Dynamic assessment of dust hazard risk in the reconstruction of old industrial buildings: coupling effects of dust distribution and personnel trajectories.

Since the old industrial buildings bear many functions of industrial production and storage in the service stage, the dust generated by the regeneration construction is often accompanied by industrial pollutants, causing irreversible damage to the personal safety of construction workers. However, little consideration has been given to the uncertainty of dust emissions and the dynamics of construction personnel movement. Therefore, reducing the risk of dust hazards during the regeneration of old industrial buildings is imperative. This study draws on the trace intersecting theory to analyse the cause of the hazard risk associated with reconstruction dust and explore the impact of the spatiotemporal distribution characteristics of reconstruction dust and its coupling effect with construction on-site personnel activity trajectories and uses the risk matrix method to assess the dust hazard risk in the reconstruction of old industrial buildings. Finally, the renovation and reinforcement process of the first floor of a printing building in Xi'an was considered as an example for verification. The results indicate that the risk assessment model results were highly consistent with the actual situation and risk value for the entire area was 6.05, indicating a risk level of IV. Immediate measures should be implemented to reduce dust concentrations or the frequency of construction personnel activity, thereby minimising potential harm.

An Opposition-Based Learning Black Hole Algorithm for Localization of Mobile Sensor Network.

The mobile node location method can find unknown nodes in real time and capture the movement trajectory of unknown nodes in time, which has attracted more and more attention from researchers. Due to their advantages of simplicity and efficiency, intelligent optimization algorithms are receiving increasing attention. Compared with other algorithms, the black hole algorithm has fewer parameters and a simple structure, which is more suitable for node location in wireless sensor networks. To address the problems of weak merit-seeking ability and slow convergence of the black hole algorithm, this paper proposed an opposition-based learning black hole (OBH) algorithm and utilized it to improve the accuracy of the mobile wireless sensor network (MWSN) localization. To verify the performance of the proposed algorithm, this paper tests it on the CEC2013 test function set. The results indicate that among the several algorithms tested, the OBH algorithm performed the best. In this paper, several optimization algorithms are applied to the Monte Carlo localization algorithm, and the experimental results show that the OBH algorithm can achieve the best optimization effect in advance.

Health damage assessment of reconstruction dust from old industrial buildings under multi-process.

The regeneration of old industrial buildings produces considerable construction dust, thereby seriously threatening the occupational health of construction workers. The existing articles concerning the exposure and health impacts of reconstruction dust in enclosed spaces are limited, but this research field has received increasing attention. In this study, multi-process during the demolition and reinforcement stages of a reconstruction project were monitored to determine the respirable dust concentration distribution. A questionnaire survey was conducted to obtain the exposure parameters of reconstruction workers. Moreover, a health damage assessment system for the reconstruction process of old industrial buildings was established by applying the disability-adjusted life year and human capital method to explore the health damage caused by the generated dust at different stages to the construction personnel. The assessment system was applied to the reconstruction stage of an old industrial building regeneration project in Beijing to obtain dust health damage values for different work types and to conduct comparative analysis. The results indicate that there are significant differences in the dust concentration and health damage at different stages. During the demolition stage, the manual demolition of concrete structures has the highest dust concentration, reaching 0.96 mg/m3. This exceeds the acceptable concentration by 37%, and the health damage cost is 0.58 yuan per person per day. In the reinforcement stage, the dust concentration generated by mortar/concrete mixing is the highest, but the risk level is acceptable. The health damage cost of concrete grinding, which is 0.98 yuan per person per day, is the highest. Therefore, it is necessary to strengthen the protective facilities and improve the reconstruction technology to reduce dust pollution. The results of this study can help in improving the existing dust pollution control measures at construction sites to reduce the risk of dust hazards during reconstruction.

Structure-free antibody paratope similarity prediction for in silico epitope binning via protein language models.

Antibodies are an important group of biological molecules that are used as therapeutics and diagnostic tools. Although millions of antibody sequences are available, identifying their structural and functional similarity and their antigen binding sites remains a challenge at large scale. Here, we present a fast, sequence-based computational method for antibody paratope prediction based on protein language models. The paratope information is then used to measure similarity among antibodies via protein language models. Our computational method enables binning of antibody discovery hits into groups as the function of epitope engagement. We further demonstrate the utility of the method by identifying antibodies targeting highly similar epitopes of the same antigens from a large pool of antibody sequences, using two case studies: SARS CoV2 Receptor Binding Domain (RBD) and Epidermal Growth Factor Receptor (EGFR). Our approach highlights the potential in accelerating antibody discovery by enhancing hit prioritization and diversity selection.

Antibody apparent solubility prediction from sequence by transfer learning.

Developing therapeutic monoclonal antibodies (mAbs) for the subcutaneous administration requires identifying mAbs with superior solubility that are amenable for high-concentration formulation. However, experimental screening is often material and labor intensive. Here, we present a strategy (named solPredict) that employs the embeddings from pretrained protein language modeling to predict the apparent solubility of mAbs in histidine (pH 6.0) buffer. A dataset of 220 diverse, in-house mAbs were used for model training and hyperparameter tuning through 5-fold cross validation. solPredict achieves high correlation with experimental solubility on an independent test set of 40 mAbs. Importantly, solPredict performs well for both IgG1 and IgG4 subclasses despite the distinct solubility behaviors. This approach eliminates the need of 3D structure modeling of mAbs, descriptor computation, and expert-crafted input features. The minimal computational expense of solPredict enables rapid, large-scale, and high-throughput screening of mAbs using sequence information alone during early antibody discovery.

Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks.

Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm.

High-throughput profiling of antibody self-association in multiple formulation conditions by PEG stabilized self-interaction nanoparticle spectroscopy.

Affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) is an assay developed to monitor the propensity of antibody self-association, hence assessing its colloidal stability. It has been widely used by pharmaceutical companies to screen antibodies at the early discovery stages, aiming to flag potential issues with high concentration formulation. However, the original assay format is not suitable for certain formulation conditions, in particular histidine buffer. In addition, the previous data extrapolation method is suboptimal and cumbersome for processing large amounts of data (100s of molecules) in a high-throughput fashion. To address these limitations, we developed an assay workflow with two major improvements: 1) use of a stabilizing reagent to enable screening of a broader range of formulation conditions beyond phosphate-buffered saline, pH 7.4; and 2) inclusion of a novel algorithm and robust data processing schema that empowers streamlined data analysis. The optimized assay format expands the screening applicability to a wider range of formulation conditions critical for downstream development. Such capability is enhanced by a custom data management workflow for optimal data extraction, analysis, and automation. Our protocol and the R/Shiny application for analysis are publicly available and open-source to benefit the broader scientific community.

EEG and ECG Power Spectrum Analysis of Sedative Effects on Propofol-Anesthetized Rats with Electroacupuncture.

Background: In previous studies, electroacupuncture (EA) with 2/15 Hz has been shown to enhance the sedative effects in general anesthesia patients. Central lateral thalamic stimulation of 50 Hz showed an arousal effect in macaques. Therefore, it is worth studying the sedative effect of EA at peripheral acupoints with different frequencies, especially the frequency of around 50 Hz. Methods: Rats were anesthetized under the constant infusion of propofol and EA at Zusanli (ST36) and Neiguan (PC6) locations. Electroencephalography (EEG) and heart rate were continuously recorded before and after the intervention by EA in the C group (control), LEA group (low-frequency group, 2/15 Hz diffuse/dense wave EA stimulation), and HEA group (high-frequency group, 50 Hz stimulation). Results: In the LEA group, a significant increase in the power of the delta component with a decrease in the alpha component (p < 0.05) was observed after EA stimulation. In the HEA group, significant increases in the powers of alpha and beta components of EEG (p < 0.05) and a decrease in the delta component of EEG were observed (p < 0.05). The phenomenon is also shown in full-frequency waves. In addition, a significant decrease in the low-frequency/high-frequency ratio parameter was observed in the LEA group. Conclusions: EA at bilateral ST36 and PC6 can enhance the sedative effects of propofol anesthesia in low-frequency stimulation but lighten the sedative effects in high-frequency (50 Hz) stimulation. The sympathetic-vagal balance was affected due to low-frequency EA.

Development of QSAR models for in silico screening of antibody solubility.

Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R2 of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics.

The chiral coating on an achiral nanostructure by the secondary effect in focused ion beam induced deposition.

Optical metamaterials are widely used in electromagnetic wave modulation due to their sub-wavelength feature sizes. In this paper, a method to plate an achiral nanopillar array with chiral coating by the secondary effect in focused ion beam induced deposition is proposed. Guided by the pattern defined in a bitmap with variable residence time, the beam scan strategy suppresses the interaction between adjacent nanostructures. A uniform chiral coating is formed on the target nanostructure without affecting the adjacent nanostructure, under carefully selected beam parameters and the rotation angle of the sample stage. Energy dispersive x-ray spectroscopy results show that the chiral film has high purity metal, which enables the generation of localized surface plasmon resonances and causes the circular dichroism (CD) under circularly polarized light illumination. Finally, the tailorable CD spectrum of the coated array is verified by the finite difference time domain method.

Population-based Incidence of Myopericarditis After COVID-19 Vaccination in Danish Adolescents.

In this prospective nationwide multicenter study from Denmark, myopericarditis after Pfizer-BioNTech mRNA COVID-19 vaccination was identified in 13 males and 2 females between May 15 and September 15, 2021, among 133,477 vaccinated males and 127,857 vaccinated females 12-17 years of age, equaling 97 males and 16 females per million. In conclusion, the incidence of myopericarditis after COVID-19 vaccination among males appears higher than reports from the United States.

Impact of IgG subclass on molecular properties of monoclonal antibodies.

Immunoglobulin G-based monoclonal antibodies (mAbs) have become a dominant class of biotherapeutics in recent decades. Approved antibodies are mainly of the subclasses IgG1, IgG2, and IgG4, as well as their derivatives. Over the decades, the selection of IgG subclass has frequently been based on the needs of Fc gamma receptor engagement and effector functions for the desired mechanism of action, while the effect on drug product developability has been less thoroughly characterized. One of the major reasons is the lack of systematic understanding of the impact of IgG subclass on the molecular properties. Several efforts have been made recently to compare molecular property differences among these IgG subclasses, but the conclusions from these studies are sometimes obscured by the interference from variable regions. To further establish mechanistic understandings, we conducted a systematic study by grafting three independent variable regions onto human IgG1, an IgG1 variant, IgG2, and an IgG4 variant constant domains and evaluating the impact of subclass and variable regions on their molecular properties. Structural and computational analysis revealed specific molecular features that potentially account for the differential behavior of the IgG subclasses observed experimentally. Our data indicate that IgG subclass plays a significant role on molecular properties, either through direct effects or via the interplay with the variable region, the IgG1 mAbs tend to have higher solubility than either IgG2 or IgG4 mAbs in a common pH 6 buffer matrix, and solution behavior relies heavily on the charge status of the antibody at the desirable pH.

Ternary compound ontology matching for cognitive green computing.

Cognitive green computing (CGC) dedicates to study the designing, manufacturing, using and disposing of computers, servers and associated subsystems with minimal environmental damage. These solutions should provide efficient mechanisms for maximizing the efficiency of use of computing resources. Evolutionary algorithm (EA) is a well-known global search algorithm, which has been successfully used to solve various complex optimization problems. However, a run of population-based EA often requires huge memory consumption, which limited their applications in the memory-limited hardware. To overcome this drawback, in this work, we propose a compact EA (CEA) for the sake of CGC, whose compact encoding and evolving mechanism is able to significantly reduce the memory consumption. After that, we use it to address the ternary compound ontology matching problem. Six testing cases that consist of nine ontologies are used to test CEA's performance, and the experimental results show its effectiveness.

Description of the voided urinary microbiota in asymptomatic prepubertal children - A pilot study.

BACKGROUND: Recently, it has been established that the urine of a healthy adult bladder contains a microbiota and that urinary dysbiosis may be involved in the development of urinary tract diseases. The urinary microbiota and its relation to bladder health and disease in children is yet to be established. The objective of the present study was to investigate the voided urinary microbiota in asymptomatic prepubertal children. STUDY DESIGN: Thirty asymptomatic children (15 boys and 15 girls) participated in the study. Bacterial DNA in "clean-catch" midstream urine (CC MSU) samples was analysed using Illumina MiSeq sequencing of the V4 region of the bacterial 16 S rRNA gene. All children had normal bladder function as ensured by uroflowmetry, ultrasonic post-void residual, and frequency-volume charts. Bladder-related parameters and gender comparisons were analysed statistically by parametric and non-parametric tests. Alpha diversity, beta diversity, and a Venn diagram were used to analyse sequencing data. RESULTS: All CC MSU samples contained bacterial DNA. The voided urinary microbiota differed significantly between girls and boys in terms of operational taxonomic unit (OTU) richness, Shannon diversity index, and relative abundances of bacterial genera, but not for evenness. The urine of girls was dominated by Prevotella (18.2%), Porphyromonas (12.9%), Ezakiella (8.1%), Prevotella 6 (7.4%), and Dialister (7.0%). Porphyromonas (22.4%) was the most abundant genus in boys, followed by Ezakiella (12.0%), Campylobacter (11.6%), Prevotella (8.6%), and Dialister (3.7%). Girls had 10 unique core OTUs, whereas boys had no unique core OTUs. Porphyromonas appeared as a shared core OTU between genders. DISCUSSION: Contrary to previous findings, this study found significant differences in the voided urinary bacterial composition among asymptomatic prepubertal children. Moreover, the bacterial composition diverged from that found among healthy adults by other research groups. Among adults, the gender specific urinary microbiota has been hypothesised to be caused by anatomical differences in the reproductive organs and differences in sex hormone levels. This could also be evident for asymptomatic prepubertal children as sex hormone levels are different even at the prepubertal stage. The limitations of the study encompass small sample size and urine collection by CC MSU with risk of contamination from surrounding areas. CONCLUSIONS: This study documents that CC MSU samples of asymptomatic prepubertal children are not sterile. The composition of the voided urinary microbiota seems gender specific and unequal to that of healthy adults. The role of the urinary microbiota in paediatric urological disorders should be considered in future studies.

Automatic Measurement of Pennation Angle from Ultrasound Images using Resnets.

In this study, an automatic pennation angle measuring approach based on deep learning is proposed. Firstly, the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the ultrasound image. Secondly, a reference line are introduced between the deep and superficial aponeuroses to assist the detection of the orientation of muscle fibers. The Deep Residual Networks (Resnets) are used to judge the relative orientation of the reference line and muscle fibers. Then, reference line is revised until the line is parallel to the orientation of the muscle fibers. Finally, the pennation angle is obtained according to the direction of the detected aponeuroses and the muscle fibers. The angle detected by our proposed method differs by about 1° from the angle manually labeled. With a CPU, the average inference time for a single image of the muscle fibers with the proposed method is around 1.6 s, compared to 0.47 s for one of the image of a sequential image sequence. Experimental results show that the proposed method can achieve accurate and robust measurements of pennation angle.

Silver nanoparticle-activated COX2/PGE2 axis involves alteration of lung cellular senescence in vitro and in vivo.

Silver nanoparticles (AgNPs) are widely used as antimicrobial agents and resulted in their accumulation in environment. The purpose of this study was to investigate the detailed molecular mechanisms underlying AgNP-induced lung cellular senescence which has been proposed as a pathogenic driver of chronic lung disease. Herein, we demonstrate that exposure to AgNPs elevates multiple senescence biomarkers in lung cells, with cell cycle arrest in the G2/M phase, and potently activates genes of the senescence-associated secretory phenotype (SASP) in human fetal lung fibroblast cell line MRC5. Fluorescence-based assay also reveals that apoptosis induced by AgNPs is associated with senescence. Furthermore, we show that AgNPs cause premature senescence through an increase in transcription factor nuclear factor kappa B (NF-κB), cyclooxygenase-2 (COX2) expression and over-production of prostaglandin E2 (PGE2) in lung cells. Inhibition of COX2 reduces AgNPs-induced senescence to a normal level. Moreover, AgNPs also induce upregulation of COX2 and accelerate lung cellular senescence in vivo and cause mild fibrosis in the lung tissue of mice. Taken together, our studies support a critical role of AgNPs in the induction of lung cellular senescence via the upregulation of the COX2/PGE2 intracrine pathway, and suggest the adverse effects to the human respiratory system.

3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm.

In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole (BH) algorithm. The performance of the EBH algorithm is verified by the CEC 2013 test suit, and shows better results than the original BH. In addition, the EBH and other celebrated algorithms can be used to solve node coverage problems of Wireless Sensor Network (WSN) in 3-D terrain with satisfactory performance.

Genome Sequence of a Human Coxsackievirus A6 Strain Isolated from a Severe Hand, Foot, and Mouth Disease Case in Qingdao, China, in 2017.

In the present study, we describe the genome sequence of coxsackievirus A6 (CVA6) strain 17ES4/QD/CHN/2017, which was isolated in Qingdao, China, in 2017. According to the phylogenetic analyses, the isolate belongs to subgenotype D3a.

Foliar fungal endophyte community structure is independent of phylogenetic relatedness in an Asteraceae common garden.

Phylogenetic distance among host species represents a proxy for host traits that act as biotic filters to shape host-associated microbiome community structure. However, teasing apart potential biotic assembly mechanisms, such as host specificity or local species interactions, from abiotic factors, such as environmental specificity or dispersal barriers, in hyperdiverse, horizontally transmitted microbiomes remains a challenge. In this study, we tested whether host phylogenetic relatedness among 18 native Asteraceae plant species and spatial distance between replicated plots in a common garden affects foliar fungal endophyte (FFE) community structure. We found that FFE community structure varied significantly among host species, as well as host tribes, but not among host subfamilies. However, FFE community dissimilarity between host individuals was not significantly correlated with phylogenetic distance between host species. There was a significant effect of spatial distance among host individuals on FFE community dissimilarity within the common garden. The significant differences in FFE community structure among host species, but lack of a significant host phylogenetic effect, suggest functional differences among host species not accounted for by host phylogenetic distance, such as metabolic traits or phenology, may drive FFE community dissimilarity. Overall, our results indicate that host species identity and the spatial distance between plants can determine the similarity of their microbiomes, even across a single experimental field, but that host phylogeny is not closely tied to FFE community divergence in native Asteraceae.