Trends of occupational exposure to ionizing radiation in Central China for the period 2000-2021

A retrospective analysis of occupational exposure to ionizing radiation from medical uses and industrial uses in the three provinces of Central China from 2000 to 2021 was conducted. The average annual effective dose in medical uses and industrial uses decreased from 2.042 mSv and 2.334 mSv in 2000-2002 to 0.476 mSv and 0.371 mSv in 2021 respectively; the fraction of monitored workers receiving annual dose not exceeding 1 mSv increased from 60.78% and 74.45% in 2000-2002 to 94.20% and 96.85% in 2021 respectively, while receiving annual doses exceeding 20 mSv declined from 1.35% and 1.91% in 2000-2002 to 0.18% and 0.03% in 2021 respectively. The average annual effective dose and NR20 in the period 2000-2021 were relatively high in professional public health institutions (0.955 mSv and 0.004) and hospitals (0.815 mSv and 0.004). In 2021, the average annual effective dose to monitored workers in different occupational categories in medical uses in the three provinces of Central China were in the range of 0.199-0.692 mSv, with interventional radiology received the highest dose and NR20 (0.692 mSv and 0.005); the average annual effective dose ranged from 0.161 to 0.493 mSv in industrial uses, with industrial radiography received the highest dose and NR20 (0.493 mSv and 0.001). Occupational exposure in medical uses and industrial uses declined obviously in Central China, and the groups receiving higher doses are the radiation workers working in hospitals and professional public health institutions, or engaged in interventional radiology, nuclear medicine and industrial radiography, warranting more effective radiation protection measures.

Efficiency assessment of a novel automatic mosquito pupae sex separation system in support of area-wide male-based release strategies

This study provides a comparative analysis of two state-of-the-art automatic mosquito pupae sex sorters currently available: the ORINNO and the WOLBAKI Biotech pupae sex separation systems, which both exploit the sexual size dimorphism of pupae. In Aedes aegypti, the WOLBAKI sex sorter and the ORINNO with a sieve mesh size of 1.050 mm achieved sex separation with female contamination rates below 1%, low pupae mortality rates and high male flight capacity. However, in Ae. albopictus, there was more variability, with female contamination rates above the 1% threshold and pupae mortality reaching 27% when using the ORINNO sorter. On the other hand, the WOLBAKI sorter achieved a male pupae recovery of 47.99 ± 8.81% and 50.91 ± 11.77% in Ae. aegypti and Ae. albopictus, respectively, while the ORINNO sorter with a smaller sieve size achieved male pupae recoveries of 38.08 ± 9.69% and 40.16 ± 2.73% in Ae. aegypti and Ae. albopictus, respectively. This study provides valuable information for researchers and practitioners in the field, assisting in the selection of the most suitable system for mosquito control, management and research programs depending on their specific requirements.

Effects of Cage Implantation Depth on Sagittal Parameters and Functional Outcomes in Posterior Lumbar Interbody Fusion for the Treatment of L4-L5 Lumbar Degenerative Spondylolisthesis

OBJECTIVE

In the treatment of lumbar degenerative spondylolisthesis (LDS) with Posterior lumbar interbody fusion (PLIF) surgery, interbody fusion implants play a key role in supporting the vertebral body and facilitating fusion. The objective of this study was to assess the impact of implantation depth on sagittal parameters and functional outcomes in patients undergoing PLIF surgery.

METHODS

This study reviewed 128 patients with L4-L5 LDS between January 2016 and August 2019. All patients underwent an open PLIF surgery that included intravertebral decompression, implantation of pedicle screws and cage. We grouped according to the position of the center of the cage relative to the L5 vertebral endplate. Patients with the center of the cage located at the anterior 1/2 of the upper end plate of the L5 vertebral body were divided into Anterior group, and located at the posterior 1/2 of the upper end plate of the L5 vertebral body were divided into Posterior group. The lumbar lordosis (LL), segmental lordosis (SL), sacral slope (SS), pelvic incidence (PI), pelvic tilt (PT) and slope degree (SD) was measured for radiographic outcomes. We used the visual analog scale (VAS) and the oswestry disability index (ODI) score to assess functional outcomes. Paired t-test was used to compare imaging and bedside data before and after surgery between the two groups, and independent sample t-test, χ2 test and Fisher exact test were used to compare the data between the two groups.

RESULT

The mean follow-up of Anterior group was 44.13 ± 9.23 months, and Posterior group was 45.62 ± 10.29 months (P > 0.05). The LL, SL, PT, SS, SD and PI-LL after operation showed great improvements, relative to the corresponding preoperative values in both groups (P < 0.05). Compared to Posterior group, Anterior group exhibited far enhanced SL (15.49 ± 3.28 vs. 13.67 ± 2.53, P < 0.05), LL (53.47 ± 3.21 vs. 52.08 ± 3.15, P < 0.05) outcomes and showed depressed PI-LL (8.87 ± 5.05 vs. 10.73 ± 5.39, P < 0.05) outcomes at the final follow-up. Meanwhile, the SL in Anterior group (16.18 ± 3.99) 1 months after operation were also higher than in Posterior group (14.12 ± 3.57) (P < 0.05). We found that VAS and ODI at the final follow-up in Anterior group (3.62 ± 0.96, 25.19 ± 5.25) were significantly lower than those in Posterior group (4.12 ± 0.98, 27.68 ± 5.13) (P < 0.05).

CONCLUSIONS

For patients with LDS, the anteriorly placed cage may provide better improvement of SL after PLIF surgery. Meanwhile, the anteriorly placed cage may achieve better sagittal parameters of LL and PI-LL and functional outcomes at the final follow-up.

Diet changes due to urbanization in South Africa are linked to microbiome and metabolome signatures of Westernization and colorectal cancer

Transition from traditional high-fiber to Western diets in urbanizing communities of Sub-Saharan Africa is associated with increased risk of non-communicable diseases (NCD), exemplified by colorectal cancer (CRC) risk. To investigate how urbanization gives rise to microbial patterns that may be amenable by dietary intervention, we analyzed diet intake, fecal 16 S bacteriome, virome, and metabolome in a cross-sectional study in healthy rural and urban Xhosa people (South Africa). Urban Xhosa individuals had higher intakes of energy (urban: 3,578 ± 455; rural: 2,185 ± 179 kcal/d), fat and animal protein. This was associated with lower fecal bacteriome diversity and a shift from genera favoring degradation of complex carbohydrates (e.g., Prevotella) to taxa previously shown to be associated with bile acid metabolism and CRC. Urban Xhosa individuals had higher fecal levels of deoxycholic acid, shown to be associated with higher CRC risk, but similar short-chain fatty acid concentrations compared with rural individuals. Fecal virome composition was associated with distinct gut bacterial communities across urbanization, characterized by different dominant host bacteria (urban: Bacteriodota; rural: unassigned taxa) and variable correlation with fecal metabolites and dietary nutrients. Food and skin microbiota samples showed compositional differences along the urbanization gradient. Rural-urban dietary transition in South Africa is linked to major changes in the gut microbiome and metabolome. Further studies are needed to prove cause and identify whether restoration of specific components of the traditional diet will arrest the accelerating rise in NCDs in Sub-Saharan Africa.

Experimental demonstration of 17.5 Gb/s physical layer key distribution over 100 km fiber link based on channel physical intrinsic property and polarization reciprocity

Secure key distribution (SKD) schemes based on fiber channel reciprocity provide information-theoretic security as well as a simple symmetric structure. However, the nonlinear effects and backscattering effects introduced during the bidirectional transmission process degrade the channel reciprocity. Recent unidirectional SKD schemes avoid non-reciprocal factors but require additional negotiation mechanisms to aggregate the transmitter and receiver data. Here, we propose a unidirectional SKD scheme based on channel physical intrinsic property and polarization reciprocity. The designed loopback structure constructs asymmetry between legitimate and illegitimate parties while aggregating data. The deployment of a broadband chaotic entropy source significantly improves the key generation rate (KGR). In the experiment, the KGR reaches 17.5 Gb/s, and the distribution distance reaches 100 km.

The predictive value of T-cell chimerism for disease relapse after allogeneic hematopoietic stem cell transplantation

Introduction

Chimerism is closely correlated with disease relapse after allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, chimerism rate is dynamic changes, and the sensitivity of different chimerism requires further research.

Methods

To investigate the predictive value of distinct chimerism for relapse, we measured bone marrow (BM), peripheral blood (PB), and T-cell (isolated from BM) chimerism in 178 patients after allo-HSCT.

Results

Receiver operating characteristic (ROC) curve showed that T-cell chimerism was more suitable to predict relapse after allo-HSCT compared with PB and BM chimerism. The cutoff value of T-cell chimerism for predicting relapse was 99.45%. Leukemia and myelodysplastic syndrome (MDS) relapse patients' T-cell chimerism was a gradual decline from 2 months to 9 months after allo-HSCT. Higher risk of relapse and death within 1 year after allo-HSCT. The T-cell chimerism rates in remission and relapse patients were 99.43% and 94.28% at 3 months after allo-HSCT (P = 0.009), 99.31% and 95.27% at 6 months after allo-HSCT (P = 0.013), and 99.26% and 91.32% at 9 months after allo-HSCT (P = 0.024), respectively. There was a significant difference (P = 0.036) for T-cell chimerism between early relapse (relapse within 9 months after allo-HSCT) and late relapse (relapse after 9 months after allo-HSCT) at 2 months after allo-HSCT. Every 1% increase in T-cell chimerism, the hazard ratio for disease relapse was 0.967 (95% CI: 0.948-0.987, P<0.001).

Discussion

We recommend constant monitoring T-cell chimerism at 2, 3, 6, and 9 months after allo-HSCT to predict relapse.

Vectorized dataset of silted land formed by check dams on the Chinese Loess Plateau

Check dams on the Chinese Loess Plateau (CLP) have captured billions of tons of eroded sediment, substantially reducing sediment load in the Yellow River. However, uncertainties persist regarding the precise sediment capture and the role of these dams in Yellow River flow and sediment dynamics due to the lack of available spatial distribution datasets. We produced the first vectorized dataset of silted land formed by check dams on the CLP, combining high-resolution and easily accessible Google Earth images with object-based classification methods. The accuracy of the dataset was verified by 1947 collected test samples, and the producer's accuracy and user's accuracy of the dam lands were 88.9% and 99.5%, respectively. Our dataset not only provides fundamental information for accurately assessing the ecosystem service functions of check dams, but also helps to interpret current changes in sediment delivery of the Yellow River and plan future soil and water conservation projects.

Exploring ncRNA-Drug Sensitivity Associations Via Graph Contrastive Learning

Increasing evidence has shown that noncoding RNAs (ncRNAs) can affect drug efficiency by modulating drug sensitivity genes. Exploring the association between ncRNAs and drug sensitivity is essential for drug discovery and disease prevention. However, traditional biological experiments for identifying ncRNA-drug sensitivity associations are time-consuming and laborious. In this study, we develop a novel graph contrastive learning approach named NDSGCL to predict ncRNA-drug sensitivity. NDSGCL uses graph convolutional networks to learn feature representations of ncRNAs and drugs in ncRNA-drug bipartite graphs. It integrates local structural neighbours and global semantic neighbours to learn a more comprehensive representation by contrastive learning. Specifically, the local structural neighbours aim to capture the higher-order relationship in the ncRNA-drug graph, while the global semantic neighbours are defined based on semantic clusters of the graph that can alleviate the impact of data sparsity. The experimental results show that NDSGCL outperforms basic graph convolutional network methods, existing contrastive learning methods, and state-of-the-art prediction methods. Visualization experiments show that the contrastive objectives of local structural neighbours and global semantic neighbours play a significant role in contrastive learning. Case studies on two drugs show that NDSGCL is an effective tool for predicting ncRNA-drug sensitivity associations. Source code and datasets can be available on https://github.com/altriavin/NDSGCL.

COVID-19 vaccines and adverse events of special interest: A multinational Global Vaccine Data Network (GVDN) cohort study of 99 million vaccinated individuals

BACKGROUND

The Global COVID Vaccine Safety (GCoVS) Project, established in 2021 under the multinational Global Vaccine Data Network™ (GVDN®), facilitates comprehensive assessment of vaccine safety. This study aimed to evaluate the risk of adverse events of special interest (AESI) following COVID-19 vaccination from 10 sites across eight countries.

METHODS

Using a common protocol, this observational cohort study compared observed with expected rates of 13 selected AESI across neurological, haematological, and cardiac outcomes. Expected rates were obtained by participating sites using pre-COVID-19 vaccination healthcare data stratified by age and sex. Observed rates were reported from the same healthcare datasets since COVID-19 vaccination program rollout. AESI occurring up to 42 days following vaccination with mRNA (BNT162b2 and mRNA-1273) and adenovirus-vector (ChAdOx1) vaccines were included in the primary analysis. Risks were assessed using observed versus expected (OE) ratios with 95 % confidence intervals. Prioritised potential safety signals were those with lower bound of the 95 % confidence interval (LBCI) greater than 1.5.

RESULTS

Participants included 99,068,901 vaccinated individuals. In total, 183,559,462 doses of BNT162b2, 36,178,442 doses of mRNA-1273, and 23,093,399 doses of ChAdOx1 were administered across participating sites in the study period. Risk periods following homologous vaccination schedules contributed 23,168,335 person-years of follow-up. OE ratios with LBCI > 1.5 were observed for Guillain-Barré syndrome (2.49, 95 % CI: 2.15, 2.87) and cerebral venous sinus thrombosis (3.23, 95 % CI: 2.51, 4.09) following the first dose of ChAdOx1 vaccine. Acute disseminated encephalomyelitis showed an OE ratio of 3.78 (95 % CI: 1.52, 7.78) following the first dose of mRNA-1273 vaccine. The OE ratios for myocarditis and pericarditis following BNT162b2, mRNA-1273, and ChAdOx1 were significantly increased with LBCIs > 1.5.

CONCLUSION

This multi-country analysis confirmed pre-established safety signals for myocarditis, pericarditis, Guillain-Barré syndrome, and cerebral venous sinus thrombosis. Other potential safety signals that require further investigation were identified.

Phase III, multicenter, randomized trial of 45 Gy versus 30 Gy thoracic radiation for extensive-stage small cell lung cancer (ES-SCLC): Study protocol

BACKGROUND

Consolidative thoracic radiotherapy (cTRT) has previously shown benefit to patients with extensive stage small cell lung cancer (ES-SCLC) who respond to chemotherapy. However, the optimum dose of cTRT is unknown. The purpose of this randomized trial is to compare the efficacy of 45 Gy in 15 fractions with 30 Gy in 10 fractions cTRT in ES-SCLC.

METHODS

This phase III, multicenter, randomized trial is designed to evaluate the safety and efficacy of different cTRT dose in ES-SCLC. Eligible patients with pathologically confirmed ES-SCLC who responded to 4-6 cycles of etoposide plus cisplatin (EP) or carboplatin (EC) chemotherapy were randomized 1:1 to receive either 30 Gy in 10 fractions (standard dose) or 45 Gy in 15 fractions (high dose) cTRT. The primary endpoint is 2-year overall survival (OS). Secondary endpoints include 2-year progression-free survival (PFS), 2-year local control (LC) and treatment related toxicity as measured by adverse events according to the Common Terminology Criteria for Adverse Events (version 4.0).

DISCUSSION

The present study is the first randomized phase III trial designed to evaluate the efficacy of higher versus lower dose cTRT in ES-SCLC, providing evidence for future clinical practice in prolonging survival of patients with ES-SCLC.

Tomato CYP94C1 inactivates bioactive JA-Ile to attenuate jasmonate-mediated defense during fruit ripening

As the master regulators of the ET signaling pathway, EIL transcription factors directly activate the expression of CYP94C1 to inactivate bioactive JA-Ile, thereby attenuating JA-mediated defense during fruit ripening. Knockout of CYP94C1 improves tomato fruit resistance to necrotrophs without compromising fruit quality.

Psychological Distress, Emergency Room Utilization, and Mortality Risk Among US Adults With History of Prostate Cancer

PURPOSE

Adults with a history of prostate cancer experience several physical and mental stressors. However, limited information is available about the prevalence of psychological distress in this population and its association with clinical outcomes in a nationally representative sample.

METHODS

We identified adults with history of prostate cancer from a nationally representative cohort (2000-2018 US National Health Interview Survey) and its linked mortality files through December 31, 2019. The six-item Kessler Psychological Distress Scale (K6) was used to assess psychological distress. The associations between psychological distress severity, emergency room (ER) usage, and mortality risk were estimated using multivariable logistic and Cox proportional hazards models, which were both adjusted for age, survey year, race/ethnicity, region, education, health insurance, comorbidities, functional limitations, and time since cancer diagnosis.

RESULTS

Among the 3,451 adults with history of prostate cancer surveyed, 96 (2.4%), 434 (11.3%), and 2,921 (86.3%) reported severe, moderate, or low/no mental distress, respectively. During the 12 months preceding the survey, 812 (22.8%) adults with history of prostate cancer visited the ER. After a median follow-up of 81 months, 937 (25.5%) deaths occurred. Compared with participants with low/no mental distress, those with severe mental distress reported the highest utilization of the ER (adjusted odds ratio [aOR], 2.57 [95% CI, 1.51 to 4.37]) and exhibited the highest all-cause mortality (adjusted hazard ratio [aHR], 1.83 [95% CI, 1.29 to 2.60]), followed by those with moderate mental distress (ER use aOR, 1.76 [95% CI, 1.29 to 2.42]; all-cause mortality aHR, 1.22 [95% CI, 0.92 to 1.62]).

CONCLUSION

Among US adults with history of prostate cancer, psychological distress was associated with increased ER use and mortality risk. Notably, severe psychological distress was correlated with the highest rates of ER visits and mortality risk. However, given the retrospective nature of this study, uncontrolled confounding variables need to be considered when interpreting the findings.

Melatonin-encapsuled silk fibroin electrospun nanofibers promote vascularized bone regeneration through regulation of osteogenesis-angiogenesis coupling

The repair of critical-sized bone defects poses a significant challenge due to the absence of periosteum, which plays a crucial role in coordinating the processes of osteogenesis and vascularization during bone healing. Herein, we hypothesized that melatonin-encapsuled silk Fibronin electrospun nanofibers (SF@MT) could provide intrinsic induction of both osteogenesis and angiogenesis, thereby promoting vascularized bone regeneration. The sustained release of melatonin from the SF@MT nanofibers resulted in favorable biocompatibility and superior osteogenic induction of bone marrow mesenchymal stem cells (BMMSCs). Interestingly, melatonin promoted the migration and tube formation of human umbilical vein endothelial cells (HUVECs) in a BMMSC-dependent manner, potentially through the upregulation of vascular endothelial growth factor (VEGFA) expression in SF@MT-cultured BMMSCs. SF@MT nanofibers enhanced the BMMSC-mediated angiogenesis by activating the PI3K/Akt signaling pathway. In vivo experiments indicated that the implantation of SF@MT nanofibers into rat critical-sized calvarial defects significantly enhances the production of bone matrix and the development of new blood vessels, leading to an accelerated process of vascularized bone regeneration. Consequently, the utilization of melatonin-encapsulated silk Fibronin electrospun nanofibers shows great promise as a potential solution for artificial periosteum, with the potential to regulate the coupling of osteogenesis and angiogenesis in critical-sized bone defect repair.

Stability Analysis for H∞-Controlled Active Quarter-Vehicle Suspension Systems With a Resilient Event-Triggered Scheme Under Periodic DoS Attacks

The stability analysis is studied for H∞ controlled networked active quarter-vehicle suspension systems with a resilient event-triggered scheme (RETS) under periodic denial-of-service (DoS) jamming attacks in this article. For the networked suspension system, the system-state signals are measured by sensors and transmitted to the cloud controller through a wireless network and then the control signal is transferred to the actuator to control it. An event-triggered scheme (ETS) is designed to reduce the workload of data transmission, which is effective to select some most useful information to transmit and discard some redundant data. DoS attacks can block the data transmission when it is active, so a resilient event-triggered H∞ control method is built based on the Lyapunov stability theory. The exponential stability of the controlled suspension system, as well as the H∞ performance, is analyzed in this article. Some simulation results show that the proposed control method is effective to improve driving comfort and driving safety and reduce the workload of data transmission under periodic DoS attacks.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

Added value of spectral computed tomography quantitative parameters for differentiating tuberculosis-associated fibrosing mediastinitis from endobronchial lung cancer: initial results

OBJECTIVE

The objective of this study was to explore the added value of spectral computed tomography (CT) parameters to conventional CT features for differentiating tuberculosis-associated fibrosing mediastinitis (TB-associated FM) from endobronchial lung cancer (EBLC).

METHODS

Chest spectral CT enhancement images from 109 patients with atelectasis were analyzed retrospectively. These patients were divided into two distinct categories: the TB-associated FM group (n = 77) and the EBLC group (n = 32), based on bronchoscopy and/or pathological findings. The selection of spectrum parameters was optimized with the least absolute shrinkage and selection operator regression analysis. The relationship between the spectrum parameters and conventional parameters was explored using Pearson's correlation. Multivariate logistic regression analysis was used to build spectrum model. The spectrum parameters in the spectrum model were replaced with their corresponding conventional parameters to build the conventional model. Diagnostic performances were evaluated using receiver operating characteristic curve analyses.

RESULTS

There was a moderate correlation between the parameters ㏒(L-AEFNIC) - ㏒(L-AEFC) (r= 0.419; p< 0.0001), ㏒(O-AEF40KeV) - ㏒(O-AEFC) (r= 0.475; p< 0.0001), [L-A-hydroxyapatite {HAP}(I)] - (L-U-CT) (r= 0.604; p< 0.0001), {arterial enhancement fraction (AEF) derived from normalized iodine concentration (NIC) of lymph node (L-AEFNIC), AEF derived from CT40KeV of bronchial obstruction (O-AEF40KeV), arterial-phase Hydroxyapatite (Iodine) concentration of lymph node [L-A-HAP(I)], AEF derived from conventional CT (AEFC), unenhanced CT value (U-CT)}. Spectrum model could improve diagnostic performances compared to conventional model (area under curve: 0.965 vs 0.916, p= 0.038).

CONCLUSION

There was a moderate correlation between spectrum parameters and conventional parameters. Integrating conventional CT features with spectrum parameters could further improve the ability in differentiating TB-associated FM from EBLC.

MSF-PFP: A Novel Multisource Feature Fusion Model for Protein Function Prediction

Protein function prediction is essential for disease treatment and drug development; yet, traditional biological experimental methods are less efficient in annotating protein function, and existing automated methods fail to fully leverage protein multisource data. Here, we present MSF-PFP, a computational framework that fuses multisource data features to predict protein function with high accuracy. Our framework designs specific models for feature extraction based on the characteristics of various data sources, including a global-local-individual strategy for local location features. MSF-PFP then integrates extracted features through a multisource feature fusion model, ultimately categorizing protein functions. Experimental results demonstrate that MSF-PFP outperforms eight state-of-the-art models, achieving FMax scores of 0.542, 0.675, and 0.624 for the biological process (BP), molecular function (MF), and cellular component (CC), respectively. The source code and data set for MSF-PFP are available at https://swanhub.co/TianGua/MSF-PFP, facilitating further exploration and validation of the proposed framework. This study highlights the potential of multisource data fusion in enhancing protein function prediction, contributing to improved disease therapy and medication discovery strategies.

Joint condition monitoring framework of wind turbines based on multi-task learning with poor-quality data

Effective condition monitoring can improve the reliability of the turbine and reduce its downtime. However, due to the complexity of the operating conditions, the monitoring data is always mixed with poor-quality data. Poor-quality data mixed in monitoring tasks disrupts long-term dependency on data, which challenges traditional condition monitoring methods to work. To solve it, a joint reparameterization feature pyramid network (JRFPN) is proposed. Firstly, three different reparameterization tricks are designed to reform temporal information and exchange cross-temporal information, to alleviate the damage of long-term dependency. Secondly, a joint condition monitoring framework is designed, aiming to suppress feature confounding between poor-quality data and faulty data. The auxiliary task is trained to extract the degradation trend. The main task fights against feature confounding and dynamically delineates the failure threshold. The degradation trend and failure threshold decisions are corrected for each other to make the final joint state inference. Besides, considering the different quality of the monitoring variables, a channel weighting mechanism is designed to strengthen the ability of JRFPN. The measured data proved that JRFPN is more effective than other methods.

Assessing the Efficacy of Pixel-Level Fusion Techniques for Ultra-High-Resolution Imagery: A Case Study of BJ-3A

Beijing Satellite 3 is a high-performance optical remote sensing satellite with a spatial resolution of 0.3-0.5 m. It can provide timely and independent ultra-high-resolution spatial big data and comprehensive spatial information application services. At present, there is no relevant research on the fusion method of BJ-3A satellite images. In many applications, high-resolution panchromatic images alone are insufficient. Therefore, it is necessary to fuse them with multispectral images that contain spectral color information. Currently, there is a lack of research on the fusion method of BJ-3A satellite images. This article explores six traditional pixel-level fusion methods (HPF, HCS, wavelet, modified-IHS, PC, and Brovey) for fusing the panchromatic image and multispectral image of the BJ-3A satellite. The fusion results were analyzed qualitatively from two aspects: spatial detail enhancement capability and spectral fidelity. Five indicators, namely mean, standard deviation, entropy, correlation coefficient, and average gradient, were used for quantitative analysis. Finally, the fusion results were comprehensively evaluated from three aspects: spectral curves of ground objects, absolute error figure, and object-oriented classification effects. The findings of the research suggest that the fusion method known as HPF is the optimum and appropriate technique for fusing panchromatic and multispectral images obtained from BJ-3A. These results can be utilized as a guide for the implementation of BJ-3A panchromatic and multispectral data fusion in real-world scenarios.

PinMyMetal: A hybrid learning system to accurately model metal binding sites in macromolecules

Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural (4-residue predominate), catalytic (3-residue predominate), or regulatory (2-residue predominate) roles. Computational tools for modeling metal ions in protein structures, especially for transient, reversible, and concentration-dependent regulatory sites, remain immature. We present PinMyMetal (PMM), a sophisticated hybrid machine learning system for predicting zinc ion localization and environment in macromolecular structures. Compared to other predictors, PMM excels in predicting regulatory sites (median deviation of 0.34 Å), demonstrating superior accuracy in locating catalytic sites (median deviation of 0.27 Å) and structural sites (median deviation of 0.14 Å). PMM assigns a certainty score to each predicted site based on local structural and physicochemical features independent of homolog presence. Interactive validation through our server, CheckMyMetal, expands PMM's scope, enabling it to pinpoint and validates diverse functional zinc sites from different structure sources (predicted structures, cryo-EM and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top. While currently trained on zinc, the PMM workflow can easily adapt to other metals through expanded training data.

Lignin bioconversion based on genome mining for ligninolytic genes in Erwinia billingiae QL-Z3

BACKGROUND

Bioconversion of plant biomass into biofuels and bio-products produces large amounts of lignin. The aromatic biopolymers need to be degraded before being converted into value-added bio-products. Microbes can be environment-friendly and efficiently degrade lignin. Compared to fungi, bacteria have some advantages in lignin degradation, including broad tolerance to pH, temperature, and oxygen and the toolkit for genetic manipulation.

RESULTS

Our previous study isolated a novel ligninolytic bacterial strain Erwinia billingiae QL-Z3. Under optimized conditions, its rate of lignin degradation was 25.24% at 1.5 g/L lignin as the sole carbon source. Whole genome sequencing revealed 4556 genes in the genome of QL-Z3. Among 4428 protein-coding genes are 139 CAZyme genes, including 54 glycoside hydrolase (GH) and 16 auxiliary activity (AA) genes. In addition, 74 genes encoding extracellular enzymes are potentially involved in lignin degradation. Real-time PCR quantification demonstrated that the expression of potential ligninolytic genes were significantly induced by lignin. 8 knock-out mutants and complementary strains were constructed. Disruption of the gene for ELAC_205 (laccase) as well as EDYP_48 (Dyp-type peroxidase), ESOD_1236 (superoxide dismutase), EDIO_858 (dioxygenase), EMON_3330 (monooxygenase), or EMCAT_3587 (manganese catalase) significantly reduced the lignin-degrading activity of QL-Z3 by 47-69%. Heterologously expressed and purified enzymes further confirmed their role in lignin degradation. Fourier transform infrared spectroscopy (FTIR) results indicated that the lignin structure was damaged, the benzene ring structure and groups of macromolecules were opened, and the chemical bond was broken under the action of six enzymes encoded by genes. The abundant enzymatic metabolic products by EDYP_48, ELAC_205 and ESOD_1236 were systematically analyzed via liquid chromatography-mass spectrometry (LC-MS) analysis, and then provide a speculative pathway for lignin biodegradation. Finally, The activities of ligninolytic enzymes from fermentation supernatant, namely, LiP, MnP and Lac were 367.50 U/L, 839.50 U/L, and 219.00 U/L by orthogonal optimization.

CONCLUSIONS

Our findings provide that QL-Z3 and its enzymes have the potential for industrial application and hold great promise for the bioconversion of lignin into bioproducts in lignin valorization.

ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies

Novel therapeutic alternatives for cancer treatment are increasingly attracting global research attention. Although chemotherapy remains a primary clinical solution, it often results in significant side effects for patients. In recent years, anticancer peptides (ACPs) have emerged as promising candidates for highly specific anticancer drugs, and a number of computational approaches have been developed to identify ACPs. However, existing methods do not recognize specific types of anticancer function. In this article, we propose ACPScanner, an integrated approach to predict ACPs and non-ACPs at first and then predict several specific activity types for potential ACPs. We incorporate sequential, physicochemical properties, secondary structural information, and deep representation learning embeddings which are generated from artificial intelligence methods to build feature space. Customized deep learning and statistical learning methods are combined to form an integral architecture for the comprehensive two-level prediction task. To the best of our knowledge, ACPScanner is the first approach for specific ACP activity prediction. The comparative evaluation illustrates that ACPScanner achieves competitive prediction performance in both prediction phases in independent testings. We establish a web server at http://acpscanner.denglab.org to provide convenient usage of ACPScanner and make the predictive framework, source code, and data sets publicly available.

Joint association of physical activity and diet quality with dyslipidemia: a cross-sectional study in Western China

OBJECTIVE

This study aims to investigate the prevalence of dyslipidemia and assess the joint association of physical activity (PA) and diet quality on dyslipidemia risk in urban areas of Xinjiang.

METHODS

Conducted from July 2019 to September 2021 in Xinjiang, China, this cross-sectional study involved 11,855 participants (mean age 47.1 ± 9.4 years, 53.1% male). Standard methods were used to measure plasma cholesterol levels, and validated questionnaires were employed to evaluate dietary habits and PA. The definition of dyslipidemia is based on 2023 Chinese guidelines for lipid management. PA was divided into guideline-recommended moderate-to-vigorous physical activity (MVPA) and non-MVPA, following World Health Organization guidelines. The Food Frequency Questionnaire was used to obtain the intake frequency of each dietary term. Each item was scored based on consumption frequency and divided into three groups (good, intermediate, and poor) based on total dietary score. Multivariate logistic regression analysis was performed to identify dyslipidemia risk factors, as well as the joint association of PA and diet quality.

RESULTS

Dyslipidemia prevalence among urban adults in Xinjiang was 39.3%, with notable sex disparities (52.6% in males vs. 24.3% in females, P < 0.001). Among participants with dyslipidemia, the awareness, treatment and control rates were 6.9%, 3.1%, and 1.9%, respectively. A significant multiplicative interaction between PA and diet quality is associated with dyslipidemia (P for interaction < 0.05). Less PA and poor diet quality were associated with an increased odds of dyslipidemia. Even individuals with poor (OR = 1.464, 95% CI: 1.106-1.939) or intermediate (OR = 1.229, 95% CI: 1.003-1.505) diet quality but adhering to recommended MVPA had lower odds of dyslipidemia compared to those with good diet quality but inadequate MVPA (OR = 1.510, 95% CI: 1.252-1.821).

CONCLUSIONS

Dyslipidemia prevalence was 39.3% in urban adults in Xinjiang, with limited awareness, treatment, and control. Following guideline-recommended MVPA and maintaining good diet quality were protective against dyslipidemia. Low levels of PA associated with a higher prevalence of dyslipidemia, even in individuals with good diet quality.

Tumor Characteristics and Treatment Responsiveness in Pembrolizumab-Treated Non-Small Cell Lung Carcinoma

Pembrolizumab, a widely used immune checkpoint inhibitor (ICI), has revolutionized the treatment of non-small cell lung cancer (NSCLC). Identifying unique tumor characteristics in patients likely to respond to pembrolizumab could help the clinical adjudication and development of a personalized therapeutic strategy. In this retrospective study, we reviewed the clinical data and pathological features of 84 NSCLC patients treated with pembrolizumab. We examined the correlation between the clinical and demographic characteristics and the tumor histopathologic features obtained before immunotherapy. The response to pembrolizumab therapy was evaluated via the Response Evaluation Criteria in Solid Tumors (RECIST). The clinical data and cancer tissue characteristics were assessed and compared among three groups according to the following RECIST: the responsive group (RG), the stable disease group (SD), and the progressive disease group (PD), where the RG comprised patients with either a complete response (CR) or a partial response (PR). The overall survival rate of the RG group was significantly higher than the SD and PD groups. In addition, the percentage of pre-treatment viable tumor cell content in the RG and SD groups was significantly higher. At the same time, the extracellular stroma proportion was significantly lower than that of the PD group. The number of tumor-infiltrating lymphocytes (TILs) in the RG group was significantly higher than in the PD group. There were no significant differences in tumor necrosis, the stroma composition, PD-L1 expression level (TPS 1-49% vs. ≥50%), and treatment response. In conclusion, our population of NSCLC patients who experienced positive treatment responses to pembrolizumab therapy had a better prognosis compared to patients with either SD or PD. Moreover, the relative proportions of viable tumor cells to tumor-associated lymphocytes were associated with responsiveness to treatment. It is expected that larger prospective clinical studies will further validate these findings.

Advancing Spiking Neural Networks Toward Deep Residual Learning

Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice. Residual learning and shortcuts have been evidenced as an important approach for training deep neural networks, but rarely did previous work assessed their applicability to the specifics of SNNs. In this article, we first identify that this negligence leads to impeded information flow and the accompanying degradation problem in a spiking version of vanilla ResNet. To address this issue, we propose a novel SNN-oriented residual architecture termed MS-ResNet, which establishes membrane-based shortcut pathways, and further proves that the gradient norm equality can be achieved in MS-ResNet by introducing block dynamical isometry theory, which ensures the network can be well-behaved in a depth-insensitive way. Thus, we are able to significantly extend the depth of directly trained SNNs, e.g., up to 482 layers on CIFAR-10 and 104 layers on ImageNet, without observing any slight degradation problem. To validate the effectiveness of MS-ResNet, experiments on both frame-based and neuromorphic datasets are conducted. MS-ResNet104 achieves a superior result of 76.02% accuracy on ImageNet, which is the highest to the best of our knowledge in the domain of directly trained SNNs. Great energy efficiency is also observed, with an average of only one spike per neuron needed to classify an input sample. We believe our powerful and scalable models will provide strong support for further exploration of SNNs.

A new cavefish of Sinocyclocheilus (Teleostei: Cypriniformes: Cyprinidae) from the Nanpanjiang River in Guizhou, China

A new species, Sinocyclocheilus xingyiensis, is described based on specimens collected from a karst cave in Guizhou Province, China. The authors used an integrated taxonomic approach, including morphological and molecular data, to identify the new species as a member of the Sinocyclocheilu angularis group, and it can be distinguished from all other members of this group by a combination of the following features: two pairs of long barbels and long pectoral fins, 42-46 lateral-line scales, 7 (13-14) on outer (inner) side of the first gill arch and 35 (14-15 + 4 + 16 - 17) vertebrae. Phylogenetic analyses based on the cytochrome b (cyt b) gene fragment suggest that S. xingyiensis is a sister lineage to Sinocyclocheilus flexuosdorsalis. The genetic distance (Kimura 2-parameter) between the S. xingyiensis and S. angularis groups of Sinocyclocheilus species based on cyt b gene fragment ranged from 1.2% to 15.4%.

Psychological Distress, Emergency Room Utilization, and Mortality Risk Among US Adults With History of Prostate Cancer

PURPOSE

Adults with a history of prostate cancer experience several physical and mental stressors. However, limited information is available about the prevalence of psychological distress in this population and its association with clinical outcomes in a nationally representative sample.

METHODS

We identified adults with history of prostate cancer from a nationally representative cohort (2000-2018 US National Health Interview Survey) and its linked mortality files through December 31, 2019. The six-item Kessler Psychological Distress Scale (K6) was used to assess psychological distress. The associations between psychological distress severity, emergency room (ER) usage, and mortality risk were estimated using multivariable logistic and Cox proportional hazards models, which were both adjusted for age, survey year, race/ethnicity, region, education, health insurance, comorbidities, functional limitations, and time since cancer diagnosis.

RESULTS

Among the 3,451 adults with history of prostate cancer surveyed, 96 (2.4%), 434 (11.3%), and 2,921 (86.3%) reported severe, moderate, or low/no mental distress, respectively. During the 12 months preceding the survey, 812 (22.8%) adults with history of prostate cancer visited the ER. After a median follow-up of 81 months, 937 (25.5%) deaths occurred. Compared with participants with low/no mental distress, those with severe mental distress reported the highest utilization of the ER (adjusted odds ratio [aOR], 2.57 [95% CI, 1.51 to 4.37]) and exhibited the highest all-cause mortality (adjusted hazard ratio [aHR], 1.83 [95% CI, 1.29 to 2.60]), followed by those with moderate mental distress (ER use aOR, 1.76 [95% CI, 1.29 to 2.42]; all-cause mortality aHR, 1.22 [95% CI, 0.92 to 1.62]).

CONCLUSION

Among US adults with history of prostate cancer, psychological distress was associated with increased ER use and mortality risk. Notably, severe psychological distress was correlated with the highest rates of ER visits and mortality risk. However, given the retrospective nature of this study, uncontrolled confounding variables need to be considered when interpreting the findings.

Induction PD-1 inhibitor toripalimab plus chemotherapy followed by concurrent chemoradiotherapy and consolidation toripalimab for bulky locally advanced non-small-cell lung cancer: protocol for a randomized phase II trial (InTRist study)

Background

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape for locally advanced non-small-cell lung cancer (LA-NSCLC), whereas responses to anti-programmed cell death-1 (PD-1) or anti-programmed death-ligand 1 (PD-L1) are heterogeneous. Though consolidation ICI following concurrent chemoradiotherapy (cCRT) improves survival of NSCLC, this regimen is challenging for patients with bulky tumors due to excessive target volumes and radiation-resistant hypoxia during upfront cCRT, leading to higher risk of pneumonitis and inferior local-regional control. Recent trials have demonstrated neoadjuvant ICI brought greater benefit to stage III than stage I-II NSCLC. Our previous study also supported the therapeutic advantage of 2-cycle induction ICI for patients with bulky unresectable stage III NSCLC. In the context of induction immunotherapy, radiotherapy is more likely to exert immune synergistic effects, reverse anti-PD-1 resistance, and activate abscopal immune responses. Prospective trials to determine the efficacy and safety of induction ICI for bulky LA-NSCLC are necessary.

Methods

This randomized, open-label, two-arm phase II study aims to explore whether 2 cycles of induction anti-PD-1 toripalimab plus chemotherapy can improve progression-free survival (PFS) in bulky LA-NSCLC. Bulky tumors are defined as primary lesion ≥5 cm in greatest dimension or metastatic lymph nodes ≥2 cm in shortest diameter. A total of 50 patients with bulky unresectable stage III NSCLC will be recruited and 1:1 randomized into the experimental arm: 2-cycle induction PD-1 inhibitor toripalimab plus chemotherapy followed by cCRT and consolidation toripalimab; or control arm: 2-cycle induction chemotherapy followed by cCRT and consolidation toripalimab. Patients are stratified by pathology (squamous versus non-squamous). The primary endpoint is PFS. Secondary endpoints are overall survival, overall response rate, disease control rate, duration of response, and incidence of adverse events. Exploratory analyses include PD-L1 expression and liquid biopsy-based biomarker testing, tumor microenvironment profiling at single-cell levels, and quality-of-life assessments.

Discussion

The InTRist study is the first randomized phase II trial to investigate the feasibility of induction anti-PD-1 toripalimab plus chemotherapy followed by cCRT and consolidation toripalimab in bulky LA-NSCLC, providing novel evidence for the synergistic strategy combining anti-PD-1 blockade with radiotherapy to prolong immunotherapy benefits, overcome resistance, and enhance abscopal immune response.

Clinical trial registration

ClinicalTrials.gov, identifier NCT05888402.

Real-time stealth optical transmission via fast laser frequency dithering

We report a real-time 150 kbps stealth transmission within public optical communication of 10 Gbps dual polarization QPSK. The stealth data is modulated onto the frequency tuning signals of a fast-tuning laser source in the transmitter, which causes slight frequency dithering for the transmitted optical signal. In the receiver, the stealth receiver recovers the stealth data from the estimated frequency offset by the QPSK DSP algorithm. The experiments show the stealth transmission has few impacts on the public channel over a 300 km distance. The proposed method is fully compatible with existing optical transmission systems, and the only hardware change is to upgrade the transmitter laser to support frequency tuning through an external analog port for receiving stealth signal. The proposed stealth scheme can combine with cryptographic protocols to improve the integrated security of the system, and can be used as signaling transport for low level network control to reduce the communication overhead.

Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics

It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.

Sequential Immunizations with Influenza Neuraminidase Protein Followed by Peptide Nanoclusters Induce Heterologous Protection

Enhancing cross-protections against diverse influenza viruses is desired for influenza vaccinations. Neuraminidase (NA)-specific antibody responses have been found to independently correlate with a broader influenza protection spectrum. Here, we report a sequential immunization regimen that includes priming with NA protein followed by boosting with peptide nanoclusters, with which targeted enhancement of antibody responses in BALB/c mice to certain cross-protective B-cell epitopes of NA was achieved. The nanoclusters were fabricated via desolvation with absolute ethanol and were only composed of composite peptides. Unlike KLH conjugates, peptide nanoclusters would not induce influenza-unrelated immunity. We found that the incorporation of a hemagglutinin peptide of H2-d class II restriction into the composite peptides could be beneficial in enhancing the NA peptide-specific antibody response. Of note, boosters with N2 peptide nanoclusters induced stronger serum cross-reactivities to heterologous N2 and even heterosubtypic N7 and N9 than triple immunizations with the prototype recombinant tetrameric (rt) N2. The mouse challenge experiments with HK68 H3N2 also demonstrated the strong effectiveness of the peptide nanocluster boosters in conferring heterologous protection.

Milk fat globule membrane modulates inflammatory pathways in human monocytes: A crossover human intervention study

BACKGROUND

Intake of high-fat foods raises postprandial plasma triglycerides and inflammatory markers, which may depend on the type of fat ingested. Dairy products are commonly consumed, but not much is known about the impact of milk fat and the milk fat globule membrane on postprandial inflammation. Here, we aimed to study the effect of milk fat with and without milk fat globule membrane and a vegetable fat blend on post-prandial inflammation, with a focus on blood monocyte gene expression.

METHODS

We performed a randomized, double-blind cross-over trial in 37 middle-aged healthy male and female volunteers (BMI 22-27 kg/m2). The participants consumed a meal shake containing 95.5 g of fat consisting of either a vegetable fat blend (VEGE), anhydrous milk fat (AMF, without milk fat globule membrane), or cream (CREAM, containing milk fat globule membrane). Blood monocytes were collected at 0 h and 6 h postprandially and used for bulk RNA sequencing and ex vivo stimulation with LPS.

RESULTS

Consumption of all three shakes significantly decreased the percentage of classical monocytes and increased the percentages of intermediate monocytes and non-classical monocytes. No differences in these measures were observed between shakes. Using a threshold of p < 0.01, 787 genes were differentially regulated postprandially between the three shakes. 89 genes were differentially regulated postprandially between AMF and VEGE, 373 genes between AMF and CREAM, and 667 genes between VEGE and CREAM, indicating that the effect of CREAM on monocyte gene expression was distinct from AMF and VEGE. Pathway analyses showed that VEGE significantly increased the expression of genes involved in inflammatory pathways, whereas this was less pronounced after AMF and not observed after CREAM. In addition, CREAM significantly down-regulated the expression of genes involved in energy metabolism-related pathways, such as glycolysis, TCA cycle, and oxidative phosphorylation, as well as HIF-1 signaling.

CONCLUSION

Compared to the consumption of an anhydrous milk fat without milk fat globule membrane and a vegetable fat blend, the consumption of cream with milk fat globule membrane downregulated inflammatory pathways in blood monocytes, thus suggesting a potential inflammation inhibitory effect of milk fat globule membrane.

Analyzing RNA posttranscriptional modifications to decipher the epitranscriptomic code

The discovery of RNA silencing has revealed that non-protein-coding sequences (ncRNAs) can cover essential roles in regulatory networks and their malfunction may result in severe consequences on human health. These findings have prompted a general reassessment of the significance of RNA as a key player in cellular processes. This reassessment, however, will not be complete without a greater understanding of the distribution and function of the over 170 variants of the canonical ribonucleotides, which contribute to the breathtaking structural diversity of natural RNA. This review surveys the analytical approaches employed for the identification, characterization, and detection of RNA posttranscriptional modifications (rPTMs). The merits of analyzing individual units after exhaustive hydrolysis of the initial biopolymer are outlined together with those of identifying their position in the sequence of parent strands. Approaches based on next generation sequencing and mass spectrometry technologies are covered in depth to provide a comprehensive view of their respective merits. Deciphering the epitranscriptomic code will require not only mapping the location of rPTMs in the various classes of RNAs, but also assessing the variations of expression levels under different experimental conditions. The fact that no individual platform is currently capable of meeting all such demands implies that it will be essential to capitalize on complementary approaches to obtain the desired information. For this reason, the review strived to cover the broadest possible range of techniques to provide readers with the fundamental elements necessary to make informed choices and design the most effective possible strategy to accomplish the task at hand.

Deep learning for the early identification of periodontitis: a retrospective, multicentre study

AIM

To develop a deep-learning model to help general dental practitioners diagnose periodontitis accurately and at an early stage.

MATERIALS AND METHODS

First, the panoramic radiographs (PARs) from the Second Affiliated Hospital of Nanchang University were input into the convolutional neural network (CNN) architecture to establish the PAR-CNN model for healthy controls and periodontitis patients. Then, the PARs from the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine were included in the second testing set to validate the effectiveness of the model with data from two centres. Heat maps were produced using a gradient-weighted class activation mapping method to visualise the regions of interest of the model. The accuracy and time required to read the PARs were compared between the model, periodontal experts, and general dental practitioners. Areas under the receiver operating characteristic curve (AUCs) were used to evaluate the performance of the model.

RESULTS

The AUC of the PAR-CNN model was 0.843, and the AUC of the second test set was 0.793. The heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. The accuracy of the model, periodontal experts, and general dental practitioners was 0.800, 0.813, and 0.693, respectively. The time required to read each PAR by periodontal experts (6.042 ± 1.148 seconds) and general dental practitioners (13.105 ± 3.153 seconds), which was significantly longer than the time required by the model (0.027 ± 0.002 seconds).

CONCLUSION

The ability of the CNN model to diagnose periodontitis approached the level of periodontal experts. Deep-learning methods can assist general dental practitioners to diagnose periodontitis quickly and accurately.

C:N:P stoichiometry of plants, soils, and microorganisms: Response to altered precipitation

Precipitation changes modify C, N, and P cycles, which regulate the functions and structure of terrestrial ecosystems. Although altered precipitation affects above- and belowground C:N:P stoichiometry, considerable uncertainties remain regarding plant-microbial nutrient allocation strategies under increased (IPPT) and decreased (DPPT) precipitation. We meta-analyzed 827 observations from 235 field studies to investigate the effects of IPPT and DPPT on the C:N:P stoichiometry of plants, soils, and microorganisms. DPPT reduced leaf C:N ratio, but increased the leaf and root N:P ratios reflecting stronger decrease of P compared with N mobility in soil under drought. IPPT increased microbial biomass C (+13%), N (+15%), P (26%), and the C:N ratio, whereas DPPT decreased microbial biomass N (-12%) and the N:P ratio. The C:N and N:P ratios of plant leaves were more sensitive to medium DPPT than to IPPT because drought increased plant N content, particularly in humid areas. The responses of plant and soil C:N:P stoichiometry to altered precipitation did not fit the double asymmetry model with a positive asymmetry under IPPT and a negative asymmetry under extreme DPPT. Soil microorganisms were more sensitive to IPPT than to DPPT, but they were more sensitive to extreme DPPT than extreme IPPT, consistent with the double asymmetry model. Soil microorganisms maintained stoichiometric homeostasis, whereas N:P ratios of plants follow that of the soils under altered precipitation. In conclusion, specific N allocation strategies of plants and microbial communities as well as N and P availability in soil critically mediate C:N:P stoichiometry by altered precipitation that need to be considered by prediction of ecosystem functions and C cycling under future climate change scenarios.

Comparative Study on the Efficacy of Two Perioperative Chemotherapy Regimens for Lumbar Brucellosis

Objective

The clinical efficacy of perioperative chemotherapy regimen (rifampicin, doxycycline, levofloxacin, ceftriaxone) was evaluated for lumbar brucellosis spondylitis patients with neurological injury.

Methods

In Beijing Ditan Hospital affiliated with Capital Medical University, 32 patients with lumbar brucellosis spondylitis underwent surgery and triple perioperative chemotherapy (rifampicin, doxycycline, levofloxacin) between 2011 and 2021 due to neurological injury, and 34 patients matched up with the triple group underwent rifampicin, doxycycline, levofloxacin, and ceftriaxone. Both groups were compared in terms of changes in inflammation index, low back/leg pain, lumbar function, neurological function, and adverse drug reactions.

Results

There was no significant difference in erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), low back pain visual analogue scale (VAS), leg pain VAS, lumbar Oswestry disability index (ODI) and nerve function injury rate between the two groups before chemotherapy (P>0.05). The ESR, CRP at 1 week and 2 weeks after chemotherapy and 1 week, 2 weeks, 1 month postoperatively in the quadruple group were significantly lower than those in the triple group, which is the same to ESR 3 months postoperatively (P<0.05). The low back pain VAS, leg pain VAS and lumbar ODI in the quadruple group were significantly lower than those in the triple group at 1 month and 3 months postoperatively (P<0.05). The recovery rate of neurological function in the quadruple group was significantly higher than that in the triple group at 3 and 6 months postoperatively (P<0.05). Both groups did not experience significantly different perioperative and postoperative adverse reactions (P>0.05).

Conclusion

For lumbar brucellosis spondylitis with neurological injury, quadruple perioperative chemotherapy of rifampicin, doxycycline, levofloxacin and ceftriaxone can significantly reduce perioperative inflammation, and improve low back/leg pain, as well as promoting neurological function recovery in the short term.

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation

Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.

Efficacy and safety of definitive chemoradiotherapy with or without induction immune checkpoint inhibitors in patients with stage III non-small cell lung cancer

Background

In the era of immunotherapy, the optimal combination of immune checkpoint inhibitors (ICIs) and chemoradiotherapy (CRT) for stage III non-small cell lung cancer (NSCLC) is not defined. The current study investigated the efficacy and safety of definitive CRT(dCRT) plus consolidation ICIs with or without induction ICIs in stage III NSCLC.

Methods

123 consecutive patients treated with dCRT followed by consolidation ICIs at our institution from 2018 to 2022 were retrospectively reviewed. Failure patterns, survival outcomes, and toxicity profiles were analyzed.

Results

The 1- and 2- year PFS rates were 75.3% and 56.9%, respectively, and median PFS was 30.83 months from the start of treatment. In-field failure (18.7%) was the most common failure pattern. The most common adverse event (AE) was pneumonitis caused by ICIs or RT. The incidence of Grade 3-4 and Grade 5 pneumonitis was 5.7% and 1.6%, respectively. Further analysis showed that the induction plus consolidation ICIs group has significantly lower cumulative incidence of distant metastasis rates (HR: 0.30, 95%CI: 0.09-1.00, p=0.043) and higher incidence of pneumonitis (p=0.039) compared with patients in the consolidation ICIs group.

Conclusions

Combined CRT and consolidation ICIs achieved encouraging efficacy and manageable toxicity in patients with stage III NSCLC in China. Induction plus consolidation ICIs might reduce distant metastasis and deserve further investigation.

Copy number variants landscape of multiple cancers and clinical applications based on NGS gene panel

BACKGROUND

The rapid adoption of next-generation sequencing in clinical oncology has enabled detection of molecular biomarkers which are shared between multiple tumour types. Intra-tumour heterogeneity is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the tumour-related copy number variants (CNVs), as key regulators of cancer origination, development, and progression, across various types of cancers are poorly understood.

METHODS

We performed pan-cancer CNV analysis of cancer-related genes in 15 types of cancers including 1438 cancerous patients by next-generation sequencing using a commercially available pan-cancer panel (Onco PanScan™). Downstream bioinformatics analysis was performed in order to detect CNVs, cluster analysis of the found CNVs, and comparison of the frequency of gained CNVs between different types of cancers. LASSO analysis was used for identification of the most important CNVs.

RESULTS

We also identified 523 CNVs among which 16 CNVs were common while 22 CNVs were caner-specific CNVs. Meanwhile, FAM58A was most commonly found in all studied cancers in this study and significant differences were found in FAM58A between female and male patients (p = .001). Common CNVs, such as FOXA1, NFKBIA, HEY1, MECOM, CHD7, AGO2, were mutated in 6.79%, 8.45%, 7.51%, 6.43%, 7.59%, 8.16% of tumours, while most of these mutations have proven roles in positive regulation of transcription from RNA polymerase II promoter. 11 features including sex, DIS3, EPHB1, ERBB2, FLT1, HCK, KEAP1, MYD88, PARP3, TBX3, and TOP2A were found as the key features for classification of cancers using CNVs.

CONCLUSION

The 16 common CNVs between cancers can be used to identify the target of pan-cancer drug design and targeted therapies. Additionally, 22 caner-specific CNVs can be used as unique diagnostic markers for each cancer type.

Leaf trait responses to global change factors in terrestrial ecosystems

Global change influences plant growth by affecting plant morphology and physiology. However, the effects of global change factors vary based on the climate gradient. Here, we established a global database of leaf traits from 192 experiments on elevated CO2 concentrations (eCO2), drought, N deposition, and warming. The results showed that the leaf mass per area (LMA) significantly increased under eCO2 and drought conditions but decreased with N deposition, whereas eCO2 levels and drought conditions reduced stomatal conductance and increased and decreased photosynthetic rates, respectively. Leaf dark respiration (Rd) increased in response to global change, excluding N deposition. Leaf N concentrations declined with eCO2 but increased with N deposition. Leaf area increased with eCO2, N deposition, and warming but decreased with drought. Leaf thickness increased with eCO2 but decreased with warming. eCO2 and N deposition enhanced plant water-use efficiency (WUE), eCO2 and warming increased photosynthetic N-use efficiency (PNUE), while N fertilization reduced PNUE significantly. eCO2 produced a positive relationship between WUE and PNUE, which were limited under drought but increased in areas with high humidity and high temperature. Trade-offs were observed between WUE and PNUE under drought, N deposition, and warming. These findings suggest that the effects of global change factors on plants can be altered by complex environmental changes; moreover, diverse plant water and nutrient strategy responses can be interpreted against the background of their functional traits.

Prediction of lncRNA functions using deep neural networks based on multiple networks

BACKGROUND

More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data.

RESULTS

In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage.

CONCLUSIONS

DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO.

AntiViralDL: Computational Antiviral Drug Repurposing Using Graph Neural Network and Self-Supervised Learning

Viral infections have emerged as significant public health concerns for decades. Antiviral drugs, specifically designed to combat these infections, have the potential to reduce the disease burden substantially. However, traditional drug development methods, based on biological experiments, are resource-intensive, time-consuming, and low efficiency. Therefore, computational approaches for identifying antiviral drugs can enhance drug development efficiency. In this study, we introduce AntiViralDL, a computational framework for predicting virus-drug associations using self-supervised learning. Initially, we construct a reliable virus-drug association dataset by integrating the existing Drugvirus2 database and FDA-approved virus-drug associations. Utilizing these two datasets, we create a virus-drug association bipartite graph and employ the Light Graph Convolutional Network (LightGCN) to learn embedding representations of viruses and drugs. To address the sparsity of virus-drug association pairs, AntiViralDL incorporates contrastive learning to improve prediction accuracy. We implement data augmentation by adding random noise to the embedding representation space of virus and drug nodes, as opposed to traditional edge and node dropout. Finally, we calculate an inner product to predict virus-drug association relationships. Experimental results reveal that AntiViralDL achieves AUC and AUPR values of 0.8450 and 0.8494, respectively, outperforming four benchmarked virus-drug association prediction models. The case study further highlights the efficacy of AntiViralDL in predicting anti-COVID-19 drug candidates.

A tryptophan metabolite made by a gut microbiome eukaryote induces pro-inflammatory T cells

The large intestine harbors microorganisms playing unique roles in host physiology. The beneficial or detrimental outcome of host-microbiome coexistence depends largely on the balance between regulators and responder intestinal CD4+ T cells. We found that ulcerative colitis-like changes in the large intestine after infection with the protist Blastocystis ST7 in a mouse model are associated with reduction of anti-inflammatory Treg cells and simultaneous expansion of pro-inflammatory Th17 responders. These alterations in CD4+ T cells depended on the tryptophan metabolite indole-3-acetaldehyde (I3AA) produced by this single-cell eukaryote. I3AA reduced the Treg subset in vivo and iTreg development in vitro by modifying their sensing of TGFβ, concomitantly affecting recognition of self-flora antigens by conventional CD4+ T cells. Parasite-derived I3AA also induces over-exuberant TCR signaling, manifested by increased CD69 expression and downregulation of co-inhibitor PD-1. We have thus identified a new mechanism dictating CD4+ fate decisions. The findings thus shine a new light on the ability of the protist microbiome and tryptophan metabolites, derived from them or other sources, to modulate the adaptive immune compartment, particularly in the context of gut inflammatory disorders.

PacBio Full-Length Transcriptome of a Tetraploid Sinocyclocheilus multipunctatus Provides Insights into the Evolution of Cavefish

Sinocyclocheilus multipunctatus is a second-class nationally protected wild animal in China. As one of the cavefish, S. multipunctatus has strong adaptability to harsh subterranean environments. In this study, we used PacBio SMRT sequencing technology to generate a first representative full-length transcriptome for S. multipunctatus. Sequence clustering analysis obtained 232,126 full-length transcripts. Among all transcripts, 40,487 were annotated in public databases, while 70,300 microsatellites, 2384 transcription factors, and 16,321 long non-coding RNAs were identified. The phylogenetic tree showed that S. multipunctatus shows a closer relationship to Carassius auratus and Cyprinus carpio, phylogenetically diverging from the common ancestor ~14.74 million years ago (Mya). We also found that between 15.6 and 17.5 Mya, S. multipunctatus also experienced an additional whole-genome duplication (WGD) event, which may have promoted the species evolution of S. multipunctatus. Meanwhile, the overall rates of evolutionary of polyploid S. multipunctatus were significantly higher than those of the other cyprinids, and 220 positively selected genes (PSGs) were identified in two sub-genomes of S. multipunctatus. These PSGs are likely to fulfill critical roles in the process of adapting to diverse cave environments. This study has the potential to facilitate future investigations into the genomic characteristics of S. multipunctatus and provide valuable insights into revealing the evolutionary history of polyploid S. multipunctatus.

Triacylglycerol uptake and handling by macrophages: From fatty acids to lipoproteins

Macrophages are essential innate immune cells and form our first line of immune defense. Also known as professional phagocytes, macrophages interact and take up various particles, including lipids. Defective lipid handling can drive excessive lipid accumulation leading to foam cell formation, a key feature of various cardiometabolic conditions such as atherosclerosis, non-alcoholic fatty liver disease, and obesity. At the same time, intracellular lipid storage and foam cell formation can also be viewed as a protective and anti-lipotoxic mechanism against a lipid-rich environment and associated elevated lipid uptake. Traditionally, foam cell formation has primarily been linked to cholesterol uptake via native and modified low-density lipoproteins. However, other lipids, including non-esterified fatty acids and triacylglycerol (TAG)-rich lipoproteins (very low-density lipoproteins and chylomicrons), can also interact with macrophages. Recent studies have identified multiple pathways mediating TAG uptake and processing by macrophages, including endocytosis and receptor/transporter-mediated internalization and transport. This review will present the current knowledge of how macrophages take up different lipids and lipoprotein particles and address how TAG-rich lipoproteins are processed intracellularly. Understanding how macrophages take up and process different lipid species such as TAG is necessary to design future therapeutic interventions to correct excessive lipid accumulation and associated co-morbidities.

Investigation on Pb2+ adsorption characteristics by AAEMs-rich biochar in aqueous solution: Performance and mechanism

Biochar derived from soybean straw with AAEMs (alkali and alkaline earth metals) enrichment could efficiently remove heavy metals from contaminated water. In this study, the influences of pyrolysis temperature on the physicochemical property and adsorption performance of soybean straw biochar were investigated. The contributions of different adsorption mechanisms were analyzed quantitatively. The results show that the soybean straw biochar exhibits excellent Pb2+ adsorption performance (157.2-227.2 mg g-1), with an order of BC800 > BC400 > BC600 > BC700 > BC500. The mechanisms of metal ion exchange (37.49%-72.58%) and precipitation with minerals (22.38%-58.03%) mainly control the Pb2+ adsorption, whereas complexation with organic functional groups (OFGs) and cation-Cπ interaction make the less contribution. The order of cation exchange capacity (CEC) is BC400 > BC800 > BC700 > BC600 > BC500, showing a high correlation (0.965) with the contribution of metal ion exchange with AAEMs. Moreover, Ca exhibits the strongest exchange capacity. The contribution of precipitation is consistent with the variation of soluble CO32- content in biochar. These results suggest that soybean straw biochar rich in AAEMs is a prospective adsorbent for Pb2+ elimination.

HGNNLDA: Predicting lncRNA-Drug Sensitivity Associations via a Dual Channel Hypergraph Neural Network

Drug sensitivity is critical for enabling personalized treatment. Many studies have shown that long non-coding RNAs (lncRNAs) are closely related to drug sensitivity because lncRNAs can regulate genes related to drug sensitivity to affect drug efficacy. Exploring lncRNA-drug sensitivity associations has important implications for drug development and disease treatment. However, identifying lncRNA-drug sensitivity associations based on traditional biological approaches is small-scale and time-consuming. In this work, we develop a dual-channel hypergraph neural network-based method named HGNNLDA to infer unknown lncRNA-drug sensitivity associations. To our best knowledge, HGNNLDA is the first computational framework to predict lncRNA-drug sensitivity associations. HGNNLDA applies the hypergraph neural network to obtain high-order neighbor information on the lncRNA hypergraph and the drug hypergraph, respectively, and utilizes a joint update mechanism to generate lncRNA embeddings and drug embeddings. In traditional graphs, an edge contains only two nodes. However, hyperedges in hypergraphs can contain any number of nodes and hypergraphs can well describe the higher-order connectivity of the lncRNA-drug bipartite graphs. The comprehensive experimental results show that HGNNLDA significantly outperforms the other six state-of-the-art models. Case studies on two drugs further illustrate that HGNNLDA is an effective tool to predict lncRNA-drug sensitivity associations.

Occlusal Force Maintains Alveolar Bone Homeostasis via Type H Angiogenesis

Physiologically, teeth and periodontal tissues are exposed to occlusal forces throughout their lifetime. Following occlusal unloading, unbalanced bone remodeling manifests as a net alveolar bone (AB) loss. This phenomenon is termed alveolar bone disuse osteoporosis (ABDO), the underlying mechanism of which remains unclear. Type H vessels, a novel capillary subtype tightly coupled with osteogenesis, reportedly have a role in skeletal remodeling; however, their role in ABDO is not well studied. In the present study, we aimed to explore the pathogenesis of and therapies for ABDO. The study revealed that type H endothelium highly positive for CD31 and endomucin was identified in the periodontal ligament (PDL) but rarely in the AB of the mice. In hypofunctional PDL, the density of type H vasculature and coupled osterix+ (OSX+) osteoprogenitors declined significantly. In addition, the angiogenic factor Slit guidance ligand 3 (SLIT3) was downregulated in the disused PDL, and periodontal injection of the recombinant SLIT3 protein partially ameliorated type H vessel dysfunction and AB loss in ABDO mice. With regard to the molecular mechanism, a mechanosensory signaling circuit, PIEZO1/Ca2+/HIF-1α/SLIT3, was validated by applying cyclic compression to 3-dimensional-cultured PDL cells using the Flexcell FX-5000 compression system. In summary, PDL plays a pivotal role in mechanotransduction by translating physical forces into the intracellular signaling axis PIEZO1/Ca2+/HIF-1α/SLIT3, which promotes type H angiogenesis and OSX+ cell-related osteogenensis, thereby contributing to AB homeostasis. Our findings advance the understanding of PDL in AB disorders. Further therapies targeting SLIT3 may provide new insights into preventing bone loss in ABDO.

Prediction of circRNA-MiRNA Association Using Singular Value Decomposition and Graph Neural Networks

A large number of experimental studies have shown that circRNAs can act as molecular sponges of microRNAs, interacting with miRNAs to regulate gene expression levels, thereby affecting the development of human diseases. Exploring the potential associations between circRNAs and miRNAs can help understand complex disease mechanisms. Considering that biological experiments are time-consuming and labor-intensive, this study proposes a computational model using a graph neural network and singular value decomposition (CMASG) for circRNA-miRNA association prediction. Specifically, graph neural networks are used to learn nonlinear feature representations of nodes, followed by matrix factorization algorithms to learn linear feature representations of nodes, and then combined feature representations learned from different perspectives. Finally, the lightGBM algorithm was used for circRNA-miRNA association prediction. The proposed CMASG model achieved an AUC value of 0.8804. The experimental results demonstrate the superiority and effectiveness of the CMASG model in predicting circRNA-miRNA association tasks.