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Given the increasing environmental concerns and energy consumption, the transformation of the new energy vehicle industry is a key link in the innovation of the energy structure. The shift from traditional fossil fuels to clean energy encompasses various dimensions such as technological innovation, policy support, infrastructure development, and changes in consumer preferences. Predicting the future ownership of electric vehicles (EVs) and then estimating the energy demand for transportation is a pressing issue in the field of new energy. This study starts from dimensions such as cost, technology, environment, and consumer preferences, deeply explores the influencing factors on the ownership of EVs, analyzes the mechanisms of various factors on the development of EVs, establishes a predictive model for the ownership of motor vehicles considering the penetration of electric vehicles based on system dynamics, and then simulates the future annual trends in EV and conventional vehicle (CV) ownership under different scenarios based on the intensity of government funding. Using energy consumption formulas under different power modes, this study quantifies the electrification energy demand for transportation flows as fleet structure changes. The results indicate that under current policy implementation, the domestic ownership of EVs and CVs is projected to grow to 172.437 million and 433.362 million, respectively, by 2035, with the proportion of EV ownership in vehicles and energy consumption per thousand vehicles at 28.46% and 566,781 J·km−1, respectively. By increasing the technical and environmental factors by 40% and extending the preferential policies for purchasing new energy vehicles, domestic EV ownership is expected to increase to 201.276 million by 2035. This study provides data support for the government to formulate promotional policies and can also offer data support for the development of basic charging infrastructure. [ABSTRACT FROM AUTHOR]

The structural diversity of biological macromolecules in different environments contributes complexity to enzymological processes vital for cellular functions. Fluorescence resonance energy transfer and electron microscopy are used to investigate the enzymatic reaction of T4 DNA ligase catalyzing the ligation of nicked DNA. The data show that both the ligase–AMP complex and the ligase–AMP–DNA complex can have four conformations. This finding suggests the parallel occurrence of four ligation reaction pathways, each characterized by specific conformations of the ligase–AMP complex that persist in the ligase–AMP–DNA complex. Notably, these complexes have DNA bending angles of ≈0°, 20°, 60°, or 100°. The mechanism of parallel reactions challenges the conventional notion of simple sequential reaction steps occurring among multiple conformations. The results provide insights into the dynamic conformational changes and the versatile attributes of T4 DNA ligase and suggest that the parallel multiple reaction pathways may correspond to diverse T4 DNA ligase functions. This mechanism may potentially have evolved as an adaptive strategy across evolutionary history to navigate complex environments. [ABSTRACT FROM AUTHOR]

Objective: The Duhuo–Jisheng pair is the ruling herb in Duhuo Jisheng decoction, which is a classic formula first recorded in the preparedness and urgency of the thousand jewels. Methods: We obtained the primary constituents of Duhuo–Jisheng and their associated protein targets from the TCMSP database. We constructed a composite target network using Cytoscape 3.9.1. To identify potential targets for the treatment of osteoarthritis (OA), we retrieved disease targets from OMIM and GeneCards databases and compared them with the composite targets. We imported the overlapping targets into the STRING database to construct a protein–protein interaction (PPI) network. We also conducted Gene ontology (GO) and KEGG enrichment analyses on the targets. Results: The component target network consisted of numerous nodes and edges. Notably, quercetin, ammidin, and β-sitosterol were identified as the compounds with high degrees. The PPI network identified tumour necrosis factor (TNF), TP53, and NOS2 as proteins with high degrees. The results of GO and KEGG analyses revealed that the signalling pathways used by DHQJD to treat OA included the NF-κB, PI3K-AKT, and TNF pathways. Conclusion: Our study provides insights into the effective components and potential molecular mechanisms of Duhuo–Jisheng in treating OA, thus serving as a reference for further basic research in this field. [ABSTRACT FROM AUTHOR]

BACKGROUND: The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration. OBJECTIVE: This study aimed to compare the performance of handcrafted radiomics (HR) features and deep transfer learning (DTL) features in Computed Tomography (CT) of intratumoral and peritumoral regions in predicting the metastatic status of NSCLC lymph nodes in different machine learning classifier models. METHODS: We retrospectively collected data of 199 patients with pathologically confirmed NSCLC. All patients were divided into training (n = 159) and validation (n = 40) cohorts, respectively. The best HR and DTL features in the intratumoral and peritumoral regions were extracted and selected, respectively. Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Light Gradient Boosting Machine (Light GBM), Multilayer Perceptron (MLP), and Logistic Regression (LR) models were constructed, and the performance of the models was evaluated. RESULTS: Among the five models in the training and validation cohorts, the LR classifier model performed best in terms of HR and DTL features. The AUCs of the training cohort were 0.841 (95% CI: 0.776–0.907) and 0.955 (95% CI: 0.926–0.983), and the AUCs of the validation cohort were 0.812 (95% CI: 0.677–0.948) and 0.893 (95% CI: 0.795–0.991), respectively. The DTL signature was superior to the handcrafted radiomics signature. CONCLUSIONS: Compared with the radiomics signature, the DTL signature constructed based on intratumoral and peritumoral areas in CT can better predict NSCLC lymph node metastasis. [ABSTRACT FROM AUTHOR]

Accurate prediction of tibiofemoral contact force (TFCF) during daily living activities is significant for understanding the initiation, progression, and treatment of knee osteoarthritis (KOA). However, the diversity of target activities, prediction accuracy, and computational efficiency of the current musculoskeletal simulations need to be further improved. In this study, a subject-specific musculoskeletal model considered the tibiofemoral alignment, medial-lateral contact locations, secondary tibiofemoral and all patellofemoral motions, and knee ligaments was proposed to predict the TFCFs during the five daily activities (normal walking, sit-to-stand, stand-to-sit, stair ascent, and stair descent) in OpenSim software. The standing lower-limbs-full-length radiograph, local radiograph of knee joint, motion capture data, and force plate data of eighteen subjects were acquired as the input data of the musculoskeletal model. The results showed good agreements of TFCFs between the predictions based on our proposed musculoskeletal model and the in-vivo measurements based on instrumented knee implants during the five daily activities (RMSE: 0.16 ∼ 0.31 BW, R2: 0.88 ∼ 0.97, M: −0.11 ∼ −0.02, P: 0.03 ∼ 0.10, and C: 0.04 ∼ 0.14). Additionally, the order of the peak total and lateral TFCFs from low to high was normal walking, stair ascent and stand-to-sit, and stair descent and sit-to-stand (P < 0.05), and the peak medial TFCF was stand-to-sit, sit-to-stand, normal walking, stair ascent and stair descent (P < 0.05). The outcomes of this study are valuable for further understanding the knee biomechanics during daily living activities and providing theoretical guidance for the treatments of KOA. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA), the most common type of arthritis, is an age-associated disease, characterized by the progressive degradation of articular cartilage, synovial inflammation, and degeneration of subchondral bone. Chondrocyte proliferation is regulated by the Indian hedgehog (IHH in humans, Ihh in animals) signaling molecule, which regulates hypertrophy and endochondral ossification in the development of the skeletal system. microRNAs (miRNAs, miRs) are a family of about 22-nucleotide endogenous non-coding RNAs, which negatively regulate gene expression. In this study, the expression level of IHH was upregulated in the damaged articular cartilage tissues among OA patients and OA cell cultures, while that of miR-199a-5p was the opposite. Further investigations demonstrated that miR-199a-5p could directly regulate IHH expression and reduce chondrocyte hypertrophy and matrix degradation via the IHH signal pathway in the primary human chondrocytes. The intra-articular injection of synthetic miR-199a-5p agomir attenuated OA symptoms in rats, including the alleviation of articular cartilage destruction, subchondral bone degradation, and synovial inflammation. The miR-199a-5p agomir could also inhibit the Ihh signaling pathway in vivo. This study might help in understanding the role of miR-199a-5p in the pathophysiology and molecular mechanisms of OA and indicate a potential novel therapeutic strategy for OA patients. [ABSTRACT FROM AUTHOR]

Background. A vast number of research papers are published every day on PubMed, making it difficult for scientists to retrieve relevant articles in a timely manner. Keyword-based searches are currently the most popular method, but determining a suitable set of keywords can be challenging. Moreover, searches based on keywords typically retrieve many irrelevant papers. We developed a natural language processing (NLP)-based keyword augmentation and screening (NKAS) method to help scientists easily refine their keywords in topic searches. This method can extract meaningful candidate keywords from the titles and abstracts of an initial search using prior knowledge, knowledge graphs, and machine learning. The method was tested on three atrial fibrillation topics. When the NKAS was applied, the number of remaining papers was less than those in the original search but showed much higher precision (73.83% vs. 34.6%) and recall (98.4% vs. 59.93%) compared with those of the original search results. In conclusion, the NKAS method showed that NLP and other artificial intelligence techniques can help enhance both the search comprehensiveness and accuracy. These results suggest a great potential for the application of artificial intelligence methods in medical publication searches and other text-based applications. [ABSTRACT FROM AUTHOR]

Two mapping populations were developed from crosses of the Asian indica rice (Oryza sativa L.) cultivar 'Dee Geo Woo Gen' (DGWG; PI 699210 Parent, PI 699212 Parent) and two weedy rice ecotypes, an early‐flowering straw hull (SH) biotype AR‐2000‐1135‐01 (PI 699209 Parent) collected in Arkansas and a late‐flowering black hull (BHA) biotype MS‐1996‐9 (PI 699211 Parent) collected in Mississippi. The weed and crop‐based rice recombinant inbred line (RIL) mapping populations have been used to identify genomic regions associated with weedy traits as well as resistance to sheath blight and rice blast diseases. The mapping population consists of 185 (DGWG/SH; Reg. no. MP‐9, NSL 541035 MAP) and 234 (BHA/DGWG; Reg. no. MP‐10, NSL 541036 MAP) F8 RILs, of which 175 (DGWG/SH) and 224 (BHA/DGWG) were used to construct two linkage maps using single nucleotide polymorphic markers to identify weedy traits, sheath blight, and blast resistance loci. These mapping populations and related datasets represent a valuable resource for basic rice evolutionary genomic research and applied marker‐assisted breeding efforts in disease resistance. Core Ideas: Weedy rice competing with cultivated rice is a major pest.Weedy rice can benefit germplasm improvement and weed control.Two contrasting crop–weed populations are ideal for basic evolutionary genomic research.Abundant genotypic and phenotypic data are ready for the users to analyze.SNP‐linked traits are useful for marker assisted breeding. [ABSTRACT FROM AUTHOR]

BACKGROUND: Acoustic Radiation Force Impulse (ARFI), Fibrosis-4(FIB-4) and Aspartate transaminase to platelet ratio index (APRI) are valuable non-invasive methods to evaluate fibrosis in hepatitis virus. Yet, they are rarely used in Wilson's disease. OBJECTIVE: Evaluate the diagnostic efficacy of ARFI, FIB-4, APRI, combined detection in cirrhosis with WD, and speculate the optimal high, low cutoff. METHODS: This retrospective study was authorized by hospital ethics Committee (number:2021MCZQ02). 102 patients with WD completed ARFI and laboratory examination on the same day. The intraclass correlation coeffcient (ICC) of ARFI among three sonographers was 0.896 (95%CI:0.859–0.925, p = 0.000). The stage of liver involvement was classified into 5 categories according to clinical manifestations, laboratory examination, and liver morphologic characteristics: I, normal; II, biochemical abnormal only; III, abnormal liver morphologic features without sighs of cirrhosis; IV, clinical and imaging sighs of compensateded cirrhosis (Child-Pugh A); V, decompensated cirrhosis (Child-Pugh B and C). This stage system served as the reference standard. The diagnostic efficacy was analyzed by Logistic regression, ROC curve. The optimal low cut-off with high sensitivity (SE) and low negative likelihood ratio (NLR) and high cut-off with high specificity (SP) and positive likelihood ratio (PLR) were derived. RESULTS: The diagnostic value of ARFI (0.85, 95%CI:0.77–0.92, p = 0.000) in distiguishing cirrhosis with WD was higher than FIB-4 (0.59, 95%CI: 0.47–0.70, p = 0.127), APRI (0.70, 95%CI: 0.59–0.81, p = 0.000). The low, high cut-off of ARFI for excluding, diagnosing cirrhosis with WD was 1.47 m/s(SE: 98%, NLR:0.09), 2.11 m/s(SP:98%, PLR:27.4), 37 (36%) patients could be spared a liver biopsy. When ARFI was 1.47∼2.11 m/s, liver biopsy was recommended. After combined with ARFI, the AUROC of FIB-4, APRI were increased respectively (p < 0.001), there were not different between ARFI and combined detection(p > 0.05). CONCLUSION: ARFI could replace some unnecessary liver biopsy according to high diagnostic efficacy for identifying cirrhosis of WD. The combined detection can also be used as an important model to predict cirrhosis in WD. [ABSTRACT FROM AUTHOR]

In recent years, the booming development of big data, cloud computing, Internet of Things, and other technologies provides conditions for the popularization and application of smart city. The combination of big data and medical information produces the emerging field of WITMED (Wise Information Technology of Med). WITMED is essential for the prospering growth of smart cities, which assumed a high quality of medical service is the most challenging goal for the city government. In this paper, the main attention is paid to the method of targeted gene therapy, which provides a new method for the treatment of oral cancer in inhibiting the growth, differentiation, invasion, and metastasis of oral cancer cells; therefore, the physical and psychological adverse effects of surgery and chemotherapy on patients are reduced and the survival and prognosis of patients are improved. Targeted gene therapy methods need to select the appropriate gene; that is, data mining methods are used to analyze a large number of complex genetic data from smart cities to obtain appropriate genetic markers, which makes the effect of targeted gene therapy better, and also provide some reference for the research of oral cancer gene direction and provide some basis for clinical treatment. [ABSTRACT FROM AUTHOR]