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UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides.

BMC Bioinformatics

January 2025

College of Artificial Intelligence, Nanjing Agricultural University, Weigang No.1, Nanjing, 210095, Jiangsu, China.

Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework for discovering AMPs. We observe that feature vectors used in various existing studies constructed from peptide information, such as sequence, composition, and structure, can be augmented and even replaced by information inferred by deep learning models.

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Pseudomeningoceles are among the most common postoperative neurosurgical complications, usually presenting in the early postoperative period and often responding well to nonsurgical management. Here, we present a case of a giant cranial pseudomeningocele that developed three years after parasagittal meningioma resection, without any known risk factors. Despite conservative measures, the pseudomeningocele grew significantly over two years, reaching 22 cm along its long axis.

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Background: Early diagnosis of syphilis is vital for its effective control. This study aimed to develop an Artificial Intelligence (AI) diagnostic model based on radiomics technology to distinguish early syphilis from other clinical skin lesions.

Methods: The study collected 260 images of skin lesions caused by various skin infections, including 115 syphilis and 145 other infection types.

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DICCR: Double-gated intervention and confounder causal reasoning for vision-language navigation.

Neural Netw

December 2024

School of Computer and Electronic Information, Guangxi University, University Road, Nanning, 530004, Guangxi, China. Electronic address:

Vision-language navigation (VLN) is a challenging task that requires agents to capture the correlation between different modalities from redundant information according to instructions, and then make sequential decisions on visual scenes and text instructions in the action space. Recent research has focused on extracting visual features and enhancing text knowledge, ignoring the potential bias in multi-modal data and the problem of spurious correlations between vision and text. Therefore, this paper studies the relationship structure between multi-modal data from the perspective of causality and weakens the potential correlation between different modalities through cross-modal causality reasoning.

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DUO1 Activated Zinc Finger (AtDAZ) protein role in the generative cell body morphogenesis.

Plant Mol Biol

January 2025

National Key Laboratory for Tropical Crop Breeding, Tropical Crop Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Sanya, Haikou, 572024/571101, Hainan, China.

Arabidopsis MYB transcription factor, AtDUO1 regulates generative cell body (GC) morphogenesis from round to semi and fully elongated forms before pollen mitosis-II (PM II). It was hypothesised that DUO1 might regulate morphogenesis through any of its direct target genes or components of the DUO1-DAZ1 network. The developmental analysis of plants harbouring T-DNA insertions in some DUO1 target genes using light and fluorescence microscopy revealed abnormal GC morphogenesis only in daz1 and daz2, but gcs1, trm16, mapkkk10, mapkkk20, tet11, and tip1 all undergo normal elongation indicating that these target genes have no important roles in morphogenesis or may be redundant.

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