mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
Characterizing diseases using genetic and clinical variables: A data analytics approach

Predictive analytics is crucial in precision medicine for personalized patient care. To aid in precision medicine, this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease types. To achieve this, experiments were performed on diseased tissues obtained from the L1000 dataset to assess differences in the functionality and predictive capabilities of genetic and clinical variables. In this study, the k-means technique was used for clustering the diseased tissue types, and the multinomial logistic regression(MLR) technique was applied for classifying the diseased tissue types. Dimensionality reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical variables. The results showed that landmark genes performed slightly better in clustering diseased tissue types compared to any random set of 978 nonlandmark genes, and the difference is statistically significant. Furthermore, it was evident that both clinical and genetic variables were important in predicting the diseased tissue types. The top three clinical predictors for predicting diseased tissue types were identified as morphology, gender, and age of diagnosis. Additionally, this study explored the possibility of using the latent representations of the clusters of landmark and non-landmark genes as predictors for an MLR classifier. The classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical variables. This study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine, enhancing diagnostic accuracy.
Mechanical regulation of lipid and sugar absorption by Piezo1 in enterocytes

Obesity is primarily caused by excessive intake as well as absorption of sugar and lipid.Postprandial surge in distention pressure and intestinal motility accelerates the absorption of nutrients.The response of intestinal epithelial cells to mechanical stimulation is not fully understood.Piezo1,a mechanosensitive ion channel,is widely expressed throughout the digestive tract.However,its function in intestinal nutrient absorption is not yet clear.In our study,excessive lipid deposition was observed in the duodenum of obese patients,while duodenal Piezo1-CaMKK2-AMPKa was decreased when compared to normal-weight individuals.Under high-fat diet condition,the Piezo1~(iKO) mice exhibited abnormally elevated sugar and lipid absorption as well as severe lipid deposition in the duodenum and liver.These phenotypes were mainly caused by the inhibition of duodenal CaMKK2-AMPKa and the upregulation of SGLT1 and DGAT2.In contrast,Yoda1,a Piezo1 agonist,was found to reduce intestinal lipid absorption in diet induced obese mice.Overexpression of Piezo1,stretch and Yoda1 inhibited lipid accumulation and the expression of DGAT2 and SGLT1,whereas knockdown of Piezo1 stimulated lipid accumulation and DGAT2 in Caco-2 cells.Our study reveals a previously unexplored mechanical regulation of nutrient absorption in intestinal epithelial cells,which may shed new light on the therapy of obesity.
全胃切除术后并发鸟肠球菌感染性心内膜炎1例

本例患者除发热、寒战、胸闷外,缺乏明显的感染性心内膜炎表现。术后第7天腹腔引流管首次引出黄色浑浊液体后,我们初始采用广谱抗菌药物进行经验治疗:并根据药物敏感性试验结果调整为万古霉素联合亚胺培南。患者出现胸闷心悸,心前区杂音,超声心动图发现二尖瓣赘生物,结合之前的血培养结果,符合亚急性感染性心内膜炎的临床诊断,存在二尖瓣手术指征。感染性心内膜炎患者,感染控制且规范化抗菌药物治疗2~6周可进行外科手术;鸟肠球菌感染后的手术时机尚无共识,Mirzoyev等选择在氨苄西林、庆大霉素治疗2周进行手术。本例患者由于曾发生术后吻合口漏,且无急性心力衰竭、感染难以控制等急诊手术指征,故在敏感抗菌药物治疗5周后实施瓣膜手术。我院目前缺乏感染性心内膜炎二尖瓣修复的成熟经验,且患者高龄,前后瓣叶均有感染灶,结合患者及家属意见,最终选择二尖瓣生物瓣置换术。
胸锁关节脱位治疗方式对患者肩关节功能的预后对比研究

为探讨钢板内固定手术方式、缝合锚定钉内固定手术方式及保守治疗方式对胸锁关节脱位的短期治疗效果,回顾性分析北京积水潭医院2013年5月至2022年5月收治的22例胸锁关节脱位患者的临床资料。患者年龄16 ~ 68岁,常见的致伤原因为车祸(12例)。采用R语言处理,比较患者术前及治疗后1周、4周的Constant评分资料。结果显示,22例患者中10例(45.5% )发生后脱位。8例保守治疗,尝试闭合手法复位,均失败,予以锁骨带“8”字固定。14例行切开内固定术,其中钢板内固定术6例,锚定钉固定手术8例。所有患者在术后1周、术后4周采用Constant肩关节功能评分,发现缝合锚定钉组肩关节评分优势明显。术后2例患者钢板断裂,无神经血管损伤或功能障碍者。缝合锚定钉内固定术可于短时间内稳定胸锁关节脱位,从而恢复肩关节功能。该技术简单、有效,患者术后功能恢复较快,为胸锁关节脱位患者提供较好的治疗选择。
肝血管内大B细胞淋巴瘤1例

患者男,69岁。主要症状为腹痛和发热。腹部磁共振成像检查显示肝左叶异常信号,初步考虑肝脓肿或肝内胆管癌。穿刺肝组织镜下9个汇管区均见炎性细胞浸润,汇管区血管内见异型淋巴细胞增生,细胞体积大,空泡状核,核仁明显,核分裂象易见,呈非肝血窦内生长模式。免疫组织化学结果示肿瘤细胞位于血管腔内,并表达CD20等B淋巴细胞标志物。B淋巴细胞基因克隆性重排阳性。肝脏血管内大B细胞淋巴瘤呈非肝窦内生长模式病例罕见,易被漏诊或误诊,诊断时需与反应性炎症、转移性低分化癌或上皮样血管内皮瘤等鉴别。确诊需要依赖于组织形态学、免疫组织化学和基因重排等多方面的综合分析。
Controlled semi-Pinacol rearrangement on a strained ring: Efficient access to multi-substituted cyclopropanes by group migration strategy

We describe a versatile electrophile addition/SPR sequence of readily available cyclopropyl carbinols that affords multi-substituted carbonylated cyclopropanes with high stereo-fidelity. This approach tolerates various heteroatom electrophiles, migration of carbon moiety of all possible hybridization states, facile ring reorganization and natural compound valorization. The examples represent an unprecedented version of SPR wherein migration to a non-benzylic bulky tertiary carbo-cation is realized with promising enantiocontrol.
Features of rare earth elements geochemistry in coals of Central Kazakhstan

This research presents the results of a comprehensive study of mineralogical and geochemical features of REE distribution in coals of Central Kazakhstan deposits- Karaganda coal basin and Shubarkol deposit,which have large hard coal reserves and are industrially important for the coal industry of Kazakhstan;the research is based on 205 samples of clayey interlayers and coal seams.It shows basic patterns of distribution and features of concentration for impurity elements,gives an estimate of the impurity elements concentration,including REE,defines conditions and factors of their accumulation,and studies features of their forms in coal and coal-bearing rocks,which allows estimating the mechanisms of their migration and conditions of accumulation.According to the results of geochemical indicators,the article establishes the factors of REE dislocation,reveals the composition of margin rocks that have influenced REE concentration in coal seams,and the presented latest data on mineralogy allowed to establish the ways of their transportation to the paleobasin during the synand epigenetic periods of formation of the coal deposits of Central Kazakhstan being researched.It was found that the coals are insignificantly enriched with heavy lanthanides from Ho to Lu.The distribution curves of UCC normalized REE values in the coals are similar and coincide,but they are less than the average value for world coal,and amount to only one-third of the UCC.It was found that the highest concentrations of all REE are characteristic of clayey interlayers and oxidized coals.The La/Yb ratio in this case increases upwards along the section,indicating mainly clastogenic mechanism of REE delivery to the coals.In coal and clay samples,the predominant mineral form of REE is light lanthanide phosphates.Identified particles of REE from minerals and their composition peculiarities suppose autigene nature of their formation.The formation of the bulk of autigene minerals occurred during the maturation of brown coals and their transformation into hard ones.
Machine learning prediction of methane,ethane,and propane solubility in pure water and electrolyte solutions:Implications for stray gas migration modeling

Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers.The stray gas can dissolve in groundwater leading to chemical and biological reactions,which could negatively affect groundwater quality and contribute to atmospheric emissions.The knowledge of light hydrocarbon solubility in the aqueous environment is essential for the numerical modelling of flow and transport in the subsurface.Herein,we compiled a database containing 2129 experimental data of methane,ethane,and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure.Two machine learning algorithms,namely regression tree (RT) and boosted regression tree (BRT) tuned with a Bayesian optimization algorithm (BO) were employed to determine the solubility of gases.The predictions were compared with the experimental data as well as four well-established thermodynamic models.Our analysis shows that the BRT-BO is sufficiently accurate,and the predicted values agree well with those obtained from the thermodynamic models.The coefficient of determination (R~2) between experimental and predicted values is 0.99 and the mean squared error (MSE) is 9.97 × 10~(-8).The leverage statistical approach further confirmed the validity of the model developed.
UAV swarm air combat maneuver decision-making method based on multi-agent reinforcement learning and transferring

During short-range air combat involving unmanned aircraft vehicle (UAV) swarms,UAVs must make accurate maneuver decisions based on information from both enemy and friendly UAVs.This dual requirement of competition and cooperation presents a significant challenge in the field of unmanned air combat.In this paper,a method based on multi-agent reinforcement learning (MARL) is proposed to address this issue.An actor network containing three subnetworks that can handle different types of situational information is designed.Hence,the results from simpler one-on-one scenarios are leveraged to enhance the complex swarm air combat training process.Separate state spaces for local and global information are designed for the actor and critic networks.A detailed reward function is proposed to encourage participation.To prevent lazy participants in air combat,a reward assignment operation is applied to distribute these dense rewards.Simulation testing and ablation experiments demonstrate that both the transfer operation and reward assignment operation can effectively deal with the swarm air combat scenario,and reflect the effectiveness of the proposed method.
Ceramic-based meta-material absorber with high-temperature stability

With the continuous exploration of uncharted and extreme environments, enhanced temperature robustness of passive devices has become particularly important. In this study, a ceramic-based meta-material absorber with exceptional temperature stability is developed using a fusion design approach that combines rare metal-based tungsten bronze structural ceramics and meta-materials. Specifically, the absorbance of the meta-material array based on Mie resonance exceeds 49.0% in both waveguides and free space, approaching the theoretical limit. According to impedance analysis, the absorption performance can be distinctly correlated with the dielectric loss (Qf). Notably, the high-temperature robustness is verified to still be effective at 400 ℃. These advancements in our design allow for the use of monolithic materials in fabricating temperature-stable perfect absorbers, providing greater freedom in the dielectric performance and expanding their potential applications, including in space exploration and 5G millimeter-wave scenarios.
Chemoselective epoxidation of electron-deficient olefins with molecular oxygen via conjugated olefins epoxidation and BHT hydroxylation

The chemoselective epoxidation of electron-deficient olefins in the presence of electron-rich alkene moieties is reported. This chemoselective epoxidation strategy undergoes a conjugated olefin epoxidation and BHT hydroxylation process to give various useful oxiranes in high yields, especially for the 2-substituted 2-trifluoromethyloxiranes. Importantly, this protocol features mild conditions, is transition-metal free, operationally simple, and gram-scalable, and tolerates diverse functional groups. Drug candidate HSD-16 is synthesized smoothly by this protocol. Mechanism studies indicate molecular oxygen is the terminal oxidant and the O-source of the oxiranes.
Targeting neutrophil subsets is a novel approach for myocardial ischemia-reperfusion injury

Recently, in their 2024 study, Dong et al. revealed the pivotal role of neutrophils in the immune response of myocardial ischemia- reperfusion injury (MIRI) via single-cell RNA sequencing (scRNA-seq), and identified the existence and function of a novel neutrophil subset with repair phenotype, which participates in the cardio-protection of MIRI by regulating the polarization of macrophages via Ym-1. This finding not only deepens our understanding of cellular interactions within MIRI but also opens new therapeutic avenues.
刘智斌教授运用头皮发际微针结合缠揉法治疗原发性痛经的经验介绍

原发性痛经(primary dysmenorrhea,PD)是妇科常见病之一,多见于未育的年轻女性,患者常在经期前后出现下腹部痉挛性疼痛,并伴有恶心欲吐、四肢厥冷、冷汗淋漓等症状,对患者正常生活和身心健康造成不良影响。研究表明,中国女性痛经的患病率为33.1%,其中PD占比53.2%。目前PD的发病原因尚未明确,临床上常使用非甾体抗炎药对症治疗,但长期服用药物可能对肝脏等器官产生不良影响。缠揉法为陕西省名中医刘智斌教授在一指禅推法的基础上改良创新而成的推拿手法,头皮发际微针系统是刘智斌教授和牛文民教授在长期从事针刺治疗神经系统疾病的临床研究中所创立。刘智斌教授将原创的缠揉手法与头皮发际微针疗法相结合,在诊断准确、辨证清晰的基础上,采用中医传统外治法,临床可有效减轻患者病痛。现将刘智斌教授运用头皮发际微针疗法结合缠揉法治疗原发性痛经经验介绍如下。
脓毒症相关性急性肺损伤信号通路的研究进展

脓毒症是一种因宿主对病原菌感染严重反应失调所致的以持续过度炎症和免疫抑制为特征的异质性较强的病理综合征。宿主对脓毒症的反应涉及免疫细胞、细胞因子、补体系统、凝血级联系统、内皮反应及神经内分泌系统等多因素之间复杂的相互作用,这些因素综合作用可导致脓毒症的不同表现、进展及预后,继而给脓毒症的治疗和处理造成困难。在脓毒症引发的器官功能障碍中,肺部是最早且最易受累的器官,常表现为脓毒症相关性急性肺损伤(sepsis-induced acute lung injury, S-ALI)。据报道,超过半数的脓毒症患者合并S-ALI,且病死率超过30%。尽管对S-ALI的研究日益深入,但对其机制仍有待进一步了解。S-ALI疾病进程中的潜在机制涉及复杂的信号通路,本研究拟就此综述如下。
Temperature field prediction of steel-concrete composite decks using TVFEMD-stacking ensemble algorithm

This research aims to develop an advanced deep learning-based ensemble algorithm, utilizing environmental temperature and solar radiation as feature factors, to conduct hourly temperature field predictions for steel-concrete composite decks(SCCDs). The proposed model comprises feature parameter lag selection, two non-stationary time series decomposition methods(empirical mode decomposition(EMD) and time-varying filtering-based empirical mode decomposition(TVFEMD)), and a stacking ensemble prediction model. To validate the proposed model, five machine learning(ML) models(random forest(RF), support vector regression(SVR), multilayer perceptron(MLP), gradient boosting regression(GBR), and extreme gradient boosting(XGBoost)) were tested as base learners and evaluations were conducted within independent, mixed, and ensemble frameworks. Finally, predictions are made based on engineering cases. The results indicate that consideration of lag variables and modal decomposition can significantly improve the prediction performance of learners, and the stacking framework, which combines multiple learners, achieves superior prediction results. The proposed method demonstrates a high degree of predictive robustness and can be applied to statistical analysis of the temperature field in SCCDs. Incorporating time lag features helps account for the delayed heat dissipation phenomenon in concrete, while decomposition techniques assist in feature extraction.
Polarization extension yielding ultrahigh piezoelectric response in x Pb(Nb_(2/3) Ni_(1/3))O_3 -(1-x)Pb(Zr_(0.3)Ti_(0.7))O_3 ferroelectrics ceramics

The heightened piezoelectric performance observed in most explored perovskite systems is typically attributed to the electric-field-induced phase transition near the morphotropic phase boundary (MPB) or polymorphic phase boundary (PPB). This study, however, unveils a distinct piezoelectric enhancement mechanism in the x Pb(Nb_(2/3) Ni_(1/3))O_3 -(1-x)Pb(Zr_(0.3)Ti_(0.7))O_3 (PNN-PZT) system, diverging from the MPB/PPB-centered piezoelectric systems. Notably, the composition with x = 0.55, positioned close to the tetragonalpseudocubic (T-PC) phase boundary, achieves an unprecedented piezoelectric coefficient (d_(33)) of 1264 pC/N, while retaining a tetragonal local structure. Importantly, on a local scale, electric fields do not incite phase transitions, suggesting that the exceptional piezoelectric performance in PNN-PZT stems from polarization extension near the T-PC boundary. Distinct from other mechanisms, the relative permittivity significantly increases post-poling due to this particular enhancement process. The dielectric behavior in poled specimens does not exhibit a conspicuous change at the ferroelectric-relaxor transition temperature. However, both the relative permittivity and planar electromechanical coupling coefficient experience a sharp rise in the temperature range of –25 °C to 25 °C. This investigation underscores the pivotal role of designing compositionally-driven T-PC phase boundaries, presenting a promising avenue for enhancing piezoelectric properties in ferroelectric ceramics.
Printability, microstructures and mechanical properties of a novel Co-based superalloy fabricated via laser powder bed fusion

High levels of Al and Ti in superalloy compositions normally lead to cracking formation during the laser powder bed fusion process, while these elements are key constituents of strengthening phases. In the current study, a novel Co-based superalloy with the basic chemical composition of Co-Al-W-Ta-Ti resolved this contradiction, indicating that the part was formed without cracking and simultaneously contained a large amount of strengthening precipitates in the microstructure fabricated via laser powder bed fusion. The printability, microstructures, and mechanical properties of the sample were analysed before and after heat treatment, providing a potential superalloy that can replace Ni-based superalloys fabricated by additive manufacturing in aerospace and other industries with higher temperature and more efficiency.
Nonlinear change of ion-induced secondary electron emission in the κ-Al_2O_3 surface charging from first-principle modelling

Secondary electron emission (SEE) induced by the positive ion is an essential physical process to influence the dynamics of gas discharge which relies on the specific surface material. Surface charging has a significant impact on the material properties, thereby affecting the SEE in the plasma-surface interactions. However, it does not attract enough attention in the previous studies. In this paper, SEE dependent on the charged surface of specific materials is described with the computational method combining a density functional theory (DFT) model from the firstprinciple theory and the theory of Auger neutralization. The effect of κ-Al_2O_3 surface charge, as an example, on the ion-induced secondary electron emission coefficient (SEEC) is investigated by analyzing the defect energy level and band structure on the charged surface. Simulation results indicate that, with the surface charge from negative to positive, the SEEC of a part of low ionization energy ions (such as Ei = 12.6 eV) increases first and then decreases, exhibiting a nonlinear changing trend. This is quite different from the monotonic decreasing tendency observed in the previous model which simplifies the electronic structure. This irregular increase of the SEEC can be attributed to the lower escaped probability of orbital energy. The results further illustrate that the excessive charge could cause the bottom of the conduction band close to the valence band, thus leading to the decrease of the orbital energy occupied by the excited electrons. The nonlinear change of SEEC demonstrates a more realistic situation of how the electronic structure of material surface influences the SEE process. This work provides an accurate method of calculating SEEC from specific materials, which is urgent in widespread physical scenarios sensitive to surface materials, such as increasingly growing practical applications concerning plasma-surface interactions.
Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

The stress-life curve (S-N) and low-cycle strainlife curve (E-N) are the two primary representations used to characterize the fatigue behavior of a material. These material fatigue curves are essential for structural fatigue analysis. However, conducting material fatigue tests is expensive and time-intensive. To address the challenge of data limitations on ferrous metal materials, we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S-N and E-N curves of ferrous materials. In addition, a data-augmentation framework is introduced using a conditional generative adversarial network (cGAN) to overcome data deficiencies. By incorporating the cGAN-generated data, the accuracy (R~2) of the Random Forest Algorithm-trained model is improved by 0.3-0.6. It is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.
High temperature heat flux sensor with ITO/In_2O_3 thermopile for extreme environment sensing

Hypersonic vehicles and aircraft engine blades face complex and harsh environments such as high heat flow density and high temperature, and they are generally narrow curved spaces, making it impossible to actually install them for testing. Thin-film heat flux sensors (HFSs) have the advantages of small size, fast response, and in-situ fabrication, but they are prone to reach thermal equilibrium and thus fail during testing. In our manuscript, an ITO-In_2O_3 thick film heat flux sensor (HFS) is designed, and a high-temperature heat flux test system is built to simulate the working condition of a blade subjected to heat flow impact. The simulation and test results show that the test performance of the thick-film HFS is improved by optimizing the structure and parameters. Under the condition of no water cooling, the designed HFS can realize short-time heat flux monitoring at 1450 °C and long-term stable monitoring at 1300 °C and below. With a maximum output thermopotential of 17.8 mV and an average test sensitivity of 0.035 mV/(kW/m~2), the designed HFS has superior high-temperature resistance that cannot be achieved by other existing thin (thick) film HFSs. Therefore, the designed HFS has great potential for application in harsh environments such as aerospace, weaponry, and industrial metallurgy.
Research on Improving the Thermal Conductivity of Epoxy Resin with Flexible Assisted Rigid Groups

Epoxy resins are cross-linked polymeric materials with typically low thermal conductivity.Currently,the introduction of rigid groups into epoxy resins is the main method to improve their intrinsic thermal conductivity.The researchers explored the relationship between the flexible chains of epoxy monomers and the thermal conductivity of the modified epoxy resins (MEP).The effect of flexible chain length on the introduction of rigid groups into the cross-linked structure of epoxy is worth investigating,which is of great significance for the improvement of thermal conductivity of polymers and related theories.We prepared a small molecule liquid crystal (SMLC) containing a long flexible chain via a simple synthesis reaction,and introduced rigid mesocrystalline units into the epoxy resin via a curing reaction.During high-temperature curing,the introduced mesocrystalline units underwent orientational stacking and were immobilized within the polymer.XRD and TGA tests showed that the ordering within the modified epoxy resin was increased,which improved the thermal conductivity of the epoxy resin.Crucially,during the above process,the flexible chains of SMLC provide space for the biphenyl groups to align and therefore affect the thermal conductivity of the MEP.Specifically,the MEP-VI cured with SMLC-VI containing six carbon atoms in the flexible chain has the highest thermal conductivity of 0.40 W·m~(-1)·K~(-1),which is 125% of the thermal conductivity of SMLC-IV of 0.32 W·m~(-1)·K~(-1),111% of the thermal conductivity of SMLC-VIII of 0.36 W·m~(-1)·K~(-1),and 182% of the thermal conductivity of pure epoxy of 0.22 W·m~(-1)·K~(-1).The introduction of appropriate length flexible chains for SMLC promotes the stacking of rigid groups within the resin while reducing the occurrence of chain folding.This study will provide new ideas for the enhancement of thermal conductivity of cross-linked polymeric materials.