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Graphs such as de Bruijn graphs and OLC (overlap-layout-consensus) graphs have been widely adopted for the de novo assembly of genomic short reads. This work studies another important problem in the field: how graphs can be used for high-performance compression of the large-scale sequencing data. We present a novel graph definition named Hamming-Shifting graph to address this problem. The definition originates from the technological characteristics of next-generation sequencing machines, aiming to link all pairs of distinct reads that have a small Hamming distance or a small shifting offset or both. We compute multiple lexicographically minimal k -mers to index the reads for an efficient search of the weight-lightest edges, and we prove a very high probability of successfully detecting these edges. The resulted graph creates a full mutual reference of the reads to cascade a code-minimized transfer of every child-read for an optimal compression. We conducted compression experiments on the minimum spanning forest of this extremely sparse graph, and achieved a 10 − 30% more file size reduction compared to the best compression results using existing algorithms. As future work, the separation and connectivity degrees of these giant graphs can be used as economical measurements or protocols for quick quality assessment of wet-lab machines, for sufficiency control of genomic library preparation, and for accurate de novo genome assembly.

Chitin, the most abundant aminopolysaccharide in nature, is an extracellular polymer consisting of N -acetylglucosamine (GlcNAc) units 1 . The key reactions of chitin biosynthesis are catalysed by chitin synthase 2 – 4 , a membrane-integrated glycosyltransferase that transfers GlcNAc from UDP-GlcNAc to a growing chitin chain. However, the precise mechanism of this process has yet to be elucidated. Here we report five cryo-electron microscopy structures of a chitin synthase from the devastating soybean root rot pathogenic oomycete Phytophthora sojae ( Ps Chs1). They represent the apo, GlcNAc-bound, nascent chitin oligomer-bound, UDP-bound (post-synthesis) and chitin synthase inhibitor nikkomycin Z-bound states of the enzyme, providing detailed views into the multiple steps of chitin biosynthesis and its competitive inhibition. The structures reveal the chitin synthesis reaction chamber that has the substrate-binding site, the catalytic centre and the entrance to the polymer-translocating channel that allows the product polymer to be discharged. This arrangement reflects consecutive key events in chitin biosynthesis from UDP-GlcNAc binding and polymer elongation to the release of the product. We identified a swinging loop within the chitin-translocating channel, which acts as a ‘gate lock’ that prevents the substrate from leaving while directing the product polymer into the translocating channel for discharge to the extracellular side of the cell membrane. This work reveals the directional multistep mechanism of chitin biosynthesis and provides a structural basis for inhibition of chitin synthesis. Using cryo-electron microscopy, the directional multiple step mechanism of chitin biosynthesis is revealed.

Rice (Oryza sativa L.) readily accumulates cadmium (Cd), posing dietary exposure risks in populations dependent on rice-based diets. This study investigated how sulfur (S) redox processes regulate Cd mobility in S-deficient, Cd-contaminated paddy soil under waterlogged conditions. A pot experiment was conducted with two S treatments (−S and +S, 30 mg kg−1) throughout the rice growing season. S addition markedly increased pore water S2− concentrations during early growth (tillering) and mid-season (booting) and suppressed the diffusion of SO42− from non-rhizosphere to rhizosphere at later stages (filling–maturity). Consequently, Cd in soil pore water was significantly lower in +S than −S treatments at all stages. Sulfur-amended soil showed a redistribution of Cd from labile fractions (exchangeable and carbonate-bound) to more stable fractions (Fe/Mn oxide-bound). Sulfur application also altered the rhizosphere microbiome: the relative abundance of sulfate-reducing bacteria (SRB) increased at the booting and filling stages, while sulfur-oxidizing bacteria (SOB) became more dominant at maturity. Additionally, +S enhanced Cd sequestration on rice root iron plaque by 32–67% during the grain-filling and maturity stages compared to −S. Throughout the rice growing period, redox-driven shifts in the S2−/SO42− ratio emerged as a key control on Cd behavior, with low pe + pH (strongly reducing conditions) promoting Cd sulfide precipitation and high pe + pH (more oxidizing conditions) causing Cd remobilization.

Antiphospholipid syndrome (APS) is a systemic autoimmune syndrome characterized by arterial or venous thrombosis, pregnancy complications and thrombocytopenia. The aim of this study is to investigate the association between APS and atrial fibrillation (AF) among patients in Peking University People’s Hospital. A single center retrospective study was conducted. Cases were hospitalized patients diagnosed with AF by a cardiologist while the control group patients did not exhibit cardiac diseases. The results of the study revealed that in multivariable logistic regression, APS, anticardiolipin antibody (aCL) positivity and anti-beta-2-glycoprotein antibody (anti- β 2GPI) positivity are independent risk factors of AF. APS, aCL positivity and anti- β 2 GPI positivity are statistically different between AF patients and non-AF patients. Forthcoming studies are needed to clarify the potential link between APS and AF.

Dual antiplatelet therapy (DAPT) with clopidogrel plus aspirin within 48 h of acute minor strokes and transient ischemic attacks (TIAs) has been indicated to effectively reduce the rate of recurrent strokes. However, the efficacy of clopidogrel has been shown to be affected by cytochrome P450 2C19 (CYP2C19) polymorphisms. Patients carrying loss-of-function alleles (LoFAs) at a low risk of recurrence (ESRS < 3) cannot benefit from clopidogrel plus aspirin at all and may have an increased bleeding risk. In order to optimize antiplatelet therapy for these patients and avoid the waste of medical resources, it is important to identify the subgroups that genuinely benefit from DAPT with clopidogrel plus aspirin through CYP2C19 genotyping. This study sought to assess the cost-effectiveness of CYP2C19 genotyping to guide drug therapy for acute minor strokes or high-risk TIAs in China. A decision tree and Markov model were constructed to evaluate the cost-effectiveness of CYP2C19 genotyping. We used a healthcare payer perspective, and the primary outcomes included quality-adjusted life years (QALYs), costs and the incremental cost-effectiveness ratio (ICER). Sensitivity analyses were performed to evaluate the robustness of the results. CYP2C19 genotyping resulted in a lifetime gain of 0.031 QALYs at an additional cost of CNY 420.13 (US$ 59.85), yielding an ICER of CNY 13,552.74 (US$ 1930.59) per QALY gained. Probabilistic sensitivity analysis showed that genetic testing was more cost-effective in 95.7% of the simulations at the willingness-to-pay threshold of CNY 72,100 (GDP per capita, US$ 10,300) per QALY. Therefore, CYP2C19 genotyping to guide antiplatelet therapy for acute minor strokes and high-risk TIAs is highly cost-effective in China.

Dramatic volumetric variation and poor cyclic stability are great challenges for the practical application of Si anode in lithium-ion batteries. In this work, hierarchical microsized hard carbon-supported Si encapsulated in nitrogen-doped carbon (HC/Si@NC) composites is successfully synthesized via electrostatic self-assembly between an intrinsic negatively charged hard carbon precursor and positively charged Si nanoparticles for the first time. Resorcinol formaldehyde resin sphere synthesized through a low-cost extended Stöber method is used as the carbon core precursor to support Si nanoparticles, followed by carbon coating and carbonization process to further fix Si on the carbon core and enhance the conductivity. The hierarchical structure where Si nanoparticles are tightly anchored onto the carbon core can significantly alleviate the volumetric expansion of Si, and the carbon can enhance the conductivity of the composites. As a result, the as-achieved HC/Si@NC composites exhibit outstanding cycling stability and good structural integrity maintenance. The composites deliver a reversible capacity of 541 mAh g−1 with a capacity retention of 92.1% after 100 cycles at a current density of 0.2 A g−1 and a capacity of 350 mAh g−1 after 300 cycles at a higher current density of 1 A g−1.

Background As the core functional carrier of life activities, the quality of protein representation directly affects the accuracy of downstream functional prediction. In recent years, multimodal deep learning methods have significantly improved the effectiveness of protein representation learning by virtue of their advantages in fusing sequence, structure, and chemical characteristics. However, current research still faces two core challenges: first, the guiding mechanism for structural information during multi-modal feature interaction has not been fully explored; second, existing fusion strategies mostly use static weight allocation mechanisms, which is difficult to adapt to sequence-structural features. The dynamic correlation between features leads to limited accuracy in identifying key functional residues. Results We proposed ProGraphTrans, a multimodal dynamic collaborative framework for protein representation learning. ProGraphTrans builds a dynamic attention multimodal fusion mechanism and captures local sequential patterns through a multi-scale convolutional neural network. Conclusions Experimental results on four protein downstream tasks show that ProGraphTrans not only outperforms other methods in various indicators but also demonstrates excellent interpretability, demonstrating its advantages and effectiveness as a protein representation method.

The prevalence of arrhythmia in patients with hypertension has gradually attracted widespread attention. However, the relationship between hypertension and arrhythmia still lacks more attention. Herein, we explore the biomechanical mechanism of arrhythmia in hypertensive rats and the effect of amiodarone on biomechanical properties. We applied micro-mechanics and amiodarone to stimulate single ventricular myocytes to compare changes of mechanical parameters and the mechanism was investigated in biomechanics. Then we verified the expression changes of genes and long non-coding RNAs (lncRNAs) related to myocardial mechanics to explore the effect of amiodarone on biomechanical properties. The results found that the stiffness of ventricular myocytes and calcium ion levels in hypertensive rats were significantly increased and amiodarone could alleviate the intracellular calcium response and biomechanical stimulation. In addition, experiments showed spontaneously hypertensive rats were more likely to induce arrhythmia and preoperative amiodarone intervention significantly reduced the occurrence of arrhythmias. Meanwhile, high-throughput sequencing showed the genes and lncRNAs related to myocardial mechanics changed significantly in the spontaneously hypertensive rats that amiodarone was injected. These results strengthen the evidence that hypertension rats are prone to arrhythmia with abnormal myocardial biomechanical properties. Amiodarone effectively inhibit arrhythmia by improving the myocardial biomechanical properties and weakening the sensitivity of mechanical stretch stimulation.

The damage of electrode integrity resulting from large volume change during cycling is the main reason that leads to the fast capacity loss of silicon anodes. Consequently, developing silicon anodes with integrated structure is critical for their practical application. Here, an integrated silicon anode with enhanced cycling ability using polyimide as binder and cyclized polyacrylonitrile as conductive matrix is fabricated by a facile pyrolysis process. The imidization of polyimide and cyclization of polyacrylonitrile are conducted simultaneously during the in situ heat treatment process. Owing to the synergy effect of polyimide binder and conductive polyacrylonitrile which can restrict volume expansion and maintain integrated conducting path effectively, the electrode exhibits a higher initial coulombic efficiency of 83.6% and delivers excellent cycle life with a high reversible capacity of 2362.2 mAh g−1 even after 100 cycles at a current density of 0.1 C, revealing significant improvement in cycle life in comparison with that of Si@cPAN or PI electrode.

Background Accurate prediction of compound-protein interaction (CPI) plays a crucial role in drug discovery. Existing data-driven methods aim to learn from the chemical structures of compounds and proteins yet ignore the conceptual knowledge that is the interrelationships among the fundamental elements in the biomedical knowledge graph (KG). Knowledge graphs provide a comprehensive view of entities and relationships beyond individual compounds and proteins. They encompass a wealth of information like pathways, diseases, and biological processes, offering a richer context for CPI prediction. This contextual information can be used to identify indirect interactions, infer potential relationships, and improve prediction accuracy. In real-world applications, the prevalence of knowledge-missing compounds and proteins is a critical barrier for injecting knowledge into data-driven models. Results Here, we propose BEACON, a data and knowledge dual-driven framework that bridges chemical structure and conceptual knowledge for CPI prediction. The proposed BEACON learns the consistent representations by maximizing the mutual information between chemical structure and conceptual knowledge and predicts the missing representations by minimizing their conditional entropy. BEACON achieves state-of-the-art performance on multiple datasets compared to competing methods, notably with 5.1% and 6.6% performance gain on the BIOSNAP and DrugBank datasets, respectively. Moreover, BEACON is the only approach capable of effectively predicting knowledge representations for knowledge-lacking compounds and proteins. Conclusions Overall, our work provides a general approach for directly injecting conceptual knowledge to enhance the performance of CPI prediction.