within one order of magnitude of that of the clinically relevant MBL NDM-1. Mutagenesis experiments also demonstrate that the loss of a metal-bridging aspartate residue reduces nuclease activity up to 35-fold, but improves carbapenemase activity. In addition, we propose that the oligomeric state significantly influences β-lactamase activity by modifying access to the active site pocket. Together, these observations hint at a possible new avenue of resistance via the spread of phage-borne MBL-fold enzymes with β-lactamase activity.

The agricultural industry relies on physical fences to manage livestock. However, physical fences present some practical, financial, and ecological problems, that partly may be solved by virtual fencing. However, it is necessary for the livestock to be adapted to virtual fencing. This study aims to identify if adaptation to virtual fence can be improved through social facilitation between experienced cattle and the remaining herd. Moreover, which of the two different learning processes, a gradual or instant introduction to virtual fence is the most effective in associating warnings with impulses. Three pastures containing Angus (Bos taurus) will be used to examine these investigations; A, B and C. All investigations are conducted on the Danish Island of Fanø. Three methods of learning process were used, one gradual and two instant introductions to the virtual fence. The investigations had varying amounts of experienced cattle. Impulses and warnings were recorded during a learning period and the following observation period. To quantify if the cattle adapt to the warnings, impulses per warning are used. There was no evidence of improvement with the presence of experienced cattle for any pastures, indicating no social facilitation. Gradual introduction was found to be more beneficial than instant introduction. More standardized tests are necessary to discover the full effect of experienced cattle.

Atopic dermatitis (AD) is a chronic inflammatory disorder presenting a dysregulated immune response and derangement of the epidermal barrier lipids. The Th2-type cytokines interleukin IL-4 and IL-13 play a prominent role in AD by activating the Janus Kinase/Signal Transduction and Activator of Transcription (JAK/STAT) intracellular signal axis. This study aimed to investigate the role of JAK/STAT in the lipid perturbations induced by Th2 signaling in 3D-epidermal equivalents. We used tofacitinib, a low-molecular-mass JAK inhibitor, to screen for the JAK/STAT-mediated deregulation of lipid metabolism. Th2 cytokines decreased the expression of elongases 1, 3, and 4, and serine-palmitoyl-transferase and increased that of sphingolipid delta(4)-desaturase, and carbonic anhydrase 2. Th2 cytokines inhibited the synthesis of palmitate and caused a depletion of triglycerides. This depletion was associated with altered PC profiles and fatty acid (FA) metabolism. Overall, the ceramide profiles were minimally affected. Except for most sphingolipids and very long chain FAs, the effects of Th2 on lipid pathways were reversed by co-treatment with tofacitinib. An increase in the mRNA levels of CPT1A and ACAT1, reduced by tofacitinib, suggests that Th2 cytokines promote FA beta-oxidation. In conclusion, pharmacological inhibition of JAK/STAT activation prevents the lipid disruption caused by halted homeostasis of FA metabolism.

The accuracy of cosmic ray observation by the Large High Altitude Air Shower Observatory Wide Field-of-view Cherenkov/fluorescence Telescope Array (LHAASO-WFCTA) is influenced by variations in aerosols in the atmosphere. The solar photometer (CE318-T) is extensively utilized within the Aerosol Robotic Network as a highly precise and reliable instrument for aerosol measurements. With this CE318-T 23, 254 sets of valid data samples in 394 days from October 2020 to October 2022 at the LHAASO site were obtained. Data analysis revealed that the baseline Aerosol Optical Depth (AOD) and A˚ngstro¨m Exponent (AE) at 440-870 nm (AE440−870nm) of the aerosols were calculated to be 0.03 and 1.07, respectively, suggesting that the LHAASO site is among the most pristine regions on Earth. The seasonality of the mean AOD are the order of spring > summer > autumn = winter. The monthly average maximum of AOD440nm occurred in April (0.11 ± 0.05) and the minimum was in December (0.03 ± 0.01). The monthly average of AE440−870nm exhibited slight variations. The seasonal characterization of aerosol types indicated that clean continental background aerosol predominated in autumn and winter, which is the optimal period for the absolute calibration of the WFCTA. Additionally, the diurnal daytime variations of AOD and AE across the four seasons are presented. Our analysis also indicates that the potential origins of aerosol over the LHAASO in four seasons were different and the atmospheric aerosols with higher AOD probably originate mainly from Northern Myanmar and Northeast India region. These results provide a baseline for comparison to the future on-site measurement of extinction coefficient profile and aerosol phase function by LHAASO-WFCTA, also enrich the valuable materials on aerosol observation in the Hengduan Mountains and Tibetan Plateau.

French Guiana, located in the Guiana Shield, is a natural reservoir for many zoonotic pathogens that are of considerable medical or veterinary importance. Until now, there are few data available on the description of parasites circulating in this area, especially on protozoan belonging to the phy-lum Apicomplexa; conversely, the neighbouring countries describe a high parasitic prevalence in animals and humans. Epidemiological surveillance is necessary, as new potentially virulent strains may emerge from these forest ecosystems, such as Amazonian toxoplasmosis. However, there is no standard tool for detecting protozoa in wildlife. In this study, we developed Meat-Borne-Parasite, a high-throughput meta-barcoding workflow for detecting Apicomplexa based on Oxford Nanopore Technologies sequencing platform using the 18S gene of fourteen Apicomplexa-positive samples collected in French Guiana. Sequencing reads were then analysed with MetONTIIME pipeline. Thanks to a scoring rule, we were able to classify as Apicom-plexa-positive 10 samples out of 14 and reveal the presence of co-carriages. The same samples were also sequenced with Illumina platform, for validation purposes. On samples identified as Apicomplexa-positive by both platforms, a strong positive correlation at up to genus level was reported. Overall, the presented workflow represents a reliable method for Apicomplexa detec-tion, which may pave the way for more comprehensive biomonitoring of zoonotic pathogens.

A novel determination method using flow-through cartridge purification and ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) was developed for the identification and quantification of 30 mycotoxins (aflatoxin B1, B2, G1, G2, sterigmatocysin, T-2 toxin, diacetoxyscirpenol, roquefortine C, methylergonovine, ergocornine, lysergol, enniatin A, enniatin A1, enniatin B, enniatin B1, beauvericin, deoxynivalenol, 3-acetylDeoxynivalenol, 15-acetylDeoxynivalenol, patulin, verruculogen, neosolaniol, gliotoxin, HT-2 toxin, wortmannin, zearalenone, α-zearalenol, β-zearalenol, α-zearalanol, and β-zearalanol) in feedstuffs. Mycotoxins were extracted from sample by 0.1% formic acid aqueous solution/acetonitrile (16/84 v/v), then purified by a Cleanert® MC clean-up column. As a result, the pigments and other matrices were efficient removed from the sample extract solution. The purified eluate was collected, then evaporated and redissolved by 0.1% formic acid aqueous solution/acetonitrile (70:30 v/v) for UPLC-MS/MS detection. A BEH Shield RP18 chromatographic column was employed for separation of 30 mycotoxins in a total of 14 min. Multiple-reaction monitoring (MRM) mode was applied for qualitative and quantitative analysis, and matrix calibration curves obtained with the external-standard method was used for quantitation of target analytes. Under optimized conditions, the linearity range was from 2 to 1000 ng/g, and the limit of quantification of the developed method was from 2 to 50 ng/g. The recoveries of 30 mycotoxins spiked in urine samples were from 72.0% to 118.5%, and the relative standard deviation was below 20%. The method was also well approved by certified reference sample, and applied on the real feedstuff samples testing successfully.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

The intricate mechanisms of the immune response elicited by vaccines and the interplay between the immune system and the central nervous system (CNS) are poorly understood. This review explores these mechanisms, emphasizing the interplay between the immune system and the central nervous system (CNS). A critical aspect is the examination of how vaccine-induced immune responses can interact with the CNS, influencing neuroinflammation and neuroimmune interactions. Furthermore, the link between vaccination and persistent symptoms observed in long-COVID patients is discussed. Vaccination initiates a complex cascade of events, starting with the production of specific proteins, such as S1/S2 spike proteins, leading to robust immune activation in the axillary lymph nodes. The role of macrophages and antigen-presenting cells (APCs) in cytokine production, antigen presentation, and the stimulation of B and T cells highlights the sophistication of the adaptive immune response. The migration of immune cell-derived exosomes to the brain plays a dynamic role in CNS inflammation, neurodegenerative processes and long COVID. On the other hand, the prolonged presence of viral and vaccine-derived spike proteins may contribute to the complex pathophysiology of Long-COVID, shedding light on the delicate balance between beneficial immune responses and possible adverse neurological outcomes of vaccination. It is worth rigorous monitoring and further research to understand the mechanisms of neuroinflammation and the persistence of spike proteins and their long-term effects on the brain, emphasising the importance of a nuanced approach to vaccine safety and efficacy in the context of COVID-19 and beyond.Principio del formulario

Background: The circadian system, a vital temporal regulator influencing physiological processes, has implications for cancer development and treatment response. Our study assesses circadian timing's impact on whole-brain radiotherapy outcomes in brain metastases for personalized cancer therapy insights. Aim: Evaluate circadian influence on radiation treatment timing and its correlation with clinical outcomes. Identify patient populations benefiting from interventions synchronizing circadian rhythms, considering subgroup differences and potential disparities related to social determinants of health (SDoH). Design: IRB-approved retrospective analysis of 237 patients undergoing whole-brain radiotherapy for brain metastases (2017–2021), receiving over 80% of treatments in the morning or afternoon. Survival analyses utilized Kaplan–Meier curves. Setting/Participants: Single-institution study involving patients receiving whole-brain radiotherapy. Collected demographic, disease, and socioeconomic parameters from electronic medical records. Results: Morning treatment (n=158) showed a trend towards improved overall survival vs. afternoon (n=79), median survival 158 vs. 79 days (p=0.20, HR=0.84, CI95% 0.84–0.91). Subgroup benefits for morning treatment in females (p=0.04) and trends in controlled primary disease (p=0.11) and breast cancer metastases (p=0.08). SDoH-based disparities observed, with Black and lower-income patients exhibiting diminished circadian influence. Conclusions: Emphasizes chronobiological factors' relevance in brain metastases radiation therapy. Morning treatment correlates with improved survival, particularly in specific subgroups. Identifies potential circadian influence disparities based on SDoH, laying a foundation for personalized cancer therapy and interventions synchronizing circadian rhythms for enhanced treatment efficacy.

Perinatal brain injury is a major public health problem burdened with high morbidity, mortality and severe neurological sequelae. Despite advances in neonatal life-saving technologies, neonatal encephalopathy lacks specific and effective treatment. Therefore, mechanisms of central nervous system changes associated with perinatal brain injury have been a hot topic of neonatology research. In recent years, researchers have shifted more focus from neurons themselves to perineuronal cells and intercellular interactions. As a member of the nervous system network, astrocytes are one of the dominant glial cells that regulate ion homeostasis, blood flow, and antioxidant function in the brain. Their changes in molecular mechanisms (e.g., alterations in RNA regulatory networks) during brain injury storms deserve further attention. Non-coding RNAs participate in and promote reactive pathophysiologic changes in astrocytes. The review summarizes and discusses the role of non-coding RNAs in astrocytes in recent years in studies of neonatal brain injury. It focuses on the most studied disease-related ncRNAs: miRNAs, lncRNAs and circRNAs, and hopes to provide possible directions for pathophysiological studies and therapeutic approaches of neonatal brain injury.

Brain metastasis (BrM), involving the spread of cells from a primary tumor through the blood circulation system to the brain microvasculature, eventually progresses despite multiple treatments and remains a substantial contributor to major mortality in patients with advanced- stage cancer. Molecular signatures and immune cellular components of the tumor microenvironment (TME) are emerging as essential regulators involved in establishing an organ-specific metastasis (colonization) and therapeutic response. A comprehensive understanding of detailed characterization and the immune landscape in context of process of BrM formation will greatly expand the horizon of treatments available to target these deadly diseases. In this review, we provide a comprehensive picture of the complex interactions between tumor cells and immune cellular components participated in the BrM process. Based on this knowledge, we will discuss opportunities and challenges for therapeutic strategies against BrM.

The neural crest, a unique cell population originating from the primitive neural field, has a multi-systemic and structural contribution to vertebrate development. At the cephalic level, the neural crest generates most of the skeletal tissues encasing the developing forebrain and provides the prosencephalon with functional vasculature and meninges. Over the last decade, we have demonstrated that the cephalic neural crest (CNC) exerts an autonomous and prominent control on forebrain and sense organs development. The present paper reviews the primary mechanisms by which CNC can orchestrate vertebrate encephalization. Demonstrating the role of the CNC as an exogenous source of patterning for the forebrain provides a novel conceptual framework with profound implications for understanding neurodevelopment. From a biomedical standpoint, these data suggest that the spectrum of neurocristopathies is broader than expected and that some neurological disorders may stem from CNC dysfunctions.

Curcumin is a natural dietary polyphenol which that possesses potent anti-aging and neuroprotective properties through various mechanisms and signaling pathways. This study aimed to investigate the effect of curcumin on factors involved in neurogenesis and angiogenesis in the aged brain. Old female Wistar rats were divided into three equal groups (n=7): control, sham, and curcumin (received curcumin, i.p., 30 mg/kg and 5 days/week for 8 weeks) rats. Curcumin treatment caused a significant increase in the vascular endothelial growth factor (VEGF), Brain-derived neurotrophic factor (BDNF) and anti-apoptotic protein BCl-2 in the brains of aging rats but no significant effect on anti-apoptotic Bax protein. Additionally, curcumin attenuated brain lipid peroxidation and decreased transforming growth factor β1 (TGF-β1). These results suggested that curcumin possesses neuroprotective effects against brain aging, which were mediated by improvment in neurogenesis and angiogenesis. Also, curcumin could alleviate brain aging which may be due to attenuating oxidative stress, inhibiting apoptosis and down-regulating TGF-β1, which in turn enhances VEGF and BDNF. Therefore, curcumin has potential therapeutic value in the treatment of neurological apoptosis, neurogenesis and angiogenesis changes caused by brain aging.

Ordinary computing machines prohibit self-reference because it leads to logical inconsistencies and undecidability. In contrast, the human mind can understand self-referential statements without necessitating physically impossible brain states. Why can the brain make sense of self-reference? Here, we address this question by defining the Strange Loop Model, which features causal feedback between two brain modules, and circumvents the paradoxes of self-reference and negation by unfolding the inconsistency in time. We also argue that the metastable dynamics of the brain inhibit and terminate unhalting inferences. Finally, we show that the representation of logical inconsistencies in the Strange Loop Model leads to causal incongruence between brain subsystems in Integrated Information Theory.

Cognitive scientists have paid very little attention to magic as a distinctly human activity capable of creating situations or events that are considered impossible because they violate expectations and conclude with the apparent transgression of well-established cognitive and natural laws. And even though magic techniques appeal to all known cognitive processes from sensing, attention and perception to memory and decision making, the relation between science and magic has so far been mostly unidirectional, with the primary goal of unraveling how magic works. Building up from the deconstruction of a classic magic trick, we provide here a cognitive foundation for the use of magic as a unique and largely untapped research tool to dissect cognitive processes in tasks arguably more natural than those usually exploited in artificial laboratory settings. Magicians can submerge every spectator into the precise experimental protocol they have previously designed, accounting with ease for both circumstantial and social contexts. Magicians do not base the success of their experiments in statistical measures that smear out the individual in favor of an average spectator that we know never exists in the real world. They target each and everyone in the audience and, often, with a complete accomplishment. Magicians deliver their cognitive manipulations in real-time, in tight closed-loop with the audience, and in a single trial (they cannot afford to repeat the trick if it fails). Magic has also an inherent and strong social component, merging the private cognitive processes of each spectator with the group dynamics. Finally, when combined with the wide range of precise measuring and wearable technologies available today, magic paves the way for a road not taken towards real-world cognitive science. We dare to speculate that some of the mysteries of how the brain works may be trapped in the split realities present in each magic effect.

Using a combination of pan proteomic platform associated with systemic biology analyses, we demonstrate that neonatal microglial cells derived from cortex and spinal cord expressed different phenotypes upon the physiological or pathological conditions. They also highlight great variability in protein production on both cellular and exosome levels. Bioinformatics data indicate for the cortical microglia anti-inflammatory and neurogenesis/tumorigenesis characteristics, while for the spinal cord microglia involvement in the inflammatory response. We confirmed these results by performing functional testing including neurite outgrowth assays in DRGs cell line, and glioma proliferation analysis in 3D spheroid cultures. Results from these in vitro assays indicate that the microglia located at different CNS areas reveal differential biological functions. While both microglia sources enhanced growth of DRGs axons, only the spinal microglia significantly attenuated glioma proliferation. Overall these findings are pointing to the fact that the origin of neonatal microglia affects the physio-pathological function, which may address the prevalence of the glioma in the brain in comparison with the spinal cord in adult.

Brain tumors still remain a deadly cancer that, in most cases, is difficult to diagnose at an earlier stage. Therefore, it is necessary to develop rapid, sensitive methods for detecting cancer biomarkers. Such a biomarker may be a microRNA which expression level in body fluids is strongly correlated with cancer. Numerous studies have demonstrated changes in the expression of microRNAs in cerebrospinal fluid and blood samples collected from patients with brain tumors. These new biomarkers have the potential to help diagnose brain cancers at an early stage, avoiding the most invasive procedures. This review discusses the functional role of microRNAs and the prospects for their use as diagnostic biomarkers in patients with brain tumors.

The purpose of this review is to provide an overview of brain arteriovenous malformations (AVMs) and focus on their management. The therapeutic options differ in each case depending on the location, size, and adjacent anatomical content of the AVM. The surgical resection by microsurgery is the preferred therapeutic option for a small and superficial AVM not related to an eloquent area (e.g., Spetzler-Martin grades I and II). On the other hand, when facing a very large AVM (e.g., Spetzler-Martin grades IV and V), the conservative and symptomatic treatment remains the most common management option due to the high risk-benefit ratio of any intervention. The management of AVMs with features between these two extremes (e.g., Spetzler-Martin grade III) remains less stablished. Stereotactic radiosurgery usually is the option for deep and small AVMs. Embolization has been usually considered before surgery for occlusion of deep arterial supplies of an AVM or to decrease its nidus size. Young patients and AVMs with compact nidus are features that weight in favor of a surgical approach. For the minority of cases that have the AVM diagnosed before bleeding the most common recommendation is to treat conservatively and particularly to avoid embolization. In summary, surgery is the gold-standard approach to cure an AVM and the choice for small AVMs located in non-eloquent area and with superficial venous drainage; large AVMs are mostly treated conservatively; the treatment of AVM with intermediate features is tailored, chosen with the guidance of classification systems, and encompasses radiosurgery and pre-operative embolization.

With the approval of the FDA Modernization Act 2.0, the pharmaceutical industry is poised to expand its research components with a plethora of alternative models, including organ-on-microfluidic chips in pharma and biotechnology, resulting in a personalized approach. Microfluidics opens new possibilities for the study of cell biology, especially for a better understanding of cell-cell interactions and the pathophysiology of neurodegenerative diseases in vitro and use these models to assess the efficacy of novel therapies. These thumb-sized organ-on-a-chip systems have the potential to reduce animal testing and replace simple 2D culture systems. Restoring critical aspects of endothelial-brain immune cell communication in a biomimetic system using microfluidics may accelerate the process of central nervous system (CNS) drug discovery and improve our understanding of the mechanisms of multiple neurodegenerative diseases. These organ-on-chip technologies can be used to optimize drug targets and assess drug efficacy and toxicity in real-time, which can significantly help minimize animal testing requirements, as authorized by the recent FDA Act. Recent advances in modeling cell-to-cell communication in the CNS are described in this review. This Review initially summarizes the fundamental advantages of microfluidic systems in creating a compartmentalized cell culture for the complex three-dimensional architectures of neural tissue cells such as neurons, glial cells, and endothelial cells, and their recapitulation of spatiotemporal biophysicochemical gradients and mechanical microenvironments. Brain endothelial cell-astroglia-on-a-chip models with a focus on neurodegenerative diseases such Alzheimer's disease, Parkinson's disease, and Huntington's disease and amyotrophic lateral sclerosis is introduced. Then, the current limitations of these microfluidic devices and strategies to overcome them are discussed.

We discuss what we believe could be an improvement in future discussions of the ever-changing brain. We do so by distinguishing different types of brain variability and outlining methods suitable to analyse them. We argue that, when studying brain and behaviour data, classical methods such as regression analysis and more advanced approaches both aim to decompose the total variance into sensible variance components. In parallel, we argue that a distinction needs to be made between innate and acquired brain variability. For varying high-dimensional brain data, we present methods useful to extract their low-dimensional representations. Finally, to trace potential causes and predict plausible consequences of brain variability, we discuss how to combine statistical principles and neurobiological insights to make associative, explanatory, predictive, and causal enquires; but cautions are needed to raise association- or prediction-based neurobiological findings to causal claims.

In this paper, we propose methods for brain tumor detection in MRI images based on ensemble learning. We build upon prior research on ensemble methods by testing the concatenation of pre-trained models: features extracted via transfer learning are merged and segmented by classification algorithms or a stacked ensemble of those algorithms. The proposed approach achieved accuracy scores of 0.98 , outperforming a benchmark VGG-16 model. Considerations to granular computing are given in the paper as well.

The paper presents a study of two novel visual motion onset stimulus-based brain–computer interfaces (vmoBCI). Two settings are compared with afferent and efferent to a computer screen center motion patterns. Online vmoBCI experiments are conducted in an oddball event–related potential (ERP) paradigm allowing for “aha–responses” decoding in EEG brainwaves. A subsequent stepwise linear discriminant analysis classification (swLDA) classification accuracy comparison is discussed based on two inter–stimulus–interval (ISI) settings of 700 and 150 ms in two online vmoBCI applications with six and eight command settings. A research hypothesis of classification accuracy non–significant differences with various ISIs is confirmed based on the two settings of 700 ms and 150 ms, as well as with various numbers of ERP response averaging scenarios.The efferent in respect to display center visual motion patterns allowed for a faster interfacing and thus they are recommended as more suitable for the no–eye–movements requiring visual BCIs.

A spike-timing-dependent-plasticity (STDP) device with a Ga-Sn-O (GTO) conductance change layer deposited by a mist-CVD method has been developed. First, the memristive characteristic is analyzed. Next, based on it, spike waveforms are determined. Finally, the STDP characteristic is successfully confirmed. This is the first report on the realization of a STDP characteristic using a thin film deposited by the mist-CVD method, which is achieved by the GTO properties and a well-designed clear methodology to realize a STDP characteristic from a memristive characteristic.

Dengue fever is a persistent public health issue in tropical regions, including Vietnam, where climate variability plays a crucial role in disease transmission dynamics. This study focuses on developing climate-based machine learning models to forecast dengue outbreaks in Ba Ria Vung Tau (BRVT) province, Vietnam, using meteorological data from 2003 to 2022. We utilized four predictive models—Negative Binomial Regression (NBR), Seasonal AutoRegressive Integrated Moving Average with Exogenous Regressors (SARIMAX), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM)—to predict weekly dengue incidence. Key climate variables, including temperature, humidity, precipitation, and wind speed, were integrated into these models, with lagged variables included to capture delayed climatic effects on dengue transmission. The NBR model demonstrated the best performance in terms of predictive accuracy, achieving the lowest Mean Absolute Error (MAE) compared to other models. The inclusion of lagged climate variables significantly enhanced the model’s ability to predict dengue cases. SARIMAX and LSTM models, although effective in capturing seasonal trends, struggled with overfitting and failed to accurately predict short-term outbreaks. XGBoost exhibited moderate predictive power but was sensitive to overfitting, particularly without fine-tuning. Our findings confirm that climate-based machine learning models, particularly the NBR model, offer valuable tools for forecasting dengue outbreaks in BRVT. However, improving the models’ ability to predict short-term peaks remains a challenge. The integration of meteorological data into early warning systems is crucial for public health authorities to plan timely and effective interventions. This research contributes to the growing body of literature on climate-based disease forecasting and underscores the need for further model refinement to address the complexities of dengue transmission in highly endemic regions.

The paper presents two procedures for creating 3D discrete models of apple using the finite element method (FEM). The study focused on a new Polish apple variety, Chopin at development, ripening, and senescence maturity stages. The analysis of the results allowed for the determination that, depending on the desired outcomes, the parenchymal tissue can be considered as a material with either elastic or elastoplastic properties. Model validation was performed using displacement-force relationships and changes in contact area as a function of loading force obtained from compression tests. Elastoplastic models reflect empirical studies much better. The fit of the elastic models, based on force vs. displacement curves, ranged from 79% to 83%, while the fit of the elastoplastic models to empirical data ranged from 91% to 96%. Elastic models, on the other hand, are well-suited to the modified force vs. displacement curves, which reflect the elastic behavior of apples under compression test. Contour plots of pressures for elastoplastic models align much better with the test results than those for elastic models. 4. Similarly, in the case of contact area values, the elastoplastic models outperformed the elastic models, achieving a fit between 92% and 96%, while the elastic models reached only 82% to 83% compliance.

Most available studies on the mechanical properties of apples lack the comprehensive results needed for the construction and validation of FEM models. Researchers typically focus on either the flesh and epidermis or the whole fruit, often overlooking the maturity stage of the examined apples. They usually report only firmness or the starch index as indicators of maturity. Furthermore, many studies use store-bought apples, which is impractical for industrial applications since this fruit have already undergone various treatments before reaching the shelf. This article aims to determine the mechanical properties of apples necessary for constructing FEM models that are both adequate and useful for the industry. The new Polish apple variety, Chopin, was selected as the research material. The study was conducted for three stages of apple maturity: development, ripening, and senescence. Mechanical properties of the flesh and skin were determined as material data for FEM models. Force-displacement curves and pressure-force functions were examined for future model validation. Using micro-computed tomography, the bruise volumes of fruit subjected to 20%, 50%, and 80% of the destructive force were determined. Significant differences were found between apples in the senescence stage and those in the development and ripening stages. Results of Micro-Ct and the results of modified and real compression tests of whole fruit have allowed us to formulate the research hypothesis regarding the influence of flesh cracking (characterized by local drops of force), influencing the bruise visibility and detection.

We introduce derangetropy, a novel functional measure designed to characterize the dynamics of information within probability distributions. Unlike scalar measures such as Shannon entropy, derangetropy offers a functional representation that captures the dispersion of information across the entire support of a distribution. By incorporating self-referential and periodic properties, it provides deeper insights into information dynamics governed by differential equations and equilibrium states. Through combinatorial justifications and empirical analysis, we demonstrate the utility of derangetropy in depicting distribution behavior and evolution, providing a new tool for analyzing complex and hierarchical systems in information theory.

The aim was to evaluate the agreement between plaque detection with intraoral scanner system (IOS) and a conventional clinical method and to evaluate the inter-rater reliability for scoring 3D models with and without a disclosing agent. 14 participants were recruited from the Department of Operative Dentistry, School of Dentistry, University name blinded to reviewers, it is completed after the reviewing process. Participants eligible for inclusion were adults with good general health and a minimum of 20 teeth. Participants were clinically examined with plaque assessment according to the modified Quigley-Hein plaque index before and after using a disclosing agent (GC-Tri Plaque ID-Gel, GC). Before and after the application of the disclosing agent, all study participants were scanned using the IOS (TRIOS5, 3Shape TRIOS A/S). The clinical examiner and additional three examiners blinded to the clinical examination assessed plaque status on the acquired 3D models with and without disclosing agent using the same index to evaluate the inter-rater agreement. Intraclass coefficient correlation, one sample t-test and Cronbach’s α for inter-rater reliability were calculated. All methods showed moderate to strong correlations (Spearman’s rho ranging from 0.527 to 0.618) and Cronbach’s α ranged from 0.551 to 0.766. Level of agreement between con-ventional clinical registration and registration from 3D models was acceptable overall.

Building Information Modeling (BIM) is undeniably the most important trend in the digitization of the construction sector in recent years. BIM models currently being built are extremely geometrically rich, that is, they are modeled at a high level of detail in terms of geometry. Thanks to object-oriented programming paradigms, BIM models include high-level relationships to ensure interactions between objects, rapid view generation and documentation. However, these models are not always equally rich in non-graphical data. This is true for parameters at the library object level, with which building object models are saturated, but also at the project, site, building or floor level according to the structure of the interoperable Industry Foundation Classes (IFC) format. For this reason, experimental work was undertaken on semantic enrichment in non-graphical data of a public building (Public Kindergarten, Secemin, Poland), which has its BIM model at a high level of geometric detail but is poor in non-graphical data. As a result of the research and development work, all levels of the IFC structure were saturated with non-graphical data, validated, and the possibilities of their use were shown from the perspective of the facility manager. The article contributes to the discussion on semantic enrichment from CAD3D to BIM by presenting a detailed process for entering non-graphical data into a BIM model.

To increase the calculation speed of the computational fluid dynamics (CFD) based simulation for the gas-solid flow in fluidized beds, a Markov chain model (MCM) is developed to simulate the particle movement in a two-dimensional (2D) circulating fluidized bed (CFB) riser. As a statistic model, the MCM takes the CFD-discrete element method (DEM) results as samples to calculate the transition probability matrix of particles, which can describe the macroscopic regularities of the particle movement and guide the numerical simulation of the particle motion based on the Monte Carlo method. The particle distribution snapshots, residence time distribution (RTD) and mixing obtained from both the CFD-DEM and the MCM are compared. Results show that the computational speed of the MCM is faster than that of the CFD-DEM by about 2 orders of magnitude, and the difference of the mean particle residence time calculated by the two models is less than 2%. Besides, the MCM can well describe the time-averaged particle mixing compared to the CFD-DEM.

With increasing research focus on industrial anomaly detection, numerous methods have emerged in this domain. Notably, memory bank-based approaches, coupled with kNN distance metrics, have demonstrated remarkable performance in anomaly detection (AD) and anomaly segmentation (AS). However, upon examination of the Back to the Feature (BTF) method applied to the Mvtec-3D AD dataset, it was observed that while it exhibited exceptional segmentation performance, its detection performance was lacking. To address this discrepancy, this study base improves the implementation of BTF, especially the improvement of the anomaly score metric. It posits that different "clusters" necessitate distinct k values in kNN distance metrics. For simplify, this assumption is distilled into the proposition that AD and AS tasks impose differing requirements on the k value in kNN distance metrics. Consequently, the paper introduces the BTM method, which utilizes distinct distance metrics for AD and AS tasks. This innovative approach yields superior AD and AS performance (I-AUROC 93.0%, AURPO 96.9%, P-AUROC 99.5%), representing a substantial enhancement over the BTF method (I-AUROC 5.7% ↑, AURPO 0.5% ↑, P-AUROC 0.2% ↑).

Functional partially linear regression model, contains a functional linear part and a non-parametric part, in which testing the linear relationship between the response and the functional predictor is of fundamental importance. When functional data cannot be approximated with a few principal components, based on a pseudo estimate for the unknown non-parametric component, we develop a U-statistic of order-two in this paper. Under some regularity conditions, we use the martingale central limit theorem to prove that the proposed test statistic is asymptotically normal. The finite sample performance with simulation studies and real data application are assessed to verify the proposed test procedure.

This research examines sustainable approaches in academic career development by analyzing the interactions between job satisfaction, professional commitment, and research performance among early-career researchers in Romanian institutions. Using a quantitative cross-sectional design, data were collected from 96 individuals classified as R1 and R2 by the European Commission. A detailed questionnaire and Web-Assisted Personal Interviewing (WAPI) identified key relationships. Findings show a significant positive correlation between job satisfaction and research perfor-mance, underscoring the role of professional satisfaction in fostering sustainable academic ex-cellence. Professional commitment is also a crucial predictor of research performance, aligning with existing studies and highlighting the importance of sustainable career practices. Statistical analysis via SPSS version 23.0 validated the initial hypotheses, emphasizing each dimension's significance in a sustainable academic career. Results reflect Romania's research environment, emphasizing the need for sustainable working conditions, effective research evaluation, sup-portive career frameworks, and strong professional commitment to motivate young researchers. This research proposes innovative, sustainable practices for enhancing academic performance and career management, advocating clear objectives, leadership development, continuous competency improvement, and a collaborative, inclusive work environment. Understanding these factors aids individuals and institutions in making informed decisions that support sustainable academic career trajectories and professional growth.

Abstract: Although the pathophysiology of fibromyalgia syndrome has been better understood in recent decades, a unified model of its pathogenesis and an effective therapeutic approach are still far from being realized. The main aim of this article will be to delve into the fundamental mechanisms of the pathophysiology of fibromyalgia conceptualized as stress intolerance syndrome. Using the biopsychosocial model of chronic pain syndromes, we will describe the potential role of the attachment system, C tactile fibers, and oxytocinergic system dysfunction in the pathophysiology of fibromyalgia syndrome and other central sensitivity syndromes. At the end of the article, the therapeutic implications of this new global and translational pathophysiological model will be briefly discussed.

Microcystis-dominated cyanobacterial blooms (MCBs) frequently occur in freshwaters worldwide due to massive Microcystis colonies formation and severely threaten human and ecosystems health. Quorum sensing (QS) is a direct cause of Microcystis colonies formation, possibly regulating MCBs occur and Microcystis population behaviors [1, 2]. Many novel findings in fundamental knowledge of Microcystis QS phenomenon and the signaling molecules have been documented. However, little effort has devoted to comprehensively summarize and discuss research progresses and exploration direction on QS signaling molecules-mediated QS system in Microcystis. This article summarizes action process of N-acyl homoserine lactones (AHLs) as major signaling molecules in Microcystis and discussed the detailed roles of AHLs-mediated QS system in cells aggregation, colonies formation, ecological adaptability and competitive advantage. And the progress was summarized in research on the QS mechanism in Microcystis. Compared to other QS system, the LuxI/LuxR-type QS system is more likely to found in Microcystis. Also, we introduced Quorum quenching (QQ) — QS blocking process in Microcystis, with emphasizing on the potential of QS inhibitors in MCBs control. Finally, in response to the research deficiencies and gaps in Microcystis QS and QQ, we proposed several future research directions in these fields. This review may deepen the understanding of Microcystis QS knowledge and provide theoretical guidance for the development of strategies to monitor, control and harness MCBs.

This study investigates the spatial and temporal dynamics of Paomo restaurants in Xi'an's central urban area from 2012 to 2023, leveraging Point of Interest (POI) data through a series of sophisticated analytical techniques. Methods including nearest neighbor distance, kernel density estimation, and band set statistical analysis were employed to thoroughly examine the evolutionary patterns and key drivers influencing the spatial distribution of these eateries. Our results highlight a notable trend of expansion from the central city to the periphery, characterized by significant spatial agglomeration. Initially concentrated in the city center, the distribution of Paomo restaurants has progressively shifted towards multiple distinct cores, reflecting the dual influence of urban policy initiatives and the dynamics of market competition. Furthermore, the research identifies several pivotal factors shaping this spatial distribution, particularly transportation access, residential quality of life, and the availability of commercial services. These findings demonstrate the profound effects of urban spatial reconfiguration and socioeconomic development on the specialized catering industry. The insights gleaned from this analysis offer valuable guidance for urban planners and policymakers, providing strategic recommendations for the spatial planning of specialty catering services to optimize urban layouts and foster sustainable economic progress.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd-Frank Wall Street Reform and Consumer Protection Act (referred to as the "Dodd-Frank Act"), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act’s provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of conceptual space, cognitive space, and semantic space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance.

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases.

The adapted Posner spatial cue-target paradigm was used to explore the relationship between exogenous temporal attention caused by rhythm and endogenous and exogenous spatial attention. The study was divided into two experiments. In Experiment 1, 33 participants used arrow cues to orient endogenous spatial attention and rhythm to orient exogenous temporal attention. In Experiment 2, with 29 participants, exogenous spatial attention was directed by highlighting the cue box. Exogenous temporal attention was directed by rhythm. Experiment 1 indicated that RTs were faster for attended targets than the unattended. The main effect of temporal attention was significant, with RTs being faster for synchrony and late compared to early. RTs were faster for late compared to synchrony. The main effect of stimulus repetition was significant, with faster RTs for targets when the rhythmic cue consisted of five compared to four. The interaction between spatial attention and temporal attention was not significant. Experiment 2 demonstrated that exogenous spatial attention only exhibited IOR under early four stimulus repetitions. The main effect of temporal attention was significant, with RTs being faster for synchrony and late compared to early. The main effect of stimulus repetition was significant, with faster RTs for targets when the rhythmic cue consisted of five compared to four. The interaction between spatial attention and temporal attention was significant. The results indicated that rhythmic temporal attention supported foreperiod effects. Exogenous temporal attention and endogenous spatial attention independently affect behavior, while exogenous temporal attention and exogenous spatial attention mutually influenced behavior.

Recognition, decoding and tracking of vortex patterns is of increasing importance in many fields reaching from astronomical observation of distant galaxies to turbulence phenomena in liquids or gases. Currently, coherent light beams with orbital angular momentum (OAM) are of particular interest for optical communication, metrology, micro-machining or particle manipulation. One common task is to identify characteristic spiral patterns in pixelated intensity maps at real-world signal-to-noise ratios. Recently, Fast Fourier Transform (FFT) was applied to the quantitative analysis of spiral interference patterns of OAM beams. Our study considers this approach in more detail. It is demonstrated that specific information can be extracted by combining conformal mapping by polar transform with determining one- or two-dimensional angular frequency spectra either in spatially fixed or scanning mode. Results of proof-of-principle experiments are presented.

In the area of dental healthcare services, where patients and professionals deal with multiple auditory stimuli, this cross-sectional study reports on the intricate dynamics of sound preferences, noise obstacles, and the profound impact of music on anxiety reduction within dental office set-tings. The sample comprises of 134 dental patients, primarily females (56.7%), with varying age groups, education levels, and therapy durations in the university clinics of the department of Dentistry, National and Kapodistrian University of Athens and four private dental offices at the metropolitan area of Athens, Greece. Notably, 92.5% of the participants report no hearing loss, and 56.7% exhibit some level of noise sensitivity. The study reveals a moderate to good perceived health status (M=3.84, SD=0.95) among participants, with low perceived noise disturbance in university dental clinics. Preferences for instrumental music, foreign pop, and classical music are prominent during waiting times. Potential irritations include impersonal treatment (44%) and staff behavior (41.8%). Music player usage within the settings of the dental clinic involves radios (49.3%) and headphones (41.0%). Correlations and predictors indicate associations between health status, music preferences, and noise disturbances. More specifically, visiting the university dental clinics (β = .300, p = .005) as well as younger age (β = .300, p = .034) were significant predictors of better perceived health status. Clearer perception of external sounds while using the music player (β = .190, p = .039) was a significant predictor of better perceived health status, while poorer health was predicted by the preference of Greek folk songs (β = -.200, p = .030). Also, the use of smartphones for playing music was related to poorer health (β = -.370, p < .001). Following, younger age (β = -.270, p = .015), lower education (β = -.210, p = .035), higher noise sensitivity (β = .190, p = .027), as well as feelings of anxiety and nervousness due to ambient noise from people and machines (β = .510, p = .027) were significant predictors of noise disturbance from machines. Also, visiting private clinics (β = .240, p = .018), preferences of specific playlist (β = .310, p = .012), no music (β = .280, p = .016) and use of smartphones to play music (β = .230, p = .029) were significant predictors of noise disturbance from patients or staff. Finally, the preference of listening to a specific playlist was related to better health status (r = .190, p < .05). Findings aim to optimize eco-soundscapes in dental offices, con-tributing to enhanced patient well-being. The research underscores the potential of music inter-ventions to alleviate dental anxiety and improve the overall dental experience and quality care.

The purpose of this study is to establish whether digital literacy and insurtech adoption influence insurance inclusion in Uganda. Principally, we sought to determine whether insurtech adoption mediates the nexus between digital literacy and insurance inclusion. This study adopted a cross-sectional and quantitative correlational approach. The study’s sample was 391 individuals who had used digital platforms such as mobile phones and computers to access insurance products and services in Uganda. Data were collected using structured survey questionnaires. Partial Least Squares Structural Equation Modelling (PLSEM) was employed to test the hypothesised relationships. The results demonstrate that both digital literacy and insurtech adoption significantly and positively influence insurance inclusion. We also found digital literacy to be a significant and positive determinant of insurtech adoption. Markedly, it was found that insurtech adoption mediates the association between digital literacy and insurance inclusion in Uganda. However, this study was conducted in a developing country with an underdeveloped insurance market and with low technological advancement. This may affect generalisation of the study findings. This study’s novelty lies in establishing how digital literacy and insurtech adoption interplay to influence insurance inclusion in Uganda. This is the first study to examine the effect of digital literacy and insurtech adoption on insurance inclusion.

The exceptional performance advantages of the fiber optic gyroscope (FOG) position it as a dominant player in middle and high-end inertial navigation systems. To prevent the loss of sensor precision caused by algorithm design and simplify the complex modeling strategy in traditional methods. We gradually demonstrate the significant role of Convolutional Neural Network (CNN) in the navigation system based on FOG, and utilize the particle swarm optimization algorithm (PSO) to expedite the convergence of the network. The experimental results demonstrate that the initial alignment method based on deep learning is more accurate than the traditional method. The attitude angle error is reduced by 81.25%, 92.54% and 36.53% respectively. The research provides support for the future application of deep learning in optical navigation systems.

The carbonyl iron particles were dispersed in a polychloroprene rubber (CR) matrix under an external magnetic field for practical application as microwave absorption composites film. The film prepared under external magnetic field with a thickness of only 0.54 mm showed least reflection loss of -15.98 dB and the reflection loss value less than -10.0 dB over the frequency range of 11.4~14.8 GHz. In comparison with the microwave absorption properties of calculation by transmission line theory based on the tested relative complex permittivity and permeability and film prepared by general route without external magnetic field, the film made with external magnetic field exhibited more excellent microwave absorption properties, strongly depending on the increment of anisotropy and rearrangement of magnetic particles. The results indicated the composite film made under external magnetic field have excellent microwave absorption properties, which suggest that the composites thin film could be used as a thinner and lighter microwave absorber.

The question of the relevance of human and natural capital, as well as the potential adverse effect of natural capital on economic growth, has gained increased attention in development economics. The aim of this paper is to theoretically and empirically assess the relevance of several forms of capital on economic growth in small economies that are dependent upon tourism or natural resources. The empirical framework is based on Impulse Response Functions obtained from Vector Autoregressive models in which we focus on the model where economic growth is the dependent variable for ten small economies that are dependent upon either tourism or natural resources. We find that there is evidence of the ‘’natural resource curse’’, especially in the economies that have a strong dependence on resources that are easily substitutable and whose prices constantly fluctuate. We further find that in the majority of observed cases the type of capital these small economies are most dependent on for their economic growth causes negative impulses in the majority of the observed periods. The main policy recommendation should be to assure that even these small economies should strive towards further diversification and avoid dependence on only one segment of their economy.

Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Since many users are able to share the data and the resources stored in the cloud, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done through access control schemes which allow the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with analysis. Keywords: role-based access control, attribute-based access control, attribute-based encryption

Cognitive rehabilitation is useful for many after traumatic brain injury (TBI), but we lack critical knowledge about which patients benefit the most from different approaches. Advanced neuroimaging techniques have provided important insight into brain pathology and systems plasticity after TBI and have potential to inform new practices in cognitive rehabilitation. In this study, we aimed to identify candidate structural brain measures with relevance for rehabilitation of cognitive control (executive) function after TBI. Twenty-eight patients (9 female, mean age 40.5 (SD = 13.04) years) with moderate/severe TBI (>21 months since injury) that participated in a randomized controlled cognitive rehabilitation trial (NCT02692352) were included in the analyses. Regional brain volume was extracted from T1-weighted MRI scans before treatment using tensor-based morphometry. Both positive and negative associations between treatment outcome (everyday cognitive control function) and regional brain volume were observed. The most robust structural brain measures with relevance for improvement in function were observed in midline fronto-parietal regions, including the anterior and posterior cingulate cortices. The study pro-vides proof of concept and valuable insight for planning future studies focusing on neuroimaging in cognitive rehabilitation after TBI.

Atrophy quantification is fundamental for understanding brain development and diagnosing and monitoring brain diseases. FSL-SIENA is a well-known fully-automated method that has been widely used in brain magnetic resonance imaging studies. However, intensity variations arising during image acquisition that may compromise evaluation, analysis and even diagnosis. In this work, we study whether intensity standardisation can improve longitudinal atrophy quantification. We considered seven methods comprising z-score, fuzzy c-means, Gaussian mixture model, kernel density, histogram matching, white stripe, and removal of artificial voxel effects by linear regression (RAVEL). We used a total of 330 scans from two publicly-available datasets, ADNI and OASIS. In scan-rescan assessments, that measures robustness to subtle imaging variations, intensity standardisation did not compromise the robustness of FSL-SIENA significantly (p>0.1). In power analysis assessments, that measures the ability to discern between two groups of subjects, three methods led to consistent improvements in both datasets with respect to the original: fuzzy c-means, Gaussian mixture model, and kernel density estimation. Reduction in sample size using these three methods ranged from 17% to 95%. The performance of the other four methods was affected by spatial normalisation, skull stripping errors, presence of periventricular white matter hyperintensities, or tissue proportion variations over time. Our work evinces the relevance of appropriate intensity standardisation in longitudinal cerebral atrophy assessments using FSL-SIENA.

Public-Private Partnerships (P3 or PPPs) have been utilized and studied more frequently over the past couple of decades for both infrastructure projects and building development projects. Based upon the records of the outcomes of these projects there is a plethora of data that demonstrates the benefits and challenges that this delivery method poses. As the name suggests, these partnerships allow for public goals to become realized while also providing opportunities for the private sector to gain business success. These two often divergent interests are finding mutual benefit but the process of compromises also comes with some disadvantages that should be understood in order to make decision of whether the risk/reward equation works for all stakeholders. In particular, this paper will focus on how PPPs affect the construction aspects of projects and will assert how to understand the risks and evaluate the benefits from a construction perspective.

Benveniste’s experiments – known in the lay press as the “water memory” phenomenon – are generally considered to be a closed case. However, the amount of data generated by twenty years of well-conducted experiments prevents closing the file so simply. An issue, which has been little highlighted so far, merits to be emphasized. Indeed, if Benveniste failed to persuade his peers of the value of his experiments, it was mainly because of a stumbling block, namely the difficulty of convincingly proving the causal relationship between the supposed cause (“informed water”) and the experimental outcomes in different biological models. To progress in the understanding of this phenomenon, we abandon the idea of any role of water in these experiments (“water memory” and its avatars). In other words, we assume that “control” and “test” conditions to be evaluated were physically identical and differed only by their respective designations. We show in this article how simple probabilistic considerations allow to build a model that accounts for all aspects of Benveniste’s experiments. In this model based on probability amplitudes, constructive and destructive interferences emerge – or not – according to the experimental context. The recording of a statistical regularity shapes the intertwined whole constituted by the probability amplitudes of the states of the experimenter’s cognitive structures and of the experimental system. This model provides an alternative explanation to Benveniste’s experiments where water plays no role and where the place of the experimenter is central.

The study investigated roles of institutional types and ethnic/racial background on academic credit among the traditionally underrepresented population of the U.S. study abroad program. Using archival data, the study sampled the student’s enrollment and academic credit information spanning a period of 20 years (2003 - 2022). Data analysis Using One-Way Analysis of Variance (ANOVA) indicates significant main influence of institutional type (p<.001) and significant main influence of ethnic/racial identity (p<.001) on students’ academic credit. The result was discussed in terms of its relevance in educational policy re-evaluations, improvement of the study conditions of the underrepresented students, and enhancement of the enrollment opportunities of these minority population across all U.S. institutions of learning.

(1) Background: Mixed Reality is a technology that has gained attention due to its unique capabilities for accessing and visualizing information, becoming a valuable tool for medicine, particularly for the operating room and surgical learning; (2) Methods: A systematic review was conducted following the PRISMA guidelines to answer the research questions established using the PICO framework; (3) Results: Although implementation of Mixed Reality applications in the operations room presents some challenges, when used appropriately, it can yield remarkable results. It can make learning easier, flatten the learning curve for several procedures, and facilitate various aspects of the surgical processes; (4) Conclusion: Technical complexity, equipment costs, and steep learning curves present significant obstacles to the widespread adoption of Mixed Reality and computer-assisted evaluation. The possibility of integration of distinct medical imaging modalities and innovative functionalities holds promise for clinical applications. However, it is important to consider issues related to usability, bias, and statistical analyses need consideration. Mixed Reality offers significant benefits, but there are still open challenges such as ergonomic issues, limited field of view, and battery autonomy that must be addressed to ensure widespread acceptance.

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.

In order to the effect of hydrated phase change materials on suppressing coal spontaneous combustion, the thermogravimetric experiment and reaction activation energy analysis experiment were used to explore the changes of combustion characteristic, parameters characteristic temperature and activating energy of gas coal, long flame coal, meagre coal, and lean coal before and after adding hydrated phase change materials. The research results showed that hydrated phase change materials increased the coal samples’characteristic temperature point and have effective inhibitory effects on different stages of oxidation process.However, the effect was best at low temperatures, as hydrated phase change materials undergo phase change and absorb heat when heated at low temperatures, isolating coal from contact with oxygen. The activating energy increased by 1.138 KJmol-1～23.048 KJmol-1 and the mass loss reduced by 1.6%～9.3% after the inhibition of the coal sample,indicating that the oxidation rate of various coal samples can be slowed down and the spontaneous combustion can be suppressed by using hydrated phase change materials.At the same time, this material can also reduce the combustibility index of meagre coal and lean coal, as well as the comprehensive combustion index and maximum loss rate of long flame coal and gas coal.

This review explores the transformative impact of sensor technologies in early disease detection. It highlights recent advancements in nanotechnology-based sensors, particularly focusing on their applications in identifying disease biomarkers at early stages. The integration of these sensors with artificial intelligence for predictive analytics in healthcare is also discussed. Applications in cancer, infectious diseases, and chronic disease management underscore their diverse utility. Challenges in sensor technology adoption and future directions, including multiplexed sensors and wearable devices, are examined. This review underscores sensors' potential in enhancing disease management and patient outcomes.

Legumes develop root nodules that harbour endosymbiotic bacteria, rhizobia. These rhizobia convert nitrogen to ammonia by biological nitrogen fixation. A thorough understanding of the biological nitrogen fixation in legumes and its regulation is key to develop sustainable agriculture. It is well known that plant hormones affect nodule formation; however, most studies are limited to model legumes due to their suitability for in vitro, plate-based assays. Specifically, it is almost impossible to measure the effects of exogenous hormones or other additives during nodule development in crop legumes such as soybean as they have huge root system in soil. To circumvent this issue, the present research develops suitable media and growth conditions for efficient nodule development under in vitro, soil free conditions in an important legume crop, soybean. Moreover, we also evaluate the effects of all major phytohormones during soybean nodulation under identical conditions. This versatile, inexpensive, scalable and simple protocol provides several advantages over previously established methods. It is extremely time-and resource-efficient, does not require special training or equipment, and produces highly reproducible results. The approach is expandable to other large legumes as well as for other exogenous additives.

Vectorial Boolean functions and codes are closely related, and each corresponding domain gives been to each other. On the one hand, various requirements of binary linear codes are needed for their theatrical interests but, more importantly, for their practical applicants (such as few-weight codes or minimal codes for secret sharing, locally recoverable codes for storage, etc.). On the other hand, various criteria and tables have been introduced to analyse the security of S-boxes that are related to vectorial Boolean functions, such as the Differential Distribution Table (DDT), the Boomerang Connectivity Table (BCT), and the Differential-Linear Connectivity Table (DLCT). In previous years, two new tables have been proposed for which the literature was pretty abundant: the $c$-DDT to extend the DDT and the $c$-BCT to extend the BCT. In the same vein, we propose extended concepts to study further the security of vectorial Boolean functions, especially the $c$-Walsh transform, the $c$-autocorrelation, and the $c$-differential-linear uniformity, and its accompanying table, the $c$-Differential-Linear Connectivity Table ($c$-DLCT). We study these properties of the novel functions at their optimal level concerning these concepts and describe the $c$-DLCT of the crucial inverse vectorial (Boolean) function case. Finally, the derived functions could lead to new families of binary minimal codes, as the recent achievements on minimal codes from low differential uniformity. We draw new avenues for future research toward linear code designs.

Tesso Nilo National Park is one of the protected areas designated as KPA by the Decree of the Minister of Forestry No.225/Menhut-II/2004 dated July 19, 2004 covering an area of ​​38,576 ha, then expanded again through the Decree of the Minister of Forestry No.663/Menhut/2009 dated 19 October 2009 with an area of ​​± 83,068 ha. The Tesso Nilo National Park area is experiencing the dynamics of land cover changes, namely the conversion of forest land which causes the forest area to become increasingly narrow in this area. With the reduction in forest area in this area, it causes a decrease in ecosystem network connectivity due to forest fragmentation that occurs, so that forests are separated and further apart. This study aims to analyze changes in land cover in the Tesso Nilo National Park area to find out information on changes in habitat ecosystems in the last 20 years. the observations were made using satellite images in 1999, 2010 and 2019. so that it can be seen the effect of forest fragmentation on ecosystems in the Tesso Nilo National Park area. From the results of the analysis, it was found that the area of ​​forest cover in the Tesso Nilo National Park decreased from 1999 to 2019 and caused changes in the connectivity network of forest ecosystems, which was found in the study, the weakest connectivity separated forest fragments as far as 17394.47 meters due to forest cover fragmentation.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

We presented unusual case of double left ventricular false aneurysm complicating late presentation AMI successfully treated with the combination of urgent surgical drainage of tamponade, staged PCI of non-culprit lesion, preventive implantation of ICD-DR, guideline directed heart failure medical therapy and delayed but effective reconstructive operation.

Oedaleus decorus asiaticus, Calliptamus abbreviatus, Angaracris rhodopa, and Myrmeleotettix palpalis are the main grasshoppers that harm the natural grassland in the Hexi Corridor. In this study, the MaxEnt model was employed to identify the key environmental factors affecting the distribution of the four grasshoppers habitats and to assess their distribution under current and future climate change conditions. The aim was to provide a basis for grasshopper monitoring, prediction, and precise control. In this study, 61 occurrences of O. decorus asiaticus, 68 occurrences of A. rhodopa, 58 occurrences of C. abbreviatus, and 92 occurrences of M. palpalis were recorded by GPS, and the distribution of suitable habitats for O. decorus asiaticus, C. abbreviatus, A. rhodopa, and M. palpalis were predicted under current and future climatic scenarios using a MaxEnt model. The average AUC and TSS values of the four grasshoppers were greater than 0.9, and the simulation results were excellent and highly reliable. The mean annual precipitation was the main factor limiting the current range of suitable areas for these four species. Under the current climate, O. asiaticus, C. abbreviatus, and A. rhodopa were mainly distributed in the central and eastern parts of the Hexi Corridor, and M. palpalis was distributed throughout the Hexi Corridor, with a potential distribution area of 1.44×104, 1.43×104, 1.29×104, and 2.12×104 km2, accounting for 15.3, 15.2, 13.7, and 22.5% of the total area of the grasslands in the Hexi Corridor, respectively. The highly suitable areas of O. asiaticus, C. abbreviatus, and A. rhodopa were mainly distributed in the east-central part of Zhangye City, the western part of Wuwei City, and the western and southern part of Jinchang City, with areas of 0.35×104, 0.29×104, and 0.20×104 km2, accounting for 3.7, 3, and 2.2% of the grassland area, respectively. The high habitat of M. palpalis was mainly distributed in the southeast of Jiuquan City, the west, middle, and east of Zhangye City, the west of Wuwei City, and the west and south of Jinchang City, with an area of 0.32×104 km2, accounting for 3.4% of the grassland area. In the 2030s, the range of O. asiaticus, C. abbreviatus, A. rhodopa, and M. palpalis was predicted to decrease.

Air pollution, particularly fine particulate matter (PM2.5), poses a significant threat to public health, especially in rapidly urbanizing areas like Lagos State. Despite the severity of the issue, there is a lack of comprehensive data on PM2.5 levels and the Air Quality Index (AQI) in Lagos. This study leverages data from AirQo, an innovative air quality monitoring network using low-cost sensors, to provide a detailed assessment of PM2.5 concentrations and AQI across eight locations in Lagos State, Nigeria. This approach offers real-time, continuous monitoring, filling a critical data gap in the region's air quality management efforts. This study uses AirQo data to assess the concentration levels and spatial distribution of PM2.5 and AQI across the selected eight locations. Data on PM2.5 concentrations were obtained from AirQo sensors deployed across the eight strategic locations in Lagos. The spatial distribution of PM2.5 levels and AQI were analyzed, and the results were compared with national and international air quality standards. The findings revealed that PM2.5 levels (minimum – 6.28 µg/m3 (Ikeja) and maximum - 204.68 µg/m3 (Banana Island) in many areas of Lagos exceed annual WHO and national air quality guidelines, indicating significant health risks. The spatial analysis identified pollution hotspots, particularly in densely populated and industrial regions. The data suggested vehicular emissions and industrial activities as major contributors to high PM2.5 levels. This study underscores the severe air quality issues in Lagos State, emphasizing the need for immediate and targeted interventions to mitigate PM2.5 pollution. The use of AirQo sensors proved effective in providing accurate and timely data for air quality assessment. It is recommended that Lagos State implements stricter emission controls, promotes cleaner technologies, and enhances public transportation systems to reduce traffic-related pollution. Additionally, expanding the air quality monitoring network and incorporating more environmental data will provide a more comprehensive understanding of air pollution impacts, thereby facilitating better policy and public health decisions.

In the context of smart agriculture, accurately estimating plant leaf chemical parameters is crucial for optimizing crop management and improving agricultural yield. Hyperspectral imaging, with its ability to capture detailed spectral information across various wavelengths, has emerged as a powerful tool in this regard. However, the complex and high-dimensional nature of hyperspectral data poses significant challenges in extracting meaningful features for precise estimation. To address this challenge, this study proposes an end-to-end estimation network for multiple chemical parameters of Astragalus leaves based on attention mechanism (AM) and multivariate hyperspectral features (AM-MHENet). We leveraging HybridSN and multilayer perception (MLP) to extract prominent features from the hyperspectral data of Astragalus membranaceus var. mongholicus (AMM) leaves and stems, as well as the surface and deep soil surrounding AMM roots. This methodology allows us to capture the most significant characteristics present in these hyperspectral data with high precision. The AM is subsequently used to assign weights and integrate the hyperspectral features extracted from different parts of the AMM. The MLP is then employed to simultaneously estimate the chlorophyll content (CC) and nitrogen content (NC) of AMM leaves. Compared with estimation networks that utilize only hyperspectral data from AMM leaves as input, our proposed end-to-end AM-MHENet demonstrates superior estimation performance. Specifically, AM-MHENet achieves an R2 of 0.983 with an RMSE of 0.73 for the estimation of CC in AMM leaves. For NC estimation, AM-MHENet achieves an R2 value of 0.977 with an RMSE of 0.27. These results underscore AM-MHENet’s effectiveness in significantly enhancing the accuracy of both CC and NC estimation in AMM leaves. Moreover, these findings indirectly suggest a strong correlation between the development of AMM leaves and stems, as well as the surface and deep soil surrounding the roots of AMM, and directly highlight the ability of AM to effectively focus on the relevant spectral features within the hyperspectral data. This findings from this study could offer valuable insights into the simultaneous estimation of multiple chemical parameters in plants, thereby making a contribution to the existing body of research in this field.

The chemical stability of tetramethylammonium (TMA) head groups, both with and without the presence of a choline chloride and ethylene glycol-based deep eutectic solvent (DES), was studied using Density Functional Theory (DFT) calculations and ab initio Molecular Dynamics (MD) simulations. DFT calculations of transition state energetics (ΔEreaction, ΔGreaction, ΔEactivation, and ΔGactivation) for key degradation mechanisms, ylide formation (YF) and nucleophilic substitution (SN2), suggested that the presence of DES enhances the stability of the TMA head groups compared to systems without DES. Ab initio MD simulations across hydration levels (HLs) 1 to 5 indicated that without DES, YF dominates at lower HLs, while SN2 does not occur. In contrast, both mechanisms are suppressed in the presence of DES. Temperature also plays a role: without DES, YF dominates at 298 K, while SN2 becomes prominent at 320 K and 350 K. With DES, both degradation mechanisms are inhibited. These findings suggest DES could improve the chemical stability of TMA head groups in anion exchange membranes.

Neuropathic pain (NeP) is a complex and debilitating condition that impacts millions of people globally. Although various treatment options exist, their effectiveness is often limited, and they can be accompanied by significant side effects. In recent years, there has been increasing interest in targeting the N-methyl-D-aspartate receptor (NMDAR) as a potential therapeutic approach to alleviate different types of neuropathic pain. This narrative review aims to provide a comprehensive examination of NMDAR antagonists, specifically ketamine, memantine, methadone, amantadine, carbamazepine, valproic acid, phenytoin, dextromethorphan, riluzole, and levorphanol, in the management of NeP. By analyzing and summarizing current preclinical and clinical studies, this review seeks to evaluate the efficacy of these pharmacologic agents in providing adequate relief for NeP.

Acute Respiratory Distress Syndrome (ARDS) is a severe lung condition with a high mortality rate for which there are no effective therapeutics. The failure of the alveolar-capillary barrier, composed of lung endothelial (EC) and alveolar epithelial (AEC) cells, is a critical factor leading to excessive inflammation and edema characteristics of acute lung injury (ALI). Phosphodiesterases (PDE) are enzymes well-recognized for their roles in regulating endothelial permeability and inflammation. Although PDE inhibitors are used as therapeutics for inflammatory diseases like COPD (chronic obstructive pulmonary disease), their efficacy in treating ARDS has not yet been established. In this study, we investigate the effects of ensifentrine, an FDA-approved novel dual PDE 3/4 inhibitor, on lung endothelial and epithelial dysfunction caused by methicillin-resistant S. aureus (MRSA), a pathogen involved in bacterial ARDS. Human primary lung endothelial cells and alveolar epithelial cell lines (A549 and immortalized AEC) were treated with heat-killed MRSA, and their responses were assessed in the presence or absence of ensifentrine. Ensifentrine given either pre- or post exposure attenuated MRSA-induced increased lung endothelial permeability. VE-cadherin junctions, which serve to stabilize the EC barrier, were disrupted by MRSA; however, ensifentrine effectively prevented this disruption. Pre-treatment with ensifentrine protected against MRSA-induced EC pro-inflammatory signaling by inhibiting the expression of VCAM-1, ICAM-1, and by reducing the IL-6 release. In AEC, MRSA caused upregulation of ICAM-1, activation of NF-kB and production of IL-8, all of which were inhibited by ensifentrine. These results indicate that dual inhibition of phosphodiesterases 3 and 4 by ensifentrine is barrier protective and attenuates MRSA-induced inflammation in both lung endothelial and epithelial cells. The PDE3/4 inhibitor ensifentrine may represent a promising novel strategy for the treatment of ALI.

This study examines change detection techniques in dense point clouds for the purpose of Cultural Heritage preservation, with a particular focus on the San Pietro Barisano Bell Tower in Matera, Italy. Dense point clouds, obtained via advanced laser scanning, offer detailed 3D representations of heritage structures, facilitating the precise monitoring changes over time. The investigation uses a variety of change detection algorithms, including the Iterative Closest Point (ICP) algorithm, which is renowned for its robust registration capabilities in aligning point clouds with high accuracy. The combination of ICP with deviation analysis and feature-based methods allows for the effective identification of alterations, including deformations, material loss, and surface degradation. This methodology establishes a comprehensive framework for the monitoring of Cultural Heritage, thereby enabling timely and targeted preservation efforts. The results emphasise the substantial contribution of dense point cloud analysis to the enhancement of heritage management and the safeguarding of vulnerable architectural sites.

The manuscript deals with a new two-parameter unit stochastic distribution, obtained by transforming the Gumbel distribution, using generalized logistic mapping, into a unit interval. The distribution thus obtained is called the Gumbel–Logistic Unit (abbr. GLU) distribution, and its key stochastic properties have been investigated in detail. Among others, it is shown that the GLU distribution, unlike the Gumbel one which is always positively asymmetric, can take both asymmetric forms. Also, the procedure for estimating parameters based on quantiles, along with the asymptotic properties of the obtained estimators and the study of their numerical simulation, is presented. Finally, the application of the GLU distribution in modeling some real–world data related to telecommunications is considered.

Photocatalysis is a promising method for methylene blue (MB) degradation due to its effectiveness and environmental compatibility. Among photocatalysts, titanium dioxide (TiO2) has been widely used for MB degradation due to its exceptional photocatalytic activity. However, the wide bandgap limits the degradation efficiency of TiO2 under visible light. Herein, an interstitial nitrogen-doped TiO2 (5%NT/TiO2) used thiourea as the N source is fabricated for the visible light-derived MB degradation. 5%NT/TiO2 exhibits an extended visible light absorption range. Besides, photoelectrochemical measurement shows an improvement in photocurrent response and charge transfer behavior on N/TiO2. Thus, 5%NT/TiO2 has enhanced photocatalytic activity compared to pristine TiO2 and substitutive N-doped TiO2 (5%NAB/TiO2). The accelerated photocatalytic MB degradation process on N/TiO2 should be mainly attributed to the interstitial N doping, which causes the appearance of new energy states and extended optical properties.

Electronic waste generation has been following a continuously rising trend. With electronics containing a myriad of materials some of which are hazardous, toxic, extremely rare or precious combined with more stringent legislative laws which encourage the reuse and optimised recycling of the materials included in e-waste, the need for a holistic approach is inevitable. Such an approach should not only identify the materials found in the various types of e-waste and their respective quantities but also needs to highlight the possible handling techniques and their respective impacts and associated hazards whether to the environment or human health. Performing such a task manually would be exasperating and costly while requiring extensive resources which might not be met with a justifiable economic gain; thus, the use of the advancement in computational sustainability to draw a complete picture and aid in an informed decision-making process is crucial. The e-waste ontology developed in this paper is one way of addressing the problem since it covers the treatment domain, the hazard and the materials contained within the different types of e-waste. The ontology developed can be used directly from the protégé software or by embedding it into a developed Decision Support System.

Background: Tumor mutational burden (TMB) is increasingly recognized as a vital biomarker for predicting the efficacy of immune checkpoint inhibitors (ICIs) in cancer treatment. Despite its growing importance, the effectiveness of TMB as a predictive marker are well established in lung cancer and melanoma but remain ambiguous for breast and prostate cancers. Objective: This study aims to evaluate the role of TMB in predicting response to ICIs across four major cancer types—lung, melanoma, breast, and prostate—and to address the variability in TMB's predictive value in different cancers. Methods: A comprehensive review of the current literature was performed, analysing studies that investigated TMB and its association with ICI therapy outcomes in the specified cancer types. Results: The analysis reveals a strong consensus on the predictive value of TMB in lung cancer and melanoma, where high TMB levels are associated with improved clinical outcomes and better responses to ICIs. In contrast, the evidence for breast and prostate cancers is less conclusive, with variability in results highlighting the need for further research. Specifically, high TMB in these cancers does not consistently predict better responses to ICIs, suggesting that additional biomarkers or refined criteria might be necessary. Conclusion: TMB is a promising biomarker for predicting responses to ICI therapy, particularly in lung cancer and melanoma. However, its predictive value in breast and prostate cancers remains uncertain, underscoring the need for more extensive studies. Future research should focus on standardizing TMB evaluation methods and exploring additional biomarkers to improve treatment personalization and outcomes in these cancer types.

Testicular cancer accounts for approximately 1% of adult cancers and 5% of all urologic cancers. The most common histopathological diagnosis of testicular neoplastic lesions are germ cell tumors (90-95% of cases), among which the majority of cases are seminomas, the most common malignant tumors among men aged 15-44. For better clinical diagnosis and treatment, it is important to understand the molecular mechanisms of tumor formation. In this study, the expression of the CacyBP/SIP protein and ERK1/2 and p38 kinases was analyzed for the first time in seminomas and normal testicular tissues. The research was carried out using archival tissue material from 30 patients undergoing surgery due to testicular seminoma, whereas the comparative material consisted of the adjacent normal tissues. Immunohistochemistry and qRT-PCR were used to identify the expression of CacyBP/SIP, ERK1/2, and p38. A very clear weakening of both immunoreactivity and gene expression of all tested parameters was demonstrated in seminomas compared to healthy tissues. Our findings suggest the involvement of the CacyBP/SIP protein in the ERK1/2 and p38 signalling pathways, which may be involved in the processes of testicular seminoma carcinogenesis. The results of our research provide the basis for further research in this area.

Inflammation is implicated in the etiology of obesity-related diseases. Thromboxane-prostanoid receptor (TPR) is known to play a role in mediating an inflammatory response in a variety of cells. Gut-derived lipopolysaccharide (LPS), a TLR4 agonist, is elevated in obesity. Moreover, free fatty acids (FFAs) are important mediators of obesity-related inflammation. However, the role and mechanisms by which TPR regulates the inflammatory response in human immune cells remain unclear. We sought to determine the link between TPR and obesity and the role/mechanisms by which TPR alters LPS or stearic acid (SA)-induced inflammatory response in PBMCs. Cells were pre-treated with agents blocking TPR signaling, followed by treatment with LPS or stearic acid (SA). Our findings showed that TPR mRNA levels are higher in PBMCs from individuals with obesity. Blockade of TPR as well as ROCK which acts downstream of TPR, attenuated LPS and/or SA-induced pro-inflammatory response. On the other hand, TPR activation using its agonist enhanced the pro-inflammatory effects of LPS and/or SA. Of note, TPR agonist by itself elicits an inflammatory response which was attenuated by blocking TPR or ROCK. Our data suggest that TPR plays a key role in promoting an inflammatory response in human PBMCs and this effect is mediated via TLR4 and/or ROCK signaling.

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing a most challenging health issue. We present a computational analysis of transcriptomic data of AD tissues vs. healthy controls, focused on elucidation of functional roles played by long non-coding RNAs (lncRNAs) throughout the AD progression. We first assembled our own lncRNA transcripts from the raw RNA-Seq data generated from 527 samples of dorsolateral prefrontal cortex, resulting in the identification of 31,574 novel lncRNA genes. Based on co-expression analyses between mRNAs and lncRNAs, a co-expression network is constructed. Maximal subnetworks with dense connections are identified as functional clusters. Pathway enrichment analyses are conducted over mRNAs and lncRNAs in each cluster, which serve as the basis for the inference of functional roles played by lncRNAs involved in each of the key steps in an AD development model that we have previously build based on transcriptomic data of protein-encoding genes. Detailed information is presented about the functional roles by lncRNAs in activities related to stress response, reprogrammed metabolism, cell-polarity, and development. Our analyses have also revealed that lncRNAs have discerning power in distinguishing between AD samples of each stage and healthy controls. This study represents the first of its kind.

The traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the network incorporates the Squeezed-and-Excitation (SE) attention mechanism to improve. Moreover, the YOLOv5 algorithm’s loss function has been optimized by substituting it with EIoU_loss, which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model , trained with this enhanced network, achieves a 99.4% mAP on a custom dataset, which is 6.3% higher than the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements.

In recent years, bioengineered and biotechnological products have gained tremendous attention to identify unique biologic features associated with oncology. To overwhelm the complexity of tumor personalized medicines (PM) or vaccines could be a promising way-out to be considered. Cancer is a heterogeneous disease. This characteristic variability of cancer provides itself to the emergent era of PM i.e., precision oncology. The prime objective of PM is merely to deliver the right treatment to the right patient at the right time. For instance, imatinib was effectively targeted chronic myeloid leukemia. To date, attainment in using PM approaches to cancer treatment have been mixed. The next-generation sequencing (NGS), and the use of several biomarkers (e.g., HER2, BRAF, ALK, etc.), to drive precise cancer treatment through a timely and cost-effective manner. In current review, author summarizes the advancement in the field of PM such as CRISPR-Cas9, NGS, pharmacogenomics, and the benefits of machine learning techniques. The precise communication between healthcare professionals, regulatory bodies, and patients is essential to translate advances in PM into immense benefits for patients with cancer globally.

Energy harvesting is an emerging topic recently. This harvested energy is used to operate wireless sensors for different industrial monitoring and automation. To maximize the harvested energy in spinning systems, the harvester should vibrate at resonance for different spinning frequencies which can be achieved by active fitting. In this research, a novel methodology was developed by actively customizing the length of vibrating element beam to manipulate its natural frequency according to the spinning frequency. Different models were investigated numerically and experimentally to verify the proposed methodology. COMSOL Multiphysics software V5.1 was used to develop the numerical model, then and tip deformation and voltage difference results were benchmarked. The numerical model was verified experimentally by attaching a piezoelectric MIDE PPA 1021 which its vibrating length was varied by changing the supporting length from 0 mm to 12 mm to a spinning object having variable frequency from 0-200 Hz. It is found that the MIDE PPA 1021 beam vibrated at its resonance throughout the specified spinning frequencies. The proposed harvester can be applied in battery-free sensors being used in automotives, wind-mills blades, and rotating machinery.

We analyze a time-delayed SIQR model that considers the transportation-related infections and entry-exit screening. This model aims to determine the measures for preventing and controlling major emergent infectious diseases and the associated costs. We calculate the basic reproduction number (R0), apply the linearization method, and construct an appropriate Lyapunov function to determine the stability of the disease-free equilibrium in the local and global. We collect COVID-19 infection data from two regions in the United States in 2020 for data fitting, obtain a set of optimal parameter values, and show that the model is suitable for simulating the outbreak and spread of emerging infectious diseases. We find that transportation-related infections increase the basic reproduction number, enhancing the impact on disease spread. Entry-exit screening effectively suppress the spread of disease by reducing the basic reproduction number. Furthermore, we investigate the influence of the incubation period on disease and find that a shorter incubation period results in a shorter duration but a larger scale of infection, and the peaks are reduced. We conduct a sensitivity analysis of the R0 and propose three measures to prevent the spread of new infectious diseases based on the most sensitive parameters: wearing masks, implementing urban closures, and administering medication to sick but not yet hospitalized patients promptly. In the case of COVID-19, we use Pontryagin’s minimum principle to get time-varying control measures to control the spread of the outbreak. Optimal control effectively controls the development and deterioration of the disease. Finally, several control measures are compared through cost-effectiveness analysis, and the results show that wearing masks is the most cost-effective measure. Our study provides some practical guidance for controlling the spread of major emerging infectious diseases.

A 2-month incubation study was carried out using two soil types to determine nitrogen mineralization of different inorganic-organic amendments. The following seven treatments (T) were established; T1 = control, T2 = 5 g of dry algae per kg of soil (100%DA), T3 = 136g of agri-mat per kg of soil (100%GA), T4 = 61 g of ground grass per kg of soil (100%GG), T5 = 2.5 g of dry algae + 2.5 g of nitrogen fertilizer (NF) per kg of soil (50%DA50NF), T6 = 50%GA50NF, T7 = 50%GG50NF. Three samples per treatment were drawn at 0, 3, 7, 15, 30, 45, and 60 days for mineral-N determination. A 2-month glasshouse experiment was established afterwards with the following five treatments: T1 = Control, T2 = 50%DA, T3 = 50%GA, T4 = 50%GG, and T5 = 100NF. The results indicated that nitrogen mineralization was significantly higher in organic-inorganic amendments compared to sole organic amendments. The percentage differences ranged from 157% to 195%. The 50%DA treatment increased spinach yield by 20.6% in sandy loam and 36.5% in loam soil. It is difficult to fully recommend the 50%DA treatment without field-scale evaluation, but it is a promising option to be considered.

CAMx-LPiG (Comprehensive Air Quality Model with Extensions – Linear Plume in Grid) is an online hybrid model, based on the Chemistry and Transport Model (CTM) CAMx, that includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions, called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work the model is presented and evaluated against measured NO2 concentrations for the city of Milan for the month of January 2017. The model is able to introduce road traffic induced gradient in NO2 concentration at sub-grid resolution. Moreover, CAMx-LPiG is shown to reduce the bias compared to CAMx stand-alone simulations.

Researchers are exploring solutions to meet the growing demand for protein due to the expected increase in global population by 2050. Interest in alternative protein sources like insects has risen, driven by concerns about environmental impact and the need for sustainable food production. This study aimed to develop and evaluate the physicochemical properties of soy protein-based burger enriched with insect protein from Alphitobius diaperinus. Three formulations were developed: a control (B0), and burgers with 5% (B5) and 10% (B10) insect protein - Whole Buffalo Powder (WBP). Results showed that adding insect protein decreased the burger analogue's pH. There was observed a clear trend of increasing total lipids and SFA, and decreasing MUFA and PUFA, as the WBP concentration increases from 0% to 10%. No significant differences with increasing WBP concentration in the protein content of the burger analogue, as well as the cooking yield, were noted. The WBP addition had a notable effect on the color change, especially decrease in brightness (L*). It was shown that as the WBP concentration increased, there were no significant differences in the texture profile of the burger analogues. The formulation with 5% WBP concentration was the most acceptable in sensory analysis.

The recyclable paper based on photochromic materials not only reduces the pollution in the paper manufacture process, but also reduces the pollution caused by the use of ink, which receives wide attention. In this paper, a serial of PMoA-PWA/ZnO/PVP hybrid films which had different ratio of PMoA/PWA was prepared by the ultrasonic composite method. The results indicated that the hybrid film prepared when the ratio of PMoA to PWA was 3 had the best photochromic performance. In this system, ZnO was the photosensitizer while PMoA/PWA was the chromophore. The photochromic mechanism of PMoA-PWA/ZnO/PVP hybrid film was based on the photogenerated electron transfer mechanism. ZnO generated photoelectron under the excitation of visible light, then PMoA and PWA obtained the photoelectron and produced photoreduction reaction to generate heteropolyblue. The visible light photochromic paper was prepared by loaded PMoA-PWA/ZnO/PVP hybrid film (A3) on A4 paper. Application test showed that the prepared paper had extremely stable, excellent and reversible visible light photochromic properties, whether it was printing patterns or words and could replace the ordinary paper to realize the reuse of paper.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Much research has been conducted to determine how hair regeneration is regulated, as this could provide therapeutic, cosmetic, and even psychological interventions for hair loss. The current study focused on the hair growth effect and effective utilization of fatty oil obtained from Bryde's whales through high-throughput DNA microarray approach in conjunction with immunohistochemical observations. The research also examined the mechanisms and factors involved in hair growth. In an experiment using female C57BL/6J mice, the vehicle control group (VC: propylene glycol: ethanol: water), the positive control group (MXD: 3% minoxidil), and the experimental group (WO: 20% Whale Oil) were topically applied to the back of the mouse. The results showed that 3% MXD and 20% WO were more effective than VC in promoting hair growth, especially 20% WO. Furthermore, in hematoxylin and eosin-stained skin tissue, an increase in the number of hair follicles and subcutaneous tissue thickness was observed with 20% WO. Whole-genome transcriptomic analysis also showed that 20% WO may have lower stress- and inflammation-related responses than 3% MXD. Therefore, whale oil can be expected to be used as a safe hair growth agent.

The search of DNA polymorphisms useful for the genetic improvement of dairy farm animals lasts more than 40 years with relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing have been found in casein genes and in the DGAT1. Nowadays, similar results are not reached yet in river buffaloes despite advanced genomic technologies and accurate phenotype records are available. The aim of the present study was to investigate and validate in a larger buffalo population the effect on six milk traits of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes, previously reported to be associated with or affect dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. Daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yields (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103cell/ml) and urea (mg/dl) were individually recorded in each month for the whole lactation from 2010 to 2021. A total of 15,742 individual milk test day records (2,496 lactations) were available on 680 buffalo cows with 3.6±1.7 parity (from 1 to 13) and 6.1±1.2 test day records per lactation on average. Three out four SNP in CSN1S1, CSN3 and LPL were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C&gt;T) gave favorable associations with all yield traits (dMY, p=0.022; dPY, p=0.014 ; dFY, p=0.029) and SCS (p=0.032), whereas the CSN3 (HQ677596: c.536C&gt;T) positively associated with SCS (p=0.005) and milk urea (p=0.04). Favorable effect on dMY (p=0.028), dFP (p=0.027) and dPP (p=0.050) were observed for the LPL. Conversely, SCD did not show any association with milk traits. This is the first example of confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in dairy field and it represents a very important indication for the preselection of young bulls destined to breeding programs in the view of a more sustainable dairy production.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Melioidosis is an endemic infectious disease caused by Burkholderia pseudomallei bacteria, which contaminates soil and water. To better understand the environmental changes that have contributed to melioidosis outbreaks, this study used spatiotemporal analyses to clarify the distribution pattern of melioidosis and the relationship between melioidosis morbidity rate and local environmental indicators (land surface temperature, normalised difference vegetation index, normalised difference water index) and rainfall. A retrospective study was conducted from January 2013 to December 2022, covering data from 219 sub-districts, with each exhibiting varying morbidity rate of melioidosis on a monthly basis. Spatial autocorrelation was determined using local Moran’s I, and the relationship between melioidosis morbidity rate and the environmental indicators was evaluated using geographically weighted Poisson regression. The results revealed clustered spatiotemporal patterns of melioidosis morbidity rate across sub-districts, with hotspots predominantly observed in the northern region. Furthermore, we observed a range of coefficients for the environmental indicators, varying from negative to positive, which provided insights into their relative contributions to melioidosis in each local area and month. These findings highlight the presence of spatial heterogeneity driven by environmental indicators and underscore the importance of public health offices implementing targeted monitoring and surveillance strategies for melioidosis in different locations.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

This Systematic Review examines studies focusing on tooth bleaching and its effect on healthy enamel or incipient caries and bacterial adhesion. The aim is to explore the impact of different bleaching agents on incipient caries lesions and healthy enamel. Randomized controlled trials, clinical trials, observational studies, and in vitro studies that compared at least two groups were included. A comprehensive search strategy was used to select studies from Pubmed and Scopus databases. Two evaluators performed data extraction, screening, and quality assessment independently. Only studies written in English were included. From 968 initial records, 28 studies were selected for a full-text evaluation. 7 studies were classified as cluster 1 (bacterial adherence on teeth), 12 studies as cluster 2 (no bacteria involved), 4 studies as cluster 3 (no teeth deployment), and 5 clinical studies were cluster 4. Of the selected studies, 6 (21.43%) supported increased bacterial attachment capacity and cariogenic dynamics, 4 (14.29%) decreased adhesion and cariogenic activity, 7 (25%) no difference, and 11 (39.28%) followed a differentiated methodological approach and could not be categorized. Due to different methodology strategies used in the studies of all clusters, the risk of bias was high. Thus, we cannot reach a confident conclusion.

Lactoperoxidase system (LPS) effect on quality of raw and pasteurized milk under real conditions along the dairy value chain in Ethiopia was evaluated. A total of 250 milk samples were collected from farmers, collectors, and factories. Experimental samples were LPS-activated of morning and overnight milk. The samples were tested with several chemical and microbiological tests such as total bacterial count (TBC), total coliform count (TCC) and Escherichia coli count (EC). Results indicated that all LPS-activated milk samples had a higher quality than all the control samples. For instance, both the morning and overnight farm milk samples had a mean TBCs of 5.79 log and 6.55 log cfu/ml which is significantly (p

Under the development mode of deep blending of emerging technologies, major projects are important for enhancing China's comprehensive national strength and independent innovation capability. Based on the social benefits of major projects, and considering the influence of reputation factors, an evolutionary game model composed of a general contractor and subcontractor of major projects is established. This paper studies the influence of various factors including the income distribution coefficient of collaborative innovation, and MATLAB simulation software is analyzed the influence of factors on an evolutionary path. The results show that a reasonable income distribution coefficient can promote the game behavior of both parties to the direction of active collaborative innovation; The reduction of innovation cost and the improvement of spillover technology absorption capacity can make innovation subjects tend to choose active collaborative innovation strategies. In addition, reputation loss above a specific threshold can effectively inhibit free-riding behavior and encourage innovation subjects to choose active collaborative innovation strategies. The research results provide reference for promoting major projects' technology innovation.

Sharding shows great potential for extending the blockchain. The current challenge facing sharded blockchain technology lies in addressing the extended transaction confirmation times caused by isolated states between shards and unbalanced transaction allocation strategies. These factors contribute to an increase in cross-shard transactions and disproportionate shard workload, ultimately resulting in indefinite confirmation delays for cross-shard transactions. A critical priority for sharded blockchain systems is to conduct a comprehensive qualitative analysis to better understand and mitigate the prolonged transaction confirmation times. We introduce a Product-Form Queue Network (PFQN) model to address the transaction confirmation time problem in sharded blockchains and incorporate a new confirmation queue to more accurately simulate the actual transaction confirmation process in the blockchain. In addition, we provide a detailed quantitative analysis of the relationship between network load, consensus efficiency, and security in sharded blockchains, laying a theoretical foundation for achieving robustness and efficiency in sharded blockchains. This research not only contributes to addressing the scalability issues in sharded blockchains but also offers a new perspective for future research directions.

Chymotrypsin from the digestive organs of bigfin reef squid, a major commercial squid species in Thailand, was purified to 41.27-fold with 5.74% yield by a sequential purification process including ammonium sulfate precipitation, size exclusion and ion exchange chromatography. According to SDS-PAGE, the molecular weight of purified chymotrypsin was 43 kDa. Native-PAGE analysis revealed a single band for this purified enzyme. Optimum pH and temperature for chymotrypsin activity of the purified enzyme were pH of 7.0 with a temperatures of 55°C. The purified chymotrypsin remained stable throughout a wide range of pH levels (6–11) and at relatively high temperature (50°C). It was significantly inhibited by PMSF and TPCK. The values of the kinetic constants Km and Kcat were found to be 1.33 mM and 31.46 s‒1, respectively. The purified chymotrypsin has the N-terminal amino acid IVGGQEATPGEWPWQAALQV. This study provided new information about the biochemical properties of pure chymotrypsin from bigfin reef squid, which is useful in the future investigation, aquaculture and application of bigfin reef squid.