Search

Introduction Blended learning is a teaching approach that integrates online self-learning and classroom teaching. It emphasizes students’ self-directed learning, actively engaging, as well as communication and collaboration. Blended learning develops the qualities in nursing undergraduate students with problem-solving ability, clinical thinking, and communication skills as nurses have. Currently, learners’ experience has become an important factor in determining the success of blended learning. Aim To learn about the experiences and perceptions of undergraduate nursing students regarding blending learning in pediatric nursing. Methods A mixed methods design beginning with a quantitative data collection and analysis phase to inform the subsequent qualitative phase was utilized. This study was conducted among undergraduate nursing students at one university in China. In the quantitative study phase, a convenience sample of 59 students completed the online survey while the qualitative study phase included nine students recruited by purposive sampling. The qualitative data were collected using individual semistructured in-depth interviews. Audio data were transcribed and subjected to thematic analysis. The findings from those two phases were integrated into the final analysis. Results The majority (89.8%) of students reported satisfaction with blended learning. Among various teaching methods, case-based learning (CBL) was the most preferred (98.3%). Regarding the qualitative data, students reported positive experiences with blended learning including well-organized course structure, resourceful course content, and high teacher quality. They stated that blended learning improved the learning outcomes such as improving problem-solving ability and developing clinical thinking. They also provided suggestions for achieving better learning outcomes, including providing stressful learning activities, optimizing online course design, and improving the quality of group learning. Conclusions The blended learning model of pediatric nursing based on the Community of Inquiry framework provides a positive learning experience for nursing undergraduate students. However, ongoing assessment and improvement of the blended learning process are also needed.

BackgroundThe active metabolite of benzo[a]pyrene (BaP), 7,8-dihydroxy-9,10-epoxybenzo[a]pyrene (BPDE), can form adducts with DNA, but the spectrum of BPDE-DNA adducts is unclear. ObjectiveTo identify the distribution of BPDE adduct sites and associated genes at the whole-genome level by chromatin immunoprecipitation followed by sequencing (ChIP-Seq), and serve as a basis for further exploring the toxicological mechanisms of BaP. MethodsHuman bronchial epithelial-like cells (16HBE) were cultured to the fourth generation inthe logarithmic growth phase. Cells were harvested and added to chromatin immunoprecipitation lysis buffer. The lysate was divided into experimental and control groups. The experimental group received a final concentration of 20 μmol·L−1 BPDE solution, while the control group received an equivalent volume of dimethyl sulfoxide solution. The cells were then incubated at 37 °C for 24 h. Chromatin fragments of 100-500 bp were obtained through sonication. BPDE-specific antibody (anti-BPDE 8E11) was used to enrich DNA fragments with BPDE adducts. High-throughput sequencing was conducted to detect BPDE adduct sites. The top 1000 peak sequences were subjected to motif analysis using MEME and DREME software. BPDE adduct target genes at the whole-genome level were annotated, and Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of BPDE adduct target genes were conducted using bioinformatics techniques. ResultsThe high-throughput sequencing detected a total of 842 BPDE binding sites, distributed across various chromosomes. BPDE covalently bound to both coding and non-coding regions of genes, with 73.9% binding sites located in intergenic regions, 19.6% in intronic regions, and smaller proportions in upstream 2 kilobase, exonic, downstream 2 kilobase, and 5' untranslated regions. Regarding the top 1000 peak sequences, four reliable motifs were identified, revealing that sites rich in adenine (A) and guanine (G) were prone to binding. Through the enrichment analysis of binding sites, a total of 199 BPDE-adduct target genes were identified, with the majority located on chromosomes 1, 5, 7, 12, 17, and X. The GO analysis indicated that these target genes were mainly enriched in nucleic acid and protein binding, participating in the regulation of catalytic activity, transport activity, translation elongation factor activity, and playing important roles in cell division, differentiation, motility, substance transport, and information transfer. The KEGG analysis revealed that these target genes were primarily enriched in pathways related to cardiovascular diseases, cancer, and immune-inflammatory responses. ConclusionUsing ChIP-Seq, 199 BPDE adduct target genes at genome-wide level are identified, impacting biological functions such as cell division, differentiation, motility, substance transport, and information transfer. These genes are closely associated with cardiovascular diseases, tumors, and immune-inflammatory responses.

Abstract Mechanical antennas have garnered considerable attention due to their ability to address the challenges posed by the significant size and high energy consumption associated with traditional electric antennas when operating at low frequencies. Here, a compact and structurally stable mechanical antenna design is presented. The proposed antenna is constructed using cylindrical piezoelectric ceramics, which have dimensions smaller than 1/1000 of the wavelength. The scrutiny of the influence exerted by the antenna feed area and material thickness on the radiation performance was undertaken, followed by an exhaustive discourse on these effects. Experimental measurements demonstrate the practical functionalities of signal coding, transmission, and reception in the low‐frequency communication system. Notably, at a frequency of 288 kHz, a single proposed antenna achieves an effective information transmission distance of 60 m using binary information coding. This remarkable outcome underscores the potential of this design in facilitating the development of portable and cost‐effective wireless communication equipment for low‐frequency applications.

The global energy industry is moving toward a trend of green and low-carbon transformation. A transparent power grid represents a new development form under this energy transition trend, integrating the power grid with new generation information and communication technologies, exemplified by the Internet. The use of micro smart sensors allows real-time monitoring of many important parameters of power systems via current, which is crucial for ensuring safe, stable, intelligent, and green power grid operation. Here, based on the development logic of current sensing technology for traditional-smart-transparent power grids, we focus our research on high-current sensing in a transparent power grid. First, we review the widely used traditional current sensing methods of shunts and Rogowski coils. More advanced optical fiber current sensing methods and microelectromechanical system current sensing chips are then introduced, which are more suitable for the smart sensing requirements of transparent power grids. In addition, we present and discuss the technical characteristics, research status, application scope, advantages and disadvantages, and future development directions of the above methods in detail. Finally, we address many essential challenges, including miniaturization, integration, low power consumption, passive wireless sensing, long-range operation, and complex environmental parameters in the comprehensive intelligent and transparent sensing of current parameters in power grids. To address these challenges, we present three possible development directions: sensor improvement, development of communication and networking technologies for sensors, and the advancement of intelligent and fast processing methods through communication.

Nasal drug delivery efficiency is highly dependent on the position in which the drug is deposited in the nasal cavity. However, no reliable method is currently available to assess its impact on delivery performance. In this study, a biomimetic nasal model based on three-dimensional (3D) reconstruction and three-dimensional printing (3DP) technology was developed for visualizing the deposition of drug powders in the nasal cavity. The results showed significant differences in cavity area and volume and powder distribution in the anterior part of the biomimetic nasal model of Chinese males and females. The nasal cavity model was modified with dimethicone and validated to be suitable for the deposition test. The experimental device produced the most satisfactory results with five spray times. Furthermore, particle sizes and spray angles were found to significantly affect the experimental device's performance and alter drug distribution, respectively. Additionally, mometasone furoate (MF) nasal spray (NS) distribution patterns were investigated in a goat nasal cavity model and three male goat noses, confirming the in vitro and in vivo correlation. In conclusion, the developed human nasal structure biomimetic device has the potential to be a valuable tool for assessing nasal drug delivery system deposition and distribution.

Hydrogels have emerged as a focal point of research in the biomedical field due to their applications in tissue repair. However, the majority of hydrogels lack the capability to release oxygen, constraining their therapeutic outcomes in environments with hypoxic tissues. In recent years, oxygen‐releasing hydrogels have garnered extensive attention in the field of tissue engineering, owing to their ability to modulate oxygen release and meet the diverse oxygenation requirements of various tissues. These hydrogels can enhance repair efficiency and promote tissue regeneration in hypoxic tissue environments. The design of oxygen‐releasing hydrogels primarily involves the utilization of diverse oxygen sources, such as algae, perfluorocarbons, and peroxides, to achieve optimal tissue oxygenation. This review provides a comprehensive summary of the design and fabrication strategies of oxygen‐releasing hydrogels, discusses deeply into their underlying oxygen‐releasing mechanisms, and their myriad applications in tissue repair along with the prospective challenges.

An artificial semisynthetic material can be derived from artemisinin (ART) called dihydroartemisinin (DHA). Although DHA has enhanced antigiardial potential, its clinical application is limited because of its poor selectivity and low solubility. The drug’s absorption has a direct impact on the cell, and mechanism research is limited to its destruction of the cytoskeleton. In this study, we used the zeolitic imidazolate framework-8 and loaded it with DHA (DHA@Zif-8) to improve its antigiardial potential. DHA@Zif-8 can enhance cellular uptake, increase antigiardial proliferation and encystation, and expand the endoplasmic reticulum compared with the DHA-treated group. We used RNA sequencing (RNA-seq) to investigate the antigiardial mechanism. We found that 126 genes were downregulated and 123 genes were upregulated. According to the KEGG and GO pathway analysis, the metabolic functions in G. lamblia are affected by DHA@Zif-8 NPs. We used real-time quantitative reverse transcription polymerase chain reaction to verify our results using the RNA-seq data. DHA@Zif-8 NPs significantly enhanced the eradication of the parasite from the stool in vivo. In addition, the intestinal mucosal injury caused by G. lamblia trophozoites markedly improved in the intestine. This research provided the potential of utilizing DHA@Zif-8 to develop an antiprotozoan drug for clinical applications.

High-strength bolts are crucial load-bearing components of wind turbine towers. They are highly susceptible to fatigue cracks over long-term service and require timely detection. However, due to the structural complexity and hidden nature of the cracks in wind turbine tower bolts, the small size of the cracks, and their variable propagation directions, detection signals carrying crack information are often drowned out by dense thread signals. Existing non-destructive testing methods are unable to quickly and accurately characterize small cracks at the thread roots. Therefore, we propose an ultrasonic phased array element arrangement method based on the Fermat spiral array. This method can greatly increase the fill rate of the phased array with small element spacing while reducing the effects of grating and sidelobes, thereby achieving high-energy excitation and accurate imaging with the ultrasonic phased array. This has significant theoretical and engineering application value for ensuring the safe and reliable service of key wind turbine components and for promoting the technological development of the wind power industry.

Abstract The pBRDF model is able to relate the properties of target materials to the polarization information of incident and reflected light, and is an important basis for obtaining polarization information of targets in space. It is an important basis for obtaining target polarization information and polarization detection of space targets. P-G model is the first strictly pBRDF model officially released, but there are still deficiencies. In this paper, we first analyze the assumption framework of the P-G model, derive the imperfections in the framework through the analysis of the assumption framework, and add scattering and phase function to enhance the existing model. On the basis of the existing P-G model and parameter inversion, the output results of the model are compared with the experimental data through simulation, and the results show that the relative error of the target's linear polarizability is reduced under the improved model, which proves the accuracy and precision of the improved model.

Abstract Aim To explore the physical and mental health status of community residents and to identify the risk factors of chronic diseases. Design A cross‐sectional, descriptive correlational study was conducted. Methods A total of 579 participants were recruited from 15 communities in Tianjin. The demographic information sheet, 7‐item Generalized Anxiety Disorder scale (GAD‐7) and Patient Health Questionnaire (PHQ‐9) were used. Data collection was undertaken based on the health management system on mobile phones from April to May 2019. Results Eighty‐four participants of the total number of surveyed were with chronic disease. The incidence of depression and anxiety in participants was 44.2% and 41.3%. Logistic regression analysis showed that age (OR = 4.905, 95%CI: 2.619–9.187), religious belief (OR = 0.445, 95%CI: 1.510–11.181) and working condition (OR = 0.161, 95%CI: 0.299–0.664) entered the regression equation. Old age is a risk factor for chronic diseases. No religious belief and working condition are protective factors for chronic diseases.

As a typical biomarker of Alzheimer’s disease, rapid and specific detection of tau protein can help improve the early diagnosis and prognosis of the disease. In this study, a simple sandwich electrochemical immunosensor was developed for rapid detection of tau protein. Primary monoclonal antibodies (mAb1) against the middle domain of tau protein (amino acids 189–195) were immobilized on the gold electrode surface through a self-assembled monolayer (SAM) of 3,3′-dithiobis (sulfosuccinimidyl propionate) (DTSSP). Then the tau protein was captured through the specific adsorption between the antigen and the antibody, resulting in a change in the impedance. Secondary monoclonal antibodies (mAb2) against the N-terminal region of tau protein were used for further amplification of the binding reaction between mAb1 and tau protein. A linear correlation between the total change in impedance and the logarithm of tau concentration was found from 2 × 10−6 mg mL−1 to 2 × 10−3 mg mL−1, with a detection limit as low as 1 × 10−6 mg mL−1. No significant interference was observed from human serum albumin. Furthermore, the fabricated sandwich immunosensor successfully detected target tau protein in artificial cerebrospinal fluid (aCSF) samples, indicating good potential for clinical applications in the future.

Weak bonds usually make macromolecules stronger; therefore, they are often used to enhance the mechanical strength of polymers. Not enough studies have been reported on the use of weak bonds in flame retardants. A water-soluble polyelectrolyte complex composed of polyethyleneimine (PEI), sodium tripolyphosphate (STPP) and melamine (MEL) was designed and utilized to treat bio-based polyamide 56 (PA56) by a simple three-step process. It was found that weak bonds cross-linked the three compounds to a 3D network structure with MEL on the surface of the coating under mild conditions. The thermal stability and flame retardancy of PA56 fabrics were improved by the controlled coating without losing their mechanical properties. After washing 50 times, PA56 still kept good flame retardancy. The cross-linking network structure of the flame retardant enhanced both the thermal stability and durability of the fabric. STPP acted as a catalyst for the breakage of the PA56 molecular chain, PEI facilitated the char formation and MEL released non-combustible gases. The synergistic effect of all compounds was exploited by using weak bonds. This simple method of developing structures with 3D cross-linking using weak bonds provides a new strategy for the preparation of low-cost and environmentally friendly flame retardants.

BackgroundBenzo[a]pyrene (BaP) has neurotoxicity, which can induce the loss of hippocampal neurons in humans and animals and lead to spatial learning and memory dysfunction, but its mechanism is still unclear. ObjectiveTo observe the ferroptosis of mouse hippocampal neuron HT22 cells induced by 7,8-dihydroxy-9,10-epoxybenzo[a]pyrene (BPDE), an active metabolite of BaP, and to explore its potential mechanism, so as to provide a basis for the study of BaP neurotoxicity mechanism. MethodMouse hippocampal neuron HT22 cells were selected and divided into four groups: solvent control group and low, medium, and high concentration BPDE exposure groups (0.25, 0.50, and 0.75 μmol·L−1). Cell survival was detected by CCK8 method. Cell morphology and ultrastructure were observed under light and electron microscopes. The levels of reactive oxygen species (ROS) and Fe2+ were detected by fluorescence probe method. Iron, 4-hydroxynonenoic acid (4-HNE), malondialdehyde (MDA), glutathione (GSH), and glutathione peroxidase (GSH-PX) levels were detected with commercial kits. The expression levels of acyl-CoA synthase long chain family member 4 (ACSL4), cyclooxygenase 2 (COX2), solute carrier family 7 member 11 (SLC7A11), and glutathione peroxidase 4 (GPX4) were detected by Western blotting. After interventions with ferroptosis inhibitors 20 μmol·L−1 deferoxamine (DFO) and 10 μmol·L−1 ethyl 3-amino-4-cyclohexylaminobenzoate (Fer-1), the cell survival rate of each BPDE exposure group and the changes of the ferroptosis characteristic indicators and protein expression levels were observed. ResultsWith the increase of BPDE concentration, the survival rate of HT22 cells decreased gradually, and the survival rate of each BPDE group was significantly lower than that of the solvent control group (P

Abstract Seismic wave arrival time measurements form the basis for numerous downstream applications. State‐of‐the‐art approaches for phase picking use deep neural networks to annotate seismograms at each station independently, yet human experts annotate seismic data by examining the whole network jointly. Here, we introduce a general‐purpose network‐wide phase picking algorithm based on a recently developed machine learning paradigm called Neural Operator. Our model, called Phase Neural Operator, leverages the spatio‐temporal contextual information to pick phases simultaneously for any seismic network geometry. This results in superior performance over leading baseline algorithms by detecting many more earthquakes, picking more phase arrivals, while also greatly improving measurement accuracy. Following similar trends being seen across the domains of artificial intelligence, our approach provides but a glimpse of the potential gains from fully‐utilizing the massive seismic data sets being collected worldwide.

The comprehensive characterization of the fine-scale genetic background of ethnolinguistically diverse populations can gain new insights into the population admixture processes, which is essential for evolutionary and medical genomic research. However, the genetic diversity and population history of southern Chinese indigenous people are underrepresented in human genetics research and their interaction with historical immigrants remains unknown. Here, we collected genome-wide SNP data from 20 Guizhou populations belonging to three primary language families [Tai-Kadai (TK), Hmong-Mien (HM), and Tibeto-Burman (TB)], including four groups newly collected here, and merged them with publicly available data from 218 modern and ancient East Asian groups to perform one comprehensive demographic and evolutionary history reconstruction. We comprehensively characterized the genetic signatures of geographically diverse populations and found language-related population stratification. We identified the unique HM genetic lineage in Southwest China and Southeast Asia as their shared ancestral component in the demographic history reconstruction. TK and TB people showed a differentiated genetic structure from HM people. Our identified admixture signals and times further supported the hypothesis that HM people originated from the Yungui Plateau and then migrated southward during the historical period. Admixture models focused on Sino-Tibetan and TK people supported their intense interaction, and these populations harbored the most extensive gene flows consistent with their shared linguistic and cultural characteristics and lifestyles. Estimates of identity-by-descent sharing and effective population size showed the extensive population stratification and gene flow events in different time scales. In short, we presented one complete landscape of the evolutionary history of ethnolinguistically different southern Chinese people and filled the gap of missing diversity in South China.

Aiming at the problem that traditional design methods make it difficult to control the polarization aberration distribution of optical systems quickly and accurately, this study proposes an automatic optimization design method for polarization optical systems based on deep learning. The unsupervised training model based on ray tracing and polarized ray tracing was constructed by learning the reference lens structural feature data from the optical lens library, and the generalization ability of the deep neural network model was improved to achieve the automatic optimization design of the polarized optical system. The design results show that the optical system structure optimized by the network model is close to the reference lens in the full field of view and the full spectrum and that the optical system structure can be designed for different focal length requirements. The success rate of 1 million groups of initial structures designed is better than 96.403%, and the polarization effect of the optical system is effectively controlled. The proposed deep learning approach to optical design provides a new solution for future complex optical systems and also provides an effective way to improve the design accuracy of special optical systems such as polarization optical systems.

Semiconductor lasers with a wavelength of 2 µm, composed of antimonide materials, find important applications in trace gas detection, laser medicine, and free-space optical communication, among others. In this paper, a more suitable microlens shape for 2 µm gallium antimonide semiconductor lasers is designed. Based on the fiber coupling efficiency model, the parameters of the designed slanting wedge-shaped microlens fiber are optimized to improve laser beam quality. The large tangent angle on both sides of the slanted, wedge-shaped microlens fiber is calculated using Snell’s law, and the fiber core diameter and small wedge angle are determined through space fiber coupling experiments. After packaging the fiber coupling module with the chip, the laser output beam exhibits good overall symmetry in the spot with a uniform intensity distribution. The maximum output power is approximately 210 mW, demonstrating good power stability.

Objective To determine the relationship of napping with incident diabetes risk and glycaemic control in people with diabetes.Design Systematic review and meta-analysis.Data sources MEDLINE (PubMed), EMBASE, Web of Science and the Cochrane Library were searched for studies published from database inception to 9 May 2023.Eligibility criteria Observational studies reporting the relationship of napping with diabetes or glycaemic control in patients with diabetes in adult populations were included.Data extraction and synthesis Two reviewers independently screened the literature, extracted data and assessed the quality of the included studies. The results were reported as ORs and 95% CIs, which were pooled by using fixed and random effects models, and subgroup analyses were performed. The Grading of Recommendations Assessment, Development and Evaluation method was used to assess the quality of the evidence.Results Forty studies were included in our review. Habitual napping was associated with an increased diabetes risk (OR 1.20, 95% CI 1.14 to 1.27) and poor glycaemic control in patients with diabetes (OR 2.05, 95% CI 1.55 to 2.73). Nap durations less than 30 min were unrelated to diabetes (OR 1.05, 95% CI 0.97 to 1.14). Nap durations of 30–60 min were associated with diabetes risk (OR 1.09, 95% CI 1.02 to 1.17), but there were differences in the subgroup analysis results. Nap durations of more than 60 min significantly increased the risk of diabetes (OR 1.31, 95% CI 1.20 to 1.44).Conclusions Napping is associated with increased diabetes risk and poor glycaemic control, and future research will need to confirm whether there are sex and regional differences. Nap durations of more than 60 min significantly increases the risk of diabetes, and the relationship between nap duration and glycaemic control in patients with diabetes needs to be further explored in the future.PROSPERO registration number CRD42021292103.

BackgroundThe global burden of digestive diseases has been rising in the last 30 years. The rates and trends of incidence, deaths, and disability-adjusted life-years (DALYs) for digestive diseases need to be investigated.MethodsWe extracted the data on overall digestive diseases and by cause between 1990–2019 from the Global Burden of Diseases 2019 website, including the absolute number and the corresponding age-standardized rates of incidence (ASIR), deaths (ASDR), and DALYs (ASDALYs).ResultsGlobally, the incident cases, deaths, and DALYs of digestive diseases in 2019 increased by 74.44, 37.85, and 23.46%, respectively, compared with that in 1990, with an increasing ASIR of 0.09%, as well as decreasing ASDR and ASDALYs of 1.38 and 1.32% annually. The sociodemographic index (SDI) of overall digestive diseases showed a slight increase in ASIR from low to middle-low regions. The downtrend in ASDR and ASDALYs was found in all SDI regions. The burden of incidence was higher in females, while the burden of deaths and DALYs was higher in males for the overall digestive diseases and most causes. The estimated annual percentage changes were significantly associated with the baseline ASIR, ASDR, and ASDALYs for the overall digestive diseases, and the negative correlations between ASDR, ASDALYs, and human development index both in 1990 (R = −0.68, R = −0.69) and 2019 (R = −0.71, R = −0.73) were noticed.ConclusionThe findings indicate that digestive diseases remain a significant public health burden, with substantial variation across countries, sexes, and age groups. Therefore, implementing age, gender, and country-specific policies for early screening and targeted interventions could significantly reduce the global burden of digestive diseases.

Abstract Sensing, Computing and Communication Integration (SC2) is widely believed as a new enabling technology. A non‐negative tensor sparse factorisation (NTSF) algorithm based on tensor analysis is proposed for sensing and classification of Small Geological Structure in coal mines. Utilising this method, advanced detection of geological anomalies hidden in coal seams was achieved. The morphological properties of geological anomalies in coal seams and the propagation characteristics of guided waves were first thoroughly studied. A three‐dimensional (3D) medium geometry model was developed for a complicated coal seam with Goaf, collapse column, scouring zone, and tiny fault based on COMSOL Multiphysics. On this model, the third‐order tensors data was constructed. Then, the TUCKER‐based NTSF algorithm was employed for feature extraction and classification. To achieve multi‐dimensional feature, the two‐dimensional data in the form of a matrix is collected, and a multiplicative update method is introduced to update the algorithm iteratively. Finally, the Support Vector Machine (SVM) multi‐classifier with Gaussian radial basis kernel function is selected for classification of Small Geological Structure. The experimental results show that the classification accuracy based on the NTSF and SVM is as high as 97.33%, which demonstrates that the proposed algorithm is suitable for Sensing and Classification of Small Geological Structure in coal mines.

The study on the return period of extreme precipitation can improve the understanding of precipitation regularity, which is of great significance for preventing urban waterlogging, farmland waterlogging. To study the characteristics of the probability distribution of extreme precipitation in Liaoning Province, we use the daily precipitation data from 14 national meteorological observational stations in Liaoning Province during May to October of 1960-2020, and select the 98th percentile as the threshold of extreme precipitation. The Poisson-generalized Pareto compound extreme value model fits the extreme precipitation days and extreme precipitation amount and estimates the precipitation amount during the return period in Liaoning Province. The results show that the days with extreme precipitation and the extreme precipitation amount in Liaoning Province follow the Poisson distribution and the generalized Pareto distribution, respectively (both passed the statistically significant test of α=0.05). Extreme precipitation shows a gradual increase from the northwest to the southeast in Liaoning Province. The amount of extreme precipitation and the days of extreme precipitation are highest in the Dandong city. The Poisson-generalized Pareto compound extreme value model and the empirical frequency method can fit the precipitation amount during the return period very well. Under different return periods, the extreme precipitation amount in southeastern Liaoning Province is the highest.The extreme precipitation amount is relatively small in the middle of Liaohe River Basin, and is the smallest in the Northwest. The extreme precipitation amount in the mountainous areas of eastern Liaoning and the hilly areas of western Liaoning is higher than that in the plains of central Liaoning, and this spatial distribution is consistent for different return periods.

Abstract Background Anxiety and depressive symptoms are associated with fear of falling and fear of falling-related activity restrictions. However, it remains unknown whether anxiety or depressive symptoms alone could predict fear of falling and activity restrictions in older adults. We sought to determine if anxiety and depressive symptoms alone could be an independent predictor of fear of falling and activity restrictions in community-dwelling older adults. Methods This longitudinal analysis used waves 5 (time 1, [T1]) and 6 (time 2, [T2], 1 year from T1) data (N = 6376) from the National Health and Aging Trends Study. The Generalized Anxiety Disorder Scale 2 and Patient Health Questionnaire 2 were used to assess anxiety and depressive symptoms, respectively. Interview questions included demographics, health-related data, and fall worry levels (no fear of falling, fear of falling but no activity restrictions, and activity restrictions). Using multinomial logistic regression models, we examined whether anxiety and depressive symptoms (T1) predicted fear of falling and activity restrictions (T2). Results In wave 5 (T1, mean age: 78 years, 58.1% female), 10 and 13% of participants reported anxiety and depressive symptoms. About 19% of participants experienced fear of falling but not activity restrictions, and 10% of participants developed activity restrictions in wave 6 (T2), respectively. Participants with anxiety symptoms at T1 had a 1.33 times higher risk of fear of falling (95% CI = 1.02–1.72) and 1.41 times higher risk of activity restrictions (95% CI = 1.04–1.90) at T2. However, having depressive symptoms did not show any significance after adjusting for anxiety symptoms. Conclusions Anxiety symptoms seemed to be an independent risk factor for future fear of falling and activity restrictions, while depressive symptoms were not. To prevent future fear of falling and activity restrictions, we should pay special attention to older individuals with anxiety symptoms.

Diagnosing the drowning site is a major challenge in forensic practice, particularly when corpses are recovered from flowing rivers. Recently, forensic experts have focused on aquatic microorganisms, including bacteria, which can enter the bloodstream during drowning and may proliferate in corpses. The emergence of 16S ribosomal RNA gene (16S rDNA) amplicon sequencing has provided a new method for analyzing bacterial composition and has facilitated the development of forensic microbiology. We propose that 16S rDNA amplicon sequencing could be a useful tool for inferring drowning sites. Our study found significant differences in bacterial composition in different regions of the Guangzhou section of the Pearl River, which led to differences in bacteria of drowned rabbit lungs at different drowning sites. Using the genus level of bacteria in the lung tissue of drowned rabbits, we constructed a random forest model that accurately predicted the drowning site in a test set with 100% accuracy. Furthermore, we discovered that bacterial species endemic to the water were not always present in the corresponding drowned lung tissue. Our findings demonstrate the potential of a random forest model based on bacterial genus and composition in drowned lung tissues for inferring drowning sites.

Antimony (Sb) is a hazardous metal element that is potentially toxic and carcinogenic. Melatonin (MT) is an indole compound with antioxidant properties that plays an essential role in plant growth and alleviates heavy metal stresses. Nevertheless, little is known about the effects and mechanisms of exogenous MT action on rice under Sb stress. The aim of this experiment was to explore the mechanism of MT reducing Sb toxicity in rice via hydroponics. The results showed that Sb stress significantly inhibited the growth of rice, including biomass, root parameters, and root viability. Exogenous MT obviously alleviated the inhibition of Sb stress on seedling growth and increased biomass, root parameters, and root viability by 15–55%. MT significantly reduced the total Sb content in rice and the subcellular Sb contents in roots by nearly 20–40% and 12.3–54.2% under Sb stress, respectively. MT significantly decreased the contents of malondialdehyde (MDA, by nearly 50%), ROS (H2O2 and O2·−, by nearly 20–30%), and RNS (NO and ONOO−) in roots under Sb stress, thus reducing oxidative stress and cell membrane damage. Furthermore, MT reversed Sb-induced phytotoxicity by increasing the activities of antioxidant enzymes (SOD, POD, CAT, and APX) by nearly 15% to 50% and by regulating the AsA–GSH cycle. In conclusion, this study demonstrates the potential of MT to maintain redox homeostasis and reduce Sb toxicity in rice cells, decreasing the content of Sb in rice and thereby alleviating the inhibition of Sb on rice growth. The results provided a feasible strategy for mitigating Sb toxicity in rice.

Defining and visualizing the three-dimensional (3D) structures of pharmaceuticals provides a new and important tool to elucidate the phenomenal behavior and underlying mechanisms of drug delivery systems. The mechanism of drug release from complex structured dosage forms, such as bilayer osmotic pump tablets, has not been investigated widely for most solid 3D structures. In this study, bilayer osmotic pump tablets undergoing dissolution, as well as after dissolution in a desiccated solid state were examined, and visualized by synchrotron radiation micro-computed tomography (SR-μCT). In situ formed 3D structures at different in vitro drug release states were characterized comprehensively. A distinct movement pattern of NaCl crystals from the push layer to the drug layer was observed, beneath the semi-permeable coating in the desiccated tablet samples. The 3D structures at different dissolution time revealed that the pushing upsurge in the bilayer osmotic pump tablet was directed via peripheral “roadways”. Typically, different regions of the osmotic front, infiltration region, and dormant region were classified in the push layer during the dissolution of drug from tablet samples. According to the observed 3D microstructures, a “subterranean river model” for the drug release mechanism has been defined to explain the drug release mechanism.

ObjectiveTo explore the optimal bolus dose of oxycodone for patient controlled intravenous analgesia (PCIA) without background dose in elderly patients after laparoscopic surgery for gastrointestinal cancer.MethodsIn this prospective, randomized, double-blind, parallel-controlled study, we recruited patients aged 65 years or older. They underwent laparoscopic resection for gastrointestinal cancer and received PCIA after surgery. Eligible patients were randomly divided into 0.01, 0.02, or 0.03 mg/kg group according to the bolus dose of oxycodone in PCIA. The primary outcome was VAS scores of pain on mobilization at 48 h after surgery. Secondary endpoints included the VAS scores of rest pain, the total and effective numbers of press in PCIA, cumulative dose of oxycodone used in PCIA, the incidence of nausea, vomiting and dizziness, as well as patients’ satisfaction at 48 h after surgery.ResultsA total of 166 patients were recruited and randomly assigned to receive a bolus dose of 0.01 mg/kg (n = 55), 0.02 mg/kg (n = 56) or 0.03 mg/kg (n = 55) of oxycodone in PCIA. The VAS scores of pain on mobilization, the total and effective numbers of press in PCIA in 0.02 mg/kg group and 0.03 mg/kg group were lower than those in 0.01 mg/kg group (P 0.05).ConclusionFor elderly patients undergoing laparoscopic surgery for gastrointestinal cancer, 0.02 mg/kg bolus dose of oxycodone in PCIA without background infusion may be a better choice.

Aiming at the problem of poor oil identification accuracy in existing oil spill detection technologies, the polarization degree model of oil spill on rough sea surface under different azimuths and zenith angles was established based on Fresnel theory. The analytical expressions of visible light polarization degree in calm and fluctuating water surface were derived respectively, and the polarization degree model of oil spill in reflection space was constructed. The effectiveness of the method and its influence on the polarization distribution of oil spill were analyzed by simulation. A portable turntable was designed to test the polarization characteristics of the experiment, and the visible light polarization detection experiment was carried out. The visible light polarization images of five typical oil spills at different observation azimuth and zenth angles were obtained. The differences in the polarization degrees of different oil species were analyzed, and the correctness of the theoretical model was proved by experiments. The polarization detection experiment of visible light pBRDF was completed, which more intuitively showed the variation law of the polarization characteristics of light reflected by different oil spills in different spatial positions. Using polarization information to distinguish oil species is a useful supplement to the traditional oil spill detection method and has important significance to improve the marine pollution control ability.

Selective catalytic reduction (SCR) systems are distributed systems with strong time-varying parameter characteristics such that an accurate model for it is difficult to establish. Its control task simultaneously achieving high NOx conversion efficiency and low NH3 slip is a typical multi-objective and multi-constraint problem, which is suitable to be solved in the framework of the model predictive control (MPC). However, how to find a data-driven identification method based on the dynamic characteristics of an SCR system and a corresponding MPC method for satisfying its emission requirements remain a formidable challenge. The sufficient identification for the traditional identification method with fixed subspace model requires an excessively high order subspace matrix, such that a degradation in real-time performance is caused and the generality of the method under non-identification conditions is limited. In this paper, utilizing the transient data of the SCR system under the WHTC cycle, a novel identification method for some lower order subspace matrices excited by the segmented data referring to the dynamic of the ammonia coverage ratio is established. A corresponding predictive controller with the switched subspace matrices according to working conditions is designed in order to further improve its real-time performance, generality and robustness. The simulation results show that under the identification condition the proposed predictive controller compared to the traditional method can improve the emissions of NOx and NH3, that under the non-identification condition the proposed predictive controller can also improve the emissions and its optimization effects have better robustness to uncertainties of the transient cycle, and that the proposed predictive controller saves an significant computation time.

Changes in structure of oral solid dosage forms (OSDF) elementally determine the drug release and its therapeutic effects. In this research, synchrotron radiation X-ray micro-computed tomography was utilized to visualize the 3D structure of enteric coated pellets recovered from the gastrointestinal tract of rats. The structures of pellets in solid state and in vitro compendium media were measured. Pellets in vivo underwent morphological and structural changes which differed significantly from those in vitro compendium media. Thus, optimizations of the dissolution media were performed to mimic the appropriate in vivo conditions by introducing pepsin and glass microspheres in media. The sphericity, pellet volume, pore volume and porosity of the in vivo esomeprazole magnesium pellets in stomach for 2 h were recorded 0.47, 1.55 × 108 μm3, 0.44 × 108 μm3 and 27.6%, respectively. After adding pepsin and glass microspheres, the above parameters in vitro reached to 0.44, 1.64 × 108 μm3, 0.38 × 108 μm3 and 23.0%, respectively. Omeprazole magnesium pellets behaved similarly. The structural features of pellets between in vitro media and in vivo condition were bridged successfully in terms of 3D structures to ensure better design, characterization and quality control of advanced OSDF.

The beam-only connected reinforced concrete shear wall (BRW) is used as a reinforcing component to enhance the seismic performance of concrete-filled, double-skin steel tube (CFDST) frames. The effects of the BRW on seismic risks of CFDST frames are investigated. Three performance levels of limit states are defined and described according to the typical failure of test specimens and the existing definition of current guidance. A simplified numerical model is calibrated for CFDST frame-BRW structures, and test results validate it. Nonlinear dynamic analyses on a nine-story CFDST-BRW building are performed to investigate the effects of BRW on reducing the seismic risk of CFDST buildings. The results show that the BRW reduces the probability of collapse of the CFDST frame to 2.76% in 50 years, which can effectively reduce the probability of different degrees of damage in the service cycle of the structure. The results provide information for risk-informed decision-making on the design of CFDST frame-BRW structures.

Infrared small-target enhancement in complex contexts is one of the key technologies for infrared search and tracking systems. The effect of enhancement directly determines the reliability of the monitoring equipment. To address the problem of the low signal-to-noise ratio of small infrared moving targets in complex backgrounds and the poor effect of traditional enhancement algorithms, an accurate enhancement method for small infrared moving targets based on two-channel information is proposed. For a single frame, a modified curvature filter is used in the A channel to weaken the background while an improved PM model is used to enhance the target, and a modified band-pass filter is used in the B channel for coarse enhancement followed by a local contrast algorithm for fine enhancement, based on which a weighted superposition algorithm is used to extract a single-frame candidate target. The results of the experimental data analysis prove that the method has a good enhancement effect and robustness for small IR motion target enhancement in complex backgrounds, and it outperforms other advanced algorithms by about 43.7% in ROC.

Extremely fine-grained fault rocks are intrinsically weak and behave as fluids even at low temperatures and fast deformation rates. Local production of fine-grained material during fault movement can lead to an earthquake instability.

Abstract It is well accepted that environmental stressors experienced over a one’s life, from microbial infections to chemical toxicants to even psychological stressors, ultimately shape central nervous system (CNS) functioning but can also contribute to its eventual breakdown. The severity, timing and type of such environmental “hits”, woven together with genetic factors, likely determine what CNS outcomes become apparent. This focused review assesses the current COVID-19 pandemic through the lens of a multi-hit framework and disuses how the SARS-COV-2 virus (causative agent) might impact the brain and potentially interact with other environmental insults. What the long-term consequences of SAR2 COV-2 upon neuronal processes is yet unclear, but emerging evidence is suggesting the possibility of microglial or other inflammatory factors as potentially contributing to neurodegenerative illnesses. Finally, it is critical to consider the impact of the virus in the context of the substantial psychosocial stress that has been associated with the global pandemic. Indeed, the loneliness, fear to the future and loss of social support alone has exerted a massive impact upon individuals, especially the vulnerable very young and the elderly. The substantial upswing in depression, anxiety and eating disorders is evidence of this and in the years to come, this might be matched by a similar spike in dementia, as well as motor and cognitive neurodegenerative diseases.

Metal-organic frameworks (MOFs), comprised of organic ligands and metal ions/metal clusters via coordinative bonds are highly porous, crystalline materials. Their tunable porosity, chemical composition, size and shape, and easy surface functionalization make this large family more and more popular for drug delivery. There is a growing interest over the last decades in the design of engineered MOFs with controlled sizes for a variety of biomedical applications. This article presents an overall review and perspectives of MOFs-based drug delivery systems (DDSs), starting with the MOFs classification adapted for DDSs based on the types of constituting metals and ligands. Then, the synthesis and characterization of MOFs for DDSs are developed, followed by the drug loading strategies, applications, biopharmaceutics and quality control. Importantly, a variety of representative applications of MOFs are detailed from a point of view of applications in pharmaceutics, diseases therapy and advanced DDSs. In particular, the biopharmaceutics and quality control of MOFs-based DDSs are summarized with critical issues to be addressed. Finally, challenges in MOFs development for DDSs are discussed, such as biostability, biosafety, biopharmaceutics and nomenclature.

Objectives: First-year college students had exposure to unhealthy lifestyle behaviors that correlate with a high prevalence of anxiety and depression. Regarding to the modifiable lifestyle behaviors factors, this study investigated the prevalence and correlation of multiple lifestyle behaviors, anxiety and depression in a sample of Chinese first-year college students. Methods: Cross-sectional data were extracted from Residents eHealth app of health lifestyle behaviors survey from September to October 2019. Anxiety, depression, eating regular meals, consumption of snacks in-between meals, consumption of fruit, dessert and sugar-sweetened beverages, smoking and secondhand smoke exposure, consuming alcohol, physical activity, sedentary time were assessed by self-report. Socio-demographic including age, gender, education, family income, religion, and health condition were captured. Logistic regression was used to explore the association of multiple lifestyle behaviors, anxiety and depression. Results: Totally 1,017 participants were included in the study. The prevalence of anxiety and depression (from mild to severe) were 40.3% and 45.3%, respectively. In multivariable analyses, religion (believe in Buddhism, OR = 2.438, 95%CI: 1.097–5.421; believe in Christian, OR = 5.886, 95%CI: 1.604–21.597), gender (Female, OR = 1.405, 95%CI: 1.001–1.971), secondhand smoke exposure (OR = 1.089, 95%CI: 1.001–1.184), and eating regular meals (OR = 0.513, 95%CI: 0.346–0.759) were associated with anxiety. Family income (OR = 0.732, 95%CI: 0.596–0.898), eating regular meals (OR = 0.641, 95%CI: 0.415–0.990), frequency of breakfast (OR = 0.813, 95%CI: 0.690–0.959), with a chronic disease (OR = 1.902, 95%CI: 1.335–2.712), and consumption of nocturnal snack (OR = 1.337, 95%CI: 1.108–1.612) were associated with depression. Conclusions: These results highlighted the need for early lifestyle behavior intervention, especially modifying diet patterns considering the background of religion, health condition, and social-economic status in first-year college students to improve their mental health.

While Alzheimer's disease (AD) can cause a severe economic burden, the specific pathogenesis involved is yet to be elucidated. To identify feature genes associated with AD, we downloaded data from three GEO databases: GSE122063, GSE15222, and GSE138260. In the filtering, we used AD for search keywords, Homo sapiens for species selection, and established a sample size of > 20 for each data set, and each data set contains Including the normal group and AD group. The datasets GSE15222 and GSE138260 were combined as a training group to build a model, and GSE122063 was used as a test group to verify the model's accuracy. The genes with differential expression found in the combined datasets were used for analysis through Gene Ontology (GO) and The Kyoto Encyclopedia of Genes and Genome Pathways (KEGG). Then, AD-related module genes were identified using the combined dataset through a weighted gene co-expression network analysis (WGCNA). Both the differential and AD-related module genes were intersected to obtain AD key genes. These genes were first filtered through LASSO regression and then AD-related feature genes were obtained for subsequent immune-related analysis. A comprehensive analysis of three AD-related datasets in the GEO database revealed 111 common differential AD genes. In the GO analysis, the more prominent terms were cognition and learning or memory. The KEGG analysis showed that these differential genes were enriched not only in In the KEGG analysis, but also in three other pathways: neuroactive ligand-receptor interaction, cAMP signaling pathway, and Calcium signaling pathway. Three AD-related feature genes (SST, MLIP, HSPB3) were finally identified. The area under the ROC curve of these AD-related feature genes was greater than 0.7 in both the training and the test groups. Finally, an immune-related analysis of these genes was performed. The finding of AD-related feature genes (SST, MLIP, HSPB3) could help predict the onset and progression of the disease. Overall, our study may provide significant guidance for further exploration of potential biomarkers for the diagnosis and prediction of AD.

Background: Necrotizing enterocolitis (NEC) is a potentially fatal inflammatory gastrointestinal disease in preterm infants with unknown pathogenesis. Mucosal-associated invariant T (MAIT) cells primarily accumulate at sites where exposure to microbes is ubiquitous and regulate immunological responses. As the implications of these cells in NEC development in premature infants remain unknown, we investigated the role and characteristics of MAIT cells in NEC pathogenesis.Methods: The percentage of different MAIT cell subsets in peripheral blood samples of 30 preterm infants with NEC and 22 control subjects was estimated using flow cytometry. The frequency of MAIT cells in the intestinal tissues of five NEC patients and five control subjects was also examined. The level of serum cytokines was estimated using cytometric bead array. Potential associations between the different measurements were analyzed using the Spearman’s correlation test.Results: Compared with controls, the NEC patients were found to have significantly reduced percentages of circulating CD161+ CD3+ CD8αα+ T cells and CD161+ CD3+ TCRγδ-TCRVa7.2+ MAIT cells. In the intestinal tissues, the percentage of MAIT cells was significantly higher in samples from the NEC patients than the controls. Furthermore, the percentage of circulating MAIT cells in the peripheral blood samples was inversely correlated with that in the intestinal tissues of the NEC patients. The percentage of CD8αα+ MAIT cells was found to be significantly reduced in both peripheral blood and intestinal tissues of NEC patients. Following treatment, the frequency of circulating MAIT cells significantly increased in NEC patients and reached a level similar to that in the control subjects. However, there was no difference in the percentage of circulating CD8αα+ MAIT cells before and after treatment in the NEC patients.Conclusion: Our results suggested that during the development of NEC MAIT cells accumulate in the inflammatory intestinal tissues, while the percentage of CD8aa+ MAIT cells is significantly decreased, which may lead to the dysfunction of MAIT cells in gut immunity.

In this paper, infrared polarization detection information acquisition technology is proposed, and the polarization characteristics of oil spills are modeled and studied. A set of long-wave infrared polarization detection equipment for oil spills is designed and built, and modeling research on oil spill polarization characteristics is carried out to accurately detect and identify oil spill types and for the faster processing of oil spill events. Oil spill accuracy is increased by defining the polarization maintenance method of the polarization optical system and reducing the polarization measurement error brought on by the imaging system. As a result, a higher than 3% contrast exists between the polarization degree image and the corrected infrared intensity image. Outdoor tests using oil, palm oil, crude oil, gasoline, and diesel oil spill types are carried out in a controlled environment to collect data on the polarization of various oil species. According to the findings, each oil species’ infrared polarization contrast with seawater is typically greater than its infrared intensity contrast. However, the polarization data of saltwater, diesel, and palm oil, which are difficult to identify in intensity data, show a noticeable difference, further proving the viability of utilizing polarization to discern oil spills.

In the Yuan Dynasty, Lord Yan 晏公 was worshipped by the people of Jiangxi 江西 as a water god, but there was no consensus on the identity of the god and the process of his deification. During the transitional period between the Yuan and Ming dynasties, the cult of Yan Gong was increasingly popular among different social groups in the Qingjiang 清江 region. Later, thanks to a combination of officials, merchants, and immigrants, its spatial scope was extended to Hubei 湖北 Province. During the Hongwu 洪武 (r. 1368–1398) period, the cult of Lord Yan in Hubei was so prevalent that multiple groups of people were enthusiastically involved in the construction of Lord Yan temples; thus, many temples shot up along lakes and the main tributaries of the Yangtze River, constituting a geographical distribution pattern with a concentration in the central and eastern parts and a scarcity in the west. The reason for this was the multidimensional interaction of migration activities, the cross-regional economic activities of merchants, and the promotion of folk beliefs by local officials since the Ming–Qing era, which encompasses the historical evolutionary features of actors competing for the cult of gods and control of regional social power.

Mitochondrial DNA (mtDNA) is an effective genetic marker in forensic practice, especially for aged bones and hair shafts. Detection of the whole mitochondrial genome (mtGenome) using traditional Sanger-type sequencing is laborious and time-consuming. Additionally, its ability to distinguish point heteroplasmy (PHP) and length heteroplasmy (LHP) is limited. The application of massively parallel sequencing in mtDNA detection helps researchers to study the mtGenome in-depth. The ForenSeq mtDNA Whole Genome Kit, which contains a total of 245 short amplicons, is one of the multiplex library preparation kits for the mtGenome. We used this system to detect the mtGenome in the blood samples and hair shafts of thirty-three individuals from eight two-generation pedigrees, one three-generation pedigree, and one four-generation pedigree. High-quality sequencing results were obtained. Ten unique mtGenome haplotypes were observed in the mothers from the ten pedigrees. A total of 26 PHPs were observed using the interpretation threshold of 6%. Eleven types of LHPs in six regions were evaluated in detail. When considering homoplasmic variants only, consistent mtGenome haplotypes were observed between the twice-sequenced libraries and between the blood and hair shafts from the same individual and among maternal relatives in the pedigrees. Four inherited PHPs were observed, and the remainder were de novo/disappearing PHPs in the pedigrees. Our results demonstrate the effective capability of the ForenSeq mtDNA Whole Genome Kit to generate the complete mtGenome in blood and hair shafts, as well as the complexity of mtDNA haplotype comparisons between different types of maternal relatives when heteroplasmy is considered.

Abstract Background Mesenchymal stem cells (MSCs) hold promising potential to treat systemic inflammatory diseases including severe acute pancreatitis (SAP). In our previous study, placental chorionic plate-derived MSCs (CP-MSCs) were found to possess superior immunoregulatory capability. However, the therapeutic efficacy of CP-MSCs on SAP and their underlying mechanism remain unclear. Methods The survival and colonization of exogenous CP-MSCs were observed by bioluminescence imaging and CM-Dil labeling in rodent animal models of SAP. The therapeutic efficacy of CP-MSCs on SAP rats was evaluated by pathology scores, the levels of pancreatitis biomarkers as well as the levels of inflammatory factors in the pancreas and serum. The potential protective mechanism of CP-MSCs in SAP rats was explored by selectively depleting M1 or M2 phenotype macrophages and knocking down the expression of TSG-6. Results Exogenous CP-MSCs could survive and colonize in the injured tissue of SAP such as the lung, pancreas, intestine, and liver. Meanwhile, we found that CP-MSCs alleviated pancreatic injury and systemic inflammation by inducing macrophages to polarize from M1 to M2 in SAP rats. Furthermore, our data suggested that CP-MSCs induced M2 polarization of macrophages by secreting TSG-6, and TSG-6 played a vital role in alleviating pancreatic injury and systemic inflammation in SAP rats. Notably, we found that a high inflammation environment could stimulate CP-MSCs to secrete TSG-6. Conclusion Exogenous CP-MSCs tended to colonize in the injured tissue and reduced pancreatic injury and systemic inflammation in SAP rats through inducing M2 polarization of macrophages by secreting TSG-6. Our study provides a new treatment strategy for SAP and initially explains the potential protective mechanism of CP-MSCs on SAP rats.

Transition metal dichalcogenides (TMDCs) are widely used in biosensing applications due to their excellent physical and chemical properties. Due to the properties of biomaterial targets, the biggest challenge that biosensors face now is how to improve the sensitivity and stability. A lot of materials had been used to enhance the target signal. Among them, TMDCs show excellent performance in enhancing biosensing signals because of their metallic and semi-conducting electrical capabilities, tunable band gap, large specific surface area and so on. Here, we review different functionalization methods and research progress of TMDCs-based biosensors. The modification methods of TMDCs for biosensor fabrication mainly include two strategies: non-covalent and covalent interaction. The article summarizes the advantages and disadvantages of different modification strategies and their effects on biosensing performance. The authors present the challenges and issues that TMDCs need to be addressed in biosensor applications. Finally, the review expresses the positive application prospects of TMDCs-based biosensors in the future.

The consistency evaluation, which is largely unexplored is crucial in regulating the quality and efficacy of generic drugs. In this report, after generic ibuprofen (IBU) sustained-release pellets were developed and validated as bioequivalent to the reference list drug (RLD) in 48 healthy human volunteers (p

Nowadays, kinship testing is very common in forensic caseworks, but the power of autosomal short tandem repeats (A-STRs) may be limited in complex cases. X-Chromosome short tandem repeats (X-STRs), having a unique heritage mode, should be of special use in some deficient cases. To evaluate and compare the potential of A-STR and X-STR as supplement genetic markers in deficient kinship testing, we simulated 10,000 duos for each of 18 kinds of relationships involving full sibling, half-sibling, grandparent-grandchild, and uncle/aunt-nephew/niece. Loci from STRTyper10, PowerPlex 16, and Investigator Argus X-12 were studied in Southern Han Chinese and the distribution of likelihood ratio (LR) values was analysed. With the addition of the X-12 system, the distribution of LR values for the full sisters, paternal half-sisters, paternal grandmother-granddaughters, maternal aunt-nieces, and maternal aunt-nephews separated much more obviously from those of unrelated duos, and the effectiveness was 1.0000, 0.99865, 0.9991, 0.8996 and 0.9634, respectively, which was more efficient than A-STRs. For the individual duos with other relationships, the effects of adding X-STRs and A-STRs were similar. Therefore, for the Southern Han Chinese, X-STRs can be very useful in kinship testing involving full sisters, paternal half-sisters, paternal grandmother-granddaughters, and maternal aunt-nieces/nephews.

Abstract Background Recent work has established that Parkinson’s disease (PD) patients have an altered gut microbiome, along with signs of intestinal inflammation. This could help explain the high degree of gastric disturbances in PD patients, as well as potentially be linked to the migration of peripheral inflammatory factors into the brain. To our knowledge, this is the first study to examine microbiome alteration prior to the induction of a PD murine model. Methods We presently assessed whether pre-treatment with the probiotic, VSL #3, or the inflammatory inducer, dextran sodium sulphate (DSS), would influence the PD-like pathology provoked by a dual hit toxin model using lipopolysaccharide (LPS) and paraquat exposure. Results While VSL #3 has been reported to have anti-inflammatory effects, DSS is often used as a model of colitis because of the gut inflammation and the breach of the intestinal barrier that it induces. We found that VSL#3 did not have any significant effects (beyond a blunting of LPS paraquat-induced weight loss). However, the DSS treatment caused marked changes in the gut microbiome and was also associated with augmented behavioral and inflammatory outcomes. In fact, DSS markedly increased taxa belonging to the Bacteroidaceae and Porphyromonadaceae families but reduced those from Rikencellaceae and S24-7, as well as provoking colonic pro-inflammatory cytokine expression, consistent with an inflamed gut. The DSS also increased the impact of LPS plus paraquat upon microglial morphology, along with circulating lipocalin-2 (neutrophil marker) and IL-6. Yet, neither DSS nor VSL#3 influenced the loss of substantia nigra dopamine neurons or the astrocytic and cytoskeleton remodeling protein changes that were provoked by the LPS followed by paraquat treatment. Conclusions These data suggest that disruption of the intestinal integrity and the associated microbiome can interact with systemic inflammatory events to promote widespread brain-gut changes that could be relevant for PD and at the very least, suggestive of novel neuro-immune communication.

Multivariate Time series data play important roles in our daily life. How to use these data in the process of prediction is a highly attractive study for many researchers. To achieve this goal, in this paper, we present a novel multivariate time series prediction method based on multi-attention generative adversarial network. This method includes three phases to explore multivariate time series prediction. Firstly, the encoder stage consists of two modules, from which the input-attention and self-attention can encode the exogenous sequence into latent space. Secondly, the decoder stage consists of the temporal-convolution-attention module, which can extract long-term temporal patterns. To solve the problem of low accuracy in long-term prediction, inspired by the weight clipping method, we design an improved discrimination network finally. The experiment results indicate that multi-attention mechanism is useful and the discrimination network can improve the performance in multivariate time series prediction. We also tested extensive empirical studies with five real world datasets (NASDAQ100, SML2010, Energy, EEG and Air Quality) demonstrate the effectiveness and robustness of our proposed approach.

Magnetic flux leakage testing (MFL) is the most widely used nondestructive testing technology in the safety inspection of oil and gas pipelines. The analysis of MFL test data is essential for pipeline safety assessments. In recent years, deep-learning technologies have been applied gradually to the data analysis of pipeline MFL testing, and remarkable results have been achieved. To the best of our knowledge, this review is a pioneering effort on comprehensively summarizing deep learning for MFL detection and evaluation of oil and gas pipelines. The majority of the publications surveyed are from the last five years. In this work, the applications of deep learning for pipeline MFL inspection are reviewed in detail from three aspects: pipeline anomaly recognition, defect quantification, and MFL data augmentation. The traditional analysis method is compared with the deep-learning method. Moreover, several open research challenges and future directions are discussed. To better apply deep learning to MFL testing and data analysis of oil and gas pipelines, it is noted that suitable interpretable deep-learning models and data-augmentation methods are important directions for future research.

Abstract A novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of BP neural network input layer neurons and the training and learning process of subsequent neural network data. Finally, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and then the vertical handover algorithm will select the right wireless network to perform vertical handover decision. The simulation results on MATLAB platform show that the vertical handover algorithm designed in this paper has a handover success rate up to 90% and realizes efficient handover and seamless connectivity between multi-heterogeneous networks.

Abstract Parkinson’s disease (PD) is characterized by the formation of toxic, fibrillar form alpha-synuclein (α-Syn) protein aggregates in dopaminergic neurons. Accumulating evidence has shown a multifactorial interplay between the intracellular calcium elevation and α-Syn dynamics. However, whether membrane depolarization regulates toxic α-Syn aggregates remains unclear. To understand this better, we used an in vitro α-Syn preformed fibrils (PFF) model of PD in human neural cells. We demonstrated functional membrane depolarization in differentiated SH-SY5Y cells induced by two independent treatments: high extracellular K+ and the GABAA receptor blocker picrotoxin. We then observed that these treatments significantly alleviated toxic α-Syn aggregation in PFF-treated SH-SY5Y cells. Moreover, clinically relevant direct current stimulation (DCS) also remarkably decreased toxic α-Syn aggregation in PFF-treated SH-SY5Y cells. Taken together, our findings suggest that membrane depolarization plays an important role in alleviating PFF-induced toxic α-Syn aggregates, and that it may represent a novel therapeutic mechanism for PD.

Task offloading could optimize computational resource utilization in edge computing environments. However, how to assign and offload tasks for different behavior users is an essential problem since the systems dynamic, intelligent application diversity, and user personality. With user behavior prediction, this paper proposes soCoM, a semi-online Computational Offloading Model. We explore the user behaviors in sophisticated action space by reinforcement learning for catching unknown environment information. With Dueling Deep-Q Network, both the prediction accuracy of users' behaviors and the server load balance are well-considered, while increasing the computational efficiency and decreasing the resource costing. We propose a dynamic simulation environment of edge computing to demonstrate that user behavior is the critical factor for impacting system utilization. As the action space increasing, Dueling DQN performs better than state-of-art DQN and other improved strategies, and also load balance in multiple different server scenario.