FGF: A web tool for Fishing Gene Family in a whole genome database

Gene duplication is an important process in evolution. The availability of genome sequences of a number of organisms has made it possible to conduct comprehensive searches for duplicated genes enabling informative studies of their evolution. We have established the FGF (Fishing Gene Family) program to efficiently search for and identify gene families. The FGF output displays the results as visual phylogenetic trees including information on gene structure, chromosome position, duplication fate and selective pressure. It is particularly useful to identify pseudogenes and detect changes in gene structure. FGF is freely available on a web server at http://fgf.genomics.org.cn

Effects of different altitudes on the structure and properties of potato starch

The main element influencing the quality of potato starch is the environment. To investigate the effects of different altitude cultivation locations on the molecular structure and physicochemical properties of starch, two potato varieties, Jiusen No.1 B1 and Qingshu No.9 B2, were planted in three different altitude zones: A1 at low altitude (Chongzhou 450 m), A2 at middle altitude (Xichang 2800 m), and A3 at high altitude (Litang 3650 m). The results showed that the average volume, number, surface area diameter, average branched polymerization degree, crystallinity, and gelatinization temperature of two potato granules in high altitude areas were significantly lower than those in middle and low altitude areas were, and the gelatinization performance of potato starch was affected according to the correlation of starch structure characteristics. Potato starch with more short-branched chains and less long branched chains resulted in a lower gelatinization temperature in high altitude areas. The results showed that Jiusen No. 1 and Qingshu No. 9 were mainly affected by accumulated radiation and accumulated rainfall in Litang, a high altitude area, and by effective accumulated temperature in Xichang, a middle altitude area. This study quantified the influence of meteorological factors on the main starch quality of potato tubers. The results can be used as a theoretical basis for the scientific planting of high-quality potatoes

Surgical intervention combined with weight-bearing walking training improves neurological recoveries in 320 patients with clinically complete spinal cord injury: a prospective self-controlled study

Although a large number of trials in the SCI field have been conducted, few proven gains have been realized for patients. In the present study, we determined the efficacy of a novel combination treatment involving surgical intervention and long-term weight-bearing walking training in spinal cord injury (SCI) subjects clinically diagnosed as complete or American Spinal Injury Association Impairment Scale (AIS) Class A (AIS-A). A total of 320 clinically complete SCI subjects (271 male and 49 female), aged 16-60 years, received early (≤ 7 days, n = 201) or delayed (8-30 days, n = 119) surgical interventions to reduce intraspinal or intramedullary pressure. Fifteen days post-surgery, all subjects received a weight-bearing walking training with the "Kunming Locomotion Training Program (KLTP)" for a duration of 6 months. The neurological deficit and recovery were assessed using the AIS scale and a 10-point Kunming Locomotor Scale (KLS). We found that surgical intervention significantly improved AIS scores measured at 15 days post-surgery as compared to the pre-surgery baseline scores. Significant improvement of AIS scores was detected at 3 and 6 months and the KLS further showed significant improvements between all pair-wise comparisons of time points of 15 days, 3 or 6 months indicating continued improvement in walking scores during the 6-month period. In conclusion, combining surgical intervention within 1 month post-injury and weight-bearing locomotor training promoted continued and statistically significant neurological recoveries in subjects with clinically complete SCI, which generally shows little clinical recovery within the first year after injury and most are permanently disabled. This study was approved by the Science and Research Committee of Kunming General Hospital of PLA and Kunming Tongren Hospital, China and registered at ClinicalTrials.gov (Identifier: NCT04034108) on July 26, 2019

The diploid genome sequence of an Asian individual

Here we present the first diploid genome sequence of an Asian individual. The genome was sequenced to 36-fold average coverage using massively parallel sequencing technology. We aligned the short reads onto the NCBI human reference genome to 99.97% coverage, and guided by the reference genome, we used uniquely mapped reads to assemble a high-quality consensus sequence for 92% of the Asian individual's genome. We identified approximately 3 million single-nucleotide polymorphisms (SNPs) inside this region, of which 13.6% were not in the dbSNP database. Genotyping analysis showed that SNP identification had high accuracy and consistency, indicating the high sequence quality of this assembly. We also carried out heterozygote phasing and haplotype prediction against HapMap CHB and JPT haplotypes (Chinese and Japanese, respectively), sequence comparison with the two available individual genomes (J. D. Watson and J. C. Venter), and structural variation identification. These variations were considered for their potential biological impact. Our sequence data and analyses demonstrate the potential usefulness of next-generation sequencing technologies for personal genomics

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

A Hybrid Extragradient-Like Method for Variational Inequalities, Equilibrium Problems, and an Infinitely Family of Strictly Pseudocontractive Mappings

The purpose of this paper is to consider a new scheme by the hybrid extragradient-like method for finding a common element of the set of solutions of a generalized mixed equilibrium problem, the set of solutions of a variational inequality, and the set of fixed points of an infinitely family of strictly pseudocontractive mappings in Hilbert spaces. Then, we obtain a strong convergence theorem of the iterative sequence generated by the proposed iterative algorithm. Our results extend and improve the results of Issara Inchan (2010) and many others

Analysis of Learning Behavior Characteristics and Prediction of Learning Effect for Improving College Students’ Information Literacy Based on Machine Learning

Information literacy is a basic ability for college students to adapt to social needs at present, and it is also a necessary quality for self-learning and lifelong learning. It is an effective way to reveal the information literacy teaching mechanism to use the rich and diverse information literacy learning behavior characteristics to carry out the learning effect prediction analysis. This paper analyzes the characteristics of college students’ learning behaviors and explores the predictive learning effect by constructing a predictive model of learning effect based on information literacy learning behavior characteristics. The experiment used 320 college students’ information literacy learning data from Chinese university. Pearson algorithm is used to analyze the learning behavior characteristics of college students’ information literacy, revealing that there is a significant correlation between the characteristics of information thinking and learning effect. The supervised classification algorithms such as Decision Tree, KNN, Naive Bayes, Neural Net and Random Forest are used to classify and predict the learning effect of college students’ information literacy. It is determined that the Random Forest prediction model has the best performance in the classification prediction of learning effect. The value of Accuracy is 92.50%, Precision is 84.56%, Recall is 94.81%, F1-Score is 89.39%, and Kapaa coefficient is 0.859. This paper puts forward differentiated intervention suggestions and management decision-making reference in the information literacy teaching process of college students, with a view to adjusting the information literacy teaching behavior, improving the information literacy teaching quality, optimizing educational decision-making, and promoting the sustainable development of high-quality and innovative talents in the information society.Our work involving research of the thinking and direction of the sustainable development of information literacy training proved to be encouraging

ScoreSeg: Leveraging Score-Based Generative Model for Self-Supervised Semantic Segmentation of Remote Sensing

The performance of semantic segmentation of remote sensing images (RSIs) heavily depends on the number of pixel-level annotations. In practice, the accumulation of pixel-level annotations for large RSIs is quite expensive or even impossible under certain scenarios. Here, we try to solve this data-intensive problem from the novel aspect of score-based self-supervise learning (SSL) and introduce a robust RSI semantic segmentation model called ScoreSeg. Unlike traditional pixel-level SSL paradigms, the generative SSL mechanism in ScoreSeg is simple in loss design and stable in pretraining, granting it an indispensable ability in dense feature learning from very large RSIs. In the model implementation, ScoreSeg first extracts pixelwise representations of RSIs by pretraining a time-dependent score-based model on abundant off-the-shelf unlabeled RSIs. Then, to address the sparse feature problem in RSIs, the collected features from different timesteps and resolutions are aggregated together forming a rich feature map for downstream semantic segmentation. Experimental results on three datasets show that our proposed ScoreSeg outperforms state-of-the-art (SOTA) SSL methods and alternative pretraining models on ImageNet by nontrivial margins, especially with very limited annotations