mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
Advances of machining techniques for gradient structures in multi-principal-element alloys

Multi-principal-element alloys (MPEAs) are attracting increasing attentions because of their high strength and ductility, high fracture toughness, excellent corrosion resistance, outstanding thermal-softening resistance and high oxidation resistance. Moreover, gradient structures (GSs) have been shown to be effective in alleviating the strength-ductility trade-off although strength and ductility are mutually exclusive properties for metals, which provides an opportunity for developing high-performance MPEAs. Here,we summarized four processing methods for creating GSs in MPEAs, including rotationally accelerated shot peening (RASP), ultra-precision machining technology (UPMT), cyclic dynamic torsion (CDT),and ultrasonic surface rolling processing (USRP). Principles, advantages, disadvantages, and typical applications of these methods are discussed in this work.
Synergistically enhancing cycleability and rate performance of sodium titanate nanowire anode via hydrogenation and carbon coating for advanced sodium ion batteries

Layered alkali-metal titanate materials are considered as attractive anodes for sodium ion batteries due to their favorable safety and low cost. However, their practical implementation faces major challenges of low electronic conductivity and inevitable volume variation during Na~+ intercalation and de-intercalation, which are generally difficult to conquer by a single modification method. Herein, a synergistically enhancing strategy to promote the electrochemical performance of Na_2Ti_2O_5 nanowire array anode via simultaneous hydrogenation and carbon coating is developed. Hydrogenation leads to partially reduced titanium; together with conductive carbon layer, it endows Na_2Ti_2O_5 with fast electron transport and structural stability. The resulting H-Na_2Ti_2O_5@C anode exhibits enhanced rate capability (8.0C, 165 mAh·g~(-1)) and stable cycle performance up to 1000 times in sodium-ion half-cells (the capacity of H-Na_2Ti_2O_5 without carbon fades drastically after only 100 cycles). In addition, a new-coupling full cell is further designed with graphene hybridized high-voltage Na_3(VO_(0.5))_2(PO_4)_2F_2 as cathode, capable of delivering a high specific energy density of 212.1 Wh·kg~(-1) (based on the mass of both anode and cathode) and good rate and cycling stability. This work may offer inspiration for synergistic optimization of electrode materials for advanced electrochemical energy storage devices.
Highly Luminescent Positively Charged Quantum Dots Interacting with Proteins and Cells

We have studied interactions between positively charged MUTAB-stabilized quantum dots (QDs) and model proteins,serum and live cells using fluorescence correlation spectroscopy (FCS),dynamic light scattering (DLS),time-resolved photoluminescence (PL) and live-cell fluorescence imaging.Using human serum albumin (HSA) as a model protein,we measured the growth of a protein adsorption layer ("protein corona") via time-resolved FCS.Corona formation was characterized by an apparent equilibrium dissociation coefficient,K D ≈ 10 µM.HSA adlayer growth was surprisingly slow (timescale ca.30 min),in stark contrast to many similar measurements with HSA and other proteins and different NPs.Time-resolved PL data revealed a characteristic quenching behavior depending on the QD surface coverage with HSA.Taken together,we found that MUTAB-QDs initially bind HSA molecules weakly (K D ≈ 700 µM);however,the affinity is enhanced over time,presumably due to proton injection into the MUTAB layer by HSA triggering ligand dissociation.This process was also observed with human blood serum,showing equal kinetics for comparable HSA concentration.Moreover,imaging experiments with cultured human cells (HeLa) revealed that MUTAB-QDs bind to the cell membrane and perforate it.This process is reduced upon pre-adsorption of proteins on the MUTAB-QD surfaces.
~(18)F-FDG PET/CT提示息肉-色素沉着-脱发-甲营养不良综合征患者结肠息肉恶变1例

CCS为罕见非遗传性疾病,临床可表现为脱发、甲营养不良、皮肤色素沉着、腹泻及广泛胃肠道息肉,出现蛋白丢失性肠病可致严重营养不良,死亡率高。CCS息肉有通过腺瘤-癌序列途径恶变潜能,约10%~20% CCS患者伴发胃肠道肿瘤;CT表现为胃、小肠及结直肠黏膜粗大,部分呈蚓状隆起。息肉和腺癌~(18) F-FDG PET/CT均可呈放射性摄取增高;CCS患者肠道多发息肉中存在FDG摄取较高者提示可能存在恶变。鉴别诊断:①胃肠道感染性病变,可表现为管壁水肿增厚、放射性摄取增高,需结合临床;②家族性腺瘤性息肉病,为常染色体显性结直肠癌综合征,结直肠内存在数百个腺瘤性息肉,多在1年内发展为结直肠癌。诊断CCS需结合临床、内镜及病理特征;~(18) F-FDG PET/CT有助于筛查息肉恶变并提示活检部位。
胃上皮样黏液纤维肉瘤1例

黏液纤维肉瘤是中老年人常见软组织肉瘤之一; EMFS在其中占3%,无明显性别倾向,多见于四肢及其附近,其次为躯干,少数可发生于头颈部,临床多表现为无痛性肿块,体积较大,直径约2~15cm,少数可伴疼痛,发生于腹腔者可表现为腹胀。MRI上,EMFS多呈不均质混杂信号,增强后呈轻中度或明显不均匀强化,可伴囊变或分隔。本例EMFS CT表现为体积较大的不规则混杂密度软组织肿块,增强后实性部分明显不均匀强化,其内片状液性低密度区强化不明显,符合肿瘤黏液样区与致密区并存的病理特点。鉴别诊断:①胃神经鞘瘤,CT密度多较均匀,增强后多呈轻中度渐进性强化,出血、坏死、囊变、钙化等少见;②胃间质瘤,CT见胃部肿块向腔内或腔外生长,边缘光滑,增强后明显强化,免疫组织化学示CD117阳性。确诊有赖病理学检查。
1例右侧睾丸旁混合型脂肪肉瘤超声表现

脂肪肉瘤约占软组织肉瘤的20%,分为高分化、去分化、黏液样、多形性及混合型。睾丸旁脂肪肉瘤较罕见,多起源于精索,其次为睾丸被膜及附睾;好发于中老年,表现为腹股沟或阴囊无痛性软组织肿块,以高分化和去分化脂肪肉瘤多见;术后易复发。本例术前二维超声误诊睾丸鞘膜内肿物为睾丸实质肿瘤,而难以判断另一肿物的性质;二者超声造影分别呈“快进快退”等增强和“快进慢退”高增强模式,其“快进”及“高增强”模式与其他部位脂肪肉瘤表现一致,而“慢退”模式可能与不同组织学亚型及分化程度有关。本病需与腹股沟疝和睾丸肿瘤相鉴别。确诊有赖于病理学检查。
不同包装材料、储藏条件对防风质量的影响

目的确定防风药材适宜的贮藏条件及包装材料。方法采用HPLC法对同一批药材12个月内在不同包装材料、储藏条件下含水量和4种色原酮(升麻素苷、5-O-甲基维斯阿米醇苷、升麻素、亥茅酚苷)含量变化进行测定,液质联用法对第12个月的药材进行黄曲霉毒素、农药残留、重金属、有害元素含量的测定。结果含水量最低是纸箱阴凉避光,最高是纸箱低温避光;色原酮含量最高是纸箱低温避光,最低是聚乙烯塑料编织袋低温避光。相关性分析发现,储藏温度、时间、含水量是影响防风质量的重要因素。灰色关联度分析得出,纸箱包装与色原酮的保留关系最为紧密。主成分分析显示,纸箱包装较为稳定,其次是麻袋包装。AHP综合评价分析显示,纸箱低温避光包装的评分最高,麻袋低温避光包装次之。黄曲霉毒素、农药残留、重金属、有害元素均未检测出。结论在保证防风含水量符合规定的情况下,纸箱为较佳包装材料,建议低温储存,并且储藏时间不宜过久。但应用于防风大批量生产中时,建议采用麻袋低温避光储藏。
德国皮肤病学会肛周湿疹诊断和治疗指南解读(2020版)

肛周湿疹是肛周常见的非感染性皮肤疾病。近年来,关于肛周常见疾病的临床指南相继发布,但未对肛周湿疹作详细阐述。2020年《德国皮肤病杂志》(Journal of the German Society of Dermatology)首次发表了由德国皮肤病学会和德国结肠直肠学会等制定的肛周湿疹临床治疗指南,该指南就疾病的定义、流行病学、发病机制、分类、诊断与鉴别诊断、治疗等方面进行阐述,对皮肤科和肛肠科医师在疾病的治疗及我国肛周湿疹指南的制定具有重要的指导意义。该文对指南的主要内容进行解读。
早发儿童系统性红斑狼疮合并Klinefelter综合征1例

患儿男,2岁5月龄,以间断发热合并血小板下降为首发表现,伴苍白、黄疸、皮肤出血点、浮肿及反复呼吸道感染,肾脏、肺部、肝脏、心血管、神经及血液等多系统受累,系统性红斑狼疮家族史阳性。抗双链DNA抗体、抗心磷脂抗体阳性,补体下降。基因检查发现RIPK1、STAT4基因杂合变异。染色体核型47,XXY,诊断早发型系统性红斑狼疮合并Klinefelter综合征。
Activity-density and spatial distribution of termites on a fine-scale in a tropical rainforest in Xishuangbanna,southwest China

The community composition and activity-density of termites can influence nutrient cycling and other ecological functions.However,the spatial distribution and the activity-density of termites on a finescale in tropical forests are still unknown.We checked the spatial distribution patterns of the feeding groups and species of termites and their co-occurrence pattern in a 1-ha (100 m × 100 m) plot,and their correlation with the environmental factors.We used a standard protocol to collect termite assemblages and classified them into five feeding groups based on their preferred diet:fungus growers,litter feeders,soil feeders,soil-wood feeders,and wood feeders.We measured the environmental factors:soil pH,litter mass,aboveground plant biomass,and topographic position index (TPI).Soil-wood feeders showed the highest activity-density,followed by wood feeders,fungus growers,soil feeders,and litter feeders.Soil-wood feeders and fungus growers demonstated a strong correlation while litter feeders showed weak correlations with other feeding groups.Termite feeding groups and most of the termite species displayed a positive association with the high TPI and the low soil pH patches.Our results indicated that the examined environmental factors influenced the termite community assemblages and distribution patterns on a fine-scale in tropical rainforests.
Effects of perfluoroalkyl and polyfluoroalkyl substances (PFAS) on soil structure and function

Soils are impacted globally by several anthropogenic factors,including chemical pollutants.Among those,perfluoroalkyl and polyfluoroalkyl substances (PFAS) are of concern due to their high environmental persistence,and as they might affect soil structure and function.However,data on impacts of PFAS on soil structure and microbially-driven processes are currently lacking.This study explored the effects of perfluorooctanesulfonic acid (PFOS),perfluorooctanoic acid (PFOA) and perfluorobutanesulfonic acid (PFBS) at environmental-relevant concentrations on soil health,using a 6-week microcosm experiment.PFAS (even at 0.5 ng g~(-1) for PFBS) significantly increased litter decomposition,associated with positive effects on β-glucosidase activities.This effect increased with PFAS concentrations.Soil pH was significantly increased,likely as a direct consequence of increased litter decomposition affected by PFAS.Soil respiration was significantly inhibited by PFAS in week 3,while this effect was more variable in week 6.Water-stable aggregates were negatively affected by PFOS,possibly related to microbial shifts.PFAS affected soil bacterial and fungal abundance,but not microbial and certain enzyme activities.Our work highlights the potential effects of PFAS on soil health,and we argue that this substance class could be a factor of environmental change of potentially broad relevance in terrestrial ecosystem functioning.
Lignocellulose pretreatment by deep eutectic solvents and related technologies: A review

Lignocellulose is the main component of plants and has a wide range of sources. The high-value production of lignocellulose lies in the biorefinery of lignin, cellulose and hemicellulose. The advantages and disadvantages of traditional lignocellulose pretreatment methods were summarized, and the effective pretreatment parameters were listed. As a green solvent system with excellent performance, deep eutectic solvents (DES) are considered to be the most potential biomass pretreatment system. Based on this, the new trend and progress of DES in lignocellulose pretreatment were reviewed, focusing on the effects of distinct kinds of lignocellulose raw materials, distinct components of DES, distinct reaction conditions and assisted by microwave ultrasound on the pretreatment of lignocellulose, and the recyclability of DES solution system was discussed. Finally, the application and development direction of DES in lignocellulose pretreatment are proposed and prospected.
Mixing trait-based corn (Zea mays L.) cultivars increases yield through pollination synchronization and increased cross-fertilization

Abiotic stress such as high temperature at flowering is one of many conditions reducing yield of corn (Zea mays L.).Mixing corn cultivars with diverse functional traits increases within-crop diversity and provides a potential means of mitigating yield losses under stress conditions.We conducted a three-year field study to investigate the effects of cultivar mixtures on kernel setting rate,pollen sources,and yield.This study consisted of six treatments,including two high temperature-tolerant (HTT) monocrops of WK702 and DH701,two high temperature-sensitive (HTS) monocrops of DH605 and DH662,and two HTT-HTS mixtures of WK702-DH605 and DH701-DH662.The anthesis-silking interval (ASI) was 0.9- 1.6 days shorter in mixtures than in monocrops.Kernel setting rate was increased in mixtures (86.4%- 88.7%) compared with those in monocrops (74.7%-84.1%) as a result of synchrony and complementarity of pollination.Grain yields of the HTT-HTS mixtures increased by 13.3%-18.7%,equivalent to 1169 to 1605 kg ha~(-1),in comparison with HTS corn monocrops.The results of SSR markers showed that crossfertilization percentage in corn cultivar mixtures ranged from 29.3% to 47.8%,partially explaining yield improvement.Land equivalent ratio (LER) was 1.12 for corn mixtures and the partial land equivalent ratio (e.g.,>0.5) showed the complementary benefits in corn mixtures.The results indicated that mixing corn cultivars with diverse flowering and drought-tolerance traits increased yields via pollination synchrony.
Time-resolved multiomics analysis of the genetic regulation of maize kernel moisture

Maize kernel moisture content (KMC) at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate (KDR) before and after physiological maturity.KMC and KDR are complex traits governed by multiple quantitative trait loci (QTL).Their genetic architecture is incompletely understood.We used a multiomics integration approach with an association panel to identify genes influencing KMC and KDR.A genome-wide association study using time-series KMC data from 7 to 70 days after pollination and their transformed KDR data revealed respectively 98 and 279 loci significantly associated with KMC and KDR.Time-series transcriptome and proteome datasets were generated to construct KMC correlation networks,from which respectively 3111 and 759 module genes and proteins were identified as highly associated with KMC.Integrating multiomics analysis,several promising candidate genes for KMC and KDR,including Zm00001d047799 and Zm00001d035920,were identified.Further mutant experiments showed that Zm00001d047799,a gene encoding heat shock 70 kDa protein 5,reduced KMC in the late stage of kernel development.Our study provides resources for the identification of candidate genes influencing maize KMC and KDR,shedding light on the genetic architecture of dynamic changes in maize KMC.
Artificial neural network-based one-equation model for simulation of laminar-turbulent transitional flow

A mapping function between the Reynolds-averaged Navier-Stokes mean flow variables and transition intermittency factor is constructed by fully connected artificial neural network (ANN),which replaces the governing equation of the intermittency factor in transition-predictive Spalart-Allmaras (SA)- γ model.By taking SA- γ model as the benchmark,the present ANN model is trained at two airfoils with various angles of attack,Mach numbers and Reynolds numbers,and tested with unseen airfoils in different flow states.The a posteriori tests manifest that the mean pressure coefficient,skin friction coefficient,size of laminar separation bubble,mean streamwise velocity,Reynolds shear stress and lift/drag/moment coefficient from the present two-way coupling ANN model almost coincide with those from the benchmark SA- γ model.Furthermore,the ANN model proves to exhibit a higher calculation efficiency and better convergence quality than traditional SA- γ model.
Centrifuge modelling of landslides and landslide hazard mitigation: A review

Landslides are serious geohazards that occur under a variety of climatic conditions and can cause many casualties and significant economic losses. Centrifuge modelling, as a representative type of physical modelling, provides a realistic simulation of the stress level in a small-scale model and has been applied over the last 50 years to develop a better understanding of landslides. With recent developments in this technology, the application of centrifuge modelling in landslide science has significantly increased. Here, we present an overview of physical models that can capture landslide processes during centrifuge modelling. This review focuses on (i) the experimental principles and considerations, (ii) landslide models subjected to various triggering factors, including centrifugal acceleration, rainfall, earthquakes, water level changes, thawing permafrost, excavation, external loading and miscellaneous conditions, and (iii) different methods for mitigating landslides modelled in centrifuge, such as the application of nails, piles, geotextiles, vegetation, etc. The behaviors of all the centrifuge models are discussed, with emphasis on the deformation and failure mechanisms and experimental techniques. Based on this review, we provide a best-practice methodology for preparing a centrifuge landslide test and propose further efforts in terms of the seven aspects of model materials, testing design and equipment, measurement methods, scaling laws, full-scale test applications, landslide early warning, and 3D modelling to better understand the complex behaviour of landslides.
Check dam extraction from remote sensing images using deep learning and geospatial analysis: A case study in the Yanhe River Basin of the Loess Plateau, China

Check dams are widely used on the Loess Plateau in China to control soil and water losses, develop agricultural land, and improve watershed ecology. Detailed information on the number and spatial distribution of check dams is critical for quantitatively evaluating hydrological and ecological effects and planning the construction of new dams. Thus, this study developed a check dam detection framework for broad areas from high-resolution remote sensing images using an ensemble approach of deep learning and geospatial analysis. First, we made a sample dataset of check dams using GaoFen-2(GF-2)and Google Earth images. Next, we evaluated five popular deep-learning-based object detectors, including Faster R-CNN, You Only Look Once(version 3)(YOLOv3), Cascade R-CNN, YOLOX, and VarifocalNet(VFNet), to identify the best one for check dam detection. Finally, we analyzed the location characteristics of the check dams and used geographical constraints to optimize the detection results. Precision, recall, average precision at intersection over union(IoU)threshold of 0.50(AP_(50)), IoU threshold of 0.75(AP_(75)), and average value for 10 IoU thresholds ranging from 0.50-0.95 with a 0.05 step(AP_(50-95)), and inference time were used to evaluate model performance. All the five deep learning networks could identify check dams quickly and accurately, with AP_(50-95), AP_(50), and AP_(75) values higher than 60.0%, 90.0%, and 70.0%, respectively, except for YOLOv3. The VFNet had the best performance, followed by YOLOX. The proposed framework was tested in the Yanhe River Basin and yielded promising results, with a recall rate of 87.0% for 521 check dams. Furthermore, the geographic analysis deleted about 50% of the false detection boxes, increasing the identification accuracy of check dams from 78.6% to 87.6%. Simultaneously, this framework recognized 568 recently constructed check dams and small check dams not recorded in the known check dam survey datasets. The extraction results will support efficient watershed management and guide future studies on soil erosion in the Loess Plateau.
Deep Learning Aided SCL Decoding of Polar Codes with Shifted-Pruning

Recently, a generalized successive cancellation list (SCL) decoder implemented with shiftedpruning (SP) scheme, namely the SCL-SP-ω decoder, is presented for polar codes, which is able to shift the pruning window at most ω times during each SCL re-decoding attempt to prevent the correct path from being eliminated. The candidate positions for applying the SP scheme are selected by a shifting metric based on the probability that the elimination occurs. However, the number of exponential/logarithm operations involved in the SCL-SP-ω decoder grows linearly with the number of information bits and list size, which leads to high computational complexity. In this paper, we present a detailed analysis of the SCL-SP-ω decoder in terms of the decoding performance and complexity, which unveils that the choice of the shifting metric is essential for improving the decoding performance and reducing the re-decoding attempts simultaneously. Then, we introduce a simplified metric derived from the path metric (PM) domain, and a custom-tailored deep learning (DL) network is further designed to enhance the efficiency of the proposed simplified metric. The proposed metrics are both free of transcendental functions and hence, are more hardware-friendly than the existing metrics. Simulation results show that the proposed DL-aided metric provides the best error correction performance as comparison with the state of the art.
Chlorogenic acid alleviates hypoxic-ischemic brain injury in neonatal mice

Recent studies have shown that chlorogenic acid (CGA),which is present in coffee,has protective effects on the nervous system.However,its role in neonatal hypoxic-ischemic brain injury remains unclear.In this study,we established a newborn mouse model of hypoxic-ischemic brain injury using a modified Rice- Vannucci method and performed intraperitoneal injection of CGA.We found that CGA intervention effectively reduced the volume of cerebral infarct,alleviated cerebral edema,restored brain tissue structure after injury,and promoted axon growth in injured brain tissue.Moreover,CGA pretreatment alleviated oxygenglucose deprivation damage of primary neurons and promoted neuron survival.In addition,changes in ferroptosis-related proteins caused by hypoxic-ischemic brain injury were partially reversed by CGA.Furthermore,CGA intervention upregulated the expression of the key ferroptosis factor glutathione peroxidase 4 and its upstream glutamate/cystine antiporter related factors SLC7A11 and SLC3A2.In summary,our findings reveal that CGA alleviates hypoxic-ischemic brain injury in neonatal mice by reducing ferroptosis,providing new ideas for the treatment of neonatal hypoxic-ischemic brain injury.
Security breach:peripheral nerves provide unrestricted access for toxin delivery into the central nervous system

We explore the hypothesis that a potential explanation for the initiation of motor neuron disease is an unappreciated vulnerability in central nervous system defense,the direct delivery of neurotoxins into motor neurons via peripheral nerve retrograde transport.This further suggests a mechanism for focal initiation of neuro-degenerative diseases in general,with subsequent spread by network degeneration as suggested by the Frost-Diamond hypothesis.We propose this vulnerability may be a byproduct of vertebrate evolution in a benign aquatic environment,where external surfaces were not exposed to concentrated neurotoxins.
Rock characteristics and dynamic fragmentation process of the 2018 Daanshan rockslide in Beijing, China

This study investigated the failure mechanism associated with the rock mass structure and the dynamic fragmentation process of blocky rocks of the 2018 Daanshan rockslide that occurred on 11 August, 2018. It was found that the initially collapsed rock of this rockslide was partitioned along the unconformity and strata interfaces. We analyzed how the unique rock mass structure, coupled with the road cut and the antecedent rainfall, jointly resulted in its failure. Based on the rock types and geological structures, the initial stratified configuration of a discrete element model was setup to reveal the influences of the local structure. The numerical model was divided into three parts. Part 1 is the basalt of the Nandaling Formation, the normal and shear stiffnesses of the basalt particles are set as 80 MPa and 40 MPa. Parts 2 and 3 are the sandstones interbedded with mudstone and sandstone of the Shihezi Formation, and the normal and shear stiffnesses of these parts were set as 6 MPa and 10 MPa, respectively. The dynamic process of the rockslide, particularly the rock fragmentation process, was numerically analyzed using a 3D discrete element method. The numerical results were compared with real-time videos and field investigations. The results show that the rock fragmentation and the final deposition range match well with the real disaster phenomenon, and the calculation accuracy of the rockslide reaches 82.41%. Moreover, a parameter sensitivity analysis was conducted, and classical uniform models under different bonding forces were established; the stratified model can better restore the true state of the fragmentation, movement, and deposition processes of rockslides. Therefore, for complicated rocks with significant differences in lithology, clarifying the rock mass stratigraphy is essential for an accurate reconstruction of the dynamic process of rockslides.