A new loach species of the genus Oreonectes (Teleostei, Cypriniformes, Nemacheilidae) from Guangxi, China.

A new loach species, Oreonectesandongensissp. nov. is described from the Guangxi Zhuang Autonomous Region, China. The new species can be differentiated from other members of the genus by combinations of characters: a developed posterior chamber of the swim bladder, 13-14 branched caudal-fin rays, 8-16 lateral-line pores, body width 12-15% of standard length (SL), interorbital width 42-47% of head length (HL), and caudal peduncle length 11-16% of SL. Bayesian inference phylogenetic analysis based on mitochondrial Cyt b provided strong support for validity of O.andongensissp. nov. (uncorrected p-distance 6.0-7.5%).

Identifying potential drug-target interactions based on ensemble deep learning.

Introduction: Drug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious. Methods: In this study, we developed a novel DTI prediction method called EnGDD by combining initial feature acquisition, dimensional reduction, and DTI classification based on Gradient boosting neural network, Deep neural network, and Deep Forest. Results: EnGDD was compared with seven stat-of-the-art DTI prediction methods (BLM-NII, NRLMF, WNNGIP, NEDTP, DTi2Vec, RoFDT, and MolTrans) on the nuclear receptor, GPCR, ion channel, and enzyme datasets under cross validations on drugs, targets, and drug-target pairs, respectively. EnGDD computed the best recall, accuracy, F1-score, AUC, and AUPR under the majority of conditions, demonstrating its powerful DTI identification performance. EnGDD predicted that D00182 and hsa2099, D07871 and hsa1813, DB00599 and hsa2562, D00002 and hsa10935 have a higher interaction probabilities among unknown drug-target pairs and may be potential DTIs on the four datasets, respectively. In particular, D00002 (Nadide) was identified to interact with hsa10935 (Mitochondrial peroxiredoxin3) whose up-regulation might be used to treat neurodegenerative diseases. Finally, EnGDD was used to find possible drug targets for Parkinson's disease and Alzheimer's disease after confirming its DTI identification performance. The results show that D01277, D04641, and D08969 may be applied to the treatment of Parkinson's disease through targeting hsa1813 (dopamine receptor D2) and D02173, D02558, and D03822 may be the clues of treatment for patients with Alzheimer's disease through targeting hsa5743 (prostaglandinendoperoxide synthase 2). The above prediction results need further biomedical validation. Discussion: We anticipate that our proposed EnGDD model can help discover potential therapeutic clues for various diseases including neurodegenerative diseases.

A deep ensemble learning-based automated detection of COVID-19 using lung CT images and Vision Transformer and ConvNeXt.

Since the outbreak of COVID-19, hundreds of millions of people have been infected, causing millions of deaths, and resulting in a heavy impact on the daily life of countless people. Accurately identifying patients and taking timely isolation measures are necessary ways to stop the spread of COVID-19. Besides the nucleic acid test, lung CT image detection is also a path to quickly identify COVID-19 patients. In this context, deep learning technology can help radiologists identify COVID-19 patients from CT images rapidly. In this paper, we propose a deep learning ensemble framework called VitCNX which combines Vision Transformer and ConvNeXt for COVID-19 CT image identification. We compared our proposed model VitCNX with EfficientNetV2, DenseNet, ResNet-50, and Swin-Transformer which are state-of-the-art deep learning models in the field of image classification, and two individual models which we used for the ensemble (Vision Transformer and ConvNeXt) in binary and three-classification experiments. In the binary classification experiment, VitCNX achieves the best recall of 0.9907, accuracy of 0.9821, F1-score of 0.9855, AUC of 0.9985, and AUPR of 0.9991, which outperforms the other six models. Equally, in the three-classification experiment, VitCNX computes the best precision of 0.9668, an accuracy of 0.9696, and an F1-score of 0.9631, further demonstrating its excellent image classification capability. We hope our proposed VitCNX model could contribute to the recognition of COVID-19 patients.

The Dilemma of Service Productivity and Service Innovation: An Empirical Exploration in Financial Services.

We conduct a systematic exploratory investigation of the effects of firms' existing service productivity on the success of their new service innovations. Although previous research extensively addresses service productivity and service innovation, this is the first empirical study that bridges the gap between these two research streams and examines the links between the two concepts. Based on a comprehensive data set of new service introductions in a financial services market over a 14-year period, we empirically explore the relationship between a firm's existing service productivity and the firm's success in introducing new services to the market. The results unveil a fundamental service productivity-service innovation dilemma: Being productive in existing services increases a firm's willingness to innovate new services proactively but decreases the firm's capabilities of bringing these services to the market successfully. We provide specific insights into the mechanism underlying the complex relationship between a firm's productivity in existing services, its innovation proactivity, and its service innovation success. For managers, we not only unpack and elucidate this dilemma but also demonstrate that a focused customer scope and growth market conditions may enable firms to mitigate the dilemma and successfully pursue service productivity and service innovation simultaneously.