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Frog-derived synthetic peptides display anti-infective activity against Gram-negative pathogens

Trends in Biotechnology 2025年

作者： Ageitos, Lucía Boaro, Andreia Cesaro, Angela Torres, Marcelo D.T. Broset, Esther de la Fuente-Nunez, Cesar Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Penn Institute for Computational Science University of Pennsylvania PhiladelphiaPA United States

Novel antibiotics are urgently needed since bacteria are becoming increasingly resistant to existing antimicrobial drugs. Furthermore, available antibiotics are broad spectrum, often causing off-target effects on host cells and the beneficial microbiome. To overcome these limitations, we used structure-guided design to generate synthetic peptides derived from Andersonin-D1, an antimicrobial peptide (AMP) produced by the odorous frog Odorrana andersonii. We found that both hydrophobicity and net charge were critical for its bioactivity, enabling the design of novel, optimized synthetic peptides. These peptides selectively targeted Gram-negative pathogens in single cultures and complex microbial consortia, showed no off-target effects on human cells or beneficial gut microbes, and did not select for bacterial resistance. Notably, they also exhibited in vivo activity in two preclinical murine models. Overall, we present synthetic peptides that selectively target pathogenic infections and offer promising preclinical antibiotic candidates. © 2025 The Authors

关键词： Invertebrates

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High-Efficiency Photo-Induced Charge Transfer for SERS Sensing in N-Doped 3D-Graphene on Si Heterojunction

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Energy & Environmental Materials 2024年第2期7卷 268-276页

作者： Guanglin Zhang Jie Sun Genwang Wei Shan Zhang Zhengyi He Huijuan Wu Bingkun Wang Siwei Yang Guqiao Ding Zhiduo Liu Zhiwen Jin Caichao Ye Gang Wang Department of Microelectronic Science and Engineering School of Physical Science and TechnologyNingbo UniversityNingbo 315211China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen UniversityShenzhen 518071China Academy for Advanced Interdisciplinary Studies&Department of Materials Science and Engineering Guangdong Provincial Key Laboratory of Computational Science and Material DesignSouthern University of Science and TechnologyShenzhen 518055China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)School of PhysicsBeijing Institute of TechnologyBeijing 100081China School of Physical Science and Technology&Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education Lanzhou UniversityLanzhou 730000China

Nitrogen-doped three-dimensional graphene(N-doped 3D-graphene)is a graphene derivative with excellent adsorption capacity,large specific surface area,high porosity,and optoelectronic ***,N-doped 3D-graphene/Si heterojunctions were grown in situ directly on silicon(Si)substrates via plasma-assisted chemical vapor deposition(PACVD),which is promising for surface-enhanced Raman scattering(SERS)substrates *** analyses of theoretical simulation,incorporating N atoms in 3D-graphene are beneficial to increase the electronic state density of the system and enhance the charge transfer between the substrate and the target *** enhancement of the optical and electric fields benefits from the stronger light-matter interaction improved by the natural nano-resonator structure of N-doped *** as-prepared SERS substrates based on N-doped 3D-graphene/Si heterojunctions achieve ultra-low detection for various molecules:10^(-8)M for methylene blue(MB)and 10^(-9)M for crystal violet(CRV)with rhodamine(R6G)of 10^(10)*** practical detected,10^(-8)M thiram was precisely detected in apple peel *** results indicate that N-doped 3D-graphene/Si heterojunctions based-SERS substrates have promising applications in low-concentration molecular detection and food safety.

关键词： charge transfer DFT simulation N-doped 3D-graphene SERS Thiram detection

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Orchard: building large cancer phylogenies using stochastic combinatorial search

arXiv

引用

arXiv 2023年

作者： Kulman, Ethan Kuang, Rui Morris, Quaid Computational and Systems Biology Program Sloan Kettering Institute New YorkNY United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN United States

Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point mutations detected with bulk DNA sequencing. However, these methods become inaccurate when reconstructing phylogenies with more than 30 mutations, or, in some cases, fail to recover a phylogeny altogether. Here, we introduce Orchard, a cancer phylogeny reconstruction algorithm that is fast and accurate using up to 1000 mutations. Orchard samples without replacement from a factorized approximation of the posterior distribution over phylogenies, a novel result derived in this paper. Each factor in this approximate posterior corresponds to a conditional distribution for adding a new mutation to a partially built phylogeny. Orchard optimizes each factor sequentially, generating a sequence of incrementally larger phylogenies that ultimately culminate in a complete tree containing all mutations. Our evaluations demonstrate that Orchard outperforms state-of-the-art cancer phylogeny reconstruction methods in reconstructing more plausible phylogenies across 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). Remarkably, Orchard accurately reconstructs cancer phylogenies using up to 1,000 mutations. Additionally, we demonstrate that the large and accurate phylogenies reconstructed by Orchard are useful for identifying patterns of somatic mutations and genetic variations among distinct cancer cell subpopulations. © 2023, CC BY.

关键词： Plant diseases

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User-experience oriented POI recommendation with pseudo rating

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Multimedia Tools and Applications 2024年 1-21页

作者： Yi, Kun Yamagishi, Ryu Li, Jiyi Li, Taishan Bai, Zhengyang Ma, Qiang Kyoto Institute of Economic Research Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Graduate School of Informatics Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Department of Computer Science and Engineering University of Yamanashi Takeda 4-3-11 Kofu 400- 8511 Yamanashi Japan Research Institute of Non-Timber Forestry Chinese Academy of Forestry Key Laboratory of Non-Timber Forest Germplasm Enhancement & ampUtilization of State Administration of Forestry and Grassland No. 3 Weiwu Road Jinshui District Zhengzhou450003 China RIKEN Center for Computational Science RIKEN Nihonbashi 1-chome Mitsubishi Bld. 15th floor 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Information Science Kyoto Institute of Technology Matsugasaki Sakyo-ku Kyoto Kyoto606-8585 Japan

Point-of-interest (POI) recommendation faces challenges in managing data sparsity and cold-start problems, particularly in terms of personalized recommendation. Firstly, most existing users only visit a small number of POIs, which results in severe data sparsity and makes most conventional methods unsuitable because they require adequate historical data from each user to learn the model. In addition, new users have little or no historical record. Existing ask-to-rate methods for cold start user scenarios treat user answers as historical data, which is inappropriate and causes difficulty in capturing user preferences. Therefore, in this paper, to tackle the data sparsity problem, we propose a user-experience model (UEM) to uncover user behaviors from several aspects. The proposed UEM involves four factors: Who (does it), What (the user does), Where (the user does it), and When (the user does it). In contrast to other methods that model user behavior from individual records, our model introduces a latent variable of user experience to extract similarity among users. This feature makes our model suitable for mitigating the severe sparse-data issue in POI recommendations. Furthermore, to handle the cold-start problem for new users, we propose a pseudo-rating mechanism (PRM) to capture new users’ preferences and recommend POIs. For new users, photos are provided for rating, and these ratings are then used to capture their preferences and estimate the probability of their visits. Extensive experimental results demonstrate that the proposed method achieves better recommendation performance and fairness than the state-of-the-art methods. Moreover, user study results validates that the users’ preferences captured via PRM are satisfactory, and UEM provides satisfactory POI recommendations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Behavioral research

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MatHub-2d:二维材料输运数据库及其高迁移率二维半导体材料高通量筛选应用

引用

science China Materials 2023年第7期66卷 2768-2776页

作者：姚明佳计嘉琳李鑫朱振宇葛军饴 David J.Singh 奚晋扬杨炯张文清 Materials Genome Institute Shanghai UniversityShanghai 200444China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Zhejiang Laboratory Hangzhou 311100China Department of Physics and Astronomy University of MissouriColumbiaMO 65211USA Department of Materials Science and Engineering and Shenzhen Institute for Quantum Science&Technology Southern University of Science and TechnologyShenzhen 518055China Shenzhen Municipal Key-Lab for Advanced Quantum Materials and Devices and Guangdong Provincial Key Lab for Computational Science and Materials Design Southern University of Science and TechnologyShenzhen 518055China

近些年来二维材料因其独特的物理化学性质引起了广泛关注.载流子迁移率是材料在电子设备应用中最重要的特性之一.在本文中,我们介绍了如何通过高通量计算筛选来发现高迁移率二维半导体材料.基于最近开发的MatHub-2d数据库(包含约1900个... 详细信息

近些年来二维材料因其独特的物理化学性质引起了广泛关注.载流子迁移率是材料在电子设备应用中最重要的特性之一.在本文中,我们介绍了如何通过高通量计算筛选来发现高迁移率二维半导体材料.基于最近开发的MatHub-2d数据库(包含约1900个二维材料的结构信息及其第一性原理计算结果),以带隙、磁性、弹性模量和形变势作为搜索标准,通过初步筛选,得到133个候选者.对这些体系,我们使用形变势方法和玻尔兹曼输运理论预测了迁移率.最终,我们预测19种二维材料在室温下(300 K)具有高迁移率(>10^(3)cm^(-2)V^(-1)s^(-1))和良好的稳定性.这些材料高迁移率的来源主要是较小的形变势常数、较大的弹性模量,以及较小的有效质量.其中有两种类型的化合物值得关注,BX(X=P,As,Sb)和ZO_(2)(Z=Ge,Sn,Pb),它们具有面内各向同性高迁移率.BX中“flower-like”化学键有利于p型和n型电输运,而Z-O反键态是ZO_(2)型二维材料良好电子传导的原因.除了这些二维材料,Si_(2)P_(2)、Ga_(2)O_(2)、Ge_(2)N_(2)等同样也表现出高的电子迁移率.这些高迁移率二维材料在新型半导体电子器件中具有潜在的应用前景.

关键词： two-dimensional materials high-throughput computational screening MatHub-2d mobility

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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Natural Graphene Plasmonic Nano-Resonators for Highly Active Surface-Enhanced Raman Scattering Platforms

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Energy & Environmental Materials 2023年第5期6卷 269-280页

作者： Xiaoqiang Feng Zhiduo Liu Guanglin Zhang Shan Zhang Shuiping Huang Zhengyi He Genwang Wei Siwei Yang Yangguang Zhu Caichao Ye Cheng-Te Lin Guqiao Ding Gang Wang Department of Microelectronic Science and Engineering School of Physical Science andNingbo UniversityNingbo 315211China State Key Laboratory of Integrated Optoelectronics Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China Academy for Advanced Interdisciplinary Studies&Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and TechnologyShenzhen 518055China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Key Laboratory of Marine Materials and Related Technologies Zhejiang Key Laboratory of Marine Materials and Protective TechnologiesNingbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China

Highly sensitive and uniform three-dimensional(3D)hybrid heterogeneous structures for use in surface-enhanced Raman scattering(SERS)experiments were fabricated by sequentially decorating high-quality,ultra-clean,graphene quantum dots(GQDs)and Ag nanoparticles(Ag-NPs)onto ***-difference time-domain calculations and scanning Kelvin probe microscopy were used to verify that the Ag-NPs/GQDs/3D-graphene system facilitates substantial electromagnetic enhancement(due to the occurrence of two kinds of"gaps"between the Ag-NPs that form 3D"hot spots")and additional chemical enhancement(in detecting someπ-conjugated molecules).The SERS mechanism was explored in further detail via experimental analysis and confirmed by performing theoretical *** large surface area of the 3D substrate(due to the large specific surface areas of the GQDs and 3D-graphene)results in a better enrichment effect which helps produce lower detection *** particular,the detection limits obtained using the Ag-NPs/GQDs/3D-graphene platform can reach 10^(-11)M for rhodamine 6G,10^(-10)M for methylene blue and dopamine,and 10^(-7)M for tetramethylthiuram disulfide and methyl parathion in apple juice(these are superior to most of the results reported using graphene-based SERS substrates).In summary,the 3D-platform Ag-NPs/GQDs/3D-graphene/Si shows outstanding SERS *** therefore has excellent application prospects in biochemical molecular detection and food safety monitoring.

关键词： Ag-NPs/GQDs/3D-graphene chemical enhancement dopamine Electromagnetic enhancement

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

引用

2024 IEEE-EMBS International Conference on Biomedical and Health informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation

arXiv

引用

arXiv 2024年

作者： Yang, Soojung Nam, Juno Dietschreit, Johannes C.B. Gómez-Bombarelli, Rafael Computational and Systems Biology Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Institute of Theoretical Chemistry Faculty of Chemistry University of Vienna Vienna1090 Austria

In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs. Obtaining an expressive CV is crucial, but often hindered by the lack of information about the particular event, e.g., the transition from unfolded to folded conformation. We propose a simulation-free data augmentation strategy using physics-inspired metrics to generate geodesic interpolations resembling protein folding transitions, thereby improving sampling efficiency without true transition state samples. This new data can be used to improve the accuracy of classifier-based methods. Alternatively, a regression-based learning scheme for CV models can be adopted by leveraging the interpolation progress parameter. © 2024, CC BY.

关键词： Geodesy

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