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Supplemental Material for The effects of street environment features on road running: An analysis using crowdsourced fitness tracker data and machine learning by Shuyang Zhang, Nianxiong Liu, Beini Ma and Shurui Yan in Environment and Planning B: Urban Analytics and City Science

Additional file 4. Review history.

Additional file 3. Supplementary methods [84–87].

Granular materials such as sand, powders, and grains are omnipresent in daily life, industrial applications, and earth-science [1]. When unperturbed, they form stable structures that resemble the ones of other amorphous solids like metallic and colloidal glasses [2]. It is commonly conjectured that all these amorphous materials show a universal mechanical response when sheared slowly, i.e., to have an elastic regime, followed by yielding [3]. Here we use X-ray tomography to determine the microscopic dynamics of a cyclically sheared granular system in three dimensions. Independent of the shear amplitude $\Gamma$, the sample shows a cross-over from creep to diffusive dynamics, indicating that granular materials have no elastic response and always yield, in stark contrast to other glasses. The overlap function [4] reveals that at large $\Gamma$ yielding is a simple cross-over phenomenon, while for small $\Gamma$ it shows features of a first order transition with a critical point at $\Gamma\approx 0.1$ at which one finds a pronounced slowing down and dynamical heterogeneity. Our findings are directly related to the surface roughness of granular particles which induces a micro-corrugation to the potential energy landscape, thus creating relaxation channels that are absent in simple glasses. These processes must be understood for reaching an understanding of the complex relaxation dynamics of granular systems.

This paper presents Segregator, a global point cloud registration framework that exploits both semantic information and geometric distribution to efficiently build up outlier-robust correspondences and search for inliers. Current state-of-the-art algorithms rely on point features to set up putative correspondences and refine them by employing pair-wise distance consistency checks. However, such a scheme suffers from degenerate cases, where the descriptive capability of local point features downgrades, and unconstrained cases, where length-preserving (l-TRIMs)-based checks cannot sufficiently constrain whether the current observation is consistent with others, resulting in a complexified NP-complete problem to solve. To tackle these problems, on the one hand, we propose a novel degeneracy-robust and efficient corresponding procedure consisting of both instance-level semantic clusters and geometric-level point features. On the other hand, Gaussian distribution-based translation and rotation invariant measurements (G-TRIMs) are proposed to conduct the consistency check and further constrain the problem size. We validated our proposed algorithm on extensive real-world data-based experiments. The code is available: https://github.com/Pamphlett/Segregator., Comment: 6 pages, 5 figures. Accepted to ICRA2023

Aims: The CYP2D6*41 variant is the second or third frequent reduced function allele in Chinese with a frequency of around 3–4%, while it is the major reduced function allele in Indians, Saudi Arabians and Caucasians with frequencies of around 10–20%. The present study was designed to explore the impact of CYP2D6*41 on the metabolic activity of CYP2D6 using phenotyping methods in urine, plasma, and saliva.Methods: We used dextromethorphan as the probe drug to analyze the phenotypes of 87 subjects with CYP2D6*1/*1 (n = 22), CYP2D6*1/*2 (n = 33), CYP2D6*2/*2 (n = 4), CYP2D6*1/*41 (n = 5), CYP2D6*2/*41 (n = 3), CYP2D6*10/*41 (n = 16), and CYP2D6*5/*41 (n = 4) for CYP2D6. The ratio of parent drug to metabolite in 3 h saliva, 3 h plasma, and in 0–3 h urine was considered the metabolic ratio (MR).Results: The CYP2D6*41 allele had substantial impact on the metabolic activity of CYP2D6 regardless of the urinary, plasma, or salivary phenotyping method used. In subjects with CYP2D6*1(or *2)/*1(or *2), *1 (or *2)/*41, *10/*41 and *5/*41 (all p < 0.001), the salivary, plasma, or urinary MR value increased. The MRs in saliva, plasma, and urine displayed high correlations.Conclusion: The activity score system or the consensus activity score system, instead of the traditional phenotype classification, could predict the CYP2D6 enzyme activity more accurately. CYP2D6*41 had similar or more impact on the CYP2D6 enzyme activity as compared with CYP2D6*10. Assigning *41 a score of 0.5 and assigning *10 a score of 0.25 according to the consensus AS system should be reconsidered.

Additional file 1: Table S1. Quality assessment of clinical outcome studies included in the meta-analysis. Figure S1. Analysis of studies for mean age of onset and mean age of diagnosis of Gly83Arg mutations in transthyretin amyloidosis of different studies. The size of the squares corresponds to the weight of each study. The diamond and its width represent the pooled estimate for mean age and standard deviation (SD). Figure S2. Subgroup analysis for mortality by subtypes of transthyretin amyloidosis. (a) The size of the squares corresponds to the weight of each subgroup. The diamond and its width represent the pooled estimate for 2-year, all-cause mortality and 95% confidence intervals (95% CIs); (b) The size of the squares corresponds to the weight of each subgroup. The diamond and its width represent the pooled estimate for 5-year, all-cause mortality and 95% confidence intervals (95% CIs); (c) The size of the squares corresponds to the weight of each subgroup. The diamond and its width represent the pooled estimate for 10-year, all-cause mortality and 95% confidence intervals (95% CIs); hATTR-PN: hereditary transthyretin amyloid polyneuropathy; hATTR-CM and MIX: hereditary transthyretin amyloid cardiomyopathy and hereditary transthyretin amyloid cardiomyopathy and polyneuropathy.

Additional file 2: Table S1. PRISMA checklist. Table S2. MOOSE Statement - Reporting Checklist for Authors, Editors, and Reviewers of Meta-analyses of Observational Studies.

Additional file 1: Table S1. Search strategies applied in each bibliographic database. Table S2. Characteristic of studies included in the present study for analyzing risk of incident diabetes post-COVID-19 versus matched controls. Table S3. Quality assessment of included studies with the Newcastle–Ottawa Scale. Figure S1. Subgroup analysis of diabetes type. Figure S2. Subgroup analysis to whether the control group was or was not upper respiratory tract infections. Figure S3. Forest plot showing the incidence of DM among different age groups after COVID-19. Figure S4. Subgroup analysis of different age groups. Figure S5. Forest plot showing the incidence of DM among different gender after COVID-19. Figure S6. Subgroup analysis of gender. Figure S7. Forest plot showing the incidence of diabetes among different follow-up time after COVID-19. Figure S8. Subgroup analysis of different follow-up time. Figure S9. Forest plot showing the incidence of diabetes based on different levels of COVID-19 severity. Figure S10. Subgroup analysis of different levels of COVID-19 severity. Figure S11. Bias factor = log(1). Figure S12. Bias factor = log(2.08).

Additional file 2: Figure S1. Relationship between baseline plasma levels of IL-6 and clinical benefits to patients with NSCLC receiving ICIs in the CICAMS cohort. Figure S2. Relationship between baseline tumor tissue levels of IL-6 and tumor response of NSCLC patients after PD-1 inhibitors in the CICAMS cohort. Figure S3. Relationship between baseline tumor tissue levels of IL-6 and clinical benefit of patients with NSCLC receiving ICIs in the CICAMS cohort. Figure S4. Western blotting analysis of PD-L1 and IL-6 expression in normal lung cell and NSCLC cells of humans. Figure S5. Correlation among IL-6 expression, PD-L1 expression, and the JAK1/Stat3 signaling pathway. Figure S6. Correlation between IL-6 expression and the tumor microenvironment based on PD-L1 and CD8+ T cell infiltration in LUAD and LUSC patients. Figure S7. Correlograms of IL-6 expression with four tumor-infiltrated immune cells in NSCLC patients. Figure S8. Correlation between IL-6 expression and immune cell infiltration in patients with LUAD and LUSC.

Additional file 1: Supplementary Figure 1. Overview of in-house data set and the ZetaSuite flowchart. a, In-house data format. Two-dimensional in-house data are generated from a siRNA screen to identify global splicing regulators. In each siRNA-treated well, 407 alternative splicing (AS) events are interrogated by RNA Annealing Selection Ligation sequencing (RASL-seq). A total number of 18,480 siRNA pools against annotated protein-coding genes in the human genome are arrayed in 57 384-well plates. Each plate also contains 6 negative controls (NS-mix), 5 positive controls (siPTBP1) and 5 killer controls (siNEK6). After screening, raw data are tabulated in a matrix as the log2 isoform ratio (exon included isoform/exon skipped isoform). b, Flowchart of the ZetaSuite software in three parts ( https://github.com/YajingHao/ZetaSuit e), as detailed in the text. Supplementary Figure 2. Data analysis using existing statistical approaches. a-b, Z-score distribution of all non-expressors (n=5006) based on 5 randomly selected AS events (a) or all interrogated AS events (b). Red-marked dots indicate hits with Z-score>=3, showing the majority (~80%, see Fig. 2c) of non-expressors scored as false-positive hits when all measured AS events are included in the analysis with the traditional Z-score-based approach. c-d, The Z-score rank distribution (from induced exon skipping on left to exon inclusion on right) of SF3B1 (c) and SRSF2 (d) responsive AS events among total detected AS events in the screen, showing skewing of SF3B1-induced splicing toward exon skipping and SRSF2-induced splicing in both directions. e, Summary of hit numbers at common FDR cutoffs using 4 different existing methods. Supplementary Figure 3. Use of weighted ζ-scores to characterize screen hits. a, List of 10 known splicing regulators displayed in the Zeta plot in main Fig. 3a. b, The density of gene expression levels for annotated core spliceosome genes compared to all other genes in HeLa cells. c, Weighted ζ-scores of representative core spliceosome genes in induced exon skipping (blue) or inclusion (purple), emphasizing that knockdown of core spliceosome components predominately induce exon skipping. d, Comparison of AUPRC among different methods in simulated datasets. Weighted ζ-score in two directions calculated by ZetaSuite are combined in this analysis to reflect the overall functional consequence. This is not applicable to other software, and we thus display the data separately. Supplementary Figure 4. Strategy to remove off-target effects and optimal readouts for two-dimensional genome-wide screens. a, Setting the threshold by using the balanced error level (BRL) approach. Arrow indicates the chosen threshold and associated FDR. b, SS plots for five genome-wide RNAi screens, showing the calculated balance points in each (top panel 1 to 5). One dataset (panel 5) was permutated 5 times to illustrate the lack of a balance point if the data quality is compromised (bottom panel). c-d, Hits with related functions or due to off-target effects. Results of our secondary screen with 4 individual siRNAs in comparison with the pool of those siRNAs (left) or with the pools of other siRNAs against genes that show significant functional similarity (right, reflected by circle size). Hits are due to related functions when multiple single siRNAs produce similar results (c) or to off-target effects when a single siRNA is responsible for the similarity to both siRNA pools (d). e, Deduced SVM curves using two different sets of positive controls. SVM1 is defined with siPTBP1 repeats and SVM2 with a set of known spliceosome components listed in Supplementary Fig. 3a. f, Diagram to illustrate the calculation of weighed ζ-scores without using a SVM curve. g, Venn diagrams showing the overlaps of high confidence hits selected using different SVM curves (left) or with and without using a SVM curve (right). h, Impact of readout (AS event) size on the efficiency in recovering a set of reference hits. Each bar represents the percentage of recovered reference hits (purple for siPTBP1 replicates; green for high confidence hits based on total AS events) by ZetaSuite using different numbers of readouts. Error bars represent the standard deviation from three independent samplings. Supplementary Figure 5. Significantly increased number of fitness genes identified by ZetaSuite from the existing DepMap and DRIVE datasets. a, Comparison of cell lines surveyed by DepMap and DRIVE. Cell lines derived from different cancer types are color-indicated. A common set of 113 cell lines has been analyzed by both projects. b, Comparison of genes interrogated by the two projects. c, Robust segregation of positive (purple: annotated essential genes) and negative (green: non-expressors) controls in DepMap (left) and DRIVE (right). d, Comparison of the performance of different methods in DRIVE and DepMap datasets. e, GO term enrichment for newly identified essential genes by ZetaSuite. f, Function enrichment on KEGG pathways for newly identified essential genes by ZetaSuite. g, Top 10 enriched complexes of newly identified essential genes by ZetaSuite. Complexes are from the CORUM database. h, Density plots of correlation between DEMETER cancer dependency score and AGO2 expression (top) or copy number variation (bottom) for different gene sets according to the color key on right. The overlapped and non-overlapped hits correspond to those displayed in main Fig. 5e. Ten genes uniquely detected by DRIVE are labeled, showing that 8 of 10 are distributed with non-hits. Supplementary Figure 6. Functional analysis of identified hits by ZetaSuite. a-b, Averaged dependency scores of MYC (a) and ATR (b) in different cancer tissues. c, Association of ZetaSuite-identified cancer dependencies with gene expression, copy number and mutation features as in main Fig. 6e. d, Clusters of hits detected by ZetaSuite that leads to improved tumor cell proliferation. e, Global network of tumor checkpoint hits. Highlighted sub-networks include those involved in the regulation of GTPase activities, DNA packaging, and protein phosphorylation. Supplementary Figure 7. Application of ZetaSuite to single-cell transcriptomics. a, ζ-plot at each bin over a full range of gene expression. The number of expressed genes is based on the benchmark dataset (PRJEB4039). High-quality cells (orange) or low-quality cells (blue) are indicated with the upper panel to compare with annotated empty cells and with lower panel with annotated broken cells. b, Violin plots of the distribution of broken, empty and high-quality cells according to different metrics. c, ROC curves are deduced using different metrics. The p-values are calculated by plot.roc in pROC R package with default parameters. d-e, UMAP of cells identified by CellRanger, EmptyDrops or ζ cut-off. Colors were labeled by T cell marker gene expression(d) or Monocyte marker gene expression (e).

Additional file 1: Table S1. Demographic characteristics of NSCLC patients treated with anti-PD-1 inhibitors. Table S2. Univariate and multivariate regression analyses of the association between baseline plasma IL-6 levels and clinical factors for the prediction of PFS. Table S3. Univariate and multivariate regression analyses of the relationship between baseline tumor tissue IL-6 levels and clinical factors for the prediction of PFS.

Supplemental material, sj-docx-1-tam-10.1177_17588359221082339 for First-line immunotherapy or angiogenesis inhibitor plus chemotherapy for HER2-altered NSCLC: a retrospective real-world POLISH study by Guangjian Yang, Yaning Yang, Runze Liu, Weihua Li, Haiyan Xu, Xuezhi Hao, Junling Li, Puyuan Xing, Shuyang Zhang, Xin Ai, Fei Xu and Yan Wang in Therapeutic Advances in Medical Oncology

Additional file 2. Review history.

Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights in various applications. Although there have been many studies on structure learning with various types of data, the structure learning on the dynamic graph has not been explored yet, and thus we study the learning problem of node feature generation mechanism on such ubiquitous dynamic graph data. In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features. These two kinds of relationships form a DAG, which could effectively characterize the feature generation process in a concise way. To learn such a DAG, we cast the learning problem as a continuous score-based optimization problem, which consists of a differentiable score function to measure the validity of the learned DAGs and a smooth acyclicity constraint to ensure the acyclicity of the learned DAGs. These two components are translated into an unconstraint augmented Lagrangian objective which could be minimized by mature continuous optimization techniques. The resulting algorithm, named GraphNOTEARS, outperforms baselines on simulated data across a wide range of settings that may encounter in real-world applications. We also apply the proposed approach on two dynamic graphs constructed from the real-world Yelp dataset, demonstrating our method could learn the connections between node features, which conforms with the domain knowledge., Comment: Accepted by AAAI23

Supplemental material, sj-docx-1-tam-10.1177_17588359221082339 for First-line immunotherapy or angiogenesis inhibitor plus chemotherapy for HER2-altered NSCLC: a retrospective real-world POLISH study by Guangjian Yang, Yaning Yang, Runze Liu, Weihua Li, Haiyan Xu, Xuezhi Hao, Junling Li, Puyuan Xing, Shuyang Zhang, Xin Ai, Fei Xu and Yan Wang in Therapeutic Advances in Medical Oncology

The 11th International Conference on Rare Diseases and Orphan Drugs (ICORD), South Africa, included the Africa-Rare initiative launch and facilitated multi-stakeholder engagement in the challenges facing, and opportunities for, Africans living with rare diseases. The following ICORD Global Call to Action, developed in collaboration with the International Rare Diseases Research Consortium, synthesizes the outcomes of the deliberations and emphasizes the international collaborative efforts required to address the global effects of rare diseases on public health.

Permanent magnet demagnetization is one of the common faults of permanent magnet synchronous motor, which seriously threatens the safe and reliable operation of the motor and reduces the service life of the motor. Based on the principle of permanent magnet demagnetization, this paper analyzes the causes of irreversible permanent magnet demagnetization. It focuses on the following three aspects: permanent magnet material, processing technology innovation and magnetic material itself improvement, permanent magnet synchronous motor electromagnetic structure optimization design and finally, the operation environment and necessary preventive measures. Combined with the actual condition and parameters of the motor, a 2D finite element simulation model of the motor was established to verify the anti-demagnetization ability of the motor and the feasibility of finite element simulation.

As the COVID-19 spreads around the world, individuals's concerns about its extensive social, economic and political impact have become increasingly prominent. Chinese overseas students are one of the most paramount bearers of the globalization of education, and one of the groups most affected by the COVID-19. The COVID-19 continues and learning does not stop. Online courses brings new opportunities and challenges to Chinese overseas students. It takes ZOOM, GOOGLE MEET, OTTER and other online softwares as the carrier, and has a slice of advantages. As an example, online teaching that is not restricted by location and convenient for recording courses has become a blowout development trend. On the contrary, some individuals are also "skeptical" about online teaching through computers, quiet a few platforms and APPs, and believe that the quality of traditional face-to-face teaching is more stable. In the research, 100 Chinese overseas students from the Faculty of Educational Studies of University Putra Malaysia were used as samples to investigate their attitudes towards online courses during the COVID-19 and evaluate current situation through the questionnaire.

Additional file 1. The items of General Form of NRDRS.

Additional file 2. List of Cohorts in NRDRS with Disease/Disease Type and Case Number.

The aim of this study was to evaluate the efficacy and safety of empirical anti-tuberculous therapy (ATT) in patients with massive pericardial effusion (MPE) of unknown etiology in China. In-hospital patients with MPE were assessed retrospectively. Based on thorough examination excluding neoplastic, autoimmune, and nontuberculous infectious diseases, patients who had no evidence of tuberculosis (TB) were treated with empirical ATT (Group A) or not treated with empirical ATT (Group C), whereas those who had evidence of TB were treated with standard ATT (Group B). Clinical outcomes and mitigation of MPE were compared among the three groups to identify the effectiveness of ATT. The survival free of composite endpoint was estimated using the Kaplan-Meier method. A total of 185 eligible patients were recruited: 77 in Group A, 80 in Group B and 28 in Group C. The average follow-up was 52.9 ± 30.7, 49.4 ± 29.7 and 51.8 ± 30.2 months for Groups A, B and C, respectively. The incidence of composite endpoint was 23.3%, 24.4% and 85.7% in Groups A, B and C, respectively (P < 0.0001). However, the clinical recovery rate was greater in Group B compared with Group A (P = 0.027). No significant difference in the safety profile of ATT was noted between Groups A and B. MPE did not spontaneously decrease in 85.7% of patients in Group C. Empirical ATT should be considered in MPE of unknown etiology in countries with a high burden of TB.

Pathophysiological Model of SARS‐CoV‐2 Infection Human‐induced pluripotent stem cell (hiPSC)‐derived skin organoids can be used to investigate the susceptibility of skin cells to SARS‐CoV‐2 infection, providing the possibility to identify various pathological mechanisms and drug screening strategies. In article number 2104192, Shuyang Zhang, Yiwu Zhou, Manli Wang, Ling Leng, and co‐workers, established an experimental skin organoid model from hiPSC with hair follicles and nervous system and proved KRT17+ hair follicles and different types of nervous cells can be infected by SARS‐CoV‐2, providing the evidence for association between COVID‐19 and hair loss. [Image: see text]

ispartof: Proceedings of ISMA2020 including USD2020 ispartof: International Conference on Noise and Vibration Engineering location:Leuven, Belgium date:7 Sep - 9 Sep 2020 status: published

Additional file 2. Figure S1 Hepatic TC, TG, LDL-C and HDL-C expression levels; Figure S2 PWV measurement details; Figure S3 Microbial regulation of intestinal and hepatic genes involved in bile acid metabolism; Figure S4 Gut microbiota taxonomic and functional composition between Con and CAD mice; Figure S5 Transcriptional results of KEGG enrichment analysis of DEGs in the liver and ileum between CAD and Con groups, respectively; Figure S6 Gating strategy for the flow cytometric analyses of lymphocytes derived cells from the spleen and small intestine lamina propria. Schematic of the screening procedure; Figure S7 The cell population of total CD4+ T-cell and CD8+ T-cell in spleen and small intestine lamina propria (siLP) in Con and CAD groups n = 10–12 for each group; Figure S8 The cell population of lymphocytes in spleen and small intestine lamina propria in Con and CAD groups; Figure S9 Spearman correlation between spleen lymphocytes distributions with serum bile acids (left) and correlation between cell population of lymphocytes in siLP and fecal bile acids (right), respectively.

Objectives This study was to analyse evolution of the left ventricular (LV) systolic function assessed by Tissue Doppler imaging (TDI) and its predictive value to the prognosis in Chinese patients with septic shock, and to evaluate the correlation between these parameters and cardiac biomarkers including cardiac tropnin I (cTnI) and N-terminal-pro-BNP (NT-proBNP). Methods This study prospectively recruited 69 patients within 24 h after the onset of septic shock. The clinical variables, cardiac biomarkers and echocardiography data were obtained and analysed on admission (day 1) and at day 2, 4, 7 and 10 or 24 h after the correction of shock. The primary endpoint was 90 days all-cause mortality. Results The 90 days all-cause mortality which was 40.6% in this study. The survivors had a lower peak myocardial systolic velocity (Sm) which emerged as an independent predictor of the 90 days mortality in the multivariate analysis at day 1. There was no difference in the baseline level of biomarkers. During the mean follow-up period of 4.8 days, NT-proBNP and cTnI decreased significantly and Sm improved significantly in the survivors; the non-survivors showed a significantly increased NT-proBNP, unchanged cTnI and Sm. The biomarkers correlated significantly with Sm and LVEF both in the baseline and in the follow-up only in the survivors. The correlation of NT-proBNP with cTnI remained significant throughout the follow-up period in both groups. Conclusions LV systolic function assessed by TDI related to the prognosis of patients with septic shock. The level of biomarkers decreased and strong correlations could be found between the biomarkers and LV systolic function in the 90 days survivors after the onset of septic shock.