Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small- and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 - Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small- and medium-scale CBIR systems with inadequate samples

Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment

Word alignment which aims to extract lexicon translation equivalents between source and target sentences, serves as a fundamental tool for natural language processing. Recent studies in this area have yielded substantial improvements by generating alignments from contextualized embeddings of the pre-trained multilingual language models. However, we find that the existing approaches capture few interactions between the input sentence pairs, which degrades the word alignment quality severely, especially for the ambiguous words in the monolingual context. To remedy this problem, we propose Cross-Align to model deep interactions between the input sentence pairs, in which the source and target sentences are encoded separately with the shared self-attention modules in the shallow layers, while cross-lingual interactions are explicitly constructed by the cross-attention modules in the upper layers. Besides, to train our model effectively, we propose a two-stage training framework, where the model is trained with a simple Translation Language Modeling (TLM) objective in the first stage and then finetuned with a self-supervised alignment objective in the second stage. Experiments show that the proposed Cross-Align achieves the state-of-the-art (SOTA) performance on four out of five language pairs.Comment: Accepted by EMNLP 202

The Edge Effects Boosting Hydrogen Evolution Performance of Platinum/Transition Bimetallic Phosphide Hybrid Electrocatalysts

Platinum (Pt) is regarded as a promising electrocatalyst for hydrogen evolution reaction (HER). However, its application in an alkaline medium is limited by the activation energy of water dissociation, diffusion of H+, and desorption of H*. Moreover, the formation of effective structures with a low Pt usage amount is still a challenge. Herein, guided by the simulation discovery that the edge effect can boost local electric field (LEF) of the electrocatalysts for faster proton diffusion, platinum nanocrystals on the edge of transition metal phosphide nanosheets are fabricated. The unique heterostructure with ultralow Pt amount delivered an outstanding HER performance in an alkaline medium with a small overpotential of 44.5 mV and excellent stability for 80 h at the current density of −10 mA cm−2. The mass activity of as-prepared electrocatalyst is 2.77 A mg−1Pt, which is 15 times higher than that of commercial Pt/C electrocatalysts (0.18 A mg−1Pt). The density function theory calculation revealed the efficient water dissociation, fast adsorption, and desorption of protons with hybrid structure. The study provides an innovative strategy to design unique nanostructures for boosting HER performances via achieving both synergistic effects from hybrid components and enhanced LEF from the structural edge effect

Identification of pathogenic mutations for a Wolfram syndrome pedigree by whole exome sequencing and analysis of its clinical characteristics

Objective·To identify the causative gene and mutations and describe the clinical traits in a Chinese diabetes pedigree suspected of Wolfram syndrome.Methods·A total of 12 subjects from one family were included. The proband was admitted to the Department of Endocrinology, The First Affiliated Hospital of Xinxiang Medical University, for the first time in May 2013. Then he visited the hospital for follow-up in July 2022 and in April 2023, respectively. The other members of this family included the proband′s sister, father, mother, paternal grandfather, paternal grandmother, uncle, aunt, as well as maternal grandfather, maternal grandmother, and two brothers of the proband′s mother. Clinical data of all subjects were collected. The whole exome sequencing was used to screen the pathogenic genes and mutation sites of six members of the family, and Sanger sequencing was used to verify the above results. Effects of the mutation of the pathogenic gene WFS1 in Wolfram syndrome on the function of the wolframin protein were evaluated by bioinformatics softwares, including CADD, DANN, MetaSVM, Polyphen-2, SIFT and M-CAP. The three-dimensional structures of wild-type and mutant wolframin proteins were constructed with Swiss-Model software, and visualized with PyMOL software. Cluster Omega software was used for evaluating species conservation of WFS1 gene mutation sites. JNetPRED software was used for online prediction of wolframin protein secondary structure.Results·The proband and his sister both carried R558H and S411Cfs*131 mutations, two compound heterozygous mutations of the Wolfram syndrome pathogenic gene WFS1. The proband′s father and parental grandfather both carried the R558H mutation, while the proband′s mother and maternal grandfather both carried the S411Cfs*131 mutation. The R558H mutation was a rare missense mutation, and the S411Cfs*131 mutation was a novel frameshift mutation. Bioinformatics analysis softwares predicted that the R558H mutation located in the α-helical structure of the wolframin protein. This mutation was a damage mutation and the amino acid sequence of the mutation region was highly conservative among 12 species with varying degrees of evolution, including humans.Conclusion·Two causative mutations of WFS1 gene are identified in a Chinese diabetes pedigree by whole exome sequencing. The study supplements the existing genotype and phenotype profiles of Wolfram syndrome, which can realize early diagnosis of diabetes pedigrees and help in performing timely follow-up of patients, so as to achieve early intervention and treatment of this disease

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe