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Automatic text summarization can help people to filter and identify information quickly,grasp the key content of news,and alleviate the problem of information overload.The mainstream abstractive summarization model is mainly based on the encoder-decoder architecture.In view of the fact that the decoder does not fully consider the text topic information when predicting the target word,and the traditional Word2Vec static word vector cannot solve the polysemy problem,an automatic summarization model for Chinese short news is proposed,which integrates the BERT word embedding representation and topic information enhancing.The encoder combines unsupervised algorithm to obtain text topic information and integrates it into the attention mechanism to improve the decoding effect of the model.At the decoder side,the BERT sentence vector extracted from the BERT pre-trained language model is used as the supplementary feature to obtain more semantic information.Meanwhile,pointer mechanism is introduced to solve the problem of out of vocabulary,and coverage mechanism is used to suppress repetition effectively.Finally,in the training process,reinforcement learning method is adopted to optimize the model for non-differentiable index ROUGE to avoid exposing bias.Experimental results on two datasets of Chinese short news summarization show that the proposed model can significantly improve the ROUGE evaluation index,effectively integrate text topic information,and generate fluent and concise summaries.

Semiconductor alloy materials are highly versatile due to their adjustable properties; however, exploring their structural space is a challenging task that affects the control of their properties. Traditional methods rely on ad hoc design based on the understanding of known chemistry and crystallography, which have limitations in computational efficiency and search space. In this work, we present ChecMatE (Chemical Material Explorer), a software package that automatically generates machine learning potentials (MLPs) and uses global search algorithms to screen semiconductor alloy materials. Taking advantage of MLPs, ChecMatE enables a more efficient and cost-effective exploration of the structural space of materials and predicts their energy and relative stability with ab initio accuracy. We demonstrate the efficacy of ChecMatE through a case study of the InxGa1−xN system, where it accelerates structural exploration at reduced costs. Our automatic framework offers a promising solution to the challenging task of exploring the structural space of semiconductor alloy materials. [ABSTRACT FROM AUTHOR]

Spirotryprostatins are representative members of medicinally interesting bioactive molecules of the spirooxindole natural products. In this communication, we present a novel enantioselective total synthesis of the spirooxindole alkaloid dihydrospirotryprostatin B. The synthesis takes advantage of copper-catalyzed tandem reaction of o-iodoanilide chiral sulfinamide derivatives with alkynone to rapidly construct the key quaternary carbon stereocenter of the natural product dihydrospirotryprostatin B. [ABSTRACT FROM AUTHOR]

By grouping nodes with similar attributes into clusters, the energy efficiency and lifespan of wireless sensor networks(WSNs) can be improved effectively. However, the number of clusters needs to be set in advance, and the optimal cluster heads (CHs) are difficult to determine, which will undoubtedly reduce the network’s overall performance. Therefore, a new clustering method using Affinity Propagation and Chaotic Lion swarm Optimization is proposed in this paper to form optimal clusters, which is called APCLO. In APCLO, Affinity Propagation (AP) is used to construct a cluster topology, forming the initial clustering according to the remaining energies of the nodes and the similarity of the distances between nodes. Moreover, the initial CHs are also determined simultaneously by AP. In order to eliminate the outliers from the initial CHs, Chaotic Lion Optimization(CLO) is presented to find the best CHs, in which a fitness function is set according to residual energy and distance to the BS, and chaotic map is used to speed up the convergence of CLO. Simulation results show that the APCLO protocol is superior to the comparison protocols in terms of energy consumption, network throughput, convergence speed, and lifetime. For network lifespan, it increases by 20.1%, 11.2%, and 13.5% respectively.

Objective To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. Methods Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. Results SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. Conclusion The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered. [ABSTRACT FROM AUTHOR]

Recently, composting cultivation method is widely used in oyster mushroom production. In this study, we focused on the effects of composting processes on nutritional qualities and antioxidant activity of Pleurotus floridanus mushroom fruiting bodies. Three treatments of different composting time (2, 4, and 5 days) were performed with an atmospheric sterilization treatment as the control. The results showed that the pH value, total carbon content, and total nitrogen content of substrate were critical parameters which would significantly affect mushroom qualities and bioactivities. Fruiting bodies of the control demonstrated significantly higher crude protein content, total amino acid content, and essential amino acid content than that of composting treatments. Moreover, fruiting bodies of treatment D4 and D5 manifested significantly higher crude polysaccharide contents. Crude polysaccharide of treatment D4 represented the highest scavenging ability toward both radical 3‐ethylbenzthiazoline‐6‐sulfonic acid (ABTS·+) and Hydroxyl radical (OH·). It suggests that composting processes is suitable for oyster mushroom cultivation based on nutritional and antioxidant qualities of fruiting bodies. [ABSTRACT FROM AUTHOR]

Anacardic acid (AA) is a phenolic acid extract found in a number of plants, crops, and fruits. It exhibits a wide range of biological activities. This study displayed that AA effectively alleviated EAE, a classical mouse model of multiple sclerosis. AA administered to the EAE greatly decreased inflammatory cell infiltration to the CNS and protected the myelin integrity in the white matter of the spinal cord. AA could block lipopolysaccharide-induced DC activation. inhibited the polarization of 2D2 mice-derived T cells by inhibiting the DCs activity. Immunoblot results indicated that the phosphorylation of NF-κB is significantly suppressed in AA-treated DCs. This work displayed that AA possessed a potential anti-inflammatory therapeutic effect for the treatment of autoimmune disease. • Anacardic acid effectively alleviated EAE. • AA greatly decreased inflammatory cell infiltration to the CNS. • Myelin integrity raised after AA treatment in the white matter. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents., Methods: Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated., Results: SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old., Conclusions: The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.

Long-term exposure to arsenic can lead to functional damage to many organs and can be life-threatening. It is of great significance to analyze the distribution characteristics of arsenic in water and evaluate its potential risk for preventing and controlling human health hazards caused by water-derived arsenic. Based on the published data from 2000 to 2022, the geographical distribution characteristics of arsenic in drinking water across China were systematically analyzed in detail, and the health risk of arsenic in drinking water was quantitatively assessed using disability-adjusted life years (DALYs), which represent the burden of disease. The results showed that the average concentration of arsenic in drinking water in China was (2.88 ±0.33) μg·L -1 , which was lower than the limit of 10 μg·L -1 set by the standard for drinking water quality (GB 5749-2022). Nevertheless, the arsenic in drinking water in some provinces, including Shanxi, Inner Mongolia, and Ningxia, was still higher than the limit. The arsenic concentration in drinking water in northern China was higher than that in southern China, and that in rural areas was higher than that in cities. The estimated health risk of arsenic in drinking water (1.63×10 -6 DALYs per person-year) was higher than the acceptable risk level of waterborne exposure of 1.0×10 -6 DALYs per person-year set by the World Health Organization. The personal health risks related to arsenic in drinking water in the six geographical regions were ranked as follows:North China > Northeast China > Central South China > Northwest China > Southwest China > East China. Almost all (99.4%) of the health burden associated with water arsenic was attributable to skin and lung cancer, which caused 2 905.25 and 1 513.96 DALYs per year, respectively. Most (78.0%) of the health burden was borne by people aged 45 years or older. In addition, given the proportion of each age group in the total population, older persons over the age of 60 bear a higher drinking-water-associated arsenic burden at the individual level than others, and attention should be consequently paid to them when controlling the risk of arsenic in water.

Objective: To investigate the clinical characteristics, diagnosis, and treatment of one patient with primary adrenal natural killer/T-cell lymphoma (PANKTCL), and to strengthen the understanding of this rare type of lymphoma., Methods: The clinical manifestations, diagnosis and treatment process, and prognosis of the patient admitted in our hospital were retrospectively analyzed., Results: Combined with pathology, imaging, bone marrow examination， etc, the patient was diagnosed with PANKTCL (CA stage, stage II; PINK-E score 3, high-risk group). Six cycles of "P-GemOx+VP-16" regimen（gemcitabine 1 g/m 2 d1 + oxaliplatin 100 mg/m 2 d 1 + etoposide 60 mg/m 2 d 2-4 + polyethylene glycol conjugated asparaginase 3 750 IU d 5） was performed, and complete response was assessed in 4 cycles. Maintenance therapy with sintilimab was administered after the completion of chemotherapy. Eight months after the complete response, the patient experienced disease recurrence and underwent a total of four courses of chemotherapy, during which hemophagocytic syndrome occurred. The patient died of disease progression 1 month later., Conclusion: PANKTCL is rare, relapses easily, and has a worse prognosis. The choice of the "P-GemOx+VP-16" regimen combined with sintilimab help to improve the survival prognosis of patient with non-upper aerodigestive tract natural killer /T-cell lymphoma.

Besides improving charge transfer, there are two key factors, such as increasing active sites and promoting water dissociation, to be deeply investigated to realize high-performance MoS 2 -based electrocatalysts in alkaline hydrogen evolution reaction (HER). Herein, we have demonstrated the synergistic engineering to realize rich unsaturated sulfur atoms and activated O-H bonds toward the water for Ni-doped MoS 2 /CoS 2 hierarchical structures by an approach to Ni doping coupled with in situ sulfurizing for excellent alkaline HER. In this work, the Ni-doped atoms are evolved into Ni(OH) 2 during alkaline HER. Interestingly, the extra unsaturated sulfur atoms will be modulated into MoS 2 nanosheets by breaking Ni-S bonds during the formation of Ni(OH) 2 . On the other hand, the higher the mass of the Ni precursor ( m Ni ) for the fabrication of our samples, the more Ni(OH) 2 is evolved, indicating a stronger ability for water dissociation of our samples during alkaline HER. Our results further reveal that regulating m Ni is crucial to the HER activity of the as-synthesized samples. By regulating m Ni to 0.300 g, a balance between increasing active sites and promoting water dissociation is achieved for the Ni-doped MoS 2 /CoS 2 samples to boost alkaline HER. Consequently, the optimal samples present the highest HER activity among all counterparts, accompanied by reliable long-term stability. This work will promise important applications in the field of electrocatalytic hydrogen evolution in alkaline environments.

Objective To evaluate the genetic polymorphisms and forensic efficiencies of 35 deletion/insertion polymorphism(DIP)loci and explore the genetic structure of the Han population in Shaanxi province.Methods Blood samples of 305 unrelated healthy individuals of the Han population in Shaanxi province were collected.And the allelic frequencies and forensic parameters of 35 DIP loci were calculated and analyzed based on their genotyping results by a self-developed amplification system.The genetic relationship of the Han population in Shaanxi province with the reference populations was explored by molecular variance analyses,phylogenetic tree reconstruction,multidimensional scale analyses,principal component analyses,and STRUCTURE analyses.Results The combined power of discrimination and probability of exclusion of the 35 DIP loci in the Han population in Shaanxi province were 0.999 999 999 999 991 119 and 0.9991,respectively.Population genetic analyses indicated that the Han population in Shaanxi province shared relatively closer genetic relationships with those in other regions of China.Conclusions The 35 DIP loci possessed high polymorphisms and could be used as an effective tool for forensic identification of individuals of the Han population in Shaanxi province.Furthermore,the 35 DIP loci could provide basic data for the genetic analysis of the Han population in Shaanxi province.