DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins.

Allergies have become an emerging public health problem worldwide. The most effective way to prevent allergies is to find the causative allergen at the source and avoid re-exposure. However, most of the current computational methods used to identify allergens were based on homology or conventional machine learning methods, which were inefficient and still had room to be improved for the detection of allergens with low homology. In addition, few methods based on deep learning were reported, although deep learning has been successfully applied to several tasks in protein sequence analysis. In the present work, a deep neural network-based model, called DeepAlgPro, was proposed to identify allergens. We showed its great accuracy and applicability to large-scale forecasts by comparing it to other available tools. Additionally, we used ablation experiments to demonstrate the critical importance of the convolutional module in our model. Moreover, further analyses showed that epitope features contributed to model decision-making, thus improving the model's interpretability. Finally, we found that DeepAlgPro was capable of detecting potential new allergens. Overall, DeepAlgPro can serve as powerful software for identifying allergens.

Impacts of Fuel Stage Ratio on the Morphological and Nanostructural Characteristics of Soot Emissions from a Twin Annular Premixing Swirler Combustor.

Soot particles emitted from aircraft engines constitute a major anthropogenic source of pollution in the vicinity of airports and at cruising altitudes. This emission poses a significant threat to human health and may alter the global climate. Understanding the characteristics of soot particles, particularly those generated from Twin Annular Premixing Swirler (TAPS) combustors, a mainstream combustor in civil aviation engines, is crucial for aviation environmental protection. In this study, a comprehensive characterization of soot particles emitted from TAPS combustors was conducted using scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), and Raman spectroscopy. The morphology and nanostructure of soot particles were examined across three distinct fuel stage ratios (FSR), at 10%, 15%, and 20%. The SEM analysis of soot particle morphology revealed that coated particles constitute over 90% of the total particle sample, with coating content increasing proportionally to the fuel stage ratio. The results obtained from HRTEM indicated that average primary particle sizes increase with the fuel stage ratio. The results of HRTEM and Raman spectroscopy suggest that the nanostructure of soot particles becomes more ordered and graphitized with an increasing fuel stage ratio, resulting in lower oxidation activity. Specifically, soot fringe length increased with the fuel stage ratio, while soot fringe tortuosity and separation distance decreased. In addition, there is a prevalent occurrence of defects in the graphitic lattice structure of soot particles, suggesting a high degree of elemental carbon disorder.

Factors associated with glycemic control in patients with T2DM: evidence from a cross-sectional study in China.

OBJECTIVE: This study aimed to analyze the factors influencing glycemic control in patients with type 2 diabetes mellitus (T2DM). METHODS: Baseline data, encompassing basic information, lifestyle habits, and treatment of 305 T2DM patients from March 2021 to January 2023, were collected and analyzed using SPSS 26.0 software. RESULTS: Univariate and multivariate logistic regression analyses identified insulin therapy (OR = 2.233; 95%Cl = 1.013-4.520; P = 0.026) and regular clinic visits (OR = 0.567; 95%Cl = 0.330-0.973; P = 0.040) as independent factors influencing glycemic control. No observed interactions between the two variables were noted. CONCLUSION: History of insulin therapy and regular clinic visits were significantly and independently associated with glycated hemoglobin control in T2DM patients. Tailored interventions based on individual circumstances are recommended to optimize glycemic control.

Homogeneous water vapor condensation with a deep neural network potential model.

Molecular-level nucleation has not been clearly understood due to the complexity of multi-body potentials and the stochastic, rare nature of the process. This work utilizes molecular dynamics (MD) simulations, incorporating a first-principles-based deep neural network (DNN) potential model, to investigate homogeneous water vapor condensation. The nucleation rates and critical nucleus sizes predicted by the DNN model are compared against commonly used semi-empirical models, namely extended simple point charge (SPC/E), TIP4P, and OPC, in addition to classical nucleation theory (CNT). The nucleation rates from the DNN model are comparable with those from the OPC model yet surpass the rates from the SPC/E and TIP4P models, a discrepancy that could mainly arise from the overestimated bulk free energy by SPC/E and TIP4P. The surface free energy predicted by CNT is lower than that in MD simulations, while its bulk free energy is higher than that in MD simulations, irrespective of the potential model used. Further analysis of cluster properties with the DNN model unveils pronounced variations of O-H bond length and H-O-H bond angle, along with averaged bond lengths and angles that are enlarged during embryonic cluster formation. Properties such as cluster surface free energy and liquid-to-vapor density transition profiles exhibit significant deviations from CNT assumptions.

An Identification Method for Road Hypnosis Based on Human EEG Data.

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers' life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

A Recognition Method for Road Hypnosis Based on Physiological Characteristics.

Road hypnosis is a state which is easy to appear frequently in monotonous scenes and has a great influence on traffic safety. The effective detection for road hypnosis can improve the intelligent vehicle. In this paper, the simulated experiment and vehicle experiment are designed and carried out to obtain the physiological characteristics data of road hypnosis. A road hypnosis recognition model based on physiological characteristics is proposed. Higher-order spectra are used to preprocess the electrocardiogram (ECG) and electromyography (EMG) data, which can be further fused by principal component analysis (PCA). The Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbor (KNN) models are constructed to identify road hypnosis. The proposed model has good identification performance on road hypnosis. It provides more alternative methods and technical support for real-time and accurate identification of road hypnosis. It is of great significance to improve the intelligence and active safety of intelligent vehicles.

Research on Recognition of Road Hypnosis in the Typical Monotonous Scene.

Road traffic safety can be influenced by road hypnosis. Accurate detection of the driver's road hypnosis is a very important function urgently required in the driver assistance system. Road hypnosis recurs frequently in a certain period, and it tends to occur in a typical monotonous scene such as a tunnel or a highway. Taking the scene of a tunnel or a highway as a typical example, road hypnosis was studied through simulated driving experiments and vehicle driving experiments. A road hypnosis recognition model based on principal component analysis (PCA) and a long short-term memory network (LSTM) was proposed, where PCA was used to extract various parameters collected by the eye tracker, and the LSTM model was constructed to identify road hypnosis. The accuracy rates of 93.27% and 97.01% in simulated driving experiments and vehicle driving experiments were obtained. The proposed method was compared with k-nearest neighbor (KNN) and random forest (RF). The results showed that the proposed PCA-LSTM model had better performance. This paper provides a novel and convenient method to realize the driver's road hypnosis detection function of the intelligent driver assistance system in practical applications.

Insights into the Profile of the Human Expiratory Microbiota and Its Associations with Indoor Microbiotas.

Microorganisms residing in the human respiratory tract can be exhaled, and they constitute a part of environmental microbiotas. However, the expiratory microbiota community and its associations with environmental microbiotas remain poorly understood. Here, expiratory bacteria and fungi and the corresponding microbiotas from the living environments were characterized by DNA amplicon sequencing of residents' exhaled breath condensate (EBC) and environmental samples collected from 14 residences in Nanjing, China. The microbiotas of EBC samples, with a substantial heterogeneity, were found to be as diverse as those of skin, floor dust, and airborne microbiotas. Model fitting results demonstrated the role of stochastic processes in the assembly of the expiratory microbiota. Using a fast expectation-maximization algorithm, microbial community analysis revealed that expiratory microbiotas were differentially associated with other types of microbiotas in a type-dependent and residence-specific manner. Importantly, the expiratory bacteria showed a composition similarity with airborne bacteria in the bathroom and kitchen environments with an average of 12.60%, while the expiratory fungi showed a 53.99% composition similarity with the floor dust fungi. These differential patterns indicate different relationships between expiratory microbiotas and the airborne microbiotas and floor dust microbiotas. The results here illustrated for the first time the associations between expiratory microbiotas and indoor microbiotas, showing a potential microbial exchange between the respiratory tract and indoor environment. Thus, improved hygiene and ventilation practices can be implemented to optimize the indoor microbial exposome, especially in indoor bathrooms and kitchens.

A Real-Time Recognition System of Driving Propensity Based on AutoNavi Navigation Data.

Driving propensity is the driver's attitude towards the actual traffic situation and the corresponding decision-making or behavior during the driving process. It is of great significance to improve the accuracy of safety early warning and reduce traffic accidents. In this paper, a real-time identification system of driving propensity based on AutoNavi navigation data is proposed. The main work includes: (1) A dynamic data acquisition method of AutoNavi navigation is proposed to obtain the time, speed and acceleration of the driver during the navigation process. (2) The dynamic data collection method of AutoNavi navigation is analyzed and verified through the dynamic data obtained in the real vehicle experiment. The principal component analysis method is used to process the experimental data to extract the driving propensity characteristics variables. (3) The fruit fly optimization algorithm combined with GRNN (generalized neural network) and the feature variable set are used to build a FOA-GRNN-based model. The results show that the overall accuracy of the model can reach 94.17%. (4) A driving propensity identification system is constructed. The system has been verified through real vehicle test experiments. This paper provides a novel and convenient method for building personalized intelligent driver assistance systems in practical applications.

DRB2 Modulates Leaf Rolling by Regulating Accumulation of MicroRNAs Related to Leaf Development in Rice.

As an important agronomic trait in rice (Oryza sativa), moderate leaf rolling helps to maintain the erectness of leaves and minimize shadowing between leaves, leading to improved photosynthetic efficiency and grain yield. However, the molecular mechanisms underlying rice leaf rolling still need to be elucidated. Here, we isolated a rice mutant, rl89, showing adaxially rolled leaf phenotype due to decreased number and size of bulliform cells. We confirmed that the rl89 phenotypes were caused by a single nucleotide substitution in OsDRB2 (LOC_Os10g33970) gene encoding DOUBLE-STRANDED RNA-BINDING2. This gene was constitutively expressed, and its encoded protein was localized to both nucleus and cytoplasm. Yeast two-hybrid assay showed that OsDRB2 could interact with DICER-LIKE1 (DCL1) and OsDRB1-2 respectively. qRT-PCR analysis of 29 related genes suggested that defects of the OsDRB2-miR166-OsHBs pathway could play an important role in formation of the rolled leaf phenotype of rl89, in which OsDRB2 mutation reduced miR166 accumulation, resulting in elevated expressions of the class III homeodomain-leucine zipper genes (such as OsHB1, 3 and 5) involved in leaf polarity and/or morphology development. Moreover, OsDRB2 mutation also reduced accumulation of miR160, miR319, miR390, and miR396, which could cause the abnormal leaf development in rl89 by regulating expressions of their target genes related to leaf development.

Protective effects of natural compounds against oxidative stress in ischemic diseases and cancers via activating the Nrf2 signaling pathway: A mini review.

Oxidative stress, an imbalance between reactive oxygen species and antioxidants, has been seen in the pathological states of many disorders such as ischemic diseases and cancers. Many natural compounds (NCs) have long been recognized to ameliorate oxidative stress due to their inherent antioxidant activities. The modulation of oxidative stress by NCs via activating the Nrf2 signaling pathway is summarized in the review. Three NCs, ursolic acid, betulinic acid, and curcumin, and the mechanisms of their cytoprotective effects are investigated in myocardial ischemia, cerebral ischemia, skin cancer, and prostate cancer. To promote the therapeutic performance of NCs with poor water solubility, the formulation approach, such as the nano drug delivery system, is elaborated as well in this review.

Impact of climate change on alpine plant community in Qilian Mountains of China.

There is growing evidence that mountains are experiencing some of the highest rates of climate warming, but assessment of the ecological impacts of climate change is often limited due to a lack of long-term monitoring data for comparative study in many ecosystems. In this study, we present an empirical work for assessing ecological responses with botanical legacy data in the Qilian Mountains of China. Plot-scale and transect-wide survey was conducted for alpine shrub communities along an elevational gradient 20 years ago. Recently, we resampled the permanent plots to investigate how the community changes may be linked to climatic variability. We found no significant temporal shifts in species richness; but the community structure underwent substantial changes, as indicated by visible shifts in the relative density of dominant shrub species and the frequency of occurrence of understory herbaceous species. This reshuffling of plant community composition reflected a series of complex responses to climate change. Specifically, wet-demanding species have become more frequent due to the recently enhanced precipitation, while the replacement of some low-statured plants with different requirements for light was indirectly regulated by climate warming via reshaping the altitudinal patterns of dominant shrubs. Climate-mediated shifts in shrub species distribution altered the expected evolutional trajectory of alpine community, which increased the complexity and nonlinearity of the responses of the communities at different altitudes to climatic variability. Our results suggested that in-depth knowledge of indirect effects can facilitate to lessen the uncertainty in predicting future community dynamics in a changing climate.

[Nucleosides-based identification model for Fritillariae Cirrhosae Bulbus].

A high performance liquid chromatography( HPLC) method was established for the fast,and precise determination of ten nucleosides in Fritillariae Cirrhosae Bulbus and its counterfeits. Then multivariate statistical analyses,such as clustering analysis,principal component analysis( PCA),and Fisher' s linear discriminant analysis( LDA),were conducted to establish a discriminant function model for an integrated analysis. The results indicated that data acquisition time of a single sample was shortened within 16 min by the HPLC method. In the range of 5-1 000 mg·kg~(-1),the mass concentrations of all nucleosides exhibited good linear relationships with the corresponding peak areas( R2> 0. 999). The spiked recoveries were in the range of 93. 83%-108. 9% with RSDs of0. 12%-1. 3%( n = 5). The limit of quantitation( LOQ) was 0. 98-4. 13 mg·kg~(-1). As revealed by the clustering analysis,Fritillariae Cirrhosae Bulbus and the counterfeits could be discriminated into two clusters based on the content of nucleosides. Fisher's LDA could achieve this discrimination,while PCA dimension reduction failed. The accuracy of the discriminant function model established on the screened characteristic indicators reached 97. 5%. The present study proposed a new identification method of Fritillariae Cirrhosae Bulbus with one-dimensional indicators,which is simple,accurate,and reliable. It can provide a scientific basis for further optimizing the identification techniques for Fritillariae Cirrhosae Bulbus and inspiration for quality control strategy development of Chinese medicinal materials.

LPS1, Encoding Iron-Sulfur Subunit SDH2-1 of Succinate Dehydrogenase, Affects Leaf Senescence and Grain Yield in Rice.

The iron-sulfur subunit (SDH2) of succinate dehydrogenase plays a key role in electron transport in plant mitochondria. However, it is yet unknown whether SDH2 genes are involved in leaf senescence and yield formation. In this study, we isolated a late premature senescence mutant, lps1, in rice (Oryza sativa). The mutant leaves exhibited brown spots at late tillering stage and wilted at the late grain-filling stage and mature stage. In its premature senescence leaves, photosynthetic pigment contents and net photosynthetic rate were reduced; chloroplasts and mitochondria were degraded. Meanwhile, lps1 displayed small panicles, low seed-setting rate and dramatically reduced grain yield. Gene cloning and complementation analysis suggested that the causal gene for the mutant phenotype was OsSDH2-1 (LOC_Os08g02640), in which single nucleotide mutation resulted in an amino acid substitution in the encoded protein. OsSDH2-1 gene was expressed in all organs tested, with higher expression in leaves, root tips, ovary and anthers. OsSDH2-1 protein was targeted to mitochondria. Furthermore, reactive oxygen species (ROS), mainly H2O2, was excessively accumulated in leaves and young panicles of lps1, which could cause premature leaf senescence and affect panicle development and pollen function. Taken together, OsSDH2-1 plays a crucial role in leaf senescence and yield formation in rice.

Impacts of Alternative Fuels on Morphological and Nanostructural Characteristics of Soot Emissions from an Aviation Piston Engine.

Soot emissions from aviation piston engines (APEs) are a major source of environment pollution in airport vicinity, stratosphere, and troposphere, and their nanostructure and surface chemistry play a critical role in determining the impact on human health and environment. In this work, the morphology and nanostructure of soot emitted from an aviation piston engine burning five different fuels including blends of promising alternative jet and biofuels were investigated via high-resolution transmission electron microscopy (HRTEM) and Raman spectroscopy. The graphitic structures were observed by analyzing primary particles in the HRTEM images. Morphological analysis demonstrated that the separation distance of the graphene layers of soot particles from the kerosene-pentanol blend combustion was larger than that from kerosene-Fischer-Tropsch blend combustion, indicating that adding pentanol tended to generate particles with more loosely stacked layers and higher oxidation tendency. Raman results were in agreement with primary particle nanostructure analysis based on the HRTEM images. Furthermore, soot particles from different fuels exhibited different concentrations of amorphous carbon and structural defects.

A novel humanized anti-tumor necrosis factor-related apoptosis-inducing ligand-R2 monoclonal antibody induces apoptotic and autophagic cell death.

It is well known that the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL/TNFSF10) is specifically expressed in various tumor cells, but less or no expression in most normal tissues and cells. While TRAIL engages with its native death receptors, TRAIL receptor 1 (TRAIL-R1) or 2 (TRAIL-R2), usually elicits the tumor cell death by apoptosis. In this study, we report that a novel humanized monoclonal antibody against TRAIL-R2 (named as zaptuzumab) well remain the biological activity of the parental mouse antibody AD5-10 inducing cell death in various cancer cells, but little effect on normal cells. Zaptuzumab also markedly inhibited the tumor growth in the mouse xenograft of NCI-H460 without toxicity to the liver and kidney, and the efficacy of tumor suppression was increased significantly while it combined with cis-dichlorodiamineplatinum. Especially, 131 I-labeled zaptuzumab injected into mouse tail vein specifically targeted to the xenograft of the lung cancer cells. Confocal analysis showed that zaptuzumab bound with TRAIL-R2 on cell surface could be quickly internalized and transferred into the lysosome. Furthermore, zaptuzumab possessed a high level of antibody-dependent cytotoxicity as well as complement-dependent cytotoxicity. Study on the mechanisms of cell death induced by zaptuzumab showed that it efficiently induced both caspase-dependent apoptosis and autophagic cell death. These data suggest that the humanized anti-TRAIL-R2 monoclonal antibody or the second generation of the antibody may have an important clinical usage for cancer immunotherapy. © 2017 IUBMB Life, 69(9):735-744, 2017.

Improved pestalotiollide B production by deleting competing polyketide synthase genes in Pestalotiopsis microspora.

Pestalotiollide B, an analog of dibenzodioxocinones which are inhibitors of cholesterol ester transfer proteins, is produced by Pestalotiopsis microspora NK17. To increase the production of pestalotiollide B, we attempted to eliminate competing polyketide products by deleting the genes responsible for their biosynthesis. We successfully deleted 41 out of 48 putative polyketide synthases (PKSs) in the genome of NK17. Nine of the 41 PKS deleted strains had significant increased production of pestalotiollide B (P < 0.05). For instance, deletion of pks35, led to an increase of pestalotiollide B by 887%. We inferred that these nine PKSs possibly lead to branch pathways that compete for precursors with pestalotiollide B, or that convert the product. Deletion of some other PKS genes such as pks8 led to a significant decrease of pestalotiollide B, suggesting they are responsible for its biosynthesis. Our data demonstrated that improvement of pestalotiollide B production can be achieved by eliminating competing polyketides.

A B-type histone acetyltransferase Hat1 regulates secondary metabolism, conidiation, and cell wall integrity in the taxol-producing fungus Pestalotiopsis microspora.

In filamentous fungi, many gene clusters for the biosynthesis of secondary metabolites often stay silent under laboratory culture conditions because of the absence of communication with its natural environment. Epigenetic processes have been demonstrated to be critical in the expression of the genes or gene clusters. Here, we report the identification of a B-type histone acetyltransferase, Hat1, and demonstrate its significant roles in secondary metabolism, conidiation, and the cell wall integrity in the fungus Pestalotiopsis microspora. An hat1 deletion strain shows a dramatic decrease of SMs in this fungus, suggesting hat1 functions as a global regulator on secondary metabolism. Moreover, the mutant strain hat1Δ delays to produce conidia with significantly decreased number of conidia, while shows little effect on vegetative growth, suggesting that it plays a critical role in conidiation. The hypersensitivity of hat1Δ to Congo red demonstrates that disruption of hat1 impairs the integrity of cell wall. Overexpression of the wild-type hat1 allele enhances conidiation by boosting the number of conidia. This is the first report on the role of a B-type histone acetyltransferase in fungal secondary metabolism and cell wall integrity.

TRAIL receptor deficiency sensitizes mice to dextran sodium sulphate-induced colitis and colitis-associated carcinogenesis.

Tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) and its receptor (TRAIL-R) play important roles in immune regulation and cancer cell death. Although TRAIL has been shown to induce chemokine release in various tumour cells, the function of TRAIL-R in the development of colitis and colitis-associated carcinogenesis has not been explored. In this study, we found that TRAIL-R-deficient mice exhibited a higher incidence of colitis and colitis-associated cancer than that of wild-type (WT) mice, and TRAIL-R expression was down-regulated in WT mice that were fed dextran sulphate sodium. Chemokines, including CCL2 and CXCL1, were highly expressed in the serum and inflammatory colon tissues of TRAIL-R(-/-) mice compared with WT mice, and TRAIL-R(-/-) mice showed a marked infiltration of immune cells during colitis. Hyperactivation of Janus kinase and nuclear factor-κB in colon epithelial cells was also observed, which correlated with the severity of colonic inflammation in TRAIL-R(-/-) mice. These data suggest that TRAIL-R plays a protective role in chemical-induced colon injury and negatively regulates mucosal immune responses.

Transcript suppression of TaGW2 increased grain width and weight in bread wheat.

Bread wheat (Triticum aestivum L.) is a major staple crop in the world. Grain weight is a major factor of grain yield in wheat, and the identification of candidate genes associated with grain weight is very important for high-yield breeding of wheat. TaGW2 is an orthologous gene of rice OsGW2 that negatively regulates the grain width and weight in rice. There are three TaGW2 homoeologs in bread wheat, TaGW2A, TaGW2B, and TaGW2D. In this study, a specific TaGW2-RNA interference (RNAi) cassette was constructed and transformed into a Chinese bread wheat variety 'Shi 4185' with small grain. The transcript levels of TaGW2A, TaGW2B, and TaGW2D were simultaneously downregulated in TaGW2-RNAi transgenic wheat lines. Compared with the controls, TaGW2-underexpressing transgenic lines displayed significantly increases in the grain width and weight, suggesting that TaGW2 negatively regulated the grain width and weight in bread wheat. Further transcript analysis showed that in different bread wheat accessions, the transcript abundance of TaGW2A was negatively associated with the grain width.