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Here, we describe a new species Calamaria jinggangensis sp. nov. from Mt. Jinggang, Jiangxi Province, China. It can be distinguished from the sister species of C. arcana by these unique characters: posterior chin shields almost whole meeting in midline, dorsal scales reduced to 6 rows above last subcaudal at tail; lower maxillary teeth, higher palatine teeth and so on. The uncorrected p-distances of cyt b sequences between this new species and other 13 Calamaria included in the study is 6.46 – 19.93%. We also studied a female specimen from Mt. Wuyi as C. arcana new record in Fujian Province. In addition, it provided a diagnostic key to all the species of Calamaria from China.

Influence maximization is the problem of finding a set of seed nodes in the network that maximizes the influence spread, which has become an important topic in social network analysis. Conventional influence maximization algorithms cause “unfair" influence spread among different groups in the population, which could lead to severe bias in public opinion dissemination and viral marketing. To address this issue, we formulate the fair influence maximization problem concerning the trade-off between influence maximization and group fairness. For the purpose of solving the fair influence maximization problem in large-scale social networks efficiently, we propose a novel attribute-based reverse influence sampling (ABRIS) framework. This framework intends to estimate influence in specific groups with guarantee through an attribute-based hypergraph so that we can select seed nodes strategically. Therefore, under the ABRIS framework, we design two different node selection algorithms, ABRIS-G and ABRIS-T. ABRIS-G selects nodes in a greedy scheduling way. ABRIS-T adopts a two-phase node selection method. These algorithms run efficiently and achieve a good trade-off between influence maximization and group fairness. Extensive experiments on six real-world social networks show that our algorithms significantly outperform the state-of-the-art approaches. This article appears in the AI & Society track.

Maintaining peace and conserving biodiversity hinge on an international system of cooperation codified in institutions, but Russia’s invasion of Ukraine brings recent progress to a crossroads. Against this backdrop, we address some implications of Russia’s invasion of Ukraine for the governance of biodiversity conservation both within and beyond Russia. The Russian invasion of Ukraine threatens the governance system for biodiversity conservation, as it pertains to Russia and beyond, due to three interacting factors: (i) isolation of Russia from the international system, (ii) halt and delay of international cooperation, and (iii) changes in international and domestic policy priorities. We recommend making the existing international system of governance for conserving biodiversity more resilient and adaptable, while aligning security agendas with biodiversity conservation goals.

Two novel Cd(ii)-based metal–organic frameworks are constructed from an imidazole-containing ligand and SiF62− anions, and both of them exhibit a fluorescence sensing capacity for arginine via a turn-on effect.

Intermolecular coulombic interactions induce physical crosslinking and thus the formation of highly asymmetric lamellar nanostructures in a broad composition range for polyzwitterionic block copolymers.

This article investigates the challenges teachers experience while learning to teach English through drama in an action research project. Through closely examining the fundamental causes, it discerns the knowledge constituents crucial for successful learning and implementation of drama for English teaching. Difficulties are identified from three aspects: text selection and interpretation, translation into drama activities, and classroom implementation. Referring to Shulman’s teacher knowledge construct (1986, 1987), the author argues that drama pedagogy for English teaching demands a changed perspective on language regarding its form and meaning and also on drama as both subject and pedagogy. Only then can teachers effectively link language and drama and create a meaningful learning experience.

ABO blood groups has been associated with risk of several cancers; however, the results for an association with ovarian cancer are inconsistent and little is known about the expression of histo-blood group (ABH) antigens and ABO gene in ovarian tumor tissues.To assess the impact of genotype-derived ABO blood types on the risk of EOC, we conducted a case-control study in 1,870 EOC and 4,829 controls. Expression of A and B antigen in 70 pairs of ovarian tumor tissues and adjacent normal tissues were detected by immunohistochemistry. Gene expression and DNA methylation profiling was conducted in ovarian tumor tissues.We identified that blood group A was associated with increased risk for EOC compared to blood group O (OR = 1.18, 95% CI = 1.03-1.36, p = 0.019). Increased frequency of aberrant expression of histo-blood group antigens was observed in patients with blood group A (76.5%) compared to patients with blood group O (21.1%) and B (5.0%) by immunohistochemistry (p 0.001). ABO gene expression was down-regulated in ovarian tumor tissues compared with paired adjacent normal tissues (p = 0.027). In addition, ABO gene expression was positively correlated with NFYB (r = 0.38, p 0.001) and inversely correlated with DNA methylation level of four CpG sites on ABO gene (cg11879188, r = - 0.3, p = 0.002; cg22535403, r = - 0.30, p = 0.002; cg13506600, r = - 0.22, p = 0.025; cg07241568, r = - 0.21, p = 0.049) in ovarian tumor tissues.We identified blood group A was associated with increased EOC risk in Chinese women and provided the clues of the possible molecular mechanisms of blood group A related to ovarian cancer risk.

Dysregulation of G6PD involved in the pentose phosphate pathway (PPP) is known to promote tumorigenesis. The PPP plays a pivotal role in meeting the anabolic demands of cancer cells. However, the detailed underlying molecular mechanisms of targeting the G6PD-regulated PPP in breast cancer remain unclear. In this study, we aimed to elucidate the molecular pathways mediating the effects of G6PD on cancer progression. Clinical sample analysis found that the expression of G6PD in breast cancer patients was higher than that in normal controls, and patients with higher G6PD expression had poor survival. Gene knockdown or inhibition of G6PD by 6-AN in MCF-7 and MDA-MB-231 cells significantly decreased cell viability, migration, and colony formation ability. G6PD enzyme activity was inhibited by 6-AN treatment, which caused a transient upregulation of ROS. The elevated ROS was independent of cell apoptosis and thus associated with abnormal activated autophagy. Accumulated ROS levels induced autophagic cell death in breast cancer. Inhibition of G6PD suppresses tumour growth in preclinical models of breast cancer. Our results indicate that targeting the G6PD-regulated PPP could restrain tumours in vitro and in vivo, inhibiting G6PD caused cell death by over-activating autophagy, therefore leading to inhibited proliferation and tumour formation.

Artifacts are irreplaceable treasures of human culture, and transportation monitoring is critical for safeguarding valuable artifacts against damage during culture exchanges. However, current collision-monitoring technologies have limitations in regard to real-time monitoring, cushioning protection, and power supply requirements. Here, we present a method for constructing a smart artifact-monitoring system (SAMS) based on a porous carbon black (CB)/Ecoflex triboelectric nanogenerator (PCE-TENG) that can monitor collisions in real time and absorb vibrations during artifact transportation. The PCE-TENG is assembled using a flexible printed circuit board (FPCB) and a porous Ecoflex layer with CB powder. It exhibits cushioning protection, stretchability, pressure sensitivity, and durability. To enhance its electrical output, modifications were made to optimize the CB content and surface structure. The SAMS comprises six PCE-TENGs attached to the inner wall of the artifact transport package and enables collision monitoring and protection in different directions. Moreover, the SAMS has the capability to instantly transmit warning information to monitoring terminals in the event of improper operations, empowering carriers to promptly and efficiently safeguard artifacts by taking necessary measures. This paper presents a practical strategy for artifact transportation monitoring and package engineering that could have significant implications for the field.

Most traditional injection-production optimization methods that treat the entire oil reservoir as a whole require re-optimization when facing new reservoirs, which is not only time-consuming but also does not make full use of historical experience information. This study decomposes the reservoir into independent basic production units to increase sample size and diversity and utilizes image enhancement techniques to augment the number of samples. Two frameworks based on convolutional neural networks (CNNs) are employed to recommend optimal control strategies for inputted well groups. Framework 1 uses bottom hole pressure (BHP) as a control variable and trains a CNN with optimal BHP obtained by reinforcement learning algorithms as labels. Framework 2 saves BHP and corresponding oil well revenue (NPV) during reinforcement learning optimization and trains a CNN with well groups and BHP as features and NPV as labels. The CNN in this framework is capable of directly outputting the NPV according to control strategies. The particle swarm algorithm (PSO) is used to generate control strategies and call CNN to predict development effects until PSO converges to the optimal production strategy. The experimental results demonstrate that the CNN-based frameworks outperform the traditional PSO-based methods in terms of accuracy and computational efficiency. Framework 1 achieves an output accuracy of 87% for predicting the optimal BHP for new well groups, while Framework 2 achieves an accuracy of 78%. Both frameworks exhibit fast running times, with each iteration taking less than 1 s. This study provides a more effective and accurate method for optimizing oil well production in oil reservoirs by decomposing oil reservoirs into independent units and using CNN to construct an algorithm framework, which is of great significance for the real-time optimization and control of oil wells in oil fields.

We describe a design principle for adaptive systems under which adaptation is driven by particular challenges that the environment poses, as opposed to average or otherwise aggregated measures of performance over many challenges. We trace the development of this "particularity" approach from the use of lexicase selection in genetic programming to "particularist" approaches to other forms of machine learning and to the design of adaptive systems more generally., Comment: Genetic Programming Theory and Practice XX

Introduction. The purpose of this study is to use deep learning and machine learning to learn and classify patients with cutaneous melanoma with different prognoses and to explore the application value of deep learning in the prognosis of cutaneous melanoma patients. Methods. In deep learning, VGG-19 is selected as the network architecture and learning model for learning and classification. In machine learning, deep features are extracted through the VGG-19 network architecture, and the support vector machine (SVM) model is selected for learning and classification. Compare and explore the application value of deep learning and machine learning in predicting the prognosis of patients with cutaneous melanoma. Result. According to receiver operating characteristic (ROC) curves and area under the curve (AUC), the average accuracy of deep learning is higher than that of machine learning, and even the lowest accuracy is better than that of machine learning. Conclusion. As the number of learning increases, the accuracy of machine learning and deep learning will increase, but in the same number of cutaneous melanoma patient pathology maps, the accuracy of deep learning will be higher. This study provides new ideas and theories for computational pathology in predicting the prognosis of patients with cutaneous melanoma.

Given the rapid development of internet and dramatic change it has brought to human life, this study examines the effect of internet use on well-being in rural China. Findings indicate that the act of engaging with internet and the increase in its usage frequency both have significant positive effects on well-being in rural China, whereas the effect attenuates with the increase of usage frequency. These results imply that internet use features diminishing marginal returns. In the sub-groups divided by education and age, the higher educated and the younger groups use internet more frequently. However, internet use significantly promotes well-being of the lower educated, the middle-aged and the elderly groups, and its effect on the higher educated and the younger groups is insignificant. A further analysis uncovers that internet use increase well-being through relieving loneliness. This study affirms the benefits of internet in rural China, but obsessive internet use could offset the benefits. Thus, the implication of using internet appropriately are highlighted.

Chronic rotator cuff tears are debilitating diseases which significantly affect patients' quality of life and pose substantial financial burden to the society. The intraoperative reparability of injured tendon and postoperative probability of tendon retear are highly associated with the quality of torn muscles, specifically, the severity of muscle atrophy and fatty infiltration. Animal models that reproduce the characteristic muscle pathology after rotator cuff injury have been developed and used to provide insight into the underlying biology and pathophysiology. In this review, we briefly summarize the current information obtained from preclinical animal studies regarding the degenerative change of cuff muscle subsequent to tendon release and/or suprascapular nerve denervation. Importantly, we focus on the potential translational therapeutic targets or agents for the prevention or reversal of muscle atrophy and fatty infiltration. While further studies are warranted to assess the safety and efficacy of novel therapies derived from these preclinical animal research, we believe that their clinical translation for the treatment of rotator cuff disorders is on the horizon.Novel therapeutic strategies described in this review from preclinical animal studies hold a great translational potential for preventing or reversing rotator cuff muscle pathology, while further assessments on their safety and efficacy are warranted.

Using a solid-state reaction strategy, nominal Li3TiO3F oxyfluorides ceramics were fabricated, and its sintering behavior, microstructure, phase assemblages, as well as microwave dielectric performances were all investigated. The main phase of Li3TiO3F with cubic structures accompanied with small amounts of the LiF or Li2TiO3 secondary phase was identified by XRD analysis. SEM analysis showed that a uniform and dense microstructure was obtained for 750 °C-sintered samples. The dielectric constant (εr) and quality factor (Q × f) were found to be strongly correlated with porosity and grain size distribution, whereas the temperature coefficient of resonance frequency (τf) was mainly dominated by the phase assemblages. In particular, the 750 °C-sintered Li3TiO3F samples exhibited good microwave dielectric performances: εr = 18, Q × f = 57,300 GHz (under 9.2 GHz), τf = −43.0 ppm/°C.

Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression, and machine learning. Due to its non-parametric and recursive nature, calculating the probability of each individual being selected by lexicase selection has been proven to be an NP-hard problem, which discourages deeper theoretical understanding and practical improvements to the algorithm. In this work, we introduce probabilistic lexicase selection (plexicase selection), a novel parent selection algorithm that efficiently approximates the probability distribution of lexicase selection. Our method not only demonstrates superior problem-solving capabilities as a semantic-aware selection method, but also benefits from having a probabilistic representation of the selection process for enhanced efficiency and flexibility. Experiments are conducted in two prevalent domains in genetic programming: program synthesis and symbolic regression, using standard benchmarks including PSB and SRBench. The empirical results show that plexicase selection achieves state-of-the-art problem-solving performance that is competitive to the lexicase selection, and significantly outperforms lexicase selection in computation efficiency., GECCO 2023

Reducing the switching energy and improving the switching speed of ferroelectrics remain an important goal in the pursuit of electronic devices with ultralow energy consumption and ultrafast response. Molecular ferroelectrics with concise dipole switching mechanism and facile structural tunability are a good platform for manipulating the ferroelectric domains. A methodology is demonstrated to manipulation of ferroelectric domain switching by tailor-made lattice parameters of molecular ferroelectrics, by following which, we succeeded in lowering the threshold electric field and improving the dynamics of ferroelectric switching. Our findings advance the fundamental understanding of microscopic mechanism and provide important insights in controllable tuning of ferroelectric domain switching.

Background The association between tea consumption and ovarian cancer (OC) risk has been reported in several epidemiology studies. However, the results were inconsistent and the causal relationship remains unclear. To explore the causal relationship between tea consumption and OC risk, we performed a two-sample Mendelian randomization (MR) analysis. Methods MR analysis was conducted using two published genome wide association studies (GWASs) (25,509 cases and 40,941 controls of European population and 3,238 cases and 4,083 controls of East Asian population) and in house GWAS (2,147 OC cases and 3,179 controls of Chinese population) by inverse variance-weighted, weighted median, and MR-Egger methods. Genetic instruments of 233 single nucleotide polymorphisms (SNPs) for European population, 382 SNPs for East Asian population, and 172 SNPs for Chinese population were created. Results We identified that tea consumption has protective effect against overall OC in European population (OR = 0.95, 95%CI: 0.90–0.99, P = 2.65E-02) and Chinese population (OR = 0.96, 95%CI: 0.92-1.00, P = 3.63E-02). When stratified by histological subtype, we found that tea consumption was significantly associated with the risk of Serous OC in European (OR = 0.92, 95%CI: 0.88–0.98, P = 4.86E-03), East Asian (OR = 0.96, 95%CI: 0.92–0.99, P = 9.87E-03), and Chinese (OR = 0.94, 95%CI: 0.90–0.99, P = 1.69E-02) population. In European population, there was a reduced risk of ovarian tumors of low malignant potential (OR = 0.90, 95%CI: 0.81-1.00, P = 4.59E-02), but an increased risk of Endometrioid OC (OR = 1.14, 95%CI: 1.03–1.27, P = 1.12E-02). Conclusion Our study suggested that there might be a causal relationship between tea consumption and OC risk in both European and East Asian populations and also may provide the evidence for cancer prevention and control.

The residential services in the context of smart cities accu- mulate massive real-time inquiry data in natural language to describe the services in need. Such inquiry requests have diverse topics in the content and considerable variate length. Besides, the responsible departments that may handle the inquiry involve a large number of organizations, from metropolitan administration to local communities. Hence accumu- lated request data is primary and central to the service of accurately dispatching requests to responsible departments. The challenge is de- vising a data centric approach to fit the data with SOTA models and improve the request classification accuracy. In this paper, we analyze the factors of embedding tokens, data segmentation, model structures, and classification methods. We devise a unified modelling process with mul- tiple dataflows that combine these factors to observe their interactions. The experiment results demonstrate the compound effects and provide insights into how SOTA models respond differently to variations in these factors. The observations allow us to fine-tune the learning task at each stage and achieve a maximum 82.4% F1-Score.

We have developed a new global eigenvalue code, Multiscale Analysis for plasma Stabilities (MAS), for studying plasma problems with wave toroidal mode number n and frequency omega in a broad range of interest in general tokamak geometry, based on a five-field Landau-fluid description of thermal plasmas. Beyond keeping the necessary plasma fluid response, we further retain the important kinetic effects including diamagnetic drift, ion finite Larmor radius, finite parallel electric field, ion and electron Landau resonances in a self-consistent and non-perturbative manner without sacrificing the attractive efficiency in computation. The physical capabilities of the code are evaluated and examined in the aspects of both theory and simulation. In theory, the comprehensive Landau-fluid model implemented in MAS can be reduced to the well-known ideal MHD model, electrostatic ion-fluid model, and drift-kinetic model in various limits, which clearly delineates the physics validity regime. In simulation, MAS has been well benchmarked with theory and other gyrokinetic and kinetic-MHD hybrid codes in a manner of adopting the unified physical and numerical framework, which covers the kinetic Alfven wave, ion sound wave, low-n kink, high-n ion temperature gradient mode and kinetic ballooning mode. Moreover, MAS is successfully applied to model the Alfven eigenmode (AE) activities in DIII-D discharge #159243, which faithfully captures the frequency sweeping of RSAE, the tunneling damping of TAE, as well as the polarization characteristics of KBAE and BAAE being consistent with former gyrokinetic theory and simulation. With respect to the key progress contributed to the community, MAS has the advantage of combining rich physics ingredients, realistic global geometry and high computation efficiency together for plasma stability analysis in linear regime., Comment: 40 pages, 21 figures

Microglial activation and sustained inflammation plays an important role in the processes of early brain injury (EBI) after subarachnoid hemorrhage (SAH). Sinomenine (SIN) has been demonstrated to have neuroprotective effects in the traumatic brain injury (TBI) model. However, the role of SIN in SAH-induced EBI and its latent mechanisms remain unclear. This study was carried out to explore the role of SIN on SAH-induced EBI and its effects on the microglial inflammatory response following SAH. In this study, a model of SAH in rats was established. Modified neurological severity scores (mNSS), encephaledema, and Nissl staining were employed to determine the effects of SIN. Western blot and immunofluorescence analysis were performed to evaluate nuclear factor erythroid 2-related factor 2 (Nrf2) expression. Nrf2-related downstream proteins, including heme oxygenase-1 (HO-1) and quinine oxidoreductase-1 (NQO-1), were detected with immunohistochemistry analyses and Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR). Microglia activation and associated inflammatory factors, factor-kappa B (NF-κB), interleukin-1β (IL-1β), and interleukin-6 (IL-6), were assessed after SAH. The results showed that SIN administration improved neurobehavior function, and attenuated neural apoptosis and brain edema after SAH. In addition, SIN inhibited microglial action and the subsequent inflammatory response after SAH through the upregulated expression of HO-1 and NQO-1 via activation of the Nrf2 pathway. These results demonstrated that SIN supplementation provided protection against SAH-induced neuronal apoptosis by microglial inflammatory response regulation and possible involvement of the Nrf2 pathway.

Collision process is crucial to the transport in magnetized plasmas. This article reviews the three typical approaches, i.e. the Fokker-Planck (FP) approach, the Bogoliubov-Born-Green-Kirwood-Yvon (BBGKY) approach, and the quasilinear (QL) approach, to deriving the kinetic equation for weakly coupled uniformly magnetized plasmas. The collision terms derived based on these three approaches are shown to be identical and satisfy the conservation laws and H theorem. Relatively speaking, the BBGKY and QL approaches are more systematic and readily to be generalized from weakly magnetized plasmas to strongly magnetized plasmas. The FP approach is pretty simple for weakly magnetized plasmas and has the advantage that the collision term derived based on it can be naturally separated into two parts, one part arising from the polarization and the other from the correlation of the fluctuating electrostatic field. However, the usual form of the FP equation is not suitable for strongly magnetized plasmas. To derive the magnetized collision term based on the FP approach, a general form of the FP equation for magnetized plasmas has to be found first.

Hepatocellular carcinoma (HCC), the most prevalent primary liver cancer, is a leading cause of cancer-related death worldwide because of rising incidence and limited therapy. Although treatment with sorafenib or lenvatinib is the standard of care in patients with advanced-stage HCC, the survival benefit from sorafenib is limited due to low response rate and drug resistance. Ibrutinib, an irreversible tyrosine kinase inhibitor (TKI) of the TEC (e.g., BTK) and ErbB (e.g., EGFR) families, is an approved treatment for B-cell malignancies. Here, we demonstrate that ibrutinib inhibits proliferation, spheroid formation, and clonogenic survival of HCC cells, including sorafenib-resistant cells. Mechanistically, ibrutinib inactivated EGFR and its downstream Akt and ERK signaling in HCC cells, and downregulated a set of critical genes involved in cell proliferation, migration, survival, and stemness, and upregulated genes promoting differentiation. Moreover, ibrutinib showed synergy with sorafenib or regorafenib, a sorafenib congener, by inducing apoptosis of HCC cells. In vivo, this TKI combination significantly inhibited HCC growth and prolonged survival of immune-deficient mice bearing human HCCLM3 xenograft tumors and immune-competent mice bearing orthotopic mouse Hepa tumors at a dose that did not exhibit systemic toxicity. In immune-competent mice, the ibrutinib–sorafenib combination reduced the numbers of BTK+ immune cells in the tumor microenvironment. Importantly, we found that the BTK+ immune cells were also enriched in the tumor microenvironment in a subset of primary human HCCs. Collectively, our findings implicate BTK signaling in hepatocarcinogenesis and support clinical trials of the sorafenib–ibrutinib combination for this deadly disease.

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