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BackgroundHepatocellular carcinoma (HCC) is the most common type of liver cancer. Small extracellular vesicles (sEVs) are bilayer lipid membrane vesicles containing RNA that exhibit promising diagnostic and prognostic potential as cancer biomarkers.AimsTo establish a miRNA panel from peripheral blood for use as a noninvasive biomarker for the diagnosis of HCC.MethodssEVs obtained from plasma were profiled using high-throughput sequencing. The identified differential miRNA expression patterns were subsequently validated using quantitative real-time polymerase chain reaction analysis.ResultsThe random forest method identified ten distinct miRNAs distinguishing HCC plasma from non-HCC plasma. During validation, miR-140-3p (p = 0.0001) and miR-3200-3p (p = 0.0017) exhibited significant downregulation. Enrichment analysis uncovered a notable correlation between the target genes of these miRNAs and cancer development. Utilizing logistic regression, we developed a diagnostic model incorporating these validated miRNAs. Receiver operating characteristic (ROC) curve analysis revealed an area under the curve (AUC) of 0.951, with a sensitivity of 90.1% and specificity of 87.8%.ConclusionThese aberrantly expressed miRNAs delivered by sEVs potentially contribute to HCC pathology and may serve as diagnostic biomarkers for HCC.

In this paper, we successfully combine convolution with a wave function to build an effective and efficient classifier for traffic signs, named the wave interference network (WiNet). In the WiNet, the feature map extracted by the convolutional filters is refined into many entities from an input image. Each entity is represented as a wave. We utilize Euler's formula to unfold the wave function. Based on the wave-like information representation, the model modulates the relationship between the entities and the fixed weights of convolution adaptively. Experiment results on the Chinese Traffic Sign Recognition Database (CTSRD) and the German Traffic Sign Recognition Benchmark (GTSRB) demonstrate that the performance of the presented model is better than some other models, such as ResMLP, ResNet50, PVT and ViT in the following aspects: 1) WiNet obtains the best accuracy rate with 99.80% on the CTSRD and recognizes all images exactly on the GTSRB; 2) WiNet gains better robustness on the dataset with different noises compared with other models; 3) WiNet has a good generalization on different datasets.

As the practical applications of fully homomorphic encryption (FHE), secure multi-party computation (MPC) and zero-knowledge (ZK) proof continue to increase, so does the need to design and analyze new symmetric-key primitives that can adapt to these privacy-preserving protocols. These designs typically have low multiplicative complexity and depth with the parameter domain adapted to their application protocols, aiming to minimize the cost associated with the number of nonlinear operations or the multiplicative depth of their representation as circuits. In this paper, we propose two differential fault attacks against a one-way function RAIN used for Rainier (CCS 2022), a signature scheme based on the MPC-in-the-head approach and an FHE-friendly cipher HERA used for the RtF framework (Eurocrypt 2022), respectively. We show that our attacks can recover the keys for both ciphers by only injecting a fault into the internal state and requiring only one normal and one faulty ciphertext blocks. Thus, we can use only the practical complexity of 226.6/228.8/230.4 bit operations to break the full-round RAIN with 128/192/256-bit keys. For full-round HERA with 80/128-bit key, our attack is practical with complexity the complexity of 220 encryptions with about 216 memory.

In this paper, we present a new algorithm for fast correlation attacks on stream ciphers with improved cryptanalysis results on the Sosemanuk stream cipher, one of the 7 finalists in the eSTREAM project in 2008. The new algorithm exploits the direct sum construction of covering codes in decoding phase which approximates the random vectors to a nearest codeword in a linear code. The new strategy provides large flexibility for the adversary and could reduce the time/memory/data complexities significantly. As a case study, we carefully revisit Sosemanuk and demonstrate a state recovery attack with a time complexity of 2134.8, which is 220 times faster than achievable before by the same kind of attack and is the fastest one among all known attacks so far. Our result indicates an inefficiency in longer keys than 135 bits and depicts that the security margin of Sosemanuk is around 28 for the 128-bit security for the first time.

Abstract The Fe-based superconductor Fe (Se,Te) combines non-trivial topology with unconventional superconductivity and may be an ideal platform to realize exotic states such as high-order topological corner modes and Majorana modes. Thin films of Fe (Se,Te) are particularly important for device fabrication and phase sensitive transport measurements. While bulk Fe (Se,Te) has been extensively studied, the nature of the superconducting order parameter in the monolayer limit has not yet been explored. In this work, we study monolayer films of Fe (Se,Te) on Bi2Te3 with scanning tunneling spectroscopy. Monolayer Fe (Se,Te)/Bi2Te3 heterostructures host a multigap superconducting state that strongly resembles the bulk. Analysis of the phase-referenced quasiparticle interference signal reveals a sign-changing s-wave order parameter similar to the bulk as well as a unique pattern of sign changes which have not been observed in the bulk. Our work establishes monolayer Fe (Se,Te)/Bi2Te3 as a robust multi-band unconventional superconductor and sets the stage for explorations of non-trivial topology in this highly-tunable system.

Abstract Background Compared with the stomach of ruminant cattle, the stomach of horse is small and mainly for chemical digestion, but the microorganisms in the stomach play an important role in maintaining the homeostasis of the internal environment. Due to the complexity of the microbes in the stomach, little is known about the diversity and structure of bacteria in the equine stomach. Grains are the main energy source for plant-eating livestock and energy is derived through enzymatic hydrolysis of grains into glucose or their microbial fermentation into Volatile fatty acids (VFA). However, the mechanism through which these ingested grains are chemically digested as well as the effect of these grains on the stomach remains elusive. This study explored the effects of feeding different grains (corn, oats, and barley) on bacterial diversity, structure, and composition in the foal’s stomach content. Furthermore, the effects of different grains on the vitality of starch digestion-related stomach enzymes were investigated. Results No significant differences were observed (P > 0.05) in the bacterial rarefaction curves of Operational Taxonomic Units (OTUs) and diversity of the stomach microbiota in all foals. This study also revealed the statistical differences for Firmicutes, Cyanobacteria, Actinobacteria, Fibrobacteres, Lactobacillaceae, Streptococcaceae, Unidentified_Clostridiales, Prevotellaceae, Lactobacillus, Streptococcus, Unidentified_Cyanobacteria, Unidentified_Clostridiales, Lactococcus, Sphingomonas, Lactobacillus_hayakitensis, Lactobacillus_equigenerosi, and Clostridium_perfringens. The linear discriminant analysis effect size analysis revealed 9 bacteria at each classification level. The functional analysis of species information by using FAPROTAX software was able to predict 35 functions, and the top 5 functions were chemoheterotrophy, fermentation, animal_parasites_or_symbionts, nitrate_reduction, and aerobic_chemoheterotrophy. The study also revealed statistical differences for pH, glucose concentration, β-amylase, maltase, and amylase. Conclusions The different grains had no significant effect on the microbial diversity of the stomach content of the foal. However, the relative bacterial abundances differed significantly in response to different diets. Particularly, oats fed to the foals significantly increased the relative abundance of Firmicutes, Lactobacillaceae, Lactobacillus, and Lactobacillus_hayakitensis. The grain had no significant effect on the pH of the stomach content, glucose concentration, and enzyme viability in the foal.

Abstract Tuberculosis (TB) remains a serious global public health threat. Accumulated evidence has demonstrated that human susceptibility to TB has a strong genetic basis. And different susceptibility single nucleotide polymorphisms (SNP) have been reported in different studies. To gain greater insight into the host susceptibility to TB, we perform a two‐stage genome‐wide association study to identify the susceptible loci of TB. In the discovery stage, 3116 (1532 TB patients and 1584 healthy controls) and 439 (211 TB patients and 228 healthy controls) individuals were genome‐wide genotyped from a western Chinese Han and Tibetan population, respectively. Based on the additive genetic model, we discovered 14 and three independent loci that had potential associations with TB susceptibility in the Chinese Han and Tibetan populations, respectively (p

Abstract SIMON is one of the lightweight block ciphers designed by the National Security Agency in 2013, and a technical report including security analysis was published by the design team nearly 4 years later. As for the linear attack, it is claimed that ‘the single‐path probabilities (and linear correlations) dip below 2−block size for 12, 16, 20, 29, and 38 rounds for SIMON32, 48, 64, 96, and 128, respectively’. However, the design team does not show details on how to get the result and there are also no published papers verified the result yet. In the present paper, an upper bound of squared correlation of SIMON‐like functions is given. As an important application of this bound, how to find optimal linear characteristics of SIMON and SIMECK under the Markov assumption with Matsui's branch‐and‐bound algorithm is shown. The authors’ results confirm the claim of the design team. Furthermore, the best‐known linear‐hull distinguishers for SIMON and SIMECK is also given.

Abstract Background To investigate the sensitivity and specificity of corneal confocal microscopy (CCM) in the diagnosis of immune-related motor neuron disease syndrome and evaluation of the response to immunosuppressive therapy. Methods Seventy-two patients with clinical manifestations of motor neuron disease (MND) were analysed. According to whether they had concomitant rheumatic immune disease or rheumatic immune antibody abnormalities, they were divided into an MND group (33 patients) and an immune-related MND syndrome group (39 patients). Another 10 healthy adults were selected as the control group. All individuals were examined by CCM. Results For Langerhans cell(LC) density, the area under the receiver operating characteristic(ROC)curve was 0.8, the best cut-off was 67.7 cells/mm2, the sensitivity was 79.5%, and the specificity was 72.7%. For inferior whorl length (IWL), the area under the ROC curve was 0.674, the best cut-off was 17.41 mm/mm2, the sensitivity was 69.2%, and the specificity was 66.7%. After immunosuppressive therapy in 5 patients with immune-related MND syndrome, the LCD was significantly reduced (P 0.05). Conclusion The LC density and IWL are ideal for distinguishing MND from immune-related MND syndrome. The LC density reflects the immunotherapy response sensitively.

We systemically identified tuberculosis (TB)-related DNA methylation biomarkers and further constructed classifiers for TB diagnosis. TB-related DNA methylation datasets were searched through October 3, 2020. Limma and DMRcate were employed to identify differentially methylated probes (DMPs) and regions (DMRs). Machine learning methods were used to construct classifiers. The performance of the classifiers was evaluated in discovery datasets and a prospective independent cohort. Eighty-nine DMPs and 24 DMRs were identified based on 67 TB patients and 45 healthy controls from 4 datasets. Nine and three DMRs were selected by elastic net regression and logistic regression, respectively. Among the selected DMRs, two regions (chr3: 195635643–195636243 and chr6: 29691631–29692475) were differentially methylated in the independent cohort (p = 4.19 × 10−5 and 0.024, respectively). Among the ten classifiers, the 3-DMR logistic regression classifier exhibited the strongest performance. The sensitivity, specificity, and area under the curve were, respectively, 79.1%, 84.4%, and 0.888 in the discovery datasets and 64.5%, 90.3%, and 0.838 in the independent cohort. The differential diagnostic ability of this classifier was also assessed. Collectively, these data showed that DNA methylation might be a promising TB diagnostic biomarker. The 3-DMR logistic regression classifier is a potential clinical tool for TB diagnosis, and further validation is needed.

Background and Objective. Tuberculosis (TB) is a major threat to human health, especially in developing countries. Its susceptibility and progression depend on interactions between mycobacterium tuberculosis, host immune system, and genetic and environmental factors. Up to now, many studies have presented the association between TB susceptibility and host genetic polymorphisms, but never regarding CD59 gene, which is an essential complement regulator. This study investigated the relationship between multiple CD59 single nucleotide polymorphisms (SNPs) and susceptibility to TB among Chinese patients. Methods. A case–control study was conducted to investigate the SNPs at CD59 rs1047581, rs7046, rs2231460, rs184251026, rs41275164, rs831633, rs704700, rs41275166, and rs10768024 by sequence-specific primer-polymerase chain reaction (SSP-PCR) in 900 tuberculosis patients and 1,534 controls. Results. The minor allele frequencies at rs2231460, rs184251026, rs41275164, and rs41275166 were extremely low both in the Cases (0.00%–0.61%) and in the Controls (0.07%–0.43%), comparatively at rs1047581, rs7046, rs831633, rs704700, and rs10768024 were notably higher both in the Cases (8.23%–48.39%) and in the Controls (8.57%–47.16%). Among the nine SNPs, only homozygous CC genotype at rs10768024 showed a significant protective effect against TB than homozygous TT genotype (OR(95% CI) = 0.59(0.38, 0.91), χ2 = 5.779, P=0.016), and homozygous TT and heterozygous CT genotypes showed a significant risk of TB infection in the recessive model (OR(95% CI) = 1.68(1.10, 2.56), χ2 = 5.769, P=0.016). Further analysis verified that rs10768024 CC genotype independently related to TB susceptibility (OR(95% CI) = 0.60(0.39, 0.91), Wald χ2 = 5.664, P=0.017) in multivariate logistic regression analysis, and its genetic mutation was independent of the other SNPs (r2 = 0.00–0.20) in haplotype analysis. Conclusions. The first investigation of the CD59 gene and susceptibility to TB suggests a significant risk with homozygous TT and heterozygous CT genotypes at rs10768024 loci. The homozygous CC mutation at rs10768024 loci showed a significant protection against TB susceptibility.

Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex background interference and subtle inter-class differences. A feasible method to address the challenges is to segment strawberry lesions from the background and learn fine-grained features of the lesions. Following this idea, we present a novel Class-Attention-based Lesion Proposal Convolutional Neural Network (CALP-CNN), which utilizes a class response map to locate the main lesion object and propose discriminative lesion details. Specifically, the CALP-CNN firstly locates the main lesion object from the complex background through a class object location module (COLM) and then applies a lesion part proposal module (LPPM) to propose the discriminative lesion details. With a cascade architecture, the CALP-CNN can simultaneously address the interference from the complex background and the misclassification of similar diseases. A series of experiments on a self-built dataset of field strawberry diseases is conducted to testify the effectiveness of the proposed CALP-CNN. The classification results of the CALP-CNN are 92.56%, 92.55%, 91.80% and 91.96% on the metrics of accuracy, precision, recall and F1-score, respectively. Compared with six state-of-the-art attention-based fine-grained image recognition methods, the CALP-CNN achieves 6.52% higher (on F1-score) than the sub-optimal baseline MMAL-Net, suggesting that the proposed methods are effective in identifying strawberry diseases in the field.

Abstract Background miR-124-3p can inhibit integrin β3 (ITGB3) expression to suppress the migration and invasion of gastric cancer (GC), and in the process lncRNA HOXA11-AS may act as a molecular sponge. Methods Luciferase reporter assay was conducted to verify the binding of miR-124-3p and HOXA11-AS. RT-PCR and western blot were performed to detect the expression of HOXA11-AS, miR-124-3p and ITGB3 in GC tissues and cells. Gene silence and overexpression experiments as well as cell migration and invasion assays on GC cell lines were performed to determine the regulation of molecular pathways, HOXA11-AS/miR-124-3p/ITGB3. Furthermore, the role of HOXA11-AS in GC was confirmed in mice models. Results We found HOXA11-AS is up-regulated in GC tissues and can bind with miR-124-3p. Through overexpression/knockdown experiments and function tests in vitro, we demonstrated HOXA11-AS can promote ITGB3 expression by sponging miR-124-3p, consequently enhance the proliferation, migration, and invasion of GC cells. Meanwhile, we validated that HOXA11-AS promotes migration and invasion of GC cells via down-regulating miR-124-3p and up-regulating ITGB3 in vivo. Conclusions We demonstrated that lncRNA HOXA11-AS can increase ITGB3 expression to promote the migration and invasion of gastric cancer by sponging miR-124-3p. Our results suggested that HOXA11-AS may reasonably serve as a promising diagnostic biomarker and a potential therapeutic target of GC.

Wheat spike detection has important research significance for production estimation and crop field management. With the development of deep learning-based algorithms, researchers tend to solve the detection task by convolutional neural networks (CNNs). However, traditional CNNs equip with the inductive bias of locality and scale-invariance, which makes it hard to extract global and long-range dependency. In this paper, we propose a Transformer-based network named Multi-Window Swin Transformer (MW-Swin Transformer). Technically, MW-Swin Transformer introduces the ability of feature pyramid network to extract multi-scale features and inherits the characteristic of Swin Transformer that performs self-attention mechanism by window strategy. Moreover, bounding box regression is a crucial step in detection. We propose a Wheat Intersection over Union loss by incorporating the Euclidean distance, area overlapping, and aspect ratio, thereby leading to better detection accuracy. We merge the proposed network and regression loss into a popular detection architecture, fully convolutional one-stage object detection, and name the unified model WheatFormer. Finally, we construct a wheat spike detection dataset (WSD-2022) to evaluate the performance of the proposed methods. The experimental results show that the proposed network outperforms those state-of-the-art algorithms with 0.459 mAP (mean average precision) and 0.918 AP50. It has been proved that our Transformer-based method is effective to handle wheat spike detection under complex field conditions.

Abstract Background Tuberculosis (TB) is one of the leading causes of morbidity and mortality in Western China. Preclinical studies have suggested the protective effect of the C-type lectin receptor of family 4 member E (CLEC4E) from TB. Herein, we investigated the association between CLEC4E gene variants and TB susceptibility in a western Chinese Han population. Methods We genotyped four single nucleotide polymorphisms (SNPs) rs10841856, rs10770847, rs10770855 and rs4480590 in the CLEC4E gene using the improved multiplex ligation detection reaction (iMLDR) assay in 900 TB cases and 1534 healthy controls. Results After stratifying the whole data by sex, it was found that males exhibited mutant allele G of rs10841856 was more strongly associated with increased TB risk after Bonferroni correction (OR = 1.334, 95% CI: 1.142–1.560; P

Magnetic Weyl semimetals in the 2D limit may behave like 2D Chern insulators and host the quantum anomalous Hall effect at high temperatures. Here, the authors report the observation of linearly dispersing topological states confined to the edges of the kagome Co3Sn terraces in the magnetic Weyl system Co3Sn2S2.

An accurate and robust pest detection and recognition scheme is an important step to enable the high quality and yield of agricultural products according to integrated pest management (IPM). Due to pose-variant, serious overlap, dense distribution, and interclass similarity of agricultural pests, the precise detection of multi-classes pest faces great challenges. In this study, an end-to-end pest detection algorithm has been proposed on the basis of deep convolutional neural networks. The detection method adopts a deformable residual network to extract pest features and a global context-aware module for obtaining region-of-interests of agricultural pests. The detection results of the proposed method are compared with the detection results of other state-of-the-art methods, for example, RetinaNet, YOLO, SSD, FPN, and Cascade RCNN modules. The experimental results show that our method can achieve an average accuracy of 77.8% on 21 categories of agricultural pests. The proposed detection algorithm can achieve 20.9 frames per second, which can satisfy real-time pest detection.

Wheat stripe rusts are responsible for the major reduction in production and economic losses in the wheat industry. Thus, accurate detection of wheat stripe rust is critical to improving wheat quality and the agricultural economy. At present, the results of existing wheat stripe rust detection methods based on convolutional neural network (CNN) are not satisfactory due to the arbitrary orientation of wheat stripe rust, with a large aspect ratio. To address these problems, a WSRD-Net method based on CNN for detecting wheat stripe rust is developed in this study. The model is a refined single-stage rotation detector based on the RetinaNet, by adding the feature refinement module (FRM) into the rotation RetinaNet network to solve the problem of feature misalignment of wheat stripe rust with a large aspect ratio. Furthermore, we have built an oriented annotation dataset of in-field wheat stripe rust images, called the wheat stripe rust dataset 2021 (WSRD2021). The performance of WSRD-Net is compared to that of the state-of-the-art oriented object detection models, and results show that WSRD-Net can obtain 60.8% AP and 73.8% Recall on the wheat stripe rust dataset, higher than the other four oriented object detection models. Furthermore, through the comparison with horizontal object detection models, it is found that WSRD-Net outperforms horizontal object detection models on localization for corresponding disease areas.

In order to compare the microscopic ultrastructure of eutopic endometrium and normal endometrium in patients with endometriosis, to study the specific pathogenesis of endometriosis. In this paper, on the basis of using B-ultrasound technology, several patients with endometriosis were subjected to B-ultrasound to observe the ultrastructure of the eutopic uterine endometrium and compared with the pictures of normal endometrium to carry out the specific analysis between the two ultrastructural comparisons. This study is based on the analysis of B-ultrasound images of patients with endometriosis, compares the difference between their ultrastructure and normal human body, and conducts specific pathological diagnosis and analysis to find out the impact of the endometrium in place. The specific factors of the occurrence of lesions and the corresponding treatment methods are proposed. The experimental results show that the ultrastructure of endometriosis eutopic endometrium is different from that of normal endometrium. The microvilli of secretory cells and the cilia of ciliated cells of the former are abnormally increased and lengthened, and they are superior to B-ultrasound technology. The success rate of the examination is 93.75%, which can play an important role in the specific examination process of patients with endometriosis, as one of the actual indicators of detection. Under the electron microscope, microvilli are tiny finger-like protrusions extending from the cell membrane and the cytoplasm on the free surface of the cell, surrounded by the cell membrane and perpendicular to the cell membrane surface.

Objectives: The aim of this study was to identify the relationship between B-cell CLL/lymphoma 2 (BCL2) polymorphisms and susceptibility to anti-tuberculous therapy-associated drug-induced liver injury (ATT-DILI). Methods: A total of 746 tuberculosis (TB) patients were enrolled in this study. Twenty-one selected single nucleotide polymorphisms in BCL2 were analyzed by custom-by-design 2 × 48-Plex SNPscan kit. The allele and genotype frequencies between patients with and without ATT-DILI were compared using three different genetic models. Results: A total of 727/746 participants were successfully genotyped, and 112 of them were diagnosed with ATT-DILI. The A allele of rs8085707, G allele of rs76986960, and A allele of rs949037 conferred an increased risk of ATT-DILI, with estimated odd ratios (ORs) of 2.181 (95% confidence interval (CI) 1.345–3.536, p = 0.001), 1.983 (95% CI 1.060–3.709, p = 0.029), and 1.390 (95% CI 1.032–1.873, p = 0.03), respectively. Bonferroni correction indicated that the A allele of rs8085707 was a risk factor for ATT-DILI (Bonferroni correction: p = 0.026). The additive model suggested that patients with the AA genotype of rs8085707 had a significantly higher risk of ATT-DILI compared with those with the GG genotype (Bonferroni correction: p = 0.036). The influence of BCL2 polymorphisms on clinical characteristics (clinical symptoms, disease subtypes, and laboratory indicators) was also identified. Conclusions: This study is novel in suggesting an association between BCL2 polymorphisms and the risk of ATT-DILI. Keywords: ATT-DILI, BCL2, Genetic polymorphisms, Susceptibility, Oxidative stress

Two-dimensional materials (2DMs) have attracted substantial attention due to their abundant active sites and their ultrahigh surface area for different catalytic applications due to the high lateral-longitudinal ratio. Transition metal dichalcogenides (TMDs), especially MoS2, as one of the 2DMs most often studied, have shown superior activity in electrochemical applications. Recently, combinations of different 2DMs have been widely studied, and they appear to be the most promising strategy available to develop state of the art catalysts for different reactions.In this article, we review the interactions between MoS2 and other materials as well as the novel assembly induced phase transitions of TMDs and their underlying mechanisms. Several methods for inducing the phase transition of TMDs by building MoS2-based heterostructures have been introduced. The electronic coupling between these counterparts has significantly enhanced their conductivity and optimized the energy states of the materials, thus introducing enhanced activity as compared to their original counterparts. The ideas summarized in this article may shed new light on and help to develop next-generation green energy materials by designing and constructing highly active two-dimensional catalysts for efficient water splitting. Keywords: Molybdenum disulfide, Heterostructure, Phase transition, Interaction, Water splitting

A serious outbreak of agricultural pests results in a great loss of corn production. Therefore, accurate and robust corn pest detection is important during the early warning, which can achieve the prevention of the damage caused by corn pests. To obtain an accurate detection of corn pests, a new method based on a convolutional neural network is introduced in this paper. Firstly, a large-scale corn pest dataset has been constructed which includes 7741 corn pest images with 10 classes. Secondly, a deep residual network with deformable convolution has been introduced to obtain the features of the corn pest images. To address the detection task of multi-scale corn pests, an attention-based multi-scale feature pyramid network has been developed. Finally, we combined the proposed modules with a two-stage detector into a single network, which achieves the identification and localization of corn pests in an image. Experimental results on the corn pest dataset demonstrate that the proposed method has good performance compared with other methods. Specifically, the proposed method achieves 70.1% mean Average Precision (mAP) and 74.3% Recall at the speed of 17.0 frames per second (FPS), which balances the accuracy and efficiency.

Specialized pest control for agriculture is a high-priority agricultural issue. There are multiple categories of tiny pests, which pose significant challenges to monitoring. Previous work mainly relied on manual monitoring of pests, which was labor-intensive and time-consuming. Recently, deep-learning-based pest detection methods have achieved remarkable improvements and can be used for automatic pest monitoring. However, there are two main obstacles in the task of pest detection. (1) Small pests often go undetected because much information is lost during the network training process. (2) The highly similar physical appearances of some categories of pests make it difficult to distinguish the specific categories for networks. To alleviate the above problems, we proposed the multi-category pest detection network (MCPD-net), which includes a multiscale feature pyramid network (MFPN) and a novel adaptive feature region proposal network (AFRPN). MFPN can fuse the pest information in multiscale features, which significantly improves detection accuracy. AFRPN solves the problem of anchor and feature misalignment during RPN iterating, especially for small pest objects. In extensive experiments on the multi-category pests dataset 2021 (MPD2021), the proposed method achieved 67.3% mean average precision (mAP) and 89.3% average recall (AR), outperforming other deep learning-based models.

Background Tuberculosis (TB) is difficult to diagnose under complex clinical conditions as electronic health records (EHRs) are often inadequate in making an affirmative diagnosis. As exosomal miRNAs emerged as promising biomarkers, we investigated the potential of using exosomal miRNAs and EHRs in TB diagnosis. Methods: A total of 370 individuals, including pulmonary tuberculosis (PTB), tuberculous meningitis (TBM), non-TB disease controls and healthy state controls, were enrolled. Exosomal miRNAs were profiled in the exploratory cohort using microarray and miRNA candidates were selected in the selection cohort using qRT-PCR. EHRs and follow-up information of the patients were collected accordingly. miRNAs and EHRs were used to develop diagnostic models for PTB and TBM in the selection cohort with the Support Vector Machine (SVM) algorithm. These models were further evaluated in an independent testing cohort. Findings: Six exosomal miRNAs (miR-20a, miR-20b, miR-26a, miR-106a, miR-191, miR-486) were differentially expressed in the TB patients. Three SVM models, ''EHR+miRNA'', ''miRNA only'' and ''EHR only'' were compared, and ''EHR + miRNA'' model achieved the highest diagnostic efficacy, with an AUC up to 0.97 (95% CI 0.80–0.99) in TBM and 0.97 (0.87–0.99) in PTB, respectively. However, ''EHR only'' model only showed an AUC of 0.67 (0.46–0.83) in TBM. After 2-month anti-tuberculosis therapy, overexpressed miRNAs presented a decreased expression trend (p= 4.80 × 10−5). Interpretation: Our results showed that the combination of exosomal miRNAs and EHRs could potentially improve clinical diagnosis of TBM and PTB. Fund: Funds for the Central Universities, the National Natural Science Foundation of China. Keywords: Exosomal miRNA, Electronic health record, Tuberculosis differential diagnosis, Machine learning

It is well recognized that aphid infestation severely reduces crop yield and further leads to significant economic loss. Therefore, accurately and efficiently detecting aphids is of vital importance in pest management. However, most existing detection methods suffer from unsatisfactory performance without fully considering the aphid characteristics, including tiny size, dense distribution, and multi-viewpoint data quality. In addition, existing clustered tiny-sized pest detection methods improve performance at the cost of time and do not meet the real-time requirements. To address the aforementioned issues, we propose a robust aphid detection method with two customized core designs: a Transformer feature pyramid network (T-FPN) and a multi-resolution training method (MTM). To be specific, the T-FPN is employed to improve the feature extraction capability by a feature-wise Transformer module (FTM) and a channel-wise feature recalibration module (CFRM), while the MTM aims at purifying the performance and lifting the efficiency simultaneously with a coarse-to-fine training pattern. To fully demonstrate the validity of our methods, abundant experiments are conducted on a densely clustered tiny pest dataset. Our method can achieve an average recall of 46.1% and an average precision of 74.2%, which outperforms other state-of-the-art methods, including ATSS, Cascade R-CNN, FCOS, FoveaBox, and CRA-Net. The efficiency comparison shows that our method can achieve the fastest training speed and obtain 0.045 s per image testing time, meeting the real-time detection. In general, our TD-Det can accurately and efficiently detect in-field aphids and lays a solid foundation for automated aphid detection and ranking.

A theoretically reliable key-recovery attack should evaluate not only the non-randomness for the correct key guess but also the randomness for the wrong ones as well. The former has always been the main focus but the absence of the latter can also cause self-contradicted results. In fact, the theoretic discussion of wrong key guesses is overlooked in quite some existing key-recovery attacks, especially the previous cube attack variants based on pure experiments. In this paper, we draw links between the division property and several variants of the cube attack. In addition to the zero-sum property, we further prove that the bias phenomenon, the non-randomness widely utilized in dynamic cube attacks and cube testers, can also be reflected by the division property. Based on such links, we are able to provide several results: Firstly, we give a dynamic cube key-recovery attack on full Grain-128. Compared with Dinur et al.’s original one, this attack is supported by a theoretical analysis of the bias based on a more elaborate assumption. Our attack can recover 3 key bits with a complexity 297.86 and evaluated success probability 99.83%. Thus, the overall complexity for recovering full 128 key bits is 2125. Secondly, now that the bias phenomenon can be efficiently and elaborately evaluated, we further derive new secure bounds for Grain-like primitives (namely Grain-128, Grain-128a, Grain-V1, Plantlet) against both the zero-sum and bias cube testers. Our secure bounds indicate that 256 initialization rounds are not able to guarantee Grain-128 to resist bias-based cube testers. This is an efficient tool for newly designed stream ciphers for determining the number of initialization rounds. Thirdly, we improve Wang et al.’s relaxed term enumeration technique proposed in CRYPTO 2018 and extend their results on Kreyvium and ACORN by 1 and 13 rounds (reaching 892 and 763 rounds) with complexities 2121.19 and 2125.54 respectively. To our knowledge, our results are the current best key-recovery attacks on these two primitives.

Neuroblastoma cell differentiation is regulated by Id proteins. Here, the authors show that CaMKII-mediated phosphorylation of Beclin 1 can activate K63-linked ubiquitination and autophagic degradation of Id proteins uncovering a role for autophagy in cell differentiation.

BACKGROUND:Pregnane X receptor (PXR) regulates the expression of drug-metabolizing enzymes and transport enzymes. NF-κB not only plays a role in liver homeostasis and injury-healing processes by regulating inflammatory responses but may also regulate the transcription of PXR. Currently, genetic polymorphisms in PXR are associated with adverse drug effects. Because little is known about the association between NF-κB1 genetic polymorphisms and adverse drug reactions, we explored the association between PXR and NF-κB1 single nucleotide polymorphisms (SNPs) and susceptibility to anti-tuberculosis drug-induced liver injury (ATDILI). MATERIALS AND METHODS:A total of 746 tuberculosis patients (118 with ATDILI and 628 without ATDILI) were prospectively enrolled at West China Hospital between December 2014 and April 2018. Nine selected SNPs (rs3814055, rs13059232, rs7643645 and rs3732360 in PXR and rs78872571, rs4647992, rs60371688, rs1598861 and rs3774959 in NF-κB1) were genotyped with a custom-designed 2x48-plex SNP Scan TM Kit. The frequencies of the alleles, genotypes and genetic models of the variants were compared between patients with or without ATDILI, while joint effect analysis of the SNP-SNP interactions was performed using multiplicative and additive models. The odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) were calculated. RESULTS:The T allele of rs3814055 in PXR was associated with a decreased risk for ATDILI (OR 0.61; 95% CI: 0.42-0.89, p = 0.0098). The T alleles of rs78872571 and rs4647992 in NF-κB1 were significantly associated with an increased risk for ATDILI (OR 1.91; 95% CI: 1.06-3.43, p = 0.028 and OR 1.81; 1.06-3.10, p = 0.029, respectively). The allele, genotype and genetic model frequencies were similar in the two groups for the other six SNPs (all P>0.05). There were no multiplicative or additive interactions between the SNPs. CONCLUSION:Our study is the first to reveal that rs3814055 variants in PXR and rs78872571 and rs4647992 variants in NF-κB1 are associated with susceptibility to ATDILI caused by first-line anti-tuberculosis combination treatment in the Han Chinese population.

Label-free optical biosensing technologies have superior abilities of quantitative analysis, unmodified targets, and ultrasmall sample volume, compared to conventional fluorescence-label-based sensing techniques, in detecting various biomolecules. In this review article, we introduce our recent results in the field of evanescent-wavebased refractive index sensing and surface enhanced Raman scattering (SERS)-based sensing, both of which are promising platforms for label-free optical biosensors. First, silicon-on-insulator (SOI) nanowire waveguide and metallic surface plasmon resonance (SPR)-based refractive index sensing are discussed. In order to improve the detection limit, phase interrogation techniques are introduced to these types of sensors based on prism-coupled SPR and SOI microring resonators. A detection limit in the order of 10−6 refractive index unit is achieved. Detection of 16.7 pM anti-IgG is also demonstrated based on the SPR devices. Second, SERS substrates based on various nanometallic structures are discussed. Metallic nanowire arrays and inverted nanopyramids and grooves with a thin metal surface are fabricated based on anisotropic wetetching of silicon substrates. Both structures have demonstrated a Raman signal enhancement on the order of 107. In order to improve the extraction efficiency of the Raman signal at a high wave number, a nano-bowtie array substrate is fabricated, which exhibits double resonances at both the excitation wavelength and the desired Raman scattering wavelength. Experimental results have shown that this double-resonance structure can further enhance the received Raman signal, as compared to conventional SERS substrates with only one resonance at the excitation wavelength.

Airborne LiDAR bathymetry (ALB) has shown great potential in shallow water and coastal mapping. However, due to the variability of the waveforms, it is hard to detect the signals from the received waveforms with a single algorithm. This study proposed a depth-adaptive waveform decomposition method to fit the waveforms of different depths with different models. In the proposed method, waveforms are divided into two categories based on the water depth, labeled as “shallow water (SW)” and “deep water (DW)”. An empirical waveform model (EW) based on the calibration waveform is constructed for SW waveform decomposition which is more suitable than classical models, and an exponential function with second-order polynomial model (EFSP) is proposed for DW waveform decomposition which performs better than the quadrilateral model. In solving the model’s parameters, a trust region algorithm is introduced to improve the probability of convergence. The proposed method is tested on two field datasets and two simulated datasets to assess the accuracy of the water surface detected in the shallow water and water bottom detected in the deep water. The experimental results show that, compared with the traditional methods, the proposed method performs best, with a high signal detection rate (99.11% in shallow water and 74.64% in deep water), low RMSE (0.09 m for water surface and 0.11 m for water bottom) and wide bathymetric range (0.22 m to 40.49 m).

Orbital angular momentum (OAM) mode division provides a promising solution to push past the already exhausted available degrees of freedom available in conventional optical communications. Nevertheless, the practical deployment of OAM within a free-space optical (FSO) communications system is still hampered by a major challenge, namely that OAM-based FSO links are vulnerable to disturbances. Though several techniques, such as using various non-diffraction beams and multiple transmit–receive apertures, are proposed to alleviate the influence of disturbances, these techniques significantly reduce the performance with regard to combating single fading for spatial blockages of the laser beam by obstructions. In this work, we initially demonstrate that a Fabry-Pérot resonant cavity has the ability to implement OAM mode healing, even for a blocking percentage of over 50%. Consequently, the proposed method will expand the use of OAM in the FSO secure communications and quantum encryption fields.

The aim of this study was to prepare a dextran–coated layered double hydroxide acetylsalicylic acid (DEX–LDH–ASA) delivery system by co-precipitation of LDH–ASA and its in-situ compositing with DEX. The structure of the system was investigated using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR) and thermogravimetry (TG). Its in vitro drug release properties and in vivo pharmacokinetics in rabbit were also determined. The results show that the DEX–LDH–ASA system retained the crystal structure of LDH–ASA and gave a marked improvement in its dispersion. It also prolonged the release of ASA and shifted the release pattern from first-order to zero-order kinetics. The pharmacokinetics of ASA administered in the DEX–LDH–ASA system to rabbits produced two absorption peaks with a Cmax of 14.8±1.7 mg/L at 2.11±0.69 h and an elimination half-life of 2.25±0.84 h for the first peak. The fact that delivery of ASA in the DEX–LDH–ASA system was sustained with improved bioavailability indicates the potential of the system as a controlled release formulation with application to other drugs.

The physics of mutual interaction of phonon quasiparticles with electronic spin degrees of freedom, leading to unusual transport phenomena of spin and heat, has been a subject of continuing interests for decades. Despite its pivotal role in transport processes, the effect of spin-phonon coupling on the phonon system, especially acoustic phonon properties, has so far been elusive. By means of inelastic neutron scattering and first-principles calculations, anomalous scattering spectral intensity from acoustic phonons was identified in the exemplary collinear antiferromagnetic nickel (II) oxide, unveiling strong spin-lattice correlations that renormalize the polarization of acoustic phonon. In particular, a clear magnetic scattering signature of the measured neutron scattering intensity from acoustic phonons is demonstrated by its momentum transfer and temperature dependences. The anomalous scattering intensity is successfully modeled with a modified magneto-vibrational scattering cross-section, suggesting the presence of spin precession driven by phonon. The renormalization of phonon eigenvector is indicated by the observed "geometry-forbidden" neutron scattering intensity from transverse acoustic phonon. Importantly, the eigenvector renormalization cannot be explained by magnetostriction but instead, it could result from the coupling between phonon and local magnetization of ions.

Revealing the positions of all the atoms in large macromolecules is powerful but only possible with neutron macromolecular crystallography (NMC). Neutrons provide a sensitive and gentle probe for the direct detection of protonation states at near-physiological temperatures and clean of artifacts caused by x rays or electrons. Currently, NMC use is restricted by the requirement for large crystal volumes even at state-of-the-art instruments such as the macromolecular neutron diffractometer at the Spallation Neutron Source. EWALD's design will break the crystal volume barrier and, thus, open the door for new types of experiments, the study of grand challenge systems, and the more routine use of NMC in biology. EWALD is a single crystal diffractometer capable of collecting data from macromolecular crystals on orders of magnitude smaller than what is currently feasible. The construction of EWALD at the Second Target Station will cause a revolution in NMC by enabling key discoveries in the biological, biomedical, and bioenergy sciences.