PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science.

Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 crops and 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease education and efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managers and pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.

Localized variation in ancestral admixture identifies pilocytic astrocytoma risk loci among Latino children.

BACKGROUND: Pilocytic astrocytoma (PA) is the most common pediatric brain tumor. PA has at least a 50% higher incidence in populations of European ancestry compared to other ancestral groups, which may be due in part to genetic differences. METHODS: We first compared the global proportions of European, African, and Amerindian ancestries in 301 PA cases and 1185 controls of self-identified Latino ethnicity from the California Biobank. We then conducted admixture mapping analysis to assess PA risk with local ancestry. RESULTS: We found PA cases had a significantly higher proportion of global European ancestry than controls (case median = 0.55, control median = 0.51, P value = 3.5x10-3). Admixture mapping identified 13 SNPs in the 6q14.3 region (SNX14) contributing to risk, as well as three other peaks approaching significance on chromosomes 7, 10 and 13. Downstream fine mapping in these regions revealed several SNPs potentially contributing to childhood PA risk. CONCLUSIONS: There is a significant difference in genomic ancestry associated with Latino PA risk and several genomic loci potentially mediating this risk.

Investigating DNA methylation as a mediator of genetic risk in childhood acute lymphoblastic leukemia.

Genome-wide association studies have identified a growing number of single nucleotide polymorphisms (SNPs) associated with childhood acute lymphoblastic leukemia (ALL), yet the functional roles of most SNPs are unclear. Multiple lines of evidence suggest that epigenetic mechanisms may mediate the impact of heritable genetic variation on phenotypes. Here, we investigated whether DNA methylation mediates the effect of genetic risk loci for childhood ALL. We performed an epigenome-wide association study (EWAS) including 808 childhood ALL cases and 919 controls from California-based studies using neonatal blood DNA. For differentially methylated CpG positions (DMPs), we next conducted association analysis with 23 known ALL risk SNPs followed by causal mediation analyses addressing the significant SNP-DMP pairs. DNA methylation at CpG cg01139861, in the promoter region of IKZF1, mediated the effects of the intronic IKZF1 risk SNP rs78396808, with the average causal mediation effect (ACME) explaining ~30% of the total effect (ACME P = 0.0031). In analyses stratified by self-reported race/ethnicity, the mediation effect was only significant in Latinos, explaining ~41% of the total effect of rs78396808 on ALL risk (ACME P = 0.0037). Conditional analyses confirmed the presence of at least three independent genetic risk loci for childhood ALL at IKZF1, with rs78396808 unique to non-European populations. We also demonstrated that the most significant DMP in the EWAS, CpG cg13344587 at gene ARID5B (P = 8.61 × 10-10), was entirely confounded by the ARID5B ALL risk SNP rs7090445. Our findings provide new insights into the functional pathways of ALL risk SNPs and the DNA methylation differences associated with risk of childhood ALL.

Pleiotropic Effects of miR5504 Underlying Plant Height, Grain Yield and Quality in Rice.

MicroRNAs (miRNAs) are known to play critical roles in regulating rice agronomic traits through mRNA cleavage or translational repression. Our previous study indicated that miR5504 regulates plant height by affecting cell proliferation and expansion. Here, the two independent homozygous mir5504 mutants (CR1 and CR2) and overexpression lines (OE1 and OE2) were further used to investigate the functions of miR5504. The panicle length, 1000-grain weight and grain yield per plant of miR5504-OE lines were identical to those of Nipponbare (NIP), but the 1000-grain weight of mir5504 mutants was reduced by about 10% and 9%, respectively. Meanwhile, the grain width and thickness of mir5504 mutants decreased significantly by approximately 10% and 11%, respectively. Moreover, the cytological results revealed a significant decrease in cell number along grain width direction and cell width in spikelet in mir5504, compared with those in NIP. In addition, several major storage substances of the rice seeds were measured. Compared to NIP, the amylose content of the mir5504 seeds was noticeably decreased, leading to an increase of nearly 10 mm in gel consistency (GC) in mir5504 lines. Further investigation confirmed that LOC_Os08g16914 was the genuine target of miR5504: LOC_Os08g16914 over-expression plants phenocopied the mir5504 mutants. This study provides insights into the role of miR5504 in rice seed development.

Evaluation of the genetic basis of familial-associated early-onset hematologic cancers in an ancestral/ethnically diverse population.

Genetic predisposition to hematologic malignancies has historically been addressed utilizing patients recruited from clinical trials and pedigrees constructed at major treatment centers. Such efforts leave unexplored the genetic basis of variations in risk by race/ethnic group shown in population-based surveillance data where cancer registration, compulsory by law, delivers universal enrollment. To address this, we performed exome sequencing on DNA isolated from newborn bloodspots derived from sibling pairs with early-onset cancers across California in which at least one of the siblings developed a hematologic cancer, using unbiased recruitment from the full state population. We identified pathogenic/likely pathogenic (P/ LP) variants among 1,172 selected cancer genes that were private or present at low allele frequencies in reference populations. Within 64 subjects from 32 families, we found 9 LP variants shared between siblings, and an additional 7 such variants in singleton children (not shared with their sibling). In 8 of the shared cases, the ancestral origin of the local haplotype that carries P/LP variants matched the dominant global ancestry of study participant families. This was the case for Latino sibling pairs on FLG and CBLB, non-Latino White sibling pairs in TP53 and NOD2, and a shared GATA2 variant for a non-Latino Black sibling pair. A new inherited mutation in HABP2 was identified in a sibling pair, one with diffuse large B-cell lymphoma and the other with neuroblastoma. Overall, the profile of P/LP germline variants across ancestral/ethnic groups suggests that rare alleles contributing to hematologic diseases originate within their race/ethnic origin parental populations, demonstrating the value of this discovery process in diverse, population-based registries.

Epigenomic signature of major congenital heart defects in newborns with Down syndrome.

BACKGROUND: Congenital heart defects (CHDs) affect approximately half of individuals with Down syndrome (DS), but the molecular reasons for incomplete penetrance are unknown. Previous studies have largely focused on identifying genetic risk factors associated with CHDs in individuals with DS, but comprehensive studies of the contribution of epigenetic marks are lacking. We aimed to identify and characterize DNA methylation differences from newborn dried blood spots (NDBS) of DS individuals with major CHDs compared to DS individuals without CHDs. METHODS: We used the Illumina EPIC array and whole-genome bisulfite sequencing (WGBS) to quantitate DNA methylation for 86 NDBS samples from the California Biobank Program: (1) 45 DS-CHD (27 female, 18 male) and (2) 41 DS non-CHD (27 female, 14 male). We analyzed global CpG methylation and identified differentially methylated regions (DMRs) in DS-CHD versus DS non-CHD comparisons (both sex-combined and sex-stratified) corrected for sex, age of blood collection, and cell-type proportions. CHD DMRs were analyzed for enrichment in CpG and genic contexts, chromatin states, and histone modifications by genomic coordinates and for gene ontology enrichment by gene mapping. DMRs were also tested in a replication dataset and compared to methylation levels in DS versus typical development (TD) WGBS NDBS samples. RESULTS: We found global CpG hypomethylation in DS-CHD males compared to DS non-CHD males, which was attributable to elevated levels of nucleated red blood cells and not seen in females. At a regional level, we identified 58, 341, and 3938 CHD-associated DMRs in the Sex Combined, Females Only, and Males Only groups, respectively, and used machine learning algorithms to select 19 Males Only loci that could distinguish CHD from non-CHD. DMRs in all comparisons were enriched for gene exons, CpG islands, and bivalent chromatin and mapped to genes enriched for terms related to cardiac and immune functions. Lastly, a greater percentage of CHD-associated DMRs than background regions were differentially methylated in DS versus TD samples. CONCLUSIONS: A sex-specific signature of DNA methylation was detected in NDBS of DS-CHD compared to DS non-CHD individuals. This supports the hypothesis that epigenetics can reflect the variability of phenotypes in DS, particularly CHDs.

A qualitative study on inner experience of self-management behavior among elderly patients with type 2 diabetes in rural areas.

BACKGROUND: As a chronic metabolic disease, diabetes poses a serious threat to human health and has become a major public health problem in China and worldwide. In 2020, 30% of Chinese people (aged ≥ 60 years) reported having diabetes mellitus. Moreover, individuals with diabetes living in rural areas face a significantly higher mortality risk compared to those in urban areas. In this study, we explored the inner experience of self-management behaviors in elderly patients with type 2 diabetes in rural areas to inform targeted interventions. METHODS: A phenomenological research design was used to explore the inner experience of self-management in rural elderly diabetes. Ten elderly diabetic patients were sampled from December 2022 to March 2023 in rural areas of Yangcheng County, Jincheng City, ShanXi Province, China. The seven-step Colaizzi phenomenological was used to analyze the interview data and generate themes. RESULTS: Four themes emerged: "Insufficient self-management cognition", "Negative self-management attitude", "Slack self-management behavior", and "No time for self-management". CONCLUSION: The level of self-management among elderly patients with type 2 diabetes in rural areas is low. Healthcare professionals should develop targeted interventions aimed at enhancing their cognitive levels, modifying their coping styles, and improving their self-management abilities to improve their quality of life.

Optical pulse interharmonic extraction and repetition rate division based on a microwave photonic phase detector.

Microwave photonic phase detectors (MPPDs) can extract ultrastable microwaves from a mode-locked laser (MLL), but their frequencies are often limited by the pulse repetition rate. Few works studied methods to break the frequency limitation. Here, a setup based on an MPPD and an optical switch is proposed to synchronize an RF signal from a voltage-controlled oscillator (VCO) to an interharmonic of an MLL and to realize the pulse repetition rate division. The optical switch is employed to realize pulse repetition rate division, and the MPPD is followed to detect the phase difference between the frequency-divided optical pulse and the microwave signal from the VCO, which is then fed back to the VCO via a proportional-integral (PI) controller. Both the optical switch and the MPPD are driven by the signal from the VCO. When the system reaches its steady state, the synchronization and repetition rate division are achieved simultaneously. An experiment is conducted to verify the feasibility. The 80½th, 80⅓rd, and 80⅔rd interharmonics are extracted, and pulse repetition rate division factors of two and three are realized. The phase noises at offset frequency of 10 kHz are improved by more than 20 dB.

Rice miR5504 regulates plant height by affecting cell proliferation and expansion.

miRNAs play critical roles in the regulation of plant growth and development by cleaving mRNA or repressing transcription. In our previous study, miR5504 with unknown functions was captured by small RNA sequencing. Here, the function and characters of miR5504 were extensively analyzed using CRISPR/Cas9, overexpression strategy, Northern blot, cytological analysis, and transcriptomics analysis. We found that the dwarf phenotype of mir5504 mutants (mir5504-1 and mir5504-2) appeared on 35-day seedlings and became more apparent at the mature stage. The cytological results showed a substantial decrease in the vascular bundle number, cell number and cell length in the mir5504 mutant compared with NIP. In addition, we found that miR5504 regulated plant height by targeting LOC_Os08g16914. The results of RNA-seq revealed that numerous biological processes were mainly enriched, including DNA-binding transcription factor activity, transferase activity, regulation of transcription, metabolic process, and protein binding. Meanwhile, KEEG analysis showed that numerous proteins were associated with cellular processes and metabolism pathways. Taken together, miR5504 may be involved in the regulation of plant height by affecting cell expansion and division of internode in rice.

Salt-Assisted Selective Growth of H-phase Monolayer VSe2 with Apparent Hole Transport Behavior.

Vanadium diselenide (VSe2) exhibits versatile electronic and magnetic properties in the trigonal prismatic (H-) and octahedral (T-) phases. Compared to the metallic T-phase, the H-phase with a tunable semiconductor property is predicted to be a ferrovalley material with spontaneous valley polarization. Herein we report an epitaxial growth of the monolayer 2D VSe2 on a mica substrate via the chemical vapor deposition (CVD) method by introducing salt in the precursor. Our first-principles calculations suggest that the monolayer H-phase VSe2 with a large lateral size is thermodynamically favorable. The honeycomb-like structure and the broken symmetry are directly observed by spherical aberration-corrected scanning transmission electron microscopy (STEM) and confirmed by giant second harmonic generation (SHG) intensity. The p-type transport behavior is further evidenced by the temperature-dependent resistance and field-effect device study. The present work introduces a new phase-stable 2D transition metal dichalcogenide, opening the prospect of novel electronic and spintronics device design.

An Enhanced Neural Network Algorithm with Quasi-Oppositional-Based and Chaotic Sine-Cosine Learning Strategies.

Global optimization problems have been a research topic of great interest in various engineering applications among which neural network algorithm (NNA) is one of the most widely used methods. However, it is inevitable for neural network algorithms to plunge into poor local optima and convergence when tackling complex optimization problems. To overcome these problems, an improved neural network algorithm with quasi-oppositional-based and chaotic sine-cosine learning strategies is proposed, that speeds up convergence and avoids trapping in a local optimum. Firstly, quasi-oppositional-based learning facilitated the exploration and exploitation of the search space by the improved algorithm. Meanwhile, a new logistic chaotic sine-cosine learning strategy by integrating the logistic chaotic mapping and sine-cosine strategy enhances the ability that jumps out of the local optimum. Moreover, a dynamic tuning factor of piecewise linear chaotic mapping is utilized for the adjustment of the exploration space to improve the convergence performance. Finally, the validity and applicability of the proposed improved algorithm are evaluated by the challenging CEC 2017 function and three engineering optimization problems. The experimental comparative results of average, standard deviation, and Wilcoxon rank-sum tests reveal that the presented algorithm has excellent global optimality and convergence speed for most functions and engineering problems.

OsAT1, an anion transporter, negatively regulates grain size and yield in rice.

Improving the grain yield of rice is a central goal of basic and applied scientific research. Here, we identified an anion transporter, OsAT1, localized in the endoplasmic reticulum and Golgi. OsAT1 is highly expressed in flag, stem, and sheath as monitored using qRT-PCR and pOsAT1::GUS. Thousand-grain weight, grain weight per plant, and content of starch were significantly increased in OsAT1 knock-down mutants (OsAT1-Ri) but significantly decreased in OsAT1 overexpressed lines (OsAT1-OE). In addition, the grain weight per plant increased by 6.17% to 6.78% in OsAT1-RNAi lines, whereas it decreased by 45.93% to 46.76% in OsAT1-OE lines, compared to wild-type. Moreover, the copper content was noticeably reduced in flag leaf of OsAT1-Ri lines and increased in OsAT1-OE lines. RNA-sequencing analysis of OsAT1-OE lines revealed that the genes related to starch biosynthesis and metabolism pathway were enriched in the down-regulated category. Thus, our results suggest that knock-down of OsAT1 in rice possibly reduces copper accumulation and improves the accumulation of storage starch, hence, increasing the grain size and weight. OsAT1 may be a useful gene to consider for cereal breeding programs.

Directly modulated azimuthally polarized vector beam laser design.

Directly modulated vector beam lasers are increasingly desirable for wide applications ranging from optical manipulation to optical communications. We report the first, to our knowledge, high-speed directly modulated vector beam laser with azimuthally polarized emission. It is a microcylinder cavity interacted with a proper second-order grating on top, which enables single mode lasing and efficient surface emission. Through theoretical and numerical analysis, the laser is designed in detail. With an optimized top grating, the emission of the laser is an azimuthally polarized vector beam. With high-differential-gain material and a small active region, the laser can be directly modulated with a high 3 dB bandwidth reach of 40 GHz in simulation. The proposed high-speed directly modulated semiconductor laser with an azimuthally polarized vector beam is promising for applications in fiber space communications or quantum information.

Forecasting Network Interface Flow Using a Broad Learning System Based on the Sparrow Search Algorithm.

In this paper, we propose a broad learning system based on the sparrow search algorithm. Firstly, in order to avoid the complicated manual parameter tuning process and obtain the best combination of hyperparameters, the sparrow search algorithm is used to optimize the shrinkage coefficient (r) and regularization coefficient (λ) in the broad learning system to improve the prediction accuracy of the model. Second, using the broad learning system to build a network interface flow forecasting model. The flow values in the time period [T-11,T] are used as the characteristic values of the traffic at the moment T+1. The hyperparameters outputted in the previous step are fed into the network to train the broad learning system network traffic prediction model. Finally, to verify the model performance, this paper trains the prediction model on two public network flow datasets and real traffic data of an enterprise cloud platform switch interface and compares the proposed model with the broad learning system, long short-term memory, and other methods. The experiments show that the prediction accuracy of this method is higher than other methods, and the moving average reaches 97%, 98%, and 99% on each dataset, respectively.

Neural Adaptive Funnel Dynamic Surface Control with Disturbance-Observer for the PMSM with Time Delays.

This paper suggests an adaptive funnel dynamic surface control method with a disturbance observer for the permanent magnet synchronous motor with time delays. An improved prescribed performance function is integrated with a modified funnel variable at the beginning of the controller design to coordinate the permanent magnet synchronous motor with the output constrained into an unconstrained one, which has a faster convergence rate than ordinary barrier Lyapunov functions. Then, the specific controller is devised by the dynamic surface control technique with first-order filters to the unconstrained system. Therein, a disturbance-observer and the radial basis function neural networks are introduced to estimate unmatched disturbances and multiple unknown nonlinearities, respectively. Several Lyapunov-Krasovskii functionals are constructed to make up for time delays, enhancing control performance. The first-order filters are implemented to overcome the "complexity explosion" caused by general backstepping methods. Additionally, the boundedness and binding ranges of all the signals are ensured through the detailed stability analysis. Ultimately, simulation results and comparison experiments confirm the superiority of the controller designed in this paper.

An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy.

Numerical optimization has been a popular research topic within various engineering applications, where differential evolution (DE) is one of the most extensively applied methods. However, it is difficult to choose appropriate control parameters and to avoid falling into local optimum and poor convergence when handling complex numerical optimization problems. To handle these problems, an improved DE (BROMLDE) with the Bernstein operator and refracted oppositional-mutual learning (ROML) is proposed, which can reduce parameter selection, converge faster, and avoid trapping in local optimum. Firstly, a new ROML strategy integrates mutual learning (ML) and refractive oppositional learning (ROL), achieving stochastic switching between ROL and ML during the population initialization and generation jumping period to balance exploration and exploitation. Meanwhile, a dynamic adjustment factor is constructed to improve the ability of the algorithm to jump out of the local optimum. Secondly, a Bernstein operator, which has no parameters setting and intrinsic parameters tuning phase, is introduced to improve convergence performance. Finally, the performance of BROMLDE is evaluated by 10 bound-constrained benchmark functions from CEC 2019 and CEC 2020, respectively. Two engineering optimization problems are utilized simultaneously. The comparative experimental results show that BROMLDE has higher global optimization capability and convergence speed on most functions and engineering problems.

A Novel Bearing Fault Diagnosis Method Based on Few-Shot Transfer Learning across Different Datasets.

At present, the success of most intelligent fault diagnosis methods is heavily dependent on large datasets of artificial simulation faults (ASF), which have not been widely used in practice because it is often costly to obtain a large number of samples in reality. Fortunately, various faults can be easily simulated in the laboratory, and these simulated faults contain a lot of fault diagnosis knowledge. In this study, based on a Siamese network framework, we propose a bearing fault diagnosis based on few-shot transfer learning across different datasets (cross-machine), using the knowledge of ASF to diagnose bearings with natural faults (NF). First of all, the model obtains a good feature encoder in the source domain, then defines a fault support set for comparison, and finally adjusts the support set with a very small number of target domain samples to improve the fault diagnosis performance of the model. We carried out experimental verification from many aspects on the ASF and NF datasets provided by Case Western Reserve University (CWRU) and Paderborn University (PU). The results show that the proposed method can fully learn diagnostic knowledge in different ASF datasets and sample numbers, and effectively use this knowledge to accurately identify the health state of the NF bearing, which has strong generalization and robustness. Our method does not need second training, which may be more convenient in some practical applications. Finally, we also discuss the possible limitations of this method.

Streamline Sulfur Redox Reactions to Achieve Efficient Room-Temperature Sodium-Sulfur Batteries.

It is vital to dynamically regulate S activity to achieve efficient and stable room-temperature sodium-sulfur (RT/Na-S) batteries. Herein, we report using cobalt sulfide as an electron reservoir to enhance the activity of sulfur cathodes, and simultaneously combining with cobalt single atoms as double-end binding sites for a stable S conversion process. The rationally constructed CoS2 electron reservoir enables the straight reduction of S to short-chain sodium polysulfides (Na2 S4 ) via a streamlined redox path through electron transfer. Meanwhile, cobalt single atoms synergistically work with the electron reservoir to reinforce the streamlined redox path, which immobilize in situ formed long-chain products and catalyze their conversion, thus realizing high S utilization and sustainable cycling stability. The as-developed sulfur cathodes exhibit a superior rate performance of 443 mAh g-1 at 5 A g-1 with a high cycling capacity retention of 80 % after 5000 cycles at 5 A g-1 .

Axial intensity distribution of a micro-Fresnel zone plate at an arbitrary numerical aperture.

The axial focus number (the number of focal spots along the axial direction) and focus intensity of a micro-Fresnel zone plate (FZP) are analyzed from deep ultraviolet to infrared using the Fourier decomposition, the vectorial angular spectrum (VAS) theory, and the three-dimensional finite-difference time-domain (FDTD) method. For a low-numerical aperture (NA) micro-FZP (NA<0.1), there are multiple axial high-order foci, and the intensity of each focus decreases slowly. However, the intensity of each high-order focus decreases rapidly with NA increasing. For a relatively high-NA micro-FZP (NA>0.3), the axial high-order foci are suppressed and there is one single focus. A fast, precise, and cost-efficient additive manufacturing method, i.e. two-photon polymerization, is used to fabricate high-NA phase-type micro-FZPs. The experiment has validated the phenomenon of linear negative focal shift of a high-NA micro-FZP. This property can be particularly applied in precise measurement of micro-displacement, film thickness, micro/nano step height, and wavelength.

Clinical and Pathological Features of Prostatic Stromal Tumor of Uncertain Malignant Potential: A Retrospective Study of 23 Chinese Cases.

INTRODUCTION: Prostatic stromal tumor of uncertain malignant potential (STUMP) is a rare disease that may coexist with prostate stromal sarcoma (PSS). We aimed to analyze the histological and clinical features of STUMP. METHODS: Twenty-three patients diagnosed with STUMP from 2008 to 2019 were included. Clinicopathological and follow-up information was collected. In the subgroup analysis, we divided the patients into a pure STUMP group (N = 18) and a mixed STUMP (STUMP coexisting with PSS) group (N = 5). Student's t test was used to compare the 2 groups. RESULTS: Patients had a mean age of 55.5 ± 19.4 years and an average follow-up time of 42.3 months. The mean prostate volume was 109.2 ± 73.5 cm3, and the mean prostate-specific antigen was 8.03 ± 10.5 ng/mL. In the subgroup analysis, 16.7% (2/12) of pure STUMP patients had disease progression, while 100% (3/3) of mixed STUMP patients suffered from recurrence. Compared with the pure STUMP group, the mixed STUMP group was younger (37.2 vs. 60.6 years, p = 0.013) and had lower expression of estrogen receptor and progesterone receptor (p = 0.004 and p < 0.001, respectively). CONCLUSION: STUMP is a rare disease with a relatively good prognosis. However, there is still a possibility of disease progression or coexistence with stromal sarcoma. Timely diagnosis and regular monitoring may be helpful in improving treatment outcomes.