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To develop emerging electrode materials and improve the performances of batteries, the machine learning techniques can provide insights to discover, design and develop battery new materials in high-throughput way. In this paper, two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage, specific capacity and specific energy. The deep learning models are trained with the multilayer perceptron as the core. The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models. Based on 10 types of ion batteries, the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V, respectively. The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms. Besides, the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries. This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

Abstract Up to 80% of the human genome produces “dark matter” RNAs, most of which are noncapped RNAs (napRNAs) that frequently act as noncoding RNAs (ncRNAs) to modulate gene expression. Here, by developing a method, NAP-seq, to globally profile the full-length sequences of napRNAs with various terminal modifications at single-nucleotide resolution, we reveal diverse classes of structured ncRNAs. We discover stably expressed linear intron RNAs (sliRNAs), a class of snoRNA-intron RNAs (snotrons), a class of RNAs embedded in miRNA spacers (misRNAs) and thousands of previously uncharacterized structured napRNAs in humans and mice. These napRNAs undergo dynamic changes in response to various stimuli and differentiation stages. Importantly, we show that a structured napRNA regulates myoblast differentiation and a napRNA DINAP interacts with dyskerin pseudouridine synthase 1 (DKC1) to promote cell proliferation by maintaining DKC1 protein stability. Our approach establishes a paradigm for discovering various classes of ncRNAs with regulatory functions.

Abstract Lithium‐ion batteries (LIBs) are currently the predominant energy storage power source. However, the urgent issues of enhancing electrochemical performance, prolonging lifetime, preventing thermal runaway‐caused fires, and intelligent application are obstacles to their applications. Herein, bio‐inspired electrodes owning spatiotemporal management of self‐healing, fast ion transport, fire‐extinguishing, thermoresponsive switching, recycling, and flexibility are overviewed comprehensively, showing great promising potentials in practical application due to the significantly enhanced durability and thermal safety of LIBs. Taking advantage of the self‐healing core–shell structures, binders, capsules, or liquid metal alloys, these electrodes can maintain the mechanical integrity during the lithiation–delithiation cycling. After the incorporation of fire‐extinguishing binders, current collectors, or capsules, flame retardants can be released spatiotemporally during thermal runaway to ensure safety. Thermoresponsive switching electrodes are also constructed though adding thermally responsive components, which can rapidly switch LIB off under abnormal conditions and resume their functions quickly when normal operating conditions return. Finally, the challenges of bio‐inspired electrode designs are presented to optimize the spatiotemporal management of LIBs. It is anticipated that the proposed electrodes with spatiotemporal management will not only promote industrial application, but also strengthen the fundamental research of bionics in energy storage.

Background and objectiveNeuroinflammatory processes have been identified as playing a crucial role in the pathophysiology of various neurodegenerative diseases, including idiopathic normal-pressure hydrocephalus (iNPH). iNPH, defined as a common disease of cognitive impairment in older adults, poses major challenges for therapeutic interventions owing to the stringent methodological requirements of relevant studies, clinical heterogeneity, unclear etiology, and uncertain diagnostic criteria. This study aims to assess the relationship between circulating inflammatory biomarkers and iNPH risk using bidirectional two-sample Mendelian randomization (MR) combined with meta-analysis.MethodsIn our bidirectional MR study, genetic data from a genome-wide association study (GWAS) involving 1,456 iNPH cases and 409,726 controls of European ancestry were employed. Single-nucleotide polymorphisms (SNPs) associated with exposures served as instrumental variables for estimating the causal relationships between iNPH and 132 types of circulating inflammatory biomarkers from corresponding GWAS data. Causal associations were primarily examined using the inverse variance-weighted method, supplemented by MR-Egger, weighted median, simple mode, and weighted mode analyses. In the results, heterogeneity was assessed using the Cochran Q test. Horizontal pleiotropy was evaluated through the MR-Egger intercept test and the MR pleiotropy residual sum and outliers test. Sensitivity analysis was conducted through leave-one-out analysis. Reverse MR analyses were performed to mitigate bias from reverse causality. Meta-analyses of identical inflammatory biomarkers from both data sources strengthened the findings.ResultsResults indicated a genetically predicted association between Interleukin-16 (IL-16) [OR: 1.228, 95% CI: 1.049–1.439, p = 0.011], TNF-related apoptosis ligand (TRAIL) [OR: 1.111, 95% CI: 1.019–1.210, p = 0.017] and Urokinase-type plasminogen activator (uPA) [OR: 1.303, 95% CI: 1.025–1.658, p = 0.031] and the risk of iNPH. Additionally, changes in human Glial cell line-derived neurotrophic factor (hGDNF) [OR: 1.044, 95% CI: 1.006–1.084, p = 0.023], Matrix metalloproteinase-1 (MMP-1) [OR: 1.058, 95% CI: 1.020, 1.098, p = 0.003] and Interleukin-12p70 (IL-12p70) [OR: 0.897, 95% CI: 0.946–0.997, p = 0.037] levels were identified as possible consequences of iNPH.ConclusionOur MR study of inflammatory biomarkers and iNPH, indicated that IL-16, TRAIL, and uPA contribute to iNPH pathogenesis. Furthermore, iNPH may influence the expression of hGDNF, MMP-1, and IL-12p70. Therefore, targeting specific inflammatory biomarkers could be promising strategy for future iNPH treatment and prevention.

Abstract Under the global carbon neutrality initiative, carbon emissions from the transportation sector are becoming increasingly prominent due to the growth in vehicle ownership. And electric mobility may be a potentially effective measure to reduce road traffic carbon emissions and achieve a green transformation of transportation. This paper systematically collates the relevant carbon accounting standards for the automotive industry and elaborates the current status of road transport greenhouse gas emissions by combining the data from the International Energy Agency. And by comparing the lifecycle carbon footprint of various energy types of vehicles, the necessity and feasibility of electric mobility to reduce carbon emissions are discussed. However, the comparison of vehicle lifecycle carbon footprints shows that electric vehicles (EVs) are not as environmentally friendly as expected, although they can significantly reduce road traffic carbon emissions. The high carbon emissions from the manufacturing process of the core components of EVs, especially the power battery, reduce the low‐carbon potential of electric mobility. Therefore, the carbon emission reduction strategies and outcomes of automakers in the automotive industry chain have been further reviewed. Finally, focusing on vehicle power batteries, this article reviews the technologies such as refined management and echelon utilization that can make EVs more environmentally friendly and promote carbon neutrality in the transportation sector.

Background Somatic symptom disorder (SSD) commonly presents in general hospital settings, posing challenges for healthcare professionals lacking specialised psychiatric training. The Neuro-11 Neurosis Scale (Neuro-11) offers promise in screening and evaluating psychosomatic symptoms, comprising 11 concise items across three dimensions: somatic symptoms, negative emotions and adverse events. Prior research has validated the scale’s reliability, validity and theoretical framework in somatoform disorders, indicating its potential as a valuable tool for SSD screening in general hospitals.Aims This study aimed to establish the reliability, validity and threshold of the Neuro-11 by comparing it with standard questionnaires commonly used in general hospitals for assessing SSD. Through this comparative analysis, we aimed to validate the effectiveness and precision of the Neuro-11, enhancing its utility in clinical settings.Methods Between November 2020 and December 2021, data were collected from 731 patients receiving outpatient and inpatient care at Shenzhen People’s Hospital in China for various physical discomforts. The patients completed multiple questionnaires, including the Neuro-11, Short Form 36 Health Survey, Patient Health Questionnaire 15 items, Hamilton Anxiety Scale and Hamilton Depression Scale. Psychiatry-trained clinicians conducted structured interviews and clinical examinations to establish a gold standard diagnosis of SSD.Results The Neuro-11 demonstrated strong content reliability and structural consistency, correlating significantly with internationally recognised and widely used questionnaires. Despite its brevity, the Neuro-11 exhibited significant correlations with other questionnaires. A test-retest analysis yielded a correlation coefficient of 1.00, Spearman-Brown coefficient of 0.64 and Cronbach’s α coefficient of 0.72, indicating robust content reliability and internal consistency. Confirmatory factor analysis confirmed the validity of the three-dimensional structure (p

Summary: The multi-scale modeling of lithium-ion battery (LIB) is difficult and necessary due to its complexity. However, it is difficult to capture the aging behavior of batteries, and the coupling mechanism between multiple scales is still incomplete. In this paper, a simplified electrochemical model (SEM) and a kinetic Monte Carlo (KMC)-based solid electrolyte interphase (SEI) film growth model are used to study the multi-scale characteristics of LIBs. The single-particle SEM (SP-SEM) is described for macro scale, and a simple and self-consistent multi-particle SEM (MP-SEM) is developed. Then, the KMC-based SEI model is established for micro-scale molecular evolution. And, the two models are coupled to construct the full-cycle multi-scale model. After modeling, validation is performed by using a commercial 18650-type LIB. Finally, the effect of parameters on the SEI model is studied, including qualitative trend analysis and quantitative sensitivity analysis. The growth of SEI film with different particle sizes is studied by MP-SEM coupling simulation.

Summary: Abscisic acid (ABA) is involved in lateral root (LR) development, but how ABA signaling interacts with auxin signaling to regulate LR formation is not well understood. Here, we report that ABA-responsive ERF1 mediates the crosstalk between ABA and auxin signaling to regulate Arabidopsis LR emergence. ABI3 is a negative factor in LR emergence and transcriptionally activates ERF1 by binding to its promoter, and reciprocally, ERF1 activates ABI3, which forms a regulatory loop that enables rapid signal amplification. Notably, ABI3 physically interacts with ERF1, reducing the cis element-binding activities of both ERF1 and ABI3 and thus attenuating the expression of ERF1-/ABI3-regulated genes involved in LR emergence and ABA signaling, such as PIN1, AUX1, ARF7, and ABI5, which may provide a molecular rheostat to avoid overamplification of auxin and ABA signaling. Taken together, our findings identify the role of the ABI3-ERF1 module in mediating crosstalk between ABA and auxin signaling in LR emergence.

Background: The present study aimed to investigate the protective effect of the water extract of Amydrium sinense (Engl.) H. Li (ASWE) against hepatic fibrosis (HF) and clarify the underlying mechanism.Methods: The chemical components of ASWE were analysed by a Q-Orbitrap high-resolution mass spectrometer. In our study, an in vivo hepatic fibrosis mouse model was established via an intraperitoneal injection of olive oil containing 20% CCl4. In vitro experiments were conducted using a hepatic stellate cell line (HSC-T6) and RAW 264.7 cell line. A CCK-8 assay was performed to assess the cell viability of HSC-T6 and RAW264.7 cells treated with ASWE. Immunofluorescence staining was used to examine the intracellular localization of signal transducer and activator of transcription 3 (Stat3). Stat3 was overexpressed to analyse the role of Stat3 in the effect of ASWE on HF.Results: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that candidate targets of ASWE, associated with protective effects against hepatic fibrosis, were related to inflammation response. ASWE ameliorated CCl4-induced liver pathological damage and reduced the liver index and alanine transaminase (ALT) and aspartate transaminase (AST) levels. ASWE also decreased the serum levels of collagen Ⅰ (Col Ⅰ) and hydroxyproline (Hyp) in CCl4-treated mice. In addition, the expression of fibrosis markers, including α-SMA protein and Acta2, Col1a1, and Col3a1 mRNA, was downregulated by ASWE treatment in vivo. The expression of these fibrosis markers was also decreased by treatment with ASWE in HSC-T6 cells. Moreover, ASWE decreased the expression of inflammatory markers, including the Tnf-α, Il6 and Il1β, in RAW264.7 cells. ASWE decreased the phosphorylation of Stat3 and total Stat3 expression and reduced the mRNA expression of the Stat3 gene in vivo and in vitro. ASWE also inhibited the nuclear shuttling of Stat3. Overexpression of Stat3 weakened the therapeutic effect of ASWE and accelerated the progression of HF.Conclusion: The results show that ASWE protects against CCl4-induced liver injury by suppressing fibrosis, inflammation, HSC activation and the Stat3 signaling pathway, which might lead to a new approach for preventing HF.

Summary: Lateral roots (LRs) are crucial for plants to sense environmental signals in addition to water and nutrient absorption. Auxin is key for LR formation, but the underlying mechanisms are not fully understood. Here, we report that Arabidopsis ERF1 inhibits LR emergence by promoting local auxin accumulation with altered distribution and regulating auxin signaling. Loss of ERF1 increases LR density compared with the wild type, whereas ERF1 overexpression causes the opposite phenotype. ERF1 enhances auxin transport by upregulating PIN1 and AUX1, resulting in excessive auxin accumulation in the endodermal, cortical, and epidermal cells surrounding LR primordia. Furthermore, ERF1 represses ARF7 transcription, thereby downregulating the expression of cell-wall remodeling genes that facilitate LR emergence. Together, our study reveals that ERF1 integrates environmental signals to promote local auxin accumulation with altered distribution and repress ARF7, consequently inhibiting LR emergence in adaptation to fluctuating environments.

Supramolecular responsive microcarriers based on chitosan microspheres were prepared and applied for nonenzymatic cell detachment. Briefly, chitosan microspheres (CSMs) were first prepared by an emulsion crosslinking approach, the surface of which was then modified with β-cyclodextrin (β-CD) by chemical grafting. Subsequently, gelatin was attached onto the surface of the CSMs via the host–guest interaction between β-CD groups and aromatic residues in gelatin. The resultant microspheres were denoted CSM-g-CD-Gel. Due to their superior biocompatibility and gelatin niches, CSM-g-CD-Gel microspheres can be used as effective microcarriers for cell attachment and expansion. L-02, a human fetal hepatocyte line, was used to evaluate cell attachment and expansion with these microcarriers. After incubation for 48 h, the cells attached and expanded to cover the entire surface of microcarriers. Moreover, with the addition of adamantane (AD), cells can be detached from the microcarriers together with gelatin because of the competitive binding between β-CD and AD. Overall, these supramolecular responsive microcarriers could effectively support cell expansion and achieve nonenzymatic cell detachment and may be potentially reusable with a new cycle of gelatin attachment and detachment.

Abstract Lithium‐ion batteries (LIBs) have become the main choice for electric vehicles (EVs). However, the thermal runaway problems of LIBs largely limit the wider promotion of EVs. To provide background and insight for the improvement of battery safety, the general working mechanism of LIBs is described in this review, followed by a discussion of the thermal runaway process, including the trigger conditions and material factors. Moreover, advances made to improve battery safety are examined from the perspective of battery materials and management systems. Thus, this review provides a general picture of the thermal runaway risks of LIBs and corresponding solutions with the aim of facilitating safer battery designs.

Objective: To describe the clinical features, laboratory data, treatment, and outcomes of anti-N-methyl-d-aspartate (NMDAR) encephalitis in Chinese patients. Methods: This retrospective study included hospitalized patients definitively diagnosed with anti-NMDAR encephalitis and positive for anti-NMDAR antibodies in the cerebrospinal fluid (CSF) in Shenzhen People’s hospital, between November 2015 and February 2020. The clinical manifestation, laboratory data, treatments and outcomes were collected retrospectively. Patients were followed up for more than 1 year. Results: The study included 31 patients (15 men, 48.4%) with a median age of 31 years (interquartile range 21–48). The most common clinical presentations were psychosis (n = 23, 74.2%), seizures (n = 20, 64.5%), and memory impairment (n = 20, 64.5%). Total magnetic resonance imaging abnormalities were found in 11 patients (35.5%), with the medial temporal and frontal lobes as the most commonly involved. Abnormal electroencephalogram was observed in 16 patients (51.6%). Five out of 31 patients (19.5%) were diagnosed as neoplasm, including five females with ovarian teratoma and one male with a central nervous system tumor. Multiple immune antibodies, including anti-SSA antibody in four patients (15.4%), anti-Ro52 antibody in four (15.4%), antinuclear antibody (ANT) in four (15.4%), anti-thyroglobulin antibodies (TGAb) in five (17.2%), and thyroid peroxidase antibodies (TPOAb) in three (10.3%) were present. All patients received first-line immunization therapy (intravenous immunoglobulin, glucocorticoids, or plasmapheresis alone or combined), and only two patients (7.3%) received second-line immunization therapy (rituximab). Mechanical ventilation was more necessary in women (37.5%) than in men (6.7%) (p = 0.04), and 29 (93.5%) had favorable clinical outcomes. At more than 12 months of follow-up, the median modified Rankin Scale score decreased from 4 to 0. Conclusions: Patients with anti-NMDAR encephalitis in China had high rates of psychosis and seizures, with low rates of underlying neoplasms. A higher proportion of female patients required mechanical ventilation. Complications with other positive autoimmune antibodies were a common clinical symptoms of anti-NMDAR encephalitis. Majority of the patients obtained satisfactory outcomes in combination with early first-line and long-term immunization therapy.

BackgroundThe neutrophil-to-lymphocyte ratio (NLR) is a biomarker reflecting the balance between inflammation (as indicated by the neutrophil count) and adaptive immunity (as indicated by the lymphocyte count). We aimed to estimate ability of NLR at admission and at day 1 for predicting stroke outcome after two reperfusion therapies: intravenous thrombolysis (IVT) and mechanical thrombectomy (MT).MethodsA retrospective analysis was performed on patients who received recombinant human tissue plasminogen activator (IVT) and/or underwent MT for acute ischemic stroke (AIS) at the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China) from January 2018 to December 2020. Blood samples were taken on admission to hospital and on day 1 after stroke onset. Binary logistic regression models were applied to investigate potential associations between NLR at admission or day 1 and the following outcomes: symptomatic intracerebral hemorrhage (sICH), dependence, and mortality at 90 days. The ability of NLR to predict AIS outcome was analyzed using receiver operating characteristic (ROC) curves.ResultsData for 927 patients (576 IVT and 351 MT) were reviewed. High admission NLR was associated with dependence in IVT treatment [adjusted odds ratio (OR) 1.21, 95% confidence interval (CI) 1.14–1.23] and 90-day mortality in MT patients (OR 1.09, 95% CI 1.04–1.13). In IVT patients, high NLR at day 1 predicted dependence (OR 1.09, 95% CI 1.02–1.11), sICH (OR = 1.07, 95% CI 1.01–1.12), and 90-day mortality (OR 1.06, 95% CI 1.01–1.15). In MT patients, high NLR at day 1 also predicted dependence (OR 1.08, 95% CI 1.02–1.11) and sICH (OR 1.03, 95% CI 1.01–1.09). ROC analysis confirmed that NLR at day 1 could predict dependence (cut-off 4.2; sensitivity 68.7%; specificity 79.6%), sICH (cut-off 5.1; sensitivity 57.9%, specificity 73.5%), and death (cut-off 5.4; sensitivity 78.8%; specificity 76.4%) in IVT patients. Z values of area under the curves were compared between admissioin and day 1 NLR in IVT patients and showed day 1 NLR can better predict dependence (Z = 2.8, p = 0.004) and 90-day death (Z = 2.8, p = 0.005).ConclusionsNLR is a readily available biomarker that can predict AIS outcome after reperfusion treatment and day 1 NLR is even better than admission NLR.

Lithium-ion batteries (LiBs) are the key power source for electric vehicles (EVs). Battery thermal management system (BTMS) is essential to ensure safety and extend service life of LIBs. This paper reviews the various refrigeration materials used in the BTMS in EVs, including liquid coolant, phase change material (PCM). The thermal properties of these refrigerant materials are summarized and the innovative ways to improve the cooling efficiency of the BTMS are analyzed. The various ways to enhance the battery’s thermal performance by modifying the materials of the electrode, separator, and electrolyte are also reviewed. Finally, the research prospect in area of BTMS is summarized. This review will inspire new BTMS design and further improvement in battery safety and performance with the aid of advanced intelligent technologies.

Lithium–sulfur batteries have received increasing research interest due to their superior theoretical capacity, cost-effectiveness, and eco-friendliness. However, the commercial realization of lithium–sulfur batteries faces critical obstacles, such as the significant volume change of sulfur cathodes over the de/lithiation processes, uncontrollable shuttle effects of polysulfides, and the lithium dendrite issue. On this basis, the lithium–sulfur battery based on solid-state electrolytes was developed to alleviate the previously mentioned problems. This article aims to provide an overview of the recent progress of solid-state lithium–sulfur batteries related to various kinds of solid-state electrolytes, which mainly include three aspects: the fundamentals and current status of lithium–sulfur solid-state batteries and several adopted solid-state electrolytes involving polymer electrolyte, inorganic solid electrolyte, and hybrid electrolyte. Furthermore, the future perspective for lithium–sulfur solid-state batteries is presented. Finally, this article proposed an initiation for new and practical research activities and paved the way for the design of usable lithium–sulfur solid-state batteries.

With the development of electric vehicles, fast-charging is greatly demanded for commercialisation on lithium-ion batteries. The rapid charging process could lead to serious side reactions on the graphite anodes, such as lithium plating and solid electrolyte interface (SEI) film growth, which severely affect the battery performances. However, there is a lack of quantitative research on their contribution ratio to battery performance and the occurrence thresholds. In this work, a P2D model of a lithium-ion battery with the correction of SEI film growth and lithium plating was built. A cyclic charge/discharge experiment was also designed to analyze the changes of SEI film and lithium plating under high charge-rate conditions. It was found that under such conditions, the battery capacity attenuation in the early stage was mainly caused by lithium plating. In the middle and late stages, as the lithium plating tended to be stable, the capacity attenuation was largely caused by the growth of the SEI film. The study provides theoretical support for the improvement of the charge/discharge strategy of lithium-ion batteries.

It is a hot issue and an effective method in botany and agronomy to improve cultivation crops using existing wild plant resources without creating invasive plants. Wild soybean is not only a wild ancestor of cultivated soybean, but also an excellent germplasm resource to improve it. The photosynthetic characteristics, ion balance, and nitrogen (N) metabolism of young and old leaves in two wild soybean genotypes (common wild soybean: W1; barren tolerant wild soybean: W2) were analyzed under different intensities of N deficiency. The results showed that N deficiency inhibited the growth and total N content in both genotypes, but W2 was less inhibited. Under the high intensity of N deficiency, the root length in W2 was significantly longer than that in the control group (P

Ischemic stroke (IS) is a deadly and debilitating disease with a high incidence and recurrence rate in elderly people worldwide. Large-artery atherosclerotic (LAA) and cardioembolic (CE) stroke are two leading subtypes and require different management. As a complementary biochemistry method for current diagnostic techniques, a sensitive and accurate phospholipid (PL) targeted lipidomic method was developed in this study. Plasma PLs were selectively extracted with titanium dioxide/fibrous silica nanosphere material, then characterized and quantified with hydrophilic interaction chromatography-mass spectrometry. A total of 31 molecular species of PLs were determined and ten biomarkers including seven molecular species of sphingomyelins (SM d18:1/18:1, d18:1/18:0, d18:1/24:1, d18:1/16:1, d18:1/22:1, d18:1/24:2, and d18:1/16:0) and three molecular species of phosphatidylcholines (16:0/18:1, 16:0/18:2 and 16:0/22:6) showed significant differences in LAA, CE, and healthy control (HC) groups. The independent diagnostic capabilities of these PL biomarkers were successfully evaluated and validated with receiver operating characteristic curves. Additionally, the oleic acid-enriched SMs, which can result in atherogenic lipoprotein aggregation, were proved to be positively related to IS and may perform as the potential risk factors in the future. Meanwhile, valuable suggestions for dietary interventions as an essential source of endogenous PLs could be obtained from this study.

The accurate estimation of the battery state of health (SOH) is crucial for the dependability and safety of battery management systems (BMS). The generality of existing SOH estimation methods is limited as they tend to primarily consider information from single-source features. Therefore, a novel method for integrating multi-feature collaborative analysis with deep learning-based approaches is proposed in this research. First, several battery degradation features are obtained through differential thermal voltammetry (DTV) analysis, singular value decomposition (SVD), incremental capacity analysis (ICA), and terminal voltage characteristic (TVC) analysis. The features highly related to SOH are selected as inputs for the deep learning model based on the results of a Pearson correlation analysis. The SOH estimation is achieved by developing a deep learning framework cored by long short-term memory (LSTM) neural network (NN), which integrates multi-source features as an input. A suggested method is validated using NASA and Oxford Battery Degradation datasets. The results demonstrate that the presented model provides great SOH estimation accuracy and generality, where the maximum root mean square error (RMSE) is less than 1%. Based on a cloud computing platform, the proposed method can be applied to provide a real-time prediction of battery health, with the potential to enhance battery full lifespan management.

Objective: Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an acute form of encephalitis of autoimmune etiology. We aimed to evaluate the risk factors that predicted the need for mechanical ventilation during the acute phase of anti-NMDAR encephalitis through an analysis of the clinical characteristics and biochemical test results of the patients with anti-NMDAR encephalitis.Methods: In this retrospective study, patients who primarily presented with anti-NMDAR encephalitis and exhibited anti-NMDAR antibody positivity in the cerebrospinal fluid (CSF) between November 2015 and February 2020 were included. Data on the clinical characteristics, biochemical test results, and treatment methods selected for the patients were collected for the analysis of factors predicting the need for mechanical ventilation.Results: Thirty-one patients with a median age of onset of 31 years (inter-quartile range: 21–48 years) were included in this study, of which 15 were male (48.4%). Psychosis (23, 74.2%), seizures (20, 64.5%), and memory deficit (20, 64.5%) were the most common clinical manifestations. At admission, 17 patients (54.8%) presented with pyrexia, of which 12 (38.7%) had a body temperature ≥38°C, and six patients (19.4%) presented with central hypoventilation. All patients received first-line therapy (glucocorticoids, intravenous immunoglobulin, or plasmapheresis alone or combined), whereas two patients (6.5%) received rituximab, a second-line agent, as well. Seven patents required mechanical ventilation. Results of univariate logistic regression analysis revealed that body temperature ≥38°C [odds ratio (OR) = 18, 95% confidence interval (CI): 1.79–181.31, P < 0.05] and central hypoventilation at admission (OR = 57.50, 95% CI: 4.32–764.89, P < 0.05) were the risk factors for mechanical ventilation. Multivariate logistic regression analysis showed that central hypoventilation at admission was the only risk factor predicting the need for mechanical ventilation.Conclusion: Central hypoventilation at admission is a key risk factor for mechanical ventilation during hospitalization in patients with anti-NMDAR encephalitis.

Few studies have focused on the connection between glymphatic dysfunction and cerebral small vessel disease (CSVD), partially due to the lack of non-invasive methods to measure glymphatic function. We established modified index for diffusion tensor image analysis along the perivascular space (mALPS-index), which was calculated on diffusion tensor image (DTI), compared it with the classical detection of glymphatic clearance function calculated on Glymphatic MRI after intrathecal administration of gadolinium (study 1), and analyzed the relationship between CSVD imaging markers and mALPS-index in CSVD patients from the CIRCLE study (ClinicalTrials.gov ID: NCT03542734) (study 2). Among 39 patients included in study 1, mALPS-index were significantly related to glymphatic clearance function calculated on Glymphatic MRI ( r = -0.772~-0.844, p < 0.001). A total of 330 CSVD patients were included in study 2. Severer periventricular and deep white matter hyperintensities (β = -0.332, p < 0.001; β = -0.293, p < 0.001), number of lacunas (β = -0.215, p < 0.001), number of microbleeds (β = -0.152, p = 0.005), and severer enlarged perivascular spaces in basal ganglia (β = -0.223, p < 0.001) were related to mALPS-index. Our results indicated that non-invasive mALPS-index might represent glymphatic clearance function, which could be applied in clinic in future. Glymphatic clearance function might play a role in the development of CSVD.

Background: Stroke-associated pneumonia (SAP) is one of the common complications of stroke patients. Higher lactic dehydrogenase (LDH) and lower albumin levels were associated with SAP, but the contribution of the LDH to albumin ratio (LAR) to the risk of SAP in acute ischemic stroke (AIS) patients remained unclear.Methods: A total of 3173 AIS patients were included in this study, divided into SAP (n = 417) and non-SAP groups (n = 2756). Characteristics were compared between these two groups. The receiver operating characteristic curves (ROC) were used to evaluate the discrimination ability of the LAR, LDH, and albumin levels in predicting SAP. Logistic regression analysis was furtherly adopted to estimate the association between LAR and SAP. We also used the restricted cubic spline (RCS) to clarify the relationship between LAR and the risk of SAP.Results: LAR in the SAP group was significantly higher than that of the non-SAP group (8.75 ± 4.58 vs. 6.10 ± 2.55, P < 0.001). According to the results of ROC, LAR had the highest prognostic accuracy compared to LDH and albumin (P < 0.05). Besides, the logistic regression model showed that higher LAR (LAR > 6.75) were more vulnerable to SAP (OR, 2.80; 95% CI, 2.18–3.59, P < 0.001), controlling the confounders. The RCS model showed that there was a non-linear relationship between LAR and the risk of SAP.Conclusion: High LAR was associated with an increased risk of SAP in patients with AIS. LAR may be a potential predictor for the incidence of SAP. Appropriate prevention measures were needed in patients with high LAR (LAR > 6.75).

Electric vehicles (EVs) in severe cold regions face the real demand for fast charging under low temperatures, but low-temperature environments with high C-rate fast charging can lead to severe lithium plating of the anode material, resulting in rapid degradation of the lithium-ion battery (LIB). In this paper, by constructing an electrode–thermal model coupling solid electrolyte interphase (SEI) growth and lithium plating, the competition among different factors of capacity degradation under various ambient temperatures and C-rates are systematically analyzed. In addition, the most important cause of rapid degradation of LIBs under low temperatures are investigated, which reveal the change pattern of lithium plating with temperature and C-rate. The threshold value and kinetic law of lithium plating are determined, and a method of lithium-free control under high C-rate is proposed. Finally, by studying the average aging rate of LIBs, the reasons for the abnormal attenuation of cycle life at lower C-rates are ascertained. Through the chromaticity diagram of the expected life of LIBs under various conditions, the optimal fast strategy is explored, and its practical application in EVs is also discussed. This study can provide a useful reference for the development of high-performance and high-safety battery management systems to achieve fine management.

With rapid development of clean energy vehicles, the health diagnosis and prognosis of lithium batteries remain challenging for practical applications. Accurate state-of-health (SOH) and remaining useful life (RUL) estimation provides crucial information for improving the safety, reliability and longevity of batteries. In this paper, a fusion of deep-learning model and feature signal analysis methods are proposed to realize accurate and fast estimation of the health conditions for battery cathodes. Specifically, the long short-term memory (LSTM) network and differential thermal voltammetry (DTV) are utilized to verify our fusion method. Firstly, the DTV feature signal analysis is executed based on battery charging and discharging data, based on which useful feature variables are extracted with Pearson correlation analysis. Next, the deep-learning model is constructed and trained with the LSTM as the core based on timeseries datasets constructed with features. Finally, the validation and error analysis of proposed model are provided, showing a max mean absolute error of 0.6%. The proposed method enables highly accurate models for SOH and RUL estimation that can be potentially deployed on cloud-end for offline battery degradation tracking.

Background: Increasing evidence demonstrates that repetitive transcranial magnetic stimulation (rTMS) treatment of the dorsolateral prefrontal cortex is beneficial for improving cognitive function in patients with Alzheimer’s disease (AD); however, the underlying mechanism of its therapeutic effect remains unclear.Objectives/Hypothesis: The aim of this study was to investigate the impact of rTMS to the dorsolateral prefrontal cortex on functional connectivity along with treatment response in AD patients with different severity of cognitive impairment.Methods: We conducted a 2-week treatment course of 10-Hz rTMS over the left dorsolateral prefrontal cortex in 23 patients with AD who were split into the mild or moderate cognitive impairment subgroup. Resting state electroencephalography and general cognition was assessed before and after rTMS. Power envelope connectivity was used to calculate functional connectivity at the source level. The functional connectivity of AD patients and 11 cognitively normal individuals was compared.Results: Power envelope connectivity was higher in the delta and theta bands but lower in the beta band in the moderate cognitive impairment group, compared to the cognitively normal controls, at baseline (p < 0.05). The mild cognitive impairment group had no significant abnormities. Montreal Cognitive Assessment scores improved after rTMS in the moderate and mild cognitive impairment groups. Power envelope connectivity in the beta band post-rTMS was increased in the moderate group (p < 0.05) but not in the mild group. No significant changes in the delta and theta band were found after rTMS in both the moderate and mild group.Conclusion: High-frequency rTMS to the dorsolateral prefrontal cortex modulates electroencephalographic functional connectivity while improving cognitive function in patients with AD. Increased beta connectivity may have an important mechanistic role in rTMS therapeutic effects.

The automatic stop-and-go task of intelligent vehicles can make the adaptive cruise control system achieve a full-speed range. However, the conventional design methods mostly focus on functional safety, without considering drivers’ behaviors, thereby leading to a poor driving experience. To improve the situation, a humanized learning control model is used instead of mechanical switching logic. Therefore, first, the common characteristics of human drivers with different driving styles are found by analyzing real drivers’ experiments. Then, the vehicle automatic starting function is designed based on iterative learning control with the fast Fourier transform for acceleration fitting. Next, the vehicle automatic braking function is designed based on dynamic time to collision. Finally, the simulation of the stop-and-go scenario is shown in CARSIM, and the real vehicle test is performed under the urban overpass driving condition. Results show that the proposed model can improve the humanization in the vehicle stop-and-go task.

Abstract Objective To explore whether poor sleep is associated with post‐stroke anxiety (PSA) in Chinese patients with acute ischemic stroke (AIS) and to verify whether poor sleep is a predictor of PSA. Methods A total of 327 patients with AIS were enrolled and followed up for 1 month. Sleep quality within 1 month before stroke was evaluated using the Pittsburgh Sleep Quality Index (PSQI) at admission. The patients were divided into the poor sleep group (PSQI > 7, n = 76) and good sleep group (PSQI ≤ 7, n = 251). One month after stroke, patients with obvious anxiety symptoms and a Hamilton Anxiety Scale score >7 were diagnosed with PSA. Results Eighty‐seven patients (26.6%) were diagnosed with PSA. Compared to the good sleep quality group, the incidence of PSA in patients with poor sleep quality was higher (42.1% vs. 21.9%, p = .001). Poor sleep quality is more common in patients with PSA (35.6% vs. 18.8%, p = .001). A logistic regression analysis indicated that poor sleep quality was significantly associated with PSA (OR: 2.265, 95% CI: 1.262–4.067, p = .003). After adjusting for conventional and identified risk factors, poor sleep quality was found to be independently associated with PSA (OR: 2.676, 95% CI: 1.451–4.936, p = .001). Conclusions Poor sleep quality before stroke was associated with PSA and may be an independent risk factor of PSA 1 month after AIS onset.

While machine learning approaches to analyzing Alzheimer disease connectome neuroimaging data have been studied, many have limited ability to provide insight in individual patterns of disease and lack the ability to provide actionable information about where in the brain a specific patient's disease is located. We studied a cohort of patients with Alzheimer disease who underwent resting state functional magnetic resonance imaging and diffusion tractography imaging. These images were processed, and a structural and functional connectivity matrix was generated using the HCP cortical and subcortical atlas. By generating a machine learning model, individual-level structural and functional anomalies detection and characterization were explored in this study. Our study found that structural disease burden in Alzheimer's patients is mainly focused in the subcortical structures and the Default mode network (DMN). Interestingly, functional anomalies were less consistent between individuals and less common in general in these patients. More intriguing was that some structural anomalies were noted in all patients in the study, namely a reduction in fibers involving parcellations in the right anterior cingulate. Alternately, the functional consequences of connectivity loss were cortical and variable. Integrated structural/functional connectomics might provide a useful tool for assessing AD progression, while few concerns have been made for analyzing the mismatch between these two. We performed a preliminary exploration into a set of Alzheimer disease data, intending to improve a personalized approach to understanding individual connectomes in an actionable manner. Specifically, we found that there were consistent patterns of white matter fiber loss, mainly focused around the DMN and deep subcortical structures, which were present in nearly all patients with clinical AD. Functional magnetic resonance imaging shows abnormal functional connectivity different within the patients, which may be used as the individual target for further therapeutic strategies making, like non-invasive stimulation technology.

Abstract Background To investigated distinct manifestations of Sjögren’s syndrome (SS) patients with neurological complications and the potential risk factors associated with neurological complications in SS, and to produce a disease evaluation and neurological involvement prediction for SS. Methods 566 patients who fulfilled the 2002 classification criteria for SS from the Rheumatology Department of the First Affiliated Hospital of Wenzhou Medical University were included in the cross-sectional study. Clinical, immunological and histological characteristics were surveyed, and potential risk factors for neurological complications were examined by multivariate analysis. Results Among 566 SS patients, 184 (32.5%) patients had neurological involvement, with more than 10% got limbs pain, limbs numbness and cerebral infarction, respectively. Of these 184 SS patients with neurological complications, secondary SS (sSS) patients had a higher prevalence of peripheral nervous system (PNS) involvement than primary SS (pSS) patients (31.1 vs. 19%). And sSS patients showed higher total ESSPRI score and higher prevalence of xerostomia and low C3, C4 levels with more liver, articular involvement and saliva gland atrophy, and more severe lymphocyte infiltration in salivary glands than pSS patients. As for the specific factors associated with neurological involvement, low C3 level were found to be significant in pSS or sSS patients who were younger 50 year old, and ANA positivity, cardiac involvement, saliva gland atrophy were demonstrated to be associated in elder pSS patients. And xerophthalmia was found to be associated in sSS patients. Conclusion Low complement (C3) levels, xerophthalmia, ANA positive, cardiac involvement and labial salivary gland histological result were good ways to predict neurological complications in different subgroups of SS, which might provide insight into better clinical decision-making, especially at early stages of the disease.

Brain-like intelligent decision-making is a prevailing trend in today’s world. However, inspired by bionics and computer science, the linear neural network has become one of the main means to realize human-like decision-making and control. This paper proposes a method for classifying drivers’ driving behaviors based on the fuzzy algorithm and establish a brain-inspired decision-making linear neural network. Firstly, different driver experimental data samples were obtained through the driving simulator. Then, an objective fuzzy classification algorithm was designed to distinguish different driving behaviors in terms of experimental data. In addition, a brain-inspired linear neural network was established to realize human-like decision-making and control. Finally, the accuracy of the proposed method was verified by training and testing. This study extracts the driving characteristics of drivers through driving simulator tests, which provides a driving behavior reference for the human-like decision-making of an intelligent vehicle.