Effective green treatment of sewage sludge from Fenton reactions: Utilizing MoS2 for sustainable resource recovery.

Effectively managing sewage sludge from Fenton reactions in an eco-friendly way is vital for Fenton technology's viability in pollution treatment. This study focuses on sewage sludge across various treatment stages, including generation, concentration, dehydration, and landfill, and employs chemical composite MoS2 to facilitate green resource utilization of all types of sludge. MoS2, with exposed Mo4+ and low-coordination sulfur, enhances iron cycling and creates an acidic microenvironment on the sludge surface. The MoS2-modified iron sludge exhibits outstanding (>95%) phenol and pollutant degradation in hydrogen peroxide and peroxymonosulfate-based Fenton systems, unlike unmodified sludge. This modified sludge maintains excellent Fenton activity in various water conditions and with multiple anions, allowing extended phenol degradation for over 14 d. Notably, the generated chemical oxygen demand (COD) in sludge modification process can be efficiently eliminated through the Fenton reaction, ensuring effluent COD compliance and enabling eco-friendly sewage sludge resource utilization.

Zn2+-dependent association of cysteine-rich protein with virion orchestrates morphogenesis of rod-shaped viruses.

The majority of rod-shaped and some filamentous plant viruses encode a cysteine-rich protein (CRP) that functions in viral virulence; however, the roles of these CRPs in viral infection remain largely unknown. Here, we used barley stripe mosaic virus (BSMV) as a model to investigate the essential role of its CRP in virus morphogenesis. The CRP protein γb directly interacts with BSMV coat protein (CP), the mutations either on the His-85 site in γb predicted to generate a potential CCCH motif or on the His-13 site in CP exposed to the surface of the virions abolish the zinc-binding activity and their interaction. Immunogold-labeling assays show that γb binds to the surface of rod-shaped BSMV virions in a Zn2+-dependent manner, which enhances the RNA binding activity of CP and facilitates virion assembly and stability, suggesting that the Zn2+-dependent physical association of γb with the virion is crucial for BSMV morphogenesis. Intriguingly, the tightly binding of diverse CRPs to their rod-shaped virions is a general feature employed by the members in the families Virgaviridae (excluding the genus Tobamovirus) and Benyviridae. Together, these results reveal a hitherto unknown role of CRPs in the assembly and stability of virus particles, and expand our understanding of the molecular mechanism underlying virus morphogenesis.

VirusPredictor: XGBoost-based software to predict virus-related sequences in human data.

MOTIVATION: Discovering disease causative pathogens, particularly viruses without reference genomes, poses a technical challenge as they are often unidentifiable through sequence alignment. Machine learning prediction of patient high-throughput sequences unmappable to human and pathogen genomes may reveal sequences originating from uncharacterized viruses. Currently, there is a lack of software specifically designed for accurately predicting such viral sequences in human data. RESULTS: We developed a fast XGBoost method and software VirusPredictor leveraging an in-house viral genome database. Our two-step XGBoost models first classify each query sequence into one of three groups: infectious virus, endogenous retrovirus (ERV) or non-ERV human. The prediction accuracies increased as the sequences became longer, i.e. 0.76, 0.93, and 0.98 for 150-350 (Illumina short reads), 850-950 (Sanger sequencing data), and 2000-5000 bp sequences, respectively. Then, sequences predicted to be from infectious viruses are further classified into one of six virus taxonomic subgroups, and the accuracies increased from 0.92 to >0.98 when query sequences increased from 150-350 to >850 bp. The results suggest that Illumina short reads should be de novo assembled into contigs (e.g. ∼1000 bp or longer) before prediction whenever possible. We applied VirusPredictor to multiple real genomic and metagenomic datasets and obtained high accuracies. VirusPredictor, a user-friendly open-source Python software, is useful for predicting the origins of patients' unmappable sequences. This study is the first to classify ERVs in infectious viral sequence prediction. This is also the first study combining virus sub-group predictions. AVAILABILITY AND IMPLEMENTATION: www.dllab.org/software/VirusPredictor.html.

Multiplex gene editing reveals cucumber MILDEW RESISTANCE LOCUS O family roles in powdery mildew resistance.

Powdery mildew (PM) is one of the most widespread and prevalent diseases that affects a wide range of crops. In cucumber (Cucumis sativus L.), previous forward genetic studies have identified MILDEW RESISTANCE LOCUS O 8 (CsMLO8) as necessary but alone insufficient for cucumber PM resistance (PMR) and suggested the involvement of other members of the CsMLO family. However, the function of other CsMLO family members in cucumber remains largely unknown. Here, we developed a highly efficient multiplex gene editing system in cucumber to generate a series of Csmlo mutants from all the 13 family members. Systematic analysis of these mutants revealed growth effects of these CsMLO family members on development and PMR. Importantly, we obtained the Csmlo1/8/11 triple mutant with complete resistance to PM. Transcriptome and proteome analysis of PM-resistant Csmlo mutants suggested that the kinesin-like calmodulin-binding protein (KCBP)-interacting Ca2+-binding protein (CsKIC), calmodulin-like protein 28 (CsCML28), and Ca2+-dependent protein kinase 11 (CsCPK11)-mediated calcium signaling pathway is involved in PMR. CsMLO8 interacted directly with CsKIC, and the simultaneous silencing of both genes resulted in a phenotype that resembled the silencing of CsKIC alone. Silencing CsCML28 and CsCPK11 increased susceptibility to PM, whereas overexpressing CsCPK11 through genetic transformation enhanced cucumber's PMR, demonstrating their positive regulatory roles in PMR. Given the importance of PMR for cucurbit crops, this research provides unprecedented insights into the function of the proteins encoded by the CsMLO gene family as well as the plant defense response to PM pathogen.

COLMARppm: A Web Server Tool for the Accurate and Rapid Prediction of 1H and 13C NMR Chemical Shifts of Organic Molecules and Metabolites.

Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. Nuclear magnetic resonance (NMR) spectroscopy has a long history of elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds (e.g., metabolites from the Human Metabolome Database (HMDB)) and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. To improve NMR prediction accuracy and applicability, an empirical prediction strategy is introduced here to provide an accurately predicted chemical shift for organic molecules and metabolites within seconds. Unique features of COLMARppm include (i) the training library exclusively consisting of high quality NMR spectra measured under standard conditions in aqueous solution, (ii) utilization of NMR motif information, and (iii) leveraging of the improved prediction accuracy for the automated assignment of experimental chemical shifts for candidate structures. COLMARppm is demonstrated in terms of accuracy and speed for a set of 20 compounds taken from the HMDB for chemical shift prediction and resonance assignment. COLMARppm is applicable to a wide range of small molecules and can be directly incorporated into metabolomics workflows.

Nanoconfined Electrokinetic Chromatography (NEC): Gradient Separation and Sensing of Short DNA Fragments at the Single-Molecule Level.

Glass nanopipets have been demonstrated to be a powerful tool for the sensing and discrimination of biomolecules, such as DNA strands with different lengths or configurations. Despite progress made in nanopipet-based sensors, it remains challenging to develop effective strategies that separate and sense in one operation. In this study, we demonstrate an agarose gel-filled nanopipet that enables hyphenated length-dependent separation and electrochemical sensing of short DNA fragments based on the electrokinetic flow of DNA molecules in the nanoconfined channel at the tip of the nanopipet. This nanoconfined electrokinetic chromatography (NEC) method is used to distinguish the mixture of DNA strands without labels, and the ionic current signals measured in real time show that the mixed DNA strands pass through the tip hole in order according to the molecular weight. With NEC, gradient separation and electrochemical measurement of biomolecules can be achieved simultaneously at the single-molecule level, which is further applied for programmable gene delivery into single living cells. Overall, NEC provides a multipurpose platform integrating separation, sensing, single-cell delivery, and manipulation, which may bring new insights into advanced bioapplication.

Holistic Prediction of AuNP Aggregation in Diverse Aqueous Suspensions Based on Machine Vision and Dark-Field Scattering Imaging.

The localized surface-plasmon resonance of the AuNP in aqueous media is extremely sensitive to environmental changes. By measuring the signal of plasmon scattering light, the dark-field microscopic (DFM) imaging technique has been used to monitor the aggregation of AuNPs, which has attracted great attention because of its simplicity, low cost, high sensitivity, and universal applicability. However, it is still challenging to interpret DFM images of AuNP aggregation due to the heterogeneous characteristics of the isolated and discontinuous color distribution. Herein, we introduce machine vision algorithms for the training of DFM images of AuNPs in different saline aqueous media. A visual deep learning framework based on AlexNet is constructed for studying the aggregation patterns of AuNPs in aqueous suspensions, which allows for rapid and accurate identification of the aggregation extent of AuNPs, with a prediction accuracy higher than 0.96. With the aid of machine learning analysis, we further demonstrate the prediction ability of various aggregation phenomena induced by both cation species and the concentration of the external saline solution. Our results suggest the great potential of machine vision frameworks in the accurate recognition of subtle pattern changes in DFM images, which can help researchers build predictive analytics based on DFM imaging data.

High-Resolution and Dynamic Visualization of Intracellular Redox Potential Using a Metal-Organic Framework-Functionalized Nanopotentiometer.

Redox potential plays a key role in regulating intracellular signaling pathways, with its quantitative analysis in individual cells benefiting our understanding of the underlying mechanism in the pathophysiological events. Here, a metal organic framework (MOF)-functionalized SERS nanopotentiometer has been developed for the dynamic monitoring of intracellular redox potential. The approach is based on the encapsulation of zirconium-based MOF (Uio-66-F4) on a surface of gold-silver nanorods (Au-Ag NRs) that is modified with the newly synthesized redox-sensitive probe ortho-mercaptohydroquinone (HQ). Thanks to size exclusion of MOF as the chemical protector, the nanopotentiometer can be adapted to long-term use and possess high anti-interference ability toward nonredox species. Combining the superior fingerprint identification of SERS with the electrochemical activity of the quinone/hydroquinone, the nanopotentiometer shows a reversible redox responsivity and can quantify redox potential with a relatively wide range of -250-100 mV. Furthermore, the nanopotentiometer allows for dynamic visualization of intracellular redox potential changes induced by drugs' stimulation in a high-resolution manner. The developed approach would be promising for offering new insights into the correlation between redox potential and tumor proliferation-involved processes such as oxidative stress and hypoxia.

A multi-dimensional approach to unravel the intricacies of lactylation related signature for prognostic and therapeutic insight in colorectal cancer.

BACKGROUND: Lactylation, a novel contributor to post-translational protein modifications, exhibits dysregulation across various tumors. Nevertheless, its intricate involvement in colorectal carcinoma, particularly for non-histone lactylation and its intersection with metabolism and immune evasion, remains enigmatic. METHODS: Employing immunohistochemistry on tissue microarray with clinical information and immunofluorescence on colorectal cell lines, we investigated the presence of global lactylation and its association with development and progression in colorectal cancer as well as its functional location. Leveraging the AUCell algorithm alongside correlation analysis in single-cell RNA sequencing data, as well as cox-regression and lasso-regression analysis in TCGA dataset and confirmed in GEO dataset, we identified a 23-gene signature predicting colorectal cancer prognosis. Subsequently, we analyzed the associations between the lactylation related gene risk and clinical characteristics, mutation landscapes, biological functions, immune cell infiltration, immunotherapy responses, and drug sensitivity. Core genes were further explored for deep biological insights through bioinformatics and in vitro experiments. RESULTS: Our study innovatively reveals a significant elevation of global lactylation in colorectal cancer, particularly in malignant tumors, confirming it as an independent prognostic factor for CRC. Through a comprehensive analysis integrating tumor tissue arrays, TCGA dataset, GEO dataset, combining in silico investigations and in vitro experiments, we identified a 23-gene Lactylation-Related Gene risk model capable of predicting the prognosis of colorectal cancer patients. Noteworthy variations were observed in clinical characteristics, biological functions, immune cell infiltration, immune checkpoint expression, immunotherapy responses and drug sensitivity among distinct risk groups. CONCLUSIONS: The Lactylation-Related Gene risk model exhibits significant potential for improving the management of colorectal cancer patients and enhancing therapeutic outcomes, particularly at the intersection of metabolism and immune evasion. This finding underscores the clinical relevance of global lactylation in CRC and lays the groundwork for mechanism investigation and targeted therapeutic strategies given the high lactate concentration in CRC.

Dynamic m6A mRNA methylation reveals the involvement of AcALKBH10 in ripening-related quality regulation in kiwifruit.

Kiwifruit ripening is a complex and highly coordinated process that occurs in conjunction with the formation of fruit edible quality. The significance of epigenetic changes, particularly the impact of N6-methyladenosine (m6A) RNA modification on fruit ripening and quality formation, has been largely overlooked. We monitored m6A levels and gene expression changes in kiwifruit at four different stages using LC-MS/MS, MeRIP, RNA-seq, and validated the function of AcALKBH10 through heterologous transgenic expression in tomato. Notable m6A modifications occurred predominantly at the stop codons and the 3' UTRs and exhibited a gradual reduction in m6A levels during the fruit ripening process. Moreover, these m6A modifications in the aforementioned sites demonstrated a discernible inverse relationship with the levels of mRNA abundance throughout the ripening process, suggesting a repression effect of m6A modification in the modulation of kiwifruit ripening. We further demonstrated that AcALKBH10 rather than AcECT9 predominantly regulates m6A levels in ripening-related genes, thereby exerting the regulatory control over the ripening process and the accumulation of soluble sugars and organic acids, ultimately influencing fruit ripening and quality formation. In conclusion, our findings illuminate the epi-regulatory mechanism involving m6A in kiwifruit ripening, offering a fresh perspective for cultivating high-quality kiwifruit with enhanced nutritional attributes.

Ignited competition: Impact of bioactive extracellular compounds on organelle functions and photosynthetic systems in harmful algal blooms.

Prevalent interactions among marine phytoplankton triggered by long-range climatic stressors are well-known environmental disturbers of community structure. Dynamic response of phytoplankton physiology is likely to come from interspecies interactions rather than direct climatic effect on single species. However, studies on enigmatic interactions among interspecies, which are induced by bioactive extracellular compounds (BECs), especially between related harmful algae sharing similar shellfish toxins, are scarce. Here, we investigated how BECs provoke the interactions between two notorious algae, Alexandrium minutum and Gymnodinium catenatum, which have similar paralytic shellfish toxin (PST) profiles. Using techniques including electron microscopy and transcriptome analysis, marked disruptions in G. catenatum intracellular microenvironment were observed under BECs pressure, encompassing thylakoid membrane deformations, pyrenoid matrix shrinkage and starch sheaths disappearance. In addition, the upregulation of gene clusters responsible for photosystem-I Lhca1/4 and Rubisco were determined, leading to weaken photon captures and CO2 assimilation. The redistribution of lipids and proteins occurred at the subcellular level based on in situ focal plane array FTIR imaging approved the damages. Our findings illuminated an intense but underestimated interspecies interaction triggered by BECs, which is responsible for dysregulating photosynthesis and organelle function in inferior algae and may potentially account for fitness alteration in phytoplankton community.

Comprehensive analyses of the cancer-associated fibroblast subtypes and their score system for prediction of outcomes and immunosuppressive microenvironment in prostate cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) drive cancer progression and treatment failure on one hand, while their tumor-restraining functions are also observed on the other. Recent single cell RNA sequencing (scRNA-seq) analyses demonstrates heterogeneity of CAFs and defines molecular subtypes of CAFs, which help explain their different functions. However, it remains unclear whether these CAF subtypes have the same or different biological/clinical implications in prostate cancer (PCa) or other malignancies. METHODS: PCa cells were incubated with supernatant from normal fibroblasts and CAFs to assess their effects on cell behaviors. Sequencing, genomic, and clinical data were collected from TCGA, MSKCC, CPGEA and GEO databases. CAF molecular subtypes and total CAF scores were constructed and grouped into low and high groups based on CAF-specific gene expression. Progression free interval (PFI), clinicopathological features, telomere length, immune cell infiltration, drug treatment and somatic mutations were compared among CAF molecular subtypes and low/high score groups. RESULTS: The PCa CAF-derived supernatant promoted PCa cell proliferation and invasion. Based on differentially expressed genes identified by scRNA-seq analyses, we classified CAFs into 6 molecular subtypes in PCa tumors, and each subtype was then categorized into score-high and low groups according to the subtype-specific gene expression level. Such score models in 6 CAF subtypes all predicted PFI. Telomeres were significantly shorter in high-score tumors. The total CAF score from 6 CAF subtypes was also associated with PFI in PCa patients inversely, which was consistent with results from cellular experiments. Immunosuppressive microenvironment occurred more frequently in tumors with a high CAF score, which was characterized by increased CTLA4 expression and indicated better responses to CTLA4 inhibitors. Moreover, this model can also serve as a useful PFI predictor in pan-cancers. CONCLUSION: By combining scRNA-seq and bulk RNA-seq data analyses, we develop a CAF subtype score system as a prognostic factor for PCa and other cancer types. This model system also helps distinguish different immune-suppressive mechanisms in PCa, suggesting its implications in predicting response to immunotherapy. Thus, the present findings should contribute to personalized PCa intervention.

Developing Salt-Rejecting Evaporators for Solar Desalination: A Critical Review.

Solar desalination, a green, low-cost, and sustainable technology, offers a promising way to get clean water from seawater without relying on electricity and complex infrastructures. However, the main challenge faced in solar desalination is salt accumulation, either on the surface of or inside the solar evaporator, which can impair solar-to-vapor efficiency and even lead to the failure of the evaporator itself. While many ideas have been tried to address this ″salt accumulation″, scientists have not had a clear system for understanding what works best for the enhancement of salt-rejecting ability. Therein, for the first time, we classified the state-of-the-art salt-rejecting designs into isolation strategy (isolating the solar evaporator from brine), dilution strategy (diluting the concentrated brine), and crystallization strategy (regulating the crystallization site into a tiny area). Through the specific equations presented, we have identified key parameters for each strategy and highlighted the corresponding improvements in the solar desalination performance. This Review provides a semiquantitative perspective on salt-rejecting designs and critical parameters for enhancing the salt-rejecting ability of dilution-based, isolation-based, and crystallization-based solar evaporators. Ultimately, this knowledge can help us create reliable solar desalination solutions to provide clean water from even the saltiest sources.

Integrative Active Sites of Cathode for Electron-Oxygen-Proton Coupling To Favor H2O2 Production in a Photoelectrochemical System.

The oxygen reduction process generating H2O2 in the photoelectrochemical (PEC) system is milder and environmentally friendly compared with the traditional anthraquinone process but still lacks the efficient electron-oxygen-proton coupling interfaces to improve H2O2 production efficiency. Here, we propose an integrated active site strategy, that is, designing a hydrophobic C-B-N interface to refine the dearth of electron, oxygen, and proton balance. Computational calculation results show a lower energy barrier for H2O2 production due to synergistic and coupling effects of boron sites for O2 adsorption, nitrogen sites for H+ binding, and the carbon structure for electron transfer, demonstrating theoretically the feasibility of the strategy. Furthermore, we construct a hydrophobic boron- and nitrogen-doped carbon black gas diffusion cathode (BN-CB-PTFE) with graphite carbon dots decorated on a BiVO4 photoanode (BVO/g-CDs) for H2O2 production. Remarkably, this approach achieves a record H2O2 production rate (9.24 µmol min-1 cm-2) at the PEC cathode. The BN-CB-PTFE cathode exhibits an outstanding Faraday efficiency for H2O2 production of ∼100%. The newly formed h-BN integrative active site can not only adsorb more O2 but also significantly improve the electron and proton transfer. Unexpectedly, coupling BVO/g-CDs with the BN-CB-PTFE gas diffusion cathode also achieves a record H2O2 production rate (6.60 µmol min-1 cm-2) at the PEC photoanode. This study opens new insight into integrative active sites for electron-O2-proton coupling in a PEC H2O2 production system that may be meaningful for environment and energy applications.

Effect of the charge rate on the mechanical response of composite graphite electrodes: in situ experiment and mathematical analysis.

The cycling lifespan and coulombic efficiency of lithium-ion batteries are crucial to high C-rate applications. The Li-ion concentration is crucial in determining the mechanical integrity and structural stability of electrodes. In this work, graphite is selected as the working electrode due to its widespread use in the electric vehicle industry. The experimental data have shown that the electrodes with a mass loading of 6.54 mg cm-2 exhibited poor cycling performance and high charge transfer resistance at high charge rates. To explain this phenomenon, an in situ stress measurement system and a C-rate-dependent stress model are established to study the mechanical properties of the composite graphite electrode during the electrochemical process at various C-rates. Moreover, the effect of the Li-ion concentration-dependent modulus and C-rate-dependent partial molar volume is taken into account in the mathematical model. The computational curvature data fit well with the corresponding experimental data, highlighting the importance of considering lithium-ion concentration in mechanical stress. It has been found that stresses along the thickness of the active layer switch between compressive and tensile stresses due to the competition between bending stress and diffusion-induced stress. The stress at the outer surface of the composite graphite electrodes reaches a maximum magnitude of 27.5 MPa at a 1.5C-rate. In contrast, the stress at the interface of the active layer is maximum at a 0.5C-rate due to the existence of more lithium ions. Our study provides a direct insight into the quantitative analysis of electrode stresses at different C-rates.

A real time study of the coupled electrochemical and mechanical behaviors of the spinel cathodes in LIBs.

Spinel cathode materials have great application prospects in lithium batteries (LIBs) due to their characteristics of abundant raw materials, simple preparation processes, and cobalt-free nature. During the electrochemical cycles, the specific capacity of the electrodes decreases significantly due to the dissolution of excess metal ions and mechanical degradation, which hinder their further application and development. Here, a bending curvature measurement system (BCMS) was designed to simultaneously measure the mechanical properties of the spinel cathodes during the electrochemical reaction. Three types of cathodes were chosen as the working cathode, and the coupled mechanical and electrochemical properties were analyzed to understand their degradation mechanism. During cycling, a hysteresis loop is observed for the curvature, modulus, plain strain, and stress, where LiMn2O4 (LMO) has the largest loop for the mechanical response while the LiNi0.5Mn1.5O4@Al2O3 (LNMO@Al) one has the smallest loop. Besides, the changing trend of LNMO@Al is the smallest in multiple cycles and it shows the more stable mechanical properties. This study shows from in situ mechanical measurements that the mechanical properties can greatly affect the electrochemical performance of the cathodes. These findings could offer new insights into the understanding of the electrochemical performance degradation in the spinel cathodes and can help develop strategies to enhance the performance of LIBs.

Rural-urban difference in the association between particulate matters and stroke incidence: The evidence from a multi-city perspective cohort study.

Available evidence suggests that air pollutants can cause stroke, but little research has investigated the confounding effects of urban-rural differences. Here, we investigated the urban-rural difference in the correlation between particulate matter (PM2.5 and PM10) exposure and stroke. This cohort study was based on a prospective multi-city community-based cohort (Guizhou Population Health Cohort Study (GPHCS)) in Guizhou Province, China. A total of 7988 eligible individuals (≥18 years) were enrolled with baseline assessments from November 2010 to December 2012, and follow-up was completed by June 2020. Two major particulate matters (PMs, including PM2.5 and PM10) were assessed monthly from 2000 by using satellite-based spatiotemporal models. The risk of stroke was estimated using a Cox proportional hazard regression model. The association between particulate matters' exposure and stroke in different areas (total, urban, and rural) and the potential modification effect of comorbidities (hypertension, diabetes, and dyslipidemia) and age (≤65/>65 years) were examined using stratified analyses. The risk of stroke increased for every 10 µg/m3 increase in mean PMs' concentrations during the previous 1 year at the residential address (HR: 1.26, 95%CI: 1.24, 1.29 (PM2.5); HR: 1.13, 95%CI: 1.11, 1.15 (PM10)). The presence of diabetes and dyslipidemia increased the risk of PM10-induced stroke in whole, urban, and rural areas. Specifically, people living in rural areas were more likely to experience the effects of PMs in causing a stroke. The risk of stroke due to PMs was statistically increased in the young and older populations living in rural areas. In conclusion, long-term exposure to PMs increased the risk of stroke and such association was more pronounced in people living in rural areas with lower income levels. Diabetes and dyslipidemia seemed to strengthen the association between PMs and stroke.

Investigation into the impact of charging rates on the stress development within silicon composite electrodes.

Silicon, renowned for its remarkable energy density, has emerged as a focal point in the pursuit of high-energy storage solutions for the next generation. Nevertheless, silicon electrodes are known to undergo significant volume expansion during the insertion of lithium ions, leading to structural deformation and the development of internal stresses, and causing a rapid decline in battery capacity and overall lifespan. To gain deeper insights into the intricacies of charge rate effects, this study employs a combination of in situ measurements and computational modeling to elucidate the cyclic performance of composite silicon electrodes. The findings derived from the established model and curvature measurement system unveil the substantial alterations in stress and deformation as a consequence of varying charge rates. Notably, the active layer experiences compressive forces that diminish as the charge rate decreases. At a charge rate of 0.2, the active layer endures a maximum stress of 89.145 MPa, providing a comprehensive explanation for the observed deterioration in cycling performance at higher charge rates. This study not only establishes a fundamental basis for subsequent stress analyses of silicon electrodes but also lays a solid foundation for further exploration of the impact of charge rates on composite silicon electrodes.

Efficacy of respiratory rehabilitation in patients with COVID-19: a retrospective study.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has resulted in millions of confirmed cases and deaths globally. The purpose of this study was to investigate the therapeutic effect of airway clearance technology combined with prone ventilation on patients infected with COVID-19. METHODS: 38 patients with COVID-19 (severe) who were treated in the intensive rehabilitation group of Shengli Oilfield Central Hospital. They were randomly divided into a control group and an observation group. The control group received prone position ventilation intervention, and the observation group received airway clearance technology combined with prone position ventilation intervention. The changes of oxygen and index, procalcitonin (PCT), interleukin-6 (IL-6) and chest X-ray image indexes were compared between the two groups. RESULT: There was no significant difference in age, gender and other general data between the control group and the observation group. The results showed that oxygen index, PCT, IL-6 and chest X-ray image index in the observation group were better than that indexes in the control group. CONCLUSION: Airway clearance technology combined with prone ventilation intervention in patients with COVID-19 can improve the total effective rate and oxygenation index, improve the inflammatory indicators and respiratory function of patients. And it may be widely promoted and used in the treatment of patients with COVID-19 (severe).

Causal relationship between bone mineral density and intervertebral disc degeneration: a univariate and multivariable mendelian randomization study.

BACKGROUND: Although previous studies have suggested a possible association between bone mineral density (BMD) and intervertebral disc degeneration (IDD), the causal relationship between them remains unclear. Evidence from accumulating studies indicates that they might mutually influence one another. However, observational studies may be affected by potential confounders. Meanwhile, Mendelian randomization (MR) study can overcome these confounders to assess causality. OBJECTIVES: This Mendelian randomization (MR) study aimed to explore the causal effect of bone mineral density (BMD) on intervertebral disc degeneration (IDD). METHODS: Summary data from genome-wide association studies of bone mineral density (BMD) and IDD (the FinnGen biobank) have been acquired. The inverse variance weighted (IVW) method was utilized as the primary MR analysis approach. Weighted median, MR-Egger regression, weighted mode, and simple mode were used as supplements. The Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and MR-Egger regression were performed to assess horizontal pleiotropy. Cochran's Q test evaluated heterogeneity. Leave-one-out sensitivity analysis was further conducted to determine the reliability of the causal relationship. Multivariate MR (MVMR) analyses used multivariable inverse variance-weighted methods to individually and jointly adjust for four potential confounders, body mass index (BMI), Type2 diabetes, hyperthyroidism and smoking. A reverse MR analysis was conducted to assess potential reverse causation. RESULTS: In the univariate MR analysis, femoral neck bone mineral density (FNBMD), heel bone mineral density (eBMD), lumbar spine bone mineral density (LSBMD), and total body bone mineral density (TB BMD) had a direct causal effect on intervertebral disc degeneration (IDD) [FNBMD-related analysis: OR(95%CI) = 1.17 (1.04 to 1.31), p = 0.008, eBMD-related analysis: OR(95%CI) = 1.06 (1.01 to 1.12), p = 0.028, LSBMD-related analysis: OR(95%CI) = 1.20 (1.10 to 1.31), p = 3.38E-7,TB BMD-related analysis: OR(95%CI) = 1.20 (1.12 to 1.29), p = 1.0E-8]. In the MVMR analysis, it was revealed that, even after controlling for confounding factors, heel bone mineral density (eBMD), lumbar spine bone mineral density (LSBMD), and total body bone mineral density (TB BMD) still maintained an independent and significant causal association with IDD(Adjusting for heel bone mineral density: beta = 0.073, OR95% CI = 1.08(1.02 to 1.14), P = 0.013; Adjusting for lumbar spine bone mineral density: beta = 0.11, OR(95%CI) = 1.12(1.02 to 1.23), P = 0.03; Adjusting for total body bone mineral density: beta = 0.139, OR95% CI = 1.15(1.06 to 1.24), P = 5.53E - 5). In the reverse analysis, no evidence was found to suggest that IDD has an impact on BMD. CONCLUSIONS: The findings from our univariate and multivariable Mendelian randomization analysis establish a substantial positive causal association between BMD and IDD, indicating that higher bone mineral density may be a significant risk factor for intervertebral disc degeneration. Notably, no causal effect of IDD on these four measures of bone mineral density was observed. Further research is required to elucidate the underlying mechanisms governing this causal relationship.