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N6-methyladenosine (m6A) is the most abundant RNA modification in mammalian cells and the best-studied epitranscriptomic mark. Despite the development of various tools to map m6A, a transcriptome-wide method that enables absolute quantification of m6A at single-base resolution is lacking. Here we use glyoxal and nitrite-mediated deamination of unmethylated adenosines (GLORI) to develop an absolute m6A quantification method that is conceptually similar to bisulfite-sequencing-based quantification of DNA 5-methylcytosine. We apply GLORI to quantify the m6A methylomes of mouse and human cells and reveal clustered m6A modifications with differential distribution and stoichiometry. In addition, we characterize m6A dynamics under stress and examine the quantitative landscape of m6A modification in gene expression regulation. GLORI is an unbiased, convenient method for the absolute quantification of the m6A methylome.The m6A modification is mapped transcriptome-wide at single-base resolution in mammalian cells.

The incidence of hyperuricemia (HUA) and gout continuously increases and has become a major public health problem. The gut microbiota, which colonizes the human intestine, has a mutually beneficial and symbiotic relationship with the host and plays a vital role in the host’s metabolism and immune regulation. Structural changes or imbalance in the gut microbiota could cause metabolic disorders and participate in the synthesis of purine-metabolizing enzymes and the release of inflammatory cytokines, which is closely related to the occurrence and development of the metabolic immune disease HUA and gout. The gut microbiota as an entry point to explore the pathogenesis of HUA and gout has become a new research hotspot. This review summarizes the characteristics of the gut microbiota in patients with HUA and gout. Meanwhile, the influence of different dietary structures on the gut microbiota, the effect of the gut microbiota on purine and uric acid metabolism, and the internal relationship between the gut microbiota and metabolic endotoxemia/inflammatory factors are explored. Moreover, the intervention effects of probiotics, prebiotics, and fecal microbial transplantation on HUA and gout are also systematically reviewed to provide a gut flora solution for the prevention and treatment of related diseases.

Porcine epidemic diarrhea virus (PEDV) has catastrophic impacts on the global pig industry. Although the fecal–oral route is generally accepted, an increased number of reports indicate that airborne transmission may contribute to PEDV outbreak. Here, we show that PEDV could cause typical diarrhea in piglets through a nasal spray. Firstly, PEDV can develop a transient nasal epithelium infection. Subsequently, PEDV-carrying dendritic cells (DCs) allow the virus to be transferred to CD3 + T cells via the virological synapse. Finally, virus-loaded CD3 + T cells reach the intestine through the blood circulation, leading to intestinal infection via cell-to-cell contact. Our study provides evidence for airborne transmission of a gastrointestinal infected coronavirus and illustrates the mechanism of its transport from the entry site to the pathogenic site. Outbreaks of porcine epidemic diarrhea virus (PEDV) have seriously affected pig farms around the world. Here, Li et al. show that PEDV can cause disease in piglets when inoculated by nasal spray, and provide insights into the cellular mechanisms underlying PEDV dissemination within the host.

Electroencephalogram (EEG) based brain-computer interfaces (BCI) in motor imagery (MI) have developed rapidly in recent years. A reliable feature extraction method is essential because of a low signal-to-noise ratio (SNR) and time-dependent covariates of EEG signals. Because of efficient application in various fields, deep learning has been adopted in EEG signal processing and has obtained competitive results compared with the traditional methods. However, designing and training an end-to-end network to fully extract potential features from EEG signals remains a challenge in MI. In this study, we propose a parallel multiscale filter bank convolutional neural network (MSFBCNN) for MI classification. We introduce a layered end-to-end network structure, in which a feature-extraction network is used to extract temporal and spatial features. To enhance the transfer learning ability, we propose a network initialization and fine-tuning strategy to train an individual model for inter-subject classification on small datasets. We compare our MSFBCNN with the state-of-the-art approaches on open datasets. The proposed method has a higher accuracy than the baselines in intra-subject classification. In addition, the transfer learning experiments indicate that our network can build an individual model and obtain acceptable results in inter-subject classification. The results suggest that the proposed network has superior performance, robustness, and transfer learning ability.

Ionic thermoelectric (i-TE) liquid cells offer an environmentally friendly, cost effective, and easy-operation route to low-grade heat recovery. However, the lowest temperature is limited by the freezing temperature of the aqueous electrolyte. Applying a eutectic solvent strategy, we fabricate a high-performance cryo-temperature i-TE liquid cell. Formamide is used as a chaotic organic solvent that destroys the hydrogen bond network between water molecules, forming a deep eutectic solvent that enables the cell to operate near cryo temperatures (down to –35 °C). After synergistic optimization of the electrode and cell structure, the as-fabricated liquid i-TE cell with cold (–35 °C) and hot (70 °C) ends achieve a high power density (17.5 W m −2 ) and a large two-hour energy density (27 kJ m −2 ). In a prototype 25-cell module, the open-circuit voltage and short-circuit current are 6.9 V and 68 mA, respectively, and the maximum power is 131 mW. The anti-freezing ability and high output performance of the as-fabricated i-TE liquid cell system are requisites for applications in frigid regions. The authors make an ionic thermoelectric cell with a eutectic solvent of formamide that can operate at temperatures as low as −35 °C by applying hydrophilic and gold coated treatments to the electrode and introducing a thermal separator.

Due to the drastic surge of COVID-19 patients, many countries are considering or already graduating health professional students early to aid professional resources. We aimed to assess outbreak-related psychological distress and symptoms of acute stress reaction (ASR) in health professional students and to characterize individuals with potential need for interventions. We conducted a prospective cohort study of 1442 health professional students at Sichuan University, China. At baseline (October 2019), participants were assessed for childhood adversity, stressful life events, internet addiction, and family functioning. Using multivariable logistic regression, we examined associations of the above exposures with subsequent psychological distress and ASR in response to the outbreak. Three hundred and eighty-four (26.63%) participants demonstrated clinically significant psychological distress, while 160 (11.10%) met the criterion for a probable ASR. Individuals who scored high on both childhood adversity and stressful life event experiences during the past year were at increased risks of both distress (ORs 2.00-2.66) and probable ASR (ORs 2.23-3.10), respectively. Moreover, internet addiction was associated with elevated risks of distress (OR 2.05, 95% CI 1.60-2.64) and probable ASR (OR 2.15, 95% CI 1.50-3.10). By contrast, good family functioning was associated with decreased risks of distress (OR 0.43, 95% CI 0.33-0.55) and probable ASR (OR 0.48, 95% CI 0.33-0.69). All associations were independent of baseline psychological distress. Our findings suggest that COVID-19 related psychological distress and high symptoms burden of ASR are common among health professional students. Extended family and professional support should be considered for vulnerable individuals during these unprecedented times.

Extracellular vesicles (EVs) contain a rich cargo of different RNA species with specialized functions and clinical applications. However, the landscape and characteristics of extracellular vesicle long RNA (exLR) in human blood remain largely unknown. We presented an optimized strategy for exLR sequencing (exLR-seq) of human plasma. The sample cohort included 159 healthy individuals, 150 patients with cancer (5 cancer types), and 43 patients with other diseases. Bioinformatics approaches were used to analyze the distribution and features of exLRs. Support vector machine algorithm was performed to construct the diagnosis classifier, and diagnostic efficiency was evaluated by ROC analysis. More than 10000 exLRs, including mRNA, circRNA, and lncRNA, were reliably detected in each exLR-seq sample from 1-2 mL of plasma. We observed that blood EVs contain a substantial fraction of intact mRNAs and a large number of assembling spliced junctions; circRNA was also enriched in blood EVs. Interestingly, blood exLRs reflected their tissue origins and the relative fractions of different immune cell types. Additionally, the exLR profile could distinguish patients with cancer from healthy individuals. We further showed that 8 exLRs can serve as biomarkers for hepatocellular carcinoma (HCC) diagnosis with high diagnostic efficiency in training [area under the curve (AUC) = 0.9527; 95% CI, 0.9170-0.9883], validation cohort (AUC = 0.9825; 95% CI, 0.9606-1), and testing cohort (AUC = 0.9627; 95% CI, 0.9263-0.9991). In summary, this study revealed abundant exLRs in human plasma and identified diverse specific markers potentially useful for cancer diagnosis.

ObjectivePancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose at resectable stage. Recent studies have suggested that extracellular vesicles (EVs) contain long RNAs. The aim of this study was to develop a diagnostic (d-)signature for the detection of PDAC based on EV long RNA (exLR) profiling.DesignWe conducted a case-control study with 501 participants, including 284 patients with PDAC, 100 patients with chronic pancreatitis (CP) and 117 healthy subjects. The exLR profile of plasma samples was analysed by exLR sequencing. The d-signature was identified using a support vector machine algorithm and a training cohort (n=188) and was validated using an internal validation cohort (n=135) and an external validation cohort (n=178).ResultsWe developed a d-signature that comprised eight exLRs, including FGA, KRT19, HIST1H2BK, ITIH2, MARCH2, CLDN1, MAL2 and TIMP1, for PDAC detection. The d-signature showed high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.960, 0.950 and 0.936 in the training, internal validation and external validation cohort, respectively. The d-signature was able to identify resectable stage I/II cancer with an AUC of 0.949 in the combined three cohorts. In addition, the d-signature showed superior performance to carbohydrate antigen 19-9 in distinguishing PDAC from CP (AUC 0.931 vs 0.873, p=0.028).ConclusionThis study is the first to characterise the plasma exLR profile in PDAC and to report an exLR signature for the detection of pancreatic cancer. This signature may improve the prognosis of patients who would have otherwise missed the curative treatment window.

Surfactin has antiviral activity against various enveloped viruses by inhibiting viral membrane fusion. However, the potential utility of surfactin as an antiviral drug is limited by its cytotoxicity. In this study, 10 surfactin analogues were obtained by chemical synthesis and evaluated to determine their anti-PEDV activities, hemolytic activities, and critical micelle concentrations. The main goal of our study was to develop a safer drug; a surfactin analogue with high anti-PEDV activity and low hemolytic activity. Compared with surfactin, one of the analogues we developed, SLP5, has lower hemolytic activity, with the same antiviral activity. The selectivity index of SLP5 is 52, while the SI for surfactin is 4, in other words, the safe and effective concentration range of SLP5 is 12 times greater than that of surfactin. Like surfactin, SLP5 has a direct antiviral effect on PEDV. Structurally, SLP5 is a linear lipopeptide with three carboxyl groups. Surfactin derivatives similar to SLP5 could be obtained by lactone bond hydrolyzation of surfactin, as well as total synthesis.

The ability to detect pistons with high accuracy over a wide range is paramount to the co-phasing of sparse aperture optical systems. This paper proposes a global piston error modulation method for sparse aperture mirrors based on convolutional neural networks. The efficacy of this approach is demonstrated by the introduction of a convolutional block attention module (CBAM) with a data generalization mechanism, which facilitates the rapid and accurate learning of key features from actual co-phasing sensor images. This is achieved with less labelled data, thereby enabling the accurate detection of piston error distribution. The experimental results demonstrate that the method exhibits high prediction accuracy, enhances the piston error detection efficiency and sensing range, and facilitates global fine phase correction (<λ/80) under closed-loop conditions. The technique demonstrates considerable potential for application in the field of simplifying the wavefront sensing and modulation process of large segmented telescopes.