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Grain refinement is the most common strategy to improve the mechanical performance of metals such as strength, wear resistance and fatigue. For the 2101 duplex stainless steel (DSS), a heterogeneous microstructure composed of sub-25 nm fiber grains and submicron elongated grains was successfully prepared by heavily drawing taking advantage of the heterogeneous microstructural design. The DSS possesses ultra-high strength of 3 GPa which is 15% higher than the reported that of DSS or a leading strength-ductility combination of 1.43 GPa and 17%. This study provides a strategy for heterogeneous microstructural design to achieve excellent mechanical performance, specifically high strength.

Abstract Background Nocardiosis is an opportunistic infection that has a low prevalence rate, its clinical manifestations are atypical and can be easily misdiagnosed as other diseases. The correct diagnosis and treatment are frequently delayed by various factors. In this case report, we present a pediatric patient with Neuromyelitis Optica Spectrum Disorders who developed Nocardia farcinica pneumonia complicated by pneumocystis jiroveci infection. Case presentation An 8-year-old girl with chest pain and cough was admitted to the hospital. She suffered from Neuromyelitis Optica Spectrum Disorders and had been taking methylprednisolone and tacrolimus orally for 3 years. She was admitted to the hospital for tests and was diagnosed with acute pneumonia. Despite empiric antibiotic treatment, her condition gradually worsened. Respiratory distress developed, and she needed to use a ventilator for breathing. The symptoms she exhibited led us to suspect the presence of a tumor. Etiological tests later confirmed the co-infection of Nocardia farcinica and Pneumocystis jiroveci. After treatment, the child’s lung infection eventually resolved. Conclusion The Nocardia bacteria and Pneumocystis jiroveci are widely distributed in the environment, possess the capability of systemic dissemination, and exhibit significant resistance to specific treatments. Invasive sampling is frequently necessary for confirming their presence. Timely and accurate diagnosis as well as treatment play a crucial role in patient survival.

Cu–12Fe-0.42Si-0.008B alloy was formed by twin-roll strip casting. Studied the strengthening mechanism after adding Si and B elements and the effect of single/double stage cold rolling aging on its microstructure and properties. The solidification structure of the two alloys was dominated by equiaxed grains, with a “sandwich” structure formed by the Cu and the central Fe-rich phases. The tensile strength of the alloy increases with the increase of aging time. The maximum strength at 16 h of two-stage cold rolling and aging (450 °C) is 682.2 MPa, 52% higher than that of the Cu–12Fe alloy, and the saturation magnetic field strength increased by 29.4 %. This is attributed to the addition of Si and B elements to increase the number of primary iron-rich phases in the alloy and promote the precipitation of nano-α-Fe phase during aging. The finer nano-Fe-rich phase particles are generated with a coherent relationship between the precipitated Fe phase and the Cu bas, that is (1-1-1)Cu//(10-1)Fe. The strength of the alloy after two-stage cold rolling aging is always better than that of single-stage cold rolling. This is because the two-stage cold rolling treatment reduces the content of fibrous α - Fe phase in the alloy and increases the nanoscale spherical α-Fe phase. Confirming that the spherical α-Fe phase has a better strengthening effect. The relative conductivity of alloy increases with the increased aging time, with the highest relative conductivity of 53.9% IACS and 54.9% IACS obtained after 16 h of two-stage cold rolling aging.

The recent surge in interest in high-entropy oxides (HEOs) as a novel class of ceramic materials can be attributed to the remarkable performance enhancements caused by both the complexity of their chemical composition and the simplicity of their structure. The extensive range of components presents challenges to the implementation of HEOs, rendering it difficult to predict the phase structure prior to the final synthesis. Herein, semi-empirical methods based on various parameters, including ΔX, VEC, ΔSmix, ΔHmix and δ, were utilized to determine the phase stability of rock salt, spinel, perovskite and fluorite structures. The combination of δ with ΔX, VEC, ΔSmix, and ΔHmix, in graphical form, is an effective indicator for determining the phase stability of rock salt, perovskite and fluorite structures. The spinel structure is stabilized outside the range of the other three structures. The phase formation rules were further verified with two newly designed and prepared HEOs via X-ray diffraction and high-resolution transmission electron microscopy. The HEOs show potential for functional applications, e.g., methyl orange degradation.

Objective The vaginal flora has been reported to be associated with human papillomavirus (HPV) infection. The purpose of this study was to investigate the characteristics of the cervical microbiota in patients with HPV infection and to analyse the changes in the vaginal flora and enzyme profiles in females with HPV infection.Methods We conducted a cross-sectional study involving 206 participants who underwent HPV genotyping, sexually transmitted diseases pathogen testing, cytology examination, and microbiome analysis. Additionally, we collected 115 HPV-negative samples and 48 HPV-positive samples for 16S rRNA amplicon sequencing. The vaginal microbial communities of both groups were analysed for diversity and differences to explore their association with HPV infection.Results The abundance of Lactobacillus was found to be reduced, while Gardnerella vaginalis was significantly more prevalent in the HPV + group. In terms of alpha diversity indices, the Shannon index (P = .0036) and Simpson index (P = .02) were higher in the HPV + group compared to the HPV − group, indicating greater community diversity in the HPV + group. Among the 10 sexually transmitted diseases pathogens analysed, Uup3 and Uup6 were significantly associated with HPV infection. Statistically significant differences were observed in Nugent scores and bacterial vaginosis between the two groups (P

Abstract The diagnosis and treatment of ovarian cancer (OC) are still a grand challenge, more than 70% of patients are diagnosed at an advanced stage with a dismal prognosis. Magnetic resonance imaging (MRI) has shown superior results to other examinations in preoperative assessment, while cisplatin‐based chemotherapy is the first‐line treatment for OC. However, few previous studies have brought together the two rapidly expanding fields. Here a technique is presented using cisplatin prodrug (Pt‐COOH), Fe3+, and natural polyphenols (Gossypol) to construct the nanoparticles (HA@PFG NPs) that have a stable structure, controllable drug release behavior, and high drug loading capacity. The acidic pH values in tumor sites facilitate the release of Fe3+, Pt‐COOH, and Gossypol from HA@PFG NPs. Pt‐COOH with GSH consumption and cisplatin‐based chemotherapy plus Gossypol with pro‐apoptotic effects displays a synergistic effect for killing tumor cells. Furthermore, the release of Fe3+ at the tumor sites promotes ferroptosis and enables MRI imaging of OC. In the patient‐derived tumor xenograft (PDX) model, HA@PFG NPs alleviate the tumor activity. RNA sequencing analysis reveals that HA@PFG NPs ameliorate OC symptoms mainly through IL‐6 signal pathways. This work combines MRI imaging with cisplatin‐based chemotherapy, which holds great promise for OC diagnosis and synergistic therapy.

Meteorological disasters occur frequently, and Gansu Province is a sensitive area for food production. Potatoes are a major crop in this province. As a result, executing risk zoning and risk prediction for potato production is quite important. However, in existing risk assessment and prediction research, the dynamic nature of risks and improving the accuracy of risk prediction are urgent scientific issues that must be addressed. Weighting, spatial econometric analysis, climate diagnosis technology, and machine learning models were used to provide a refined spatiotemporal evolution of potato disaster risk in China's Gansu Province, as well as predict future potato production risk. The findings indicate that there are significant interdecadal fluctuations in the potato disaster loss, which has decreased considerably since 2000. The average yield decrease rate in the 1980s, 1990s, 2000s, and 2010s was -13.9%, -15.4%, -9.1%, and -7.3%, respectively, and the county percentage susceptible to severe yield loss was 26.1%, 39.1%, 22.9%, and 12.9%. Second, most counties' potato production falls within the medium–low or low risk region. Eastern and southern Gansu are particularly vulnerable to catastrophic calamities. High risk counties are primarily clustered in Qingyang and Longnan, whereas low risk counties are concentrated in Wuwei and Gannan. Third, high risk locations have altered, and the migration trajectory of the risk indicator’s barycenter shows significant differences in direction and distance. The comprehensive risk moves in a southeast-west-northern direction, but the distance is short. Overall, disaster losses in most counties are decreasing, and future trends will be similar with previous patterns. The Interpolation-EMD-SVM scheme greatly increases the accuracy of the disaster loss risk prediction. The technology and methods provide a scientific foundation for accurately assessing risk dynamic characteristics, managing regional disaster risks, and preventing and mitigating disasters.

The impact of shear deformation on texture evolution in strip-cast Fe-1.5%Si non-oriented electrical steel was studied combined with in-suit experiments and crystal plasticity finite element method (CPFEM). Throughout the deformation process, the texture evolution predominantly follows a traditional route: transitioning from {211} and {411} to {111} orientation. In certain regions with specific stress conditions, orientation evolution shifts from {310} to {411}, {100}, and finally to {100} orientation. Crystal plasticity finite element (CPFE) simulation results show that the rotation of {310} through {411} to {100} orientation, {211} and {112} orientations transition to {111} and {110} orientations, respectively, which is consistent with the experimental observations. Recrystallization texture mainly includes favorable λ and near-λ (∥ND, normal direction) textures, with detrimental γ (∥ND) texture essentially diminishing. In-situ heating experiments revealed {411} as the primary recrystallization orientation, the mechanism involves both oriented nucleation and growth. In the edge region lacking stress constraints, the Schmid factor (SF) reaches up to 0.48, the (21-3)[111] slip system is preferentially activated, with shear bands appearing earlier in the same region. Indicating that when slip mechanism alone are inadequate for sustaining plastic deformation, the shear deformation becomes the favored mode of activation. {411} orientation can serve as a transitional orientation for transitioning to {100} orientation, and further as an advantageous transitional orientation during the nucleation and growth of recrystallization. Due to the texture improvement, superior magnetic properties were achieved, with the average magnetic induction (B50) and iron loss (P1.5/50) being 1.77 T and 4.25 W/kg, respectively.

The aging precipitated phases are of great significance for the strengthening and toughening of Al–Zn–Mg alloys. In this work, pre-precipitating was realized through equal channel angular pressing (ECAP) immediately followed by solid solution treatment at 475 °C for 6 h. Its corresponding strengthening and toughening effect on the room temperature (RT)-rolled Al–6Zn–1Mg alloy was systematically studied. The results indicate that pre-precipitating by ECAP can effectively improve the strength of the RT-rolled Al–6Zn–1Mg alloy and circumvent the strength-ductility trade-off. Microstructure characterization reveals that ECAP induces a large number of microshear bands, which can not only encourage recrystallization but also effectively promote the precipitation of the η′ phase. The synergistic effect of precipitation strengthening and dislocation strengthening contributes to the high strength of Al–6Zn–1Mg alloy, with UTS and YS increasing to ∼522 MPa and ∼400 MPa, respectively. Meanwhile, the ductility remains almost unchanged mainly due to the coordinated grain boundary sliding and the uniform and finely dispersed second phase particles. As a result, the strength-ductility trade-off is effectively avoided. The findings shed some light on optimizing the traditional aging treatment.

Peroxymonosulfate (PMS)-based advanced oxidation processes have shown potential for the removal of organic contaminants; however, the preparation of catalysts with high degradation efficiencies and rapid reaction rates remains a challenge. In this study, we have successfully synthesized CoFe bimetallic modified corn cob-derived biochar (CoFe/BC) for the activation of PMS, achieving the rapid and efficient degradation of bisphenol F (BPF). The synthesized CoFe/BC catalyst demonstrated excellent catalytic performance, achieving over 99% removal within 3 min and exhibiting a removal rate of 90.0% after five cycles. This could be attributed to the cyclic transformation of Co and Fe, which sustained rapid PMS activation for BPF degradation, and Co0/Fe0 played a significant role in the cyclic transformation. Furthermore, the electron paramagnetic resonance tests confirmed that •SO4− and •OH were the primary reactive oxygen species, while •O2− played a minor role in BPF degradation. This study highlights the high degradation efficiency, rapid reaction rate, excellent magnetic separation properties, and exceptional reusability of CoFe/BC catalysts for BPF removal, providing valuable insights for practical wastewater treatment.

The rapid, efficient, and thorough degradation of Bisphenol A (BPA) is challenging. In this study, we prepared an effective peroxymonosulphate (PMS) activation catalyst derived from sawdust containing calcium carbonate. The Co and Cu co-doped sawdust biochar (CoO/CuO@CBC) catalyst could activate PMS quickly, and the degradation rate of BPA reached 99.3% in 5 min, while the rate constant was approximately 30 times higher than in the CBC/PMS and CoCuOx/PMS systems. Moreover, the interaction between CoO, CuO, and CBC endows the CoO/CuO@CBC catalyst with excellent catalytic performance under different conditions, such as initial pH, temperature, water matrix, inorganic anions, and humic acid, which maintained fast PMS activation via the cyclic transformation of Cu and Co for BPA degradation. The results demonstrated that both the radical (•O2− and •SO4−) and non-radical (1O2) pathways participate in the degradation of BPA in the CoO/CuO@CBC/PMS system. The efficient and stable degradation over a wide range of pH, temperature, and aqueous matrices indicates the potential application of the CoO/CuO@CBC catalyst.

BackgroundChildren with autoimmune hepatitis (AIH) often present with symptoms similar to those of other liver diseases. This study consists of a comparison between the clinical and histological characteristics of AIH and those of other four AIH-like liver diseases [i.e., drug-induced liver injury (DILI), gene deficiency, infectious liver disease and other etiology of liver disease], as well as an evaluation of the AIH scoring system's diagnostic performance.MethodsAll children with AIH-like liver disease at our center from January 2013 to December 2022 were included. The clinical and histological characteristics of the AIH group were retrospectively analyzed and compared with those of the other four groups.ResultsA total of 208 children were included and divided into AIH group (18 patients), DILI group (38 patients), gene deficiency group (44 patients), infectious liver disease group (74 patients), and other etiology group (34 patients). The antinuclear antibodies (ANA) ≥ 1:320 rate was significantly higher in the AIH compared to the other four groups after multiple testing correction (p 0.73) and the revised systems (AUC > 0.93) for AIH have good diagnostic performance, with the latter being superior (p

The 7046 aluminum alloy possesses a favorable fatigue property, corrosion resistance and weldability, but its moderate strength and plasticity limit its wider application and development. In the present study, severe plastic deformation (SPD) was applied prior to T6 treatment to significantly enhance the strength and toughness of the 7046 aluminum alloy. The results show that the alloy processed by four passes of equal channel angular pressing (ECAP) at 300 °C prior to T6 treatment exhibits an excellent mechanical performance, achieving an ultimate tensile strength (UTS) and elongation (EL) of 485 MPa and 19%, respectively, which are 18.6% and 375% higher than that of the T6 alloy. The mechanical properties of the alloy are further improved by an additional room temperature (RT) rolling process, resulting in a UTS of 508 MPa and EL of 23.4%, respectively. The increased presence of η′ and Al6Mn phases in the 300°C4P-R80%-T6 and 300°C4P-T6 alloys contributes to a strengthening and toughening enhancement in the SPD-processed T6 alloy. The findings from this work may shed new insights into enhancing the 7046 aluminum alloy.

The effect of grain boundaries on the strength and electrical conductivity of annealed copper wires was studied. After annealing at 100 °C for 30 min, the copper wires retained a fiber-like structure. The increase in electrical conductivity is believed to stem from the decrease of crystallographic defects introduced by drawing. Recrystallization occurred when the annealing temperature reached 150 °C. The formation of small recrystallized grains led to the introduction of additional grain boundaries perpendicular to the drawing direction, resulting in a slight decrease in electrical conductivity. When the annealing temperature rose up to 200 °C, complete recrystallization took place in the copper wires. The grain boundaries parallel to the drawing direction play an important role in boundary strengthening, while the grain boundaries perpendicular to the drawing direction play an important role in both boundary strengthening and electron scattering. The electrical resistivity of grain boundaries in fully recrystallized copper wires at room temperature was determined experimentally, and an empirical relation is proposed to quickly determine the electric conductivity of copper wires at room temperature based on the boundary spacing along the drawing direction.

High-entropy materials, for both complexity in structure and superiority in performance, have been widely confirmed to be one possible kind of advanced electrocatalyst. Significant efforts have been dedicated to modeling the atomic-level details of high-entropy catalysts to improve the viability for bottom-up design of advanced electrocatalysts. In this review, first, we survey developments in various modeling methods that are based on density functional theory. We review progress in density functional theory simulations for emulating different high-entropy electrocatalysts. Then, we review the advancements in simulations of high-entropy materials for electrocatalytic applications. Finally, we present prospects in this field.Abbreviations: HEMs: high-entropy materials; CCMs: compositionally complex materials; DFT: density functional theory; LDA: local density approximation; GGA: generalized gradient approximation; VASP: Vienna Ab initio simulation package; ECP: effective core potential; PAW: projector-augmented wave potential; VCA: virtual crystal approximation; CPA: coherent potential approximation; SQS: special quasi-random structures; SSOS: small set of ordered structures; SLAE: similar local atomic environment; HEAs: high-entropy alloys; FCC: face-centered cubic; BCC: body-centered cubic; HCP: hexagonal close-packed; ORR: oxygen reduction reaction; OER: oxide evolution reaction; HER: hydrogen evolution reaction; RDS: rate-limiting step; AEM: adsorbate evolution mechanism; LOM: lattice oxygen oxidation mechanism; HEOs: high-entropy oxides; OVs: oxygen vacancies; PDOS: projected densities of states; ADR: ammonia decomposition reaction; NRR: nitrogen reduction reaction; CO2RR: CO2 reduction reaction; TMDC: transition metal dichalcogenide; TM: transition metal; AOR: alcohol oxidation reaction; GOR: glycerol oxidation reaction; UOR: urea oxidation reaction; HEI: high-entropy intermetallic.

In this study, the dissipation, terminal residues and dietary risk of the cyazofamid residue in three globally consumed minor crops, turnip (Brassica rapa L.), onion (Allium cepa L.) and romaine lettuce (Lactuca sativa L.) were assessed by supervised field trials at 6 locations of China in 2020. A reliable QuEChERS-LC-MS/MS method was developed for simultaneous analysis of cyazofamid and its primary metabolite, 4-chloro-5-p-tolylimidazole-2-carbonitrile (CCIM). The average recoveries of cyazofamid and CCIM in turnip, onion and romaine lettuce were 79 %–97 % and 77 %–99 %, respectively. The half-lives of cyazofamid in these crops were 5.7–6.5, 5.3–8.7 and 5.8–6.5 days in turnip, onion and romaine lettuce, respectively, with the final cyazofamid residues at harvest all lower than limit of quantitation (LOQ, 0.01 mg/kg). The turnip roots, onion bulbs and romaine lettuce leaves grown in China under Good Agricultural Practices conditions and harvested 10, 14 and 5 days, respectively, after the cyazofamid SC (100 g/L) application, would be reasonably safe for consumption by the general population. The maximum residue limit (MRL) values of cyazofamid could be tentatively set as 10 mg/kg for turnip leaves, 0.3 mg/kg for turnip roots, 0.05 mg/kg for onion bulbs, and 10 mg/kg for romaine lettuce leaves.

BackgroundThis investigation aimed to examine the epidemiological characteristics of children with liver disease hospitalized for the first time between June 2012 and May 2022 in a tertiary hospital.MethodsThe study retrospectively recruited children aged between 29 days and 18 years who had been hospitalized for liver disease. Clinical characteristics were categorized by age and etiology, and time trends were assessed using linear regression analysis.ResultsA total of 4,313 children were recruited, with a median age of 0.7 (0.2–4.5) years, and 54.5% of the cases were in the 0–1 years age group. Infection was the primary cause of liver disease (30.0%), followed by undiagnosed cases (25.8%), biliary obstructive disease (15.9%), inherited metabolic liver disease (13.9%), and non-alcoholic fatty liver disease (NAFLD) (3.2%). Genetic diagnoses were established in 43.9% (478/1,088) of patients. The percentage of NAFLD demonstrated an upward trend from 1.2% in 2012 to 12.6% in 2022 (p = 0.006). In contrast, the percentage of cytomegalovirus hepatitis decreased from 13.3% in 2012 to 3.4% in 2022 (p = 0.002).ConclusionsLiver disease in infancy makes up the largest group in pediatric liver disease. Infection remains the leading cause of pediatric liver disease. Hospital admissions for NAFLD in children have increased rapidly over the past decade, while cytomegalovirus hepatitis has declined markedly.

The role of inhibitor behavior in abnormal growth of Goss grains during secondary recrystallization was studied in combination with experimental observation and phase-field model simulation in grain-oriented silicon steel. The frequency advantages of both HE (high energy) and CSL (coincidence site lattice) grain boundaries around Goss grains over the matrix were identified, with frequency advantages of ∼15% and ∼2% respectively. The HE grain boundary was supposed to play a more important role in abnormal grain growth over CSL grain boundary in this study. The boundary character distribution was necessary but not sufficient for the abnormal growth of Goss grains. Appropriate inhibitor behavior was a prerequisite for secondary recrystallization. The initial content and stability of inhibitor played a key role in stabilizing the matrix, which guaranteed beneficial fine grains around Goss grains. While if the inhibitor force rapidly declined, matrix grains might grow obviously, resulting in coarsened grain colonies with Cube, {112}, {111}, {411}and {210} orientation, which further hindered the abnormal growth of Goss grains. The candidate Goss grains with different misorientations competed with each other during secondary recrystallization and the exact Goss grain exhibited an obvious growth advantage. Only with an appropriate pinning force and grain boundary character, perfect secondary recrystallization can be achieved. The present findings can provide guidance to understand and precise control of abnormal grain growth phenomenon.

The main purpose of our research was to further clarify the effectiveness and potential pathophysiological principles of metabolic resuscitation therapy in critically ill patients with sepsis and septic shock. We found that metabolic resuscitation therapy is beneficial for patients with sepsis and septic shock, shortening the length of intensive care unit (ICU) stay, reducing the duration of vasopressor use, and reducing the ICU mortality rate of patients with sepsis and septic shock, but it does not reduce the hospital mortality rate.

In this paper, we investigate multiple flexible spacecrafts attitude synchronization with parametric uncertainties and external disturbances. A large number of results on this problem rely on accurate measurements, ignoring measurement noises in attitude and angular velocity feedback. However, the existence of inherent noises and accumulated measurement errors are invertible, such that the performance of synchronization may be damaged. By developing a decentralized measurement-based attitude controller, we achieve attitude synchronization having input-to-state stability (ISS) with respect to measurement noises as external input. Moreover, thanks to the ISS-based robust design procedure, each of flexible spacecraft attitude control subsystem gains modeled external disturbance rejection, as well as unmodeled actuator disturbances attenuation and robustness with regarding to uncertain parameters.

Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning algorithms. However, recent studies on emotion classification show resource utilization because they use the fully-supervised learning methods. Therefore, in this study, we applied the self-supervised learning methods to improve the efficiency of resources usage. We employed a self-supervised approach to train deep multi-task convolutional neural network (CNN) for EEG-based emotion classification. First, six signal transformations were performed on unlabeled EEG data to construct the pretext task. Second, a multi-task CNN was used to perform signal transformation recognition on the transformed signals together with the original signals. After the signal transformation recognition network was trained, the convolutional layer network was frozen and the fully connected layer was reconstructed as emotion recognition network. Finally, the EEG data with affective labels were used to train the emotion recognition network to clarify the emotion. In this paper, we conduct extensive experiments from the data scaling perspective using the SEED, DEAP affective dataset. Results showed that the self-supervised learning methods can learn the internal representation of data and save computation time compared to the fully-supervised learning methods. In conclusion, our study suggests that the self-supervised machine learning model can improve the performance of emotion classification compared to the conventional fully supervised model.

Motor imagery (MI) electroencephalogram (EEG) signals have an important role in brain-computer interface (BCI) research. However, effectively decoding these signals remains a problem to be solved. Traditional EEG signal decoding algorithms rely on parameter design to extract features, whereas deep learning algorithms represented by convolution neural network (CNN) can automatically extract features, which is more suitable for BCI applications. However, when EEG data is taken as input in raw time series, traditional 1D-CNNs are unable to acquire both frequency domain and channel association information. To solve this problem, this study proposes a novel algorithm by inserting two modules into CNN. One is the Filter Band Combination (FBC) Module, which preserves as many frequency domain features as possible while maintaining the time domain characteristics of EEG. Another module is Multi-View structure that can extract features from the output of FBC module. To prevent over fitting, we used a cosine annealing algorithm with restart strategy to update the learning rate. The proposed algorithm was validated on the BCI competition dataset and the experiment dataset, using accuracy, standard deviation, and kappa coefficient. Compared with traditional decoding algorithms, our proposed algorithm achieved an improvement of the maximum average correct rate of 6.6% on the motion imagery 4-classes recognition mission and 11.3% on the 2-classes classification task.

The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning methods. To tackle this problem, a dual encoder variational autoencoder-generative adversarial network (DEVAE-GAN) incorporating spatiotemporal features is proposed to generate high-quality artificial samples. First, EEG data for different emotions are preprocessed as differential entropy features under five frequency bands and divided into segments with a 5s time window. Secondly, each feature segment is processed in two forms: the temporal morphology data and the spatial morphology data distributed according to the electrode position. Finally, the proposed dual encoder is trained to extract information from these two features, concatenate the two pieces of information as latent variables, and feed them into the decoder to generate artificial samples. To evaluate the effectiveness, a systematic experimental study was conducted in this work on the SEED dataset. First, the original training dataset is augmented with different numbers of generated samples; then, the augmented training datasets are used to train the deep neural network to construct the sentiment model. The results show that the augmented datasets generated by the proposed method have an average accuracy of 97.21% on all subjects, which is a 5% improvement compared to the original dataset, and the similarity between the generated data and the original data distribution is proved. These results demonstrate that our proposed model can effectively learn the distribution of raw data to generate high-quality artificial samples, which can effectively train a high-precision affective model.

Motor Imagery (MI) paradigm is critical in neural rehabilitation and gaming. Advances in brain-computer interface (BCI) technology have facilitated the detection of MI from electroencephalogram (EEG). Previous studies have proposed various EEG-based classification algorithms to identify the MI, however, the performance of prior models was limited due to the cross-subject heterogeneity in EEG data and the shortage of EEG data for training. Therefore, inspired by generative adversarial network (GAN), this study aims to propose an improved domain adaption network based on Wasserstein distance, which utilizes existing labeled data from multiple subjects (source domain) to improve the performance of MI classification on a single subject (target domain). Specifically, our proposed framework consists of three components, including a feature extractor, a domain discriminator, and a classifier. The feature extractor employs an attention mechanism and a variance layer to improve the discrimination of features extracted from different MI classes. Next, the domain discriminator adopts the Wasserstein matrix to measure the distance between source domain and target domain, and aligns the data distributions of source and target domain via adversarial learning strategy. Finally, the classifier uses the knowledge acquired from the source domain to predict the labels in the target domain. The proposed EEG-based MI classification framework was evaluated by two open-source datasets, the BCI Competition IV Datasets 2a and 2b. Our results demonstrated that the proposed framework could enhance the performance of EEG-based MI detection, achieving better classification results compared with several state-of-the-art algorithms. In conclusion, this study is promising in helping the neural rehabilitation of different neuropsychiatric diseases.

In the application of brain-computer interfaces (BCIs), electroencephalogram (EEG) signals are difficult to collect in large quantities due to the non-stationary nature and long calibration time required. Transfer learning (TL), which transfers knowledge learned from existing subjects to new subjects, can be applied to solve this problem. Some existing EEG-based TL algorithms cannot achieve good results because they only extract partial features. To achieve effective transfer, a double-stage transfer learning (DSTL) algorithm which applied transfer learning to both preprocessing stage and feature extraction stage of typical BCIs was proposed. First, Euclidean alignment (EA) was used to align EEG trials from different subjects. Second, aligned EEG trials in the source domain were reweighted by the distance between the covariance matrix of each trial in the source domain and the mean covariance matrix of the target domain. Lastly, after extracting spatial features with common spatial patterns (CSP), transfer component analysis (TCA) was adopted to reduce the differences between different domains further. Experiments on two public datasets in two transfer paradigms (multi-source to single-target (MTS) and single-source to single-target (STS)) verified the effectiveness of the proposed method. The proposed DSTL achieved better classification accuracy on two datasets: 84.64% and 77.16% in MTS, 73.38% and 68.58% in STS, which shows that DSTL performs better than other state-of-the-art methods. The proposed DSTL can reduce the difference between the source domain and the target domain, providing a new method for EEG data classification without training dataset.

Abstract Background Noninvasively urine-based diagnostic modalities for upper urinary tract urothelial carcinoma (UTUC) were still lacking. We evaluated the diagnostic value of our previously developed urine-based assay (UTC assay) in UTUC. Methods We retrospectively analyzed 90 patients with suspected UTUC and 40 donors without UTUC. Voided urine specimens were analyzed by UTC assay and fluorescence in situ hybridization (FISH). The performance of UTC assay and FISH was compared among the 60 histologically proven UTUC patients and the 40 donors with benign disease. Results Of the 60 UTUCs, there were 8 low-grade and 52 high-grade cases. Overall sensitivity for UTC assay and FISH were 85% and 73.3%, respectively (P = 0.116). Specificities for UTC assay and FISH were 92.5% and 95%, respectively (P = ns.). By grade, sensitivities of UTC assay and FISH were 87.5% vs. 37.5% for low-grade (P = 0.119), and 84.6% vs. 78.8% for high- grade UTUC (P = 0.446), respectively. By stage, UTC assay showed significantly higher sensitivity than FISH for detecting non-muscle-invasive UTUC, which were 88.5% vs. 61.5%, respectively (P = 0.025). Conclusion UTC assay has good performance for the non-invasive diagnosis of UTUC. UTC assay may improve the diagnosis and surveillance of low-grade or superficial UTUC.

The evolution of shear band (SB) and texture in strip-cast electrical steel was studied in order to investigate the crystallographic character of SB and its influence on recrystallization texture. Shear deformation in the form of SB played an effective role in tailoring the local lattice rotation to accommodate heavy deformation in the case of inhomogeneous deformation under conditions of initial coarse grain in strip-cast electrical steel. During rolling process, η-oriented SBs appeared at different stages of deformation as a result of geometric softening. Cube-oriented SB was mainly retained from initial exact Cube grain at early stage of shear deformation and Goss-oriented SB was newly developed during the formation of γ-fiber matrix after heavy deformation. {210} SB formed as a transition form among different SBs. Crystal rotation inside SB, SB dimension and SB boundary misorientation progressed in proportion to cold rolling reduction. It is demonstrated that crystallographic direction of SBs tended to be parallel to under the driving force of reduction in stored energy to maintain relatively quasi-stable state. Low orientation gradient as well as relatively low density of dislocations was developed within SB region, while high orientation gradient formed between SB region and surrounding matrix. Interestingly, the η-oriented cell blocks inside SBs provided preferential nucleation sites for Goss and Cube texture at early stage of recrystallization, which significantly contributed to beneficial recrystallization texture in a manner of texture inheritance and superior magnetic properties (B50 was as high as 1.81 T, and P1.5/50 was as low as 4.0 W/kg).

Abstract Objectives Exosomes are essential mediators of intercellular communication as they transport proteins and RNAs between cells. Owing to their tumor‐targeting capacity, immune compatibility, low toxicity, and long half‐life, mesenchymal stem cell‐derived exosomes have great potential for the development of novel antitumor strategies. In this context, the role of exosomes produced by adipose‐derived mesenchymal stem cells (ADSCs) for the treatment of bladder cancer (BC) remains unclear. Here, we investigated the use of ADSCs as a source of therapeutic exosomes, as well as their efficacy in delivering the tumor suppressor miR‐138‐5p in BC. Methods ADSCs stably expressing miR‐138‐5p were established using Lentivirus infection, and ADSC‐derived miR‐138‐5p exosomes (Exo‐miR‐138‐5p) were isolated from the cell culture medium. The effect of Exo‐miR‐138‐5p on BC cell migration, invasion, and proliferation was evaluated in vitro using wound healing, transwell invasion, and proliferation assays. The in vivo effect of Exo‐miR‐138‐5p was investigated using a subcutaneous xenograft mouse model. Results Exo‐miR‐138‐5p prevented the migration, invasion, and proliferation of BC cells in vitro. Moreover, ADSC‐derived exosomes could penetrate tumor tissues and successfully deliver miR‐138‐5p to suppress the growth of xenograft tumors in vivo. Conclusions The present results reveal that ADSC‐derived exosomes are an effective delivery vehicle for small molecule drugs in vivo, and exosome‐delivered miR‐138‐5p is a promising therapeutic agent for BC treatment.

Zn-doped MnCO3/carbon sphere (Zn-doped MnCO3/CS) composites were synthesized using a simple hydrothermal procedure. Among various samples (ZM-50, ZM-05, and ZMC-0), the ternary Zn-doped MnCO3/CS (ZMC-2) catalyst demonstrated excellent visible light-induced photocatalytic activity. This improvement comes from the Zn addition and the conductive CS, which facilitate electron movement and charge transport. The catalyst exhibited efficient degradation of methylene blue (MB) over a wide pH range, achieving a removal efficiency of 99.6% under visible light. Radical trapping experiments suggested that •OH and •O2− played essential roles in the mechanism of organic pollutant degradation. Moreover, the catalyst maintained good degradation performance after five cycles. This study offers valuable perspectives into the fabrication of carbon-based composites with promising photocatalytic activity.

The medium-entropy alloy is a newly intriguing material showing superb properties. A simple, one-step method was developed to electrodeposit CoNiFe medium-entropy alloy films from a sulfate and citrate bath. The microstructure and corrosion resistance were investigated in CoNiFe films with varying deposition current densities. It shows the face-centered cubic structure with a preferential orientation. HRTEM-EDS observations further show information on the nanostructure and element distribution. The films exhibit a strong corrosion resistance in 3.5 wt.% NaCl solution. The films electrodeposited with a current density of 44.4 A/dm2 shows a low self-corrosion current density of 4.72 × 10−6 A⋅cm−2 in 3.5 wt.% NaCl solution. The corrosion mechanism was proposed in combination with electrochemical impedance spectroscopy results. The outstanding properties were attributed to the near-equimolar ternary components. The results lay a solid foundation for developing of highly oriented medium-entropy alloy films with strong corrosion resistance.

A novel processing route involving twin-roll casting and two-stage cold rolling was proposed to manufacture Inconel 718 alloy. Due to the sub-rapid solidification rate (412.5 °C/s) of as-cast strip, the element segregation can be alleviated and the growth of Laves phase was effectively suppressed. It was also observed that the dissolution of Laves phases during subsequent homogenization was significantly accelerated because of their refined size. The corresponding homogenization time can be shortened from dozens of hours to 1 h. Additionally, the non-uniform microstructure of cold-rolled and annealed samples caused by coarse grains from homogenization treatment can be eliminated by two-stage cold rolling method. Furthermore, a large amount of granular δ phases precipitated during intermediate annealing. After double aging treatment, the final products exhibited an excellent combination of strength and elongation. The ultimate tensile strength when deformed at room temperature and at 650 °C were about 1475 and 1120 MPa, and the corresponding total elongation were ∼ 15% and ∼ 22%. These results indicated that the twin-roll casting was a promising route to produce Inconel 718 alloy.

Protocols have been proposed to optimize neuromodulation targets and parameters to increase treatment efficacies for different neuropsychiatric diseases. However, no study has investigated the temporal effects of optimal neuromodulation targets and parameters simultaneously via exploring the test–retest reliability of the optimal neuromodulation protocols. In this study, we employed a publicly available structural and resting-state functional magnetic resonance imaging (fMRI) dataset to investigate the temporal effects of the optimal neuromodulation targets and parameters inferred from our customized neuromodulation protocol and examine the test–retest reliability over scanning time. 57 healthy young subjects were included in this study. Each subject underwent a repeated structural and resting state fMRI scan in two visits with an interval of 6 weeks between two scanning visits. Brain controllability analysis was performed to determine the optimal neuromodulation targets and optimal control analysis was further applied to calculate the optimal neuromodulation parameters for specific brain states transition. Intra-class correlation (ICC) measure was utilized to examine the test–retest reliability. Our results demonstrated that the optimal neuromodulation targets and parameters had excellent test–retest reliability (both ICCs > 0.80). The test–retest reliability of model fitting accuracies between the actual final state and the simulated final state also showed a good test–retest reliability (ICC > 0.65). Our results indicated the validity of our customized neuromodulation protocol to reliably identify the optimal neuromodulation targets and parameters between visits, which may be reliably extended to optimize the neuromodulation protocols to efficiently treat different neuropsychiatric disorders.

High-entropy alloys (HEAs) represent prospective applications considering their outstanding mechanical properties. The properties in HEAs can be affected by the phase structure. Artificial neural network (ANN) is a promising machine learning approach for predicting the phases of HEAs. In this work, a deep neural network (DNN) structure using a residual network (RESNET) is proposed for the phase formation prediction of HEAs. It shows a high overall accuracy of 81.9%. Compared it with machine learning models, e.g., ANN and conventional DNN, its Micro-F1 score highlights the advantages of phase prediction of HEAs. It can remarkably prevent network degradation and improve the algorithm accuracy. It delivers a new path to develop the phase formation prediction model using deep learning, which can be of universal relevance in assisting the design of the HEAs with novel chemical compositions.

Acinetobacter baumannii is a gram-negative coccobacilli, mainly causing nosocomial infections with poor prognosis, especially in patients with prolonged hospitalization or antibiotics administration. A. baumannii pneumonia is the most common clinical form and usually occurs in critically ill patients in the intensive care unit. However, septic arthritis caused by A. baumannii is rarely reported. In this report, we describe a case of A. baumannii septic arthritis combined with incomplete Kawasaki disease in an infant. The child's chief complaint was a 2-week intermittent fever with poor response to antibiotics. Initial physical examination revealed swollen lymph nodes in the neck, pharynx congestion, and the appearance of rashes. Combined with laboratory tests, the diagnosis of incomplete Kawasaki disease was considered. After administration of high-dose intravenous immunoglobulin and corticosteroids, the child's fever improved and periungual desquamation appeared simultaneously. Swelling of the right knee occurred 5 days after the fever improved and imaging tests of MRI and ultrasound suggested the existence of infection. A diagnosis of septic arthritis was established subsequently, and arthroscopy was carried out. A. baumannii was finally identified by metagenomics next-generation sequencing of joint draining fluid for pathogenic microorganisms. Treatment with meropenem was then started. The patient eventually recovered and was discharged from the hospital after 23 days of treatment with meropenem. Although A. baumannii is not a common bacterium of septic arthritis, this rare infection can still occur in infants. Early diagnosis, pathogenic identification, and target antibiotic treatment are important to reduce the occurrence of joint sequelae.

Continuous cropping of watermelon (Citrullus lanatus) may lead to soil degradation. As a soil conditioner, microbial agent has great potential in improving soil function and enhancing plant growth. In this study, we aimed to explore how microbial agent relieves the soil sickness of watermelon by analyzing watermelon performance, soil physicochemical properties and microbial community structures. Results suggested that microbial agent treatments significantly changed the photosynthetic efficiency of upper and lower leaves, which helped improve the growth of watermelon. The single fruit weight, fruit sugar degree and total phosphorus of soil following treatment with a mixture of Paecilomyces lilacinus DZ910 and Bacillus subtilis KC1723 (treatment D_K) were higher than those in single biofertilizer treatments and control. The soil microbial community under microbial agent treatments also changed significantly, indicating the feasibility of using microbial agents as soil remediations. The proportions of Pseudomonas and Flavobacterium, changed significantly after using microbial agents. Pseudomonas increased significantly after B. subtilis KC1723 and D_K treatments, while Flavobacterium increased significantly after using all three kinds of microbial agents compared to control. Increases in these bacteria were positively correlated with agronomic variables of watermelon. The fungi Aspergillus and Neocosmospora in the soil, which create an soil sickness of watermelon, decreased after KC1723 and D_K treatments. Meanwhile, Aspergillus and Neocosmospora were positively related to Myceliophthora incidence and negatively correlated with watermelon growth (single fruit weight and photosynthetic efficiency of upper leaves). Our microbial agent, especially D_K, represents a useful technique for alleviating soil sickness in watermelon.

Abstract Background Informal caregivers are the main source of care for the critically ill, especially after discharge or during the terminal stages at home. However, the concern for informal caregivers is often overshadowed by critically ill patients. The purpose of this study is to determine the influencing factors of the subjective burden of informal caregivers and to seek solutions accordingly. Methods Between July and August 2019, a cross-sectional study was conducted in Shandong, China, focusing on family caregivers and critically ill patients. Subjective caregiver burden was measured by the Chinese version of Zarit Burden Interview (ZBI). The stress process model was used to identify conditions relevant to the caregiving burden and to assess their impact on family caregivers. Results 554 samples were selected for analysis. The average scores of Zarit Caregiver Burden Interview (ZBI) scores in this study was 30.37±19.04 (n=554). ZBI scores of older, less educated, and spouse caregivers were significantly lower (4.12; 95%CI, 0.42 to 7.81; P =0.029). Objective and subjective burdens increased proportionally. Secondary role stress factors included the higher out-of-pocket (OOP) costs of critical diseases and lower household income, both of which increased caregivers’ subjective burdens (1.28; 95%CI, -0.06 to 2.63; p=0.062). Formal medical aid systems played a positive role in reducing subjective caregiving burdens (-7.31; 95%CI, -13.23 to -1.40; p=0.016). Conclusions Health policies should address both the direct medical burdens and the intangible psychological burdens of critical diseases.

Brain neuromodulation effectively treats neurological diseases and psychiatric disorders such as Depression. However, due to patient heterogeneity, neuromodulation treatment outcomes are often highly variable, requiring patient-specific stimulation protocols throughout the recovery stages to optimize treatment outcomes. Therefore, it is critical to personalize neuromodulation protocol to optimize the patient-specific stimulation targets and parameters by accommodating inherent interpatient variability and intersession alteration during treatments. The study aims to develop a personalized repetitive transcranial magnetic stimulation (rTMS) protocol and evaluate its feasibility in optimizing the treatment efficiency using an existing dataset from an antidepressant experimental imaging study in depression. The personalization of the rTMS treatment protocol was achieved by personalizing both stimulation targets and parameters via a novel approach integrating the functional brain network controllability analysis and optimal control analysis. First, the functional brain network controllability analysis was performed to identify the optimal rTMS stimulation target from the effective connectivity network constructed from patient-specific resting-state functional magnetic resonance imaging data. The optimal control algorithm was then applied to optimize the rTMS stimulation parameters based on the optimized target. The performance of the proposed personalized rTMS technique was evaluated using datasets collected from a longitudinal antidepressant experimental imaging study in depression (n = 20). Simulation models demonstrated that the proposed personalized rTMS protocol outperformed the standard rTMS treatment by efficiently steering a depressive resting brain state to a healthy resting brain state, indicated by the significantly less control energy needed and higher model fitting accuracy achieved. The node with the maximum average controllability of each patient was designated as the optimal target region for the personalized rTMS protocol. Our results also demonstrated the theoretical feasibility of achieving comparable neuromodulation efficacy by stimulating a single node compared to stimulating multiple driver nodes. The findings support the feasibility of developing personalized neuromodulation protocols to more efficiently treat depression and other neurological diseases.

Abstract Background With an increasing number of critically ill patients, attention should be paid to both their physical health and mental health. The objective of this study is to examine the links between depression and social capital among critically ill patients. Methods Data for 1043 patients with critical illnesses was collected with a stratified cluster random sampling method in rural Shandong, China. Depression symptoms were measured using a short form version of the Center for Epidemiologic Studies Depression Scale (CESD-10) and the total scores of them were dichotomized. We associated structural social capital with social networks, social participation, and social support. Cognitive social capital includes the degree of availability of social trust and reciprocity. Binary logistic regression was used to explore whether social capital was significantly associated with depression among patients with critical illnesses. Results We found that 68.5% of the critically ill patients in our sample population had depression. CESD-10 scores were negatively correlated with social capital, including occupations of their frequent contacts, social trust in relatives and friends, distance to the nearest medical institution and medical assistance convenience from non-spouse. In addition, low economic status, and low self-rated health were more significantly correlated with depression in critically ill patients. Conclusions Our findings suggest that more attention should be paid to the mental health of critically ill patients and more formal society, community and government support form given, particularly in rural China.

Abstract Background Epidermal growth factor-like domain 7 (Egfl7), a recently identified secreted protein, was significantly increased in patients with HCC by our previous studies. However, its efficacy in the diagnosis of early HCC remains unknown. In this study, we therefore evaluate the efficacy of serum Egfl7 for early HCC diagnosis and compare it with alpha-fetoprotein (AFP). Methods Serum Egfl7 levels in testing cohort (1081 participants) and validation cohort (476 participants) were measured by a sandwich enzyme-linked immunoassay (ELISA). The cut-off value of Egfl7 was determined by Youden’s index and the efficacies of Egfl7 and AFP in diagnosing early HCC were estimated by receiver operating characteristic (ROC). Results Serum Egfl7 was significantly elevated in patients with early HCC than all non-HCC controls in whatever Testing Cohort or Validation Cohort. In the Testing Cohort, ROC curves showed the optimum cut-off value of Egfl7 was 2610 ng/mL and Egfl7 showed a significantly higher sensitivity than AFP in discriminating early HCC from healthy individuals (77.4% vs. 65.3%, P = 0.0013) but the area under ROC (AUROC) and accuracy of Egfl7 and AFP were similar (0.860 vs. 0.868, P = 0.704; 80.2% vs. 83.8%, P = 0.184). In distinguishing patients with early HCC from patients with chronic liver disease (CLD), the AUROC, sensitivity, specificity and accuracy of Egfl7 were 0.800, 75.2, 71.7 and 73.5%, which were all significantly higher than AFP (0.675, 61.8, 62.0 and 61.9% in order). Egfl7 also showed a significant higher sensitivity and accuracy than AFP (76.6% vs. 64.0%, P = 0.0031; 79.9% vs. 66.1%, P

Background: Autism spectrum disorder (ASD) is a multifactorial, pervasive, neurodevelopmental disorder, of which intestinal symptoms collectively represent one of the most common comorbidities. Methods: In this study, 1,222 children with ASD and 1,206 typically developing (TD) children aged 2-7 years were enrolled from 13 cities in China. Physical measurement and basic information questionnaires were conducted in ASD and TD children. The Childhood Autism Rating Scale (CARS), Social Responsiveness Scale (SRS), and Autism Behavior Checklist (ABC) were used to evaluate the clinical symptoms of children with ASD. The six-item Gastrointestinal Severity Index (6-GSI) was used to evaluate the prevalence of intestinal symptoms in two groups. Results: The detection rates of constipation, stool odor, and total intestinal symptoms in ASD children were significantly higher than those in TD children (40.098% vs. 25.622%, 17.021% vs. 9.287%, and 53.601% vs. 41.294%, respectively). Autistic children presenting with intestinal comorbidity had significantly higher scores on the ABC, SRS, CARS, and multiple subscales than autistic children without intestinal symptoms, suggesting that intestinal comorbidity may exacerbates the core symptoms of ASD children. Conclusion: Intestinal dysfunction was significantly more common in autistic than in TD children. This dysfunction may aggravate the core symptoms of children with ASD.

The frequent occurrence of meteorological disasters in China has caused huge losses to agriculture. Risk assessment serves as a bridge from disaster crisis management to disaster risk management. Therefore, it is necessary to carry out a refined comprehensive risk assessment of meteorological disasters in typical areas. However, several limitations remain in the disaster loss risk research, such as too coarse resolution and too single risk indicator. Additionally, less research has examined geographical information on risk clustering and barycenter migration, as well as temporal information on the sustainability of trends. Consequently, it is significant to unearth the geographical and temporal information on disaster loss and identify the refined spatial and temporal evolution pattern of crop risk. For this reason, we evaluated the risk of corn production in Gansu Province. First, based on maize yield data, a risk evaluation index system was constructed using the characteristics of variation trends, fluctuations, and extreme values of disaster losses. Then, the spatial distribution patterns and temporal evolution characteristics of maize production risks on a county scale in Gansu Province were determined using spatial analysis and climate diagnosis technology. The results show that there is a large interdecadal fluctuation in risk. In the 1980s, 1990s, 2000s, and 2010s, the average yield reduction rates of maize in Gansu Province were −11.8%, −12.6%, −8.7%, and −8.5%, and the proportions of counties with severe yield reduction were 34.8%, 44.4%, 20.8%, and 9.7%, respectively. Second, most counties belong to medium-low or low-risk areas for maize production. High-risk counties are primarily located in eastern and southern Gansu, whereas low-risk counties are mostly found along the Hexi Corridor. Third, most risk indicators exhibit some geographical aggregation. The Jiuquan region falls within the low-low-risk aggregation zone. In contrast, the Qingyang region is a high-high aggregation zone with a gradual expansion trend. Four, each risk indicator’s geographical barycenter migrates over a complicated path, but the direction and distance vary considerably. The comprehensive risk migrates along the south-northwest-southeast trajectory, albeit at a shorter distance. Five, the proportion of counties with a medium, medium-severe, severe, and total yield reduction tended to decline. In addition, the annual precipitation is significantly or very significantly correlated with most risk indicators and the comprehensive risk level. The results can guide agricultural production processes at all levels, as well as government disaster prevention.

An amendment to this paper has been published and can be accessed via the original article.

Motor control deficits are very common in stroke survivors and often lead to disability. Current clinical measures for profiling motor control impairments are largely subjective and lack precise interpretation in a “control” perspective. This study aims to provide an accurate interpretation and assessment of the underlying “motor control” deficits caused by stroke, using a recently developed novel technique, i.e., the functional brain controllability analysis. The electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were simultaneously recorded from 16 stroke patients and 11 healthy subjects during a hand-clenching task. A high spatiotemporal resolution fNIRS-informed EEG source imaging approach was then employed to estimate the cortical activity and construct the functional brain network. Subsequently, network control theory was applied to evaluate the modal controllability of some key motor regions, including primary motor cortex (M1), premotor cortex (PMC), and supplementary motor cortex (SMA), and also the executive control network (ECN). Results indicated that the modal controllability of ECN in stroke patients was significantly lower than healthy subjects (p = 0.03). Besides, the modal controllability of SMA in stroke patients was also significant smaller than healthy subjects (p = 0.02). Finally, the baseline modal controllability of M1 was found to be significantly correlated with the baseline FM-UL clinical scores (r = 0.58, p = 0.01). In conclusion, our results provide a new perspective to better understand the motor control deficits caused by stroke. We expect such an analytical methodology can be extended to investigate the other neurological or psychiatric diseases caused by cognitive control or motor control impairment.

Listeria innocua is widely distributed in the environment and food and is considered a non-pathogenic bacterium for both humans and animals. To our knowledge, only a few cases of L. innocua infection in humans and ruminants have been reported. Moreover, there has been no report on human L. innocua meningoencephalitis. Here, we report a case of severe refractory meningoencephalitis in a three-year-old boy after infection with L. innocua. The child’s first symptoms were a runny nose, high fever, and rashes, which quickly progressed to unconsciousness and convulsions. The initial analysis of cerebral spinal fluid revealed remarkably elevated protein levels and increased white blood cells count. The blood culture of the patient in the early stage was positive for L. innocua. In addition, his brain imaging tests were observed dynamically, and the result showed a speedy progression from multiple intracranial abnormal signals to hydrocephalus and interstitial cerebral edema. After receiving antibiotics and symptomatic treatment for nearly 3 months, the patient’s condition improved markedly. However, he still had residual complications such as hydrocephalus. Although L. innocua is considered harmless, it can still cause disease in humans, even severe meningoencephalitis, with rapid progression and poor prognosis. Early discovery, diagnosis, and treatment are necessary to elevate the survival rate and life quality of those patients. Antibiotics should be used with sufficient duration and dosage. Cephalosporins are not suitable for the treatment of L. innocua meningoencephalitis and penicillin antibiotics are preferred for children. The presentation of this case will help to expand our knowledge of Listeria infections and provide a potential candidate for pathogens causing severe childhood central nervous system infection.

Abstract Background Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV. Methods Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored. Results A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P

Abstract Protective coal layer (PCL) mining is a preferred regional method for preventing coal and gas outbursts; stress‐relief of PCL mining is the prerequisite of pressure‐relief gas drainage. In order to study the stress‐relief effect of PCL mining on protected coal seams and determine the protection scope of PCL mining under different inclinations, this paper uses physical simulations to study the stress‐relief law and deformation characteristics of the strata during upper PCL mining with a gentle inclination (25°), an intermediate inclination (45°), and a steep inclination (65°). The results show that as the coal seam dip increases, the degree of deformation and failure of the overburden strata gradually decreases, the severe subsidence and expansion area of the overburden strata tends to gradually develop in the uphill direction of the working face, and the floor strata underlying the protective seam significantly expand toward the goaf. However, the absolute value of the upward movement of the floor strata is much smaller than that of the roof strata; and the greater the dip angle, the greater the ratio of floor displacement to roof subsidence. The stress‐relief curve of the protected layer is approximately U‐shaped. As the inclination of the coal seam increases, the stress‐relief range decreases gradually. The maximum relief‐stress and the stress release coefficient of the protected layer initially increase and then decrease. The stress concentration coefficient of the protected coal seam initially increases first and then decreases in both uphill and downhill of the working face in the PCL. As the inclination increases, the stress‐relief angle of the downhill boundary initially increases and then decreases, while the stress‐relief angle of the uphill boundary decreases gradually. Our research results provide guidance for the delimitation of the protected scope of PCLs with different dip angles.

Abstract Background Early detection of urothelial carcinoma (UC) by noninvasive diagnostic methods with high accuracy is still underscored. This study aimed to develop a noninvasive assay incorporating both enrichment of urine exfoliated cells and immunoassays for UC detection. Methods Polystyrene dishes were exposed to oxygen plasma and modified with 3‐aminopropyltriethoxysilane to prepare amine‐functionalized nanostructured substrates (NS). Performance characterization of NS was evaluated by atomic force microscope and X‐ray photoelectron spectroscopy. Urine exfoliated cells were captured by NS and then immunostained to detect urinary tumor cells (UTCs), which was called UTC assay. The receiver operating characteristic (ROC) curve, area under ROC curve (AUC), and Youden index were used to find the cutoff value of UTC assay. ROC analysis and McNemar test were used to compare the diagnostic accuracy of UTC assay with cytology. Kappa test was used to analyze the agreement of UTC assay and cytology with pathological diagnosis. Results Nanostructured substrates had good cell binding yields of nucleated cells and tumor cells. CK20+CD45−CD11b− cells were considered as UTCs. UTC number ≥ 1 per sample could be considered as a positive result. By AUC and Kappa analysis, UTC assay showed good performance in UC detection. McNemar test demonstrated that UTC assay had a superior sensitivity even in low‐grade subgroup and a similar specificity compared to cytology in UC diagnosis. Conclusions Nanostructured substrates could be used to enrich the exfoliated cells from urine samples. UTC assay with NS has the potential to play a role in UC detection. The value of this assay still needs additional validation by large, multi‐center studies.

Objective The objective of this study is to examine the association between livelihood capital and catastrophic health expenditure (CHE).Design Between July and August 2019, a cross-sectional study was conducted in critically ill patients.Setting Shandong, China.Participants 1041 households with critically ill patients from 77 villages.Primary and secondary outcome measures We defined expenditure as being catastrophic if a household’s out-of-pocket payments were greater than or equal to 40% of their capacity to pay. Using the sustainable livelihoods framework, this study explored the associations between CHE and the various forms of livelihood capital—inclusive of human capital, natural capital, physical capital, financial capital and social capital. χ2 tests, t-tests, Wilcoxon tests and binary logistic regression analysis were performed to examine these associations.Results The incidence of CHE among households with critically ill patients was 76.37% in this study. Better livelihood capital was significantly associated with lower incidence of CHE. After controlling for confounding factors, households with healthier patients (OR 0.47, 95% CI 0.23 to 0.96), more real estate ownership (OR 0.35, 95% CI 0.19 to 0.67) and better economic status (OR 0.33, 95% CI 0.18 to 0.62) were associated with a reduction in the occurrence of CHE.Conclusions Livelihood capital was significantly associated with CHE in rural families with critically ill patients. This association suggests that, in addition to providing health insurance to the critically ill, more attention should be paid to their ability to create and preserve livelihood capital.

A phase-field model was revised to study the abnormal growth of Goss grains during the annealing process in Fe-3%Si steels, in which the interaction between the second-phase particles and Goss grain boundaries (GBs) was considered. The results indicate that the abnormal growth of Goss grains occurs due to the different dissolvability of the particles at Goss GBs compared with the other GBs. Moreover, the degree of abnormal growth increases first and then decreases with an increasing particle content. Meanwhile, the size advantage of Goss grain can further promote the degree of abnormal growth. Two types of island grains were found according to the simulated results, which is consistent with the experimental observations. A proper GB dissolvability of particles is the key factor for the formation of isolated island grains, and a higher local particle density at GBs is the main reason for the appearance of serial island grains. These findings can provide guidance for the desired texture control in silicon steels.