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Abstract Within the geo-simulation research domain, micro-simulation and agent-based modeling often require the creation of synthetic populations. Creating such data is a time-consuming task and often lacks social networks, which are crucial for studying human interactions (e.g., disease spread, disaster response) while at the same time impacting decision-making. We address these challenges by introducing a Python based method that uses the open data including that from 2020 U.S. Census data to generate a large-scale realistic geographically explicit synthetic population for America’s 50 states and Washington D.C. along with the stylized social networks (e.g., home, work and schools). The resulting synthetic population can be utilized within various geo-simulation approaches (e.g., agent-based modeling), exploring the emergence of complex phenomena through human interactions and further fostering the study of urban digital twins.

Abstract Frequent extreme heat events around the world not only pose a major threat to human health but also cause significant economic losses to the livestock industry. The existing management practices are insufficient to fully prevent heat stress with an urgent need to develop preventive medicines. The aim of this study was to develop an anti-heat stress Chinese herbal formula (CHF) via big data analysis techniques and to evaluate its anti-heat stress effect and mechanism of action via pharmacodynamic evaluation and network pharmacology analysis. Many anti-heat stress CHFs were collected from the Chinese National Knowledge Infrastructure (CNKI) database. Three alternative CHFs were obtained via unsupervised entropy hierarchical clustering analysis, and the most effective CHF against heat stress, Shidi Jieshu decoction (SJD), was obtained by screening in a mouse heat stress model. In dry and hot environments, SJD significantly improved the heat tolerance of AA broilers by 4–6°C. In a humid and hot environment, pretreatment with 2% SJD resulted in 100% survival of Wenchang chickens at high temperatures. The main active ingredients of SJD were identified as muntjacoside E, timosaponin C, macrostemonoside H and mangiferin via ultra-performance liquid chromatography/mass spectrometry (UPLC/MS) and database comparison. The active ingredients of SJD were found to target tumor necrosis factor-α (TNF-α), signal transducer activator of transcription 3 (STAT3) and epidermal growth factor receptor (EGFR). Finally, the safety of the new formulation was assessed in an acute oral toxicity study in rats. The SJDs developed in this study provide a new option for the prevention of heat stress in animal husbandry and offer new insights for further research on anti-heat stress.

Abstract Background The Platelet to High-Density Lipoprotein cholesterol Ratio (PHR) is a novel indicator of inflammatory response and metabolic disorders, linked to various chronic diseases. This study aims to investigate the relationship between PHR and hypertension. Methods Data from the National Health and Nutrition Examination Survey (NHANES), collected across seven consecutive cycles from 2005 to 2018, were analyzed. The dataset included participants’ hypertension status as reported by a doctor, their use of antihypertensive medications, and the average of three blood pressure measurements to identify hypertensive adults, along with complete information for PHR calculation. PHR was calculated based on Platelet (PLT) count and High-Density Lipoprotein cholesterol (HDL-C) using the following formula: PHR = [PLT (1000 cells/µL) / HDL-C (mmol/L)]. A multivariable logistic regression model was employed to assess the association between PHR and hypertension, and subgroup analyses were conducted to explore potential influencing factors. Additionally, Restricted Cubic Spline (RCS) curves were applied for threshold effect analysis to describe nonlinear relationships. Results Higher PHR was associated with an increased prevalence of hypertension. After adjusting for various covariates, including race, education level, Family Poverty Income Ratio (PIR), smoking, alcohol consumption, sleep disturbances, waist circumference, diabetes, coronary heart disease, angina, heart attack, and stroke, the results remained significant (OR = 1.36; 95% CI, 1.32, 1.41, P

Abstract Background Metabolic regulation of various amino acids have been proven to be effective in preventing cardiovascular disease (CVD). The impact of tryptophan, an essential amino acid, on the risk of developing CVD has not been fully elucidated. Aims The aim of this meta-analysis was to systematically review evidence of the effects of tryptophan on CVD risk. Methods The PubMed, Embase, Web of Science, Cochrane Library, and China National Knowledge Infrastructure (CNKI) databases were searched to collect relevant trials from inception to August 2024. The means and hazard ratios (HRs) were extracted and pooled. Subgroup analysis was performed to identify pooled effect estimates, and sensitivity analysis was conducted to assess the robustness of the pooled estimates. Results Data were collected from 34,370 people under follow-up for CVD events in 13 studies, including cohort studies and case-control studies. They were categorized into three groups on the basis of sample type and indicators: the plasma tryptophan level group, the plasma tryptophan CVD hazard group, and the urinary tryptophan CVD hazard group. The CVD included in this study were coronary artery disease, heart failure, and peripheral artery disease. Twelve studies on plasma tryptophan were meta-analyzed. The plasma tryptophan levels in CVD patients were generally lower than those in individuals without CVD (SMD = -8.57, 95%CI (-15.77, -1.37), P = 0.02). Decreased circulating tryptophan levels are associated with cardiovascular disease risk (HR = 0.85, 95%CI (0.78, 0.92), P

Abstract Heart failure (HF) is closely linked to platelet counts and lipid levels. The platelet-to-high-density lipoprotein cholesterol ratio (PHR) is a novel marker for assessing cardiovascular disease. This study investigates the relationship between PHR and HF. We analyzed data from ten consecutive NHANES survey cycles (1998–2018), focusing on self-reported HF diagnoses with complete PHR information. PHR was calculated as platelet count divided by HDL-C (mmol/L). A multivariate logistic regression model was used to examine the association between PHR and HF, with subgroup analyses to identify potential influencing factors. RCS curve plotting and threshold effect analysis were employed to describe non-linear relationships. The study included 31,410 adults aged 20–85 years. The multivariate logistic regression indicated that individuals with the highest PHR had 82% increased likelihood of HF compared to those with the lowest PHR (OR = 1.82; 95% CI, 1.37–2.40, P 0.05), except in those with alcohol consumption (yes/no) and BMI subgroups (P

Abstract Background Endometriosis is intricately linked to metabolic health. The Cardiometabolic Index (CMI), a novel and readily accessible indicator, is utilized to evaluate metabolic status. This study seeks to investigate the potential correlation between CMI and endometriosis. Methods Data from four consecutive survey cycles of the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2006 were utilized. This included adult females with self-reported diagnoses of endometriosis and complete information required for calculating the CMI. The calculation formula for CMI is Triglycerides(TG) / High-density lipoprotein cholesterol (HDL-C) × WHtR (WHtR = waist circumference / height). A multivariable logistic regression model was employed to investigate the linear association between CMI and endometriosis. Subgroup analyses were performed to explore potential influencing factors. Additionally, the linear relationship was validated using restricted cubic spline (RCS) curve plotting and threshold effect analysis. Results This study, based on the National Health and Nutrition Examination Survey (NHANES), included a cohort of 2,224 adult women. The multivariable logistic regression analysis demonstrated that in the fully adjusted model, individuals with the highest CMI exhibited a 78% elevated likelihood of endometriosis compared to those with the lowest CMI (OR = 1.78; 95% CI, 1.02–3.11, P 0.05), except for the subgroup stratified by stroke status (P 0.67 (OR = 1.20; 95% CI, 1.05–1.37, P

Abstract Accumulating evidence supports the concept that DNA damage response targeted therapies can improve antitumor immune response by increasing the immunogenicity of tumor cells and improving the tumor immune microenvironment. Ataxia telangiectasia mutated (ATM) is a core component of the DNA repair system. Although the ATM gene has a significant mutation rate in many human cancers, including colorectal, prostate, lung, and breast, it remains understudied compared with other DDR-involved molecules such as PARP and ATR. Here, we found that either gene knockout or drug intervention, ATM inhibition activated the cGAS/STING pathway and augmented MHC class I in CRC cells, and these effects could be amplified by radiation. Furthermore, we found that MHC class I upregulation induced by ATM inhibition is dependent on the activation of the NFκB/IRF1/NLRC5 pathway and independent of STING. Animal experiments have shown increasing infiltration and cytotoxic function of T cells and better survival in ATM-deficient tumors. This work indicated that ATM nonsense mutation predicted the clinical benefits of radiotherapy combined with immune checkpoint blockade for patients with CRC. It also provides a molecular mechanism rationale for ATM-targeted agents for patients with CRC.

Communication delays within connected and autonomous vehicles (CAVs) pose significant risks. It is imperative to address these issues to ensure the safe and effective operation of CAVs. However, the exploration of communication delays on CAV operations and their energy use remains sparse in the literature. To fill the research gap, this study leverages the facilities at America Center of Mobility (ACM) Smart City Test Center to implement and evaluate a CAV merging control algorithm through vehicle-in-the-loop testing. This study aims at achieving three main objectives: (1) develop and implement a CAV merging control strategy in the experimental test bed through vehicle-in-the-loop testing, (2) propose analytical models to quantify the impacts of communication delay on the variability of CAV speed and energy consumption based on field experiment data, and (3) create a predictive model for energy usage considering various CAV attributes and dynamics, e.g., speed, acceleration, yaw rate, and communication delays. To our knowledge, this is one of the first attempts at evaluating the impacts of communication delays on CAV merging operational control with field data, making critical advancement in the field. The results suggest that communication delay has a more substantial effect on energy consumption under high-speed volatility compared to low-speed volatility. Among all factors examined, acceleration is the dominant characteristic that influences energy usage. It also revealed that even minor improvements in communication delay can yield tangible improvements in energy efficiency. The results provide guidance on CAV field experiments and the influence of communication delays on CAV operation and energy consumption.

Thymoma is closely associated with myasthenia gravis (MG). However, due to the heterogeneity of thymoma and the intricate pathogenesis of MG, it remains unclear why some patients with thymoma develop MG and others do not. In this study, we conducted a comparative phenotype analysis of thymocytes in type B thymomas in patients with MG (MG (+) thymomas) and without MG (MG (−) thymomas) via fluorescence-activated cell sorting (FACS). Our results show that the developmental stages defined by the expression of CD3, CD4, and CD8 were largely maintained in both MG (+) and MG (−) thymomas, with CD4+CD8+ cells constituting the majority of thymocytes in type B thymoma, and no significant difference between this cell population was observed in MG (+) and MG (−) thymomas.We discovered that CD4+CD8+ thymocytes in MG (+) thymomas expressed low levels of αβ TCR and high levels of IL-7 receptor α (IL-7Rα), whereas in MG (−) thymomas, CD4+CD8+ thymocytes exhibited the opposite pattern of αβ TCR and IL-7Rα expression. These results suggest that the positive and negative selection processes of CD4+CD8+ thymocytes might differ between MG (+) thymomas and MG (−) thymomas. The expression of the Helios transcription factor is induced during negative selection and marks a group of T cells that have undergone negative selection and are likely to be deleted due to strong TCR binding with self-peptides/MHC ligands. We observed that the percentage of Helios-positive CD4SP T cells was greater in MG (−) than in MG (+) thymomas. Thus, the differentially regulated selection process of CD4+CD8+ thymocytes, which involves TCR and IL-7/IL-7Rα signaling, is associated with the presence of MG in type B thymomas.

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging due to its ill-posed nature. To achieve a unique solution, it is essential to apply sophisticated regularization constraints to restrict the solution space. Traditionally, the design of regularization terms is based on assumptions about the spatiotemporal structure of the underlying source dynamics. In this paper, we propose a novel paradigm for ESI via an Explainable Deep Learning framework, termed as XDL-ESI, which connects the iterative optimization algorithm with deep learning architecture by unfolding the iterative updates with neural network modules. The proposed framework has the advantages of (1) establishing a data-driven approach to model the source solution structure instead of using hand-crafted regularization terms; (2) improving the robustness of source solutions by introducing a topological loss that leverages the geometric spatial information applying varying penalties on distinct localization errors; (3) improving the reconstruction efficiency and interpretability as it inherits the advantages from both the iterative optimization algorithms (interpretability) and deep learning approaches (function approximation). The proposed XDL-ESI framework provides an efficient, accurate, and interpretable paradigm to solve the ESI inverse problem with satisfactory performance in both simulated data and real clinical data. Specially, this approach is further validated using simultaneous EEG and intracranial EEG (iEEG).

Abstract Background IL-15 plays a vital role in enhancing NK cell- and T-cell-mediated antitumor immune responses; however, the direct effect of IL-15 on tumor cells has not been fully elucidated. Herein, we investigated the effect of IL-15 on lung adenocarcinoma cells. Methods Silencing and overexpression techniques were used to modify endogenous IL-15 expression in tumor cells. Transwell assays were used to assess tumor cell migration and invasion; a live-cell analysis system was used to evaluate cell motility; cellular morphological changes were quantified by confocal fluorescence microscopy; the molecular mechanisms underlying the effect of IL-15 on tumor cells were analyzed by western blotting; and RhoA and Cdc42 activities were evaluated by a pulldown assay. NCG and C57BL/6 mouse models were used to evaluate the functions of IL-15 in vivo. Results Cancer cell-intrinsic IL-15 promoted cell motility and migration in vitro and metastasis in vivo via activation of the AKT-mTORC1 pathway; however, exogenous IL-15 inhibited cell motility and migration via suppression of the RhoA-MLC2 axis. Mechanistic analysis revealed that both the intracellular and extracellular IL-15-mediated effects required the expression of IL-15Rα by tumor cells. Detailed analyses revealed that the IL-2/IL-15Rβ and IL-2Rγ chains were undetected in the complex formed by intracellular IL-15 and IL-15Rα. However, when exogenous IL-15 engaged tumor cells, a complex containing the IL-15Rα, IL-2/IL-15Rβ, and IL-2Rγ chains was formed, indicating that the differential actions of intracellular and extracellular IL-15 on tumor cells might be caused by their distinctive modes of IL-15 receptor engagement. Using a Lewis lung carcinoma (LLC) metastasis model, we showed that although IL-15 overexpression facilitated the lung metastasis of LLC cells, IL-15-overexpressing LLC tumors were more sensitive to anti-PD-L1 therapy than were IL-15-wild-type LLC tumors via an enhanced antitumor immune response, as evidenced by their increased CD8+ T-cell infiltration compared to that of their counterparts. Conclusions Cancer cell-intrinsic IL-15 and exogenous IL-15 differentially regulate cell motility and migration. Thus, cancer cell-intrinsic IL-15 acts as a double-edged sword in tumor progression. Additionally, high levels of IL-15 expressed by tumor cells might improve the responsiveness of tumors to immunotherapies.

Aquaculture contributes significantly to the global economy and has become the key to global food security and nutrition strategies. In order to supply sustainable and equitable aquatic food, studies on aquaculture genomics, genetics and selective breeding are required. Genomic selection (GS) captures diversity and estimates breeding values based on genome-wide distributed markers enhancing aquaculture production efficiency, sustainability, product quality, and profitability. However, the application of GS in aquaculture is still in its initial stage compared with that in plants and livestock. The complex preprocessing, quality control and postprocessing steps, complicated statistical models, fussy file format conversions between various interfaces and frequent switches among different running environments prevent smooth and large-scale applications. In this study, we have developed AquaGS, an open-source Graphic User Interface (GUI) Genomic Selection pipeline offering click-by-click running from inputting raw data for phenotype and genotype to the final mate allocation scheme. AquaGS is a C++ based application that uses QT to create a GUI and integrates the functions needed for the GS process by calling various programs and specific tools in the background, such as C++, Python, R, PLINK and AlphaMate. AquaGS includes phenotype preprocessing, quality control, testing the significance of effects, breeding value predictions, cross-validation and mating allocation scheme generation, integrated from widely-used standalone methods and tools, such as BLUP, GBLUP, SSBLUP, Bayes A, Bayes B, Bayes Cπ, and Bayesian Lasso model. Additionally, AquaGS includes a mating design module based on optimum contribution selection to avoid inbreeding depression and maximize genetic gains. To accommodate various application scenarios, AquaGS offers a flexible, interactive and customized processing pipeline.

IntroductionRecently, Google introduced Pathways as its next-generation AI architecture. Pathways must address three critical challenges: learning one general model for several continuous tasks, ensuring tasks can leverage each other without forgetting old tasks, and learning from multi-modal data such as images and audio. Additionally, Pathways must maintain sparsity in both learning and deployment. Current lifelong multi-task learning approaches are inadequate in addressing these challenges.MethodsTo address these challenges, we propose SEN, a Sparse and Expandable Network. SEN is designed to handle multiple tasks concurrently by maintaining sparsity and enabling expansion when new tasks are introduced. The network leverages multi-modal data, integrating information from different sources while preventing interference between tasks.ResultsThe proposed SEN model demonstrates significant improvements in multi-task learning, successfully managing task interference and forgetting. It effectively integrates data from various modalities and maintains efficiency through sparsity during both the learning and deployment phases.DiscussionSEN offers a straightforward yet effective solution to the limitations of current lifelong multi-task learning methods. By addressing the challenges identified in the Pathways architecture, SEN provides a promising approach for developing AI systems capable of learning and adapting over time without sacrificing performance or efficiency.

Our brain excels at recognizing objects, even when they flash by in a rapid sequence. However, the neural processes determining whether a target image in a rapid sequence can be recognized or not remains elusive. We used electroencephalography (EEG) to investigate the temporal dynamics of brain processes that shape perceptual outcomes in these challenging viewing conditions. Using naturalistic images and advanced multivariate pattern analysis (MVPA) techniques, we probed the brain dynamics governing conscious object recognition. Our results show that although initially similar, the processes for when an object can or cannot be recognized diverge around 180 ms post-appearance, coinciding with feedback neural processes. Decoding analyses indicate that gist perception (partial conscious perception) can occur at ∼120 ms through feedforward mechanisms. In contrast, object identification (full conscious perception of the image) is resolved at ∼190 ms after target onset, suggesting involvement of recurrent processing. These findings underscore the importance of recurrent neural connections in object recognition and awareness in rapid visual presentations.

Abstract Over 15 million colonoscopies were performed yearly in North America, during which biopsies were taken for pathological examination to identify abnormalities. Distinguishing between true- and pseudo-invasion in colon polyps is critical in treatment planning. Surgical resection of the colon is often the treatment option for true invasion, whereas observation is recommended for pseudo-invasion. The task of identifying true- vs pseudo-invasion, however, could be highly challenging. There is no specialized software tool for this task, and no well-annotated dataset is available. In our work, we obtained (only) 150 whole-slide images (WSIs) from the London Health Science Centre. We built three deep neural networks representing different magnifications in WSIs, mimicking the workflow of pathologists. We also built an online tool for pathologists to annotate WSIs to train our deep neural networks. Results showed that our novel system classifies tissue types with 95.3% accuracy and differentiates true- and pseudo-invasions with 83.9% accuracy. The system’s efficiency is comparable to an expert pathologist. Our system can also be easily adjusted to serve as a confirmatory or screening tool. Our system (available at http://ai4path.ca ) will lead to better, faster patient care and reduced healthcare costs.

Abstract The transformation from one compound to another involves the breaking and formation of chemical bonds at the single-bond level, especially during catalytic reactions that are of great significance in broad fields such as energy conversion, environmental science, life science and chemical synthesis. The study of the reaction process at the single-bond limit is the key to understanding the catalytic reaction mechanism and further rationally designing catalysts. Here, we develop a method to monitor the catalytic process from the perspective of the single-bond energy using high-resolution scanning tunneling microscopy single-molecule junctions. Experimental and theoretical studies consistently reveal that the attack of a halogen atom on an Au atom can reduce the breaking energy of Au−S bonds, thereby accelerating the bond cleavage reaction and shortening the plateau length during the single-molecule junction breaking. Furthermore, the distinction in catalytic activity between different halogen atoms can be compared as well. This study establishes the intrinsic relationship among the reaction activation energy, the chemical bond breaking energy and the single-molecule junction breaking process, strengthening our mastery of catalytic reactions towards precise chemistry.

Recently, considerable attention has been given to (2+1)-dimensional Kadomtsev-Petviashvili equations due to their extensive applications in solitons that widely exist in nonlinear science. Therefore, developing a reliable numerical algorithm for the Kadomtsev-Petviashvili equations is crucial. The lattice Boltzmann method, which has been an efficient simulation method in the last three decades, is a promising technique for solving Kadomtsev-Petviashvili equations. However, the traditional higher-order moment lattice Boltzmann model for the Kadomtsev-Petviashvili equations suffers from low accuracy because of error accumulation. To overcome this shortcoming, a splitting lattice Boltzmann scheme for (2+1)-dimensional Kadomtsev-Petviashvili-Ⅰ type equations is proposed in this paper. The variable substitution method is applied to transform the Kadomtsev-Petviashvili-Ⅰ type equation into two macroscopic equations. Two sets of distribution functions are employed to construct these two macroscopic equations. Moreover, three types of soliton solutions are numerically simulated by this algorithm. The numerical results imply that the splitting lattice Boltzmann schemes have an advantage over the traditional high-order moment lattice Boltzmann model in simulating the Kadomtsev-Petviashvili-Ⅰ type equations.

Recently, there has been a growing interest in skutterudite (SKD) compounds containing electronegative elements such as Br, Cl, S, Se, and Te, owing to their increased diversity and the versatility of filler atoms. This study focused on the thermoelectric performance of a series of (Ni, Te) co-doped SKDs filled with the electronegative element S, denoted as SxNi0.4Co3.6Sb11.2Te0.8 (x = 0, 0.1, 0.2, and 0.3). These compounds were prepared using a combination of a solid-state reaction and spark plasma sintering techniques. The results showed that (Ni, Te) co-doping introduced excess electrons in the SKD lattice, while the incorporation of the element S into the SKD voids optimized carrier concentration. This led to a considerable increase in the absolute Seebeck coefficient to 110.6 μV K−1 at ambient temperatures. The presence of S fillers induced phonon resonance scattering and point scattering, which reduced lattice thermal conductivity and ultimately improved the thermoelectric figure of merit zT, which reached 0.93 for S0.3Ni0.4Co3.6Sb11.2Te0.8 at 823 K.

Ecological zoning management aims to ensure ecological functions and improve environmental quality, serving as an essential means to optimize the development and protection of territorial space. However, comprehensive research on ecological zoning management that combines human needs with natural resources is still relatively scarce. In this study, we selected water yield (WY), food provision (FP), and carbon sequestration (CS) as the critical ecosystem services (ES) in China. An InVEST model, ecosystem services supply–demand index (ESI), random forest (RF), and geographically and temporally weighted regression (GTWR) were used to analyze the spatiotemporal characteristics and influencing factors of ES supply and demand, and the four-quadrant model was used to analyze the spatial matching patterns. The results showed that: (1) from 2005 to 2020, the supply and demand of WY, FP, and CS increased. Among them, WY, FP, and CS supply increased by 16.06%, 34%, and 22.53%, respectively, while demand increased by 5.63%, 12.4%, and 83.02%, respectively; (2) the supply of WY and CS follow a “high in the southeast and low in the northwest” pattern, while all of the demands exhibit a “high in the east and low in the west” pattern; and (3) the average ecosystem service supply–demand index (ESI) values for WY, FP, and CS in China are 0.45, 0.12, and −0.24, respectively, showing an overall upward trend. The study identified three dominant functional zones for WY, FP, and CS, and four classification management zones, including protection zones, conservation zones, improvement zones, and reconstruction zones. These research findings provide a scientific basis for future territorial space planning in China and the application of ecosystem service supply and demand in sustainable development.

Abstract Research on van der Waals heterostructures based on stacked two‐dimensional atomic thickness crystals has received considerable attention because of their unique characters and great potential applications in flexible transparent electronics and optoelectronics. In this study, a nonvolatile memory device with a few‐layer bipolar material WS2 as channel and charge‐trapping layers is designed with a floating‐gate structure in which charges (electrons and holes) can be stored in the charge‐trapping layer using a dual‐mode processing program by changing the metal–semiconductor contact type. The device exhibits different programming currents during programming, in particular, the device has a low programming current for programming voltages ˂20 V. Moreover, the heterostructure exhibits a remarkable long retention time (≈10,000 s), with no apparent degradation and a strong endurance, retaining its original performance even after 1,000 programming/erasing cycles. This study proposes a novel method for reducing power consumption while programming to facilitate artificial synapse applications.

In this study, a series of flexible neutron/gamma shielding composites are fabricated through the doping of Gd2O3 into the matrix of SEBS with (MGd2O3: MSEBS) % from 5% to 100%. Neutron transmittance test shows an exponential attenuation with the increase of areal density of Gd, in which the transmittance T ranges from 59.1440% to 35.3026%, with standard deviation less than 2.2743%, mass attenuation coefficient μm from 0.3194 cm2/g to 0.4999 cm2/g, and half value layer-HVL value from 2.4530 mm to 1.1313 mm. Shielding efficiency of the Gd2O3/SEBS composites is basically improved in comparison with that of B4C/SEBS. The transmittance T, mass/linear attenuation coefficient μm and μ, HVL and effective atomic number Zeff for the shielding of γ rays (39 keV, 59 keV and 122 keV) are measured and calculated with XCOM as well as MCX programs. Finally, plots of the three dimensional relationships between transmittance, doping amount and thickness are provided to the guidance for engineering shielding design. In summary, the Gd2O3/SEBS composite is proved to be an effective flexible neutron/low energy γ rays shielding material, which could be of potential applications in the field of nuclear technology and nuclear engineering.

Focusing on the problems of uncertainty and carbon emissions in the manufacturing process, this paper studies the low-carbon-emission scheduling optimization problem. Firstly, the variations in workpiece processing time and delivery date are selected as the uncertainty factors. A low-carbon-emission scheduling model for uncertain job shops is constructed with the optimization objectives of the time index, carbon emission index, and robustness index. Secondly, an improved third-generation non-dominated sorting genetic algorithm (NSGA-III) is proposed. Based on the original NSGA-III algorithm, this algorithm introduces the state transition algorithm to perform state transformation, neighborhood sampling, selection update, and alternate rotation operations on the parent population, generating new candidate solutions. Finally, the scheduling model and the improved algorithm are applied to a workshop example. Through case study computation and result analysis, the feasibility and effectiveness of the model and algorithm in addressing the low-carbon-emission job shop scheduling problem under uncertainty are further verified.

Gear factories are most typical discrete manufacturing factories. Many gear factories are striving to explore how to achieve intelligent manufacturing in order to improve efficiency and reduce costs. Digital twin technology is currently one of the most reliable ways to achieve intelligent manufacturing. This article aims to establish a real-time control system in order to promote intelligent manufacturing for discrete manufacturing factories. Firstly, a model for a digital twin gear factory is put forward based on the characteristics of gear factories, and the composition of a real-time control system for gear factories is clarified. Then, a human–computer interaction architecture for the real-time control system is proposed. The real-time control system consists of three parts as follows: a monitoring module, a virtualizing module, and a controlling module. At work, it appears as a kind of human–machine interaction form with the three following interfaces: a monitoring window, a virtualizing window, and a controlling window. Finally, a gear factory, which is specialized in manufacturing the intermediate shaft dual gear of a new energy vehicle gearbox, develops a set of software for the real-time control system. The prototype software is obtained through some development activities such as 3D MAX and WebGL virtualization modeling and OPC UA and REST communication design.

The Côte d’Ivoire Basin is one of the most important oil production areas in Africa; and it is also the focus of global oil and gas exploration, but the degree of oil exploration and development in this basin are still in the early stages. Based on a comprehensive analysis of drilling, logging, and geochemical data in the Côte d’Ivoire Basin, this study comprehensively evaluates the Cretaceous lacustrine source rocks in the study area, establishes a total organic carbon (TOC) quantitative prediction model by using the total organic carbon method, determines the relationship between seismic reflections and the geochemical characteristics of the source rocks, and tracks the main source rocks horizontally. The results show that the TOC of the Lower Cretaceous Albian stage source rocks is 2.63 wt.%, the kerogen type II is dominant, and the vitrinite reflectance (RO) ranges from 0.3% to 0.9%. The source rocks are mainly in the low-mature to mature stage, and they are generally of good and premium quality. The distribution range of high-quality source rocks with TOC values of 4.00~5.00% is predicted using the total organic carbon method, and useing the seismic method, we determine that high-quality source rocks with larger thickness values and better continuity are mainly developed in the eastern depression center of the basin.

Abstract Background The changes in demographic and family structures have weakened the traditional norms of filial piety and intergenerational relationships dramatically. This study aims to examine the dynamic association between financial support of adult children to their parents and informal care provision in China and its differences in household registration, residence arrangement and community-based care services. Methods Data was derived from the 2008–2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), which is a longitudinal survey of a nationally representative sample of individuals aged 60 and over. Random effects model was used to assess the association between financial support and informal care provision of adult children to their parents. Results It was found that financial support showed an upward trend while informal care provision showed a download trend from 2008 to 2018. The result indicated a significant and negative association between financial support and informal care provision of adult children to their parents (B = -0.500, 95% confidence interval (CI) = -0.761 to -0.239). And the association was significant among elderly people who were from urban areas (B = -0.628, 95% CI = -0.970 to -0.287), co-resided with adult children (B = -0.596, 95% CI = -0.939 to -0.253), and had community-based services (B = -0.659, 95% CI = -1.004 to -0.315). Conclusion Financial support was negatively associated with informal care provision of adult children to their parents in China, and the association has differences in household registration, residence arrangement and community-based care services. It is suggested that policymakers should prioritize planning interventions for elderly care services and establish a family caregiver support system.

Objective: Recently, artificial neural networks (ANNs) have been proven effective and promising for the steady-state visual evoked potential (SSVEP) target recognition. Nevertheless, they usually have lots of trainable parameters and thus require a significant amount of calibration data, which becomes a major obstacle due to the costly EEG collection procedures. This paper aims to design a compact network that can avoid the over-fitting of the ANNs in the individual SSVEP recognition. Method: This study integrates the prior knowledge of SSVEP recognition tasks into the attention neural network design. First, benefiting from the high model interpretability of the attention mechanism, the attention layer is applied to convert the operations in conventional spatial filtering algorithms to the ANN structure, which reduces network connections between layers. Then, the SSVEP signal models and the common weights shared across stimuli are adopted to design constraints, which further condenses the trainable parameters. Results: A simulation study on two widely-used datasets demonstrates the proposed compact ANN structure with proposed constraints effectively eliminates redundant parameters. Compared to existing prominent deep neural network (DNN)-based and correlation analysis (CA)-based recognition algorithms, the proposed method reduces the trainable parameters by more than ${90}\%$ and ${80}\%$ respectively, and boosts the individual recognition performance by at least ${57}\%$ and ${7}\%$ respectively. Conclusion: Incorporating the prior knowledge of task into the ANN can make it more effective and efficient. The proposed ANN has a compact structure with less trainable parameters and thus requires less calibration with the prominent individual SSVEP recognition performance.

Objective: Multi-frequency-modulated visual stimulation scheme has been shown effective for the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) recently, especially in increasing the visual target number with less stimulus frequencies and mitigating the visual fatigue. However, the existing calibration-free recognition algorithms based on the traditional canonical correlation analysis (CCA) cannot provide the merited performance. Approach: To improve the recognition performance, this study proposes a phase difference constrained CCA (pdCCA), which assumes that the multi-frequency-modulated SSVEPs share a common spatial filter over different frequencies and have a specified phase difference. Specifically, during the CCA computation, the phase differences of the spatially filtered SSVEPs are constrained using the temporal concatenation of the sine-cosine reference signals with the pre-defined initial phases. Main results: We evaluate the performance of the proposed pdCCA-based method on three representative multi-frequency-modulated visual stimulation paradigms (i.e., based on the multi-frequency sequential coding, the dual-frequency, and the amplitude modulation). The evaluation results on four SSVEP datasets (Dataset Ia, Ib, II, and III) show that the pdCCA-based method can significantly outperform the current CCA method in terms of recognition accuracy. It improves the accuracy by 22.09% in Dataset Ia, 20.86% in Dataset Ib, 8.61% in Dataset II, and 25.85% in Dataset III. Significance: The pdCCA-based method, which actively controls the phase difference of the multi-frequency-modulated SSVEPs after spatial filtering, is a new calibration-free method for multi-frequency-modulated SSVEP-based BCIs.

Tunnels excavated in a combination of hard and soft rock strata with high ground stress are prone to large deformations, collapse, and other disasters. The Yongfeng Tunnel, a reconstruction and expansion of the G544 line, suffered severe high ground stress from plate compression. This paper studied the surrounding rock pressure and supporting structure stress characteristics of tunnels with a combination of hard and soft rock strata with high ground stress by using earth pressure cells, surface strain gauges, and embedded strain gauges to test all stress related to the surrounding rock, primary support, and secondary lining. It was found that the contact pressure (P1) between the initial support and the surrounding rock and the contact pressure (P2) between the initial support of the leading tunnel were distributed in the direction of vertical stratification, while the contact pressures (P1 and P2) of the lagging tunnel were different due to the excavation unloading of the leading tunnel. The maximum stress positions of the initial support of the leading tunnel and the lagging tunnel were located in the left arch waist and the vault, respectively. However, the maximum stress position of the secondary lining was generally located on the side wall. The research results presented herein can guide future tunnel construction projects.

Aquaculture has become the fastest growing sector in global agriculture. The environmental degradation, diseases, and high density of mariculture has made for an inevitable shift in mariculture production from coastal to deep-sea areas. The influence that traditional coastal and emerging deep-sea farming environments exert on aquatic growth, immunity and gut microbial flora is unclear. To address this question, we compared the growth performance, physiological indicators and intestinal microbiological differences of deep-sea and coastal aquaculture in the Guangxi Beibu Gulf of China. The results showed that the growth performance and the complement of C3 and C4 (C3, C4), superoxide dismutase (SOD), and lysozyme (LYS), these physiological and biochemical indicators in the liver, kidney, and muscle of Trachinotus ovatus (T. ovatus), showed significant differences under different rearing conditions. Metagenome sequencing analysis showed Ascomycota, Pseudomonadota, and Bacillota were the three dominant phyla, accounting for 52.98/53.32 (coastal/deep sea), 24.30/22.13, and 10.39/11.82%, respectively. Aligned against the CARD database, a total of 23/2 (coastal/deep-sea) antibiotic resistance genes were screened and grouped into 4/2 genotypes. It indicated that compared with deep-sea fish, higher biological oxygen levels (3.10 times), inorganic nitrogen (110.00 times) and labile phosphate levels (29.00 times) in coastal waters might contributed to the existence of eutrophication with antibiotic resistance. The results of the study can provide complementary data on the study of the difference between deep-sea farming and traditional coastal farming, serving as a reference to future in-depth work on the transformation of fisheries development and scientific standardization of deep-sea farming.

Flow batteries provide promising solutions for stationary energy storage but most of the systems are based on expensive metal ions or synthetic organics. Here, the authors show a chlorine flow battery capitalizing the electrolysis of saltwater where the redox reaction is stabilized by the saltwater-immiscible organic flow.

Stimulator of interferon genes (STING) pathway activation predicts the effectiveness of targeting the PD-1/PD-L1 axis in lung cancer. Active IFN-γ signaling is a common feature in tumors that respond to PD-1/PD-L1 blockade. The connection between IFN-γ and STING signaling in cancer cells has not been documented. We showed that IFN-γ caused DNA damage and the accumulation of cytosolic dsDNA, leading to the activation of the cGAS- and IFI16-dependent STING pathway in lung adenocarcinoma cells. IFN-γ-induced iNOS expression and nitric oxide production were responsible for DNA damage and STING activation. Additional etoposide treatment enhanced IFN-γ-induced IFN-β and CCL5 expression. Tumor-infiltrating T cells stimulated with a combination of anti-CD3 and anti-PD-1 antibodies caused STING activation and increased IFN-β and CCL5 expression in lung adenocarcinoma. These effects were abrogated by the addition of an IFN-γ neutralizing antibody. Our results suggest that the activation of tumor-infiltrating T cells could alter the tumor microenvironment via the IFN-γ-mediated activation of STING signaling in cancer cells.

Wind speed forecasting is critical to renewable energy generation, agriculture, and disaster prevention. Due to the uncertainty and intermittence of wind, conventional forecasting methods with numerical weather prediction (NWP) models fall short of achieving satisfactorily high accuracy. Post-processing of the predicted results is necessary for enhancing the prediction accuracy. The industry generally employs time-series prediction (TSP) methods for error correction, yet it is time-consuming since repeated modeling is needed if the location changes. Aiming at addressing this problem, this paper discusses the application of a deep learning algorithm in the post-processing period of wind speed prediction. NWP results are utilized as the forecasting basis, and deep learning algorithms are used for minimizing errors. An experimental study is conducted with industrial data. The functionality and performance of TSP-based algorithms including rolling mean, exponential smoothing, and autoregressive integrated moving average algorithms are compared with deep learning-based algorithms, including long-short term memory and convolutional neural network. From the numerical results, both TSP and deep-learning error-correction methods can effectively increase the accuracy of day-level NWP model prediction results, while deep-learning methods are data-driven, and no modeling process is needed. This work also poses an insight into the future development of wind speed prediction in meteorology.

Grasslands provide important livestock products such as beef and milk, the main source of daily nutrients for human beings. However, ecological degradation has occurred in nearly 50% of global grasslands, which seriously restricts livestock product supply and impairs human well-beings. Sustainable management measures are explored both in developed and developing regions to curb grassland degradation trend and achieve economic-ecological coordination. We focused on the differences of grassland management measures and their economic-ecological effects between developed regions (US, New Zealand and Western Europe) and developing regions (Inner Mongolia, China) along the “global gold pastures” in middle-latitude zone, by analyzing human-induced grassland productivity changes based on satellite remote sensing, and livestock carrying pressure based on forage-livestock relationship. The results showed the human-induced GPP on grasslands were higher in those developed regions than Inner Mongolia, while the livestock carrying pressures of grasslands were lower, which indicated that the ecological effect of grassland management measures was more positive in the developed regions. Although strict grassland-livestock balance policies had effectively altered the degradation trends in Inner Mongolia grassland, the economic growth still strongly relies on increasing livestock quantity within the grazing limitation, while those developed regions increased the economic benefits and also reduce the ecological pressure caused by livestock grazing in the meantime. Artificial pastures construction together with improved production efficiency effectively mitigated the ecological pressure on grasslands and also brought more economic benefits in those developed regions, which could also enlighten other developing regions to promoting sustainable utilization of grasslands and development of animal husbandry.

Abstract Immunotherapy has revolutionized cancer treatment, dramatically improving survival rates of melanoma and lung cancer patients. Nevertheless, immunotherapy is almost ineffective against ovarian cancer (OC) due to its cold tumor immune microenvironment (TIM). Many traditional medications aimed at remodeling TIM are often associated with severe systemic toxicity, require frequent dosing, and show only modest clinical efficacy. In recent years, emerging nanomedicines have demonstrated extraordinary immunotherapeutic effects for OC by reversing the TIM because the physical and biochemical features of nanomedicines can all be harnessed to obtain optimal and expected tissue distribution and cellular uptake. However, nanomedicines are far from being widely explored in the field of OC immunotherapy due to the lack of appreciation for the professional barriers of nanomedicine and pathology, limiting the horizons of biomedical researchers and materials scientists. Herein, a typical cold tumor‐OC is adopted as a paradigm to introduce the classification of TIM, the TIM characteristics of OC, and the advantages of nanomedicines for immunotherapy. Subsequently, current nanomedicines are comprehensively summarized through five general strategies to substantially enhance the efficacy of immunotherapy by heating the cold OC. Finally, the challenges and perspectives of this expanding field for improved development of clinical applications are also discussed.

Electronegative guest‐filled skutterudites (EN‐SKDs) are considered one of the most promising thermoelectric materials for next‐generation thermoelectric generators (TEGs). However, the practical application of EN‐SKDs in TEGs is still a huge challenge because of their unsatisfactory zT value (less than 1.0). Herein, Ge, Te‐codoped electronegative sulfur‐filled skutterudites of S x Co4Sb11Ge0.2Te0.8 (x = 0, 0.05, 0.1, 0.2, 0.25) are synthesized. The optimal zT value of as high as 1.45 at 823 K with a low room‐temperature lattice thermal conductivity of 1.98 W m−1 K−1 is achieved by S0.25Co4Sb11Ge0.2Te0.8. Such performance is competitive with those of traditional TE materials and among one of the best EN‐SKDs. The excellence is attributed to the synergistic effect of the disorder caused by Ge, Te co‐doping at Sb‐site and the “rattling” effect of electronegative element S. Furthermore, a novel EN‐SKD‐based TEG is prepared for the first time through an integrated welding process and exhibits a high efficiency of 6.0% at the temperature difference of 475 K. This work provides valuable guidance for designing advanced thermoelectric materials and paves a way toward high‐performance EN‐SKD‐based TEGs.

BackgroundFew studies have examined the association between reproductive history and the multidimensional health of older adults with more diverse reproductive histories and poorer health status in rural China. The purpose of this study is to explore the effect of parity, sex ratio of children and late childbearing on multidimensional health and its gender differences.MethodsThe analytical sample consisted of 3,377 older adults in rural China who participated in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2018. Linear regression models were applied to estimate the relationship between reproductive history and multidimensional health, with separate models for each indicator of health outcomes.ResultsOlder adults in rural areas with greater parity were more likely to have better cognitive function (β = 0.409, 95% CI: 0.255–0.563), fewer Activities of Daily Living (ADL) limitations (β = −0.085, 95% CI: −0.137 to −0.034) and symptoms of depression (β = −0.396, 95% CI: −0.577 to −0.216). The social mechanism of intergenerational support from children later in life partly explained the positive effect of parity. Late childbearing had negative effects on cognitive function (β = −1.220, 95% CI: −1.895 to −0.545), ADL (β = 0.253, 95% CI: 0.028–0.478) and symptoms of depression (β = 1.025, 95% CI: 0.237–1.812). Women were more likely to be influenced by the positive effect of parity; the association between late childbearing and health was only significant in the male group.ConclusionsParity and late childbearing are associated with cognitive function, activities of daily living, and symptoms of depression in the older adults in rural China. Older adults with more children might be in better health, and this finding is especially significant in women. However, late childbearing had a negative effect on multidimensional health, especially for men. The social mechanism and gender differences between reproductive history and health need to be further explored.

Alopecia has gradually become a problem that puzzles an increasing number of people. Dermal papilla cells (DPCs) play an important role in hair follicle (HF) growth; thus, exploring the effective chemicals or natural extracts that can remediate the growth of DPCs is vital. Our results showed that Schizochytrium sp.-extracted lipids (SEL) significantly promoted proliferation (up to 1.13 times) and survival ratio (up to 2.45 times) under oxidative stress. The treatment with SEL can protect DPCs against oxidative stress damage, reducing the reactive oxygen species (ROS) level by 90.7%. The relative gene transcription and translation were thoroughly analyzed using RNA-Seq, RT-qPCR, and Western blot to explore the mechanism. Results showed that SEL significantly inhibited the ferroptosis pathway and promoted the expression of antioxidant genes (up to 1.55–3.52 times). The in vivo application of SEL improved hair growth, with the length of new hair increasing by 16.7% and the length of new HF increasing by 92.6%, and the period of telogen shortening increased by 40.0%. This study proposes a novel therapeutic option for alopecia, with the effect and regulation mechanism of SEL on DPC systematically clarified.

Maritime transportation plays a critical role in global trade as it accounts for over 80% of all merchandise movement. Given the growing volume of maritime freight, it is vital to have an efficient system for handling ships and cargos at ports. The current first-come-first-serve method is insufficient in maintaining operational efficiency, especially under complicated conditions such as parallel scheduling with different cargo setups. In addition, in the face of rising demand, data-driven strategies are necessary. To tackle this issue, this paper proposes a mixed-integer model for allocating quay cranes, terminals, and berths. It considers not only cargo types, but also the time required for a quay crane setup. The proposed model features a greedy-insert-based offline algorithm that optimizes berth allocation when vessel information is available. In situations where vessel information is uncertain, the model utilizes an online optimization strategy based on a reinforcement-learning algorithm that is capable of learning from feedback and of adapting quickly in real time. The results of the numerical experiments demonstrate that both the offline and online algorithms can significantly enhance cargo handling efficiency and overall harbor operation. Furthermore, they have the potential to be extended to other complex settings.

In this paper, a kind of flexible shield for thermal neutron is fabricated through blending functional B4C powder with thermoplastic elastomer polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene(SEBS) in a banbury mixer, and weight fractions[(MB4C: MSEBS)%] of the composites are prepared as 10%, 20%, 30%, 40%, 60%, 100% respectively. B4C powders are well dispersed in the matrix of SEBS through microscopic observation, even without chemical interaction between the chemical groups of SEBS and the fillers. The elongations at break and tensile strengths of the B4C/SEBS composites deteriorate rapidly compared with the virgin sample, but it seems the tensile and tear strength could still maintain a satisfactory value suitable for preparing super flexible shields. The hardness of the composites is in the range of 15–25 HA. Based on a self developed nondestructive testing device, shielding abilities of the samples are estimated by neutron transmittance, 10B areal density, linear attenuation coefficient, and thickness of half value layer. The neutron transmittance is 71.31%–32.43% corresponding to 10B areal density ranging from 2.1873 mg/cm2 to 17.6314 mg/cm2, linear attenuation coefficient is 0.1932–0.6435 mm−1, and half value layer value is 3.59–1.08 mm. It is also proved that the neutron transmittance measured shows an exponential attenuation with the increase of 10B areal density. The most importantly, the areal uniformity of neutron transmittance, with standard deviation less than 0.76%, is validated for the achievement of excellent attenuation of thermal neutrons in all directions. Consequently, results show excellent flexible and shielding performances of the functional composites.

Recently, Siamese trackers have shown excellent performance in both accuracy and speed. However, traditional trackers have poor robustness against similar objects due to the use of single deep features and the limitation of cosine windows. In this paper, a novel Siamese network combining information fusion with rectangular window filtering named SiamFF is introduced. First, a multilevel fusion network is proposed. At feature-level, the shallow and deep features of the network are fused through a layer-hopping connection to obtain complementary feature maps. Then, the score maps generated by the complementary feature maps are further fused at the score-level to improve the robustness. In addition, based on the continuity and stationarity of objects movement in reality, a score map filtering strategy is proposed. The relative displacement of the target can be predicted by obtaining the interframe information, and the moving direction is applied to filter the score map to further eliminate the analog interference. Experimental results on OTB2015 and VOT2016 benchmarks indicate that SiamFF performs favorably against many state-of-the-art trackers in terms of accuracy while maintaining real-time tracking speed.

In order to optimize the interface performance of copper/steel bimetallic composite and avoid the degradation of the composite performance owing to the difference of thermal-physical properties, HS211/ER50-6 composite structure was fabricated by arc additive technology, and Fe–Ni and Cu–Al flux-cored wires were designed as transition materials to realize the gradient connection of the interface area. Microstructure observation and mechanical properties testing were used to characterize the properties of the samples. The results showed that gradient connection can be achieved by adding the transition layer of Fe–Ni and Cu–Al flux-cored wires. Fe-rich phases with different morphologies were found in the interface region, which strengthened the matrix as the second phase. The corrosion resistance of the Fe–Ni transition layer region was better than Cu–Al, and the impact absorption energy reached 925 kJ/m2. The tensile strength of Fe–Ni samples reached 326.5 MPa, and the fracture location of the two groups of samples were both the copper side deposition.

The parameter tuning optimization design is realized for an active disturbance rejection controller (ADRC) in combination with the improvement of the existing swarm intelligence algorithm. Taking the optimization design and application of ADRC as an example, this paper is focused on investigating the improvement of the hybrid algorithm composed of fish swarm algorithm and particle swarm optimization algorithm and its application in parameter tuning of ADRC. The main contents are as follows. First, the parameters that need to be tuned are determined based on the composition and principle of the ADRC. The module building technology of S-function is adopted to create the module library of ARDC in terms of the modular construction idea and a complete simulation example of ADRC is built in Simulink. Second, the parameters are improved according to the proposed hybrid algorithm composed of the artificial fish swarm algorithm and the standard particle swarm optimization algorithm, and the control performance is tested by the MATLAB simulation of the ADRC whose parameters are optimized by using the algorithm. Finally, the flight attitude control of the unmanned aerial vehicle (UAV) is taken as an application example, and the fixed-wing UAV is selected as the research object. Through the analysis of the experimental results, the effectiveness of the optimized design is verified for the ADRC in the attitude control of the UAV.

Ecological degradation has occurred in global grasslands and has impaired their ecosystem services severely, so ecological conservation of grasslands should be focused more on the effectiveness of management measures. The green-up process decides the year-round forage yield and ecological conditions of grasslands. Adopting rest-grazing during the green-up process can guarantee a successful green-up, thus realizing more economic benefits without grassland degradation. Therefore, studies should pay more attention to whether the green-up process is really covered by the rest-grazing period or not. We analyze the spatiotemporal variations and the stability of the annual green-up date in Xilin Gol Grassland from 2000 to 2018 based on MODIS time series images and compare the green-up date with the rest-grazing period to assess the effectiveness of the rest-grazing policy. The results show that the green-up date of Xilin Gol Grassland had advanced 15 days on average because of the increasing trend of both temperature and precipitation during 2000~2018. The green-up date is mostly 120~130 d in the east, about 10 days earlier than the west (130~140 d) and 20 days earlier than in the central areas (140~150 d), also because of the spatial variations of temperature and precipitation. The coefficient of variation (CV) of the green-up date showed a significant negative correlation with precipitation, so the green-up date is more unstable in the arid areas. The rest-grazing period started more than 45 days earlier than the green-up date and failed to cover it in several years, which occurred more frequently in southern counties. The average green-up date appeared after rest-grazing started in over 98% of areas, and the time gap is 15~45 days in 88% of areas, which not only could not avoid grassland degradation effectively but also increased herdsmen’s life burden. This study aims to accurately grasp the temporal and spatial variations of the green-up date in order to provide references for adjusting a more proper rest-grazing period, thus promoting ecological conservation and sustainable development of animal husbandry.

Aurantiochytrium sp. is a major source of n-3 polyunsaturated fatty acids (n-3 PUFAs), which are essential nutrients for marine fish. n-3 PUFAs have drawn increasing attention because of their great potential for improving the biological functions of fish. The growth performance and immune response of Trachinotus ovatus were studied by applying diets with various microalgae content from 1.00 to 11.00% for 8 weeks. The results showed that, with the addition of Aurantiochytrium sp., the survival rate, weight gain rate, and specific growth rate of fish increased by a maximum of 1.02, 1.16, and 1.08 times, respectively, indicating that the intake of marine microalgae Aurantiochytrium sp. was beneficial for fish growth. As the microalgae content increased, the feed utilization efficiency index feed conversion ratio decreased (maximum 15.00%) and feed efficiency increased (maximum 1.17 times), showing that the addition of Aurantiochytrium sp. contributed to the assimilation of fish feed. Furthermore, our results showed that as the addition of Aurantiochytrium sp. increased (from 1.00 to 11.00%), the glucose content increased in the blood (from 9.04 to 27.80%). The content of fatty acids ARA, ALA, DHA, and EPA in fish was significantly increased after adding Aurantiochytrium sp. in diets. In liver, ARA content increased from 1.17 to 1.63%, ALA increased from 0.56 to 0.85%, DHA increased from 14.44 to 20.61%, and EPA increased from 1.86 to 4.40%. In muscle, ARA content increased from 0.97 to 1.24%, ALA increased from 0.59 to 0.81%, DHA increased from 14.63 to 14.82%, and EPA increased from 4.58 to 5.19%. Positive changes were observed in the blood cell count of immune related cells (white blood cells, lymphocytes, monocytes, neutrophils, and red blood cells). These results indicated that microalgae rich in n-3 PUFAs could increase the number of immune cells, thus helping to improve fish immunity and disease resistance.

A kind of novel and compact magnetic field sensor has been proposed and investigated experimentally. The proposed sensor consists of a tapered single mode fiber coupled with a nanostructured magnetic fluid-infiltrated photonic crystal fiber, which is easy to be fabricated. The response of magnetic fluid to magnetic field is used to measure the intensity of magnetic field via whispering gallery mode. The magnetic field-dependent shift in resonance wavelength is observed. The maximum magnetic field intensity sensitivity is 53 pm/mT. The sensor sensitivity is inversely proportional to the thickness of the photonic crystal fiber cladding.

Grassland has always had a difficult economic–ecological relationship, as coordination between its ecological conservation and the sustainable development of animal husbandry is required. Nature-based Solutions (NbS), who make full use of the natural ecosystem services, have successfully solved some economic–ecological issues, but still have unclear implementation prospects for grassland management. The Xilin Gol grassland is one of the most typical pastoral areas in China; there is a village chief named Bateer, who has already used NbS for grassland management. To confirm whether the solutions employed by Bateer have been effective for both increasing economic profits and protecting grassland ecosystem, we interviewed him, and many other herdsmen, using questionnaires about their livelihood. Based on these questionnaires, we calculated and compared their income–cost ratios. Meanwhile, we analyzed the NDVI variations inside their rangelands through high-resolution remote sensing images. The results showed that the herdsmen in Bateer’s village had a much higher disposable income and income–cost ratio than others, and their rangelands also had a higher value and a more obvious increasing trend of NDVI. Bateer’s success proves that the NbS can also play a positive role in grassland management, which can provide a valuable guidance for economic–ecological coordination in pastoral areas.

During early postnatal development in mammals, cardiomyocytes exit the cell cycle, losing their regenerative capacity. Here the authors show that, following myocardial infarction, loss of microRNA-128 promotes cardiomyocyte proliferation and cardiac regeneration in adult mice partly via enhancing the expression of the chromatin modifier SUZ12.

Fiber-reinforced concrete (FRC) and engineered cementitious composite materials have demonstrated promising requisite in construction industry owing to its superior mechanical and durability properties. In this study, a sustainable approach was taken, i.e., to use industry waste as a reinforcement with improved interfacial bonding leading to enhanced mechanical performance of FRC. An efficient in situ recycling process allowed the authors to extract glass fibers from glass fiber-reinforced polymer acrylic waste. Concrete mixes with low fiber dosages including 0.1%, 0.2%, and 0.3% (by volume) of recycled as well as virgin glass fibers were prepared. The slump of concrete was maintained ∼150 mm by using high water-reducing admixture (HWRA). Notably, lower amount of HWRA was required for raw glass fibers vis-à-vis recycled ones due to its hydrophobic nature. Overall, FRC enclosing 0.3% recycled glass fiber demonstrated >20% enhancement in compressive, split tensile, and flexural strength as compared to control (after 28 days of curing), also supported by morphological analysis.