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Abstract The activation of ferroptosis presents a versatile strategy for enhancing the antitumor immune responses in cancer therapy. However, developing ferroptosis inducers that combine high biocompatibility and therapeutic efficiency remains challenging. In this study, we propose a novel approach using biological nanoparticles derived from outer membrane vesicles (OMVs) of Escherichia coli for tumor treatment, aiming to activate ferroptosis and stimulate the immune responses. Specifically, we functionalize the OMVs by anchoring them with ferrous ions via electrostatic interactions and loading them with the STING agonist-4, followed by tumor-targeting DSPE-PEG-FA decoration, henceforth referred to as OMV/SaFeFA. The anchoring of ferrous ions endows the OMVs with peroxidase-like activity, capable of inducing cellular lipid peroxidation by catalyzing H2O2 to •OH. Furthermore, OMV/SaFeFA exhibits pH-responsive release of ferrous ions and the agonist, along with tumor-targeting capabilities, enabling tumor-specific therapy while minimizing side effects. Notably, the concurrent activation of the STING pathway and ferroptosis elicits robust antitumor responses in colon tumor-bearing mouse models, leading to exceptional therapeutic efficacy and prolonged survival. Importantly, no acute toxicity was observed in mice receiving OMV/SaFeFA treatments, underscoring its potential for future tumor therapy and clinical translation.

Abstract Background Exploring novel biomarkers for gastric cancer holds promise for enhancing patients’ therapy and survival rates. lncRNAs and miRNAs have emerged as important biomarkers for various human cancers. However, the role of lncRNA RMST (RMST) in gastric cancer development and the mechanism underlying its function remains unclear. Results Significant upregulation of RMST was observed in gastric cancer tumor tissues. RMST levels showed strong correlation with patients’ lymph node metastasis and TNM stage and serving as a predictor of adverse prognosis RMST negatively regulated miR-204-5p, which in turn mediated the inhibitory effects of RMST knockdown on gastric cancer cell growth and metastasis. Conclusion RMST served as both a prognostic biomarker and tumor promoter by modulating miR-204-5p. Inhibiting RMST could represent a novel and potential therapeutic strategy for gastric cancer.

To understand the seasonal variations in shellfish community structure, the composition and diversity of shellfish in the intertidal zone of the Changshan Archipelago, China, were investigated. We investigated the composition and diversity of shellfish species in the intertidal zone (including NCD1, NCD2, BCD1, BCD2, TJD1, TJD2, DQD1, DQD2, NHD1, and NHD2) of five representative islands and 10 survey stations in the Changshan Archipelago during the low tide period in July and October 2021 and February and May 2022, and the water quality parameters of point water and interstitial water were analyzed. The results showed that the bottom material of the Changshan Archipelago was generally rock or gravel and the salinity fluctuated with the seasons. The salinity was high in summer and fall while low in spring and winter, with an average of 30.92±0.84. Compared with spring, summer, and fall, the temperature changed significantly in winter, with an average of (4.51±0.32) ℃ and the highest temperature being 23.48 ℃ during spring, summer, and fall. In general, the dissolved oxygen in the main islands of Changshan Archipelago (North and South Changshan Islands) was relatively low, with an average of (8.59±0.38) mg/L, while that in Tuoji Island, Daqin Island, and Nanhuangcheng Island was relatively high, with an average of (9.95±0.51) mg/L. A total of 12 species of shellfish were collected in this survey, including Crassostrea sp., Monodonta labio Linnaeus, Littorina brevicula, Mytilus galloprovincialis, Patelloida pygmaea, Chlorostoma rustica, Reishia clavigera, Thais luteostoma, Ocinebrellus inornata, Cellana toreuma, Ruditapes philippinarum, and Rapana venosa. The number of shellfish species in each season was the highest in spring, with an average of eight species, followed by in fall, with an average of six species. Winter had the least number of shellfish species collected, with an average of four species. In addition, the North and South Changshan Islands presented the most shellfish species at eight and nine species, respectively. The number of shellfish collected in DQD was the least (six species). There were differences in shellfish density at different survey sites, among which DQD2 had the highest density, with an average density of 41.26 ind./m2. The lowest density was in NCD2, and the average density was 13.71 ind./m2. In the same season, the shellfish density at different survey sites was different, the proportion of dominant species at different survey sites was also different. In spring, the density of Crassostrea sp., Cellana toreuma, and Chlorostoma rustica was high, while in summer, the density of Crassostrea sp., Mytilus galloprovincialis, Littorina brevicula, and Cellana toreuma was high. In fall, the density of Crassostrea sp., Littorina brevicula, and Mytilus galloprovincialis was the highest, while in winter, the density of Crassostrea sp., Patelloida pygmaea, and Cellana toreuma was the highest. The species diversity (H´) of shellfish in each tidal area of the survey site is shown as middle tidal zone (1.44) > low tidal zone (1.40) > high tidal zone (0.44). Overall, the average shellfish species diversity in the 10 survey sites was spring (1.26) > winter (1.21) > fall (1.11) > summer (0.95). The average evenness index of species (J´) in the 10 survey stations was 0.55, among which the evenness index of DQD2, NCD2, NHD2, and TJD1 shellfish was 0.67, 0.64, 0.64, and 0.55, respectively. The evenness index of shellfish in each tidal area of the survey site was in order of middle tidal zone (0.67) > low tidal zone (0.59) > high tidal zone (0.29). The seasonal change was not obvious. The average shellfish abundance at the survey site was 0.533, including 0.82, 0.68, 0.60, and 0.53 shellfish abundance at NCD2, NHD2, BCD2, and NCD1, respectively. The abundance index of shellfish in each tidal area of the survey site was in order of middle tidal zone (0.56) > low tidal zone (0.52) > high tidal zone (0.17). Overall, the average shellfish abundance in the 10 survey sites was spring (0.681) > fall (0.573) > summer (0.458) > winter (0.418). Seasons (temperatures) have the most obvious impact on the diversity and abundance of intertidal shellfish resources. Anthropogenic activities, such as aquaculture and tourism, also affect the distribution and quantity of shellfish species. The results of this study not only enrich the data of intertidal shellfish resources on islands in China but also provide basic information and reference for biodiversity and environmental protection of intertidal shellfish on the Changshan Islands.

Identifying workers’ construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress. However, current activity analysis methods for construction workers rely solely on manual observations and recordings, which consumes considerable time and has high labor costs. Researchers have focused on monitoring on-site construction activities of workers. However, when multiple workers are working together, current research cannot accurately and automatically identify the construction activity. This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers. In this framework, multiple deep neural network models are designed and used to complete worker key point extraction, worker tracking, and worker construction activity analysis. The designed framework was tested at an actual construction site, and activity recognition for multiple workers was performed, indicating the feasibility of the framework for the automated monitoring of work efficiency.

Abstract The white-crowned sparrow, Zonotrichia leucophrys, is a passerine bird with a wide distribution and it is extensively adapted to environmental changes. It has historically acted as a model species in studies on avian ecology, physiology and behaviour. Here, we present a high-quality chromosome-level genome of Zonotrichia leucophrys using PacBio and OmniC sequencing data. Gene models were constructed by combining RNA-seq and Iso-seq data from liver, hypothalamus, and ovary. In total a 1,123,996,003 bp genome was generated, including 31 chromosomes assembled in complete scaffolds along with other, unplaced scaffolds. This high-quality genome assembly offers an important genomic resource for the research community using the white-crowned sparrow as a model for understanding avian genome biology and development, and provides a genomic basis for future studies, both fundamental and applied.

Magnesium alloys are widely used as biodegradable medical biomaterials due to their density and modulus of elasticity close to that of human bone. However, too rapid degradation in the physiological environment limits their application. In this paper, the non-toxic and easy alloying properties of liquid metal gallium are utilized to make it a coating material for biomedical Mg–Nd alloys, which is used to solve the problem of the rapid degradation rate of Mg alloys. The corrosion resistance of the Ga–Mg alloy layer was investigated by in vitro immersion in simulated body fluid (SBF) and electrochemical experiments, and a microhardness tester characterized the surface hardness. In addition, the phase transformation of the Ga–Mg alloy layer was systematically investigated in combination with XRD diffraction results. The results showed that by adjusting the alloying time of Ga with Mg–Nd in liquid metal, a “retarded layer” with high hardness, stability, and good corrosion resistance was obtained. This study is expected to expand the application of Mg alloy materials in medicine.

Chronic wound infections, particularly multidrug-resistant microbe-caused infections, have imposed severe challenges in clinical administration. The therapeutic effectiveness of the current strategy using conventional antibiotics is extremely unsatisfactory. The development of novel treatment strategies to inhibit the infections caused by multidrug-resistant bacteria is highly desired. In this work, based on the combination of nanocompounds with the assistance of NIR laser, an antibacterial strategy was designed for MRSA-infected abscesses in diabetic mice. The nanocompounds named Ag@Chi-PB were prepared by using chitosan-coated Prussian blue (PB) as a nanocarrier for silver nanoparticles anchoring. Combined with near-infrared (NIR) laser, the nanocompounds were more efficient at killing Escherichia coli (E. coli) and Methicillin-resistant staphyllococcus aureus (MRSA) in vitro. Notably, MRSA was significantly removed in vivo and promoted diabetic abscess healing by the combined therapy of this nanocompound and NIR laser, owing to the synergistic antibacterial effect of photothermal therapy and release of Ag+. Meanwhile, the nanocompound showed satisfactory biocompatibility and superior biosafety. Collectively, the combination therapy of this nanocompound with the assistance of NIR laser may represent a promising strategy for clinical anti-infection.

For the application of continuously adjustable optical fiber delay lines, a large delay range can increase the instrument’s measurement range. Increased insertion loss of components will reduce the signal-to-noise ratio of the collected signal, ultimately limiting the measurement accuracy of the instrument. In order to take into account both large delay range and low insertion loss in design, a spatial light transmission and fiber coupling model based on scalar diffraction theory was established and the insertion loss was equivalently calculated through the coupling efficiency of the coupling lens. Then, we analyze the impact of optical fiber delay line’s structural parameters and adjustment errors on coupling efficiency. By optimizing lens group parameters and limiting adjustment tolerances, we can reduce the insertion loss of optical fiber delay lines in a large delay range and conduct experimental verification. Considering various influencing factors, we use a lens group with a focal length of 12 mm, achieving a coupling efficiency of no less than 92.7% within the delay range of 0–1 m. This theoretical model provides a new quantitative analysis method for improving the performance of the optical fiber delay line.

C16H26Cl2CoN2O14S2, monoclinic, P21/n (no. 14), a = 5.3261(4) Å, b = 39.755(2) Å, c = 6.2518(4) Å, β = 96.880(6)∘, V = 1314.21(15) Å3, Z = 2, Rgt(F) = 0.0487, wRref(F2) = 0.1040, T = 153(2) K.

Abstract Reliable mechanical fault diagnosis of high-voltage circuit breakers is important to ensure the safety of electric power systems. Recent fault diagnosis approaches are mostly based on a single classifier whose performance relies heavily on expert prior knowledge. In this study, we propose an improved Dempster–Shafer evidence theory fused echo state neural network, an ensemble classifier for fault diagnosis. Evidence credibility is calculated through the evidence deviation matrix and the segmented circle function and employed as credibility weights to rectify the raw evidence. Then, an improved Dempster–Shafer evidence fusion algorithm is proposed to fuse evidence from different echo state network modules and sensors. Unlike conventional classifiers, the proposed methodology consists of multiple echo state neural network modules. It has better flexibility and stronger robustness, and its model performance is not sensitive to network parameters. Comparative analysis indicates that it can handle the paradox evidence fusion analysis and thus can achieve better diagnostic performance. The superiority of the reported fault diagnosis approaches is verified with the experimental data of a ZN12 high-voltage circuit breaker.

Societal Impact Statement Increasing atmospheric nitrogen (N) deposition represents a major global change factor, but its long‐term effect on tree growth and carbon (C) sequestration remains uncertain. Our manipulation experiment and meta‐analysis reveal that N deposition in temperate and boreal forests promoted tree growth and the allocation of more C into wood than into leaves in China and worldwide. Thus, N deposition may increase forest C sequestration through enhanced wood production and distribution of C into stable sinks. In the context of achieving “Carbon Neutrality,” understanding how N deposition affect long‐term forest C sinks will help us with mitigation strategies under climate change. Summary Increased nitrogen (N) deposition is driving many temperate and boreal forests in the Northern Hemisphere towards N saturation. However, it is uncertain how long‐term N deposition affects tree growth and carbon (C) allocation in these forests. To investigate this, we treated temperate larch and mixed forests in northeastern China with N additions for 8 years. In addition, we collected data from 25 N‐addition experiments in temperate and boreal forests worldwide to reveal the overall effects of N on tree growth and C allocation. Nitrogen additions significantly promoted total biomass increment by 24% in both study forests, with on average additional 8 kg C per kg N gain into woody biomass over the study period. Nitrogen additions increased the ratio of woody biomass increment to foliage litterfall production in the larch forest (by 34%). Literature data analysis also revealed greater N promotion on wood (24%) over foliage (9%) production. However, the positive effect on foliage diminished over time. These results combined imply that N deposition may promote tree growth in temperate and boreal regions and drive proportionally more photosynthate allocation into wood than leaves, thus may enhance forest C sequestration in the long run.

Antimicrobial peptides (AMPs) are vital components of innate immunotherapy. Existing approaches mainly rely on either deep learning for the automatic extraction of sequence features or traditional manual amino acid features combined with machine learning. The peptide sequence contains symmetrical sequence motifs or repetitive amino acid patterns, which may be related to the function and structure of the peptide. Recently, the advent of large language models has significantly boosted the representational power of sequence pattern features. In light of this, we present a novel AMP predictor called UniproLcad, which integrates three prominent protein language models—ESM-2, ProtBert, and UniRep—to obtain a more comprehensive representation of protein features. UniproLcad utilizes deep learning networks, encompassing the bidirectional long and short memory network (Bi-LSTM) and one-dimensional convolutional neural networks (1D-CNN), while also integrating an attention mechanism to enhance its capabilities. These deep learning frameworks, coupled with pre-trained language models, efficiently extract multi-view features from antimicrobial peptide sequences and assign attention weights to them. Through ten-fold cross-validation and independent testing, UniproLcad demonstrates competitive performance in the field of antimicrobial peptide identification. This integration of diverse language models and deep learning architectures enhances the accuracy and reliability of predicting antimicrobial peptides, contributing to the advancement of computational methods in this field.

The prefabricated composite slab (PCS) is an essential horizontal component in a building, which is made of a precast part and a cast-in-place concrete layer. In practice, the floor should be split into many small PCSs for the convenience of manufacturing and installation. Currently, the splitting design of PCS mostly relies on sound knowledge and valuable experience of construction. While rule-based parametric design tools using building information modeling (BIM) can facilitate PCS splitting, the generated solution is suboptimal and limited. This paper presents an intelligent BIM-based framework to automatically complete the splitting design of PCSs. A collaborative optimization model is formulated to minimize the composite costs of manufacturing and installation. Individuals with similar area information are grouped into a subpopulation, and the optimization objective is to minimize the specifications and quantities of PCSs. Through the correlation information within the subpopulation and the shared information among each other, the variable correlation is eliminated to accomplish the task of collaborative optimization. The multipopulation coevolution particle swarm optimization (PSO) algorithm is implemented for the collaborative optimization model to determine the sizes and positions of all PCSs. The proposed framework is applied in the optimized splitting design of PCSs in a standard floor to demonstrate its practicability and efficiency.

Abstract Background In poultry, the population structure of local breeds is usually complex mainly due to unrecorded breeding. Local chicken breeds offer an interesting proxy to understand the complexity of population structure in the context of human-mediated development of diverse morphologies and varieties. We studied 37 traditional Dutch chicken breeds to investigate population structure and the corresponding genomic impact using whole-genome sequence data. Results Looking at the genetic differences between breeds, the Dutch chicken breeds demonstrated a complex and admixed subdivided structure. The dissection of this complexity highlighted the influence of selection adhering to management purposes, as well as the role of geographic distance within subdivided breed clusters. Identification of signatures of genetic differentiation revealed genomic regions that are associated with diversifying phenotypic selection between breeds, including dwarf size (bantam) and feather color. In addition, with a case study of a recently developed bantam breed developed by crossbreeding, we provide a genomic perspective on the effect of crossbreeding. Conclusions This study demonstrates the complex population structure of local traditional Dutch chicken, and provides insight into the genomic basis and the factors involved in the formation of this complexity.

Abstract By facilitating the data delivery in wireless sensor networks, the movement of mobile sink can enhance the network connectivity and sensory coverage. However, the optimal path determination of mobile sink is a NP-hard optimization problem. By jointly considering the cluster-based routing and sink mobility, this paper proposes an enhanced ACO-based movement scheduling of mobile sink for data gathering in wireless sensor networks. To meet the delay requirements and balance the energy consumption of the sensor nodes, the optimal cluster heads selection is introduced. Then, an enhanced ACO-based movement scheduling algorithm is proposed to obtain the shortest path of mobile sink by traversing the network. The simulation results show that our proposed method can offer a promising performance in terms of reducing data delivery latency and extending the lifetime of the network.

When confronted with rainstorms and flood disturbances, the operational processes of urban metro systems demonstrate vulnerabilities to attacks, inadequate resistance, and sluggish recovery characteristics. The flood resilience of UMS operational processes requires urgent enhancements. This paper aims to enhance the flood resilience of urban metro operation processes by proposing a three-stage PEL resilience enhancement framework: prevention resilience, response resilience, and learning resilience. Additionally, it summarizes the influencing factors on UMS flood resilience from five dimensions: natural-physical-social-management-economic (NPSME). By employing system dynamics as a simulation tool, this study elucidates the logical interconnections among these influential factors. Furthermore, by utilizing economic change conditions as an illustrative example, it effectively simulates the response characteristics of both standardized benchmark scenarios and economic change scenarios. Based on these simulation results, corresponding strategies for flood resilience enhancement are proposed to offer valuable insights for metro operation management. The Nanjing metro system was taken as a case study, where relevant historical data were collected and strategies were simulated for different development scenarios to validate the effectiveness and rationality of the proposed method for enhancing resilience. The simulation results demonstrate that changes in economic conditions and population structure are the primary factors influencing the enhancement of flood resilience in UMS operations.

Green silver nanoparticles (AgNPs) possess tremendous promise for diverse applications due to their versatile characteristics. Coriander and other plant extracts have become popular for greenly synthesizing AgNPs as an economical, biocompatible, cost-effective, and environmentally beneficial alternative to chemical processes. In this study, we synthesized AgNPs from coriander leaves and evaluated their antibacterial, anti-inflammatory, antioxidant, and wound-healing acceleration properties in comparison to chemically synthesized AgNPs. The zeta potentials of AgNPs extracted from green and chemical processes were −32.4 mV and −23.4 mV, respectively. TEM images showed a cuboidal shape of green and chemical AgNPs with a diameter of approximately 100 nm. The FTIR spectra of green AgNPs showed an extreme absorption peak at 3401 cm−1, which signifies O-H stretching vibrations, typically linked to hydroxyl groups. In vitro results elaborated that AgNPs from coriander exerted a stronger effect on anti-Klebsiella pneumoniae (KP) through interrupting cell integrity, generating ROS, depleting ATP, and exhibiting significant antioxidant activity, compared with AgNPs synthesized chemically. In vivo experiments showed that AgNPs from coriander, as opposed to chemically manufactured AgNPs, greatly accelerated the healing of wounds contaminated with Klebsiella pneumoniae bacteria by effectively eliminating the bacteria on the wounds and stimulating skin regeneration and the deposition of dense collagen. In vivo assays further demonstrated that green AgNPs effectively enhanced Klebsiella pneumoniae-infected wound healing by extenuating local inflammatory responses and up-regulating VEGF and CD31 expression. In conclusion, green AgNPs significantly alleviated the inflammation without significantly harming the organism.

Long-distance water transfer is a critical engineering measure to rectify disparities in water resource distribution across regions. The effective operation and safety of such projects are paramount to their success, as localized issues can have cascading consequences, potentially disrupting the entire network. Conventional ground-based monitoring methods have limitations in measuring the deformation of large-scale structures. In this paper, InSAR is employed to monitor the deformation of the Shuangwangcheng (SWC) Reservoir, which features a long embankment dam as part of the South-to-North Water Diversion Project in China. We utilize data from both Sentinel-1 and TerraSAR-X satellites to derive 7-year deformation. Results reveal that the entire dam experiences continuous subsidence, with the maximum deformation in the line-of-sight direction measuring ~160 mm. While minor differential settlements are noted in different sections of the dam, the gradient is not significant due to the dam’s substantial length. The InSAR deformation results from multiple geometries demonstrate good consistency, with the highest correlation observed between the Sentinel-1 ascending and descending datasets, exceeding 0.9. Validation against the GNSS observations of the three sites on the SWC Dam shows the accuracy of InSAR displacements is ~8 mm. Water level changes do impact deformation, but consolidation settlement appears to be the primary controlling factor during the monitoring period. This study underscores the potential of InSAR in long-distance water transfer projects and highlights that spatially continuous deformation is the most significant advantage.

Abstract The effective use of wind energy is an essential part of the sustainable development of human society, in particular, at the recent unprecedented pressure in shaping a low carbon energy environment. Accurate wind resource and power forecasting play a key role in improving the wind penetration. However, it has not been well adopted in the real‐world applications due to the strong stochastic characteristics of wind energy. In recent years, the application boost of deep learning methods provides new effective tools in wind forecasting. This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy. Featured approaches include time‐series‐based recurrent neural networks, restricted Boltzmann machines, convolutional neural networks as well as auto‐encoder‐based approaches. In addition, future development directions of deep‐learning‐based wind energy forecasting have also been discussed.

IntroductionAs one of the megadiverse countries, the effectiveness of wildlife protection in China is of great significance to global biodiversity conservation. With continued evolution and revisions, China’s Wildlife Protection Law has listed over 140 marine species; however, it is still inclined toward terrestrial animals.MethodsTo narrow the gap between compliance and enforcement, we collected 1,309 effective responses from various coastal cities of China through an anonymous online questionnaire survey, to investigate their exposure, understanding and attitudes toward Wildlife Protection Law for marine species (mWPL).ResultsMost respondents demonstrated an overall good understanding about the context, necessity and effectiveness of mWPL. The fisher communities were found to be more aware of the dissemination and implementation of mWPL. However, they understood less of the penal system, and exhibited negative attitudes toward the necessity and punishment of the legislation, probably due to the conflicts between resource utilizations and legislative interventions. The participants also indicated that seahorses, horseshoe crabs and corals were commonly subjected to illegal exploitations.DiscussionWhile most respondents suggested greater fines, tighter laws and better public enforcement, we advocate the exploration of bottom-up options such as community engagement and environmental education to improve compliance and implementation of mWPL for the benefit of marine wildlife conservation in China.

With the rapid development of modern Interferometric Synthetic Aperture Radar (InSAR) missions, SAR instruments with wider coverage can be used to monitor the ground surface deformation from regional to global scale. However, the ocean tide loading (OTL) displacement is becoming a primary source of errors. It contributes to a long-wavelength signal in InSAR interferograms, leading to errors from millimeter to centimeter-level in InSAR deformation monitoring, especially over coastal areas. Although the state-of-the-art has applied ocean tide models to mitigate the errors, the difference between them and their impact on InSAR measurements are rarely discussed. In this paper, we compare representative ocean tide models and investigate their effects in the correction of OTL errors. We found that (i) the modeled OTL displacements from different models show little difference over interiors far from the ocean, while disagreement becomes larger over coastal areas; (ii) the magnitude of OTL artifacts may be greater than the atmospheric delays in some coastal areas, and the correction using ocean tide models can effectively attenuate the OTL effects for large-scale InSAR measurements; (iii) when correcting the OTL errors for InSAR measurements, the global model TPXO and FES are recommended because of their better overall performance, while the NAO model performs the worst. The local models with high spatial resolution can help improve the capability of coarse global models in complex topographic areas.

Abstract The growth of animals is a complex trait, in chicken resulting in a diverse variety of forms, caused by a heterogeneous genetic basis. Bantam chicken, known as an exquisite form of dwarfism, has been used for crossbreeding to create corresponding dwarf counterparts for native fowls in the Dutch populations. Here, we demonstrate the heterogeneity of the bantam trait in Dutch chickens and reveal the underlying genetic causes, using whole‐genome sequence data from matching pairs of bantam and normal‐sized breeds. During the bantam‐oriented crossbreeding, various bantam origins were used to introduce the bantam phenotype, and three major bantam sources were identified and clustered. The genome‐wide association studies revealed multiple genetic variants and genes associated with bantam phenotype, including HMGA2 and PRDM16, genes involved in body growth and stature. The comparison of associated variants among studies illustrated differences related to divergent bantam origins, suggesting a clear heterogeneity among bantam breeds. We show that in neo‐bantam breeds, the bantam‐related regions underwent a strong haplotype introgression from the bantam source, outcompeting haplotypes from the normal‐sized counterpart. The bantam heterogeneity is further confirmed by the presence of multiple haplotypes comprising associated alleles, which suggests the selection of the bantam phenotype is likely subject to a convergent direction across populations. Our study demonstrates that the diverse history of human‐mediated crossbreeding has contributed to the complexity and heterogeneity of the bantam phenotype.

In this paper, shale gas cores from the Lower Silurian Longmaxi Formation in the Zhaotong National Shale Gas Demonstration Area were selected to study the hydration effect of different fluids on the fracture morphology inside the shale, the rock tensile strength and the Brazilian tensile failure mode. Fresh water and slick water were adopted for hydration pretreatments and the CT technique was used to compare the changes of the fabric in the shale. Then, Brazilian tensile tests were carried out to study the tensile strength and tensile failure modes of the shale specimen after hydration pretreatment. Finally, two horizontal wells in the study area were selected to perform pilot tests of hydration pretreatment in their fracturing operation sites. And the following research results were obtained. First, in the process of spontaneous imbibition, the hydration effect of fresh water is superior to that of slick water in promoting the fracture complexity of marine shale in the same hydration duration. Second, fresh water has greater surface tension and lower viscosity and its hydration effect can not only promote the propagation of original fractures but induce new micro-fractures or branches, while the hydration effect of slick water mainly promotes the propagation of original fractures. Third, due to hydration effect, marine shale is damaged and its tensile strength is reduced. After the hydration pretreatment by fresh water and slick water, the tensile strength of the shale specimens are reduced by 35.6% and 18.1%, respectively. Fourth, according to the propagation paths of main fractures, the Brazilian tensile failure modes of hydrated shale can be divided into four types (i.e., step shaped, dog-leg shaped, branching shaped and arc shaped) or their combinations, while the tensile failure mode of unhydrated shale is only a straight line. Fifth, hydration effect can effectively increase the complexity of the hydraulic fractures in marine shale, so if the conditions permit, it is recommended to inject a certain amount of fresh water at a high pumping rate within the limited pressure after perforation and to shut in the well until the hydraulic fracturing operation. And in order to reduce the difficulties of pumping the proppant during the hydraulic fracturing operation, a pumping strategy of “low proppant concentration, large volume of slurries” can be adopted to reach the expected proppant volume.

Abstract Background Hepatocellular carcinoma (HCC) is a malignant tumor with frequent intrahepatic metastases; extrahepatic metastases are not rare but less frequent compared to intrahepatic ones. The most frequent sites of extrahepatic metastases are the lungs, followed by the lymph nodes, bones, and adrenal glands. Case report covering gastrointestinal (GI) tract involvement from HCC is limited. Case presentation A 60-year-old man was referred to us in May 2019 with a diagnosis of sigmoid colon tumor. The patient had a history of HCC and had received two stages of open resections for the primary and the abdominal metastasis successively and many times of transcatheter arterial chemoembolization (TACE). The sigmoid colon tumor received Hartmann procedure after abdominal enhanced computerized tomography (CT) scan and colonoscopy, while postoperative pathology and immunohistochemistry identified it as extrahepatic colonic metastasis from HCC. Conclusions The ratio of extrahepatic metastasis to the digestive tract was very low, and the majority was upper gastrointestinal involvement because of direct invasion or intraperitoneal implantation. TACE may be the risk factor of retrograde hematogenous metastasis to the downstream colon. Keywords Hepatocellular carcinoma; Extrahepatic metastases; Colon metastasis; Transcatheter arterial chemoembolization

Abstract Background Many studies have found that the hippocampus plays a very important role in major depressive disorder (MDD). The hippocampus can be divided into three subfields: the cornu ammonis (CA), dentate gyrus (DG) and subiculum. Each subfield of the hippocampus has a unique function and are differentially associated with the pathological mechanisms of MDD. However, no research exists to describe the resting state functional connectivity of each hippocampal subfield in MDD. Methods Fifty-five patients with MDD and 25 healthy controls (HCs) matched for gender, age and years of education were obtained. A seed-based method that imposed a template on the whole brain was used to assess the resting-state functional connectivity (rsFC) of each hippocampal subfield. Results Patients with MDD demonstrated increased connectivity in the left premotor cortex (PMC) and reduced connectivity in the right insula with the CA seed region. Increased connectivity was reported in the left orbitofrontal cortex (OFC) and left ventrolateral prefrontal cortex (vlPFC) with the DG seed region. The subiculum seed region revealed increased connectivity with the left premotor cortex (PMC), the right middle frontal gyrus (MFG), the left ventrolateral prefrontal cortex (vlPFC) and reduced connectivity with the right insula. ROC curves confirmed that the differences between groups were statistically significant. Conclusion The results suggest that the CA, DG and subiculum have significant involvement with MDD. Specifically, the abnormal functional connectivity of the CA may be related to bias of coding and integration of information in patients with MDD. The abnormal functional connectivity of the DG may be related to the impairment of working memory in patients with MDD, and the abnormal functional connectivity of the subiculum may be related to cognitive impairment and negative emotions in patients with MDD.

In this paper, the influence of data imbalance on neural networks is discussed, and an improved learning algorithm to solve this problem is proposed. The experimental results show that in the case of imbalanced data, the training error of neural network converges slowly and the generalization ability is poor. Our theoretical analysis shows that in the process of training, the gradient descent direction of the weights is dominated by the major-classes, which accounts for the slow convergence of the training error. Based on these results, we propose the Equilibration Mini-batch Stochastic Gradient Descent (EMSGD) method to ensure the equilibrium of the data in the mini-batch. The advantage of this technique is that it makes full use of the existing random sampling step of MSGD without increasing the computational complexity. In addition, by over-sampling of minor-classes in the mini-batch, duplicated instances would be greatly reduced, thus preventing the model from overfitting. The experimental results show that under the condition of the imbalanced training data, EMSGD can make the neural network training error converge rapidly.

Abstract Multi-beam antenna technology can effectively mitigate the impact of deep fading caused by multipath propagation on communication quality through spatial diversity and increase the reliability of wireless mobile communication systems. At the same time, the multi-beam antenna with beamforming algorithm as the key technology can realize spatial division multiple access (SDMA) by automatically adapting the weight vector of the antenna array, enhance the expected value, and suppress the interference. A novel beamforming technology increases the inner circle beam width and reduces the outer ring beam width to minimize the difference between the internal and external cell link SNR. This study considers reducing the number of cells by reasonably allocating beams, which satisfies both the requirements of coverage link quality and the difficulty of forming antenna beams. This research subject applies the scheme and strategy of improving multi-beam antenna technology in the same system. Under the requirement of cell coverage and partitioning, the related technologies and strategies combining multi-beamforming technology and space-time block code technology can obtain high coding gain and diversity gain without sacrificing bandwidth and effectivelyx improve the wireless systematic capacity.

Despite the successful control of highly contagious tumorigenic Marek’s disease (MD) by vaccination, a continuous increase in MD virus (MDV) virulence over recent decades has put emphasis on the development of more MD-resistant chickens. The cell types and genes involved in resistance therefore need to be recognized. The virus is primarily lymphotropic, but research should also focus on innate immunity, as innate immune cells are among the first to encounter MDV. Our previous study on MDV–macrophage interaction revealed significant differences between MHC-congenic lines 61 (MD-resistant) and 72 (MD-susceptible). To investigate the role of dendritic cells (DCs) in MD resistance, bone-marrow-derived DCs from these lines were infected with MDV in vitro. They were then characterized by cell sorting, and the respective transcriptomes analysed by RNA-seq. The differential expression (DE) of genes revealed a strong immune activation in DCs of the susceptible line, although an inherent immune supremacy was shown by the resistant line, including a significant expression of tumour-suppressor miRNA, gga-mir-124a, in line 61 control birds. Enrichment analysis of DE genes revealed high expression of an oncogenic transcription factor, AP-1, in the susceptible line following MDV challenge. This research highlights genes and pathways that may play a role in DCs in determining resistance or susceptibility to MDV infection.

Background: The liver is the central metabolic organ of animals. In chicken, knowledge on the relationship between gene expression in the liver and fat deposition during development is still limited. A time-course transcriptomic study from the embryonic (day 12) to the egg-producing period (day 180 after hatch) was performed to profile slow-growing meat type chicken liver gene expression and to investigate its correlation with abdominal fat deposition.Results: The transcriptome profiles showed a separation of the different developmental stages. In total, 13,096 genes were ubiquitously expressed at all the tested developmental stages. The analysis of differentially expressed genes between adjacent developmental stages showed that biosynthesis of unsaturated fatty acids pathway was enriched from day 21 to day 140 after hatch. The correlation between liver gene expression and the trait abdominal fat weight (AFW) was analyzed by weighted gene co-expression network analysis. The genes MFGE8, HHLA1, CKAP2, and ACSBG2 were identified as hub genes in AFW positively correlated modules, which suggested important roles of these genes in the lipid metabolism in chicken liver.Conclusion: Our results provided a resource of developmental transcriptome profiles in chicken liver and suggested that the gene ACSBG2 among other detected genes can be used as a candidate gene for selecting low AFW chickens.

Abstract Background Disturbances in clock genes affect almost all patients with Alzheimer’s disease (AD), as evidenced by their altered sleep/wake cycle, thermoregulation, and exacerbation of cognitive impairment. As microglia-mediated neuroinflammation proved to be a driver of AD rather than a result of the disease, in this study, we evaluated the relationship between clock gene disturbance and neuroinflammation in microglia and their contribution to the onset of AD. Methods In this study, the expression of clock genes and inflammatory-related genes was examined in MACS microglia isolated from 2-month-old amyloid precursor protein knock-in (APP-KI) and wild-type (WT) mice using cap analysis gene expression (CAGE) deep sequencing and RT-PCR. The effects of clock gene disturbance on neuroinflammation and relevant memory changes were examined in 2-month-old APP-KI and WT mice after injection with SR9009 (a synthetic agonist for REV-ERB). The microglia morphology was studied by staining, neuroinflammation was examined by Western blotting, and cognitive changes were examined by Y-maze and novel object recognition tests. Results CLOCK/BMAL1-driven transcriptional negative feedback loops were impaired in the microglia from 2-month-old APP-KI mice. Pro-inflammatory genes in microglia isolated from APP-KI mice were significantly higher than those isolated from WT mice at Zeitgeber time 14. The expression of pro-inflammatory genes was positively associated with NF-κB activation and negatively associated with the BMAL1 expression. SR9009 induced the activation of microglia, the increased expression of pro-inflammatory genes, and cognitive decline in 2-month-old APP-KI mice. Conclusion Clock gene disturbance in microglia is involved in the early onset of AD through the induction of chronic neuroinflammation, which may be a new target for preventing or slowing AD.

Abstract Channel allocation is the prerequisite for the HAPS (high-altitude platform station) 5G communication network to transmit information. An intelligent wireless channel allocation algorithm for HAPS 5G massive MIMO (multiple-input multiple-output) system based on reinforcement learning was proposed. Q-learning reinforcement learning algorithm and the back-propagation neural network were combined, which enabled HAPS 5G massive MIMO systems autonomous learn according to the environment and allocate channel resources of the system efficiently. Agents perceive the state information in the channel environment through continuous interaction with the channel environment, learn from the environment state to the action mapping, and use the back-propagation neural network for learning training, use the neural network instead of the Q value table, and train the network with each Q update as a training example to update the evaluation function. It effectively improves the overall performance of the system.

Summary: Various in situ synthesis methods have been developed for the polymerization of 3,4-ethylenedioxythiophene monomers, such as electropolymerization, oxidative chemical vapor deposition, and vapor phase polymerization. Meeting industrial requirements through these techniques has, however, proven challenging. Here, we introduce an alternative method to fabricate highly conductive poly(3,4-ethylenedioxythiophene) (PEDOT) films in situ by solution means. The process involves sequential deposition of oxidants (V2O5 in this case) and monomers. Excess reactants and by-products can be completely removed from the PEDOT film by MeOH rinsing. The obtained PEDOT films possess good crystallinity and high doping level, with carrier concentration three orders of magnitude higher than that of the commercial product (PH1000, Heraeus GmbH). The electrical conductivity of the as-cast PEDOT film reaches up to 1,420 S/cm. In addition, this method is fully compatible with large-scale printing techniques. These PEDOT conducting films enable the realization of flexible touch sensors, which demonstrate superior flexibility and sensitivity. : Chemistry; Polymer Chemistry; Electronic Materials Subject Areas: Chemistry, Polymer Chemistry, Electronic Materials

Layered or chain materials have received significant research attention owing to their interesting physical properties, which can dramatically change when the material is thinned from bulk (three-dimensional) to thin two-dimensional sheet or one-dimensional (1D) chain form. Materials with the stoichiometry AX2 with A = Si or Ge and X = S or Se form an especially intriguing semiconducting class. For example, bulk silicon dichalcogenides (SiX2) consist of 1D chains held together by van der Waals forces. Although this structural configuration has the potential to reveal interesting physical phenomena within the 1D limit, obtaining SiX2 single chains has been challenging. We here examine experimentally and theoretically SiX2 materials in the low chain number limit. Carbon nanotubes serve as growth templates and stabilize and protect the structures, and atomic-resolution scanning transmission electron microscopy directly identifies the atomic structure. Two distinct chain structures are observed for SiX2. SixGe1-xS2(1-y)Se2y quaternary alloy chains are also synthesized and characterized, demonstrating tunable semiconducting properties at the atomic-chain level. Density functional theory calculations reveal that the band gap of these alloy chains can be widely tuned through composition engineering. This work offers the possibilities for synthesizing and controlling semiconductor compositions at the single-chain limit to tailor material properties.

As a physiological response to injury in the internal body organs, inflammation is responsible for removing dangerous stimuli and initiating healing. However, persistent and exaggerative chronic inflammation causes undesirable negative effects in the organs. Inflammation occurring in the brain and spinal cord is known as neuroinflammation, with microglia acting as the central cellular player. There is increasing evidence suggesting that chronic neuroinflammation is the most relevant pathological feature of Alzheimer’s disease (AD), regulating other pathological features, such as the accumulation of amyloid-β (Aβ) and hyperphosphorylation of Tau. Systemic inflammatory signals caused by systemic disorders are known to strongly influence neuroinflammation as a consequence of microglial activation, inflammatory mediator production, and the recruitment of peripheral immune cells to the brain, resulting in neuronal dysfunction. However, the neuroinflammation-accelerated neuronal dysfunction in AD also influences the functions of peripheral organs. In the present review, we highlight the link between systemic inflammatory disorders and AD, with inflammation serving as the common explosion. We discuss the molecular mechanisms that govern the crosstalk between systemic inflammation and neuroinflammation. In our view, inflammation spreading indicates a negative spiral between systemic diseases and AD. Therefore, “dampening inflammation” through the inhibition of cathepsin (Cat)B or CatS may be a novel therapeutic approach for delaying the onset of and enacting early intervention for AD.

The use of agents that target both glia and neurons may represent a new strategy for the treatment of ageing disorders. Here, we confirmed the presence of the novel cyclic peptide Naturido that originates from a medicinal fungus (Isaria japonica) grown on domestic silkworm (Bombyx mori). We found that Naturido significantly enhanced astrocyte proliferation and activated the single copy gene encoding the neuropeptide VGF and the neuron-derived NGF gene. The addition of the peptide to the culture medium of primary hippocampal neurons increased dendrite length, dendrite number and axon length. Furthermore, the addition of the peptide to primary microglial cultures shifted CGA-activated microglia towards anti-inflammatory and neuroprotective phenotypes. These findings of in vitro glia-neuron interactions led us to evaluate the effects of oral administration of the peptide on brain function and hair ageing in senescence-accelerated mice (SAMP8). In vivo analyses revealed that spatial learning ability and hair quality were improved in Naturido-treated mice compared with untreated mice, to the same level observed in the normal ageing control (SAMR1). These data suggest that Naturido may be a promising glia-neuron modulator for the treatment of not only senescence, but also Alzheimer's disease and other neurodegenerative diseases.

The feather rate phenotype in chicks, including early-feathering and late-feathering phenotypes, are widely used as a sexing system in the poultry industry. The objective of this study was to obtain candidate genes associated with the feather rate in Shouguang chickens. In the present study, we collected 56 blood samples and 12 hair follicle samples of flight feathers from female Shouguang chickens. Then we identified the chromosome region associated with the feather rate by genome-wide association analysis (GWAS). We also performed RNA sequencing and analyzed differentially expressed genes between the early-feathering and late-feathering phenotypes using HISAT2, StringTie, and DESeq2. We identified a genomic region of 10.0–13.0 Mb of chromosome Z, which is statistically associated with the feather rate of Shouguang chickens at one-day old. After RNA sequencing analysis, 342 differentially expressed known genes between the early-feathering (EF) and late-feathering (LF) phenotypes were screened out, which were involved in epithelial cell differentiation, intermediate filament organization, protein serine kinase activity, peptidyl-serine phosphorylation, retinoic acid binding, and so on. The sperm flagellar 2 gene (SPEF2) and prolactin receptor (PRLR) gene were the only two overlapping genes between the results of GWAS and differential expression analysis, which implies that SPEF2 and PRLR are possible candidate genes for the formation of the chicken feathering phenotype in the present study. Our findings help to elucidate the molecular mechanism of the feather rate in chicks.

Agricultural mechanization is essential to increase farmers’ income in modern agriculture. However, the use of machinery for crop production in China is quite inefficient. To understand the obstacles limiting farmers’ use of machinery, we conducted face-to-face interview surveys with 1023 farmers (including cooperative directors, machine operators, and farmers without machines) in two major cereal-producing regions with large differences in farming scale: the North China Plain (2.7 ha per capita) and the Northeast China Plain (12.8 ha per capita). The results revealed that farmers in both regions had strong will to use machines. The obstacle preventing farmers from buying machines was the lack of machinery training in the Northeast China Plain and land fragmentation in the North China Plain. Among different farmer groups, land fragmentation was the main barrier for cooperative directors. Farmers without machines thought that there was lack of machinery training and that the cost of machinery purchase was high. Machine operators believed that machine maintenance was too expensive. The income and age also had an effect on the different groups of farmer. It is concluded that, to improve mechanization efficiency and stimulate farmers’ intention to use machinery, the government should make policies to encourage the merge of fragmented farmlands, provide targeted subsidies for agricultural machinery, and organize machinery training in an efficient way.