Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic-dysfunction associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The efficacy of the model was assessed through the evaluation of the Out-of-Bag (OOB) error estimate. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using 3 models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest), the AUCs were 0.882, 0.972 and 0.96 respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Background. Gene rearrangements affecting KMT2A are frequent in acute myeloid leukemia (AML) and are often associated with a poor prognosis. KMT2A gene fusions are often detected by chromosome banding analysis and confirmed by fluorescence in-situ hybridization. However, small intragenic insertions, termed KMT2A partial tandem duplication (PTD), are particularly challenging to detect using standard molecular and cytogenetic approaches. Methods. We have validated the use of a custom hybrid capture based next-generation sequencing (NGS) panel for comprehensive profiling of AML patients seen at our institution. This NGS panel targets the entire consensus coding DNA sequence of KMT2A; to deduce the presence of a PTD we used the relative ratio of KMT2A exons coverage. We sought to corroborate the KMT2A-PTD NGS results using (1) multiplex-ligation probe amplification (MLPA) and (2) optical genome mapping (OGM). Results. We analyzed 932 AML cases and identified 41 individuals harboring a KMT2A-PTD. MLPA, NGS and OGM confirmed the presence of a KMT2A-PTD in 22 of the cases analyzed where orthogonal testing was possible. Two false positive PTD calls by NGS could be explained by the presence of cryptic structural variants impacting KMT2A and interfering with PTD analysis. OGM revealed the nature of these previously undetected gene rearrangements in KMT2A, while MLPA yielded in-conclusive results. MLPA analysis for KMT2A-PTD is limited to exon 4, whereas NGS and OGM resolved KMT2A-PTD sizes and copy number levels. Conclusion. KMT2A-PTDs are complex gene rearrangements that cannot be fully ascertained using a single genomic platform. MLPA, NGS panels and OGM are complementary technologies applied in standard of care testing for AML patients. MLPA and NGS panels are designed for targeted copy number analysis; however, our results show that integration of concurrent genomic alterations is needed for accurate KMT2A-PTD identification. Unbalanced chromosomal rearrangements overlapping with KMT2A can interfere with the diagnostic sensitivity and specificity of copy number based KMT2A-PTD detection methodologies.

Parasomnias are a group of sleep disorders characterized by abnormal and unpleasant motor, verbal or behavioral events that occur during sleep or during transitions between wake and sleep states. They disrupt sleep and can have a detrimental impact on the individual experiencing them. Our goal was to identify types of parasomnias and their prevalence in the post-secondary student population and to explore students' coping strategies for parasomnias they found distressing. Seventy-seven post-secondary students completed the 21-item Munich Parasomnia Screening (MUPS) frequency scale. They also rated, on a 10-point scale, how disturbing each parasomnia was. Not only did 92% percent of students report at least one parasomnia, but our results also indicate that the vast majority of students experienced more than one parasomnia. This led us to investigate the likelihood of the co-occurrence of different parasomnias. With respect to the level of subjectively experienced distress, the most prevalent parasomnias were not always the more disturbing ones. Coded open-ended responses about what students do about the disturbing parasomnias indicate that grounding strategies and physical manipulation of one’s body were the most common, although most participants indicated that in spite of distress, they do nothing to cope. In conclusion, our study found a strikingly high prevalence of parasomnias in this sample of young adults and a lack of knowledge about effective means of dealing with these.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

The interest in the Spark Plasma Sintering (SPS) technique has continuously increased over the last few years. This article shows the possibility of the development of a SPS device used for material processing and synthesis both in both scientific and industrial applications. This work presents an example of processing Arc-Meleted (half-Heusler, cobalt triantimonide) and SHS-synthesized semiconductors (bismuth telluride) materials with SPS device. The system functionalities expansion is presented, showing the possible way of increasing the information amount obtained about SPS processes and using the SPS apparatus for conducting synthesis of materials and increasing reproducibility and accuracy of process parameters control.

Oligopeptide transporters (OPTs) are a group of membrane localized proteins that have a broad range of substrate transport capabilities and contribute to numerous biological processes. However, limited information has been reported on OPTs in higher plants. In this study, a comprehensive analysis of the OPT gene family in Medicago truncatula was performed. A total of 26 OPT genes (MtOPT01-MtOPT26) have been identified in the Medicago truncatula genome. Phylogenetic analyses indicated that MtOPTs consisted of two distinct subgroups, 12 MtOPTs belonged to the peptide transport subgroup (PT-OPT) based on their predicted amino acid sequences containing the two highly conserved motifs (NPG and KIPPR) and 14 MtOPTs belonged to yellow stripe subgroup (YS-OPT). The MtOPTs distributed on each of 8 chromosomes in Medicago truncatula. Sequence analysis verified that MtOPTs significant similar to those in other plants. The copy number of MtOPTs was low and the multiply of MtOPTs was simple relatively. Gene structure analysis showed that most of the MtOPTs have various numbers of introns. The multiple of MtOPTs might play different biological roles which were supported by the fact that MtOPTs have a distinct tissue-specific expression pattern. The data obtained in this study will help to better understand the complexity of the MtOPTs gene family and provide new evidence for the function and evolution of the OPT gene family in higher plants.

Background and Objectives: Knee osteoarthritis (KOA) is a degenerative disease that is targeting continuously people of different ages, but especially the elderly population, the number of which tends to increase continuously at the global level. Apart from age, excess weight can influence the evolution of the disease, with obesity being associated with a weak inflammation stage, and an imbalance between pro-inflammatory and anti-inflammatory cytokines. The present work aimed to identify specific biomarkers alike ACRP-30, IL-10, TNF-α, and IL-6 in synovial fluid and correlate them with patients’ clinical data. Materials and Methods: 24 subjects with KOA and over 50 years of age are involved in the present study. All patients performed a supine X-ray at the target knee. Ultrasound was performed using an ESAOTE MyLab Omega (ESAOTE Spa, Italy) ultrasound device and a 6-19 MHz linear array transducer. Results: The obtained results highlighted a significant correlation between age and adiponectin level (p=0.0451, r=-0.412). Also, the IL-10 values are lower in cases where the intensity of the pain is more pronounced (p=0.0405, r=-0.421). In addition, analyzing the data by gender, it was observed that in the case of males, stiffness is more related to age (p=0.0079, r=0.7993), compared to women (p=0.0203, r=0.6223). In the case of women, the progression of the disease tends to increase more intensively total WOMAC score’s values (p=0.00031, r=0.8342), compared with men (p=0.0289, r=7013). Regarding interleukins and BMI, significant correlations were observed only in the case of men. Conclusions: A significant correlation between age and adiponectin, and adiponectin and IL-6, suggests that advanced age may contribute to adiponectin reduction. Comparing men with women, it was observed that men's age is more related to rigidity, and IL-6 and IL-10 are directly correlated to BMI and women seem to be more sensitive to pain and stiffness.

Image segmentation is the process of partitioning an image into multiple segments where the goal is to simplify the representation of the image and make the image more meaningful and easier to analyze. In particular, semantic segmentation is an approach of detecting the classes of objects, based on each pixel. In the past, most semantic segmentation models were for only one single style, such as urban street views, medical images, or even Manga. In this paper, we proposed a semantic segmentation model called Dual-Pyramid Wide Residual Network (DPWRN) to solve the segmentation for cross-style datasets, which is suitable for diverse segmentation applications. The DPWRN integrated the Pyramid of Kernel paralleled with Dilation (PKD) and Multi-Feature Fusion (MFF) to improve the accuracy of segmentation. To evaluate the generalization of the DPWRN and its superiority over most state-of-the-art models, three datasets with completely different styles are tested in the experiments. Finally, the DPWRN verified its generalization and also showed its superiority over most state-of-the-art models.

Following the adoption of the Kunming-Montreal Global Biodiversity Framework (KM-GBF), with the Convention on Biological Diversity serving as its guiding treaty, the European Union (EU) has just reached an agreement on an EU Nature Restoration Regulation. This study carries out a systematic cross-walk between the restoration regulation and the KM-GBF, focusing on their implications for forest ecosystems. This paper identifies areas of alignment, divergence, and potential gaps related to habitat restoration, species protection, climate resilience, and the sustainable use of natural resources. The methodology adopts a grounded approach, starting with the 23 targets outlined in the KM-GBF and proceeding to the 28 articles set out in the restoration regulation. The results highlight the need for better alignment between the KM-GBF, the restoration regulation, and other forest-relevant EU policy instruments. The study stresses the need for a coherent and integrated EU policy approach that can address the diverse challenges and policy objectives facing forests. It concludes that amendments to the restoration regulation have significantly diluted its potential impact, limiting the EU Members States accountability and ability to meet KM-GBF goals and targets. It further stresses the need for strategies that can reconcile divergent EU policy pathways, support forest management and restoration efforts, while aligning with global biodiversity objectives.

At least 50% of factors predisposing to alcohol dependence (AD) are genetic and women affected with this disorder present with more psychiatric comorbidities, probably indicating different genetic factors involved. We aimed to run a genome-wide association study (GWAS) followed by a bioinformatic functional annotation of associated genomic regions in male and female patients with AD and eight related clinical measures. A genome-wide significant association of rs220677 with AD (p-value = 1.33×10^-8 calculated with the Yates-corrected Chi-square test under the assumption of dominant inheritance) was discovered in female patients. Associations of AD and related clinical measures with seven other single nucleotide polymorphisms listed in previous GWAS of psychiatric and addiction traits were differently replicated in male and female patients. The bioinformatic analysis showed that regulatory elements in the eight associated linkage disequilibrium blocks define the expression of 80 protein-coding genes. Nearly 68% of these and of 120 previously published coding genes associated with alcohol phenotypes directly interact in a single network. This study indicates that a number of genes behind the pathogenesis of AD are different in male and female patients, but implicated molecular mechanisms are functionally connected. The results also suggest the genetic basis of sex-specific psychiatric comorbidities of AD.

Based on the topology of wide area protection system (WAPS), after studying the reliability of hardware system and information flow in the WAPS and establishing the reliability assessment model, the multi-service reliability analysis method with multi monitoring and protection tasks in WAPS was proposed. In the model, the impact of network quality of service (QoS) such as information flow loss and delay, is studied. On the base of the model, the multi-service reliability evaluation method is employed to analyze the reliability of a WAPS of IEEE14 node power system, and the key nodes of the WAPS is given, which provides a basis for improving the reliability of the WAPS.

This project designed a set of network security mechanisms for cloud applications in VANETs. At present, cloud computing is one of the government’s development priorities in the industry. We divide cloud into public, private, and hybrid clouds in this project. Vehicles or passengers can access road conditions or public transport information through the public cloud; public transportation can access current driving records and users can access relevant enterprise information on the private cloud and hybrid cloud is a combination of public and private clouds. Both cloud computing or VANETs require network and information security. At present, many researches on VANETs security only focus on message communication, and neglect information storage. Researches on cloud computing security only focus on information protection, and neglect user privacy and anonymity. This project designed a set of network and information security mechanisms in line with the requirements of Confidentiality, Authentication, Non-repudiation, Conditional Anonymity, and Conditional Untraceability. This project primarily needs to achieve the following: 1.an authentication mechanism to verify the identity of each other between the passenger and the vehicle and to verify the identity with Single Sign-On; 2. vehicle or user privacy and anonymity, which need to be able to replace the anonymous ID and related parameters for the vehicle or the user; 3. a private communication mechanism, which enables any vehicle or user to communicate privately; and 4. an information security encryption method, which can encrypt the information on a cloud server to avoid unauthorized access by internal personnel or hackers.

In the famous medieval magical manual called Picatrix, the unknown author describes the phenomenon of magic with the term nigromantia. As is well known, the original meaning of the Greek term necromantia has a more concise meaning. It is used for a special kind of divination, i.e., divination through the parts of a cadaver and the conjured spirit of the dead. Seemingly in the Picatrix, no necromantic ritual can be found; moreover, the author stresses that his main goal is pious, i.e., to find the path leading to the ultimate source of the universe, the one and only God. In my paper, I show that on some pages of the Picatrix, there are traces of divinatory practices that may be connected to the original meaning of the term nigromantia. In the third book of the manual, descriptions of some interesting rituals attributed to the pagan Sabeans of Harran and their teacher, the god Hermes can be found. During the practices, the magician involved conjured spirits of the heavenly bodies and powers as well. Because of this, by looking closely at and analyzing the given text, it is possible to piece together a complex web of necromantic and demonic divinatory rituals.

Hepatitis C virus (HCV) is a major public health threat associated with serious clinical consequences worldwide. The WHO reports an estimated 58 million people globally living with chronic hepatitis C virus (HCV) infection, with 1.5 million new cases and over 350,000 deaths annually due to HCV-related diseases. Despite the existence of multiple diagnostic methods, the accessibility of diagnosis and treatment is often hindered across the continent by limited resources, insufficient healthcare infrastructure, and low public awareness of Hepatitis C virus Hepatitis C virus infection is a major public health concern in African countries, which have the highest prevalence rates of HCV in the world (1–26%). Egypt, African country which had the highest prevalence of Hepatitis C virus infection in the world. Its national hepatitis C elimination initiative emerges as an exemplary case of success an having screened over 60 million individuals, and treated more than 4 million, thereby markedly diminishing both the prevalence and incidence of the disease. The strategies employed by Egypt offer critical insights and adaptable frameworks for other African Countries facing analogous challenges. The Aim of this Review is to highlight the triumphs and hurdles of Egypt's approach, while also providing strategic guidance for African Countries to accelerate the journey towards the eradication of hepatitis C.

An innovative wireless sensor network (WSN) based on Ultra-Wide Band (UWB) technology for 3D accurate superficial monitoring of ground deformations, as landslides and subsidence, is proposed. The system has been designed and developed as part of an European Life+ project, called Wi-GIM (Wireless Sensor Network for Ground Instability Monitoring). The details of the architecture, the localization via wireless technology and data processing protocols are described. The flexibility and accuracy achieved by the UWB two-way ranging technique is analysed and compared with the traditional systems, such as robotic total stations (RTSs), Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR), highlighting the pros and cons of the UWB solution to detect the surface movements. An extensive field trial campaign allows the validation of the system and the analysis of its sensitivity to different factors (e.g., sensor nodes inter-visibility, effects of the temperature, etc.). The Wi-GIM system represents a promising solution for landslide monitoring and it can be adopted in conjunction with traditional systems or as an alternative in areas where the available resources are inadequate. The versatility, easy/fast deployment and cost-effectiveness, together with the good accuracy, make the Wi-GIM system a possible solution for municipalities that cannot afford expensive/complex systems to monitor potential landslides in their territory.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

Short-range Internet of Things (IoT) sensor nodes operating at 2.4 GHz need to provide ubiquitous wireless sensor networks (WSNs) with energy-efficient, wide-range output power (POUT) and fully integrated on a single chip for wireless body area networks (WBANs) and wireless personal area networks (WPANs) using low-power Bluetooth (BLE) and Zigbee standards. The proposed fully integrated transmitter (TX) utilizes a digitally controllable current-mode class-D (CMCD) power amplifier (PA) with a second harmonic distortion (HD2) suppression to reduce VCO pulling in an integrated system while meeting harmonic limit regulations. The CMCD PA is divided into 7-bit slices that can be reconfigured between differential and single-ended topologies. Duty cycle distortion compensation is performed for HD2 suppression, and an HD2 rejection filter and a modified C-L-C low-pass filter (LPF) reduce HD2 further. Implemented in a 28 nm CMOS process, the TX achieves a wide POUT range of from 12.1 to -31 dBm and provides a maximum efficiency of 39.8% while consuming 41.1 mW at 12.1 dBm POUT. The calibrated HD2 level is -82.2 dBc at 9.93 dBm POUT, resulting in a transmitter figure of merit (TX_FoM) of -97.52 dB. Higher order harmonic levels remain below -41.2 dBm even at 12.1 dBm POUT, meeting regulatory requirements.

Diacylglycerol kinase (DGK) is a lipid kinase that phosphorylates diacylglycerol (DAG) to generate phosphatidic acid (PA). Based on converting one important signaling molecule (DAG) to another (PA), DGK plays an important role in plant responses to abiotic stress including waterlogging stress. However, no studies have been reported on the characterization of the DGK gene family in the waterlogging-tolerant kiwifruit germplasm, Actinidia valvata Dunn. Here, we identified 18 AvDGK genes in the A. valvata genome. The phylogenetic analysis showed that AvDGKs can be classified into three clusters, and members within the same cluster have similar domain distribution, exon-intron structures and conserved motif compositions. Chromosome localization analysis revealed that all the AvDGK genes are located across 18 different chromosomes. There were 29 duplicated gene pairs in kiwifruit and all had undergone purifying selection during evolution. Promoter cis-element analysis revealed that the cis-elements within AvDGK genes are associated with multiple functions, including phytohormone signal transduction, stress response, and plant growth and development. The expression pattern analyses indicated that AvDGK play an important role in the fruit development and plant response to waterlogging stress. AvDGKs gene family in the tetraploid A. valvata genome might promote PA synthesis and subsequent signal transduction both under short-term and long-term waterlogging stress. These results provide information regarding the structural characteristics and potential function of DGK genes within kiwifruit and lay a fundamental basis for further research into breeding for enhancing the kiwifruit’s tolerance to waterlogging stress.

The way people innovate and create new ideas and bring them to the market is undergoing a fundamental change from closed innovation to open innovation. Why and how do firms perform open innovation? Firms’ open innovation is measured through the levels of firms’ joint patent applications. Next, we analyze network structures and characters of firms’ joint patent applications such as betweenness and degree centrality, structure hole, and closure. From this research, we drew conclusions as follows. First, the structure of collaboration networks has both direct and indirect effects on firms’ innovative performance. Second, in the process of joint patent applications, there is a long tail phenomenon in networks of joint patent applications. Third, the number of patents and International Patent Classification (IPC) subclasses together constitute a meaningful measure of the innovation performance of firms.

The central nervous system (CNS) is the final frontier in drug delivery because of the blood-brain barrier (BBB), which poses significant barriers to the access of most drugs to their targets. Kynurenic acid (KYNA), a tryptophan (Trp) metabolite, plays an important role in behavioral functions and abnormal KYNA levels have been observed in neuropsychiatric conditions. The current challenge lies in delivering KYNA to the CNS owing to its polar side chain. Recently, C-3 side chain modified KYNA analogs have been shown to cross the BBB; however, it is unclear whether they retain the biological functions of the parent molecule. This study examined the impact of KYNA analogs, specifically SZR-72, SZR-104, and the newly developed SZRG-21, on behavior. The analogs were administered intracerebroventricularly (i.c.v.), and their effects on the motor domain were compared with those of KYNA. Specifically, open field (OF) and rotarod (RR) tests were employed to assess motor activity and skills. SZR-104 increased horizontal exploratory activity in the OF test at a dose of 0.04 μmol/4 μL, while SZR-72 decreased vertical activity at doses of 0.04 and 0.1 μmol/4 μL. In the RR test, however, neither KYNA nor its analogs showed any significant differences in motor skills at either dose. Side chain modification affects affective motor performance and exploratory behavior, as the results show for the first time. In this study, we showed that KYNA analogs alter emotional components such as motor-associated curiosity and emotions. Consequently, drug design necessitates the development of precise strategies to traverse the BBB while paying close attention to modifications in their effects on behavior.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

The ZIPs (ZRT/IRT-like proteins) play critical roles in the absorption, transport, and intracellular balance of metal ions essential for various physiological processes in plants. However, little is known about the pan-genomic characteristics and properties of ZIP genes in cucumber (Cucumis sativus L.). In this study, we identified 10 CsZIP genes from the pan-genome of 13 C. sativus accessions. Among them, only CsZIP10 showed no variation in protein sequence length. We analyzed the gene structure, conserved domains, promoter cis-elements, and phylogenetic relationships of these 10 CsZIP genes derived from "9930". Based on phylogenetic analysis, the CsZIP genes were classified into three branches. Amino acid sequence comparison revealed the presence of conserved histidine residues in the ZIP proteins. Analysis of promoter cis-elements showed that most promoters contained elements responsive to plant hormones. Expression profiling in different tissues showed that most CsZIP genes were expressed at relatively low levels in C. sativus leave, stem, and tendril, and CsZIP7 and CsZIP10 were specifically expressed in root, indicating their potential involvement in the absorption and transport of metal ions. Transcriptomic data indicated that these 10 ZIP genes displayed responses to both downy mildew and powdery mildew, and CsZIP1 were significantly downregulated after both salt and heat treatments. In conclusion, this study deepens our understanding of the ZIP gene family and enhances our knowledge of the biological functions of CsZIP genes in C. sativus.

Delve into the dynamic realms of neuroscience, neurology, and psychiatry with this groundbreaking editorial, unveiling the intricate mechanisms of brain function and dysfunction. Explore the latest advancements in molecular biology, genetics, epigenetics, pharmacology, and neuroimaging that are revolutionizing our understanding of neurological and psychiatric disorders. Uncover the challenges and gaps in translating cutting-edge research into practical clinical applications, aiming to enhance the lives of patients and their families. Discover the pivotal role of neuroplasticity in reshaping the brain's adaptability throughout life, alongside the promising potential of non-invasive brain stimulation techniques like transcranial magnetic stimulation and transcranial direct current stimulation. Embark on a journey of innovation and collaboration as researchers and clinicians strive to bridge the gap between bench and bedside, offering hope for improved diagnosis, prevention, and treatment of complex neurological and psychiatric conditions. Join us in this exploration of transformative ideas and novel interventions that hold the key to a brighter future for individuals and society.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

We propose a neural-network-based watermarking method that introduces the quantized activation function that approximates the quantization of JPEG compression. Many neural-network-based watermarking methods have been proposed. Conventional methods have acquired robustness against various attacks by introducing an attack simulation layer between the embedding network and the extraction network. The quantization process of JPEG compression was replaced by the noise addition process in the attack layer of the conventional methods. In this paper, we propose a quantized activation function that can simulate the JPEG quantization standard as it is in order to improve the robustness against the JPEG compression. Our quantized activation function consists of several hyperbolic tangent functions and is applied as an activation function for neural networks. Our network was introduced in the attack layer of ReDMark proposed by Ahmadi et al. to compare it with their method. That is, the embedding and extraction networks had the same structure. We compared the usual JPEG compressed images and the images applying the quantized activation function. The results showed that a network with quantized activation functions can approximate JPEG compression with high accuracy. We also compared the bit error rate (BER) of estimated watermarks generated by our network with those generated by ReDMark. We found that our network was able to produce estimated watermarks with lower BERs than those of ReDMark. Therefore, our network outperformed the conventional method with respect to image quality and BER.

M-polynomial of different molecular structures helps to calculate many topological indices. A topological index of graph G is a numerical parameter related to G which characterizes its molecular topology and is usually graph invariant. In the field of quantitative structure-activity (QSAR) quantitative structure-activity structure-property (QSPR) research, theoretical properties of the chemical compounds and their molecular topological indices such as the Zagreb indices, Randic index, Symmetric division index, Harmonic index, Inverse sum index, Augmented Zagreb index, multiple Zagreb indices etc. are correlated. In this report, we compute closed forms of M-polynomial, first Zagreb polynomial and second Zagreb polynomial of Octagonal network. From the M-polynomial we recover some degree-based topological indices for Octagonal network. Moreover, we give a graphical representation of our results.

Neurodegeneration is a major problem in neuroscience and medicine as it causes and worsens many neurological and psychiatric disorders. It involves gradual loss of neurons at different levels of the brain, affecting cognition, emotion, behavior, consciousness, and attention. Understanding the causes and consequences of neurodegeneration is crucial for identifying the risk factors, biomarkers, and treatments. However, the current therapies are mostly symptomatic and ineffective. Therefore, new and innovative methods, such as noninvasive brain stimulation (NIBS), are needed to modulate brain activity and plasticity in a safe and reversible manner. This field is rapidly developing, with a focus on exploring new clinical applications, mechanisms, and combinations of NIBS and drugs. This special issue covers the latest progress and challenges in the study of neurodegeneration in cognitive impairment and mood disorders from the experimental, clinical, and translational perspectives. It addresses the complexity and diversity of these conditions, the lack of disease-modifying treatments, the ethical and practical issues of conducting research in vulnerable and diverse populations, the integration and interpretation of data from multiple sources and modalities, and the development and validation of novel methods such as NIBS, artificial intelligence, and drug repurposing. Collaborative efforts among various stakeholders are needed to improve our knowledge and treatment of neurodegeneration underlying cognitive impairment and mood disorders.

Wide-field (WF) retinal imaging is becoming a standard diagnostic tool for diseases involving the retinal periphery. Technological progress elicited the advent of wide-field optical coherence tomography (WF-OCT) and WF-OCT angiography (WF-OCTA) examinations. This review presents the results of studies that analyzed the implementation of these procedures in clinical practice and refers to them as traditional and ultra-wide-field fluorescein angiography (UWF-FA). The PUBMED search was performed using the terms WF-OCT OR WF-OCTA OR UWF-FA AND the specific clinical entity, including diabetic retinopathy (DR), retinal vein occlusion (RVO), Coats disease, peripheral retinal telangiectasia, peripheral retinal degeneration, lattice degeneration, and posterior vitreous detachment. The analysis only included the studies in which the analyzed field of view for the OCT or OCTA exam was larger than 55 degrees. The evaluation of the extracted studies indicates that WF imaging with OCT and OCTA provides substantial information on retinal disorders involving the retinal periphery. Vascular diseases, such as DR or RVO, can be reliably evaluated using WF-OCTA with results superior to standard-field fluorescein angiography. Nevertheless, UWF-FA provides a larger field of view and still has advantages over WF-OCTA concerning the evaluation of areas of non-perfusion and peripheral neovascularization. Detailed information on the vascular morphology of peripheral changes should be obtained via WF-OCTA, not angiographic examinations. WF-OCT can serve as a valuable tool for the detection and evaluation of vitreoretinal traction, posterior vitreous detachment, and peripheral retinal degeneration, and guide therapeutic decisions on a patient’s eligibility for surgical procedures.

Optical backbone networks constitute the fundamental infrastructure employed today by network operators to deliver services to users. As network capacity is a key factor influencing optical network performance, it is important to understand how topological and physical properties impact its behavior and to have the capability to estimate its value. In this context, we propose here a method to evaluate the network capacity that relies on the optical reach to account for physical layer aspects, in conjunction with constrained routing techniques for traffic routing. As this type of routing can lead to traffic blocking, particularly due to the limitation on the number of wavelengths per fiber, we also propose a fiber assignment algorithm designed to deal with this problem. We apply this method to a set of randomly generated networks using a modified Waxman model, and for a network with 60 nodes, in a scenario without blocking, we obtain capacities of about 2.5 Pbit/s for a symbol rate of 64 Gbaud and about 5 Pbit/s for a symbol rate of 128 Gbaud. Remarkably, this duplication in the total network capacity is achieved by an increase in the total fiber length of only about 51%.

River bed materials serve multiple environmental functions as a habitat for aquatic invertebrates and fishes. At the same time, the particle size of the bed material reflects the tractive force of the flow regime in a flood and provides useful information for flood control. The traditional river bed particle size surveys, such as sieving, require time and labor to investigate river bed materials. The authors proposed a method to classify aerial images taken by unmanned aerial vehicle (UAV) using convolutional neural networks (CNN), our previous study showed that terrestrial riverbed material could be classified with high accuracy. In this study, we attempted to classify riverbed materials distributed in shallow waters where the bottom can be seen from UAVs. After training the CNN to classify the images with the same grain size as being in the same class even if the surface flow types taken overlapping the riverbed material were different, the total accuracy reached 90.3%. Moreover, the proposed method was applied to the wide-ranging area to determine the distribution of the particle size. In parallel, the microtopography was surveyed using Lidar-UAV, and the relationship between the microtopography and particle size distribution was discussed. In the steep section, coarse particles were distributed and formed a rapid. Fine particles were deposited on the upstream side of those rapids, where the slope had become gentler due to the damming. There was good agreement between the microtopographical trends and the grain size distribution.

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection which detects the patient's exhaled VOCs combined with sensor arrays of Convolutional Neural Network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, due to low accuracy, high complexity computation and large number of parameters make the CNN algorithms difficult transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models such as EfficientNet, LTNet has fewer parameters (32K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than that of the five existing models’, and it also offers the shortest training (844.38s and 584.67s) and inference time (23s and 14s) in the same validation set. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in CMB-MAXP engine. Both the RBM-MAXP engine and the CMB-MAXP engine were implemented using VHSIC Hardware Description Language (VHDL) and verified by simulations. They have been employed for and tested in our CNN accelerator targeting at the CNN model YOLOv4-CSP-S-Leaky for object detection.

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing the urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological and infrastructural. A hybrid (FAHP-EWM)-TOPSIS model was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) from 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR are highly correlated with urban infrastructure development. However, the ecological resilience in the subsystem showed a fluctuating downward trend, because of the influence of the surface area of lakes and rivers. (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

Water networks are critical infrastructure components, ensuring the continuous supply of high-quality drinking water to consumers. To secure such water supply, regular maintenance, including the replacement of deteriorating pipelines, is essential. In this study, a methodology has been developed for determining optimal pipeline replacement solutions in water supply systems at water utilities with limited data availability. Hydraulic analysis has been conducted on the segment of 25 km of the water supply network using the free software EPANET. Applying water network optimization, eight pipeline replacement projects according to 13 pre-defined criteria have been identified and evaluated. The paper outlines the methods for evaluating the criteria, including defining specific quantitative limits. The Analytical Hierarchical Process (AHP) method was used in the paper to determine the weights of the criteria. The reason for applying this method refers to problems that involve a set of criteria with a mixed structure, including both quantitative and qualitative aspects. Also, the paper describes the steps of the multi-criteria optimization method VIKOR, used to select the optimal project. This paper, considered as case studie, describes a method, i.e. application of a new principles and an innovative way to solve a problem for developing countries.

The research of object classification and part segmentation is a hot topic in computer vision. A considerable number of studies have been carried out about deep learning on 3D point clouds. However,it is challenging to achieve effective feature learning due to sparsity of point clouds. Recently, a variety of Transformers have been adopted to improve point cloud processing and display great potential. Nevertheless, large numbers of Transformer layers tend to incur huge computational and memory costs. PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks, this does not mean that PointNet++ has no potential.Thus, this paper offer two major contributions that significantly improve PointNet++ performance. Firstly, we introduce a novel contextual feature extraction (CFE) block that significantly enhances the feature extraction capabilities of PointNet++ networks. Secondly, to further enhance feature fusion, we seamlessly integrate a mutual learning (ML) block into the network architecture. By embedding these two innovative blocks within each layer of the network, we not only enrich the network's functionality but also impart it with greater robustness and adaptability. The specific experiments were conducted on the S3DIS (6-fold cross-validation) and Modelnet40 datasets with 86.5% and 92.7% accuracy, respectively, which proved that our model is comparable or even better than most existing methods for classification and segmentation tasks.

Primary biliary cholangitis (PBC) is a cholestatic liver disease characterized by immune-mediated injury to small bile ducts. Although PBC is an autoimmune disease, the results of traditional immunosuppressive therapy are disappointing. Nearly 40% of PBC patients do not respond to the first-line drug UDCA. Without appropriate intervention, PBC patients would eventually progress to liver cirrhosis and even death. There is an urgent need to develop new therapies. The gut-liver axis emphasizes the interconnection between the gut and the liver, and there is increasing evidence that gut microbiota and bile acids play an important role in cholestatic diseases. Dysbiosis of gut microbiota, imbalance of bile acids, and immune-mediated bile duct injury constitute the triad of pathophysiology in PBC. Understanding the gut microbiota-bile acid network and corresponding immune functions in PBC provides a new perspective for its treatment strategies. Therefore, we summarize the latest advancements in research of gut microbiota and bile acids in PBC and, for the first time, explore the potential of immune factors related to them as novel immunotherapy targets. This article discusses potential therapeutic approaches focusing on regulating gut microbiota, maintaining bile acid homeostasis, their interactions, and related immune factors.

Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel evolutionarily divergent RNA virus etiological agent of COVID-19, is responsible for present devastating pandemic respiratory illness. To explore the genomic signatures, we comprehensively analyzed 2,492 complete and/or near-complete genome sequences of SARS-CoV-2 strains reported from across the globe to the GISAID database up to 30 March 2020. Genome-wide annotations revealed 1,407 nucleotide-level mutations at different positions throughout the entire genome of SARS-CoV-2. Moreover, nucleotide deletion analysis found nine deletions throughout the genome, including in polyprotein (n=6), ORF10 (n=1) and 3´-UTR (n=2). Evidence from the systematic gene-level mutational and protein profile analyses revealed a large number of amino acid (aa) substitutions (n=722), making the viral proteins heterogeneous. Notably, residues of receptor-binding domain (RBD) having crucial interactions with angiotensin-converting enzyme 2 (ACE2), and cross-reacting neutralizing antibody were found to be conserved among the analyzed SARS-CoV-2 strains, except for replacement of Lysine with Arginine at 378 position of the cryptic epitope of a Shanghai isolate, hCoV-19/Shanghai/SH0007/2020 (EPI_ISL_416320). Our method of genome annotation is a promising tool for monitoring and tracking the epidemic, the associated genetic variants, and their implications for the development of effective control and prophylaxis strategy.

Autoimmune hemolytic anemias (AIHAs) are conditions whereby a person produces an antibody to their own red blood cells (RBCs). These conditions can be primary with unknown cause or secondary, associated with diseases or infections. There are seven different categories of AIHA, warm-antibody AIHA (wAIHA); cold-antibody AIHA that includes cold agglutinin syndrome (CAS) and cold agglutin disease (CAD); mixed AIHA, also called combined cold and warm AIHA; paroxysmal cold hemoglobinuria (PCH) also termed Donath-Lansteiner test-positive AIHA; direct antiglobulin test-negative AIHA (DAT-neg AIHA); drug-induced immune hemolytic anemia (DIIHA); and passenger lymphocyte syndrome. This review will discuss each of these AIHAs and provide information on diagnosis and treatment.

There are several challenges on how to attain energy efficiency while maintaining balance among factors affecting energy consumption such as power rating and temperature setpoints of cooling units, room temperature and humidity, and indoor air quality (IAQ). A real-time energy and IAQ monitoring system were installed in an educational building to profile the operational power consumption of inverter-based air condition unit (ACU) installed in each room. Polynomial curve and neural fitting regression analysis were applied to the real-time power consumption, indoor temperature and humidity, and carbon dioxide (CO2) level data. The derived models were able to provide the stabilizing indoor thermal and air conditions of the room to reach steady state ACU power consumption. These collected data and calculated parameters can be used to define rules in automating control of cooling appliances for an efficient energy utilization. The regression approaches, using real-time data, have determined the influence of indoor heat conditions and carbon dioxide levels on ACU power consumption. These parameters were utilized to establish consistent values for temperature, humidity, and carbon dioxide levels under stable settings of inverter-based air conditioning units.

Sorghum (Sorghum bicolor L.) ranks fifth as the most crucial cereal crop globally, yet its seed morphology remains relatively unexplored. This study investigated seed morphology in sorghum based on 115 mini core and 130 Senegalese germplasms. Eight seed morphology traits encompassing size, shape, and color parameters were assessed. Statistical analyses explored potential associations between these traits and resistance to three major sorghum diseases: anthracnose, head smut, and downy mildew. Furthermore, genome-wide association studies (GWAS) were conducted using phenotypic data from over 24,000 seeds and over 290,000 publicly available single nucleotide polymorphisms (SNPs) through the Genome Association and Prediction Integrated Tool (GAPIT) R package. Significant SNPs associated with various seed morphology traits were identified and mapped onto the reference sorghum genome to identify novel candidate defense genes.

The features of a class of cubic curves with a shape factor are analyzed by means of the theory of envelope and topological mapping. The effects of the shape factor on the cubic curves are made clear. Necessary and sufficient conditions are derived for the curve to have one or two inflection points, a loop or a cusp, or to be locally or globally convex. Those conditions are completely characterized by the relative position of the edge vectors of the control polygon and the shape factor. The results are summarized in a shape diagram, which is useful when the cubic parametric curves are used for geometric modeling. Furthermore we discussed the influences of the shape factor on the shape diagram and the ability for adjusting the shape of the curve.

Today's city needs more than ever methodological devices that ensure a specific urbanism, in keeping with the complexity of relationships that occur in the urban space. Networks or systems of diverse nature must be activated to achieve a certain success of the city, even more so in its fragile central fabrics. The study is focused on the historic neighborhoods of the city of Jaén (Spain), which have a high vulnerability index. The aim is to establish a strategy to revitalize their urban and social capital and improve their articulation with the rest of the urban and territorial structure. A methodological proposal for the analysis of vulnerability factors and an urban and cartographic analysis of the public space is addressed through an interweaving of a triple network: environmental, social, and urban. For this purpose, the configuration of this overlapping network of analysis and formulation of criteria is presented, which considers the urban multiplicity and promotes new dynamics for the integrated urban regeneration of the area and for the activation of public and collective urban spaces.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

The stromal vascular cell fraction (SVF) of visceral and subcutaneous adipose tissue (VAT and SAT) has increasingly come into focus in stem cell research, since these compartments represent a rich source of multipotent adipose-derived stem cells (ASCs). ASCs exhibit a self- renewal potential and differentiation capacity. Our aim was to study the different expression of embryonic stem cell markers NANOG, SOX2 and OCT3/4 and to evaluate if there exists a hierarchal role in this network in ASCs derived from both SAT and VAT. ASCs were isolated from SAT and VAT biopsies of 72 consenting patients (23 men, 47 women; age 45 ± 10; BMI between 25 and 30 range) undergoing elective open-abdominal surgery. Sphere-forming capability was evaluated by plating cells in low adhesion plastic. Stem cell markers CD90 and CD105 were analyzed by flow cytometry and stem cell transcription factors NANOG, SOX2 and OCT3/4 were detected by immunoblotting and Real-Time PCR. NANOG, SOX2 and OCT3/4 interplay was explored by gene silencing. ASCs from VAT and SAT confirmed their mesenchymal stem cell (MSC) phenotype expressing the specific MSC markers CD90, CD105, NANOG, SOX2 and OCT3/4. NANOG silencing induced a significant OCT 3/4 (70% ± 0.05) and SOX2 (75% ± 0.03) down-regulation whereas SOX2 silencing did not affect NANOG gene expression. Adipose tissue is an important source of MSC, and siRNA experiments endorse a hierarchical role of NANOG in the complex transcription network that regulates pluripotency and plasticity.

Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

In this short communication, we conducted first-principles calculations to explore the stability of boron monochalcogenides (BX, X=S, Se or Te), as a new class of two-dimensional (2D) materials. We predicted BX monolayers with two different atomic stacking sequences of ABBA and ABBC, referred in this work to 2H and 1T, respectively. Analysis of phonon dispersions confirm the dynamical stability of BX nanosheets with the both 2H and 1T atomic lattices. Ab-initio molecular dynamics simulations reveal the outstanding thermal stability of all predicted monolayers at high temperatures over 1500 K. BX structures were found to exhibit high elastic modulus and tensile strengths. It was found that BS and BTe nanosheets can show high stretchability, comparable to that of the graphene. It was found that all predicted monolayers exhibit semiconducting electronic character, in which 2H structures present lower band-gaps as compared with 1T lattices. The band-gap values were found to decrease from BS to BTe. According to the HSE06 results, 1T-BS and 2H-BTe show respectively, the maximum (4.0 eV) and minimum (2.06 eV) electronic band-gaps. This investigation introduces boron monochalcogenides as a novel class of 2D semiconductors with remarkable thermal, dynamical and mechanical stability.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

Open source software such as OpenDSS has given a lot of help to distribution network researchers and educators. With high penetration of distributed renewable energy resources into distribution network, tradition distribution steady state analysis software such as OpenDSS is faced with difficulty in handling distributed generators. Three-phase distributed generators are often modeled in sequence frame while unbalanced distribution network are usually modeled in phase frame. So a load flow in sequence-phase coupled frame is proposed to handle models described in both frames. Voltage controlled DGs which are difficult to cope with in OpenDSS are handled in proposed program. The steady state analysis platform is programmed with open source Modelica language and the main aim of this paper is to introduce an open source platform for active distribution network steady analysis include load flow and short circuit analysis which can be easily adopted and improved by other educators and researchers.

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

The editorial explores the realm of emerging translational research in neurology and psychiatry, providing insights into various subjects, including neurodegenerative disorders and innovative therapeutic methods. The main objective is to study neurological and psychiatric disorders, such as Alzheimer's disease, with a particular emphasis on techniques for neural regeneration and repair. The editorial highlights the importance of taking a comprehensive approach to studying and treating complex neurological and psychiatric conditions. This involves thoroughly reviewing research papers on the causes, development, biomarkers, and treatment options for these conditions. The editorial emphasizes the necessity of using multidisciplinary and integrative methods to gain a deeper understanding and improve the treatment of these conditions. The development of an animal model for recurrent primary dysmenorrhea showcases the innovative techniques employed in the investigation of gynecological disorders, illustrating the capacity of translational research to connect fundamental scientific knowledge with practical medical applications. The cooperative endeavors of researchers and clinicians emphasized in this editorial lay the foundation for revolutionary advancements that hold the potential to enhance outcomes for individuals afflicted with neurological and psychiatric disorders globally.

Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.

Lead-acid batteries utilised in electrical substations release hydrogen and oxygen when these are charged. These gases could be dangerous and cause a risk of fire if they are not properly ventilated. Therefore, this research seeks to design and implement a network control panel for heating, ventilation, and air-conditioning systems (HVACS). This is done using a specific range of controllers, which have more than thirty loops of proportional, integral and derivative (PID) control to achieve a cost-effective design. It performs the required function of extracting hydrogen, oxygen, and maintaining the desired temperature of the battery storage room within the recommended limits (i.e. 25°C ±1°C tolerance) without compromising quality as set out in the user requirement specification in Appendix-A. The system control panel allows the user to access control parameters such as changing temperature set-points, fan-speed, sensor database amongst others. The hardware is configured to detect extreme hydrogen and oxygen gas content in the battery room and ensure that the HVACS extract the gas content to the outside environment. The results of the system show that the network control panel effectively operates as per the recommended system requirements. Therefore, the effective operation of the HVACS ensures sufficient gas ventilation, thus mitigating the risk of fire in a typical battery storage room. Furthermore, the effective operation of HVACS enhances battery lifespan because of regulated operating temperature, which is conducive to minimise the effect of sulfation in lead acid Batteries (LABs).

This review highlights the potential and limitations of mRNA vaccines with a particular focus on mucosal delivery as an alternative to parenteral administration. The strengths of mRNA vaccines are: a) Versatility: they can be adapted to target a wide range of pathogens; b) Faster development: designed and produced more quickly than traditional vaccines; c) High efficacy: effective in preventing severe disease, as demonstrated during the COVID-19 pandemic. Current challenges: a) Delivery: current methods (mainly intramuscular injection) may not be optimal for inducing mucosal immunity; b) Stability: mRNA is fragile and needs to be protected from degradation; c) Safety: potential side effects, including myocarditis, blood clotting, allergic reactions need further investigation. Mucosal delivery could be more effective in preventing the transmission of pathogens and is generally better accepted by the population. Pros: offers the potential to induce local immunity at the site of pathogen entry, potentially providing broader protection; Cons: developing effective delivery systems and adjuvants for mucosal delivery is difficult. Extracellular vesicles as a mucosal delivery system are promising and offer a potentially safe, scalable, and needle-free approach. Future directions: a) Optimizing mRNA constructs: strategies like codon optimization and self-amplifying RNA can improve stability and translation efficiency; b) Rigorous pre-clinical studies are essential before human trials.

Research Highlights: This study identified the cell cycle genes in birch that likely play important roles during plant growth and development. This analysis provides a basis for understanding the regulatory mechanism of various cell cycles in Betula pendula. Background and Objectives: The cell cycle factors not only influence cell cycle progression together, but also regulate accretion, division and differentiation of cells, and then regulate growth and development of plant. In this study, we identified the putative cell cycle genes in B. pendula genome, based on the annotated cell cycle genes in A. thaliana. It could serve as a foundation for further functional studies. Materials and Methods: The transcript abundance was determined for all the cell cycle genes in xylem, root, leaf and flower tissues using RNA-seq technology. Results: We identified 59 cell cycle gene models in the genome of B. pendula, 17 highly expression genes among them. These genes were BpCDKA.1, BpCDKB1.1, BpCDKB2.1, BpCKS1.2, BpCYCB1.1, BpCYCB1.2, BpCYCB2.1, BpCYCD3.1, BpCYCD3.5, BpDEL1, BpDpa2, BpE2Fa, BpE2Fb, BpKRP1, BpKRP2, BpRb1 and BpWEE1. Conclusions: We identified 17 core cell cycle genes in the genome of birch by combining phylogenetic analysis and tissue specific expression data.

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data is made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist’s perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

The use of large-scale antenna arrays grants considerable benefits in energy and spectral efficiency to wireless systems due to spatial resolution and array gain techniques. By assuming a dominant line-of-sight environment in a massive MIMO scenario with favorable propagation, we derive analytical expressions for the sum-capacity. % Then, we show that convenient simplifications on the sum-capacity expressions are possible when working at low and high SNR regimes. % Furthermore, in the case of a high SNR regime, it is demonstrated that the Gamma PDF can approximate the PDF of the instantaneous channel sum-capacity as the number of BS antennas grows. A second important demonstration presented in this work is that a Gamma PDF can also be used to approximate the PDF of the summation of the channel's singular values as the number of devices increases. Finally, it is important to highlight that the presented framework is useful for LPWAN's as we show that the transmit power of each device can be made inversely proportional to the number of BS antennas.

Network Embedding (NE) is an important method to learn the representations of network via a low-dimensional space. Conventional NE models focus on capturing the structure information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structure and semantic information of edges. BimoNet is composed of two parts, i.e., the bi-mode embedding part and the deep neural network part. For bi-mode embedding part, the first mode named add-mode is used to express the entity-shared features of edges and the second mode named subtract-mode is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then we take the nodes' adjacent matrix as the input of the deep neural network as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structure information of edges. In experiments, we evaluate BimoNet on three real-world datasets and task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently and significantly.

Zanthoxylum armatum is an forest tree with apomixis characteristic, and RWP-RKs are plant-specific transcription factors known to regulate nitrogen response and embryonic development. However, the genome-wide analysis of RWP-RK family genes in Z.armatum is unclear. In this study, 36 ZaRWP-RK transcription factors were identified in the genome of Z.armatum, among which 15 genes were belonged to the RKD subfamily and 21 belonged to the NLP subfamily. Duplication events of ZaRWP-RK genes were mainly segmental duplication, and synteny analysis revealed close phylogenetic relationship between Z.armatum and Arabidopsis. The analysis of cis-elements indicated that ZaRWP-RK genes may be involved in the regulation of embryonic development of Z.armatum by responding to plant hormones such as abscisic acid, auxin, and gibberellin. Real-time PCR showed that the expression levels of most ZaRWP-RK genes were significantly increased from flowers to young fruits. Protein-protein interaction network analysis further revealed the potential roles of the ZaRWP-RK proteins in apomixis. Collectively, this study provide a theoretical basis for future investigations into the ZaRWP-RK genes and their regulatory mechanisms in the apomixis process of Z.armatum.

The purpose of this study was to investigate the horizontal force and contribution of shoulder horizontal adductor and elbow extensor muscles activity during wide and narrow grip bench press (BP) in various conditions, such as resistance trained/non-trained, concentric/eccentric and muscle fatigue/non-fatigue.　We measured the horizontal force on the bar and the electromyographic (EMG) muscle activity of pectoralis major (PM) and triceps brachii (TB) during 10RM BP with wide grip (81cm) and narrow grip (40cm)　in 7　resistance trained men and 7 non-trained men. The all-reps average of the horizontal to vertical force ratio both in resistance trained and non-trained subjects was about 30% outward for the wide grip, and about 10% inward for the narrow grip. The EMG contribution ratio PM/TB shows no significant differences between narrow and wide grip in all evaluated conditions except in non-trained subjects muscle fatigue eccentric phase. Both resistance trained and non-trained subjects did not push the bar straight upward, and the EMG PM/TB was almost unchanged by hand width. The direction adjustment of the force on the bar that achieves almost the same muscle activity degree of the shoulder and elbow joints might be optimal BP kinetics.

(1) Background: The goal of our study was to identify SNPs, metabolic pathways (KEGG), and gene ontology (GO) terms significantly associated with calving and workability; (2) Methods: Based on the EuroGenomics reference data set, we analyzed direct (DCE) and maternal (MCE) calving ease, direct (DSB), and maternal (MSB) stillbirth, milking speed (MSP), and temperament (TEM). We estimated SNP effects using a multi-SNP mixed-model. Further, SNP positions were mapped to genes, and GO terms/KEGG pathways of the corresponding genes were assigned. The estimation of GO term/KEGG pathway effects was based on a mixed-model using SNP effects as dependent variables; (3) Results: The number of significant SNPs comprised 59 for DCE, 25 for DSB and MSP, 17 for MCE and MSB, and 7 for TEM. Significant KEGG pathways were found for MSB (2), TEM (2), and MSP (1), while 11 GO terms were significant for MSP, 10 for DCE, 8 for DSB and TEM, 5 for MCE, and 3 for MSB.; (4) Conclusions: From the perspective of a better understanding of the genomic background of the phenotypes, traits with low heritabilities suggest that the focus should be moved from single genes to metabolic pathways or gene ontologies significant for the phenotype.

This paper shows that the empirical distribution of cross-sectional analyst coverage in China's stock markets follows an exponential law in a given month from 2011 to 2020. The findings hold in both the emerging (Shanghai) and the developed market (Hong Kong). Moreover, the unique distribution parameter (i.e., mean) is directly related to the amount of market-wide information. Average analyst coverage exhibits a significant negative predictive power for stock-market uncertainty, highlighting the role of security analysts in diminishing the total uncertainty. The exponential law can be derived from the maximum entropy principle (MEP). When analysts, who are constrained by average ability in generating information (i.e., the first-order moment), strive to maximize the amount of market-wide information, this objective yields the exponential distribution. Contrary to the conventional wisdom that security analysts specialize in the generation of firm-specific information, empirical findings suggest that analysts primarily produce market-wide information for 25 countries. Nevertheless, it remains unclear why cross-sectional analyst coverage reflects market-wide information, this paper provides an entropy-based explanation.

Keywords: SAR; interferometry; bistatic

A real-time two-way direct load control (TWDLC) of central air-conditioning chillers in wide area is proposed to provide demand response. The proposed TWDLC scheme is designed to optimize the load shedding ratio of every customer under control to ensure the target load to be shed is met at every scheduling period. In order to overcome the load reduction uncertainties of TWDLC, an innovative solution is proposed by applying a certain degree of loosening on the constraint of the actual shed load. Fuzzy linear programming is utilized to solve the optimization problem with fuzzy constraints. The proposed fuzzy linear programming problem is solved by delicately transforming it into a regular liner programming problem. A selection scheme used to obtain the feasible candidates set for load shedding at every sampling interval of TWDLC is also designed along with the fuzzy linear programming.

Organic solar cells are contributing extensively to the production of future large area and cost-effective sources of renewable energy with a compact environmental impact. Moreover, due to tunable optical properties of the active medium, the semi-transparent organic solar cell can be fabricated and become a promising nominee for building Integrated Photovoltaics (BIPV), vehicles, and mobile electronic devices. Therefore, three newly synthesized polymers used as were host materials for the active medium and blended with beetroot dye. Hence, based on BHJ structure two ternary systems have been constructed and utilized to fabricate semitransparent solar cells and characterized. The results show that the natural dye has a crucial impact on solar cell characteristic parameters. Consequently, large value of V_OC close to 1V were obtained for ternary systems and acceptor material named PINDOLE exhibits potentials for electron transport layer (ETL) and shows an improvement in both fill factor and device efficiency.

Owing to the rising demand for vegetable soybean products, there is an increasing need for high-yield soybean varieties to raise the output. However, complex correlation patterns among quantitative traits with genetic interactions bring a challenge for vegetable soybean breeding. Here, a genome-wide association study (GWAS) was applied for six yield-related traits with 188 vegetable soybean accessions. A total of 81 SNPs were identified for pod length, plant height, pods number, pod thickness, pod width, and seed weight per plant by Blink model. Among these significant SNPs, 79 were novel SNPs, while 2 overlapped with previously reported SNPs. A total of 220 genes were found in 100 kb upstream and downstream regions of significant SNPs, of which 17 genes were functional proteins. Among these function proteins, four candidate genes, Glyma.13G109100, Glyma.03G183200, Glyma.09G102200, and Glyma.09G102300 were analyzed as significant haplotype variations, which encode MYB-related transcription factor, auxin-responsive family protein, F-box protein and CYP450, respectively. In addition, the relative expression with four candidate genes in the V030 and V071 (Among them, the plant height, pod number and fresh pod weight of V030 were lower than V071 strains) vegetable soybean were significantly different and these genes could be involved in plant growth and development via various pathways. Therefore, we proposed four genes from vegetable soybean germplasm that could be the key candidate genes for pod yield and plant height and these studies will accelerate the process of high-yield vegetable soybean breeding.

Plant protein phosphatase 2Cs (PP2Cs) serve as negative regulators of protein kinase cascades activated in different processes and play important roles in plant development and abiotic-stress-mediated signaling pathways. In this study, a genome-wide study was conducted on the CqPP2C gene family. A total of putative 117 CqPP2C genes were identified. Comprehensive analyses of physical and chemical feature, chromosome localization and subcellular localization were conducted. According to phylogenetic analysis, CqPP2Cs were divided into 13 subfamilies. CqPP2Cs in the same subfamily had similar gene structure, conserved motif and all the CqPP2C proteins had the type 2C phosphatase domains. Gene duplication revealed that segmental duplication was the major driving force for CqPP2Cs expansion and all duplicated CqPP2Cs evolved from purifying selection. The expression of CqPP2Cs in various tissues under different abiotic stresses was analyzed using RNA-seq data. The results revealed that CqPP2C genes were involved in regulating development and stress responses of quinoa. Real-time quantitative reverse transcription PCR (qRT-PCR) analysis of six CqPP2C genes in subfamily A revealed that they were up-regulated or down-regulated under salt and drought treatments. Furthermore, the results of yeast two-hybrid assays revealed that subfamily A CqPP2Cs interacted not only with subclass III CqSnRK2s, but also with subclass II CqSnRK2s. Subfamily A CqPP2Cs could interact with CqSnRK2s in different combinations and intensities to respond to various biological processes and stresses. Overall, our results will be useful for understanding the functions of CqPP2C in regulating ABA signals and responding to abiotic stress.

The basic (region) leucine zippers (bZIPs) are evolutionarily conserved transcription factors widely distributed in eukaryotic organisms. In plants, they are not only involved in growth and development, defense and stress responses, and regulation of physiological processes, but also play a pivotal role in regulating secondary metabolism. To explore the function related to the bZIP gene family in Stevia rebaudiana Bertoni, we identified 105 SrbZIP genes at the genome-wide level and classified into 12 subfamilies using bioinformation methods. Analysis of physicochemical properties showed that the amino acid length readings ranged from 96 aa (SrbZIP13) to 704 aa (SrbZIP17), and the isoelectric point ranged from 4.64 (SrbZIP21) to 10.08 (SrbZIP101). The majority of these proteins (94.29%) were localized in the nucleus. Three main classes of cis-acting elements found in the SrbZIP promoter regions, including development-related elements (CAT-box), defense and stress-responsive elements (LTR/ARE/MBS), and phytohormone respon- sive elements (ABRE/TGACG-mitif/TCA-element). Through protein-protein interac- tion network of 105 SrbZIP proteins, SrbZIP proteins were mainly classified into four major categories: ABF2/ABF4/ABI5 (SrbZIP51/SrbZIP38/SrbZIP7), involved in phy- tohormone signaling, GBF1/GBF3/GBF4 (SrbZIP29/SrbZIP63/SrbZIP60) involved in environmental signaling, AREB3 (SrbZIP88), PAN (SrbZIP12), TGA1 (SrbZIP69), TGA4 (SrbZIP82), TGA7 (SrbZIP31), TGA9 (SrbZIP95), TGA10 (SrbZIP79) and HY5 (SrbZIP96) involved in cryptochrome signaling, and FD (SrbZIP23, SrbZIP24, SrbZIP47, SrbZIP67, SrbZIP68, SrbZIP71, SrbZIP72) promoted flowering. The transcriptomic data showed that SrbZIP genes differentially expressed in six S. rebaudiana cultivars (‘023’, ‘110’, ‘1188’, ‘11-14’, ‘GP’ and ‘GX’). Moreover, the expression levels of selected 15 SrbZIP genes in response to light, abiotic stress (low temperature, salt and drought), phytohormones (methyl jasmonate, gibberellic acid and salicylic acid) treatment and in different tissues were analyzed utilizing qRT-PCR. Six SrbZIP (SrbZIP54, SrbZIP63, SrbZIP32, SrbZIP45, SrbZIP60 and SrbZIP9) genes were further identified to be highly induced by factors affecting glycoside synthesis. Among them, three SrbZIP genes (SrbZIP54, SrbZIP63 and SrbZIP32) were predicted to be related to stress-responsive terpenoid synthesis in S. rebaudiana. The protein-protein interaction network expanded the potential functions of SrbZIP genes. This study firstly provided the comprehensive genome-wide report of the SrbZIP gene family, laying a foundation for further research on evolution, function and regulatory role of bZIP gene family in terpenoid synthesis in S. rebaudiana.

Convolutional neural networks (CNN) have attracted much attention as a commonly used method for hyperspectral image (HSI) classification in recent years, however, CNNs can only be applied to Euclidean data and have limitations in dealing with relationships due to the limitations of local feature extraction. However, each pixel of a hyperspectral image contains a set of spectral bands that are correlated and interact with each other, and the methods used to process Euclidean data cannot effectively obtain these correlations. In contrast, the graph convolutional network (GCN) can be used in non-Euclidean data, but usually leads to oversmoothing and ignoring local detail features due to the need for superpixel segmentation processing to reduce computational effort. To overcome the above problems, we constructed a network a fusion network based on GCN and CNN, which contains two branches: a graph convolutional network based on superpixel segmentation and a convolutional network with added attention mechanism. The graph convolu-tional branch can extract the structural features and capture the relationships between the nodes, and the convolutional branch can extract the detailed features in the local fine region. Owing to the fact that the features extracted from the two branches are different, the classification performance can be improved by fusing the complementary features extracted from the two branches. To vali-date the proposed algorithm, experiments were conducted on three widely used datasets, namely Indian Pines, Pavia University, and Salinas, and the overall accuracy of 98.78% was obtained in the Indian Pines dataset, and the overall accuracy of 98.99% and 98.69% was obtained in the other two datasets. The results showed that the proposed fusion network can obtain richer features and achieve high classification accuracy.

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by the rapid population growth in the sub-region. SERVIR West Africa, a regional consortium jointly supported by USAID and NASA to foster the use of geospatial and earth observation data and science for decision-making on critical environmental and food security challenges, in collaboration with the Food and Agriculture Organization of the United Nations (FAO), is building technical capacity in the sub-region to develop and implement harmonized regional land cover classification system for LULC change monitoring in West Africa, based on the Land Cover Meta Language (LCML-ISO 19144-2). This article focuses on the process, potential opportunities, outputs of this collaboration, exemplary use cases, and recommendations for the next steps.

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost‐effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naïve integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with Fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study.

Codon use bias is an important characteristic in the process of genetic information transmission of species. With the publication of the genome of C. panzhihuaensis, it is of great significance to investigate the codon use bias for understanding the genetic evolution of this species and for molecular breeding. In this study, Perl language and CodonW 1.4.2 software was used to systematically analyze the coding gene sequences of the C. panzhihuaensis genome, obtain the characteristics of codon use, and identify the sources of variation affecting codon use. The results showed that GC3s content, GCall content, and ENC value were 46.75%, 47.67%, and 52.8 respectively. As the first seed plant, the GC3 content of Cycas is similar to that of most gymnosperms and most dicotyledons, but considerably different from that of monocotyledons.RSCU value was greater than 1, among which 21 codons ended in U or A. Enc-plot analysis, PR2-plot neutral plot analysis, and correlation coefficient analysis of Axis1 with GC3s and CAI revealed that the codon bias was mainly affected by natural selection, but also by mutation pressure and other factors. Thirty-one optimal codons of Cycas genes were screened. This research can provide a reference for the phylogeny and genome codon evolution of Cycas.

Supply chain management requires regular updates of the location of assets, which can be enabled by low power wide area networks, such as Sigfox. While it is useful to localize a device simply by its communication signals, this is very difficult to do with Sigfox because of wide area and ultra narrowband nature. On the other hand, installing a satellite localization element on the device greatly increases its power consumption. We investigated using information about nearby Wi-Fi access points as a way to localize the asset over the Sigfox network, so without connecting to those Wi-Fi networks. This paper reports the location error that can be achieved by this type of outdoor localization. By using a combination of two databases, we could localize the device on all 36 test locations with a median location error of 39 m. This shows that the localization accuracy of this method is promising enough to warrant further study, most specifically the minimal power consumption.

This study examined the tourism spatial distribution of nine cities in the Fujian province and assessed the impacts of COVID-19. The modified gravity model found that it was widely dispersed, with uneven and relatively independent tourism development in different cities. The social network analysis showed that tourism connections across cities were significantly reduced after the pandemic. The impacts of brand awareness and transport accessibility on spatial networks were positive in pre-pandemic but became negative during the pandemic. In contrast, tourist volume had negative impacts on spatial networks in pre-pandemic but had positive ones during the pandemic. Tourism resources and market performance had significantly positive impacts in the post-pandemic era. These findings provide advice on tourism recovery and destination management in coping with future critical events. It also contributes to the theoretical gravity framework in tourism and the research scope of the social networks analysis at the city level.

Ultra-wide field optical coherence tomography (UWF-OCT) has been recently introduced into clinical ophthalmological practice. To date, there is little data on the reference values of retinal thickness (RT) and choroidal thickness (CT) measured with this technique. This study aimed to analyze the variance of RT and CT in healthy eyes with UWF-OCT tests performed with the largest available scan size of 23 x 20 mm. The data were analyzed with reference to the patients' age, gender, and the axial length of the eyeball. The results of UWF-OCT scanning enabled us to picture the shape of the retina and choroid in a large portion of the eyeball. Both anatomical entities become significantly thinner at the periphery. The peripheral CT is greater in the upper and temporal sectors; the RT is higher in the nasal compared to the temporal sectors. Both choroid and retina lose thickness with age, however, CT and RT do not show a statistically significant correlation with the axial length after adjusting for age and gender. Age-related variations in thickness are especially prominent in the choroid. The CT in UWF-OCT testing is significantly greater in females while the RT is greater in males. UWF-OCT testing provides additional information on the anatomical structure of the retina and choroid compared to standard-field OCT

(1) Background: Brassinosteroids (BRs) are an important hormone that participates in almost all stages of plant growth and development, and sterol dehydrogenase is a key enzyme that is involved in the biosynthesis of BRs. However, the steroid dehydrogenase gene family in Populus yunnanensis Dode (P. yunnanensis) has not been studied; (2) Methods: The PyDET2 gene family was identified and bioinformatics analyzed. Three genes were screened based on the RNA-seq of stem tip, the PyDET2e was further investigated by RT-qPCR (real-time quantitative polymerase chain reaction) and subcellular localization; (3) Results: We identified 14 DET2 family genes in P. yunnanensis that were classified into 4 groups according to sequence similarity and phylogenetic relationship, and 10 conserved protein motifs had been identified. The gene structure, chromosome distribution, collinearity, and codon preference of all PyDET2 genes identified in the genome of P. yunnanensis were analyzed. The codon of this family is biased to the end of A/U, and natural selection has a great influence on it. The expression level of PyDET2e in September was higher than in July, and it was significantly expressed in stems, stem tips, and leaves. The PyDET2e protein was localized in chloroplasts; (4) Conclusions: The PyDET2e is involved in the development of P. yunnanensis in the rapid growth stage. The systematic analysis provided a basis for the genome-wide identification of genes related to brassinolide biosynthesis processes in P. yunnanensis, and laid a foundation for the study on the mechanism of rapid growth of P. yunnanensis.

This paper presents a novel dynamic camera parameter control method for position and posture estimation of highly miniaturized AR markers (micro AR markers) using a low cost general camera. The proposed method performs iterative calculation of the marker’s position and posture to converge them to specified accuracy with dynamically updating the camera’s zoom, focus and other parameter values based on the detected marker’s depth distances. For a 10 mm square micro AR marker, the proposed system demonstrated recognition accuracy of better than ±1.0% for depth distance and 2.5∘ for posture angle with a maximum recognition range of 1.0 m. In addition, the iterative calculation time was at most 0.7 seconds in an initial detection of the marker. These experimental results suggest that the proposed method and system can be applied to a robotic precise handling of small objects with low cost.

Apathy in patients with Parkinson's disease (PD) is one of the least studied manifestations of a wide range of neuropsychiatric disorders (PND). The frequency of apathy in PD, according to various researchers, is 17–80%. Structural and neurochemical changes associated with PD are considered as the leading pathophysiological factors of apathy. Possible general pathophysiological mechanisms of apathy and hypokinesia, depression, executive (frontal) cognitive functions, sleep disorders in PD are discussed. The basis of the pathophysiological commonality of apathy, hypokinesia, and executive functions is probably bilateral disturbances in the functional connections of the stratum and dorsolateral, medial parts of the prefrontal cortex. The combination of apathy and depression in PD may be due to dysfunction of the structures of the limbic system and the medial orbital prefrontal cortex responsible for motivational determined behavior. The variability of the relationship between apathy and hypokinesia, depression, cognitive impairments, sleep disorders at different stages of PD may be associated with the phenomenological heterogeneity of apathy.Apathy worsens the quality of life, makes a significant contribution to disorders of both household and social adaptation of PD patients. It is promising to study the possibility of correcting apathy using dopaminergic therapy. Against the background of the appointment of pramipexole (1.5-3.0 mg / day) to 20 patients with PD (middle stage, according to Hen-Yar-2.5) to correct movement disorders after 4-6 weeks of therapy, a statistically significant positive dynamics of apathy were noted. According to the total assessment of the scale of apathy SE Starkstein (AS). As a result of the study, no statistically significant correlation was found between the dynamics of the total apathy indicator and the dynamics of motor symptoms of PD according to the unified rating scale for assessing PD (“Motor functions”), which, we believe, indicates an independent effect of pramipexole therapy in relation to motivational disorders.

Background: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Also, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. Methods: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including N=229 African American and N=381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, N = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (N = 27,955) and Hispanic/Latino (N = 28,324) ancestry participants. Results: Our results revealed 24 suggestive signals (p < 1×10^(-4)) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (N=802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. Conclusions: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

SARS-CoV2 (corona virus) has spread globally at an unprecedented rate; so far, increasing SARS-CoV2-infected individuals have been identified. Although the situation in China is improving and is currently under control, the outbreak in other countries and its pandemic management is only beginning to develop. Based on 154 SARS-CoV2 genome sequence analyses, we used receptor–ligand docking to identify one potential point mutation (V354F) on the spike structure which enhances spike binding to ACE2 receptors underlying potential super infection. Importantly, the V354F site on spike S1 had been identified in 5/10 infected French patients living in Paris, who sharing 100% identical SARS-CoV2 genomes. With Covid-19 cases increasing rapidly in France that could lead to a new explosion, we suggest that the French government should identify all potential super spreaders and treat them accordingly. In summary, our study provides on of the measures to avoid the potential second worldwide explosion of SARS-CoV2.

Convolutional Neural Networks (CNNs) are often criticized for their lack of transparency, acting as 'black boxes' in decision-making, a challenge compounded by class imbalance in ECG datasets, which limits their clinical application. This study introduces a CNN-based ECG signal classification model that enhances interpretability and addresses class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). The model also integrates Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and SHAP value analysis, facilitating the visualization of decision boundaries and the assessment of feature contributions. Our evaluation using the MIT-BIH Arrhythmia Database highlights the model's high performance, with accuracy and precision nearing 1.00 for Normal ECG (NOR), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and Ventricular Premature Beat (PVC) in the six-class task. In the ten-class task, the model demonstrated robustness, particularly with an accuracy of 0.9846, precision of 0.9783, recall of 0.9736, and F1 score of 0.9760 in Pacemaker Fusion Beat (PFHB), supported by an AUC of 0.9999 and AP of 0.9885. These results underscore the model's efficacy in cardiac rhythm recognition and resilience to class imbalance. Future research will explore sophisticated model architectures and feature extraction methods to enhance the model's generalization and clinical applicability for early heart disease diagnosis and personalized treatment.

in our increasingly interconnected world, sensor networks are critical in gathering and sending data for various applications, from environmental monitoring and industrial automation to healthcare and smart cities. However, as sensor networks expand in importance, so does the need to solve the multidimensional concerns of security, privacy, and forensics. This article explores the complex world of sensor network security, the delicate balance between data privacy and utility, and the emerging area of sensor network forensics. This article focuses on risk assessment of a network.

Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.

Entamoeba histolytica, the causative agent of amebiasis, is the third leading cause of death among parasitic diseases globally. Its life cycle includes encystation, which has been mostly studied in Entamoeba invadens, responsible of reptilian amebiasis. However, the molecular mechanisms underlying this process are not fully understood. Therefore, we focused on the identification and characterization of Myb proteins, that regulate the expression of encystation-related genes in various protozoan parasites. Through bioinformatic analysis, we identified 47 genes in E. invadens encoding MYB-domain-containing proteins. These were classified into single-repeat 1R (19), 2R-MYB proteins (27), and one 4R-MYB protein. The in silico analysis suggests that these proteins are multifunctional, participating in transcriptional regulation, chromatin remodeling, telomere maintenance, and splicing. Transcriptomic data analysis revealed expression signatures of eimyb genes, suggesting a potential orchestration in the regulation of early and late encystation-excystation genes. Furthermore, we identified 3135 probable target genes associated with reproduction, meiotic cell cycle, ubiquitin-dependent protein catabolism, and endosomal transport. In conclusion, our findings suggest that E. invadens Myb proteins regulate stage-specific proteins and a wide array of cellular processes. This study provides a foundation for further exploration of the molecular mechanisms governing encystation and unveils potential targets for therapeutic intervention in amebiasis.

This paper has designed a four-channel wide dynamic microwave rectifier circuit for the shortcomings of uneven output voltage of each branch of the multi-channel rectification technology. A voltage-doubling rectifier circuit with identical structure was used in each branch. The four rectifier branches shared the same load at the output terminal, allowing the rectifier circuit to have an RF-DC conversion efficiency greater than 50% over the input power range from 12mW to 274mW, widened by 274% compared to a single channel. Not only the advantages of multi-channel technology with wide dynamics were retained, but also the output voltage was highly consistent and could be directly output in parallel without complex power electronics conversion.

Transcription factors (TFs) in the homeodomain-leucine zipper (HD-ZIP) family serve as essential regulators of plant development and control responses to environmental stimuli. To date, however, the HD-ZIP gene family in Prunus nana remains to be fully characterized. Accordingly, a genome-wide analysis of P. nana HD-ZIP family genes was performed using the most recent genomic data available, leading to the classification of 30 HD-ZIP TFs. These genes were annotated and subjected to systematic analyses of phylogenetic relationships, protein physicochemical properties, sequence-based structural characteristics, chromosomal distributions, and associated cis-acting regulatory elements. High levels of diversity were observed with respect to the gene structures for these PnaHD-Zip genes and the cis-regulatory elements found in the promoter regions upstream of these genes, suggesting that they play diverse roles in a range of biological contexts. These 30 PnaHD-Zip genes were further classified into four subgroups based on the results of phylogenetic, gene structure, and conserved motif analyses. Subsequent qPCR analyses indicated that PnaHD-Zip1 and PnaHD-Zip7 expression levels tended to increase was continuously inhibited in response to cold stress, suggesting that proteins in this HD-ZIP gene family may exhibit distinct responses to low-temperature stress exposure. Overall, these results offer a robust foundation for future studies seeking to explore the functional roles that HD-ZIP TFs play as regulators of cold stress tolerance in P. nana, in addition to offering more general insight regarding the regulatory functions and characteristics of these different HD-ZIP genes in P. nana.

The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super-Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as data augmentation better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly from a personal laptop.

Sentinel-1 (S1) extra-wide (EW) swath data in cross-polarization (horizontal-vertical, HV or vertical-horizontal, VH) are strongly affected by the scalloping effect and thermal noise, particularly over areas with weak backscattered signals, such as sea surfaces. Although noise vectors in both the azimuth and range directions are provided in the standard S1 EW data for subtraction, the residual thermal noise still significantly affects sea ice detection by the EW data. In this paper, we improve the denoising method developed in previous studies to remove the additive noise for the S1 EW data in cross-polarization. Furthermore, we propose a new method for eliminating the residual noise (i.e. multiplicative noise) at the sub-swath boundaries of the EW data, which cannot be well processed by simply subtracting the reconstructed 2-D noise field. The proposed method of removing both the additive and multiplicative noise was applied to EW HV-polarized images processed using different Instrument Processing Facility (IPF) versions. The results suggest that the proposed algorithm significantly improves the quality of EW HV-polarized images under various sea ice conditions and sea states in marginal ice zone (MIZ) of the Arctic. This is of great support for the utilization of cross-polarization SAR images in wide swaths for intensive sea ice monitoring in polar regions.

Millimeter wave wide-band imaging is widely studied for a variety of applications. However real-time millimeter wave wide-band imaging at frequencies above 30GHz for moving targets in a large field of view has not been realized commercially. A 2D sparse array with transmitter multiplexing is a promising solution to this problem. In this article, a method combining compressed sensing and orthogonal coded multiplexing was proposed, and the imaging performance was analyzed for different reconstruction algorithms and observation matrices by imaging simulation for a continuous object. Also the influence on the dynamic range of the original signal introduced by orthogonal coded multiplexing was studied. This work demonstrated that the proposed method was effective in reconstructing the image with a real-time capability. It is shown that different algorithms and matrices resulted in distinct performances, while the evaluation parameter selection also played a role. This work provided useful instructions for both the hardware and software design of a real-time 3D millimeter wave imaging system in the future.

A recent study claimed that genome-wide polygenic scores (GPSs) for five common diseases could identify individuals with risk equivalent to monogenic mutations. Receiver operator curve analyses were reported to have areas under the curve (AUCs) ranging from 0.63 for inflammatory bowel disease up to 0.81 for coronary artery disease (CAD) but these models also included age and sex, themselves strong predictors of risk. The GPS for CAD identified 8% of the population at threefold increased risk, which it was claimed was comparable to the excess risk from monogenic mutations. In the present study attempts were made to model the distribution of the GPS for CAD to match the information provided. These models were based on the reported distribution of prevalence by centile of GPS and on the distribution of GPS in controls and cases and were fitted to the reported results using linear approximations to the distributions and using simulations of a liability-threshold model. It was impossible to produce a compatible model in which the GPS produced an AUC as high as 0.81 and the most plausible estimate was that the true AUC was only 0.65. The reported distributions of the GPS in cases and controls overlap so much that they are not compatible with an AUC of 0.7 or higher. The AUC of the GPS for these diseases is modest. Furthermore, the literature robustly demonstrates that true CAD risk associated with monogenic mutations is much higher than the threefold increase which is predicted by the GPS. Together, these findings cast doubt on the clinical utility of the GPS.

Pasmo is one of the most widespread diseases threatening flax production. To identify genetic regions associated with pasmo resistance (PR), a genome-wide association study was performed on 370 accessions from the flax core collection. Evaluation of pasmo severity was performed in the field from 2012 to 2016 in Morden, MB, Canada. Genotyping-by-sequencing has identified 258,873 single nucleotide polymorphisms (SNPs) distributed on all 15 flax chromosomes. Marker-trait associations were identified using ten different statistical models. A total of 692 unique quantitative trait nucleotides (QTNs) associated with 500 putative quantitative trait loci (QTL) were detected from six phenotypic PR datasets (five individual years and average across years). Different QTNs were identified with various statistical models and from individual PR datasets, indicative of the complementation between analytical methods and/or genotype × environment interactions of the QTL effects. The single-locus models tended to identify large-effect QTNs while the multi-loci models were able to detect QTNs with smaller effects. Among the putative QTL, 67 had large effects (3-23%), were stable across all datasets and explained 32-64% of the total variation for PR in the various datasets. Forty-five of these QTL spanned 85 resistance gene analogs including a large toll interleukin receptor, nucleotide-binding site, leucine-rich repeat (TNL) type gene cluster on chromosome 8. The number of positive effect QTL (NPQTL) in accessions was significantly correlated with PR (R²=0.55), suggesting additive effects. NPQTL was also significantly associated with morphotype (R²=0.52) and major positive effect QTL were present in the fiber type accessions. The 67 large effect QTL are suited for marker-assisted selection and the 500 QTL for effective genomic prediction in PR molecular breeding.

For a wide field of view (FoV) wavefront coding athermalized infrared imaging system with a single decoding kernel, the off-axis aberration tends to cause artefacts. In order to correct off-axis aberration, many pieces of lenses will reduce the transmission efficiency and increase the weight and cost. To meet requirements for wide FoV, wide operating temperature and low weight of infrared imaging systems, this paper reports a wide-FoV wavefront coding athermalized infrared imaging system with a two-piece lens. Its principle, design, manufacture, measurement and performance validation are successively discussed. This paper constructs an optimization problem which maximizes the weighted mean of PSF consistency for both the FoV and operating temperature range. The two-piece lens contains four surfaces, where three aspheric surfaces are introduced to reduce optical off-axis aberrations and a cubic surface is introduced to achieve athermalization. The optical phase mask containing an aspheric surface and a cubic surface is manufactured by nano-metric machining of ion implanted material(NiIM). Experimental results validate that our wide-FoV wavefront coding athermalized infrared imaging system has a full FoV of 26.10° and an operating temperature over -20°C to +70°C.

The development of modern genomic tools has sped up the breeding program of food crops. More recently, genomic resources are being developed for tropical forages and will be key resources for developing climate resilient high yielding forage varieties. In this study, we present a genome-wide association study for biomass yield and feed quality traits in buffel grass (Cenchrus ciliaris L. aka Pennisetum ciliare L.,). Genome-wide markers generated using the DArTSeq platform and mapped onto the reference genome of Setaria italica were used for the genome-wide association study. The results revealed several markers associated with biomass yield and feed quality traits. A total of 78 marker trait associations were identified with R2 values ranging from 0.138 to 0.236. The marker trait associations were distributed across different chromosomes. Of these associations, the most marker trait associations (23) were observed on Chr9, followed by Chr5 with 12. The fewest number of marker trait associations were observed on Chr4 with 2. In terms of traits, 17 markers were associated with biomass yield, 24 with crude protein, 26 with TDN, 14 with ADF, 10 with NDF and six with DMI. Twenty of the identified markers were associated with at least two traits. The identified marker trait associations provide a useful genomic resource for future improvement and breeding of buffel grass.

Sulfate transporters (SULTRs) are responsible for the uptake of the sulfate (SO42−) ions in the rhizosphere by the roots and their distribution in plant organs. In this study, SULTR family members in the genome of the two oilseed crops, Camelina sativa, and Brassica napus, were identified and characterized based on their sequence structure, duplication events, phylogenetic relationships, phosphorylation sites, and expression levels. Herein, 36 and 45 putative SULTR genes were recognized from the genome of C. sativa, and B. napus, respectively. SULTR proteins were predicted as basophilic proteins with low hydrophilicity in both studied species. According to phylogenetic relationships, we divided SULTRs into five groups, in which SULTRs 3 showed highest variation. Besides, several duplication events were observed between SULTRs. The first duplication event was predicted approximately five million years ago between three SULTRs 3.1 in C. sativa. Two subunits were indicated in the 3D structure of SULTRs that the active binding sites differed between C. sativa and B. napus. According to available RNA-seq data, SULTRs showed diverse expression in tissues and response to stimuli. SULTRs 3 showed an expression in all tissues. SULTRs 3.1 were more upregulated in response to abiotic stresses in C. sativa, while SULTRs 3.3, and SULTRs 2.1 showed an upregulation in B. napus. Furthermore, SULTRs 3 and SULTRs 4.1 showed an upregulation in response to biotic stresses in B. napus. Based on the distribution of cis-regulatory elements in the promoter region, we speculated that SULTRs might be controlled by phytohormones such as ABA, and MeJA. Therefore, it seems that SULTR genes in C. sativa have been more influenced by evolutionary processes and have acquired more diversity.

The electronic structure and some of its derived properties of Li2CaGeO4 compound have been investigated. The calculations have been performed using the full-potential linearized augmented plane wave plus local orbitals method and ultra-soft pseudo-potentials . The optimized lattice parameters are found to be ingood accord with experiment. Features such as bulk modulus and its pressure derivative, electronic band structure and density of states are reported. The elastic anisotropy of the crystal is discussed and visualized. Moreover, the optical properties reveal that Li2CaGeO4 compound are suitable candidates for optoelectronic devices in the visible and ultraviolet (UV) regions.

A novel configuration for a super-wide impedance planar antenna is presented based on a 2 × 2 microstrip patch antenna (MPA). The antenna comprises a symmetrical arrangement of four-square patches that are interconnected to each other with cross-shaped high impedance microstrip lines. The antenna array is exciting through a single feedline connected to one of the patches. The proposed antenna array configuration overcomes the main drawback of conventional MPA of narrow bandwidth that is typically <5%. The antenna exhibits a super-wide frequency bandwidth from 20 GHz to 120 GHz for S₁₁<−15 dB, which corresponds to a fractional bandwidth of 142.85%. The antenna’s performance of bandwidth, impedance match, and radiation gain were enhanced by etching slots on the patches. The slotted microstrip patch essentially acts like a left-handed capacitance to exhibit metamaterial properties over a given frequency range. With the inclusion of the slot the maximum radiation gain and efficiency of the MPA have increased to 15.11 dBi and 85.79% at 80 GHz, which show an improvement of 2.58 dBi and 12.54%, respectively. The dimension of each patch antenna is 30 × 30 mm². The results show that the proposed MPA is useful for various communications existing and emerging systems such as ultra-wideband (UWB) communications, RFID systems, massive multiple-output multiple-input (MIMO) for 5G, and radar systems.