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Abstract Recent advances in Artificial Intelligence (AI), such as the development of large language models like ChatGPT, have blurred the boundaries between human and AI-generated text. This has led to a pressing need for tools that can determine whether text has been created or revised using AI. A general and universally effective detection model would be extremely useful, but appears to be beyond the reach of current technology and detection methods. The research described in this study adopts a domain and task specific approach and shows that specialized detection models can attain high accuracy. The study focuses on the higher education graduate admissions process, with the specific goal of identifying AI-generated and AI-revised Letters of Recommendation (LORs) and Statements of Intent (SOIs). Detecting such application materials is essential to ensure that applicants are evaluated on their true merits and abilities, and to foster an equitable and trustworthy admissions process. Our research is based on 3755 LORs and 1973 SOIs extracted from the application records of Fordham University’s Master’s programs in Computer Science and Data Science. To facilitate the construction and evaluation of detection models, we generated AI counterparts for each LOR and SOI using the GPT-3.5 Turbo API. The prompts for AI-generation text were derived from the admission data of the respective applicants, and the AI-revised LORs and SOIs were generated directly from the human-authored versions. We also utilize an open-access GPT-wiki-intro dataset to further validate our hypothesis regarding the feasibility of constructing domain-specific AI content detectors. Our experiments yield promising results in developing classifiers tailored to a specific domain when provided with sufficient training samples. Additionally, we present a comparative analysis of the word frequency and statistical characteristics of the text, which provides convincing evidence that ChatGPT employs distinctive vocabulary and paragraph structure compared to human-authored text. The code for this study is available on GitHub, and the models can be executed on user-provided data via an interactive web interface.

Abstract Purpose 46,XY disorders of sex development (46,XY DSD) are characterized by incomplete masculinization of genitalia with reduced androgenization. Accurate clinical management remains challenging, especially based solely on physical examination. Targeted next-generation sequencing (NGS) with known pathogenic genes provides a powerful tool for diagnosis efficiency. This study aims to identify the prevalent genetic variants by targeted NGS technology and investigate the diagnostic rate in a large cohort of 46,XY DSD patients, with most of them presenting atypical phenotypes. Methods Two different DSD panels were developed for sequencing purposes, targeting a cohort of 402 patients diagnosed with 46,XY DSD, who were recruited from the Department of Urology at Children’s Hospital, Zhejiang University School of Medicine (Hangzhou, China). The detailed clinical characteristics were evaluated, and peripheral blood was collected for targeted panels to find the patients’ variants. The clinical significance of these variants was annotated according to American College of Medical Genetics and Genomics (ACMG) guidelines. Results A total of 108 variants across 42 genes were found in 107 patients, including 46 pathogenic or likely pathogenic variants, with 45.7%(21/46) being novel. Among these genes, SRD5A2, AR, FGFR1, LHCGR, NR5A1, CHD7 were the most frequently observed. Besides, we also detected some uncommon causative genes like SOS1, and GNAS. Oligogenic variants were also identified in 9 patients, including several combinations PROKR2/FGFR1/CYP11B1, PROKR2/ATRX, PROKR2/AR, FGFR1/LHCGR/POR, FGFR1/NR5A1, GATA4/NR5A1, WNT4/AR, MAP3K1/FOXL2, WNT4/AR, and SOS1/FOXL2. Conclusion The overall genetic diagnostic rate was 11.2%(45/402), with an additional 15.4% (62/402) having variants of uncertain significance. Additionally, trio/duo patients had a higher genetic diagnostic rate (13.4%) compared to singletons (8.6%), with a higher proportion of singletons (15.1%) presenting variants of uncertain significance. In conclusion, targeted gene panels identified pathogenic variants in a Chinese 46,XY DSD cohort, expanding the genetic understanding and providing evidence for known pathogenic genes’ involvement.

In the study of coal powder reburning technology, combining the coal powder delivery via recirculating flue gas and natural gas conditioning was proposed. The purpose of this approach is to enhance the reduction capability of coal powder reburning, thereby reducing the emission of nitrogen oxides from coal-fired boilers. The main objective of the research is to explore how to utilize the highly reactive components in natural gas to create a more favorable environment for coal powder to reduce nitrogen oxides. To achieve this, a detailed investigation of the entire process of natural gas conditioning was conducted using a multi-reaction control section with a flow reactor experimental system simulating actual coal-fired boiler conditions. The results revealed that the abundant reduction substances generated during natural gas conditioning, such as CO*, OH*, H, and CHi, optimized the physical and chemical structure of coal char, thereby enhancing its ability to reduce nitrogen oxides. Compared with traditional coal powder delivery via recirculating flue gas, the addition of natural gas conditioning significantly increased the efficiency of nitrogen oxide reduction by approximately 12.23%. Through experimental investigation and data analysis, the significant impact of natural gas conditioning technology on coal powder reburning was elucidated, and the specific parameter requirements for engineering applications were proposed. From an engineering perspective, the precise control of the excess air ratio, residence time, and fuel ratio in the reburning zone is essential to achieve a high-efficiency nitrogen oxide reduction while ensuring a complete coal powder combustion and economic feasibility. Overall, adopting natural gas conditioning technology can not only guarantee ultra-low nitrogen oxide emissions but also minimize the use of reactive gases, thereby enhancing the economic efficiency of system operation. This flexible use of natural gas technology has demonstrated some promising results in laboratory conditions and offers feasible solutions for engineering practice, ensuring the economic efficiency of system operation.

BackgroundChildhood acute myeloid leukemia (cAML) is the second most common pediatric blood cancer, with high heterogeneity and poor prognosis. Recent studies have highlighted cuproptosis, a newly discovered form of programmed cell death triggered by the accumulation of intracellular copper ions, as a critical mechanism influencing cancer survival and resistance. Given its emerging role in cancer biology, we investigated cuproptosis-related genes (CRGs) in cAML to explore their potential in prognostic prediction and therapeutic targeting.MethodsGene expression data from publicly available sources were analyzed to identify differentially expressed CRGs. Samples were categorized based on their expression profiles, followed by the development of a prognostic risk model using multivariable Cox regression, LASSO, and univariable analyses. The model’s performance was evaluated through Kaplan-Meier survival analysis and ROC analysis. Immune infiltration in the tumor microenvironment was assessed using ssGSEA, validated by CIBERSORT. Drug sensitivity correlations were analyzed, and functional validation experiments were conducted on THP-1 and MOLM13 cell lines to assess the role of CNN3.ResultsA set of 12 differential CRGs was used to build a robust prognostic risk model, with high accuracy in predicting patient outcomes (P < 0.001). Significant differences in immune cell composition were identified between risk groups, particularly in T cells, B cells, monocytes, and dendritic cells. Drug sensitivity analysis revealed altered IC50 values for drugs like 5-fluorouracil and bortezomib. Knockdown of CNN3 in leukemia cell lines led to reduced cell proliferation.ConclusionOur CRGs-based prognostic model shows potential for guiding personalized treatment strategies in cAML. The differences in immune cell infiltration between risk groups suggest that immune modulation is key in cAML progression. CNN3 and LGR4 were identified as modulators of cAML progression, making them potential therapeutic targets. Future studies with larger cohorts are essential to validate these findings and further explore CRGs-targeted therapies.

Electrochemical CO2 reduction can convert CO2 into high-value-added products for special forms of energy storage and efficient carbon utilization for renewable electricity. To investigate the influence of biochar-Cu-based catalysts properties on electrochemical CO2 reduction performance, Cu is loaded onto rice husk-based biochar by impregnation method combined with pyrolysis and calcination in this study. The three synthesized biochar-Cu-based catalysts are tested for activity and electrochemical CO2 reduction performance in Flow Cell. The results show that biochar's properties, such as its high specific surface area, rich pore structure, and adjustable pore structure, provide sufficient sites for CO2 reduction. Urea can relatively increase the copper loading by 44 %, but it will also increase the clustering of copper. In the reduction performance test, the current density of char-Cu-700 is 2.08 times higher than that of char-Cu and 1.45 times higher than char-Cu-N at a reduction potential of -0.45 (V vs. RHE). The current density enhancement of the catalyst loaded on biochar with Cu particle size of 10 nm is about 50 % higher than that of the catalyst with a particle size of 20 nm. It indicates that the smaller the particle size of Cu at the nanoscale, the lower the average coordination of surface atoms and the greater the catalyst's reactivity. This study provides novel ideas for synthesizing biochar-Cu-based catalysts, lays part of the theoretical foundation for using biochar-Cu-based catalysts for electrochemical CO2 reduction, and provides experimental support for optimizing the catalyst structure.

BackgroundNeutrophil extracellular traps (NETs) can be attributed to the metastasis, occurrence, and immune evasion of cancer cells. We investigated the prognostic value of NET-related genes in childhood acute lymphoblastic leukemia (cALL) patients.MethodsDifferential gene expression analysis was conducted on samples collected from public databases. Grouping them based on the expression level of NET-related genes, we assessed the correlation between immune cell types and the risk score for having a poor prognosis of cALL, with an evaluation of the sensitivity of drugs used in cALL. We further divided the groups, integrating survival data. Subsequently, methods including multivariable Cox algorithms, least absolute shrinkage and selection operator (LASSO), and univariable were utilized to create a risk model predicting prognosis. Experiments in cell lines and animals were performed to explore the functions of TRIM8, a gene selected by the model. To validate the role of TRIM8 in leukemia development, lentivirus-mediated overexpression or knockdown of TRIM8 was employed in mice with T-ALL and B-ALL.ResultsKaplan–Meier (KM) analysis underscored the importance of differentially expressed genes identified in the groups divided by genes participated in NETs, with enrichment analysis showing the mechanism. Correlation analysis revealed significant associations with B cells, NK cells, mast cells, T cells, plasma cells, dendritic cells, and monocytes. The IC50 values of drugs such as all-trans-retinoic acid (ATRA), axitinib, doxorubicin, methotrexate, sorafenib, and vinblastine were increased, while dasatinib exhibited a lower IC50. A total of 13 NET-related genes were selected in constructing the risk model. In the training, testing, and merged cohorts, KM analysis demonstrated significantly improved survival for low-risk cALL patients compared to high-risk cALL patients (p < 0.001). The area under the curve (AUC) indicated strong predictive performance. Experiments in Jurkat and SUP-B15 revealed that TRIM8 knockdown decreased the proliferation of leukemia cell lines. Further experiments demonstrated a more favorable prognosis in mice with TRIM8-knockdown leukemia cells. Results of cell lines and animals showed better outcomes in prognosis when TRIM8 was knocked down.ConclusionWe identified a novelty in a prognostic model that could aid in the development of personalized treatments for cALL patients. Furthermore, it revealed that the expression of TRIM8 is a contributing factor to the proliferation of leukemia cells and worsens the prognosis of cALL.

Abstract The smart materials with multi‐color and stimuli‐responsive luminescence are very promising for next generation of optical information encryption and anti‐counterfeiting, but these materials are still scarce. Herein, a multi‐level information encryption strategy is developed based on the polychromatic emission of Sb‐doped double perovskite powders (SDPPs). Cs2NaInCl6:Sb, Cs2KInCl6:Sb, and Cs2AgInCl6:Sb synthesized through coprecipitation methods exhibit broadband emissions with bright blue, cyan, and orange colors, respectively. The information transmitted by specific SDPP is encrypted when different SDPPs are mixed. The confidential information can be decrypted by selecting the corresponding narrowband filter. Then, an encrypted quick response (QR) code with improved security is demonstrated based on this multi‐channel selection strategy. Moreover, the three types of SDPPs exhibit three different water‐triggered luminescence switching behaviors. The confidential information represented by Cs2NaInCl6:Sb can be erased/recovered through a simple water spray/drying. Whereas, the information collected from the green channel is permanently erased by moisture, which fundamentally avoids information leakage. Therefore, different encryption schemes can be designed to meet a variety of encryption requirements. The multicolor and stimuli‐responsive luminescence greatly enrich the flexibility of optical information encryption, which leaps the level of security and confidentiality.

Conventional CO2 capture technologies often focus on the simple capture process, while the absorption and high-value utilization of carbon capture products have profound significance, especially when combined with the utilization of biomass energy. Novel ammonia-based CO2 capture coupled with selective silicon dissolution was proposed with process regulation of external fields (i.e. magnetic field, microwave field, ultrasound field) used to prepare nano silica. The results show that the magnetic field plays a dual role of “vibration stirring” and adding energy (11.2 J/mol) on the formation and dispersion of nano silica. The specific surface area of nano-silica is increased from 140.24 m2/g to 280 m2/g by microwave field (250 W), and its “internal heating” promotes the formation of crystal nuclei. The ultrasonic field reduces the primary particle size of nano-silica by 3.17 nm, and inhibits the particle growth through cavitation and mechanical vibration. It would provide parameter support and optimization guidance for the low-cost high-quality nano silica for high-value utilization of ammonia-based CO2 capture products.

More and more connected and autonomous vehicle (CAV) open test roads reconstructed on the basis of traditional roads have appeared in China. However, the management policies vary, which makes the traffic environment complicated. This paper takes CAV test road safety management as the research aim and investigates the open test condition through the evaluation of the traffic safety facilities. Indicators were rigorously screened, then the game theory model was used to determine the combination weight of the indicators, and the set pair analysis was applied to solve the uncertain problems. A case study for the CAV test road network of a city in central China was implemented and the results show, regarding the traffic safety facilities’ condition, among the 20 sections of the city’s CAV test road network, 15% of which are at an excellent level, 75% of which are at a good level and 10% of which are at a moderate level; road signs, guardrail facilities, isolation facilities and road features are the main limiting factors affecting the level of traffic safety facilities. Based on the results, recommendations have been made for the transport management authorities in the aspects of safety management policy-making and facilities maintenance.

Abstract Letters of Recommendation (LORs) are widely utilized for admission to both undergraduate and graduate programs, and are becoming even more important with the decreasing role that standardized tests play in the admissions process. However, LORs are highly subjective and thus can inject recommender bias into the process, leading to an inequitable evaluation of the candidates’ competitiveness and competence. Our study utilizes natural language processing methods and manually determined ratings to investigate gender and cultural differences and biases in LORs written for STEM Master’s program applicants. We generate features to measure important characteristics of the LORs and then compare these characteristics across groups based on recommender gender, applicant gender, and applicant country of origin. One set of features, which measure the underlying sentiment, tone, and emotions associated with each LOR, is automatically generated using IBM Watson’s Natural Language Understanding (NLU) service. The second set of features is measured manually by our research team and quantifies the relevance, specificity, and positivity of each LOR. We identify and discuss features that exhibit statistically significant differences across gender and culture study groups. Our analysis is based on approximately 4000 applications for the MS in Data Science and MS in Computer Science programs at Fordham University. To our knowledge, no similar study has been performed on these graduate programs.

Objective. Age-related mitochondrial dysfunction and associated oxidative stress may contribute to the development of osteoarthritis. The aim of this study was to identify hub genes associated with mitochondrial dysfunction in osteoarthritis (OA) patients, helping predict the risk of OA, and revealing the mechanism of OA progression. Methods. OA expression data and mitochondrial dysfunction genes were downloaded from GEO (GSE55235, GSE82107, and GSE114007) and GeneCard databases. The differentially expressed mitochondrial dysfunction genes (DEMDFGs) between OA and control samples were screened. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes pathways were analyzed for DEMDFGs. The hub genes were determined by WGCNA and LASSO regression analysis. ROC curves manifested the diagnostic efficacy of each hub gene. A nomogram model was constructed and validated to predict OA risk. The expression of hub genes in OA and normal chondrocytes was verified by external datasets, qRT-PCR and western blotting. Results. A total of 31 DEMDFGs were identified, with 15 genes upregulated and 16 genes downregulated. GO functional enrichment analysis revealed that DEMDFGs were enriched in biological processes related to energy metabolism and cellular respiration. By employing weighted gene coexpression network analysis, we identified four distinct coexpression modules, among which the blue module exhibited the strongest correlation with OA. The intersection between DEMDFGs and this module yielded eight candidate genes. After LASSO analysis of the data, four hub genes (ACADL, CYBA, SLC19A2, and UCP2) were identified as potential biomarkers for OA. The expression levels of these four genes were externally validated in the GSE114007 dataset. And the biologically differential expression of these four genes has been verified in OA and normal chondrocytes. Moreover, the four hub genes had good sensitivity and specificity by ROC curve analysis, and the risk model constructed with these four genes showed promising performance. In conclusion, our study may provide novel mitochondrial dysfunction hub genes with potential clinical applications for understanding the pathology, diagnosis, and treatment of OA.

Summary: Hematopoietic stem cells (HSCs) sustain hematopoiesis during homeostasis and regeneration. However, their limited availability poses a challenge for protein analysis. Here, we present a protocol for performing high-sensitivity western blot on HSCs using two techniques that enhance HSC isolation from mice and boost sensitivity for low cell numbers. We describe steps for isolating murine bone marrow cells, antibody staining, and cell sorting and post-sort analysis. We then detail a western blot procedure suitable for low numbers of HSCs.For complete details on the use and execution of this protocol, please refer to Li et al (2022).1,2 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

In this work, pseudo-bilayer planar heterojunction (PPHJ) all-polymer solar cells (APSCs) were constructed on the basis of the commonly used PY-IT and PM6 as the acceptor and donor, respectively. A nonfullerene small molecular acceptor (NF-SMA) BTP-eC9 was incorporated into the PY-IT layer as the solid additive in consideration of its similar building block to PY-IT. BTP-eC9 can serve as a photon capture reinforcer and morphology-regulating agent to realize more adequate photon capture, as well as a more orderly molecular arrangement for effective carrier transport. By incorporating 2 wt% BTP-eC9, the efficiency of PM6/PY-IT-based PPHJ-APSCs was boosted from 15.11% to 16.47%, accompanied by a synergistically enhanced short circuit current density (JSC, 23.36 vs. 24.08 mA cm−2) and fill factor (FF, 68.83% vs. 72.76%). In another all-polymer system, based on PBQx-TCl/PY-DT as the active layers, the efficiency could be boosted from 17.51% to 18.07%, enabled by the addition of 2 wt% L8-BO, which further verified the effectiveness of using an NF-SMA as a solid additive. This work demonstrates that incorporating an NF-SMA as a solid additive holds great potential for driving the development of PPHJ-APSCs.

Abstract The uvula flapping is one of the most distinctive features of snoring and is critical in affecting airway aerodynamics and vibrations. This study aimed to elucidate the mechanism of pharyngeal vibration and pressure fluctuation due to uvula flapping employing fluid–structure interaction simulations. The followings are the methodology part: we constructed an anatomically accurate pediatric pharynx model and put attention on the oropharynx region where the greatest level of upper airway compliance was reported to occur. The uvula was assumed to be a rigid body with specific flapping frequencies to guarantee proper boundary conditions with as little complexity as possible. The airway tissue was considered to have a uniform thickness. It was found that the flapping frequency had a more significant effect on the airway vibration than the flapping amplitude, as the flapping uvula influenced the pharyngeal aerodynamics by altering the jet flow from the mouth. Breathing only through the mouth could amplify the effect of flapping uvula on aerodynamic changes and result in more significant oropharynx vibration.

Objective: The present study aimed to investigate the safety, feasibility, and efficacy of three-dimensional visualization technique (3DVT)-guided hepatectomy in the treatment of complicated hepatolithiasis. Methods: The clinical and follow-up data of 279 patients with complicated hepatolithiasis were retrospectively analyzed. The patients were divided into a 3DVT group (group A, 66 cases) and a non-3DVT group (group B, 213 cases). After baseline data were balanced using propensity score matching (PSM), the clinical characteristics and follow-up data of the two groups were observed. Results: After 1:1 PSM, 58 patients in each group were successfully matched with each other. When the groups were compared, the surgical duration (p = 0.033) and intraoperative blood loss (p = 0.002) of group A were lower than those of group B. The immediate stone clearance rate (91.4% vs. 75.9%, p = 0.024) and quality of life outcome (p = 0.034) of group A were significantly higher than those of group B. Logistic regression analysis showed that history of two or more biliary tract operations (odds ratio [OR] = 6.544, 95% confidence interval [CI] = 1.193–35.890, p = 0.031), bilateral stone distribution (OR = 4.198, 95% CI = 1.186–14.854, p = 0.026), and Geng grade III or IV (OR = 12.262, 95% CI = 2.224–67.617, p = 0.004) were independent risk factors for poor outcomes in patients with complicated hepatolithiasis. Conclusion: Compared to conventional imaging examinations, 3DVT can be used to guide and achieve accurate preoperative diagnosis of complicated hepatolithiasis and has good safety, feasibility, and efficacy.

Most stroke survivors have mobility deficits and show a pathological gait pattern. Seeking to enhance the gait performance among this population, we have developed a hybrid cable-driven lower limb exoskeleton (called SEAExo). This study aimed to determine the effects of SEAExo with personalized assistance on immediate changes in gait performance of people after stroke. Gait metrics (i.e., the foot contact angle, knee flexion peak, temporal gait symmetry indices) and muscle activities were the primary outcomes to evaluate the assistive performance. Seven subacute stroke survivors participated and completed the experiment with three comparison sessions, i.e., walking without SEAExo (served as baseline) and without/with personalized assistance, at their preferred walking speeds. Compared to the baseline, we observed increases in the foot contact angle and knee flexion peak by 70.1% ( ${p} < 0.05$ ) and 60.0% ( ${p} < 0.05$ ) with personalized assistance. Personalized assistance contributed to the improvements in temporal gait symmetry of more impaired participants ( ${p} < 0.05$ ), and it led to a 22.8% and 51.3% ( ${p} < 0.05$ ) reduction in the muscle activities of ankle flexor muscles. These results demonstrate that SEAExo with personalized assistance has the potential to enhance post-stroke gait rehabilitation in real-world clinical settings.

In the real-world surveillance system application, the accuracy of person re-identification (Re-ID) still suffers from occlusion. Occluded Re-ID aims to retrieve occluded person images from multiple cameras that do not overlap. To address this issue, we present a multi-branch feature enhancement network in this paper. It has the pose correction module (PCM), the feature reconstruction module (FRM), and the part align module (PAM). PCM builds the key-points confidence corrected mechanism of the self-adaptive weights learning before the part matching stage to strengthen part-level features in the non-occluded region and weaken ones in the occluded region. FRM reconstructs the feature distribution of inter-domain and class-domain using the maximum mean discrepancy to generalize the global features. The proposed feature separation mechanism enables the two branches to attend to distinct pedestrian feature. To increase the power of representation, PAM combines part-level and global features in an outer product. The experimental results on three holistic datasets, two partial datasets, and two occluded datasets demonstrate that our method is superior. It significantly outperforms state-of-the-art by 5.7% and 4.4% rank-1 scores on Occluded-Duke and Occluded-REID, respectively.

Abstract Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease characterized by flares ranging from mild to life-threatening. Severe flares and complications can require hospitalizations, which account for most of the direct costs of SLE care. This study investigates two machine learning approaches in predicting SLE hospitalizations using longitudinal data from 925 patients enrolled in a multicenter electronic health record (EHR)-based lupus cohort. Our first Differential approach accounts for the time dependencies in sequential data by introducing additional lagged variables between consecutive time steps. We next evaluate the performance of LSTM, a state-of-the-art deep learning model designed for time series. Our experimental results demonstrate that both methods can effectively predict lupus hospitalizations, but each has its strengths and limitations. Specifically, the Differential approach can be integrated into any non-temporal machine learning algorithms and is preferred for tasks with short observation periods. On the contrary, the LSTM model is desirable for studies utilizing long observation intervals attributing to its capability in capturing long-term dependencies embedded in the longitudinal data. Furthermore, the Differential approach has more options in handling class imbalance in the underlying data and delivers stable performance across different prognostic horizons. LSTM, on the other hand, demands more class-balanced training data and outperforms the Differential approach when there are sufficient positive samples facilitating model training. Capitalizing on our experimental results, we further study the optimal length of patient monitoring periods for different prediction horizons.

BackgroundColorectal cancer (CRC) patients with BRAF mutation have very poor prognosis. It is urgent to search for prognostic factors of BRAF mutant CRC. RNF43 is a ENF ubiquitin ligase of Wnt signaling. Mutation of RNF43 has been observed frequently in various types of human cancers. However, few studies have evaluated the role of RNF43 in CRC. The present study aimed to explore the impact of RNF43 mutations on molecular characteristics and prognosis in BRAF mutant CRC.MethodsSamples of 261 CRC patients with BRAF mutation were retrospectively analyzed. Tumor tissue and matched peripheral blood samples were collected and subjected to targeted sequencing with a panel of 1021 cancer-related genes. The association of molecular characteristics and survival in patients were then analyzed. 358 CRC patients with BRAF mutation from the cBioPortal dataset were used for further confirmation.ResultsThis study was inspired by a CRC patient with BRAF V600E and RNF43 co-mutation, who achieved a best remission of 70% and a progression free survival (PFS) of 13 months. Genomic analysis indicated that RNF43 mutation affected the genomic characteristics of patients with BRAF mutation, including microsatellite instability (MSI), tumor mutation burden (TMB) and the proportion of common gene mutations. Survival analysis showed that RNF43 mutation was a predictive biomarker for better PFS and OS in BRAF mutant CRC.ConclusionCollectively, we identified that RNF43 mutations were correlated with favorable genomic features, resulting in a better clinical outcome for BRAF mutant CRC patients.

The occurrence of cyanobacterial blooms in summer are frequently accompanied by the succession of phytoplankton communities in freshwater. However, little is known regarding the roles of viruses in the succession, such as in huge reservoirs. Here, we investigated the viral infection characteristics of phytoplankton and bacterioplankton during the summer bloom succession in Xiangxi Bay of Three Gorges Reservoir, China. The results indicated that three distinct bloom stages and two successions were observed. From cyanobacteria and diatom codominance to cyanobacteria dominance, the first succession involved different phyla and led to a Microcystis bloom. From Microcystis dominance to Microcystis and Anabaena codominance, the second succession was different Cyanophyta genera and resulted in the persistence of cyanobacterial bloom. The structural equation model (SEM) showed that the virus had positive influence on the phytoplankton community. Through the Spearman’s correlation and redundancy analysis (RDA), we speculated that both the increase of viral lysis in the eukaryotic community and the increase of lysogeny in cyanobacteria may contributed to the first succession and Microcystis blooms. In addition, the nutrients supplied by the lysis of bacterioplankton might benefit the second succession of different cyanobacterial genera and sustain the dominance of cyanobacteria. Based on hierarchical partitioning method, the viral variables still have a marked effect on the dynamics of phytoplankton community, although the environmental attributes were the major factors. Our findings suggested that viruses played multiple potential roles in summer bloom succession and may help the blooms success of cyanobacteria in Xiangxi Bay. Under the background of increasingly serious cyanobacterial blooms worldwide, our study may have great ecological and environmental significance for understanding the population succession in phytoplankton and controlling the cyanobacterial blooms.

National parks provide a considerable number of co-benefits to society, including the balance of ecosystems, conservation of heritage values, and tourism. However, studies on zoning approaches for the management of national parks are lacking. The landscape characterization approach is a holistic method for identifying regional landscapes and helps improve zoning management, thus promoting sustainable planning. Here, we propose a landscape character classification (LCC) approach for national parks by integrating a k-means clustering algorithm and geographic information system (GIS). We used Laoshan National Park (LNP) as a case study and aimed to (1) quantify the major landscape factors (altitude, topography relief, soil type, and heritage impact intensity) that influence the landscape classification of mountainous protected areas; (2) create a map of landscape character types and areas to guide a zoning boundary; and (3) further examine how decision makers assign different conservation strategies to each landscape character area. Our results indicate that different landscape character areas reflect distinct ecological environments and heritage values and that differentiated zoning management can effectively mitigate the impact of natural disasters and human activities. Our study suggests that national parks require scientific landscape character zoning, rational descriptions of landscape character types, and targeted management measures to achieve the dual objectives of zoning and landscape conservation.

Motivated to address the inconsistency between the essential i.i.d. assumption in machine learning theory and the data heterogeneity in real-world applications, we propose a novel calibrated ensemble (CE) algorithm to facilitate learning with diverse data subgroups. Unlike the traditional ensemble framework in which each learner is trained independently using the entire dataset, our method exploits the strengths of various machine learning models by training them simultaneously and forming model-ergonomic data subgroups as part of the training process. Consequently, each learner is calibrated to a unique subset of data based on their individualized predictive strength. Clinically, we can interpret each model as an expert specializing in treating patients with particular disease manifestations. We evaluate the CE model in our motivating domain of identifying lupus patients with severe SLE flares using 1541 clinical encounters in the Mass General Brigham (MGB) Lupus Cohort. Our experimental results demonstrate the efficacy of our CE model across seven performance evaluation metrics compared to five individual machine learning models and regular ensemble approaches. We further utilize ANOVA and Tukey HSD post-hoc statistical analysis to discover characteristic features of individual model clusters for clinical interpretations.

The graduate admissions process is time-consuming, subjective, and complicated by the need to combine information from diverse data sources. Letters of recommendation (LORs) are particularly difficult to evaluate and it is unclear how much impact they have on admissions decisions. This study addresses these concerns by building machine learning models to predict admissions decisions for two STEM graduate programs, with a focus on examining the contribution of LORs in the decision-making process. We train our predictive models leveraging information extracted from structured application forms (e.g., undergraduate GPA, standardized test scores, etc.), applicants' resumes, and LORs. A particular challenge in our study is the different modalities of application data (i.e., text vs. structured forms). To address this issue, we converted the textual LORs into features using a commercial natural language processing product and a manual rating process that we developed. By analyzing the predictive performance of the models using different subsets of features, we show that LORs alone provide only modest, but useful, predictive signals to admission decisions; the best model for predicting admissions decisions utilized both LOR and non-LOR data and achieved 89% accuracy. Our experiments demonstrate promising results in the utility of automated systems for assisting with graduate admission decisions. The findings confirm the value of LORs and the effectiveness of our feature engineering methods from LOR text. This study also assesses the significance of individual features using the SHAP method, thereby providing insight into key factors affecting graduate admission decisions.

The novel ammonia carbon capture system of carbonated solvent solventing out crystallization process, the crystal regeneration instead of rich liquid regeneration, to achieve the reduction of regeneration energy consumption. The enhanced crystallization by solventing out method requires the control of crystal yield as well as grain size, and the exploration of the kinetics of solventing out crystallization of carbonated solvent becomes necessary. The crystals produced by solvation crystallization are mainly ammonium bicarbonate, so this paper focuses on the crystallization kinetics of ammonium bicarbonate under different working conditions are studied by the intermittent dynamic method. The kinetic model of the crystal under the working conditions study in this paper is determined by calculation. The growth kinetic model of crystal nucleation rate versus growth rate is shown in Eq. B0=2.994 × 108G1.907. Finally, the integrated rate equations of the crystallization process under different working conditions are derived from the experimental data. The results show that an increase in supersaturation promotes the nucleation rate directly. The stirring rate has positive and negative effects on the crystal growth process. The effect on the nucleation rate is significant when the drop acceleration rate of the solubilizer is high. Under the action of the static magnetic field, by changing the hydrogen bonds in water molecules, the viscosity of water and the surface tension of solution is reduced so that the diffusion process of solute is accelerated and the crystal growth is facilitated. The addition of ammonia, ammonium carbamate and crystalline seeds influence the crystallization process mainly by affecting the supersaturation of the solution. In turn, the width of the metastable zone width is regulated to avoid explosive nucleation and to maintain typical nucleation and growth of the crystallization process.

With the global pandemic of COVID-19, it has been striking psychological burdens on individuals. Under this background, more and more people get wellbeing by watching live broadcasts. However, the psychological mechanism behind this phenomenon is still a black box. This study finds that when people watch a live broadcast and interact with anchors and other people, an interaction ritual chain is formed, and emotional energy is generated, thus making people experience and understand the meaning of the live interaction ritual chains. Under the effect of the meaning transfer model, people will generate wellbeing. Specifically, the basic meaning of live interaction (emotional meaning and functional meaning) drives people’s generation of wellbeing. The meanings of self-participation, self-display, self-concept, and self-renewal play a role in mediation in enhancing people’s wellbeing with the basic meaning of live broadcast interaction.

Uremic neuropathy in children encompasses a wide range of central nervous system (CNS), peripheral nervous system (PNS), autonomic nervous system (ANS), and psychological abnormalities, which is associated with progressive renal dysfunction. Clinically, the diagnosis of uremic neuropathy in children is often made retrospectively when symptoms improve after dialysis or transplantation, due to there is no defining signs or laboratory and imaging findings. These neurological disorders consequently result in increased morbidity and mortality among children population, making uremia an urgent public health problem worldwide. In this review, we discuss the epidemiology, potential mechanisms, possible treatments, and the shortcomings of current research of uremic neuropathy in children. Mechanistically, the uremic neuropathy may be caused by retention of uremic solutes, increased oxidative stress, neurotransmitter imbalance, and disturbance of the blood-brain barrier (BBB). Neuroimmune, including the change of inflammatory factors and immune cells, may also play a crucial role in the progression of uremic neuropathy. Different from the invasive treatment of dialysis and kidney transplantation, intervention in neuroimmune and targeted anti-inflammatory therapy may provide a new insight for the treatment of uremia.

Abstract Musculoskeletal disorders affect the locomotor system and are the leading contributor to disability worldwide. Patients suffer chronic pain and limitations in mobility, dexterity, and functional ability. Musculoskeletal (bone) X-ray is an essential tool in diagnosing the abnormalities. In recent years, deep learning algorithms have increasingly been applied in musculoskeletal radiology and have produced remarkable results. In our study, we introduce a new calibrated ensemble of deep learners for the task of identifying abnormal musculoskeletal radiographs. Our model leverages the strengths of three baseline deep neural networks (ConvNet, ResNet, and DenseNet), which are typically employed either directly or as the backbone architecture in the existing deep learning-based approaches in this domain. Experimental results based on the public MURA dataset demonstrate that our proposed model outperforms three individual models and a traditional ensemble learner, achieving an overall performance of (AUC: 0.93, Accuracy: 0.87, Precision: 0.93, Recall: 0.81, Cohen’s kappa: 0.74). The model also outperforms expert radiologists in three out of the seven upper extremity anatomical regions with a leading performance of (AUC: 0.97, Accuracy: 0.93, Precision: 0.90, Recall:0.97, Cohen’s kappa: 0.85) in the humerus region. We further apply the class activation map technique to highlight the areas essential to our model’s decision-making process. Given that the best radiologist performance is between 0.73 and 0.78 in Cohen’s kappa statistic, our study provides convincing results supporting the utility of a calibrated ensemble approach for assessing abnormalities in musculoskeletal X-rays.

Abstract This study aimed to investigate the clinicopathological and radiological significance of the collateral vessel of renal cell carcinoma (RCC) on preoperative computed tomography (CT). Preoperative contrast-enhanced CT of 236 consecutive patients with pathological documented RCC were retrospectively reviewed during the period of 2014. The associations of the presence of collateral vessels with perioperative clinicopathological and radiological features, as well as long term survival outcomes were analyzed. Totally, collateral vessels were detected by contrast-enhanced CT in 110 of 236 patients. The presence of collateral vessels was significantly associated with higher pathologic T stage, higher Fuhrman grade, higher overall RENAL scores, greater tumor size and enhancement, and more tumor necrosis (all P

Asian American students have experienced additional physical and emotional hardships associated with the COVID-19 pandemic due to increased xenophobic and anti-Asian discrimination. This study investigates different coping patterns and risk factors affecting Asian and non-Asian college students in response to COVID-19 challenges by studying the differences in their responses within four domains after the onset of the pandemic: academic adjustment, emotional adjustment, social support, and discriminatory impacts related to COVID-19. We first employed a machine learning approach to identify well-adjusted and poorly adjusted students in each of the four domains for the Asian and non-Asian groups, respectively. Next, we applied the SHAP method to study the principal risk factors associated with each classification task and analyzed the differences between the two groups. We based our study on a proprietary survey dataset collected from U.S. college students during the initial peak of the pandemic. Our findings provide insights into the risk factors and their directional impact affecting Asian and non-Asian students’ well-being during the pandemic. The results could help universities establish customized strategies to support these two groups of students in this era of uncertainty. Applications for international communities are discussed.

Abstract Background Splenogondal fusion (SGF) is a rare congenital anomaly characterized by abnormal association between the splenic tissue and the gonads or mesonephric remnants. SGF that requires separate two-stage laparoscopic staged Fowler-Stephen orchiopexy on both the left and right sides is extremely rare. SGF could be misdiagnosed as testicular malignancy and leads to unnecessary orchiectomy. Case presentation This is a case of an 8-month old male infant presented with bilateral cryptorchidism, B-mode ultrasound visualized the left and right testes in the lower abdominal cavity and the upper margin of the left testicle as a hypoechoic mass extending to the spleen, indicating an undescended right testis and possible SGF on the left side. Single-site laparoscopic examination confirmed the diagnosis of SGF on the left side and an undescended right testis. As both testes were high and the right spermatic vessel was poorly developed and short, a routine single stage orchiopexy would be difficult and risky, therefore, separate two-stage laparoscopic staged Fowler-Stephen orchiopexies for both sides were implemented. Stage 1 of the staged Fowler-Stephen orchiopexy for the right side was performed first without treating the left side, Stage 2 for the right side, separation of the left testis from the spleen as well as Stage 1 for the left side were performed 7 months later, and Stage 2 for the left side was performed 7 months after that. Follow-up ultrasound 1 year after the surgery revealed no obvious abnormalities in the shapes of the testes or their blood supply. This treatment strategy prevented unnecessary orchiectomy. Conclusions We reported a rare case of SGF that needed separate two-stage laparoscopic staged Fowler-Stephen orchiopexies for both sides, and a review of the recent literature. SGF is a rare congenital anomaly often diagnosed incidentally during exploration/surgery for scrotal swelling/mass, cryptorchidism or inguinal hernia in young patients. Surgeons, especially pediatric surgeons should be aware of this rare condition to avoid unnecessary, life-altering radical orchiectomy. When routine single stage orchiopexy is not feasible or risky for either side, separate two-stage laparoscopic staged Fowler-Stephen orchiopexies could be performed on both the left and right sides to avoid unnecessary orchiectomy.

With the rapid development of hardware and software technology, modern industry has produced a large amount of high-dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace can find clusters in the subspace, which solves the problem of high data dimension. However, their clustering process based on only one single feature, which results in their performance being particularly sensitive to this single view. Affected by the integrated algorithm, a large number of multi-view methods began to emerge. These methods improve the clustering performance by integrating the subspace expressions of multiple views, but the problem is that the complementary information of multiple views cannot be fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high-dimensional data sets. In this paper, based on the multi-view subspace clustering method, a clustering algorithm based on tensor low rank expression is proposed to solve the clustering problem of high-dimensional datasets. On the one hand, this paper solves the problem of noise and data corruption by combining the method of ℓ2,1 norms, which transform the optimization problem of solving multi-view subspace expression into a low-rank expression problem of tensor to fully consider the complementarities between views. On the other hand, the proposed scheme solves the problem of nonuniform distribution in clusters in high-dimensional data by combining with the density peak algorithm based on hierarchical strategy. Experiment results show the effectiveness of the algorithm.

The COVID-19 pandemic affects all population segments and is especially detrimental to university students because social interaction is critical for a rewarding campus life and valuable learning experiences. In particular, with the suspension of in-person activities and the adoption of virtual teaching modalities, university students face drastic changes in their physical activities, academic careers, and mental health. Our study applies a machine learning approach to explore the gender differences among U.S. university students in response to the global pandemic. Leveraging a proprietary survey dataset collected from 322 U.S. university students, we employ association rule mining (ARM) techniques to identify and compare psychological, cognitive, and behavioral patterns among male and female participants. To formulate our task under the conventional ARM framework, we model each unique question-answer pair of the survey questionnaire as a market basket item. Consequently, each participant's survey report is analogous to a customer's transaction on a collection of items. Our findings suggest that significant differences exist between the two gender groups in psychological distress and coping strategies. In addition, the two groups exhibit minor differences in cognitive patterns and consistent preventive behaviors. The identified gender differences could help professional institutions to facilitate customized advising or counseling for males and females in periods of unprecedented challenges.

Abstract The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients’ clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A “threshold” was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course.

The COVID-19 pandemic has presented unprecedented challenges for university students, creating uncertainties for their academic careers, social lives, and mental health. Our study utilized a machine learning approach to examine the degree to which students' college adjustment and coping styles impacted their adjustment to COVID-19 disruptions. More specifically, we developed predictive models to distinguish between well-adjusted and not well-adjusted students in each of five psychological domains: academic adjustment, emotionality adjustment, social support adjustment, general COVID-19 regulations response, and discriminatory impact. The predictive features used for these models are students' individual characteristics in three psychological domains, i.e., Ways of Coping (WAYS), Adaptation to College (SACQ), and Perceived Stress Scale (PSS), assessed using established commercial and open-access questionnaires. We based our study on a proprietary survey dataset collected from 517 U.S. students during the initial peak of the pandemic. Our models achieved an average of 0.91 AUC score over the five domains. Using the SHAP method, we further identified the most relevant risk factors associated with each classification task. The findings reveal the relationship of students' general adaptation to college and coping in relation to their adjustment during COVID-19. Our results could help universities identify systemic and individualized strategies to support their students in coping with stress and to facilitate students' college adjustment in this era of challenges and uncertainties.

With the development of Internet e-commerce channels, online shopping platforms have become the main channel for consumers to buy nearly expired food. Date labels, as one of the main external clues, play a decisive role in nearly expired food purchasing. Therefore, based on attention-related theory, this study attempts to explore the influence exerted by different time frames on consumers’ willingness to buy and its mechanism. The results show that compared with the date, consumers have a higher willingness to buy nearly expired food when the expiration time is framed by delay. More specifically, compared to the date, the delay causes the individual to have a longer time perception, thus more preference for nearly expired food. Meanwhile, the mediating effect of time perception is moderated by food type. The conclusion of this research is helpful to expand the theoretical framework of time frames and related fields on nearly expired food, as well as provide practical guidance for marketers to effectively promote nearly expired food.

Silica is widely applied in industrial fields due to its good properties, while preparation of high-quality SiO2 by carbonization is an important way to utilize CO2. The experiment was carried out in a microbubble/bubbling experimental device, with its mass transfer kinetics of carbonization analyzed according to the pH-t curves. Under microbubble conditions, with the increasing temperature of 20∼60°C, the specific surface area, oil absorption value and particle size of silica varies at 336.80∼231.29 m2/g, 1.12∼1.81 ml/g and 16.49∼14.60 μm, respectively, which is similar as that with bubble. Aging at 20∼90 °C, they involve in the range of 35.82∼128.76 m2/g, 1.59∼2.44 ml/g and 23.15∼14.46 μm. With total concentration of solution >0.5 M, the concentration increases, the specific surface area and particle size of microbubbles are larger than those of the bubbling silica, and oil absorption value increases at 1.81∼2.44 ml/g. The kinetic analysis shows that it is more consistent with theoretical value, which can be judged as the secondary-reaction process. The microbubbles increase the CO2 utilization rate and volumetric mass transfer coefficient of carbonization by 2-4 times, while the liquid-phase mass transfer coefficient decreases. It indicates that microbubble mass transfer is achieved by significantly increasing the specific surface area.

As a chemical absorption method, the new ammonia carbon capture technology can capture CO2. Adding ethanol to ammonia can reduce the escape of ammonia to a certain extent and increase the absorption rate of CO2. The dissolution and crystallization of ethanol can realize the crystallization of ammonium bicarbonate and generate solid products. The induction of the crystallization process is influenced by many parameters, such as solution temperature, supersaturation, and solvating precipitant content. The basic nucleation theory is related to the critical size of nucleation. Accurate measurement of the induction period and investigating relevant factors can help to assess the nucleation kinetics. The effects of solubilizer content, temperature, and magnetic field on the induction period of the crystallization process of ammonium bicarbonate in the ethanol–H2O binary solvent mixture and determining the growth mechanism of the crystal surface by solid–liquid surface tension and surface entropy factor are investigated. The results indicate that under the same conditions of mixed solution temperature, the crystallization induction period becomes significantly longer, the solid–liquid surface tension increases, and the nucleation barrier becomes more significant and less likely to form nuclei as the content of solvating precipitants in the components increases. At the same solubilizer content, there is an inverse relationship between the solution temperature and the induction period, and the solid–liquid surface tension decreases. The magnetic field can significantly reduce the induction period of the solvate crystallization process. This gap tends to decrease with an increase in supersaturation; the shortening reduces from 96.9% to 84.0%. This decreasing trend becomes more and more evident with the rise of solvent content in the solution. The variation of surface entropy factor under the present experimental conditions ranges from 0.752 to 1.499. The growth mode of ammonium bicarbonate in the ethanol–H2O binary solvent mixture can be judged by the surface entropy factor as continuous growth.

In-scanner head motion often leads to degradation in MRI scans and is a major source of error in diagnosing brain abnormalities. Researchers have explored various approaches, including blind and nonblind deconvolutions, to correct the motion artifacts in MRI scans. Inspired by the recent success of deep learning models in medical image analysis, we investigate the efficacy of employing generative adversarial networks (GANs) to address motion blurs in brain MRI scans. We cast the problem as a blind deconvolution task where a neural network is trained to guess a blurring kernel that produced the observed corruption. Specifically, our study explores a new approach under the sparse coding paradigm where every ground truth corrupting kernel is assumed to be a “combination” of a relatively small universe of “basis” kernels. This assumption is based on the intuition that, on small distance scales, patients’ moves follow simple curves and that complex motions can be obtained by combining a number of simple ones. We show that, with a suitably dense basis, a neural network can effectively guess the degrading kernel and reverse some of the damage in the motion-affected real-world scans. To this end, we generated 10,000 continuous and curvilinear kernels in random positions and directions that are likely to uniformly populate the space of corrupting kernels in real-world scans. We further generated a large dataset of 225,000 pairs of sharp and blurred MR images to facilitate training effective deep learning models. Our experimental results demonstrate the viability of the proposed approach evaluated using synthetic and real-world MRI scans. Our study further suggests there is merit in exploring separate models for the sagittal, axial, and coronal planes.

Although numerous studies have explored the factors influencing entrepreneurial activity, there is a lack of a theoretical basis for linking these factors to entrepreneurship behavioral intention. The current study uses the theory of self-regulating attitude to construct a theoretical model of examining the relationship among cognitive bias, entrepreneurial emotion, and entrepreneurship intention. A total of 312 valid samples were collected from college students at a Chinese university. The bootstrapping method was used to test the multi-mediation hypotheses. Our research found that positive entrepreneurial emotion plays a mediating role in the relationship between optimism and entrepreneurship intention, whereas negative entrepreneurial emotion plays a mediating role in the relationship between overconfidence and entrepreneurship intention. These findings underline the importance of a correct understanding of cognitive bias and entrepreneurial emotion in the process of entrepreneurship.

Gas therapy has received widespread attention from the medical community as an emerging and promising therapeutic approach to cancer treatment. Among all gas molecules, nitric oxide (NO) was the first one to be applied in the biomedical field for its intriguing properties and unique anti-tumor mechanisms which have become a research hotspot in recent years. Despite the great progress of NO in cancer therapy, the non-specific distribution of NO in vivo and its side effects on normal tissue at high concentrations have impaired its clinical application. Therefore, it is important to develop facile NO-based nanomedicines to achieve the on-demand release of NO in tumor tissue while avoiding the leakage of NO in normal tissue, which could enhance therapeutic efficacy and reduce side effects at the same time. In recent years, numerous studies have reported the design and development of NO-based nanomedicines which were triggered by exogenous stimulus (light, ultrasound, X-ray) or tumor endogenous signals (glutathione, weak acid, glucose). In this review, we summarized the design principles and release behaviors of NO-based nanomedicines upon various stimuli and their applications in synergistic cancer therapy. We also discuss the anti-tumor mechanisms of NO-based nanomedicines in vivo for enhanced cancer therapy. Moreover, we discuss the existing challenges and further perspectives in this field in the aim of furthering its development.

This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard based on quantitative and qualitative characteristics of the companies. Collaborating with the management team of Rose Street Capital (RSC), we explore the most influential factors of their balanced scorecard using their retrospective investment decisions of successful and failed startup companies. Our study employs six standard machine learning models and their counterparts with an additional feature selection technique. Our findings suggest that “planning strategy” and “team management” are the two most determinant factors in the firm’s investment decisions, implying that qualitative factors could be more important to startup evaluation. Furthermore, we analyzed which machine learning models were most accurate in predicting the firm’s investment decisions. Our experimental results demonstrate that the best machine learning models achieve an overall accuracy of 78% in making the correct investment decisions, with an average of 87% and 69% in predicting the decision of companies the firm would and would not have invested in, respectively. Our study provides convincing evidence that qualitative criteria could be more influential in investment decisions and machine learning models can be adapted to help provide which values may be more important to consider for a venture capital firm.

Antioxidant activities of amino acids and synthetic dipeptides were studied to elucidate the structure–activity relationship of antioxidant dipeptides. Results showed that Tyr- and Trp-containing dipeptides showed the highest radical scavenging activities in both ABTS and ORAC assays with Trolox equivalent (TE) values ranging from 0.69 to 4.97 µmol TE/µmol peptide, followed by Cys- and Met-containing dipeptides. With respect to their reducing power and Fe2+ chelating abilities, only Cys-containing dipeptides showed moderate reducing power, and none of them displayed Fe2+chelating ability. It indicated that antioxidant dipeptides mainly acted as radical scavengers and the presence of Tyr, Trp, Cys or Met residue with electron/hydrogen donating ability was the driving force for dipeptides to scavenge radicals. Additionally, Tyr- and Trp-containing dipeptides with Tyr/Trp residue at the N-terminus showed stronger antioxidant activities than that at the C-terminus, and the neighboring residue also affected their activities by steric effect, hydrophobicity and hydrogen bonding among others.

A correction to this paper has been published: https://doi.org/10.1038/s41746-020-00361-9 .

OBJECTIVE:To explore the value of machine learning methods for predicting multiple sclerosis disease course. METHODS:1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up. RESULTS:Baseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%). Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group. INTERPRETATION:SVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

In the present study, the expansion characteristics of plant chemical alcohol waste liquid were experimentally studied with a vertical tube furnace system. The results showed that the droplet quality, heating temperature, and atmosphere directly influenced the droplet expansion. The droplet mass had nothing to do with the swelling volume index (SVI) but had a significant influence on the expansion time, with a larger droplet mass and longer expansion time. The heating temperature had a significant influence on the expansion characteristics of the waste liquid. As the heating temperature increased, the droplet SVI became larger with a shorter expansion time. The nitrogen atmosphere was more conducive to droplet volume expansion than the air atmosphere but had less of an effect on the expansion time. The volume of waste liquid droplets expanded more than 5 times, forming an internal porous structure, thereby increasing the comparative area and the probability of contact with oxygen to facilitate the combustion of the waste liquid.

In order to illustrate the importance of a coupling effect in the physical-chemical structure of char-based catalysts on in-situ biomass tar reforming, three typical char-based catalysts (graphite, Zhundong coal char, and sawdust biochar) were studied in the fixed-bed/fluidized-bed reactor. The physical-chemical properties of carbon-based catalysts associated with their catalytic abilities were characterized by inductively coupled plasma-atomic emission spectroscopy (ICP-AES), Raman, X-ray photoelectron spectroscopy (XPS), scanning electron microscope−energy dispersive spectrometer (SEM-EDS) and N2 adsorption. The relationship between the specific reactivity and tar reforming ability of carbon-based catalysts was discussed through a micro fluidized bed reaction analyzer (MFBRA−MR). The results indicate that the char-based catalyst has a certain removal ability for in-situ biomass tar of corn straw in an inert atmosphere, which is as follows: sawdust biochar > Zhundong (ZD) coal char > graphite. During the in-situ tar reforming, the alkali and alkaline earth metal species (AAEMs) act as adsorption/reaction sites, affecting the evolution of the aromatic ring structure and oxygen-containing functional groups of the char-based catalyst, and also its pore structure. AAEM species on the surface of char-based catalysts are the active sites for tar reforming, which promotes the increase of active intermediates (C-O bond and C-O-AAEMs), and enhances the interactions between char-based catalysts and biomass tar. The abundant AAEMs may lead to the conversion of O=C−O and C=O to C−O. For tar reforming, the internal pore structure of char-based catalysts is little changed, mainly with the carbon deposit forming on the surface pore structure. The carbon deposit from the reformation of straw tar on the char surface has better reactivity than the inherent carbon structure of ZD coal char and sawdust biochar. There is a positive relationship between the MFBRA−MR specific reactivity and tar catalytic reforming ability of char-based catalysts (decided by the coupling effect in their physical-chemical structure), which can be used to determine the catalytic ability of char-based catalysts on tar reforming directly.

The effect of chemical speciation (H2O/NH4Ac/HCl-soluble and insoluble) of alkali and alkaline earth metallic species on the steam gasification of sawdust biochar was investigated in a lab-scale, fixed-bed reactor, with the method of chemical fractionation analysis. The changes in biochar structures and the evolution of biochar reactivity are discussed, with a focus on the contributions of the chemical speciation of alkali and alkaline earth metallic species (AAEMs) on the steam gasification of biochar. The results indicate that H2O/NH4Ac/HCl-soluble AAEMs have a significant effect on biochar gasification rates. The release of K occurs mainly in the form of inorganic salts and hydrated ions, while that of Ca occurs mainly as organic ones. The sp3-rich or sp2-sp3 structures and different chemical-speciation AAEMs function together as the preferred active sites during steam gasification. H2O/HCl-soluble AAEMs could promote the transformation of biochar surface functional groups, from ether/alkene C-O-C to carboxylate COO− in biochar, while they may both be improved by NH4Ac-soluble AAEMs. H2O-soluble AAEMs play a crucial catalytic role in biochar reactivity. The effect of NH4Ac-soluble AAEMs is mainly concentrated in the high-conversion stage (biochar conversion >30%), while that of HCl-soluble AAEMs is reflected in the whole activity-testing stage.