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Abstract As the main cause of visual function deficits in children and adolescents worldwide, amblyopia causes serious impairment of monocular visual acuity and stereopsis. The recovery of visual functions from amblyopia beyond the critical period is slow and incomplete due to the limited plasticity of the mature cortex; notably, visual stimulation training seems to be an effective therapeutic strategy in clinical practice. However, the precise neural basis and cellular mechanisms that underlie amblyopia and visual stimulation treatment remain to be elucidated. Using monocular deprivation in juvenile mice to model amblyopia, we employed two-photon calcium imaging and chemogenetic techniques to investigate the visual responses of individual excitatory neurons and parvalbumin (PV+) interneurons in the primary visual cortex (V1) of amblyopic mice. We demonstrate that amblyopic mice exhibit an excitation/inhibition (E/I) imbalance. Moreover, visual stimulation decreases the response of PV+ interneurons, reactivates the ocular dominance plasticity of excitatory neurons, and promotes vision recovery in adult amblyopic mice. Our results reveal a dynamic E/I balance between excitatory neurons and PV+ interneurons that may underlie the neural mechanisms of amblyopia during cortical development and visual stimulation-mediated functional recovery from adult amblyopia, providing evidence for therapeutic applications that rely on reactivating adult cortical plasticity.

Abstract Diabetic macular edema (DME) has emerged as the foremost cause of vision loss in the population with diabetes. Early detection of DME is paramount, yet the prevailing screening, relying on two-dimensional and labor-intensive fundus photography (FP), results in frequent unwarranted referrals and overlooked diagnoses. Self-imaging optical coherence tomography (SI-OCT), offering fully automated, three-dimensional macular imaging, holds the potential to enhance DR screening. We conducted an observational study within a cohort of 1822 participants with diabetes, who received comprehensive assessments, including visual acuity testing, FP, and SI-OCT examinations. We compared the performance of three screening strategies: the conventional FP-based strategy, a combination strategy of FP and SI-OCT, and a simulated combination strategy of FP and manual SD-OCT. Additionally, we undertook a cost-effectiveness analysis utilizing Markov models to evaluate the costs and benefits of the three strategies for referable DR. We found that the FP + SI-OCT strategy demonstrated superior sensitivity (87.69% vs 61.53%) and specificity (98.29% vs 92.47%) in detecting DME when compared to the FP-based strategy. Importantly, the FP + SI-OCT strategy outperformed the FP-based strategy, with an incremental cost-effectiveness ratio (ICER) of $8016 per quality-adjusted life year (QALY), while the FP + SD-OCT strategy was less cost-effective, with an ICER of $45,754/QALY. Our results were robust to extensive sensitivity analyses, with the FP + SI-OCT strategy standing as the dominant choice in 69.36% of simulations conducted at the current willingness-to-pay threshold. In summary, incorporating SI-OCT into FP-based screening offers substantial enhancements in sensitivity, specificity for detecting DME, and most notably, cost-effectiveness for DR screening.

Foodborne pathogens are one of the important factors affecting food safety. Efficient and rapid detection technology of foodborne pathogens is particularly important to ensure food safety. Among the rapid detection methods of foodborne pathogens, molecular biology technology has been widely used because of its advantages of strong specificity, high accuracy, and fast detection speed. This paper introduces the application of various molecular biological techniques in the rapid detection of foodborne pathogens, including polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), nuclear acid sequence-based amplification (NAS-BA), recombinase polymerase amplification (PRA), recombinase-aided isothermal amplification (RAA), and gene chip (GC). The application status, application scope and existing problems of these technologies are also analyzed and summarized to provide theoretical reference for the rapid detection of foodborne pathogens in the future.

BackgroundClozapine is widely regarded as a highly efficacious psychotropic drug that is largely underused worldwide. Recent disproportionality analyses and nationwide case-control studies suggested a potential association between clozapine use and hematological malignancy (HM). Nevertheless, the absolute rate difference is not well-established due to the absence of analytic cohort studies. The clinical significance of such a potential risk remains unclear.Methods and findingsWe extracted data from a territory-wide public healthcare database from January 2001 to August 2022 in Hong Kong to conduct a retrospective cohort study of anonymized patients aged 18+ years with a diagnosis of schizophrenia who used clozapine or olanzapine (drug comparator with highly similar chemical structure and pharmacological mechanisms) for 90+ days, with at least 2 prior other antipsychotic use records within both groups. Weighted by inverse probability of treatment (IPTW) based on propensity scores, Poisson regression was used to estimate the incidence rate ratio (IRR) of HM between clozapine and olanzapine users. The absolute rate difference was also estimated. In total, 9,965 patients with a median follow-up period of 6.99 years (25th to 75th percentile: 4.45 to 10.32 years) were included, among which 834 were clozapine users. After IPTW, the demographic and clinical characteristics of clozapine users were comparable to those of olanzapine users. Clozapine users had a significant weighted IRR of 2.22 (95% confidence interval (CI) [1.52, 3.34]; p < 0.001) for HM compared to olanzapine users. The absolute rate difference was estimated at 57.40 (95% CI [33.24, 81.55]) per 100,000 person-years. Findings were consistent across subgroups by age and sex. Sensitivity analyses all supported the robustness of the results and showed good specificity to HM but no other cancers. The main limitation of this observational study is the potential residual confounding effects that could have arisen from the lack of randomization in clozapine or olanzapine use.ConclusionsAbsolute rate difference in HM incidence associated with clozapine is small despite a 2-fold elevated rate. Given the rarity of HM and existing blood monitoring requirements, more restrictive indication for clozapine or special warnings may not be necessary.

Abstract Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising diagnostic tool, but challenging due to the complex and unclear etiology of autism. And it is difficult to effectively identify ASD patients with a single data source (single task). Therefore, to address this challenge, we propose a novel multi-task learning framework for ASD identification based on rs-fMRI data, which can leverage useful information from multiple related tasks to improve the generalization performance of the model. Meanwhile, we adopt an attention mechanism to extract ASD-related features from each rs-fMRI dataset, which can enhance the feature representation and interpretability of the model. The results show that our method outperforms state-of-the-art methods in terms of accuracy, sensitivity and specificity. This work provides a new perspective and solution for ASD identification based on rs-fMRI data using multi-task learning. It also demonstrates the potential and value of machine learning for advancing neuroscience research and clinical practice.

BackgroundAlthough descriptive studies have found an association between thyroid dysfunction (TD) and alopecia areata (AA), however, the causal relationship between TD and AA remains unclear. The purpose of this study is to investigate the causal relationship between the two and the specific directions.MethodsWe performed large-scale, two-sample Mendelian randomization (MR) analyses to examine whether there was an association between TD (such as Graves’ disease (GD), Hashimoto’s thyroiditis (HT), thyroid cancer (TC), thyroid stimulating hormone (TSH), thyrotropin-releasing hormone (TRH), etc.) and AA. Genome-wide association study (GWAS) summary statistics for TD and AA were from the IEU OpenGwas project. The inverse variance-weighted (IVW) method was used as the primary analysis method to evaluate the causality between TD and AA, supplemented by the weighted median, MR-Egger, simple mode and weighted mode. In addition, sensitivity analyses were performed to assess the reliability of the study results.ResultsOur study found that single nucleotide polymorphisms (SNPs) in HT (IVW OR = 1.396, 95% CI 1.030-1.892, P=0.031) and hypothyroidism (IVW OR = 1.431, 95% CI 1.138-1.799, P=0.002) significantly increased the risk of AA. Reverse MR analysis indicated that genetic susceptibility to AA (β=-0.029, 95%CI=-0.051 to -0.007, P=0.009) may be a risk for TRH. Positive MR analysis observed no statistically significant causal relationship between other TD and AA (IVW P>0.05). Reverse MR analysis also showed no statistically significant association between AA and other TD (IVW P>0.05) other than TRH. Furthermore, additional sensitivity analyses were performed, including a leave-one-out test, a heterogeneity test, and a pleiotropy test to assess the robustness of the results.ConclusionsThis study provides a very comprehensive analysis of the causal relationship between TD and AA, providing convincing genetic evidence to support the causal relationship between TD and alopecia areata. It reveals some causes of AA patients, which is of great significance for the management and treatment of AA patients.

Group 1 innate lymphoid cells (ILCs) comprise conventional natural killer (cNK) cells and type 1 innate lymphoid cells (ILC1s). The main functions of liver cNK cells and ILC1s not only include directly killing target cells but also regulating local immune microenvironment of the liver through the secretion of cytokines. Uncovering the intricate mechanisms by which transcriptional factors regulate and influence the functions of liver cNK cells and ILC1s, particularly within the context of liver tumors, presents a significant opportunity to amplify the effectiveness of immunotherapies against liver malignancies. Using Ncr1-drived conditional knockout mouse model, our study reveals the regulatory role of Prdm1 in shaping the composition and maturation of cNK cells. Although Prdm1 did not affect the killing function of cNK cells in an in vivo cytotoxicity model, a significant increase in cancer metastasis was observed in Prdm1 knockout mice. Interferon-gamma (IFN-γ), granzyme B, and perforin secretion decreased significantly in Prdm1-deficient cNK cells and liver ILC1s. Single-cell RNA sequencing (scRNA-seq) data also provided evidences that Prdm1 maintains functional subsets of cNK cells and liver ILC1s and facilitates communications between cNK cells, liver ILC1s, and macrophages. The present study unveiled a novel regulatory mechanism of Prdm1 in cNK cells and liver ILC1s, showing promising potential for developing innovative immune therapy strategies against liver cancer.

The rapid development of blockchain transactions highlights the importance of privacy protection (including anonymity and confidentiality) and underscores the necessity for auditability. Some schemes, such as PGC and Miniledger, support privacy protection and auditability. However, they only offer incomplete privacy protection (i.e., supporting anonymity or confidentiality exclusively). In response to these issues, we propose a scheme that achieves partial anonymity, confidentiality, auditability, and traceability. By integrating a variant of Pedersen commitments and randomizable signatures, we achieve partial anonymity for users and the auditability of transactions, thereby protecting user privacy under audit conditions. Based on the twisted ElGamal encryption algorithm and specially constructed zero-knowledge proofs, we achieve confidentiality of transaction amounts under legal and regulatory conditions. System test results indicate that this scheme effectively meets the above requirements. The feasibility of this scheme is confirmed through system testing, comparative analysis, and security analysis.

The concern and opposition to centralized management of user identities by third-party authorities have led to the emergence of Self-Sovereign Identity (SSI). With the help of blockchain, SSI effectively restore users’ control over their own identities, but privacy concerns due to blockchain transparency, coupled with the absence of accountability like Know Your Customer (KYC) and Anti-Money Laundering (AML), have cast uncertainties upon the viability of SSI. In this paper, we bridge this gap by introducing AASSI, a pioneering SSI protocol meticulously designed to balance the twin imperatives of privacy and accountability. Specifically, AASSI extends support for anonymity, self-derivation, fine-grained tracing and selective revocation. In the realm of anonymity and self-derivation, AASSI introduces redactable signatures, which empowers users to autonomously derive distinctive credentials for each user-service-provider interaction, effectively improving privacy protection and self-management capabilities. Pertaining to fine-grained tracing, the protocol employs a dual-tag system that facilitates tracing users’ real identities as well as a granular historical records of derived credentials. For selective revocation, AASSI leverages dynamic accumulators as a building block to enable the revocation of offending users. Ultimately, we provide functional comparison, which shows that AASSI has robust features, in particular, it supports novel self-derived features that further increase user control over their identity, and fine-grained tracking, which provides more flexibility for tracers. We demonstrate protocol efficiency through off-chain time-consuming comparison, which show that AASSI dramatically reduces the time overhead of credential verification. Moreover, We test the on-chain communication overhead and experimentally prove the feasibility of AASSI.

Sea surface height anomaly (SSHA) plays a pivotal role in ocean dynamics and climate systems. This article develops a graph-based memory recall recurrent neural network (GMR-Net) to achieve accurate and reliable mid-term spatiotemporal prediction of the SSHA field. The proposed method designs a newly developed long-term memory recall cell as the building block of the network, which utilizes the proposed memory store recall (MSR) module to learn and capture the mid- and long-term temporal dependencies of the SSHA field. The MSR module can efficiently recall memories stored in the memory bank across multiple timestamps through the proposed graph representation mechanism even after long periods of disturbance. The mid-term SSHA forecasting is performed with a 30-day ahead, and our proposed GMR-Net model achieves high prediction accuracy in different geographical regions: the Tropical Western Pacific and the South China Sea, yielding an RMSE of 0.026 and 0.035 m, respectively. Compared with advanced prediction models, our proposed GMR-Net model exhibits high reliability and superior performance in mid-term SSHA forecasting. Moreover, marine phenomena, such as Rossby waves, which can cause dramatic changes in sea-surface height, are successfully observed from our forecast data, further verifying the effectiveness of our prediction method.

Abstract Lignocellulosic biomass is an abundant renewable feedstock, but its complex structure of lignocellulose poses barriers to its enzymatic hydrolysis and fermentation. Fungi possess diverse lignocellulolytic enzyme systems that synergistically deconstruct lignocellulose into soluble sugars for fermentation. This review elucidates recent advances in understanding the molecular mechanisms underpinning fungal degradation of lignocellulose. We analyze major enzyme classes tailored by fungi to depolymerize cellulose, hemicellulose, and lignin. Highlighted are the concerted actions and intimate partnerships between these biomass‐degrading enzymes. Current challenges impeding large‐scale implementation of enzymatic hydrolysis are discussed, along with emerging biotechnological opportunities. Advanced pretreatments, high‐throughput enzyme engineering platforms, and machine learning or artificial intelligence‐guided lignocellulolytic enzyme cocktail optimization represent promising ways to improve hydrolytic efficiencies. Elucidating the coordinated interplay and regulation of fungal lignocellulolytic machinery can facilitate optimization of fungal biotechnology platforms. Harnessing the efficiency of fungal biomass deconstruction promises to enhance the development of biorefinery processes for sustainable bioenergy.

Abstract Background Mycoplasma ovipneumoniae (M. ovipneumoniae) is one of the main pathogens of sheep pneumonia, causing a series of clinical symptoms, such as depression, anorexia, hyperthermia, cough, dyspnea, and tract secretions. In recent years, the prevalence of M. ovipneumoniae pneumonia has become increasingly serious in sheep farms in Ningxia, China, leading to the death of sheep, and causing significant economic losses. In this study, the pathological organs infected by M. ovipneumoniae were collected to observe histopathological change, to determine the tissue localization of M. ovipneumoniae, and to analyze the cytokine changes, which lays a basis for the diagnosis and pathogenesis of M. ovipneumoniae disease. Results In this study, M. ovipneumoniae was detected in 97 of 105 samples collected from 13 large-scale sheep farms for nucleic acid by PCR. One representative isolate per farm was isolated from 13 farms. The lesions caused by M. ovipneumoniae were mainly in the trachea, bronchus, and lung, including necrosis of tracheal mucosal epithelial cells, disintegration of some epithelial cells, edema of mucosal lamina propria, with inflammatory cell infiltration, cytoplasmic vacuolization of epithelial cells of bronchial mucosa, massive infiltration of inflammatory cells in the alveolar space of lung, necrosis and hyperplasia of alveolar epithelial cells. Immunohistochemical analysis showed that the proportion of M. ovipneumoniae positive area in the lung was the largest, followed by that in the bronchus and trachea. Compared to healthy animals, diseased animals exhibited up-regulated gene expression levels of IL-1β, IL-6, and NF-κB in the trachea, bronchus, and lungs. In contrast, the expression of IL-10, IL-12, and IFN-γ was primarily limited to the trachea and bronchus. The expression of IL-1β showed differential patterns across different lung regions, with variations observed among lung lobes. Additionally, other cytokines consistently showed significant up-regulation specifically in the bronchus. Conclusions M. ovipneumoniae is primarily found in the lungs of infected individuals. NF-κB, an essential transcription factor, is involved in the regulation of IL-1β transcription. IL-12 may enhance the cytotoxic function of natural killer cells during M. ovipneumoniae infection. Those findings demonstrate the distinct expression profiles of cytokines in various anatomical sites throughout disease progression, suggesting the potential role of bronchial tissue as a major site of immune response.

Abstract Single-cell protein (SCP) production in the methylotrophic yeast Pichia pastoris has the potential to achieve a sustainable protein supply. However, improving the methanol fermentation efficiency and reducing carbon loss has been a long-standing challenge with far-reaching scientific and practical implications. Here, comparative transcriptomics revealed that PAS_0305, a gene directly associated with cell wall thickness under methanol stress, can be used as a target for unlocking cell wall sensors. Intracellular trehalose accumulation confirmed that cell wall sensors were activated after knocking out PAS_0305, which resulted in increased cell wall permeability. Genome-wide signal perturbations were transduced through the HOG module and the CWI pathway, which was confirmed to connected by Pbs2-Mkk. As a consequence of CWI pathway activation, ΔPAS_0305 elicited a rescue response of cell wall remodeling by increasing the β-1,3-glucan content and decreasing the chitin/mannose content. Remarkably, perturbations in global stress signals led to a fine-tuning of the metabolic network of ΔPAS_0305, resulting in a superior phenotype with highest crude protein and methanol conversion rate of 67.21% and 0.46 gDCW/g. Further genome-scale metabolic models were constructed to validate the experimental results, confirming that unlocking cell wall sensors resulted in maximized flux from methanol towards SCP and effectively addressing the issue of carbon loss in methanol fermentation. This work sheds new light on the potential of manipulating cellular signaling pathways to optimize metabolic networks and achieve exceptional phenotypic characteristics, providing new strategies for constructing versatile cell factories in P. pastoris.

Cf/TaxHf1−xC–SiC composites are ideal thermal structural materials for service under extreme conditions of hypersonic vehicles. However, how to synthesize TaxHf1-xC powders and efficiently fabricate Cf/TaxHf1-xC–SiC composites still faces some challenges. Furthermore, mechanical properties and thermophysical properties of TaxHf1−xC vary with the composition, but not monotonically. In-depth analysis of mechanical behaviors of the Cf/TaxHf1−xC–SiC composites is extremely important for their development and applications. In this study, the TaxHf1−xC powders (x = 0.2, 0.5, 0.8) were successfully synthesized via solid solution of TaC and HfC at a relatively low temperature of 1800 ℃, with a small amount of Si as an additive. Subsequently, the efficient fabrication of 2D-Cf/TaxHf1–xC–SiC composites was achieved by slurry impregnation and lamination (SIL) combined with precursor infiltration and pyrolysis (PIP). In addition, the mechanical behavior of the composites was investigated systematically. It is demonstrated that the composites present remarkable non-brittle fractures, including a large number of fiber pull out and interphase debonding. Also, the fracture failure involves a complex process of microcrack generation and propagation, matrix cracking, and layer fracture. Moreover, the interfacial bonding between the fibers and the matrix is enhanced as the Ta∶Hf ratio decreases from 4∶1 to 1∶4. As a result, Cf/Ta0.2Hf0.8C–SiC composites exhibit exceptional flexural strength of 437±19 MPa, improved by 46% compared with Cf/Ta0.8Hf0.2C–SiC (299±19 MPa). This study provides a new perception of design and fabrication of ultra-high-temperature ceramic (UHTC) matrix composites with high performance.

Natural ventilation (NV) has been considered a simple and effective method of ventilation. However, the introduction of NV does not achieve better indoor air quality (IAQ) when the outdoor atmospheric environment is polluted. Therefore, portable air cleaners (PACs) are increasing in use in recent years, but their effectiveness is highly dependent on the residents' habits. A typical residence in Xi'an, China was selected to examine the effects of the use of NV alone and the use of NV and PACs together on IAQ in the three occupant states, i.e., unoccupied, sleeping and leisure. Parameters, such as temperature, relative humidity, CO2, and PM2.5 concentration were measured when changing the window opening and the position of the PAC. The results showed that in the unoccupied state, opening the inner door can promote a more uniform thermal and humid environment. In the sleeping state, the I/O ratio of the PM2.5 concentration was the lowest when the window opening of the bedroom was 1/2 or 3/4, with a mean value of 0.3. In the leisure state, only using NV, when the purification rate reaches 90%, the mean purification time of each window opening in the living room is 87.5 min. The mean purification time was reduced to 25 min when both NV and PAC were used. The on-site purification efficiencies were 91.0% and 94.5%, when the window opening was 1/2 (i.e., the PAC was placed in the center of the room) and 3/4 (i.e., the PAC was placed away from the outer window), respectively.

The shift towards autonomous excavators in construction and mining is a significant leap towards enhancing operational efficiency and ensuring worker safety. However, it presents challenges, such as the need for sophisticated decision making and environmental perception due to complex terrains and diverse conditions. Our study introduces the E-GTN framework, a novel approach tailored for autonomous excavation that leverages advanced multisensor fusion and a custom-designed convolutional neural network to address these challenges. Results demonstrate that GridNet effectively processes grid data, enabling the reinforcement learning algorithm to make informed decisions, thereby ensuring efficient and intelligent autonomous excavator performance. The study concludes that the E-GTN framework offers a robust solution for the challenges in unmanned excavator operations, providing a valuable platform for future advancements in the field.

Abstract Background Methanol, synthesized from CO2, is a potentially sustainable one-carbon (C1) resource for biomanufacturing. The use of methanol as a feedstock to produce single cell protein (SCP) has been investigated for decades as an alternative to alleviate the high global demand for animal-derived proteins. The methylotrophic yeast Pichia pastoris is an ideal host for methanol-based SCP synthesis due to its natural methanol assimilation ability. However, improving methanol utilization, tolerance to higher temperature, and the protein content of P. pastoris are also current challenges, which are of great significance to the economical industrial application using methanol as a feedstock for SCP production. Results In the present work, adaptive laboratory evolution (ALE) has been employed to overcome the low methanol utilization efficiency and intolerance to a higher temperature of 33 °C in P. pastoris, associated with reduced carbon loss due to the lessened detoxification of intracellular formaldehyde through the dissimilation pathway and cell wall rearrangement to temperature stress resistance following long-term evolution as revealed by transcriptomic and phenotypic analysis. By strengthening nitrogen metabolism and impairing cell wall synthesis, metabolic engineering further increased protein content. Finally, the engineered strain via multi-strategy produced high levels of SCP from methanol in a pilot-scale fed-batch culture at 33 °C with a biomass of 63.37 g DCW/L, methanol conversion rate of 0.43 g DCW/g, and protein content of 0.506 g/g DCW. SCP obtained from P. pastoris contains a higher percentage of protein compared to conventional foods like soy, fish, meat, whole milk, and is a source of essential amino acids, including methionine, lysine, and branched-chain amino acids (BCAAs: valine, isoleucine, leucine). Conclusions This study clarified the unique mechanism of P. pastoris for efficient methanol utilization, higher temperature resistance, and high protein synthesis, providing a P. pastoris cell factory for SCP production with environmental, economic, and nutritional benefits.

Objective: The incidence and mortality of hepatocellular carcinoma (HCC) have been increasing around the world. Current guidelines recommend HCC screening in high-risk population. However, the strength of evidence of benefits and harms of HCC screening to support the recommendation was unclear. The objective is to systematically synthesize current evidence on the benefits and harms of HCC screening. Methods: We searched PubMed and nine other databases until August 20, 2021. We included cohort studies and RCTs that compared the benefits and harms of screening and non-screening in high-risk population of HCC. Case series studies that reported harms of HCC screening were also included. Pooled risk ratio (RR), according to HCC screening status, was calculated for each benefit outcome (e.g., HCC mortality, survival rate, proportion of early HCC), using head-to-head meta-analysis. The harmful outcomes (e.g., proportion of physiological harms provided by non-comparative studies were pooled by prevalence of meta-analysis. Analysis on publication bias and quality of life, subgroup analysis, and sensitivity analysis were also conducted. Results: We included 70 studies, including four random clinical trials (RCTs), 63 cohort studies,three case series studies. The meta-analysis of RCTs showed HCC screening was significantly associated with reduced HCC mortality (RR [risk ratio], 0.73 [95% CI, 0.56–0.96]; I2 = 75.1%), prolonged overall survival rates (1-year, RR, 1.72 [95% CI, 1.13–2.61]; I2 = 72.5%; 3-year, RR, 2.86 [95% CI, 1.78–4.58]; I2 = 10.1%; and 5-year, RR, 2.76 [95% CI, 1.37–5.54]; I2 = 28.3%), increased proportion of early HCC detection (RR, 2.68 [95% CI, 1.77–4.06]; I2 = 50.4%). Similarly, meta-analysis of cohort studies indicated HCC screening was more effective than non-screening. However, pooled proportion of physiological harms was 16.30% (95% CI: 8.92%–23.67%) and most harms were of a mild to moderate severity. Conclusion: The existing evidence suggests HCC screening is more effective than non-screening in high-risk population. However, harms of screening should not be ignored.

Deciphering patterns of connectivity between neurons in the brain is a critical step toward understanding brain function. Imaging-based neuroanatomical tracing identifies area-to-area or sparse neuron-to-neuron connectivity patterns, but with limited throughput. Barcode-based connectomics maps large numbers of single-neuron projections, but remains a challenge for jointly analyzing single-cell transcriptomics. Here, we established a rAAV2-retro barcode-based multiplexed tracing method that simultaneously characterizes the projectome and transcriptome at the single neuron level. We uncovered dedicated and collateral projection patterns of ventromedial prefrontal cortex (vmPFC) neurons to five downstream targets and found that projection-defined vmPFC neurons are molecularly heterogeneous. We identified transcriptional signatures of projection-specific vmPFC neurons, and verified Pou3f1 as a marker gene enriched in neurons projecting to the lateral hypothalamus, denoting a distinct subset with collateral projections to both dorsomedial striatum and lateral hypothalamus. In summary, we have developed a new multiplexed technique whose paired connectome and gene expression data can help reveal organizational principles that form neural circuits and process information.

Alzheimer's disease(AD) poses a significant challenge due to its widespread prevalence and the lack of effective treatments, highlighting the urgent need for early detection. This research introduces an enhanced neural network, named ADnet, which is based on the VGG16 model, to detect Alzheimer's disease using two-dimensional MRI slices. ADNet incorporates several key improvements: it replaces traditional convolution with depthwise separable convolution to reduce model parameters, replaces the ReLU activation function with ELU to address potential issues with exploding gradients, and integrates the SE(Squeeze-and-Excitation) module to enhance feature extraction efficiency. In addition to the primary task of MRI feature extraction, ADnet is simultaneously trained on two auxiliary tasks: clinical dementia score regression and mental state score regression. Experimental results demonstrate that compared to the baseline VGG16, ADNet achieves a 4.18% accuracy improvement for AD vs. CN classification and a 6% improvement for MCI vs. CN classification. These findings highlight the effectiveness of ADnet in classifying Alzheimer's disease, providing crucial support for early diagnosis and intervention by medical professionals. The proposed enhancements represent advancements in neural network architecture and training strategies for improved AD classification.

Soil salinization is a common environmental problem that seriously threatens crop yield and food security, especially through its impact on seed germination. Nanoparticle priming, an emerging seed treatment method, is receiving increasing attention in improving crop yield and stress resistance. This study used alfalfa seeds as materials to explore the potential benefits of cerium oxide nanoparticle (CeO2NP) priming to promote seed germination and improve salt tolerance. CeO2NPs at concentrations up to 500 mg/L were able to significantly alleviate salt stress in alfalfa seeds (200 mM), with 50 mg/L of CeO2NP having the best effect, significantly (P< 0.05) increasing germination potential (from 4.0% to 51.3%), germination rate (from 10.0% to 62.7%), root length (from 8.3 cm to 23.1 cm), and seedling length (from 9.8 cm to 13.7 cm). Priming treatment significantly (P< 0.05) increased seed water absorption by removing seed hardness and also reducing abscisic acid and jasmonic acid contents to relieve seed dormancy. CeO2NP priming increased α-amylase activity and osmoregulatory substance level, decreased reactive oxygen species and malonaldehyde contents and relative conductivity, and increased catalase enzyme activity. Seed priming regulated carotenoid, zeatin, and plant hormone signal transduction pathways, among other metabolic pathways, while CeO2NP priming additionally promoted the enrichment of α-linolenic acid and diterpenoid hormone metabolic pathways under salt stress. In addition, CeO2NPs enhanced α-amylase activity (by 6.55%) in vitro. The optimal tested concentration (50 mg/L) of CeO2NPs was able to improve the seed vigor, enhance the activity of α-amylase, regulate the osmotic level and endogenous hormone levels, and improve the salt tolerance of alfalfa seeds. This study demonstrates the efficacy of a simple seed treatment strategy that can improve crop stress resistance, which is of great importance for reducing agricultural costs and promoting sustainable agricultural development.

Summary: The morphology and spatial distribution of axon arbors and boutons are crucial for neuron presynaptic functions. However, the principles governing their whole-brain organization at the single-neuron level remain unclear. We developed a machine-learning method to separate axon arbors from passing axons in single-neuron reconstruction from fluorescence micro-optical sectioning tomography imaging data and obtained 62,374 axon arbors that displayed distinct morphology, spatial patterns, and scaling laws dependent on neuron types and targeted brain areas. Focusing on the axon arbors in the thalamus and cortex, we revealed the segregated spatial distributions and distinct morphology but shared topographic gradients between feedforward and feedback projections. Furthermore, we uncovered an association between arbor complexity and microglia density. Finally, we found that the boutons on terminal arbors show branch-specific clustering with a log-normal distribution that again differed between feedforward and feedback terminal arbors. Together, our study revealed distinct presynaptic structural organizations underlying diverse functional innervation of single projection neurons.

Abstract. Background:. Current guidelines recommend hepatocellular carcinoma (HCC) screening in high-risk populations. However, the ideal HCC screening interval and screening modality have not been determined. This study aimed to compare the screening efficacy among different modalities with various intervals. Methods:. PubMed and other nine databases were searched through June 30, 2021. Binary outcomes were pooled using risk ratio (RR) with 95% confidence intervals (CIs). Survival rates were also pooled using RR with 95% CIs because most eligible studies only provided the number of survival patients instead of hazard ratio. Results:. In all, 13 studies were included. Two random controlled trials (RCTs) and six cohort studies compared screening intervals for ultrasonography (US) screening and found no significant differences between shorter (3- or 4-month) and longer (6- or 12-month) screening intervals in terms of early HCC proportion, HCC significant mortality, 1-year survival rate; screening at 6-month interval significantly increased the proportion of early HCC (RR = 1.17, 95% confidence interval [CI]: 1.08–1.26) and prolonged the 5-year survival rate (RR = 1.39, 95% CI: 1.07–1.82) relative to the 12-month interval results. Three other RCTs and two cohort studies compared different screening modalities in cirrhosis or chronic hepatitis B, which indicated no statistical differences in the proportion of early HCC (RR = 0.89, 95% CI: 0.40–1.96) and HCC mortality (RR = 0.69, 95% CI: 0.23–2.09) between the biannual US and annual computed tomography (CT screening). Biannual US screening showed a lower proportion of early HCC than biannual magnetic resonance imaging (MRI) (RR = 0.60, 95% CI: 0.37–0.97) and biannual US combined with annual CT (RR = 1.31, 95% CI: 1.13–1.51) screening. The proportion of early HCC in the contrast-enhanced US group was slightly higher than that in the B-mode US (RR = 1.08, 95% CI: 1.00–1.23) group. Conclusions:. The evidence suggests that 6 months may be the best HCC screening interval for US screening. The effectiveness of CT and MRI is better than US during same screening intervals. However, MRI and CT are more expensive than US, and CT also can increase the risk of radiation exposure. The selection of CT or MRI instead of US should be carefully considered. Registration:. No. CRD42020148258 at PROSPERO website (https://www.crd.york.ac.uk/PROSPERO/).

Abstract Background Reactivation of Epstein Barr virus (EBV) leads to modulation of the viral and cellular epitranscriptome. N6-methyladenosine (m6A) modification is a type of RNA modification that regulates metabolism of mRNAs. Previous reports demonstrated that m6A modification affects the stability and metabolism of EBV encoded mRNAs. However, the effect of reactivation on reprograming of the cellular mRNAs, and how this contributes to successful induction of lytic reactivation is not known. Methods Methylated RNA immunoprecipitation sequencing (MeRIP-seq), transcriptomic RNA sequencing (RNA-seq) and RNA pull-down PCR were used to screen and validate differentially methylated targets. Western blotting, quantitative real-time PCR (RT-qPCR) and immunocytochemistry were used to investigate the expression and localization of different proteins. RNA stability and polysome analysis assays were used to detect the half-lives and translation efficiencies of downstream genes. Insertion of point mutation to disrupt the m6A methylation sites was used to verify the effect of m6A methylation on its stability and expression levels. Results We report that during EBV reactivation the m6A eraser ALKBH5 is significantly downregulated leading to enhanced methylation of the cellular transcripts DTX4 and TYK2, that results in degradation of TYK2 mRNAs and higher efficiency of translation of DTX4 mRNAs. This resulted in attenuation of IFN signaling that promoted progression of viral lytic replication. Furthermore, inhibition of m6A methylation of these transcripts led to increased production of IFN, and a substantial reduction in viral copy number, which suggests abrogation of lytic viral replication. Conclusion Our findings illuminate the significance of m6A modification in overcoming the innate immune response during EBV reactivation. We now report that during lytic reactivation EBV targets the RNA methylation system of the host to attenuate the innate immune response by suppressing the interferon signaling which facilitates successful lytic replication of the virus.

Abstract There is a high level of conservation of tryptophans within the active site architecture of the cellulase family, whereas the function of the four tryptophans in the catalytic domain of Cel7A is unclear. By mutating four tryptophan residues in the catalytic domain of Cel7A from Penicillium piceum (PpCel7A), the binding affinity between PpCel7A and p‐nitrophenol‐d‐cellobioside (pNPC) was reduced as determined by Michaelis–Menten constants, molecular dynamics simulations, and fluorescence spectroscopy. Furthermore, PpCel7A variants showed a reduced level of cellobiohydrolase (CBH) activity against cellulose analogs or natural cellulose. Therefore, it could be concluded four tryptophan residues in Cel7A played a critical role in substrate binding. Mutagenesis results indicated that the W390 stacking interactions at the −2 site played an essential role in facilitating substrate distortion to the −1 site. As soon as the function was altered, the mutation would inevitably affect the catalytic activity against the natural substrate. Interestingly, no clear relationship was found between the CBH activity of PpCel7A variants against pNPC and Avicel. p‐Nitrophenol contains many electrophilic groups that may result in overestimation of the binding constant between tryptophan residues and pNPC in comparison with the natural substrate. Consequently, screening improved cellulase using cellulose analogs would divert attention from the target direction for lignocellulose biorefinery. Clarifying mechanism of catalytic diversity on the natural cellulose or cellulose analogs may give better insight into cellulase screening and selecting strategy.

Abstract Trichoderma reesei endoglucanases (EGs) have limited industrial applications due to its low thermostability and activity. Here, we aimed to improve the thermostability of EGs from T. reesei without reducing its activity counteracting the activity–stability trade‐off. A cross‐strategy combination of machine learning and B‐factor analysis was used to predict beneficial amino acid substitution in EG loop optimization. Experimental validation showed single‐site mutated EG concomitantly improved enzymatic activity and thermal properties by 17.21%–18.06% and 49.85%–62.90%, respectively, compared with wild‐type EGs. Furthermore, the mechanism explained mutant variants had lower root mean square deviation values and a more stable overall structure than the wild type. According to this study, EGs loop optimization is crucial for balancing the activity–stability trade‐off, which may provide new insights into how loop region function interacts with enzymatic characteristics. Moreover, the cross‐strategy between machine learning and B‐factor analysis improved superior enzyme activity–stability performance, which integrated structure‐dependent and sequence‐dependent information.

Abstract. Background:. Data on the immunogenicity and safety of heterologous immunization schedules are inconsistent. This study aimed to evaluate the immunogenicity and safety of homologous and heterologous immunization schedules. Methods:. Multiple databases with relevant studies were searched with an end date of October 31, 2021, and a website including a series of Coronavirus disease 2019 studies was examined for studies before March 31, 2022. Randomized controlled trials (RCTs) that compared different heterologous and homologous regimens among adults that reported immunogenicity and safety outcomes were reviewed. Primary outcomes included neutralizing antibodies against the original strain and serious adverse events (SAEs). A network meta-analysis (NMA) was conducted using a random-effects model. Results:. In all, 11 RCTs were included in the systematic review, and nine were ultimately included in the NMA. Among participants who received two doses of CoronaVac, another dose of mRNA or a non-replicating viral vector vaccine resulted in a significantly higher level of neutralizing antibody than a third CoronaVac 600 sino unit (SU); a dose of BNT162b2 induced the highest geometric mean ratio (GMR) of 15.24, 95% confidence interval [CI]: 9.53–24.39. Following one dose of BNT162b2 vaccination, a dose of mRNA-1273 generated a significantly higher level of neutralizing antibody than BNT162b2 alone (GMR = 1.32; 95% CI: 1.06–1.64), NVX-CoV2373 (GMR = 1.60; 95% CI: 1.16–2.21), or ChAdOx1 (GMR = 1.80; 95% CI: 1.25–2.59). Following one dose of ChAdOx1, a dose of mRNA-1273 was also more effective for improving antibody levels than ChAdOx1 (GMR = 11.09; 95% CI: 8.36–14.71) or NVX-CoV2373 (GMR = 2.87; 95% CI: 1.08–3.91). No significant difference in the risk for SAEs was found in any comparisons. Conclusions:. Relative to vaccination with two doses of CoronaVac, a dose of BNT162b2 as a booster substantially enhances immunogenicity reactions and has a relatively acceptable risk for SAEs relative to other vaccines. For primary vaccination, schedules including mRNA vaccines induce a greater immune response. However, the comparatively higher risk for local and systemic adverse events introduced by mRNA vaccines should be noted. Registration:. PROSPERO; https://www.crd.york.ac.uk/PROSPERO/; No. CRD42021278149.

Typhoon landing is frequent in the southeast coastal areas of China, and the typhoon-induced landslide caused by associated rainstorms has caused serious economic losses and casualties. Some studies have made some achievements in the slope hydrological response process and stability analysis of single-peak and multi-peaks typhoon rainstorms, but research on mechanism analysis on the hysteresis effect of typhoon-induced landslide is lacked. Therefore, taking “Lekima” in Qingtian county, Zhejiang Province as an example, based on the statistics of the number and rainfall of typhoon-type landslides, this paper constructs 16 groups of slope models with different structural combinations to simulate the seepage-stability changes of slopes under different rainfall conditions during the landing of a typhoon. The results show that there are still 26.4% landslides in Qingtian county after typhoon “Lekima” left the land, which is lagging. The stability of slopes with different structural combinations is the lowest after landing, which is 13.82% lower than that before typhoon landing. Under the action of typhoon landing rainstorm, the slope is the most sensitive among the influencing parameters of slope stability structure in Qingtian county. It is considered that rainfall at different landing stages of the typhoon will lead to the difference of infiltration characteristics of the slope, thus forming a hysteresis effect. The conclusion has a certain guiding significance for the early identification of typhoon-induced landslides in this area.

Abstract Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks. Methods Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model. Results Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively. Limitations Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity. Conclusions These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.

The development of the DC distribution system (DCDS) has attracted increasing attention, due to its technological superiorities in integrating new energy sources and DC power loads more friendly and effectively. In this paper, a comprehensive evaluation method is proposed for the medium–low​ voltage DCDS to handle the voltage sag assessment problem. Firstly, the topology structure and control strategy of the DCDS are stated, and the pragmatic characteristics of DC voltage sag on the medium-voltage and low-voltage sides based on the dual active bridge (DAB) converter are analyzed. Then, considering the difference in the evaluation indexes of the low-voltage and medium-voltage sides of the DCDS, a comprehensive evaluation system is established. Further, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) weighting method are applied to achieve comprehensive weighting, and the TOPSIS model is used to describe the severity of DC voltage sag. Using MATLAB, a 10 kV/ 750 V DCDS with the DAB is built, and comparative assessments are performed. The results show that the proposed method can reasonably evaluate the DC voltage sag of the low-voltage and medium-voltage sides. Compared to the evaluation schemes based on the single index, the proposed method can more effectively reflect the waveform features of the DC voltage sag, and it is very appropriate for the voltage dip evaluation of the medium–low voltage DCDS.

In this paper, a method for extracting browning features from cooked shrimps based on a simplified color model was proposed. A large number of images of normal cooked shrimps and melanotic cooked shrimps in the dynamic production line were obtained by online image acquisition equipment, and the region of interest of cooked shrimps was extracted by using the grey-scale difference method and morphological operations, and the browning features of cooked shrimps were extracted by three color spaces; a color model was constructed for 151 images of normal shrimps and 66 images of melanotic shrimps in the training set, and the percentage of browning areas of cooked shrimps were used as the benchmark condition to determine whether browning occurred in cooked shrimps, and were graded. The results showed that the RGB color model with the B-component range of 0~77 was the most effective, and the recognition rate could reach 91.36 ms/each of the 116 normal and 50 browned shrimp images, which reduced the complexity of the algorithm from O(n²) to O(n) and improved the recognition accuracy by 7.3% to 92.77% compared with the traditional binary linear discriminant analysis method. The method has greater practical advantages and application prospects, providing performance support for the application of machine vision technology to the online non-destructive inspection of the appearance quality of South American white shrimp.

Abstract The diversity of consumer demand makes the manufacturing industry shift from the production mode of small variety and large batch to the production mode of multi-variety and small batch, which leads to the increase of production cost and the decrease of production efficiency, especially the increase of energy consumption. In this paper, the energy-aware scheduling problem of two-stage flexible flow shop based on group technology is studied, in which the process differences of jobs in the two stages are inconsistent. To solve the problem of unbalanced load of machines caused by group technology, this paper uses a dynamic splitting strategy to balance the load of machines on the basis of increasing the setup energy consumption as little as possible. Considering that the research problem is a multi-objective optimization problem, this study develops an improved multi-objective scatter search algorithm. According to the characteristics of the problem, the coding scheme, decoding scheme, diversity generation method and combination method are designed. The experimental results show that the group technology can effectively reduce energy consumption, and the dynamic split strategy can balance the load of machines effectively. Moreover, the results show the proposed method is better than the existing methods in terms of obtaining better solutions.

Human papillomavirus (HPV) plays a significant role in the development of cervical cancers in the setting of co-infection with HIV. Botswana has a high prevalence of HIV and cervical cancer. In this study, we investigated the distribution of HPV subtypes in cervical cancer biopsy samples from patients in Botswana using a highly sensitive pan-pathogen microarray technology, PathoChip, to detect both high- (HR-HPV) and low-risk HPV (LR-HPV) subtypes in women living with HIV (WLWH) and women living without HIV. We analyzed samples from 168 patients, of which 73% (n = 123) were WLWH with a median CD4 count of 479.5 cells/μL. Five HR-HPV subtypes were detected in the cohort: HPV 16, 18, 26, 34, and 53. The most prevalent subtypes were HPV 26 (96%) and HPV 34 (92%); 86% of WLWH (n = 106) had co-infection with four or more HR-HPV subtypes compared to 67% (n = 30) of women without HIV (p 200 cells/μL and HIV-negative patients. Although the majority of cervical cancer specimens in this cohort were determined to have multiple HPV infections, the most prevalent HR-HPV subtypes (HPV 26 and HPV34) found in these cervical cancer samples are not covered in the current HPV vaccines. Though no conclusions can be made on the direct carcinogenicity of these subtypes the results do underlie the need for continued screening for prevention of cervical cancer.

Accurate retinal vessel segmentation from fundus images is essential for eye disease diagnosis. Many deep learning methods have shown great performance in this task but still struggle with limited annotated data. To alleviate this issue, we propose an Attention-Guided Cascaded Network (AGC-Net) that learns more valuable vessel features from a few fundus images. Attention-guided cascaded network consists of two stages: the coarse stage produces a rough vessel prediction map from the fundus image, and the fine stage refines the missing vessel details from this map. In attention-guided cascaded network, we incorporate an inter-stage attention module (ISAM) to cascade the backbone of these two stages, which helps the fine stage focus on vessel regions for better refinement. We also propose Pixel-Importance-Balance Loss (PIB Loss) to train the model, which avoids gradient domination by non-vascular pixels during backpropagation. We evaluate our methods on two mainstream fundus image datasets (i.e., DRIVE and CHASE-DB1) and achieve AUCs of 0.9882 and 0.9914, respectively. Experimental results show that our method outperforms other state-of-the-art methods in performance.

Abstract Background It was urgent and necessary to synthesize the evidence for vaccine effectiveness (VE) against SARS-CoV-2 variants of concern (VOC). We conducted a systematic review and meta-analysis to provide a comprehensive overview of the effectiveness profile of COVID-19 vaccines against VOC. Methods Published randomized controlled trials (RCTs), cohort studies, and case-control studies that evaluated the VE against VOC (Alpha, Beta, Gamma, Delta, or Omicron) were searched until 4 March 2022. Pooled estimates and 95% confidence intervals (CIs) were calculated using random-effects meta-analysis. VE was defined as (1-estimate). Results Eleven RCTs (161,388 participants), 20 cohort studies (52,782,321 participants), and 26 case-control studies (2,584,732 cases) were included. Eleven COVID-19 vaccines (mRNA-1273, BNT162b2, ChAdOx1, Ad26.COV2.S, NVX-CoV2373, BBV152, CoronaVac, BBIBP-CorV, SCB-2019, CVnCoV, and HB02) were included in this analysis. Full vaccination was effective against Alpha, Beta, Gamma, Delta, and Omicron variants, with VE of 88.0% (95% CI, 83.0–91.5), 73.0% (95% CI, 64.3–79.5), 63.0% (95% CI, 47.9–73.7), 77.8% (95% CI, 72.7–82.0), and 55.9% (95% CI, 40.9–67.0), respectively. Booster vaccination was more effective against Delta and Omicron variants, with VE of 95.5% (95% CI, 94.2–96.5) and 80.8% (95% CI, 58.6–91.1), respectively. mRNA vaccines (mRNA-1273/BNT162b2) seemed to have higher VE against VOC over others; significant interactions (p interaction < 0.10) were observed between VE and vaccine type (mRNA vaccines vs. not mRNA vaccines). Conclusions Full vaccination of COVID-19 vaccines is highly effective against Alpha variant, and moderate effective against Beta, Gamma, and Delta variants. Booster vaccination is more effective against Delta and Omicron variants. mRNA vaccines seem to have higher VE against Alpha, Beta, Gamma, and Delta variants over others.

Thermophilic β-1,4-endoglucanases (Cel5A) have garnered significant interest due to their potential applications in various industries, particularly in biofuel production and biorefineries. However, despite inherent stability, thermophilic Cel5A still face challenges in terms of further enhancing their catalytic efficiency and thermostability. In this study, a novel B-factor analysis method was used to predict beneficial amino acid substitutions within a 4 Å radius of the catalytic site in the tunnel of thermophilic Cel5A from Acidomyces richmondensis (ArCel5A). A combined strategy involving site-saturation mutagenesis and high-throughput screening was employed to identify the variants with the highest endoglucanase activity. Genomic sequencing revealed a mutation at position 299 in the starting strain T. reesei A2H, where the nucleotide sequence changed from TAC to TGC, resulting in a corresponding amino acid substitution from Tyrosine(Y) to Cystine(C). The endoglucanase activity of the mutant ArCel5A reached 3251 IU/mL, representing an 85.2% increase compared to wild-type ArCel5A at the fermentation time of 94 h. Significantly, the ArCel5A-Y299C mutant showed superior thermostability, retaining 93.8% of its initial activity after 30 min at 70 °C, and 91.5% after 10 min at 80 °C. Various computational simulation methods confirmed that the Y299C mutation enhanced the stability of the catalytic pocket, thereby improving the overall stability and catalytic efficiency of ArCel5A. This study offers an effective strategy for mining target sites for rational mutagenesis based on highly conserved sequences, which simultaneously improves both the thermostability and catalytic efficiency of thermophilic Cel5A.

The small GTPases of the Rho family are known to regulate various biological processes in filamentous fungi. In this study, we investigated the impact of deleting Rho proteins on the growth and cellulase production of Trichoderma reesei. Our findings revealed that deletion of cdc42 led to the most severe growth defect and impaired cellulase production. Conversely, overexpression of cdc42 resulted in a hyperbranched phenotype, significantly enhancing cellulase production. Furthermore, the cdc42-overexpressing (OCdc42) strain showed an increased expression of multiple cellulase genes and Rho GTPase genes. Analysis of the secretome in the OCdc42 strain unveiled an increased abundance and diversity of extracellular proteins compared to the parent strain. These discoveries provide valuable insights into the functionality of Rho GTPases in T. reesei and offer potential targets for engineering fungi to improve plant biomass deconstruction in biorefineries.

Soybean (Glycine max (L.) Merr.), a typical short-day plant, is sensitive to photoperiod, which limits the geographical range for its cultivation. In the flowering pathway regulated by photoperiod, E1, a flowering inhibitor in soybean, plays the dominant role in flowering time regulation. Two E1 homologs, E1-like-a (E1La) and E1-like-b (E1Lb), play overlapping or redundant roles in conjunction with E1. In the present study, E1 and E1La/b were simultaneously silenced via RNA interference (RNAi) in Zigongdongdou (ZGDD), an extremely late-flowering soybean landrace from southern China. As a result, RNAi lines showed a much earlier-flowering phenotype and obvious photoperiod insensitivity compared with wild-type (WT) plants. In RNAi transgenic plants, the expression levels of flowering inhibitor GmFT4 and flowering promoters GmFT2a/GmFT5a were significantly down- and up-regulated, respectively. Further, the maturity group (MG) of the RNAi lines was reduced from WT ZGDD's MG VIII (extremely late-maturity) to MG 000 (super-early maturity), which can even grow in the northernmost village of China located at a latitude of 53.5°N. Our study confirms that E1 and E1La/b can negatively regulate flowering time in soybean. The RNAi lines generated in this study, with early flowering and maturity traits, can serve as valuable materials and a technical foundation for breeding soybeans that are adapted to high-latitude short-season regions.

This article developed a scalable deep-learning model, the AlgaeNet model, for floating Ulva prolifera (U. prolifera) detection in moderate resolution imaging spectroradiometer (MODIS) and synthetic aperture radar (SAR) images. We labeled 1055/4071 pairs of samples, among which 70%/30% were used for training/validation. As a result, the model reached an accuracy of 97.03%/99.83% and a mean intersection over union of 48.57%/88.43% for the MODIS/SAR images. The model was designed based on the classic U-Net model with two tailored modifications. First, the physics information input was a multichannel multisource remote sensing data. Second, a new loss function was developed to resolve the class-unbalanced samples (algae and seawater) and improve model performance. In addition, this model is expandable to process images from optical sensors (e.g., MODIS/GOCI/Landsat) and SAR (e.g., Sentinel-1/GF-3/Radarsat-1 or 2), reducing the potential biases due to the selection of extraction thresholds during the traditional threshold-based segmentation. We process satellite images containing U. prolifera in the Yellow Sea and draw two conclusions. First, adding the 10-m high-resolution SAR imagery shows a 63.66% increase in algae detection based on the 250-m resolution MODIS image alone. Second, we define a floating and submerged ratio number (FS ratio) based on the floating and submerged parts of U. prolifera detected by SAR and MODIS. A research vessel measurement confirms the FS ratio to be a good indicator for representing different life phases of U. prolifera.

Abstract Background tRNA-derived fragments (tRFs) have been shown to have critical regulatory roles in cancer biology. However, the contributions of tRFs to colorectal cancer (CRC) remain largely unknown. Methods tRF3008A (a tRFRNA derived from tRNAVal) was identified by RNA sequencing and validated by quantitative reverse transcription PCR. The role of tRF3008A in CRC progression was assessed both in vitro and in vivo, and its downstream target genes were identified and validated in CRC cells. RNA pull-down with mass spectrometry and AGO-RIP were used to confirm the interaction of tRF3008A and AGO proteins. The clinical implications of tRF3008A were assessed in CRC tissues and blood samples. Results The expression of tRF3008A was reduced in colorectal cancer, and its reduction was significantly correlated with advanced and metastatic disease in CRC. Patients with low tRF3008A expression showed significantly shorter DFS, and multivariate analysis identified tRF3008A as an independent prognostic biomarker in CRC. Functionally, tRF3008A inhibits the proliferation and migration of CRC in vivo and in vitro by repressing endogenous FOXK1, a positive regulator of the Wnt/β-catenin pathway. Mechanistically, tRF3008A binds to AGO proteins as a guide to destabilize oncogenic FOXK1 transcript. Conclusions tRF3008A suppresses the metastasis and progression of colorectal cancer by destabilizing FOXK1 in an AGO-dependent manner.

PurposeSuicide is a global concern, especially among young people. Suicide prediction models have the potential to make it easier to identify patients who are at a high risk of suicide, but they have very little predictive power when there is a positive value for suicide mortality. Therefore, the aim of the study is to uncover potential risk factors associated with suicide by self-poisoning and further to provide a trustworthy nomogram to predict self-poisoning suicide among poisoned patients.MethodsThis study prospectively enrolled 237 patients who were treated for poisoning at the Fifth Medical Center of PLA General Hospital (Beijing) between May 2021 and May 2022. Patient's basic characteristics, daily activities, mental health status, and history of psychological illnesses were gathered to examine their predictive power for self-poisoning suicide. On developing a prediction model, patients were split 8:2 into a training (n = 196) group and a validation (n = 41) group at random via computer. The training group worked on model development, while the validation group worked on model validation. In this study, the Hosmer and Lemeshow test, accuracy, and area under the curve were the primary evaluation criteria. Shapley Additive exPlanations (SHAP) was determined to evaluate feature importance. To make the prediction model easy for researchers to utilize, it was presented in nomogram format. Two risk groups of patients were identified based on the ideal cut-off value.ResultsOf all poisoned patients, 64.6% committed suicide by self-poisoning. With regard to self-poisoning attempted suicide, multivariate analysis demonstrated that female gender, smoking, generalized anxiety disorder-7 (GAD-7), and beck hopelessness scale-20 (BHS-20) were significant risk factors, whereas married status, relatively higher education level, a sedentary time of 1–3 h per day, higher sport frequency per week, higher monthly income were significant protective features. The nomogram contained each of the aforementioned nine features. In the training group, the area under curve (AUC) of the nomogram was up to 0.938 (0.904–0.972), whereas in the validation group, it reached a maximum of 0.974 (0.937–1.000). Corresponding accuracy rates were up to 0.883 and 0.927, respectively, and the P-values for the Hosmer and Lemeshow test were 0.178 and 0.346, respectively. SHAP demonstrated that the top three most important features were BHS-20, GAD-7, and marital status. Based on the best cut-off value of the nomogram (40%), patients in the high-risk group had a nearly six-time larger likelihood of committing suicide by self-poisoning than patients in the low-risk group (88.68 vs. 15.38%, P < 0.001). The dynamic nomogram was made available at the following address: https://xiaobo.shinyapps.io/Nomogramselfpoisoningsuicide/.ConclusionsThis study proposes a prediction model to stratify patients at a high risk of suicide by self-poisoning and to guide individual preventive strategies. Patients in the high-risk group require further mental health counseling to alleviate anxiety and hopelessness, healthy lifestyle like quitting smoking and exercising more, and restriction of access to poison and psychiatric drugs.

Summary: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death in the world. Mitochondrial fission regulator 2 (MTFR2) is involved in the development of various cancers. However, the roles of MTFR2 in HCC remain unknown. In this study, we conducted a comprehensive analysis of MTFR2 in HCC, which was generated from integrative MTFR2 analyses of eight HCC cell lines, and three datasets (public dataset, real-world dataset, and immunotherapy dataset) derived from bulk HCC tissues, survival, and immunotherapy data. We demonstrated that the expression level of MTFR2 is upregulated in HCC, leading to poor prognosis. MTFR2 is positively correlated with the level of immune cell infiltration, multiple immune checkpoints and immunotherapy response prediction pathways, and acts as an important role in cancer-immunity cycle. In conclusion, our work indicates that MTFR2 can shape a barrier of immune microenvironment and result in poor prognosis in hepatocellular carcinoma, but the immune barrier may be broken by immunotherapy.

Rapid economic development and industrialization may include environmentally harmful human activities that cause heavy-metal accumulation in soils, ultimately threatening the quality of the soil environment and human health. Therefore, accurate identification of pollution sources is an important weapon in efforts to control and prevent pollution. The self-organizing map (SOM) method is widely used in pollution source identification because of its capacity for visualization of high-dimensional data. The SOM ignores the graph structure relationship among chemical elements in soils; the SOM analysis of pollution sources has high uncertainty. Here, we propose a new analysis method, i.e., the graph convolutional self-organizing map (GCSOM), which uses a graph convolutional network (GCN) to extract the graph structure relationship among the chemical elements in soils, then performs data visualization using an SOM. We compared the performances of GCSOM and SOM, then assessed the pollution source characteristics of trace metal(loid)s (TMs, mostly heavy metals) in Jiangmen City using the GCSOM. Our experimental results showed that the GCSOM is superior to the SOM for identification of TM sources, while the TMs in the soil of Jiangmen originate from three main sources: agricultural activities (mainly in Taishan City, Jiangmen), traffic emissions (mainly in Xinhui and Pengjiang Districts), and industrial activities (mainly in Xinhui District). The risk assessment indicated that the risk of all TMs was within threshold.

Purpose: We conducted a systematic review and meta-analysis to compare the screening performance of synthesized mammography (SM) plus digital breast tomosynthesis (DBT) with digital mammography (DM) plus DBT or DM alone. Methods: Medline, Embase, Web of Science, and the Cochrane Library databases were searched from January 2010 to January 2021. Eligible population-based studies on breast cancer screening comparing SM/DBT with DM/DBT or DM in asymptomatic women were included. A random-effect model was used in this meta-analysis. Data were summarized as risk differences (RDs), with 95 % confidence intervals (CIs). Results: Thirteen studies involving 1,370,670 participants were included. Compared with DM/DBT, screening using SM/DBT had similar breast cancer detection rate (CDR) (RD = −0.1/1000 screens, 95 % CI = −0.4 to 0.2, p = 0.557, I2 = 0 %), but lower recall rate (RD = −0.56 %, 95 % CI = −1.03 to −0.08, p = 0.022, I2 = 90 %) and lower biopsy rate (RD = −0.33 %, 95 % CI = −0.56 to −0.10, p = 0.005, I2 = 78 %). Compared with DM, SM/DBT improved CDR (RD = 2.0/1000 screens, 95 % CI = 1.4 to 2.6, p

Federated learning is a distributed machine learning framework, which allows users to save data locally for training without sharing data. Users send the trained local model to the server for aggregation. However, untrusted servers may infer users’ private information from the provided data and mistakenly execute aggregation protocols to forge aggregation results. In order to ensure the reliability of the federated learning scheme, we must protect the privacy of users’ information and ensure the integrity of the aggregation results. This paper proposes an effective secure aggregation verifiable federated learning scheme, which has both high communication efficiency and privacy protection function. The scheme encrypts the gradients with a single mask technology to securely aggregate gradients, thus ensuring that malicious servers cannot deduce users’ private information from the provided data. Then the masked gradients are hashed to verify the aggregation results. The experimental results show that our protocol is more suited for bandwidth-constraint and offline-users scenarios.

Abstract The lack of effective lignin valorization technology remains a major barrier to utilize lignocellulose for producing biofuels and biochemicals. In this work, a new technology via the three‐phase three‐dimensional electro‐Fenton reaction was employed for the first time to convert lignin or its derived aromatics. The operating parameters were optimized by central composite design, which was controlled to prompt the synthesis of long‐chain fatty acids from lignin instead of degrading lignin to CO2. Under the optimized condition, the yields of palmitic acid and octadecanoic acid from lignin reached 138.41 and 112.31 mg/g, respectively. Key intermediators were identified during the electrolysis of lignin model compound using gas chromatography–quantitative time of flight mass spectrometry. The mechanism for the production of long‐chain fatty acids from lignin was proposed. Lignin successively underwent degradation, opening ring, and couple reactions and produced free radicals. This study is the first to report the conversion of lignin into the precursor of biodiesel or advanced biofuels through electro‐Fenton electrolysis at room temperature and atmosphere pressure. This study provides an environment‐friendly route for direct conversion of lignin into high‐value added products as a new strategy to valorize this undervalued component in lignocellulosic biomass.

BackgroundImmunotherapy is a promising anti-cancer strategy in hepatocellular carcinoma (HCC). However, a limited number of patients can benefit from it. There are currently no reliable biomarkers available to find the potential beneficiaries. Methylcytosine (m5C) is crucial in HCC, but its role in forecasting clinical responses to immunotherapy has not been fully clarified.MethodsIn this study, we analyzed 371 HCC patients from The Cancer Genome Atlas (TCGA) database and investigated the expression of 18 m5C regulators. We selected 6 differentially expressed genes (DEGs) to construct a prognostic risk model as well as 2 m5C-related diagnostic models.ResultsThe 1-, 3-, and 5-year area under the curve (AUC) of m5C scores for the overall survival (OS) was 0.781/0.762/0.711, indicating the m5C score system had an ideal distinction of prognostic prediction for HCC. The survival analysis showed that patients with high-risk scores present a worse prognosis than the patients with low-risk scores (p< 0.0001). We got consistent results in 6 public cohorts and validated them in Xiangya real-world cohort by quantitative real-time PCR and immunohistochemical (IHC) assays. The high-m5C score group was predicted to be in an immune evasion state and showed low sensitivity to immunotherapy, but high sensitivity to chemotherapy and potential targeted drugs and agents, such as sepantronium bromide (YM-155), axitinib, vinblastine and docetaxel. Meanwhile, we also constructed two diagnostic models to distinguish HCC tumors from normal liver tissues or liver cirrhosis.ConclusionIn conclusion, our study helps to early screen HCC patients and select patients who can benefit from immunotherapy. Step forwardly, for the less likely beneficiaries, this study provides them with new potential targeted drugs and agents for choice to improve their prognosis.

Summary: Background: This study aims to evaluate the association between thromboembolic events and hemorrhagic stroke following BNT162b2 and CoronaVac vaccination. Methods: Patients with incident thromboembolic events or hemorrhagic stroke within 28 days of covid-19 vaccination or SARS-CoV-2 positive test during 23 February to 30 September 2021 were included. The incidence per 100,000 covid-19 vaccine doses administered and SARS-CoV-2 test positive cases were estimated. A modified self-controlled case series (SCCS) analysis using the data from the Hong Kong territory-wide electronic health and vaccination records. Seasonal effect was adjusted by month. Findings: A total of 5,526,547 doses of BNT162b2 and 3,146,741 doses of CoronaVac were administered. A total of 334 and 402 thromboembolic events, and 57 and 49 hemorrhagic stroke cases occurred within 28 days after BNT162b2 and CoronaVac vaccination, respectively. The crude incidence of thromboembolic events and hemorrhagic stroke per 100,000 doses administered for both covid-19 vaccines were smaller than that per 100,000 SARS-CoV-2 test positive cases. The modified SCCS detected an increased risk of hemorrhagic stroke in BNT162b2 14-27 days after first dose with adjusted IRR of 2.53 (95% CI 1.48-4.34), and 0-13 days after second dose with adjusted IRR 2.69 (95% CI 1.54-4.69). No statistically significant risk was observed for thromboembolic events for both vaccines. Interpretation: We detected a possible safety signal for hemorrhagic stroke following BNT162b2 vaccination. The incidence of thromboembolic event or hemorrhagic stroke following vaccination is lower than that among SARS-CoV-2 test positive cases; therefore, vaccination against covid-19 remains an important public health intervention. Funding: This study was funded by a research grant from the Food and Health Bureau, The Government of the Hong Kong Special Administrative Region (reference COVID19F01).

Circular RNA (circRNA), a novel class of non-coding RNA, has been reported in various diseases, especially in tumors. However, the key signatures of circRNA-competitive endogenous RNA (ceRNA) network are largely unclear in colorectal cancer (CRC). We first characterized circRNAs profile by using circRNA-seq analysis from real-word dataset. The expression level of hsa_circ_0066351 in CRC tissues and cell lines was detected by quantitative real-time PCR. Then, cell proliferation assay was used to confirm the proliferation function of hsa_circ_0066351. Next, Cytoscape was used to construct circRNA–miRNA–mRNA networks. Last but not least, the landscape of hsa_circ_0066351–miRNA–mRNA in CRC had been investigated in the bulk tissue RNA-Seq level and single-cell Seq level. We proved that hsa_circ_0066351 was significantly downregulated in CRC cell lines and tissues (P