Distributed feedforward and feedback cortical processing supports human speech production.

Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and temporal cortical networks, but the degree and timing of their recruitment and dynamics remain poorly understood. We present a deep learning architecture that translates neural signals recorded directly from the cortex to an interpretable representational space that can reconstruct speech. We leverage learned decoding networks to disentangle feedforward vs. feedback processing. Unlike prevailing models, we find a mixed cortical architecture in which frontal and temporal networks each process both feedforward and feedback information in tandem. We elucidate the timing of feedforward and feedback-related processing by quantifying the derived receptive fields. Our approach provides evidence for a surprisingly mixed cortical architecture of speech circuitry together with decoding advances that have important implications for neural prosthetics.

Prognostic characterization of immune molecular subtypes in non-small cell lung cancer to immunotherapy.

BACKGROUND: Non-small cell lung cancer (NSCLC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable biomarkers, especially immunotherapy-associated biomarkers, that can predict outcomes of these patients. METHODS: Gene expression profiles of 1026 NSCLC patients were collected from The Cancer Genome Atlas (TCGA) datasets with their corresponding clinical and somatic mutation data. Based on immune infiltration scores, molecular clustering classification was performed to identify immune subtypes in NSCLC. After the functional enrichment analysis of subtypes, hub genes were further screened using univariate Cox, Lasso, and multivariate Cox regression analysis, and the risk score was defined to construct the prognostic model. Other microarray data and corresponding clinical information of 603 NSCLC patients from the GEO datasets were applied to conduct random forest models for the prognosis of NSCLC with 100 runs of cross-validation. Finally, external datasets with immunotherapy and chemotherapy were further applied to explore the significance of risk-scores in clinical immunotherapy response for NSCLC patients. RESULTS: Compared with Subtype-B, the Subtype-A, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration scores and up-regulation of immunotherapy markers. In addition, we found and validated an eleven -gene signatures for better application of distinguishing high- and low-risk NSCLC patients and predict patients' prognosis and therapeutical response to immunotherapy. Furthermore, combined with other clinical characteristics based on multivariate Cox regression analysis, we successfully constructed and validated a nomogram to effectively predict the survival rate of NSCLC patients. External immunotherapy and chemotherapy cohorts validated the patients with higher risk-scores exhibited significant therapeutic response and clinical benefits. CONCLUSION: These results demonstrated the immunological and prognostic heterogeneity within NSCLC and provided a new clinical application in predicting the prognosis and benefits of immunotherapy for the disease.

Reproducibility and validity of dietary patterns identified using factor analysis among Chinese populations.

In the present study, we evaluated the reproducibility and validity of dietary patterns among Chinese adult populations. A random subsample of 203 participants (aged 31-80 years) from a community-based nutrition and health survey was enrolled. An eighty-seven-item FFQ was administered twice (FFQ1 and FFQ2) 1 year apart; four 3 consecutive day, 24-h dietary recalls (24-HDR, as a reference method) were performed between the administrations of the two FFQ every 3 months. Dietary patterns from three separate dietary sources were derived using factor analysis based on twenty-eight predefined food groups. Comparisons between dietary pattern scores were made by using Pearson's or intraclass correlation coefficients (ICC), cross-classification analysis, weighted κ statistic and Bland-Altman plots; the four major dietary patterns identified from FFQ1, FFQ2 and 24-HDR were similar. Regarding reproducibility, ICC for z-scores between FFQ1 and FFQ2 were all >0·6 for dietary patterns. The 'animal and plant protein' pattern had the highest ICC of 0·870. For validity, the adjusted Pearson's correlation coefficients for dietary pattern z-scores between two FFQ and the mean of four 3 consecutive day 24-HDR ranged from 0·387 for the 'Chinese traditional' pattern to 0·838 for the 'animal and plant protein' pattern. More than 75 % of the participants were classified into the same or adjacent quartile, and <5 % were misclassified into opposite quartiles. The weighted κ ranged from 0·259 to 0·680. Bland-Altman plots indicated that no significant deviation was found between two dietary assessment methods. Our findings indicate a good reasonable reproducibility and a reasonable validity of dietary patterns derived by factor analysis in China.

Reproducibility and validity of an FFQ developed for adults in Nanjing, China.

We evaluated the reproducibility and validity of an FFQ used in a Chinese community-based nutrition and health survey. A total of ninety-nine males and 104 females aged 31-80 years completed four three consecutive 24-h dietary recalls (24-HDR, served as a reference method, one three consecutive 24-HDR for each season) and two FFQ (FFQ1 and FFQ2) over a 1-year interval. The reproducibility of the FFQ was estimated with correlation coefficients, misclassification and weighted κ statistic. The validity was evaluated by comparing the data obtained from FFQ2 with the mean 24-HDR (m24-HDR). Compared with the m24-HDR, the FFQ tended to underestimate intake of most nutrients and food groups. For all nutrients and food groups, the Spearman's and intra-class correlation coefficients between FFQ1 and FFQ2 ranged from 0·66 to 0·88 and from 0·65 to 0·87, respectively. Most correlation coefficients decreased after adjusting for energy. More than 90% of the subjects were classified into the same or adjacent categories by both FFQ. For all nutrients and food groups, the crude, energy-adjusted and de-attenuated Spearman's correlation coefficients between FFQ2 and the m24-HDR ranged from 0·21 to 0·69, 0·19 to 0·58 and 0·25 to 0·71, respectively. More than 70% of the subjects were classified into the same and adjacent categories by both instruments. Both weighted κ statistic and the Bland-Altman plots showed reasonably acceptable agreement between the FFQ2 and the m24-HDR. The FFQ developed for adults in the Nanjing area can be used to reliably and validly measure usual intake of major nutrients and food groups.

Genomic characteristics and comparative genomics analysis of Penicillium chrysogenum KF-25.

BACKGROUND: Penicillium chrysogenum has been used in producing penicillin and derived ß-lactam antibiotics for many years. Although the genome of the mutant strain P. chrysogenum Wisconsin 54-1255 has already been sequenced, the versatility and genetic diversity of this species still needs to be intensively studied. In this study, the genome of the wild-type P. chrysogenum strain KF-25, which has high activity against Ustilaginoidea virens, was sequenced and characterized. RESULTS: The genome of KF-25 was about 29.9 Mb in size and contained 9,804 putative open reading frames (orfs). Thirteen genes were predicted to encode two-component system proteins, of which six were putatively involved in osmolarity adaption. There were 33 putative secondary metabolism pathways and numerous genes that were essential in metabolite biosynthesis. Several P. chrysogenum virus untranslated region sequences were found in the KF-25 genome, suggesting that there might be a relationship between the virus and P. chrysogenum in evolution. Comparative genome analysis showed that the genomes of KF-25 and Wisconsin 54-1255 were highly similar, except that KF-25 was 2.3 Mb smaller. Three hundred and fifty-five KF-25 specific genes were found and the biological functions of the proteins encoded by these genes were mainly unknown (232, representing 65%), except for some orfs encoding proteins with predicted functions in transport, metabolism, and signal transduction. Numerous KF-25-specific genes were found to be associated with the pathogenicity and virulence of the strains, which were identical to those of wild-type P. chrysogenum NRRL 1951. CONCLUSION: Genome sequencing and comparative analysis are helpful in further understanding the biology, evolution, and environment adaption of P. chrysogenum, and provide a new tool for identifying further functional metabolites.

The Preparation of Acryloxyl Group Functionalized Siloxane Polymers and the Study of Their Ultra Violet Curing Properties.

Polysiloxane with multiple acryloxyl groups at the terminal site of the polymer chain was synthesized by the condensation reaction between hydroxyl-terminated polysiloxane and acryloyl chloride and used to improve the cross-linking density of UV-curable silicone materials initiated from dual acryloxy-terminated symmetric polysiloxane or single acryloxy-terminated asymmetric polysiloxane with the mixture of Irgacure 1173 and Irgacure 184 at a mass ratio of 1:1 as the photoinitiator. The effects of factors such as initiator composition, UV irradiation time, structure, and molecular weight of linear dual acryloxy-terminated or single acryloxy-terminated asymmetric siloxane oligomers on the gelation yield, thermal properties, water absorption, and water contact angle of UV-cured film were investigated. The synthesized cross-linking density modifier can be copolymerized with acryloxy-functionalized linear polysiloxanes under the action of a photoinitiator to increase the cross-link density of UV-cured products effectively. Both linear dual acryloxy-terminated or single acryloxy-terminated asymmetric siloxane oligomers can be copolymerized with cross-link density modifiers within 20 s of UV irradiation. The gelation yields of the UV-cured products obtained from the dual acryloxy-terminated siloxane oligomers were greater than 85%, and their surface water contact angles increased from 72.8° to 95.9° as the molecular weight of the oligomers increased. The gelation yields of UV-cured products obtained from single acryloxy-terminated asymmetric siloxane oligomers were less than 80%, and their thermal stabilities were inferior to those obtained from the dual acryloxy-terminated siloxane oligomers. However, the water contact angles of UV-cured products obtained from these single acryloxy-terminated asymmetric siloxane oligomers were all greater than 90°.

Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals.

Objective: This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface (ECoG) and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements and the trained model should perform well on participants unseen during training. Approach: We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes, by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train both subject-specific models using data from a single participant as well as multi-patient models exploiting data from multiple participants. Main Results: The subject-specific models using only low-density 8x8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC=0.817), over N=43 participants, outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N=39) led to further improvement (PCC=0.838). For participants with only sEEG electrodes (N=9), subject-specific models still enjoy comparable performance with an average PCC=0.798. The multi-subject models achieved high performance on unseen participants, with an average PCC=0.765 in leave-one-out cross-validation. Significance: The proposed SwinTW decoder enables future speech neuroprostheses to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. Importantly, the generalizability of the multi-patient models suggests the exciting possibility of developing speech neuroprostheses for people with speech disability without relying on their own neural data for training, which is not always feasible.

Combined analysis of chromatin accessibility and gene expression profiles provide insight into Fucoxanthin biosynthesis in Isochrysis galbana under green light.

Isochrysis galbana, as a potential accumulator of fucoxanthin, has become a valuable material to develop functional foods for humans. Our previous research revealed that green light effectively promotes the accumulation of fucoxanthin in I. galbana, but there is little research on chromatin accessibility in the process of transcriptional regulation. This study was conducted to reveal the mechanism of fucoxanthin biosynthesis in I. galbana under green light by analyzing promoter accessibility and gene expression profiles. Differentially accessible chromatin regions (DARs)-associated genes were enriched in carotenoid biosynthesis and photosynthesis-antenna protein formation, including IgLHCA1, IgLHCA4, IgPDS, IgZ-ISO, IglcyB, IgZEP, and IgVDE. The motifs for the MYB family were also identified as candidates controlling metabolic regulation responses to green light culture of I. galbana, including IgMYB1, IgMYB2, IgMYB33, IgMYB42, IgMYB98, IgMYB118, and IgMYB119. The results of differential expression analysis and WGCNA showed that several genes or transcription factors (TFs) related to carotenoid metabolism and photosynthesis exhibited a higher expression level and were significantly upregulated in A-G5d compared with A-0d and A-W5d, including IgMYB98, IgLHCA1, IgLHCX2, IgLHCB4, and IgLHCB5. This suggests that upregulation of these genes by green light may be the key factor leading to fucoxanthin accumulation by regulating the photosynthesis-antenna protein pathway. An integrated analysis of ATAC-seq and RNA-seq showed that 3 (IgphoA, IgPKN1, IgOTC) of 34 DARs-associated genes displayed obvious changes in their chromatin regions in ATAC-seq data, suggesting that these genes specific for green light may play a key role in fucoxanthin biosynthesis in I. galbana through a complex regulatory network of multiple metabolic pathways interacting with each other. These findings will facilitate in-depth understanding the molecular regulation mechanisms of fucoxanthin in I. galbana and its role in response to green light regulation, providing technical support for the construction of high fucoxanthin content strains.

A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis.

Decoding human speech from neural signals is essential for brain-computer interface (BCI) technologies restoring speech function in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity, and high dimensionality, and the limited publicly available source code. Here, we present a novel deep learning-based neural speech decoding framework that includes an ECoG Decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable Speech Synthesizer that maps speech parameters to spectrograms. We develop a companion audio-to-audio auto-encoder consisting of a Speech Encoder and the same Speech Synthesizer to generate reference speech parameters to facilitate the ECoG Decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Among three neural network architectures for the ECoG Decoder, the 3D ResNet model has the best decoding performance (PCC=0.804) in predicting the original speech spectrogram, closely followed by the SWIN model (PCC=0.796). Our experimental results show that our models can decode speech with high correlation even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. We successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with speech deficits resulting from left hemisphere damage. Further, we use an occlusion analysis to identify cortical regions contributing to speech decoding across our models. Finally, we provide open-source code for our two-stage training pipeline along with associated preprocessing and visualization tools to enable reproducible research and drive research across the speech science and prostheses communities.

Prognostic value of SOX9 in cervical cancer: Bioinformatics and experimental approaches.

Among gynecological cancers, cervical cancer is a common malignancy and remains the leading cause of cancer-related death for women. However, the exact molecular pathogenesis of cervical cancer is not known. Hence, understanding the molecular mechanisms underlying cervical cancer pathogenesis will aid in the development of effective treatment modalities. In this research, we attempted to discern candidate biomarkers for cervical cancer by using multiple bioinformatics approaches. First, we performed differential expression analysis based on cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas database, then used differentially expressed genes for weighted gene co-expression network construction to find the most relevant gene module for cervical cancer. Next, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on the module genes, followed by using protein-protein interaction network analysis and Cytoscape to find the key gene. Finally, we validated the key gene by using multiple online sites and experimental methods. Through weighted gene co-expression network analysis, we found the turquoise module was the highest correlated module with cervical cancer diagnosis. The biological process of the module genes focused on cell proliferation, cell adhesion, and protein binding processes, while the Kyoto Encyclopedia of Genes and Genomes pathway of the module significantly enriched pathways related to cancer and cell circle. Among the module genes, SOX9 was identified as the hub gene, and its expression was associated with cervical cancer prognosis. We found the expression of SOX9 correlates with cancer-associated fibroblast immune infiltration in immune cells by Timer2.0. Furthermore, cancer-associated fibroblast infiltration is linked to cervical cancer patients' prognosis. Compared to those in normal adjacent, immunohistochemical and real-time quantitative polymerase chain reaction (qPCR) showed that the protein and mRNA expression of SOX9 in cervical cancer were higher. Therefore, the SOX9 gene acts as an oncogene in cervical cancer, interactive with immune infiltration of cancer-associated fibroblasts, thereby affecting the prognosis of patients with cervical cancer.

A Multiphysics Model for Predicting Microstructure Changes and Microhardness of Machined AerMet100 Steel.

The machined-surface integrity plays a critical role in corrosion resistance and fatigue properties of ultra-high-strength steels. This work develops a multiphysics model for predicting the microstructure changes and microhardness of machined AerMet100 steel. The variations of stress, strain and temperature of the machined workpiece are evaluated by constructing a finite-element model of the orthogonal cutting process. Based on the multiphysics fields, the analytical models of phase transformation and dislocation density evolution are built up. The white layer is modeled according to the phase-transformation mechanism and the effects of stress and plastic strain on real phase-transformation temperature are taken into consideration. The microhardness changes are predicted by a model that accounts for both dislocation density and phase-transformation evolution processes. Experimental tests are carried out for model validation. The predicted results of cutting force, white-layer thickness and microhardness are in good agreement with the measured data. Additionally, from the proposed model, the correlation between the machined-surface characteristics and processing parameters is established.

Characterization of Pyroptosis-Related Subtypes via RNA-Seq and ScRNA-Seq to Predict Chemo-Immunotherapy Response in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. There is an urgent need to screen for reliable indices, especially immunotherapy-associated biomarkers that can predict patient outcomes. Pyroptosis, as an inflammation-induced type of programmed cell death, is shown to create a tumor-suppressive environment and improve the chemotherapeutic response in multiple tumors. However, the specific therapeutic effect of pyroptosis in TNBC remains unclear. In this study, we present a consensus clustering by pyroptosis-related signatures of 119 patients with TNBC into two subtypes (clusterA and clusterB) with distinct immunological and prognostic characteristics. First, clusterB, associated with better outcomes, was characterized by a significantly higher pyroptosis-related signature expression, tumor microenvironment prognostic score, and upregulation of immunotherapy checkpoints. A total of 262 differentially expressed genes between the subtypes were further identified and the Ps-score was built using LASSO and COX regression analyses. The external GEO data set demonstrated that cohorts with low Ps-scores consistently had higher expression of pyroptosis-related signatures, immunocyte infiltration levels, and better prognosis. In addition, external immunotherapy and chemotherapy cohorts validated that patients with lower Ps-scores exhibited significant therapeutic response and clinical benefit. Combined with other clinical characteristics, we successfully constructed a nomogram to effectively predict the survival rate of patients with TNBC. Finally, using the scRNA-seq data sets, we validated the landscape of cellular subtypes of TNBC and successfully constructed an miRNA-Ps-score gene interaction network. These findings indicated that the systematic assessment of tumor pyroptosis and identification of Ps-scores has potential clinical implications and facilitates tailoring optimal immunotherapeutic strategies for TNBC.

Advances in the use of nanomaterials for nucleic acid detection in point-of-care testing devices: A review.

The outbreak of the coronavirus (COVID-19) has heightened awareness of the importance of quick and easy testing. The convenience, speed, and timely results from point-of-care testing (POCT) in all vitro diagnostic devices has drawn the strong interest of researchers. However, there are still many challenges in the development of POCT devices, such as the pretreatment of samples, detection sensitivity, specificity, and so on. It is anticipated that the unique properties of nanomaterials, e.g., their magnetic, optical, thermal, and electrically conductive features, will address the deficiencies that currently exist in POCT devices. In this review, we mainly analyze the work processes of POCT devices, especially in nucleic acid detection, and summarize how novel nanomaterials used in various aspects of POCT products can improve performance, with the ultimate aims of offering new ideas for the application of nanomaterials and the overall development of POCT devices.

Regulating carbon and water balance as a strategy to cope with warming and drought climate in Cunninghamia lanceolata in southern China.

Human activities have increased the possibility of simultaneous warming and drought, which will lead to different carbon (C) allocation and water use strategies in plants. However, there is no conclusive information from previous studies. To explore C and water balance strategies of plants in response to warming and drought, we designed a 4-year experiment that included control (CT), warming (W, with a 5°C increase in temperature), drought (D, with a 50% decrease in precipitation), and warming and drought conditions (WD) to investigate the non-structural carbohydrate (NSC), C and nitrogen (N) stoichiometry, and intrinsic water use efficiency (iWUE) of leaves, roots, and litter of Cunninghamia lanceolata, a major tree species in southern China. We found that W significantly increased NSC and starch in the leaves, and increased NSC and soluble sugar is one of the components of NSC in the roots. D significantly increased leaves' NSC and starch, and increased litter soluble sugar. The NSC of the WD did not change significantly, but the soluble sugar was significantly reduced. The iWUE of leaves increased under D, and surprisingly, W and D significantly increased the iWUE of litter. The iWUE was positively correlated with NSC and soluble sugar. In addition, D significantly increased N at the roots and litter, resulting in a significant decrease in the C/N ratio. The principal component analysis showed that NSC, iWUE, N, and C/N ratio can be used as identifying indicators for C. lanceolata in both warming and drought periods. This study stated that under warming or drought, C. lanceolata would decline in growth to maintain high NSC levels and reduce water loss. Leaves would store starch to improve the resiliency of the aboveground parts, and the roots would increase soluble sugar and N accumulation to conserve water and to help C sequestration in the underground part. At the same time, defoliation was potentially beneficial for maintaining C and water balance. However, when combined with warming and drought, C. lanceolata growth will be limited by C, resulting in decreased NSC. This study provides a new insight into the coping strategies of plants in adapting to warming and drought environments.

Dynamic characteristics of a COVID-19 outbreak in Nanjing, Jiangsu province, China.

Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage B.1.617.2 (also named the Delta variant) was declared as a variant of concern by the World Health Organization (WHO). This study aimed to describe the outbreak that occurred in Nanjing city triggered by the Delta variant through the epidemiological parameters and to understand the evolving epidemiology of the Delta variant. Methods: We collected the data of all COVID-19 cases during the outbreak from 20 July 2021 to 24 August 2021 and estimated the distribution of serial interval, basic and time-dependent reproduction numbers (R0 and Rt), and household secondary attack rate (SAR). We also analyzed the cycle threshold (Ct) values of infections. Results: A total of 235 cases have been confirmed. The mean value of serial interval was estimated to be 4.79 days with the Weibull distribution. The R0 was 3.73 [95% confidence interval (CI), 2.66-5.15] as estimated by the exponential growth (EG) method. The Rt decreased from 4.36 on 20 July 2021 to below 1 on 1 August 2021 as estimated by the Bayesian approach. We estimated the household SAR as 27.35% (95% CI, 22.04-33.39%), and the median Ct value of open reading frame 1ab (ORF1ab) genes and nucleocapsid protein (N) genes as 25.25 [interquartile range (IQR), 20.53-29.50] and 23.85 (IQR, 18.70-28.70), respectively. Conclusions: The Delta variant is more aggressive and transmissible than the original virus types, so continuous non-pharmaceutical interventions are still needed.

A Research on Durability Degradation of Mineral Admixture Concrete.

In order to study the degradation laws and mechanisms of admixture concretes with single-added SO42- and composite of Mg2+ and SO42-, respectively, the durability tests were conducted on three types of mineral admixture concretes (concretes with single-added metakaolin (MK), single-added ultra-fine fly ash (UFA), and composite of metakaolin and ultra-fine fly ash (MF), and one reference concrete. In these tests, the 10% Na2SO4 solution and the 10% MgSO4 solution were used as the erosion medium, and the drying-wetting circle method was applied. It can be seen from the compressive tests and grey relational analysis that the MK admixture can improve the anti-Na2SO4-erosion capability of the concrete significantly, but weaken its anti-MgSO4-erosion capability; the UFA admixture can improve both the anti-Na2SO4-erosion and the anti-MgSO4-erosion capability of the concrete; and the composite admixture has superimposed effects and can enhance erosion resistance against these two erosion mediums. The phase composition and the changes of the macro morphology and the micro structure during the erosion process caused by mono sulfate ions and complex ions has been observed through X-ray diffraction (XRD), FTIR spectrum (FTIR), and scanning electron microscope (SEM), based on which it was determined that the erosion of single-added SO42- ions can produce erosive outputs of ettringite, gypsum, and mirabilite in the concrete, and cause corner scaling or deformation. Mg2+ and SO42- reacted in the concrete and produced brucite, M-S-H, ettringite, and gypsum, etc. The erosion of complex ions can cause scaling of the cement mortar and aggregate from the surface or the desquamation of corners.

A Preliminary Inquiry Into the Potential Mechanism of Huang-Lian-Jie-Du Decoction in Treating Rheumatoid Arthritis via Network Pharmacology and Molecular Docking.

Huang-Lian-Jie-Du decoction (HLJDD) has been widely applied to treat inflammation-associated diseases for thousands of years in China. However, the concrete molecular mechanism of HLJDD in the treatment of rheumatoid arthritis (RA) remains unclear. In this work, network pharmacology and molecular docking were applied to preliminarily analyze the potential active ingredients, drug targets, and related pathways of HLJDD on treating RA. A total of 102 active compounds with corresponding 189 targets were identified from HLJDD, and 41 common targets were further identified by intersecting with RA-related targets. Functional enrichment analysis was performed to screen the biological pathways associated with RA. Ten hub targets were further identified through constructing the protein-protein interaction (PPI) network of common targets, which were mainly enriched in the interleukin-17 (IL-17) signaling pathway, tumor necrosis factor (TNF) signaling pathway, and Toll-like receptor signaling pathway. Furthermore, a complex botanical drugs-ingredients-hub-targets-disease network was successfully constructed. The molecular docking results exhibited that these vital ingredients of HLJDD had a stable binding to the hub targets. Among these ingredients, quercetin (MOL000098) was the most common molecule with stable binding to all the targets, and PTGS2 was considered the most important target with multiple regulations by the most active ingredients. In vitro, we successfully validated the inhibitory role of quercetin in the cellular proliferation of human RA fibroblast-like synoviocyte cell line (MH7A cells). These findings indicated that the potential mechanisms of HLJDD for RA treatment might be attributed to inhibiting the immune-inflammatory response, reducing the release of chemokines, and alleviating the destruction of extracellular matrix (ECM) in the synovial compartment.

Characteristics of the Immune Cell Infiltration Landscape in Gastric Cancer to Assistant Immunotherapy.

Background: Gastric cancer (GC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable indices especially immunotherapy-associated parameters that can predict the therapeutic responses to immunotherapy of GC patients. Methods: Gene expression profile of 854 GC patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets (GSE84433) with their corresponding clinical and somatic mutation data. Based on immune cell infiltration (ICI) levels, molecular clustering classification was performed to identify subtypes and ICI scores in GC patients. After functional enrichment analysis of subtypes, we further explored the correlation between ICI scores and Tumor Mutation Burden (TMB) and the significance in clinical immunotherapy response. Results: Three subtypes were identified based on ICI scores with distinct immunological and prognostic characteristics. The ICI-cluster C, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration and up-regulation of PD-L1. ICI scores were identified through using principal component analysis (PCA) and the low ICI scores were consistent with the increased TMB and the immune-activating signaling pathways. Contrarily, the high-ICI score cluster was involved in the immunosuppressive pathways, such as TGF-beta, MAPK and WNT signaling pathways, which might be responsible for poor prognosis of GC. External immunotherapy and chemotherapy cohorts validated the patients with lower ICI scores exhibited significant therapeutic responses and clinical benefits. Conclusion: This study elucidated that ICI score could sever as an effective prognostic and predictive indicator for immunotherapy in GC. These findings indicated that the systematic assessment of tumor ICI landscapes and identification of ICI scores have crucial clinical implications and facilitate tailoring optimal immunotherapeutic strategies.

Two Stream Active Query Suggestion for Active Learning in Connectomics.

For large-scale vision tasks in biomedical images, the labeled data is often limited to train effective deep models. Active learning is a common solution, where a query suggestion method selects representative unlabeled samples for annotation, and the new labels are used to improve the base model. However, most query suggestion models optimize their learnable parameters only on the limited labeled data and consequently become less effective for the more challenging unlabeled data. To tackle this, we propose a two-stream active query suggestion approach. In addition to the supervised feature extractor, we introduce an unsupervised one optimized on all raw images to capture diverse image features, which can later be improved by fine-tuning on new labels. As a use case, we build an end-to-end active learning framework with our query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics. With the framework, we curate, to our best knowledge, the largest connectomics dataset with dense synapses and mitochondria annotation. On this new dataset, our method outperforms previous state-of-the-art methods by 3.1% for synapse and 3.8% for mitochondria in terms of region-of-interest proposal accuracy. We also apply our method to image classification, where it outperforms previous approaches on CIFAR-10 under the same limited annotation budget. The project page is https://zudi-lin.github.io/projects/#two_stream_active.

Detection of Nitrogen Content in Rubber Leaves Using Near-Infrared (NIR) Spectroscopy with Correlation-Based Successive Projections Algorithm (SPA).

Near-infrared spectroscopy is an efficient, low-cost technology that has potential as an accurate method in detecting the nitrogen content of natural rubber leaves. Successive projections algorithm (SPA) is a widely used variable selection method for multivariate calibration, which uses projection operations to select a variable subset with minimum multi-collinearity. However, due to the fluctuation of correlation between variables, high collinearity may still exist in non-adjacent variables of subset obtained by basic SPA. Based on analysis to the correlation matrix of the spectra data, this paper proposed a correlation-based SPA (CB-SPA) to apply the successive projections algorithm in regions with consistent correlation. The result shows that CB-SPA can select variable subsets with more valuable variables and less multi-collinearity. Meanwhile, models established by the CB-SPA subset outperform basic SPA subsets in predicting nitrogen content in terms of both cross-validation and external prediction. Moreover, CB-SPA is assured to be more efficient, for the time cost in its selection procedure is one-twelfth that of the basic SPA.