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Spatiotemporal crowdsourcing involves the use of various Internet of Things (IoT) devices distributed across industrial environments to collect and transmit spatiotemporal data related to industrial operations. Unmanned aerial vehicles (UAVs) play a crucial role in further collecting this data from IoT devices, especially in spatiotemporal crowdsourcing tasks. In the realm of industrial IoT energy management, allocating spatiotemporal crowdsourcing resources to UAVs poses substantial challenges. Traditional approaches to this problem have focused on optimizing the Age of Information (AoI) to ensure timely and equitable data updates. However, these methods often overlook critical operational constraints such as UAV no-fly zones and the risk of data interception by eavesdroppers. These issues can adversely affect the freshness and integrity of the information being gathered and transmitted. To address these shortcomings, this paper presents a novel deep reinforcement learning-based framework for UAV spatiotemporal crowdsourcing resource allocation. This approach aims to minimize the average AoI across the network while also reducing the energy consumption of IoT devices. It incorporates spatial constraints imposed by UAV no-fly zones and actively manages the transmission of jamming signals to mitigate the threat posed by eavesdroppers, thus ensuring data security. However, the complexity of allocating spatiotemporal crowdsourcing resources to UAVs is notable owing to numerous decision variables, which increase linearly with the duration of the service. Furthermore, the relationship between performance metrics and decision variables is intricate, requiring adherence to quality of service requirements. This problem is formalized as a Markov decision process (MDP), providing a structured approach to model the decision-making scenario faced by UAVs in a dynamic environment. To solve this MDP, we employ the soft actor critic (SAC) algorithm, an advanced deep reinforcement learning method known for its sample efficiency and stability. The SAC algorithm is adept at handling the continuous action spaces typical of UAV flight paths and power control problems, making it particularly well-suited for our application. We rigorously test our proposed methods in scenarios involving multiple UAVs, demonstrating the algorithm’s effectiveness in managing the spatiotemporal allocation of resources. Our results show that the SAC algorithm achieves faster convergence speed and better solutions than existing state-of-the-art methods, such as the twin delayed deep deterministic policy gradient (TD3) and the deep deterministic policy gradient (DDPG) algorithms. Furthermore, the paper delves into the strategic selection of the optimal number of UAVs to balance the trade-offs between coverage, energy consumption, and operational efficiency. By analytically and empirically examining the impact of the UAV fleet size on system performance, we provide insights into configuring UAV networks to achieve optimal outcomes in terms of AoI, energy management, and security. In conclusion, our research introduces a robust and intelligent framework for UAV resource allocation. The demonstrated efficacy of the SAC algorithm in this context paves the way for its future application in other domains where secure, efficient, and intelligent resource management is paramount.

Objective The present study aimed to investigate the functional connectivity (FC) of the anterior and posterior hypothalamus with the whole brain in insomnia disorder (ID) patients. Additionally, we explored the relationship between FC values and serum levels of arousal-promoting neurotransmitters (orexin-A and histamine) in ID patients. Methods This study enrolled 30 ID patients and 30 age- and gender-matched healthy controls. Resting-state functional magnetic resonance imaging (RS-fMRI) was employed to assess the FC of the anterior and posterior hypothalamus with the whole brain. Serum concentrations of orexin-A and histamine were measured using enzyme-linked immunosorbent assay (ELISA). Moreover, Spearman correlation analysis was conducted to investigate the relationship between FC values and serum levels of arousal-promoting neurotransmitters in ID patients. Results Our findings showed decreased FC between the posterior hypothalamus and several brain regions including the bilateral orbital superior frontal gyrus, the bilateral angular gyrus, the right anterior cingulate cortex, the left precuneus, and the right medial superior frontal gyrus in ID patients. Additionally, decreased FC was observed between the anterior hypothalamus and the right anterior cingulate cortex among ID patients. Compared to the healthy controls, ID patients showed significantly elevated serum concentrations of orexin-A and histamine. Furthermore, we identified a positive correlation between the FC of the right medial superior frontal gyrus with posterior hypothalamus and histamine levels in ID patients. Conclusion ID patients exhibited aberrant FC in brain regions related to sleep-wake regulation, particularly involving the default mode network and anterior cingulate cortex, which may correlate with the peripheral levels of histamine. These findings contribute to our understanding of the potential neuroimaging and neurohumoral mechanism underlying ID patients.

IntroductionThe plant genome encodes a plethora of proteins with structural similarity to animal receptor protein kinases, collectively known as receptor-like protein kinases (RLKs), which predominantly localize to the plasma membrane where they activate their kinase domains to convey extracellular signals to the interior of the cell, playing crucial roles in various signaling pathways. Despite the large number of members within the RLK family, to date, only a few have been identified as pattern-recognition receptors (PRRs), leaving many potential RLKs that could play roles in plant immunity undiscovered.MethodsIn this study, a recombinant strategy was initially employed to screen the kinase domains of 133 RLKs in the Arabidopsis genome to determine their involvement in the pathogen-triggered immunity (PTI) pathway. Subsequently, 6 potential immune-related recombinant RLKs (rRLKs) were selected for the creation of transgenic materials and underwent functional characterization analysis. Finally, a sequence analysis was conducted on the kinase domains of these 133 RLKs as well as the known immune RLK receptor kinase domains from other species.ResultsIt was found that 24 rRLKs activated the PTI response in Arabidopsis fls2 mutant protoplasts following flg22 treatment. Consistently, when 6 of these rRLKs were individually expressed in fls2 background, they exhibited diverse PTI signal transduction capabilities via different pathways while all retained membrane localization. Intriguingly, sequence analysis revealed multiple conserved amino acid sites within kinase domains of these experimentally identified immune-related RLKs in Arabidopsis. Importantly, these patterns are also preserved in RLKs involved in PTI in other species.DiscussionThis study, on one hand, identifies common features that theoretically can enhance our understanding of immune-related RLKs and facilitate the discovery of novel immune-related RLKs in the future. On the other hand, it provides experimental evidence for the use of recombinant technique to develop diverse rRLKs for molecular breeding, thereby conferring high resistance to plants without compromising their normal growth and development.

Abstract Background The number of spikelets per spike is a key trait that affects the yield of bread wheat (Triticum aestivum L.). Identification of the QTL for spikelets per spike and its genetic effects that could be used in molecular assistant breeding in the future. Results In this study, four recombinant inbred line (RIL) populations were generated and used, having YuPi branching wheat (YP), with Supernumerary Spikelets (SS) phenotype, as a common parent. QTL (QSS.sicau-2 A and QSS.sicau-2D) related to SS trait were mapped on chromosomes 2 A and 2D through bulked segregant exome sequencing (BSE-Seq). Fourteen molecular markers were further developed within the localization interval, and QSS.sicau-2 A was narrowed to 3.0 cM covering 7.6 Mb physical region of the reference genome, explaining 13.7 − 15.9% the phenotypic variance. Similarly, the QSS.sicau-2D was narrowed to 1.8 cM covering 2.4 Mb physical region of the reference genome, and it explained 27.4 − 32.9% the phenotypic variance. These two QTL were validated in three different genetic backgrounds using the linked markers. QSS.sicau-2 A was identified as WFZP-A, and QSS.sicau-2D was identified a novel locus, different to the previously identified WFZP-D. Based on the gene expression patterns, gene annotation and sequence analysis, TraesCS2D03G0260700 was predicted to be a potential candidate gene for QSS.sicau-2D. Conclusion Two significant QTL for SS, namely QSS.sicau-2 A and QSS.sicau-2D were identified in multiple environments were identified and their effect in diverse genetic populations was assessed. QSS.sicau-2D is a novel QTL associated with the SS trait, with TraesCS2D03G0260700 predicted as its candidate gene.

Abstract Autonomic nervous system disorders play a pivotal role in the pathophysiology of cardiovascular diseases. Regulating it is essential for preventing and treating acute ventricular arrhythmias (VAs). Photothermal neuromodulation is a nonimplanted technique, but the response temperature ranges of transient receptor potential vanilloid 1 (TRPV1) and TWIK-related K+ Channel 1 (TREK1) exhibit differences while being closely aligned, and the acute nature of VAs require that it must be rapid and precise. However, the low photothermal conversion efficiency (PCE) still poses limitations in achieving rapid and precise treatment. Here, we achieve a nearly perfect blackbody absorption and a high PCE in the second near infrared (NIR-II) window (73.7% at 1064 nm) via a Pt nanoparticle shell (PtNP-shell). By precisely manipulating the photothermal effect, we successfully achieve rapid and precise multimodal neuromodulation encompassing neural activation (41.0–42.9 °C) and inhibition (45.0–46.9 °C) in a male canine model. The NIR-II photothermal modulation additionally achieves multimodal reversible autonomic modulation and confers protection against acute VAs associated with myocardial ischemia and reperfusion injury in interventional therapy.

To bolster agricultural efficiency and precision, this study introduces the YOLOv8-MRF model (multi-path coordinate attention, receptive field attention convolution, and Focaler-CIoU-optimized YOLOv8), a groundbreaking advancement in automated detection of wheat tillers. This model transcends traditional manual methods prone to subjectivity and inefficiency. This approach integrates an enhanced multi-path coordinate attention (MPCA) mechanism within the backbone network, capturing multi-scale features and significantly elevating tillers recognition. The innovative replacement of the CSPDarknet53 to 2-Stage FPN (C2F) module with receptive field attention convolution (RFCAConv) addresses parameter-sharing limitations, accentuating feature significance, and amplifying network performance. Coupled with the Focaler-CIoU loss for superior detection accuracy, YOLOv8-MRF outperforms RTDETR, YOLOv5, YOLOv7, and YOLOv8 by impressive margins in mAP50, while operating with merely 11 % of the parameters of YOLOv7, achieving a detection precision of 91.7 %, and with enhancements of 2.5 % in precision, 5.5 % in recall, and 4.1 % in mAP50 over the original model. The experimental results demonstrate that this method can realize tillering detection under complex backgrounds, contributing to advancing intelligent farming practices for wheat. Importantly, the YOLOv8-MRF model not only achieves significant technological advancements but also shows strong potential in practical applications, providing an effective tool for agricultural automation and intelligence, which could become pivotal in the development of future precision agriculture technologies.

Pandemic outbreaks significantly disrupt human work activity, which is a crucial aspect of urban daily life, potentially causing reduced income or unemployment. These disruptions often vary across different population groups and regions. However, most existing studies focus on general human mobility patterns with limited attention specific to work activity, and conduct separate analyses on population groups and regions, overlooking intra-population differences across regions and inter-population variations within the same region. To this end, we first introduce the concept of work intensity to quantify the work activity. Using large-scale mobile phone data, we then estimate an individual’s work intensity, and characterize the changes in work intensity based on the concept of resilience, i.e., the ability to withstand and recover from a disaster. Finally, we propose a novel analytical framework that integrates both population groups and regions to assess differences in resilience. Herein, we take the pandemic outbreak in Beijing after the sudden relaxation of dynamic zero-COVID policy as a case study due to less policy intervention. Results reveal that females and younger people exhibit lower work intensity resilience, respectively. We also find significant regional differences and several negative features for resilience: short distance to city center, long home-to-work distance, high density of high-paying jobs, low road density, and high density of subway stations. By integrating both population group and region perspectives, we identify vulnerable population groups in specific regions. This integrated perspective can help design more targeted response and recovery strategies, and thereby promote health-related urban resilience and sustainability.

IntroductionAutoimmune encephalitis (AE) comprises a group of inflammatory brain disorders mediated by autoimmune responses. Anti–N-methyl-D-aspartate receptor (NMDAR) encephalitis, anti–leucine-rich glioma-inactivated 1 (LGI1) encephalitis, and anti–γ-aminobutyric acid-B receptor (GABABR) encephalitis are the most prevalent forms, characterized by the presence of antibodies against neuronal cell-surface antigens. Efgartigimod, an antagonist of the neonatal Fc receptor, has proven efficacy in myasthenia gravis treatment. This clinical case report describes the clinical progression and functional outcomes of AE in three patients who received efgartigimod treatment.Case presentationsCase 1 was a 60-year-old man exhibiting memory impairment and psychiatric disturbances over 11 days. Case 2 was a 38-year-old man with a 1-month history of rapid cognitive decline and seizures. Case 3 was a 68-year-old woman with mental behavioral changes and seizures for 4 months. Anti-GABABR, anti-LGI1, and anti-NMDAR antibodies were confirmed in the respective patients’ cerebrospinal fluid or serum. All three patients experienced marked and swift symptomatic relief after four cycles of efgartigimod treatment, with no complication.ConclusionCurrent first-line and second-line treatments for AE have limitations, and efgartigimod has demonstrated potential in the rapid and efficacious treatment of AE, emerging as a promising option for the management of this disease.

BackgroundThe present study explored the serum glutamate (Glu), glutamine (Gln), glutamic acid dehydrogenase (GAD) concentrations and the mRNA expression levels of the N-methyl-D-aspartate receptor (NMDAR) NR1 subunit in the peripheral blood of patients with insomnia disorder (ID). To our knowledge, this is the first study showing an increase in the mRNA expression levels of the NMDAR NR1 subunit in patients with ID.MethodsThis study included 30 ID patients and 30 matched healthy controls. We investigated the demographic and illness information and assessed subjective sleep quality using the Pittsburgh Sleep Quality Index. The Hamilton Depression Scale-17 and Hamilton Anxiety Scale were used to evaluate the patients’ symptoms of depression and anxiety, respectively. The quantifications of Glu, Gln and GAD concentrations were performed by Enzyme-linked immunosorbent assay (ELISA). Real-time PCR was used to detect the mRNA expression levels of the NMDAR NR1 subunit in peripheral blood.ResultsCompared with the healthy control group, the serum Glu concentrations and the mRNA expression levels of the NMDAR NR1 subunit in the ID group were significantly higher. However, there was no significant difference in Gln and GAD between the two groups. The receiver operating characteristic (ROC) analysis showed that the mRNA expression levels of the NMDAR NR1 subunit could distinguish ID patients from healthy individuals (area under the curve: 0.758; sensitivity: 73.3%; specificity: 76.7%). A negative correlation was found between the mRNA expression levels of the NMDAR NR1 subunit for age, total duration of illness, and age of first onset in the ID group, whereas a positive correlation was detected for daytime dysfunction.ConclusionGlutamatergic neurotransmission was abnormal in ID patients. Additionally, the mRNA expression levels of the NMDAR NR1 subunit appeared to have potential as a clinical biomarker for ID. However, the sample size of our study was limited, and future studies with larger sample sizes are needed to further validate and explore the mechanisms involved and to assess the reliability of the biomarker.

IntroductionTobacco is a critical economic crop, yet its cultivation heavily relies on chemical pesticides, posing health risks to consumers, therefore, monitoring pesticide residues in tobacco is conducive to ensuring food safety. However, most current research on pesticide residue detection in tobacco relies on traditional chemical methods, which cannot meet the requirements for real-time and rapid detection.MethodsThis study introduces an advanced method that combines hyperspectral imaging (HSI) technology with machine learning algorithms. Firstly, a hyperspectral imager was used to obtain spectral data of tobacco samples, and a variety of spectral pre-processing technologies such as mean centralization (MC), trend correction (TC), and wavelet transform (WT), as well as feature extraction methods such as competitive adaptive reweighted sampling (CARS) and least angle regression (LAR) were used to process the spectral data, and then, grid search algorithm (GSA) is used to optimize the support sector machine (SVM).ResultsThe optimized MC-LAR-SVM model achieved a pesticide classification accuracy of 84.1%, which was 9.5% higher than the original data model. The accuracy of the WT-TC-CARS-GSA-SVM model in the fenvalerate concentration classification experiment was as high as 91.8 %, and it also had excellent performance in other metrics. Compared with the model based on the original data, the accuracy, precision, recall, and F1-score are improved by 8.3 %, 8.2 %, 7.5 %, and 0.08, respectively.DiscussionThe results show that combining spectral preprocessing and feature extraction algorithms with machine learning models can significantly enhance the performance of pesticide residue detection models and provide robust, efficient, and accurate solutions for food safety monitoring. This study provides a new technical means for the detection of pesticide residues in tobacco, which is of great significance for improving the efficiency and accuracy of food safety detection.

Tobacco stems constitute a large amount of waste biomass generated during tobacco production, and their recycling is of great significance to the environment and the conservation of resources. In this study, an efficient, inexpensive, and less toxic strategy is reported for recycling waste tobacco stem, wherein the spherical tobacco stem nanocellulose (STsN) with a size of 10 to 100 nm was prepared from waste stems using a NaOH/urea/thiourea aqueous system. The morphology of STsN was characterized using scanning electron microscopy. The crystal structure of STsN was determined using X-ray diffractometry. The nanocellulose exhibited the crystal structure of cellulose II. Fourier-transform infrared spectra of the STsN indicated that STsN retained the typical chemical structure of cellulose. The thermal properties of STsN were investigated by thermogravimetry. It is concluded that the STsN had better thermal stability than cellulose. The product has potential for practical application with high thermal stability requirements, such as transistors and batteries.

Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. The burgeoning development of integrated sensing and communication (ISAC) provides a new paradigm for data collection. A diverse data collection framework is established where the uplink communication and sensing functions are both considered, which can also be referred to as the uplink ISAC system. An optimization is formulated to minimize the data freshness indicator for communication and the detection freshness indicator for sensing by optimizing the UAV paths, the transmitted power of IoT devices and UAVs, and the transmission allocation indicators. Three state-of-the-art deep reinforcement learning (DRL) algorithms are utilized to solve this optimization. Experiments are conducted in both single-UAV and multi-UAV scenarios, and the results demonstrate the effectiveness of the proposed algorithms. In addition, the proposed algorithms outperform the benchmark in terms of accuracy and efficiency. Moreover, the effectiveness of the data collection mode with only communication or sensing functions is also verified. Also, the numerical Pareto front between communication and sensing performance is obtained by adjusting the importance parameter.

Phosphorus (P) deficiency is one of the most critical factors for plant growth and productivity, including its inhibition of lateral root initiation. Auxin response factors (ARFs) play crucial roles in root development via auxin signaling mediated by genetic pathways. In this study, we found that the transcription factor ZmARF1 was associated with low inorganic phosphate (Pi) stress-related traits in maize. This superior root morphology and greater phosphate stress tolerance could be ascribed to the overexpression of ZmARF1. The knock out mutant zmarf1 had shorter primary roots, fewer root tip number, and lower root volume and surface area. Transcriptomic data indicate that ZmLBD1, a direct downstream target gene, is involved in lateral root development, which enhances phosphate starvation tolerance. A transcriptional activation assay revealed that ZmARF1 specifically binds to the GC-box motif in the promoter of ZmLBD1 and activates its expression. Moreover, ZmARF1 positively regulates the expression of ZmPHR1, ZmPHT1;2, and ZmPHO2, which are key transporters of Pi in maize. We propose that ZmARF1 promotes the transcription of ZmLBD1 to modulate lateral root development and Pi-starvation induced (PSI) genes to regulate phosphate mobilization and homeostasis under phosphorus starvation. In addition, ZmERF2 specifically binds to the ABRE motif of the promoter of ZmARF1 and represses its expression. Collectively, the findings of this study revealed that ZmARF1 is a pivotal factor that modulates root development and confers low-Pi stress tolerance through the transcriptional regulation of the biological function of ZmLBD1 and the expression of key Pi transport proteins.

C-repeat binding factor (CBF) subfamily genes encoding transcriptional activators are members of the AP2/ERF superfamily. CBFs play important roles in plant tolerance to abiotic stress. In this study, we identified and analyzed the structure, phylogeny, conserved motifs, and expression profiles of 12 CBFs of the grass species Lolium perenne cultured under abiotic stress. The identified LpCBFs were grouped into three phylogenetic clades according to their protein structures and motif organizations. LpCBF expression was differentially induced by cold, heat, water deficit, salinity, and abscisic acid, among which cold treatment induced LpCBF gene expression significantly. Furthermore, association network analysis indicated that different proteins, including certain stress-related proteins, potentially interact with LpCBFs. Altogether, these findings will enhance our understanding of LpCBFs protein structure and function in the regulation of L. perenne stress responses. Our results will provide valuable information for further functional research of LpCBF proteins in L. perenne stress resistance.

The total number of spikelets (TSPN) and the number of fertile spikelets (FSPN) affect the final number of grains per spikelet in wheat. This study constructed a high-density genetic map using 55K single nucleotide polymorphism (SNP) arrays from a population of 152 recombinant inbred lines (RIL) from crossing the wheat accessions 10-A and B39. Twenty-four quantitative trait loci (QTLs) for TSPN and 18 QTLs for FSPN were localized based on the phenotype in 10 environments in 2019–2021. Two major QTLs, QTSPN/QFSPN.sicau-2D.4 (34.43–47.43 Mb) and QTSPN/QFSPN.sicau-2D.5(32.97–34.43 Mb), explained 13.97%–45.90% of phenotypic variation. Linked kompetitive allele-specific PCR (KASP) markers further validated these two QTLs and revealed that QTSPN.sicau-2D.4 had less effect on TSPN than QTSPN.sicau-2D.5 in 10-A×BE89 (134 RILs) and 10-A×Chuannong 16 (192 RILs) populations, and one population of Sichuan wheat (233 accessions). The alleles combination haplotype 3 with the allele from 10-A of QTSPN/QFSPN.sicau-2D.5 and the allele from B39 of QTSPN.sicau-2D.4 resulted in the highest number of spikelets. In contrast, the allele from B39 for both loci resulted in the lowest number of spikelets. Using bulk-segregant analysis–exon capture sequencing, six SNP hot spots that included 31 candidate genes were identified in the two QTLs. We identified Ppd-D1a from B39 and Ppd-D1d from 10-A and further analyzed Ppd-D1 variation in wheat. These results identified loci and molecular markers with potential utility for wheat breeding and laid a foundation for further fine mapping and cloning of the two loci.

Abstract Schizophrenia (SZ) places a tremendous burden on public health as one of the leading causes of disability and death. SZ patients are more prone to developing obesity than the general population from the clinical practice. The development of obesity frequently causes poor psychiatric outcomes in SZ patients. In turn, maternal obesity during pregnancy has been associated with an increased risk of SZ in offspring, suggesting that these two disorders may have shared neuropathological mechanisms. The gut microbiota is well known to serve as a major regulator of bidirectional interactions between the central nervous system and the gastrointestinal tract. It also plays a critical role in maintaining physical and mental health in humans. Recent studies have shown that the dysbiosis of gut microbiota is intimately associated with the onset of SZ and obesity through shared pathophysiological mechanisms, particularly the stimulation of immune inflammation. Therefore, gut microbiota may serve as a common biological basis for the etiology in both SZ and obesity, and the perturbed gut–brain axis may therefore account for the high prevalence of obesity in patients with SZ. On the basis of these findings, this review provides updated perspectives and intervention approaches on the etiology, prevention, and management of obesity in SZ patients by summarizing the recent findings on the role of gut microbiota in the pathogenesis of SZ and obesity, highlighting the role of gut‐derived inflammation.

Tiller angle (TA) strongly influences plant architecture and grain yield in cereals. However, the genetic basis of TA in wheat is largely unknown. We identified three TA-related quantitative trait loci (QTL). One of them was QTa.sau-2B-769, a major QTL localized on chromosome arm 2BL. QTa.sau-2B-769 was detected in seven environments, explaining 18.1%–51.1% of phenotypic variance. We developed a linked Kompetitive Allele-Specific Polymerase chain reaction (KASP) marker, KASP-AX-108792274, to further validate this locus in three additional populations in multiple environments. QTa.sau-2B-769 increased TA by up to 24.9% in these populations. There were significant and positive correlations between TA and flag leaf angle (FLANG). However, TA was not correlated with plant height or anthesis date, suggesting that expression of QTa.sau-2B-769 is independent of vernalization. TraesCS2B01G583800, a gene known to be involved in leaf angle regulation, was identified as the most likely candidate gene for QTa.sau-2B-769. These results enrich our understanding of the mechanisms regulating wheat TA at maturity and may support precise mapping and cloning of gene(s) underlying QTa.sau-2B-769.

Summary: Cell type-specific expression of the developmental gene is conferred by distinct enhancer elements. Current knowledge about mechanisms in Nkx2-5 transcriptional regulation and its specific roles in multistage heart morphogenesis is limited. We comprehensively interrogate enhancers U1 and U2 in controlling Nkx2-5 transcription during heart development. Serial genomic deletions in mice reveal U1 and U2 function redundantly to confer Nkx2-5 expression at early stages, but U2 instead of U1 supports its expression at later stages. Combined deletions markedly reduce Nkx2-5 dosage as early as E7.5, despite being largely reinstated two days later, displaying heart malformations with precocious differentiation of cardiac progenitors. Cutting-edge low-input chromatin immunoprecipitation sequencing (ChIP-seq) confirmed that not only genomic NKX2-5 occupancy but also its regulated enhancer landscape is mostly disturbed in the double-deletion mouse hearts. Together, we propose a model that the temporal and partially compensatory regulatory function of two enhancers dictates a transcription factor (TF)’s dosage and specificity during development.

The centrality of a place reflects its relative importance in serving other places, and quantifying centrality is a prerequisite for assessing the urban spatial structure and facilitates policy-making of sustainable urban development. Two important aspects in quantifying the centrality of a place are the amount of service it provides to other places and the spatial diversity of the places it serves. For example, a central city attracts not only many people but also people from various cities. However, few existing centrality measures can address the two aspects simultaneously. In this study, following the idea of the scholar’s H-index, we propose a novel flow-based locational measure, the X-index, which quantifies the centrality of a place by considering both the volume and directions of flows toward the place in an automated way. We demonstrate the effectiveness and advantages of the X-index over other common centrality measures by quantifying the centrality of Chinese cities and spatial grid cells in Beijing. The results show that: (1) The X-index can successfully detect all the official national central cities and reveal the hierarchy of cities. (2) The X-index is positively correlated with the variety of urban functions in grid cells, which is consistent with central place theory.

Plant trichomes are epidermal structures with a wide variety of functions in plant development and stress responses. Although the functional importance of trichomes has been realized, the tedious and time-consuming manual phenotyping process greatly limits the research progress of trichome gene cloning. Currently, there are no fully automated methods for identifying maize trichomes. We introduce TrichomeYOLO, an automated trichome counting and measuring method that uses a deep convolutional neural network, to identify the density and length of maize trichomes from scanning electron microscopy images. Our network achieved 92.1% identification accuracy on scanning electron microscopy micrographs of maize leaves, which is much better performed than the other 5 currently mainstream object detection models, Faster R-CNN, YOLOv3, YOLOv5, DETR, and Cascade R-CNN. We applied TrichomeYOLO to investigate trichome variations in a natural population of maize and achieved robust trichome identification. Our method and the pretrained model are open access in Github (https://github.com/yaober/trichomecounter). We believe TrichomeYOLO will help make efficient trichome identification and help facilitate researches on maize trichomes.

Background: In the emergent situation of COVID-19, off-label therapies and newly developed vaccines may bring the patients more adverse drug event (ADE) risks. Data mining based on spontaneous reporting systems (SRSs) is a promising and efficient way to detect potential ADEs to help health professionals and patients get rid of the risk.Objective: This pharmacovigilance study aimed to investigate the ADEs of some attractive drugs (i.e., “hot drugs” in this study) in COVID-19 prevention and treatment based on the data from the US Food and Drug Administration (FDA) adverse event reporting system (FAERS).Methods: The FAERS ADE reports associated with COVID-19 from the 2nd quarter of 2020 to the 2nd quarter of 2022 were retrieved with hot drugs and frequent ADEs were recognized. A combination of support, lower bound of 95% confidence interval (CI) of the proportional reporting ratio (PRR) was applied to detect significant hot drug and ADE signals by the Python programming language on the Jupyter notebook.Results: A total of 66,879 COVID-19 associated cases were retrieved with 22 hot drugs and 1,109 frequent ADEs on the “preferred term” (PT) level. The algorithm finally produced 992 significant ADE signals on the PT level among which unexpected signals such as “hypofibrinogenemia” of tocilizumab and “disease recurrence” of nirmatrelvir\ritonavir stood out. A picture of signals on the “system organ class” (SOC) level was also provided for a comprehensive understanding of these ADEs.Conclusion: Data mining is a promising and efficient way to assist pharmacovigilance work, and the result of this study could help timely recognize ADEs in the prevention and treatment of COVID-19.

Abstract Background Improvement of wheat gercTriticum aestivum L.) yield could relieve global food shortages. Kernel size, as an important component of 1000-kernel weight (TKW), is always a significant consideration to improve yield for wheat breeders. Wheat related species possesses numerous elite genes that can be introduced into wheat breeding. It is thus vital to explore, identify, and introduce new genetic resources for kernel size from wheat wild relatives to increase wheat yield. Results In the present study, quantitative trait loci (QTL) for kernel length (KL) and width (KW) were detected in a recombinant inbred line (RIL) population derived from a cross between a wild emmer accession ‘LM001’ and a Sichuan endemic tetraploid wheat ‘Ailanmai’ using the Wheat 55 K single nucleotide polymorphism (SNP) array-based constructed linkage map and phenotype from six different environments. We identified eleven QTL for KL and KW including two major ones QKL.sicau-AM-3B and QKW.sicau-AM-4B, the positive alleles of which were from LM001 and Ailanmai, respectively. They explained 17.57 to 44.28% and 13.91 to 39.01% of the phenotypic variance, respectively. For these two major QTL, Kompetitive allele-specific PCR (KASP) markers were developed and used to successfully validate their effects in three F3 populations and two natural populations containing a panel of 272 Chinese wheat landraces and that of 300 Chinese wheat cultivars, respectively. QKL.sicau-AM-3B was located at 675.6–695.4 Mb on chromosome arm 3BL. QKW.sicau-AM-4B was located at 444.2–474.0 Mb on chromosome arm 4BL. Comparison with previous studies suggested that these two major QTL were likely new loci. Further analysis indicated that the positive alleles of QKL.sicau-AM-3B and QKW.sicau-AM-4B had a great additive effect increasing TKW by 6.01%. Correlation analysis between KL and other agronomic traits showed that KL was significantly correlated to spike length, length of uppermost internode, TKW, and flag leaf length. KW was also significantly correlated with TKW. Four genes, TRIDC3BG062390, TRIDC3BG062400, TRIDC4BG037810, and TRIDC4BG037830, associated with kernel development were predicted in physical intervals harboring these two major QTL on wild emmer and Chinese Spring reference genomes. Conclusions Two stable and major QTL for KL and KW across six environments were detected and verified in three biparental populations and two natural populations. Significant relationships between kernel size and yield-related traits were identified. KASP markers tightly linked the two major QTL could contribute greatly to subsequent fine mapping. These results suggested the application potential of wheat related species in wheat genetic improvement.

Kernel weight (KW), together with kernel number per unit area, determines yield of cereal crops. Here, two barley recombinant inbred lines (RILs) populations with a shared parent were used to identify loci controlling KW. One is Baudin/AWCS276 (BA) for which a linkage map was available. Several large-effect QTL controlling KW were detected in this population. Another is Morex/AWCS276 (MA). A linkage map with 5273 makers formed 1454 clusters, was constructed by the genotyping by sequence (GBS) data of 201 RILs from this population. A single marker was selected to represent each of the clusters and the linkage map constructed with these markers covers a total length of 1022.4 cM with an average interval of approximately 0.7 cM between loci. Three of the large-effect loci controlling KW (located on 2HL, 6HL, and 7HL, respectively) identified from the BA population were also detected in the MA population under different environments. The locus on 6HL was detected in each of the experiments conducted for both populations thus was selected for developing near isogenic lines (NILs). Apart from KW, the two isolines for each pair of the putative NILs obtained showed no significant difference for any of the morphological characteristics assessed. The average difference in KW between the isolines for all the NILs obtained was about 15% based on assessments under both glasshouse and field conditions. Taken advantage that high quality genome assemblies for both Morex and AWCS276 are available, we identified candidate genes underlying two of the three loci based on an orthologous analysis. The NILs developed and the candidate genes identified in this study should facilitate the cloning and functional analysis of genes regulating KW in barley.

Fusarium crown rot (FCR), caused by various Fusarium species, is a primary fungal disease in most wheat-growing regions worldwide. A. tauschii, the diploid wild progenitor of the D-genome of common wheat, is a reservoir of genetic diversity for improving bread wheat biotic and abiotic resistance/tolerance. A worldwide collection of 286 A. tauschii accessions was used to evaluate FCR resistance. Population structure analysis revealed that 115 belonged to the A. tauschii ssp. strangulata subspecies, and 171 belonged to the A. tauschii ssp. tauschii subspecies. Five accessions with disease index values lower than 20 showed moderate resistance to FCR. These five originated from Afghanistan, China, Iran, Uzbekistan, and Turkey, all belonging to the tauschii subspecies. Genome-wide association mapping using 6,739 single nucleotide polymorphisms (SNPs) revealed that two SNPs on chromosome 2D and four SNPs on chromosome 7D were significantly associated with FCR resistance. Almost all FCR resistance alleles were presented in accessions from the tauschii subspecies, and only 4, 11, and 19 resistance alleles were presented in accessions from the strangulata subspecies. Combining phenotypic correlation analysis and genome-wide association mapping confirmed that FCR resistance loci were independent of flowering time, heading date, and plant height in this association panel. Six genes encoding disease resistance-related proteins were selected as candidates for further validation. The identified resistant A. tauschii accessions will provide robust resistance gene sources for breeding FCR-resistant cultivars. The associated loci/genes will accelerate and improve FCR in breeding programs by deploying marker-assisted selection.

Optimizing root system architecture (RSA) allows crops to better capture water and nutrients and adapt to harsh environment. Parental reproductive environment (PRE) has been reported to significantly affect growth and development throughout the life cycle of the next generation. In this study, 10 RSA-related traits were evaluated in seedling stage from five independent hydroponic tests using seeds harvested from five different PREs. Based on the Wheat55K SNP array-based genetic map, quantitative trait loci (QTL) for these traits were detected in a recombinant inbred line population. Twenty-eight putative QTL for RSA-related traits were detected, covering thirteen chromosomal regions. A major QTL, QTrl.sicau-2SY-4D for total root length (TRL), which was likely independent of PREs, explained 15.81–38.48% of phenotypic variations and was located at 14.96–19.59 Mb on chromosome arm 4DS. Interestingly, it showed pleiotropic effects on TRL, root area, root volume, root forks, root dry weight, and shoot dry weight. The functional marker KASP-Rht-D1 for Rht-D1 was used to genotype 2SY population and remapping QTL for TRL showed that QTrl.sicau-2SY-4D was not linked to Rht-D1. The kompetitive allele-specific PCR (KASP) marker, KASP-AX-110527441 linked to this major QTL, was developed and used to successfully validate its effect in three different genetic populations. Further analysis suggested that the positive allele at QTrl.sicau-2SY-4D was mainly utilized in wheat breeding of northwest China where precipitation was significantly lower, indicating that wheat requires longer TRL to capture water and nutrients in arid or semi-arid regions due to deficient precipitation. Additionally, four genes (TraesCS4D03G0059800, TraesCS4D03G0057800, TraesCS4D03G0064000, and TraesCS4D03G0064400) possibly related to root development were predicted in physical interval of QTrl.sicau-2SY-4D. Taken together, these results enrich our understanding on the genetic basis of RSA and provide a potentially valuable TRL QTL for wheat breeding.

Introduction: Autoimmune encephalitis (AE) comprises a group of inflammatory brain disorders mediated by autoimmune responses. Anti-N-methyl-Daspartate receptor (NMDAR) encephalitis, anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis, and anti-g-aminobutyric acid-B receptor (GABABR) encephalitis are the most prevalent forms, characterized by the presence of antibodies against neuronal cell-surface antigens. Efgartigimod, an antagonist of the neonatal Fc receptor, has proven efficacy in myasthenia gravis treatment. This clinical case report describes the clinical progression and functional outcomes of AE in three patients who received efgartigimod treatment. Case presentations: Case 1 was a 60-year-old man exhibiting memory impairment and psychiatric disturbances over 11 days. Case 2 was a 38-yearold man with a 1-month history of rapid cognitive decline and seizures. Case 3 was a 68-year-old woman with mental behavioral changes and seizures for 4 months. Anti-GABABR, anti-LGI1, and anti-NMDAR antibodies were confirmed in the respective patients' cerebrospinal fluid or serum. All three patients experienced marked and swift symptomatic relief after four cycles of efgartigimod treatment, with no complication. Conclusion: Current first-line and second-line treatments for AE have limitations, and efgartigimod has demonstrated potential in the rapid and efficacious treatment of AE, emerging as a promising option for the management of this disease. [ABSTRACT FROM AUTHOR]

Abstract Background Phosphorus deficiency is a major limiting factors for affecting crop production globally. To understand the genetic variation of phosphorus-deficiency-tolerance, a total of 15 seedling traits were evaluated among 707 Chinese wheat landraces under application of phosphorus (AP) and non-application of phosphorus (NP). A total of 18,594 single-nucleotide polymorphisms and 38,678 diversity arrays technology sequencing markers were used to detect marker-trait associations under AP and NP. Results Top ten genotypes with extremely tolerance and bottommost ten genotypes with extremely sensitivity were selected from 707 Chinese wheat landraces for future breeding and genetic analysis. A total of 55 significant markers (81 marker-trait associations) for 13 traits by both CMLM and SUPER method. These were distributed on chromosomes 1A, 1B, 2A, 2B, 2D, 3A, 4B, 5A, 5B, 6A, 6B, 6D, 7A and 7B. Considering the linkage disequilibrium decay distance, 25 and 12 quantitative trait loci (QTL) were detected under AP and NP, respectively (9 QTL were specific to NP). Conclusions The extremely tolerant landraces could be used for breeding phosphorus-deficiency-tolerant cultivars. The QTL could be useful in wheat breeding through marker-assisted selection. Our findings provide new insight into the genetic analysis of P-deficiency-tolerance, and will be helpful for breeding P-deficiency-tolerant cultivars.

Wheat (Triticum aestivum L.) is one of the most important crops in the world. Here, four yield-related traits, namely, spike length, spikelets number, tillers number, and thousand-kernel weight, were evaluated in 272 Chinese wheat landraces in multiple environments. Five multi-locus genome-wide association studies (FASTmrEMMA, ISIS EN-BLASSO, mrMLM, pKWmEB, and pLARmEB) were performed using 172,711 single-nucleotide polymorphisms (SNPs) to identify yield-related quantitative trait loci (QTL). A total of 27 robust QTL were identified by more than three models. Nine of these QTL were consistent with those in previous studies. The remaining 18 QTL may be novel. We identified a major QTL, QTkw.sicau-4B, with up to 18.78% of phenotypic variation explained. The developed kompetitive allele-specific polymerase chain reaction marker for QTkw.sicau-4B was validated in two recombinant inbred line populations with an average phenotypic difference of 16.07%. After combined homologous function annotation and expression analysis, TraesCS4B01G272300 was the most likely candidate gene for QTkw.sicau-4B. Our findings provide new insights into the genetic basis of yield-related traits and offer valuable QTL to breed wheat cultivars via marker-assisted selection.

Spikelet number per spike (SNS) is the primary factor that determines wheat yield. Common wheat breeding reduces the genetic diversity among elite germplasm resources, leading to a detrimental effect on future wheat production. It is, therefore, necessary to explore new genetic resources for SNS to increase wheat yield. A tetraploid landrace “Ailanmai” × wild emmer wheat recombinant inbred line (RIL) population was used to construct a genetic map using a wheat 55K single- nucleotide polymorphism (SNP) array. The linkage map containing 1,150 bin markers with a total genetic distance of 2,411.8 cm was obtained. Based on the phenotypic data from the eight environments and best linear unbiased prediction (BLUP) values, five quantitative trait loci (QTLs) for SNS were identified, explaining 6.71–29.40% of the phenotypic variation. Two of them, QSns.sau-AM-2B.2 and QSns.sau-AM-3B.2, were detected as a major and novel QTL. Their effects were further validated in two additional F2 populations using tightly linked kompetitive allele-specific PCR (KASP) markers. Potential candidate genes within the physical intervals of the corresponding QTLs were predicted to participate in inflorescence development and spikelet formation. Genetic associations between SNS and other agronomic traits were also detected and analyzed. This study demonstrates the feasibility of the wheat 55K SNP array developed for common wheat in the genetic mapping of tetraploid population and shows the potential application of wheat-related species in wheat improvement programs.

Abstract Background Kernel length (KL), kernel width (KW) and thousand-kernel weight (TKW) are key agronomic traits in wheat breeding. Chuannong16 (‘CN16’) is a commercial cultivar with significantly longer kernels than the line ‘20828’. To identify and characterize potential alleles from CN16 controlling KL, the previously developed recombinant inbred line (RIL) population derived from the cross ‘20828’ × ‘CN16’ and the genetic map constructed by the Wheat55K SNP array and SSR markers were used to perform quantitative trait locus/loci (QTL) analyses for kernel traits. Results A total of 11 putative QTL associated with kernel traits were identified and they were located on chromosomes 1A (2 QTL), 2B (2 QTL), 2D (3 QTL), 3D, 4A, 6A, and 7A, respectively. Among them, three major QTL, QKL.sicau-2D, QKW.sicau-2D and QTKW.sicau-2D, controlling KL, KW and TKW, respectively, were detected in three different environments. Respectively, they explained 10.88–18.85%, 17.21–21.49% and 10.01–23.20% of the phenotypic variance. Further, they were genetically mapped in the same interval on chromosome 2DS. A previously developed kompetitive allele-specific PCR (KASP) marker KASP-AX-94721936 was integrated in the genetic map and QTL re-mapping finally located the three major QTL in a 1- cM region flanked by AX-111096297 and KASP-AX-94721936. Another two co-located QTL intervals for KL and TKW were also identified. A few predicted genes involved in regulation of kernel growth and development were identified in the intervals of these identified QTL. Significant relationships between kernel traits and spikelet number per spike and anthesis date were detected and discussed. Conclusions Three major and stably expressed QTL associated with KL, KW, and TKW were identified. A KASP marker tightly linked to these three major QTL was integrated. These findings provide information for subsequent fine mapping and cloning the three co-localized major QTL for kernel traits.

Abstract Background As one of the most important food crops in the world, increasing wheat (Triticum aestivum L.) yield is an urgent task for global food security under the continuous threat of stripe rust (caused by Puccinia striiformis f. sp. tritici) in many regions of the world. Molecular marker-assisted breeding is one of the most efficient ways to increase yield. Here, we identified loci associated to multi-environmental yield-related traits under stripe rust stress in 244 wheat accessions from Sichuan Province through genome-wide association study (GWAS) using 44,059 polymorphic markers from the 55 K single nucleotide polymorphism (SNP) chip. Results A total of 13 stable quantitative trait loci (QTLs) were found to be highly associating to yield-related traits, including 6 for spike length (SL), 3 for thousand-kernel weight (TKW), 2 for kernel weight per spike (KWPS), and 2 for both TKW and KWPS, in at least two test environments under stripe rust stress conditions. Of them, ten QTLs were overlapped or very close to the reported QTLs, three QTLs, QSL.sicau-1AL, QTKW.sicau-4AL, and QKWPS.sicau-4AL.1, were potentially novel through the physical location comparison with previous QTLs. Further, 21 candidate genes within three potentially novel QTLs were identified, they were mainly involved in the regulation of phytohormone, cell division and proliferation, meristem development, plant or organ development, and carbohydrate transport. Conclusions QTLs and candidate genes detected in our study for yield-related traits under stripe rust stress will facilitate elucidating genetic basis of yield-related trait and could be used in marker-assisted selection in wheat yield breeding.

The research on the mobile communication network optimization for the Internet of things large connection and differential service quality has great significance.Mobile communication network optimization is a multi-parameter complex optimization problem with high computational cost function.In order to provide the basis for parallelization of operations,the calculation method of the coverage quality assessment of the mobile communication network based on the computational graph was introduced.Based on the calculation graph,the derivative calculation method of the coverage quality index was obtained,by back propagation to guide the optimization of the antenna parameters.The momentum method was used to accelerate the convergence speed of the optimization algorithm.The simulation results show that the algorithm is suitable for the coverage optimization in mobile communication network.