RESULTS: Here, we present draft genome information for five agriculturally, biologically, medicinally, and economically important underutilized plants native to Africa: Vigna subterranea, Lablab purpureus, Faidherbia albida, Sclerocarya birrea, and Moringa oleifera. Assembled genomes range in size from 217 to 654 Mb. In V. subterranea, L. purpureus, F. albida, S. birrea, and M. oleifera, we have predicted 31,707, 20,946, 28,979, 18,937, and 18,451 protein-coding genes, respectively. By further analyzing the expansion and contraction of selected gene families, we have characterized root nodule symbiosis genes, transcription factors, and starch biosynthesis-related genes in these genomes.
CONCLUSIONS: These genome data will be useful to identify and characterize agronomically important genes and understand their modes of action, enabling genomics-based, evolutionary studies, and breeding strategies to design faster, more focused, and predictable crop improvement programs.
METHODS: Eight electronic databases (Web of Science, PubMed, ScienceDirect, American Psychological Association PsycNet, Cochrane Library, Scopus, Embase, and Ovid) were searched for the study. Articles published from January 1 to December 31, 2022, were considered for this review. A random-effects meta-analysis and between-study heterogeneity analysis were conducted using Comprehensive Meta-Analysis V3.0 software.
RESULTS: We identified 7829 articles of which 28 met the full inclusion criteria and were included in the systematic review and analyses. Our pooled analysis suggested that participants with MCI can be differentiated from HC by significant P200, P300, and N200 latencies. The P100 and P300 amplitudes were significantly smaller in participants with MCI when compared with those in the HCs, and the patients with MCI showed increased N200 amplitudes. Our findings provide new insights into potential electrophysiological biomarkers for diagnosing MCI.
METHODS: This study aimed to compile and synthesize the existing studies on the effects of PT on healthy athletes' technical skill performance. A comprehensive search of SCOPUS, PubMed, Web of Science Core Collection, and SPORTDiscus databases was performed on 3rd May 2023. PICOS was employed to establish the inclusion criteria: 1) healthy athletes; 2) a PT program; 3) compared a plyometric intervention to an active control group; 4) tested at least one measure of athletes' technical skill performance; and 5) randomized control designs. The methodological quality of each individual study was evaluated using the PEDro scale. The random-effects model was used to compute the meta-analyses. Subgroup analyses were performed (participant age, gender, PT length, session duration, frequency, and number of sessions). Certainty or confidence in the body of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE).
RESULTS: Thirty-two moderate-high-quality studies involving 1078 athletes aged 10-40 years met the inclusion criteria. The PT intervention lasted for 4 to 16 weeks, with one to three exercise sessions per week. Small-to-moderate effect sizes were found for performance of throwing velocity (i.e., handball, baseball, water polo) (ES = 0.78; p < 0.001), kicking velocity and distance (i.e., soccer) (ES = 0.37-0.44; all p < 0.005), and speed dribbling (i.e., handball, basketball, soccer) (ES = 0.85; p = 0.014), while no significant effects on stride rate (i.e., running) were noted (ES = 0.32; p = 0.137). Sub-analyses of moderator factors included 16 data sets. Only training length significantly modulated PT effects on throwing velocity (> 7 weeks, ES = 1.05; ≤ 7 weeks, ES = 0.29; p = 0.011). The level of certainty of the evidence for the meta-analyzed outcomes ranged from low to moderate.
CONCLUSION: Our findings have shown that PT can be effective in enhancing technical skills measures in youth and adult athletes. Sub-group analyses suggest that PT longer (> 7 weeks) lengths appear to be more effective for improving throwing velocity. However, to fully determine the effectiveness of PT in improving sport-specific technical skill outcomes and ultimately enhancing competition performance, further high-quality research covering a wider range of sports is required.
METHODS: In order to solve this problem, this paper proposes an end-to-end framework based on BERT for NER and RE tasks in electronic medical records. Our framework first integrates NER and RE tasks into a unified model, adopting an end-to-end processing manner, which removes the limitation and error propagation of multiple independent steps in traditional methods. Second, by pre-training and fine-tuning the BERT model on large-scale electronic medical record data, we enable the model to obtain rich semantic representation capabilities that adapt to the needs of medical fields and tasks. Finally, through multi-task learning, we enable the model to make full use of the correlation and complementarity between NER and RE tasks, and improve the generalization ability and effect of the model on different data sets.
RESULTS AND DISCUSSION: We conduct experimental evaluation on four electronic medical record datasets, and the model significantly out performs other methods on different datasets in the NER task. In the RE task, the EMLB model also achieved advantages on different data sets, especially in the multi-task learning mode, its performance has been significantly improved, and the ETE and MTL modules performed well in terms of comprehensive precision and recall. Our research provides an innovative solution for medical image and signal data.