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Identifying disease–gene associations is a fundamental and critical biomedical task towards understanding molecular mechanisms, the diagnosis and treatment of diseases. It is time-consuming and expensive to experimentally verify causal links between diseases and genes. Recently, deep learning methods have achieved tremendous success in identifying candidate genes for genetic diseases. The gene prediction problem can be modeled as a link prediction problem based on the features of nodes and edges of the gene–disease graph. However, most existing researches either build homogeneous networks based on one single data source or heterogeneous networks based on multi-source data, and artificially define meta-paths, so as to learn the network representation of diseases and genes. The former cannot make use of abundant multi-source heterogeneous information, while the latter needs domain knowledge and experience when defining meta-paths, and the accuracy of the model largely depends on the definition of meta-paths. To address the aforementioned challenges above bottlenecks, we propose an end-to-end disease–gene association prediction model with parallel graph transformer network (DGP-PGTN), which deeply integrates the heterogeneous information of diseases, genes, ontologies and phenotypes. DGP-PGTN can automatically and comprehensively capture the multiple latent interactions between diseases and genes, discover the causal relationship between them and is fully interpretable at the same time. We conduct comprehensive experiments and show that DGP-PGTN outperforms the state-of-the-art methods significantly on the task of disease–gene association prediction. Furthermore, DGP-PGTN can automatically learn the implicit relationship between diseases and genes without manually defining meta paths.

Accurate prediction of promoter regions driving miRNA gene expression has become a major challenge due to the lack of annotation information for pri-miRNA transcripts. This defect hinders our understanding of miRNA-mediated regulatory networks. Some algorithms have been designed during the past decade to detect miRNA promoters. However, these methods rely on biosignal data such as CpG islands and still need to be improved. Here, we propose miProBERT, a BERT-based model for predicting promoters directly from gene sequences without using any structural or biological signals. According to our information, it is the first time a BERT-based model has been employed to identify miRNA promoters. We use the pre-trained model DNABERT, fine-tune the pre-trained model on the gene promoter dataset so that the model includes information about the richer biological properties of promoter sequences in its representation, and then systematically scan the upstream regions of each intergenic miRNA using the fine-tuned model. About, 665 miRNA promoters are found. The innovative use of a random substitution strategy to construct a negative dataset improves the discriminative ability of the model and further reduces the false positive rate (FPR) to as low as 0.0421. On independent datasets, miProBERT outperformed other gene promoter prediction methods. With comparison on 33 experimentally validated miRNA promoter datasets, miProBERT significantly outperformed previously developed miRNA promoter prediction programs with 78.13% precision and 75.76% recall. We further verify the predicted promoter regions by analyzing conservation, CpG content and histone marks. The effectiveness and robustness of miProBERT are highlighted.

Accurate and effective drug–target interaction (DTI) prediction can greatly shorten the drug development lifecycle and reduce the cost of drug development. In the deep-learning-based paradigm for predicting DTI, robust drug and protein feature representations and their interaction features play a key role in improving the accuracy of DTI prediction. Additionally, the class imbalance problem and the overfitting problem in the drug–target dataset can also affect the prediction accuracy, and reducing the consumption of computational resources and speeding up the training process are also critical considerations. In this paper, we propose shared-weight-based MultiheadCrossAttention, a precise and concise attention mechanism that can establish the association between target and drug, making our models more accurate and faster. Then, we use the cross-attention mechanism to construct two models: MCANet and MCANet-B. In MCANet, the cross-attention mechanism is used to extract the interaction features between drugs and proteins for improving the feature representation ability of drugs and proteins, and the PolyLoss loss function is applied to alleviate the overfitting problem and the class imbalance problem in the drug–target dataset. In MCANet-B, the robustness of the model is improved by combining multiple MCANet models and prediction accuracy further increases. We train and evaluate our proposed methods on six public drug–target datasets and achieve state-of-the-art results. In comparison with other baselines, MCANet saves considerable computational resources while maintaining accuracy in the leading position; however, MCANet-B greatly improves prediction accuracy by combining multiple models while maintaining a balance between computational resource consumption and prediction accuracy.

Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but knowledge on long-term complications is limited. In particular, a large portion of survivors has respiratory complications, but currently, experienced radiologists and state-of-the-art artificial intelligence systems are not able to detect many abnormalities from follow-up computerized tomography (CT) scans of COVID-19 survivors. Here we propose Deep-LungParenchyma-Enhancing (DLPE), a computer-aided detection (CAD) method for detecting and quantifying pulmonary parenchyma lesions on chest CT. Through proposing a number of deep-learning-based segmentation models and assembling them in an interpretable manner, DLPE removes irrelevant tissues from the perspective of pulmonary parenchyma, and calculates the scan-level optimal window, which considerably enhances parenchyma lesions relative to the lung window. Aided by DLPE, radiologists discovered novel and interpretable lesions from COVID-19 inpatients and survivors, which were previously invisible under the lung window. Based on DLPE, we removed the scan-level bias of CT scans, and then extracted precise radiomics from such novel lesions. We further demonstrated that these radiomics have strong predictive power for key COVID-19 clinical metrics on an inpatient cohort of 1,193 CT scans and for sequelae on a survivor cohort of 219 CT scans. Our work sheds light on the development of interpretable medical artificial intelligence and showcases how artificial intelligence can discover medical findings that are beyond sight.

Osteosarcoma (OS) is the commonest malignant bone tumor in adolescent patients, and patients face amputation, tumor metastasis, chemotherapy resistance, and even death. We investigated the potential connection between abnormal methylation differentially expressed genes and the survival rate of osteosarcoma patients. GSE36002 and GSE12865 datasets of GEO database were utilized for abnormal methylation differentially expressed genes, followed by function and pathway enrichment analyses, the protein-protein interaction network in the STRING database, and cluster analysis in the MCODE app of Cytoscape. The RNA-seq and clinical data from the TARGET-OS project of TCGA were used for univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses to predict the risk genes of osteosarcoma. 1191 hypermethylation-downregulated genes might function through plasma membrane, negative regulation of transcription from the RNA polymerase II promoter, and pathways, including transcriptional misregulation in cancer. 127 hypomethylation-upregulated genes were enriched in proteolysis, negative regulation of the canonical Wnt signaling pathway, and metabolic signaling pathways. The univariate Cox analysis revealed 638 genes ( P < 0.01 ), including 50 hypermethylation-downregulated genes and 4 hypomethylation-upregulated genes, subsequently based on LASSO Cox regression analysis for 54 aberrant methylation-driven genes, and three genes (COL13A1, MXI1, and TBRG1) were selected to construct the risk score model. The three genes (COL13A1, MXI1, and TBRG1) regulated by DNA methylation were identified to relate with the outcomes of OS patients, which might provide a new insight to the pathological mechanism of osteosarcoma.

BACKGROUND: The finite element method (FEM) is an efficient and powerful tool for studying human spine biomechanics. OBJECTIVE: In this study, a detailed asymmetric three-dimensional (3D) finite element (FE) model of the upper cervical spine was developed from the computed tomography (CT) scan data to analyze the effect of ligaments and facet joints on the stability of the upper cervical spine. METHODS: A 3D FE model was validated against data obtained from previously published works, which were performed in vitro and FE analysis of vertebrae under three types of loads, i.e. flexion/extension, axial rotation, and lateral bending. RESULTS: The results show that the range of motion of segment C1–C2 is more flexible than that of segment C2–C3. Moreover, the results from the FE model were used to compute stresses on the ligaments and facet joints of the upper cervical spine during physiological moments. CONCLUSION: The anterior longitudinal ligaments (ALL) and interspinous ligaments (ISL) are found to be the most active ligaments, and the maximum stress distribution is appear on the vertebra C3 superior facet surface under both extension and flexion moments.

Modifying the wide band gap semiconductor hexagonal boron nitride (hBN) can bring new chances in photonics. By virtue of the solvothermal/hydrothermal oxidation or functionalization, hBN can be converted into fluorescent nanodots. Until now, it has been a big challenge to drily oxidize hBN and turn it into bright fluorescent structures due to its superior chemical stability. Here, we report the oxidation of multilayer hBN into fluorescent structures by ultraviolet (UV, λ = 172 nm) photodissociated directional oxygen radical [O(

Nickel-based alloys have excellent corrosion resistance. They are also well-known for their mechanical properties, high temperature durability, and heat transfer performance. Because nickel-based alloys have these properties, they are widely used in creating nuclear safety level pressure equipment. It is the first-choice material for the heat exchanger tube plate and U-tube plate of the nuclear island steam generator, which has a profound impact on the safe operation of the reactor. Therefore, it is necessary to calibrate the main elements in nickel-based alloys. Accurate detection of the main elements in nickel-based alloys is important in ensuring the advancement of process research. A method for determining chromium, iron, and nickel in a nickel-based alloy by X-ray fluorescence spectroscopy is presented here. The detection range of chromium, iron, and nickel is 0.2–1.6 g/L, 0.2–2.3 g/L, and 0.5–3.3 g/L, respectively. After the three elements are corrected respectively, good correlation is obtained, R2 (chromium)= 0.999406, R2 (iron) = 0.999202, and R2 (nickel)= 0.999114. The relative standard deviations for chromium, iron, and nickel are 0.75%, 0.65%, and 0.65% respectively. The standard sample is tested to confirm the measured value is in agreement with the certified value.

Polycomb group (PcG) protein-mediated histone methylation (H3K27me3) controls the correct spatiotemporal expression of numerous developmental regulators in Arabidopsis. Epigenetic silencing of the stem cell factor gene WUSCHEL (WUS) in floral meristems (FMs) depends on H3K27me3 deposition by PcG proteins. However, the role of H3K27me3 in silencing of other meristematic regulator and pluripotency genes during FM determinacy has not yet been studied. To this end, we report the genome-wide dynamics of H3K27me3 levels during FM arrest and the consequences of strongly depleted PcG activity on early flower morphogenesis including enlarged and indeterminate FMs. Strong depletion of H3K27me3 levels results in misexpression of the FM identity gene AGL24, which partially causes floral reversion leading to ap1-like flowers and indeterminate FMs ectopically expressing WUS and SHOOT MERISTEMLESS (STM). Loss of STM can rescue supernumerary floral organs and FM indeterminacy in H3K27me3-deficient flowers, indicating that the hyperactivity of the FMs is at least partially a result of ectopic STM expression. Nonetheless, WUS remained essential for the FM activity. Our results demonstrate that PcG proteins promote FM determinacy at multiple levels of the floral gene regulatory network, silencing initially floral regulators such as AGL24 that promotes FM indeterminacy and, subsequently, meristematic pluripotency genes such as WUS and STM during FM arrest.

Identifying gene regulatory networks (GRNs) at the resolution of single cells has long been a great challenge, and the advent of single-cell multi-omics data provides unprecedented opportunities to construct GRNs. Here, we propose a novel strategy to integrate omics datasets of single-cell ribonucleic acid sequencing and single-cell Assay for Transposase-Accessible Chromatin using sequencing, and using an unsupervised learning neural network to divide the samples with high copy number variation scores, which are used to infer the GRN in each gene block. Accuracy validation of proposed strategy shows that approximately 80% of transcription factors are directly associated with cancer, colorectal cancer, malignancy and disease by TRRUST; and most transcription factors are prone to produce multiple transcript variants and lead to tumorigenesis by RegNetwork database, respectively. The source code access are available at: https://github.com/Cuily-v/Colorectal_cancer.

Objective To provide updated, pooled evidence on clinical outcomes among octogenarians (aged ≥80 years) with abdominal aortic aneurysm (AAA) managed by elective endovascular repair, compared to conventional open repair. Methods PubMed, Embase, and Scopus databases were systematically searched. Studies that were either observational or randomized controlled trials were considered for the review. Included studies were conducted in elderly subjects (≥80 years) with AAA, and clinical and mortality outcomes were compared between endovascular and open surgical repair. Those reporting on outcomes of patients with urgent repair were excluded. The primary outcomes of interest were mortality and risk of complications. The pooled effect sizes were reported as odds ratio (OR) for categorical outcomes and weighted mean difference (WMD) for continuous outcomes. STATA software was used for statistical analysis. Results The meta-analysis included 15 studies. Compared to those undergoing open repair, patients receiving endovascular repair had significantly reduced risk of immediate post-operative mortality (OR .23, 95% CI: .20, .27), overall complication (OR .30, 95% CI: .20, .44), cardiac (OR .23, 95% CI: .16, .35), renal (OR .29, 95% CI: .18, .46), pulmonary (OR .14, 95% CI: .09, .21) and bleeding related (OR .59, 95% CI: .42, .83) complications. The risk of mortality at latest follow up (at 36 months and 60 months) was similar in the two groups. The total blood loss (ml) (WMD -1126.47, 95% CI: −1497.81, −755.13), operative time (min) (WMD -29.40, 95% CI: −56.19, −2.62), length of intensive care unit stay (days) (WMD -2.27, 95% CI: −3.43, −2.12) and overall hospital stay (days) (WMD −6.64, 95% CI: −7.60, −5.68) was significantly lower in those undergoing endovascular repair. Conclusions Endovascular repair appears to be better than open repair of AAA in this high-risk, frail population, with respect to short term outcomes. The benefits of reduced risk of short term mortality, complications, and better peri and post-operative outcomes may be considered when making a choice between these two surgical approaches. Randomized controlled trials are needed to provide reliable evidence on the effect of EVAR on long term survival.

Heart transplantation is the definitive therapy for patients with end-stage heart failure. Antecedent studies reported that a substantial proportion of heart transplant recipients developed postoperative cognitive impairment in the long term. However, no studies have explored the association between postoperative cognitive impairment and survival after heart transplantation.The data of 43 adult patients who underwent heart transplantation were consecutively enrolled and assessed using the MMSE and MoCA neuropsychological tests. Kaplan-Meier curves and Cox proportional hazards models were used for survival analyses. Primary component analysis was performed to integrate MoCA subtests into the "Attention factor," "Naming factor," and "Orientation factor."About 30% of the patients were diagnosed with short-term postoperative cognitive impairment. The impairment group was older and had lower baseline cognitive performances, larger LV diameter, worse MMSE decline and higher ratio of significant MoCA decline. Postoperative cognitive impairment was significantly associated with worse survival (P = .028). Multivariate Cox analyses showed that higher postoperative MoCA score was significantly associated with lower mid-term post-transplant mortality (HR = .744 [.584, .949], P = .017), in which "Attention factor" contributed to this association most (HR = .345 [.123, .970], P = .044) rather than "Naming factor" or "Orientation factor." Notably, preoperative cognitive impairment was closely related with postoperative cognitive impairment and also indicated the worse post-transplant survival (P = .015).Postoperative as well as preoperative cognitive impairments were associated with a worse mid-term survival after heart transplantation, indicating that neuropsychological assessments before and after heart transplantation should be routinely performed for heart transplant recipients for better risk stratification. This article is protected by copyright. All rights reserved.

BACKGROUND Dysmenorrhea, including primary and secondary, is defined as pain in the lower abdomen occurring before or during menstruation. It was reported that dysmenorrhea accounted for 600 million lost work hours and $2 billion lost productivity annually. The loss in working hours and school days induced by dysmenorrhea has affected national economy. OBJECTIVE The purpose of this study is to provide valid data on the scientific progress of this disease through bibliometric analysis, which may help researchers to know its status and pick up the most active hotspots and trends quickly. METHODS We retrieved literatures on WOSCC from Jan 1st, 2001 to Dec 31th, 2021 and finally acquired 944 papers. Bibliometric analysis software CiteSpace was used in combination with VOSviewer. RESULTS The research trend over the last 21 years in dysmenorrhea can be evaluated through the number of papers in each year. An upward trend in articles continued in this field on the whole. Through the map, China contributed the most, followed by The United States and Turkey. For institutions, Beijing University of Chinese Medicine in China contributed most, followed by National Yang-Ming University in Taiwan, China, and Chengdu University of Traditional Chinese Medicine in China. Hsieh JC and Hellman KM were both the most prolific author with 14 articles. We can find 5 major research groups, respectively with Hsieh JC, Hellman KM, Zhu J, Liang F and Dun W as the core of the group. Dawood MY is the most famous author and the most frequently cited author with 488 citations. Advances in pathogenesis and management for primary dysmenorrhea wrote by Dawood MY was most cited of 197 citations and most influential. Pathophysiology, the potential central mechanism, syndrome, evaluation index, diagnosis to adenomyosis-associated dysmenorrhea, treatment, et al, were the main trends and hotspots. CONCLUSIONS The cooperative relationships among authors, countries, and institutions, as well as research hotspots and trends in the field of dysmenorrhea were presented, which can guide scholars to look for new research directions. Further and closer cooperation among authors, institutions, and countries in the future is expected to accelerate the development of dysmenorrhea research.

The crystalline structures and magnetic and thermodynamic properties of a Gd4Ga2O9 single crystal grown with the optical floating zone technique have been investigated. Gd4Ga2O9 crystallizes in a monoclinic structure with the space group P21/c at room temperature. Temperature-dependent magnetic susceptibility measurements along the three crystallographic axes reveal a paramagnetic (PM) behavior between 2 and 300 K. A Curie–Weiss (CW) law fit was carried out and the CW temperature θCW and magnetic frustration parameter f were calculated; these suggest antiferromagnetic (AFM) interactions between Gd3+ spins and a strong magnetic frustration. The field dependence of the magnetization at 2 K further confirms the magnetic frustration characteristics. A distinct λ-shaped peak at 1.4 K in the heat capacity curves suggests a transition from the PM to AFM phase. The magnetic entropy is contributed solely by Gd3+ ions.

BACKGOUND B cells contribute to chronic transplant rejection by producing donor-specific antibodies and promoting T cell response , but how these processes are regulated at the transcriptional level remains unclear. Herein, we investigate the role of transcription factor interferon regulatory factor 4 (IRF4) in controlling B cell response during chronic transplant rejection. METHODS We generated the Irf4gfp reporter mice to determine IRF4 expression in B cell lineage . We then used mice with B cell-specific IRF4 deletion to define the role of IRF4 in B cell response after NP-KLH immunization or allogeneic heart transplantation . In particular, graft survival and histology, as well as B and T cell responses, were evaluated after transplantation. RESULTS IRF4 is dynamically expressed at different stages of B cell development and is absent in germinal center (GC) B cells. However, IRF4 ablation in the B cell lineage primarily eliminates GC B cells in both naive and NP-KLH immunized mice. In the transplantation setting, IRF4 functions intrinsically in B cells and governs allogeneic B cell responses at multiple levels, including GC B cell generation, plasma cell differentiation, donor-specific antibody production , and support of T cell response. B cell-specific IRF4 deletion combined with transient CTLA4-Ig treatment abrogates acute and chronic cardiac allograft rejection in naive recipient mice but not in donor skin-sensitized recipients. CONCLUSIONS B cells require IRF4 to mediate chronic transplant rejection. IRF4 ablation in B cells abrogates allogeneic B cell responses and may also inhibit the ability of B cells to prime allogenic T cells . Targeting IRF4 in B cells represents a potential therapeutic strategy for eliminating chronic transplant rejection.

Objective. To test the biomechanical properties of 3D printed tantalum and titanium porous scaffolds. Methods. Four types of tantalum and titanium scaffolds with four alternative pore diameters, #1 (1000-700 μm), #2 (700-1000 μm), #3 (500-800 μm), and #4 (800-500 μm), were molded by selective laser melting technique, and the scaffolds were tested by scanning electronic microscope, uniaxial-compression tests, and Young’s modulus tests; they were compared with same size pig femoral bone scaffolds. Results. Under uniaxial-compression tests, equivalent stress of tantalum scaffold was 411 ± 1.43 MPa, which was significantly larger than the titanium scaffolds ( P < 0.05 ). Young’s modulus of tantalum scaffold was 2.61 ± 0.02 GPa, which was only half of that of titanium scaffold. The stress-strain curves of tantalum scaffolds were more similar to pig bone scaffolds than titanium scaffolds. Conclusion. 3D printed tantalum scaffolds with varying pore diameters are more similar to actual bone scaffolds compared with titanium scaffolds in biomechanical properties.

Uranium-containing wastewater poses a major threat to human health because of its toxicity and radioactivity. To address this issue, tea waste-derived porous biochar supporting the Fe–Mn bimetallic composite (FMBC) was fabricated using impregnation-pyrolysis and applied for U(VI) removal from aqueous solution. Batch experiments and characterization analysis were performed to investigate the adsorption properties and interaction mechanisms. Results showed that FMBC exhibit a maximum U(VI) removal capacity of 510.8 mg/g at pH = 5, T = 303 k, and t = 25 min. The kinetic and isotherm date fitted well with the pseudo-second-order kinetic model and Langmuir isotherm model, respectively, indicating that uranium adsorption is dominated by chemical adsorption on FMBC. FTIR and XPS analyses further confirmed that the U(VI) adsorption mechanism by FMBC was attributed to surface complexation, π–π bonding, and reduction processes. In addition, the Cationic competitive adsorption experiment further highlighted the excellent selective performance of FMBC for U(VI) removal from aqueous solutions, which is important in radionuclides' pollution treatment.

Objective The anti-inflammatory mechanisms of hyperbaric oxygenation (HBO) treatment on traumatic brain injury (TBI)-induced neuroinflammation remain unclear. The aim of this study was expected the effect of HBO on CCL2-related signaling pathway following severe TBI in rats. Methods The severe TBI model in rats was induced by controlled cortical impact. TBI rats were treated with CCR2 antagonist, p38 inhibitor, or HBO. Modified neurological severity scores and Morris water maze were used to evaluate neurological and cognitive function. The expression levels of CCL2 and CCR2 were measured by ELISA and real-time fluorescence quantitative PCR. Phospho-p38 expression was analyzed by western blotting. Results TBI-induced upregulation of CCL2, CCR2, and p38 in the injured cortex. Application of CCR2 antagonist improved neurological and cognitive function of TBI rats. Application of p38 inhibitor decreased expression of CCL2 and CCR2 in the injured of TBI rats, meanwhile improved neurological and cognitive function. HBO improved neurological and cognitive function by decreasing the expressions of CCL2, CCR2, and phospho-p38. Conclusions This study indicates that the p38-MAPK-CCL2 signaling pathway could mediate neuroinflammation and HBO therapy can modulate neuroinflammation by modulating the p38-MAPK-CCL2 signaling pathways following TBI. This study may provide theoretical evidence for HBO treatment in the treatment of TBI.

The adsorption performance and mechanism of the amino-modified zirconium-based metal organic framework (UiO-66-NH2) for the removal of U(VI) in aqueous solution were studied. Compared with UiO-66, UiO-66-NH2 shows better adsorption performance due to the introduction of amino groups. The adsorption characteristics of factors such as pH, UiO-66-NH2 dosage and contact time were investigated. The results show that the maximum adsorption capacity is 384.6 mg g−1 at pH = 6 and T = 313 K. The adsorption conforms to the quasi-second-order kinetic model and the Langmuir isotherm model. The thermodynamic parameters indicate that the adsorption process of U(VI) is endothermic and spontaneous. After five cycles, the removal rate of U(VI) still exceeded 83.53%. The results indicate that UiO-66-NH2 is a promising adsorbent that can effectively remove U(VI) in radioactive wastewater.

Spin-orbit coupled honeycomb magnets with the Kitaev interaction have received a lot of attention due to their potential of hosting exotic quantum states including quantum spin liquids. Thus far, the most studied Kitaev systems are 4d/5d-based honeycomb magnets. Recent theoretical studies predicted that 3d-based honeycomb magnets, including Na2Co2TeO6 (NCTO), could also be a potential Kitaev system. Here, we have used a combination of heat capacity, magnetization, electron spin resonance measurements alongside inelastic neutron scattering (INS) to study NCTO's quantum magnetism, and we have found a field-induced spin disordered state in an applied magnetic field range of 7.5 T < B (vertical to b-axis) < 10.5 T. The INS spectra were also simulated to tentatively extract the exchange interactions. As a 3d-magnet with a field-induced disordered state on an effective spin-1/2 honeycomb lattice, NCTO expands the Kitaev model to 3d compounds, promoting further interests on the spin-orbital effect in quantum magnets., 19 pages and 5 figures for main text. 16 pages and 6 figures for Supplemental Material

Recently, many different types of artificial discs have been introduced to persevere the biomechanical behavior of the cervical spine. This study compares the biomechanical behavior of single- and double-level cervical disc arthroplasty, that is “Prestige LP and Mobi-C” on the index and adjacent segment. A three-dimension finite element model of C2-C7 was developed and validated. In single-level prostheses, the Prestige LP or Mobi-C was implanted in the segment C5-C6, while the double-level arthroplasty was integrated at both segments C4-C5 and C5-C6 in the FE model. The intact FE and prosthesis-modified models were constrained from the inferior endplate of the vertebra C7 and applied a compressive load of 73.6 N with a moment load of 1 Nm on the odontoid process of the vertebra C2 to produce flexion/extension, lateral bending, and axial rotation. The prosthesis-modified model’s range of motion and intradiscal pressure were determined and compared to the intact model. Also examined the impact of the prostheses on the stress at the bone-implant interface. The range of motion of the implanted segments in both single- and double-levels arthroplasty was increased while that of the adjacent segment of implanted segments decreased. The intradiscal pressure in both levels of arthroplasty was greater than in the intact model. In conclusion, Mobi-C’s cervical prostheses could better preserve the normal range of motion and maintain intradiscal pressure than the Prestige LP.

By combining angle-resolved photoemission spectroscopy (ARPES) and first-principles calculations, we have studied the electronic band structure of a topological material candidate TaPdTe5.Two inverted bulk gaps determined through the parity analysis were observed in TaPdTe5 near the Fermi level, which causes the appearance of topologically protected surface states on the (010) surface. Beside the surface state, we also observed strong surface resonance state. The hybridization between the surface resonance state and the surface state changes the quasi-one-dimensional like Fermi surface sheets of the surface resonance state into two-dimensional Fermi pockets. Our findings provide comprehensive understanding about the interesting surface states in TaPdTe5.

Background and Objectives The Alberta Stroke Program CT Score (ASPECTS) is a widely used rating system for assessing infarct extent and location. We aimed to investigate the prognostic value of ASPECTS subregions’ involvement in the long-term functional outcomes of acute ischemic stroke (AIS). Materials and Methods Consecutive patients with AIS and anterior circulation large-vessel stenosis and occlusion between January 2019 and December 2020 were included. The ASPECTS score and subregion involvement for each patient was assessed using posttreatment magnetic resonance diffusion-weighted imaging. Univariate and multivariable regression analyses were conducted to identify subregions related to 3-month poor functional outcome (modified Rankin Scale scores, 3–6) in the reperfusion and medical therapy cohorts, respectively. In addition, prognostic eficiency between the region-based ASPECTS and ASPECTS score methods were compared using receiver operating characteristic curves and DeLong’s test. Results A total of 365 patients (median age, 64 years; 70% men) were included, of whom 169 had poor outcomes. In the reperfusion therapy cohort, multivariable regression analyses revealed that the involvement of the left M4 cortical region in left-hemisphere stroke (adjusted odds ratio [aOR] 5.39, 95% confidence interval [CI] 1.53–19.02) and the involvement of the right M3 cortical region in right-hemisphere stroke (aOR 4.21, 95% CI 1.05–16.78) were independently associated with poor functional outcomes. In the medical therapy cohort, left-hemisphere stroke with left M5 cortical region (aOR 2.87, 95% CI 1.08–7.59) and caudate nucleus (aOR 3.14, 95% CI 1.00–9.85) involved and right-hemisphere stroke with right M3 cortical region (aOR 4.15, 95% CI 1.29–8.18) and internal capsule (aOR 3.94, 95% CI 1.22–12.78) affected were related to the increased risks of poststroke disability. In addition, region-based ASPECTS significantly improved the prognostic efficiency compared with the conventional ASPECTS score method. Conclusion The involvement of specific ASPECTS subregions depending on the affected hemisphere was associated with worse functional outcomes 3 months after stroke, and the critical subregion distribution varied by clinical management. Therefore, region-based ASPECTS could provide additional value in guiding individual decision making and neurological recovery in patients with AIS.

In order to identify plant pentatricopeptide repeat (PPR) proteins, a framework of variable selection has been proposed. In fact, it is an effective feature selection strategy that focuses on the performance of classification. Random forest has been used as the classifier with certain variables automatically selected for discrimination between PPR functional and non-functional proteins. However, it is found that samples regarded as PPR functional proteins are wrongly classified in a high rate. In this paper, we plan to improve the framework in order to achieve better classification results. Modifications are made on the framework for better identifying PPR functional proteins. Instead of random forest, a hybrid ensemble classifier is built with its base classifiers derived from six different classification methods. Besides, an incremental strategy and a clustering by search in descending order are alternatively used for feature selection, which can effectively select the most representative variables for identification on PPR proteins. In addition, it can be found that different base classifiers alternately play an important role in the ensemble classifier with feature dimension increasing. The experimental results demonstrate the effectiveness of our improvements.

Chromatin accessibility landscapes are essential for detecting regulatory elements, illustrating the corresponding regulatory networks, and, ultimately, understanding the molecular bases underlying key biological processes. With the advancement of sequencing technologies, a large volume of chromatin accessibility data has been accumulated and integrated in humans and other mammals. These data have greatly advanced the study of disease pathogenesis, cancer survival prognosis, and tissue development. To advance the understanding of molecular mechanisms regulating plant key traits and biological processes, we developed a comprehensive plant chromatin accessibility database (PlantCADB, https://bioinfor.nefu.edu.cn/PlantCADB/) from 649 samples of 37 species. Among these samples, 159 are abiotic stress-related (including heat, cold, drought, salt, etc.), 232 are development-related and 376 are tissue-specific. Overall, 18,339,426 accessible chromatin regions (ACRs) were compiled. These ACRs were annotated with genomic information, associated genes, transcription factors footprint, motif, and SNPs. Additionally, PlantCADB provides various tools to visualize ACRs and corresponding annotations. It thus forms an integrated, annotated, and analyzed plant-related chromatin accessibility information which can aid to better understand genetic regulatory networks underlying development, important traits, stress adaptions, and evolution.

Rapid advances in single-cell transcriptome analysis provide deeper insights into the study of tissue heterogeneity at the cellular level. Unsupervised clustering can identify potential cell populations in single-cell RNA-sequencing (scRNA-seq) data, but fail to further determine the identity of each cell. Existing automatic annotation methods using scRNA-seq data based on machine learning mainly use single feature set and single classifier. In view of this, we propose a Weighted Ensemble classification framework for Cell Type Annotation, named scWECTA, which improves the accuracy of cell type identification. scWECTA uses five informative gene sets and integrates five classifiers based on soft weighted ensemble framework. And the ensemble weights are inferred through the constrained non-negative least squares. Validated on multiple pairs of scRNA-seq datasets, scWECTA is able to accurately annotate scRNA-seq data across platforms and across tissues, especially for imbalanced data containing rare cell types. Moreover, scWECTA outperforms other comparable methods in balancing the prediction accuracy of common cell types and the unassigned rate of non-common cell types at the same time. The source code of scWECTA is freely available at https://github.com/ttren-sc/scWECTA.

Over the last decades, molecular signatures have attracted extensive attention in cancer research. However, most of the reported biomarkers show a weak distinguishing ability in predicting the survival risks of patients. Actually, univariate analysis is generally considered in regression analysis, which makes the existing statistical methods ineffective. Furthermore, there is too much human involvement in the ways of classifying patients with high and low risk. Last but not least, the participation of therapy after conservative surgery also makes the survival analysis more complex. In order to solve these problems, we propose a solid method of feature selection which combines top-down and bottom-up strategies. The top-down strategy is to randomly extract some genes each time and select candidate genes through cumulative voting. The bottom-up strategy is to fully enumerate the selected genes and to use a clustering algorithm to classify samples. We analyzed glioblastoma data from the Cancer Genome Atlas (TCGA) and got candidate signatures. The results of simulation data, as well as an independent test set the Chinese Glioma Genome Atlas (CGGA), verified the reliability of the method and validity of the selected features.

Bronchopulmonary dysplasia is a chronic pulmonary disease with a high incidence in premature infants, and there is still no effective treatment. The purpose of our study was to analyze the association between the use of probiotics and BPD in premature infants. We retrospectively collected clinical data of infants with gestational age 2 tests was used to compare the categorical variables. The t test and non-parametric Wilcoxon rank-sum test were used to compare the continuous data. Univariate and multivariate logistic regression were used to analyze the association between probiotics and BPD. A total of 318 newborns met the inclusion criteria, of which 94 received probiotics and 224 received no probiotics. There were 16 deaths and 115 newborns with BPD in the included population. The results of univariate analysis showed differences in the maternal diabetes, the proportion of systemic antibiotics given to mother within 24 h before birth, the receiving rate of invasive mechanical ventilation, the prevalence of BPD/death, PDA, RDS and Ivh between newborns with and without probiotics (p

Three sites with different surface roughness were selected to explore the turbulent/synoptic separation and self-similar wall-attached coherent structures in the atmospheric surface layer. At each site, the facility permits synchronous multi-point measurements of three-dimensional wind velocity and temperature at different heights, as well as synchronous measurements via the global positioning system among the three sites. A filter based on the linear coherent spectrum between two sites (separated by 500 m) is adopted to separate turbulent and synoptic signals. After the separation, the two-point correlations of the filtered turbulent streamwise velocity component reveal that increasing surface roughness leads to less coherence in both the wall-normal and streamwise directions. The present results with unstable stratification and different surface roughness also demonstrate good agreement with the self-similar range of the wall-attached turbulence reported in Baars et al. (J Fluid Mech 823:R2, 2017). The aspect ratio of coherent structures (defined as the ratio of streamwise wavelength to the wall-normal offset) for the streamwise and spanwise velocity components and temperature increases with increasing surface roughness.

Tiansheng Xie,1,2 Haibo Yan,3 Guohua Wang4 1Zhejiang Sino-German Institute of Life science and Healthcare, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, 310023, People’s Republic of China; 2State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital of Zhejiang University, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310003, People’s Republic of China; 3Center for Disease Prevention and Control, Shaoxing, 312030, People’s Republic of China; 4Center for Disease Prevention and Control, Jiaxing, 314500, People’s Republic of ChinaCorrespondence: Tiansheng XieZhejiang Sino-German Institute of Life science and Healthcare, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, 318 Liuhe Road, Hangzhou, Zhejiang Province, 310023, People’s Republic of ChinaTel +86 571 86021350Email tianshengxie@zust.edu.cnPurpose: Social support is increasingly recognized to be important in care of people living with HIV/AIDS (PLWH), we firstly translate and validate the disease-targeted social support instrument in Chinese and to explore the correlation with WHOQOL-HIV.Patients and Methods: We established content validity for HIV-related social support scale (HSSS) and administered the resultant questionnaire to 310 PLWH. Descriptive statistics were generated for each of the variables of general characteristics; student t-test was used to compare the different groups.Results: The HSSS demonstrated a high level of internal consistency, both within each subscale and with the total score; all Cronbach’s α values exceeded a priori threshold of ≥ 0.70. The HSSS cores were positively correlated with WHOQOL-HIV total scores (Pearson correlation: 0.39, P < 0.001). We also found that higher educational level, personal income, CD4 cell count, and shorter duration of antiretroviral therapy are significantly associated with a higher level of social support (P < 0.05).Conclusion: Social support may improve quality of life for PLWH, Chinese version of HIV-related social support scale can be used in future clinical practice.Keywords: HIV, AIDS, quality of life, social support

Polyethyleneimine (PEI) modified magnetic yeast biomaterials composites (MY@SiO2-PEI) were prepared through a two-step solution polymerization method to remove U(VI) from aqueous solutions. The impact of six anions on U(VI) removal efficiency with MY@SiO2-PEI in single- and multi-ion systems was conducted in this article. A 26–2 Fractional factorial design (FFD) and Visual MINTEQ were employed to identify primary or interacting factors and the uranium chemical speciation calculations, respectively. In the single-ion systems, PO43−, humic acids (HA) slightly increased the U(VI) removal efficiency with MY@SiO2-PEI at low pH, whereas the U(VI) removal efficiency was largely reduced with SO42−, CO32−. The factorial effects of the six selected species on U(VI) removal efficiency via FFD in the multi-ion system were in the following sequence: CO32− > Cl− > HA > PO43− > SO42− > NO3−, and U(VI) removal was affected significantly by the combined of AB (Cl− × NO3−), BD (NO3− × SO42−).

Sewage sludge-derived biochar (SSB) was prepared at 600 °C pyrolysis temperature and modified by co-precipitation with Fe3O4 to obtain Fe3O4@SSB. The adsorption process of U(VI) onto the Fe3O4@SSB was accurately described by the pseudo-second order and Langmuir isotherm model. The maximum removal capacity of U(VI) was 149.15 mg/g at 303 K and initial pH of 4.0 by Langmuir isotherm model analysis. The removal mechanisms included complexation, ion exchange, reduction and electrostatic attraction. The U(VI) removal efficiency by Fe3O4@SSB remained above 90% after five adsorption–desorption. This work provided a reference for sewage sludge resource utilization and biochar modification for uranium-containing wastewater treatment.