bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated neuronal reconstruction with super-multicolour Tetbow labelling and threshold-based clustering of colour hues
Authors: Leiwe Marcus N. , Fujimoto Satoshi , Baba Toshikazu , Moriyasu Daichi , Saha Biswanath , Sakaguchi Richi , Inagaki Shigenori , Imai Takeshi
Published: June 2024

Fluorescence imaging is widely used for the mesoscopic mapping of neuronal connectivity. However, neurite reconstruction is challenging, especially when neurons are densely labelled. Here, we report a strategy for the fully automated reconstruction of densely labelled neuronal circuits. Firstly, we establish stochastic super-multicolour labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron is labelled with a unique combination of fluorescent proteins, which are then imaged and separated by linear unmixing. We also establish an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder), which identifies neurite fragments with similar colour combinations. To classify colour combinations, we develop unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space are clustered based on a given threshold distance. Our strategy allows the reconstruction of neurites for up to hundreds of neurons at the millimetre scale without using their physical continuity.
N-terminal cysteine acetylation and oxidation patterns may define protein stability
Authors: Heathcote Karen C. , Keeley Thomas P. , Myllykoski Matti , Lundekvam Malin , McTiernan Nina , Akter Salma , Masson Norma , Ratcliffe Peter J. , Arnesen Thomas , Flashman Emily
Published: June 2024

Oxygen homeostasis is maintained in plants and animals by O2-sensing enzymes initiating adaptive responses to low O2 (hypoxia). Recently, the O2-sensitive enzyme ADO was shown to initiate degradation of target proteins RGS4/5 and IL32 via the Cysteine/Arginine N-degron pathway. ADO functions by catalysing oxidation of N-terminal cysteine residues, but despite multiple proteins in the human proteome having an N-terminal cysteine, other endogenous ADO substrates have not yet been identified. This could be because alternative modifications of N-terminal cysteine residues, including acetylation, prevent ADO-catalysed oxidation. Here we investigate the relationship between ADO-catalysed oxidation and NatA-catalysed acetylation of a broad range of protein sequences with N-terminal cysteines. We present evidence that human NatA catalyses N-terminal cysteine acetylation in vitro and in vivo. We then show that sequences downstream of the N-terminal cysteine dictate whether this residue is oxidised or acetylated, with ADO preferring basic and aromatic amino acids and NatA preferring acidic or polar residues. In vitro, the two modifications appear to be mutually exclusive, suggesting that distinct pools of N-terminal cysteine proteins may be acetylated or oxidised. These results reveal the sequence determinants that contribute to N-terminal cysteine protein modifications, with implications for O2-dependent protein stability and the hypoxic response.
Multiple recent HCAR2 structures demonstrate a highly dynamic ligand binding and G protein activation mode
Authors: Shenol Aslihan , Tenente Ricardo , Lückmann Michael , Frimurer Thomas M. , Schwartz Thue W.
Published: June 2024

A surprisingly clear picture of the allosteric mechanism connecting G protein-coupled receptor agonists with G protein binding—and back – is revealed by a puzzle of thirty novel 3D structures of the hydroxycarboxylic acid receptor 2 (HCAR2) in complex with eight different orthosteric and a single allosteric agonist. HCAR2 is a sensor of β-hydroxybutyrate, niacin and certain anti-inflammatory drugs. Surprisingly, agonists with and without on-target side effects bound very similarly and in a completely occluded orthosteric binding site. Thus, despite the many structures we are still left with a pertinent need to understand the molecular dynamics of this and similar systems.
Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS
Authors: Yan Donghui , Zhou Muqing , Adduri Abhinav , Zhuang Yihao , Guler Mustafa , Liu Sitong , Shin Hyonyoung , Kovach Torin , Oh Gloria , Liu Xiao , Deng Yuting , Wang Xiaofeng , Cao Liu , Sherman David H. , Schultz Pamela J. , Kersten Roland D. , Clement Jason A. , Tripathi Ashootosh , Behsaz Bahar , Mohimani Hosein
Published: June 2024

Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.
Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery
Authors: Tong Xiaochu , Qu Ning , Kong Xiangtai , Ni Shengkun , Zhou Jingyi , Wang Kun , Zhang Lehan , Wen Yiming , Shi Jiangshan , Zhang Sulin , Li Xutong , Zheng Mingyue
Published: June 2024

Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen’s application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine.
Beta-lactamase dependent and independent evolutionary paths to high-level ampicillin resistance
Authors: Gross Rotem , Yelin Idan , Lázár Viktória , Datta Manoshi Sen , Kishony Roy
Published: June 2024

The incidence of beta-lactam resistance among clinical isolates is a major health concern. A key method to study the emergence of antibiotic resistance is adaptive laboratory evolution. However, in the case of the beta-lactam ampicillin, bacteria evolved in laboratory settings do not recapitulate clinical-like resistance levels, hindering efforts to identify major evolutionary paths and their dependency on genetic background. Here, we used the Microbial Evolution and Growth Arena (MEGA) plate to select ampicillin-resistant Escherichia coli mutants with varying degrees of resistance. Whole-genome sequencing of resistant isolates revealed that ampicillin resistance was acquired via a combination of single-point mutations and amplification of the gene encoding beta-lactamase AmpC. However, blocking AmpC-mediated resistance revealed latent adaptive pathways: strains deleted for ampC were able to adapt through combinations of changes in genes involved in multidrug resistance encoding efflux pumps, transcriptional regulators, and porins. Our results reveal that combinations of distinct genetic mutations, accessible at large population sizes, can drive high-level resistance to ampicillin even independently of beta-lactamases.
MiDAS 5: Global diversity of bacteria and archaea in anaerobic digesters
Authors: Dueholm Morten Kam Dahl , Andersen Kasper Skytte , Korntved Anne-Kirstine C. , Rudkjøbing Vibeke , Alves Madalena , Bajón-Fernández Yadira , Batstone Damien , Butler Caitlyn , Cruz Mercedes Cecilia , Davidsson Åsa , Erijman Leonardo , Holliger Christof , Koch Konrad , Kreuzinger Norbert , Lee Changsoo , Lyberatos Gerasimos , Mutnuri Srikanth , O’Flaherty Vincent , Oleskowicz-Popiel Piotr , Pokorna Dana , Rajal Veronica , Recktenwald Michael , Rodríguez Jorge , Saikaly Pascal E. , Tooker Nick , Vierheilig Julia , De Vrieze Jo , Wurzbacher Christian , Nielsen Per Halkjær
Published: June 2024

Anaerobic digestion of organic waste into methane and carbon dioxide (biogas) is carried out by complex microbial communities. Here, we use full-length 16S rRNA gene sequencing of 285 full-scale anaerobic digesters (ADs) to expand our knowledge about diversity and function of the bacteria and archaea in ADs worldwide. The sequences are processed into full-length 16S rRNA amplicon sequence variants (FL-ASVs) and are used to expand the MiDAS 4 database for bacteria and archaea in wastewater treatment systems, creating MiDAS 5. The expansion of the MiDAS database increases the coverage for bacteria and archaea in ADs worldwide, leading to improved genus- and species-level classification. Using MiDAS 5, we carry out an amplicon-based, global-scale microbial community profiling of the sampled ADs using three common sets of primers targeting different regions of the 16S rRNA gene in bacteria and/or archaea. We reveal how environmental conditions and biogeography shape the AD microbiota. We also identify core and conditionally rare or abundant taxa, encompassing 692 genera and 1013 species. These represent 84–99% and 18–61% of the accumulated read abundance, respectively, across samples depending on the amplicon primers used. Finally, we examine the global diversity of functional groups with known importance for the anaerobic digestion process.
From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning
Authors: Chen Xinyu , Lu Shuaihua , Chen Qian , Zhou Qionghua , Wang Jinlan
Published: June 2024

Data scarcity is one of the critical bottlenecks to utilizing machine learning in material discovery. Transfer learning can use existing big data to assist property prediction on small data sets, but the premise is that there must be a strong correlation between large and small data sets. To extend its applicability in scenarios with different properties and materials, here we develop a hybrid framework combining adversarial transfer learning and expert knowledge, which enables the direct prediction of carrier mobility of two-dimensional (2D) materials using the knowledge learned from bulk effective mass. Specifically, adversarial training ensures that only common knowledge between bulk and 2D materials is extracted while expert knowledge is incorporated to further improve the prediction accuracy and generalizability. Successfully, 2D carrier mobilities are predicted with the accuracy over 90% from only crystal structure, and 21 2D semiconductors with carrier mobilities far exceeding silicon and suitable bandgap are successfully screened out. This work enables transfer learning in simultaneous cross-property and cross-material scenarios, providing an effective tool to predict intricate material properties with limited data.
Overtone photothermal microscopy for high-resolution and high-sensitivity vibrational imaging
Authors: Wang Le , Lin Haonan , Zhu Yifan , Ge Xiaowei , Li Mingsheng , Liu Jianing , Chen Fukai , Zhang Meng , Cheng Ji-Xin
Published: June 2024

Photothermal microscopy is a highly sensitive pump-probe method for mapping nanostructures and molecules through the detection of local thermal gradients. While visible photothermal microscopy and mid-infrared photothermal microscopy techniques have been developed, they possess inherent limitations. These techniques either lack chemical specificity or encounter significant light attenuation caused by water absorption. Here, we present an overtone photothermal (OPT) microscopy technique that offers high chemical specificity, detection sensitivity, and spatial resolution by employing a visible probe for local heat detection in the C-H overtone region. We demonstrate its capability for high-fidelity chemical imaging of polymer nanostructures, depth-resolved intracellular chemical mapping of cancer cells, and imaging of multicellular C. elegans organisms and highly scattering brain tissues. By bridging the gap between visible and mid-infrared photothermal microscopy, OPT establishes a new modality for high-resolution and high-sensitivity chemical imaging. This advancement complements large-scale shortwave infrared imaging approaches, facilitating multiscale structural and chemical investigations of materials and biological metabolism.
Development of a genetically encoded sensor for probing endogenous nociceptin opioid peptide release
Authors: Zhou Xuehan , Stine Carrie , Prada Patricia Oliveira , Fusca Debora , Assoumou Kevin , Dernic Jan , Bhat Musadiq A. , Achanta Ananya S. , Johnson Joseph C. , Pasqualini Amanda Loren , Jadhav Sanjana , Bauder Corinna A. , Steuernagel Lukas , Ravotto Luca , Benke Dietmar , Weber Bruno , Suko Azra , Palmiter Richard D. , Stoeber Miriam , Kloppenburg Peter , Brüning Jens C. , Bruchas Michael R. , Patriarchi Tommaso
Published: June 2024

Nociceptin/orphanin-FQ (N/OFQ) is a recently appreciated critical opioid peptide with key regulatory functions in several central behavioral processes including motivation, stress, feeding, and sleep. The functional relevance of N/OFQ action in the mammalian brain remains unclear due to a lack of high-resolution approaches to detect this neuropeptide with appropriate spatial and temporal resolution. Here we develop and characterize NOPLight, a genetically encoded sensor that sensitively reports changes in endogenous N/OFQ release. We characterized the affinity, pharmacological profile, spectral properties, kinetics, ligand selectivity, and potential interaction with intracellular signal transducers of NOPLight in vitro. Its functionality was established in acute brain slices by exogeneous N/OFQ application and chemogenetic induction of endogenous N/OFQ release from PNOC neurons. In vivo studies with fibre photometry enabled direct recording of NOPLight binding to exogenous N/OFQ receptor ligands, as well as detection of endogenous N/OFQ release within the paranigral ventral tegmental area (pnVTA) during natural behaviors and chemogenetic activation of PNOC neurons. In summary, we show here that NOPLight can be used to detect N/OFQ opioid peptide signal dynamics in tissue and freely behaving animals.
An in-situ polymerization strategy for gel polymer electrolyte Si||Ni-rich lithium-ion batteries
Authors: Bai Miao , Tang Xiaoyu , Zhang Min , Wang Helin , Wang Zhiqiao , Shao Ahu , Ma Yue
Published: June 2024

Coupling the Si-based anodes with nickel-rich LiNixMnyCo1−x−yO2 cathodes (x ≥ 0.8) in the energy-dense cell prototype suffers from the mechanical instability of the Li-Si alloys, cathode collapse upon the high-voltage cycling, as well as the severe leakage current at elevated temperatures. More seriously, the cathode-to-anode cross-talk effect of transitional metal aggravates the depletion of the active Li reservoir. To reconcile the cation utilization degree, stress dissipation, and extreme temperature tolerance of the Si-based anode||NMC prototype, we propose a gel polymer electrolyte to reinforce the mechanical integrity of Si anode and chelate with the transitional cations towards the stabilized interfacial property. As coupling the conformal gel polymer electrolyte encapsulation with the spatial arranged Si anode and NMC811 cathode, the 2.7 Ah pouch-format cell could achieve the high energy density of 325.9 Wh kg−1 (based on the whole pouch cell), 88.7% capacity retention for 2000 cycles, self-extinguish property as well as a wide temperature tolerance. Therefore, this proposed polymerization strategy provides a leap toward the secured Li batteries.
Plasmons in the Kagome metal CsV3Sb5
Authors: Shiravi H. , Gupta A. , Ortiz B. R. , Cui S. , Yu B. , Uykur E. , Tsirlin A. A. , Wilson S. D. , Sun Z. , Ni G. X.
Published: June 2024

Plasmon polaritons, or plasmons, are coupled oscillations of electrons and electromagnetic fields that can confine the latter into deeply subwavelength scales, enabling novel polaritonic devices. While plasmons have been extensively studied in normal metals or semimetals, they remain largely unexplored in correlated materials. In this paper, we report infrared (IR) nano-imaging of thin flakes of CsV3Sb5, a prototypical layered Kagome metal. We observe propagating plasmon waves in real-space with wavelengths tunable by the flake thickness. From their frequency-momentum dispersion, we infer the out-of-plane dielectric function $${{{{{{\boldsymbol{\epsilon }}}}}}}_{{{{{{\boldsymbol{c}}}}}}}$$

ϵ

c

that is generally difficult to obtain in conventional far-field optics, and elucidate signatures of electronic correlations when compared to density functional theory (DFT). We propose correlation effects might have switched the real part of $${{{{{{\boldsymbol{\epsilon }}}}}}}_{{{{{{\boldsymbol{c}}}}}}}$$

ϵ

c

from negative to positive values over a wide range of middle-IR frequencies, transforming the surface plasmons into hyperbolic bulk plasmons, and have dramatically suppressed their dissipation.
Transcription regulates the spatio-temporal dynamics of genes through micro-compartmentalization
Authors: Salari Hossein , Fourel Geneviève , Jost Daniel
Published: June 2024

Although our understanding of the involvement of heterochromatin architectural factors in shaping nuclear organization is improving, there is still ongoing debate regarding the role of active genes in this process. In this study, we utilize publicly-available Micro-C data from mouse embryonic stem cells to investigate the relationship between gene transcription and 3D gene folding. Our analysis uncovers a nonmonotonic - globally positive - correlation between intragenic contact density and Pol II occupancy, independent of cohesin-based loop extrusion. Through the development of a biophysical model integrating the role of transcription dynamics within a polymer model of chromosome organization, we demonstrate that Pol II-mediated attractive interactions with limited valency between transcribed regions yield quantitative predictions consistent with chromosome-conformation-capture and live-imaging experiments. Our work provides compelling evidence that transcriptional activity shapes the 4D genome through Pol II-mediated micro-compartmentalization.
Dual polarization-enabled ultrafast bulk photovoltaic response in van der Waals heterostructures
Authors: Zeng Zhouxiaosong , Tian Zhiqiang , Wang Yufan , Ge Cuihuan , Strauß Fabian , Braun Kai , Michel Patrick , Huang Lanyu , Liu Guixian , Li Dong , Scheele Marcus , Chen Mingxing , Pan Anlian , Wang Xiao
Published: June 2024

The bulk photovoltaic effect (BPVE) originating from spontaneous charge polarizations can reach high conversion efficiency exceeding the Shockley-Queisser limit. Emerging van der Waals (vdW) heterostructures provide the ideal platform for BPVE due to interfacial interactions naturally breaking the crystal symmetries of the individual constituents and thus inducing charge polarizations. Here, we show an approach to obtain ultrafast BPVE by taking advantage of dual interfacial polarizations in vdW heterostructures. While the in-plane polarization gives rise to the BPVE in the overlayer, the charge carrier transfer assisted by the out-of-plane polarization further accelerates the interlayer electronic transport and enhances the BPVE. We illustrate the concept in MoS2/black phosphorus heterostructures, where the experimentally observed intrinsic BPVE response time achieves 26 ps, orders of magnitude faster than that of conventional non-centrosymmetric materials. Moreover, the heterostructure device possesses an extrinsic response time of approximately 2.2 ns and a bulk photovoltaic coefficient of 0.6 V−1, which is among the highest values for vdW BPV devices reported so far. Our study thus points to an effective way of designing ultrafast BPVE for high-speed photodetection.
Ag–thiolate interactions to enable an ultrasensitive and stretchable MXene strain sensor with high temporospatial resolution
Authors: Liu Yang , Xu Zijun , Ji Xinyi , Xu Xin , Chen Fei , Pan Xiaosen , Fu Zhiqiang , Chen Yunzhi , Zhang Zhengjian , Liu Hongbin , Cheng Bowen , Liang Jiajie
Published: June 2024

High-sensitivity strain sensing elements with a wide strain range, fast response, high stability, and small sensing areas are desirable for constructing strain sensor arrays with high temporospatial resolution. However, current strain sensors rely on crack-based conductive materials having an inherent tradeoff between their sensing area and performance. Here, we present a molecular-level crack modulation strategy in which we use layer-by-layer assembly to introduce strong, dynamic, and reversible coordination bonds in an MXene and silver nanowire-matrixed conductive film. We use this approach to fabricate a crack-based stretchable strain sensor with a very small sensing area (0.25 mm2). It also exhibits an ultrawide working strain range (0.001–37%), high sensitivity (gauge factor ~500 at 0.001% and >150,000 at 35%), fast response time, low hysteresis, and excellent long-term stability. Based on this high-performance sensing element and facile assembly process, a stretchable strain sensor array with a device density of 100 sensors per cm2 is realized. We demonstrate the practical use of the high-density strain sensor array as a multichannel pulse sensing system for monitoring pulses in terms of their spatiotemporal resolution.
Interpretable video-based tracking and quantification of parkinsonism clinical motor states
Authors: Deng Daniel , Ostrem Jill L. , Nguyen Vy , Cummins Daniel D. , Sun Julia , Pathak Anupam , Little Simon , Abbasi-Asl Reza
Published: June 2024

Quantification of motor symptom progression in Parkinson’s disease (PD) patients is crucial for assessing disease progression and for optimizing therapeutic interventions, such as dopaminergic medications and deep brain stimulation. Cumulative and heuristic clinical experience has identified various clinical signs associated with PD severity, but these are neither objectively quantifiable nor robustly validated. Video-based objective symptom quantification enabled by machine learning (ML) introduces a potential solution. However, video-based diagnostic tools often have implementation challenges due to expensive and inaccessible technology, and typical “black-box” ML implementations are not tailored to be clinically interpretable. Here, we address these needs by releasing a comprehensive kinematic dataset and developing an interpretable video-based framework that predicts high versus low PD motor symptom severity according to MDS-UPDRS Part III metrics. This data driven approach validated and robustly quantified canonical movement features and identified new clinical insights, not previously appreciated as related to clinical severity, including pinkie finger movements and lower limb and axial features of gait. Our framework is enabled by retrospective, single-view, seconds-long videos recorded on consumer-grade devices such as smartphones, tablets, and digital cameras, thereby eliminating the requirement for specialized equipment. Following interpretable ML principles, our framework enforces robustness and interpretability by integrating (1) automatic, data-driven kinematic metric evaluation guided by pre-defined digital features of movement, (2) combination of bi-domain (body and hand) kinematic features, and (3) sparsity-inducing and stability-driven ML analysis with simple-to-interpret models. These elements ensure that the proposed framework quantifies clinically meaningful motor features useful for both ML predictions and clinical analysis.
Tipping point in ice-sheet grounding-zone melting due to ocean water intrusion
Authors: Bradley Alexander T. , Hewitt Ian J.
Published: June 2024

Marine ice sheets are highly sensitive to submarine melting in their grounding zones, where they transition between grounded and floating ice. Recently published studies of the complex hydrography of grounding zones suggest that warm ocean water can intrude large distances beneath the ice sheet, with dramatic consequences for ice dynamics. Here we develop a model to capture the feedback between intruded ocean water, the melting it induces and the resulting changes in ice geometry. We reveal a sensitive dependence of the grounding-zone dynamics on this feedback: as the grounding zone widens in response to melting, both temperature and flow velocity in the region increase, further enhancing melting. We find that increases in ocean temperature can lead to a tipping point being passed, beyond which ocean water intrudes in an unbounded manner beneath the ice sheet, via a process of runaway melting. Additionally, this tipping point may not be easily detected with early warning indicators. Although completely unbounded intrusions are not expected in practice, this suggests a mechanism for dramatic changes in grounding-zone behaviour, which are not currently included in ice-sheet models. We consider the susceptibility of present-day Antarctic grounding zones to this process, finding that both warm and cold water cavity ice shelves may be vulnerable. Our results point towards a stronger sensitivity of ice-sheet melting, and thus higher sea-level-rise contribution in a warming climate, than has been previously understood.
Spatial mapping of mobile genetic elements and their bacterial hosts in complex microbiomes
Authors: Grodner Benjamin , Shi Hao , Farchione Owen , Vill Albert C. , Ntekas Ioannis , Diebold Peter J. , Wu David T. , Chen Chia-Yu , Kim David M. , Zipfel Warren R. , Brito Ilana L. , De Vlaminck Iwijn
Published: June 2024

The exchange of mobile genetic elements (MGEs) facilitates the spread of functional traits including antimicrobial resistance within bacterial communities. Tools to spatially map MGEs and identify their bacterial hosts in complex microbial communities are currently lacking, limiting our understanding of this process. Here we combined single-molecule DNA fluorescence in situ hybridization (FISH) with multiplexed ribosomal RNA-FISH to enable simultaneous visualization of both MGEs and bacterial taxa. We spatially mapped bacteriophage and antimicrobial resistance (AMR) plasmids and identified their host taxa in human oral biofilms. This revealed distinct clusters of AMR plasmids and prophage, coinciding with densely packed regions of host bacteria. Our data suggest spatial heterogeneity in bacterial taxa results in heterogeneous MGE distribution within the community, with MGE clusters resulting from horizontal gene transfer hotspots or expansion of MGE-carrying strains. Our approach can help advance the study of AMR and phage ecology in biofilms.
Characterization of an expanded set of assays for immunomodulatory proteins using targeted mass spectrometry
Authors: Whiteaker Jeffrey R. , Zhao Lei , Schoenherr Regine M. , Huang Dongqing , Kennedy Jacob J. , Ivey Richard G. , Lin Chenwei , Lorentzen Travis D. , Colantonio Simona , Caceres Tessa W. , Roberts Rhonda R. , Knotts Joseph G. , Reading Joshua J. , Perry Candice D. , Garcia-Buntley Sandra S. , Bocik William , Hewitt Stephen M. , Paulovich Amanda G.
Published: June 2024

Immunotherapies are revolutionizing cancer care, but many patients do not achieve durable responses and immune-related adverse events are difficult to predict. Quantifying the hundreds of proteins involved in cancer immunity has the potential to provide biomarkers to monitor and predict tumor response. We previously developed robust, multiplexed quantitative assays for immunomodulatory proteins using targeted mass spectrometry, providing measurements that can be performed reproducibly and harmonized across laboratories. Here, we expand upon those efforts in presenting data from a multiplexed immuno-oncology (IO)-3 assay panel targeting 43 peptides representing 39 immune- and inflammation-related proteins. A suite of novel monoclonal antibodies was generated as assay reagents, and the fully characterized antibodies are made available as a resource to the community. The publicly available dataset contains complete characterization of the assay performance, as well as the mass spectrometer parameters and reagent information necessary for implementation of the assay. Quantification of the proteins will provide benefit to correlative studies in clinical trials, identification of new biomarkers, and improve understanding of the immune response in cancer.
A multi-omics dataset of the response to early plant polysaccharide ingestion in rabbits
Authors: Paës Charlotte , Beaumont Martin , Gidenne Thierry , Bébin Karine , Duperray Joël , Gohier Charly , Guené-Grand Emeline , Rebours Gwénaël , Castinel Adrien , Barilly Céline , Gabinaud Béatrice , Bannelier Carole , Gress Laure , Laperruque François , Aymard Patrick , Debrusse Anne-Marie , Cauquil Laurent , Pascal Géraldine , Combes Sylvie
Published: June 2024

The transition from a milk-based diet to exclusive solid feeding deeply modifies microbiota-host crosstalk. Specifically, early ingestion of plant polysaccharides would be one of the main nutritional components to drive host-microbiota-interaction. To capture the effects of polysaccharides early-life nutrition (starch vs rapidly fermentable fiber) on the holobiont development, we investigated on the one hand the gut bacteriome and metabolome and on the other hand the transcriptome of two host gut tissues. Rabbit model was used to study post-natal co-development of the gut microbiota and its host around weaning transition. The assessment of the microbial composition of the gut appendix together with the caecum was provided for the first time. Gene expression signatures were analyzed along the gut (ileum and caecum) through high-throughput qPCR. The data collected were completed by the analysis of animal growth changes and time-series assessment of blood biomarkers. Those accessible and reusable data could help highlight the gut development dynamics as well as biological adaptation processes at the onset of solid feeding.
The Subnational Corruption Database: Grand and petty corruption in 1,473 regions of 178 countries, 1995–2022
Authors: Crombach Lamar , Smits Jeroen
Published: June 2024

This data descriptor presents the Subnational Corruption Database (SCD), which provides data on corruption in 1,473 subnational areas of 178 countries. The SCD includes a comprehensive overall corruption index, the Subnational Corruption Index (SCI), and its two components: the Subnational Grand Corruption Index (SGCI) and Subnational Petty Corruption Index (SPCI). The SCD is constructed by combining data of 807 surveys held in the period 1995–2022 and includes the corruption experiences and perceptions of 1,326,656 respondents along 19 separate dimensions. The data are available for multiple years, allowing longitudinal analyses. At the national level, the SCI correlates strongly with established corruption indices, like the Transparency International Corruption Perceptions Index (CPI) and the World Bank Control of Corruption Index (CCI). We create subnational estimates of the CPI and CCI by superimposing the subnational variation of the SCI around the national averages of these indices. The presentation of subnational data in the SCD and the separation between grand and petty corruption significantly broaden the global knowledge base in the field of corruption.
Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data
Authors: De Silva Senuri , Alli-Shaik Asfa , Gunaratne Jayantha
Published: June 2024

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.
Stiffness analysis and structural optimization design of an air spring for ships
Authors: Cheng Yuqiang , Gao Hua , Ma Jianguo , Shuai Changgeng
Published: June 2024

An air spring (AS) for ships must have the structural strength of its bellows enhanced considerably to ensure its reliability under high internal pressure and strong impact. In this case, the stiffness of the bellows gradually dominates the overall stiffness of the AS. Nevertheless, the parameterization calculation of stiffness for an AS mainly focuses on its pneumatic stiffness. The bellows stiffness is normally analyzed by virtue of equivalent simplification or numeric simulation. There is not an effective parameterization calculation model for the stiffness of the bellows, making it difficult to achieve the structural optimization design of the bellows. In this paper, the shell theory was borrowed to build a mechanical model for the bellows. Subsequently, the state vector of the bellows was solved by precision integration and boundary condition. Iteration was conducted to identify the complex coupling relationship between the vector of the bellows and other parameters. On this basis, the parameterization calculation method was introduced for the stiffness of the bellows to obtain the vertical and horizontal stiffness of the AS. After that, a dual-membrane low-stiffness structure was designed to analyze the dominating factors affecting the strength and stiffness of the AS, which highlighted the way to the low-stiffness optimization design of high-strength ASs. In the end, three prototypes and one optimized prototype were tested to verify the correctness of the parameterization design model for stiffness as well as the effectiveness of the structural optimization design.
Rapid phenotypic differentiation in the iconic Japanese knotweed s.l. invading novel habitats
Authors: Yuan Wei , Pigliucci Massimo , Richards Christina L.
Published: June 2024

Understanding the mechanisms that underlie plant invasions is critical for management and conservation of biodiversity. At the same time, invasive species also provide a unique opportunity to study rapid adaptation to complex environmental conditions. Using four replicate reciprocal transplant experiments across three habitats, we described patterns of phenotypic response and assessed the degree of local adaptation in knotweed populations. We found plants from beach habitats were generally smaller than plants from marsh and roadside habitats when grown in their home habitat. In the marsh habitat, marsh plants were generally larger than beach plants, but not different from roadside plants. There were no differences among plants grown in the roadside habitat. We found mixed evidence for local adaptation: plants from the marsh habitat had greater biomass in their “home” sites, while plants from beaches and roadsides had greater survival in their “home” sites compared to other plants. In sum, we found phenotypic differentiation and some support for the hypothesis of rapid local adaptation of plants from beach, marsh and roadside habitats. Identifying whether these patterns of differentiation result from genetic or heritable non-genetic mechanisms will require further work.
Severity influences categorical likelihood communications: A case study with Southeast Asian weather forecasters
Authors: Liefgreen Alice , Jenkins Sarah C. , Osman Sazali , Moron Lorenzo A. , Monteverde Maria Cecilia A. , Cayanan Esperanza O. , Hoang Lam , Tran Diep Quang , Ngo Huong , Putra Agie Wandala , Novikarany Riefda , Ayuliana Sefri , Beckett Rebecca , Harris Adam J. L.
Published: June 2024

Risk assessments are common in multiple domains, from finance to medicine. They require evaluating an event’s potential severity and likelihood. We investigate the possible dependence of likelihood and severity within the domain of impact-based weather forecasting (IBF), following predictions derived from considering asymmetric loss functions. In a collaboration between UK psychologists and partners from four meteorological organisations in Southeast Asia, we conducted two studies (N = 363) eliciting weather warnings from forecasters. Forecasters provided warnings denoting higher likelihoods for high severity impacts than low severity impacts, despite these impacts being described as having the same explicit numerical likelihood of occurrence. This ‘Severity effect’ is pervasive, and we find it can have a continued influence even for an updated forecast. It is additionally observed when translating warnings made on a risk matrix to numerical probabilities.
Advantages of CoS2 nano-particles on the corrosion resistance and adhesiveness of epoxy coatings
Authors: Deyab M. A. , Alghamdi Majed M. , El-Zahhar Adel A. , El-Shamy Omnia A. A.
Published: June 2024

Researchers face significant challenges because of the inadequate corrosion resistance and weak adherence of epoxy (EP) coatings. We deal with these issues here by means of a novel nano-composite coating (EP/nano-CoS2). In order to create a composite coating (EP/nano-CoS2), CoS2 nanoparticles (nano-CoS2) were prepared and incorporated to an epoxy (EP) resin. The synthesized CoS2 was characterized using XRD and FT-IR spectroscopic techniques. The mean particle size was determined using Scherer equation and found to be 19.38 nm. The zeta potential was also determined (− 9.78 mV). Electrochemical impedance spectroscopies (EIS) as well as pull-off assessments were used to quantify the EP/nano-CoS2 coating’s anti-corrosion capabilities and adhesive power. The findings demonstrate that the EIS variables of the EP/nano-CoS2 composite coating are markedly improved when compared to raw EP coating. The corrosion resistance or even adhesion of EP protective layer can be markedly increased by using the synthesized nanoparticles as nano-fillers.
Design optimization of large-scale bifacial photovoltaic module frame using deep learning surrogate model
Authors: Han Dongwoon , Kim Seongtak
Published: June 2024

Recently, the wafers used in solar cells have been increasing in size, leading to larger module sizes and weights. The increased weight can cause deflection of photovoltaic (PV) module, which may lead to decreased cell efficiency. In this study, we developed a deep neural network (DNN)-based finite element (FE) surrogate model to obtain the optimal frame design factors that can improve deflection in large-scale bifacial PV module. Initially, an FE model was constructed for large-scale bifacial PV module. Based on this, the FE surrogate model was trained using 243 FEA datasets generated within the proposed range of factors. Furthermore, it was improved through Bayesian optimization and k-fold validation. As a result, the final loss value was $$3.743 \times 10^{-4}$$

3.743
×

10

-
4

, and the average mean absolute percentage error (MAPE) and coefficient of determination ($$R^2$$

R
2

) values for deflection and weight were 0.0017, 0.9972 for the training set, and 0.0020, 0.9962 for the test set, respectively. This indicates that the trained FE surrogate model possesses significant accuracy. After generating 1 million datasets within the range of frame design factors, the trained model was used to obtain predictions. Based on this data, the frame design factors that minimize both deflection and weight were identified as about a = 1.5, b = 13.7, c = 1.5, d = 3.0, e = 4.3. At this point, the deflection was 11.1 mm, and the weight was 3.6 kg. After altering the frame shape with the derived factors, FEA was conducted. The results matched for both deflection and weight, with almost no error. At this point, the weight increased by approximately 12.8% compared to the existing, while the deflection decreased by about 9.6%. Additionally, we analyzed the relationship between deflection and weight for each factor and secured the basis for the derived results. Consequently, our FE surrogate model accurately predicted the FEA results and quickly identified the optimal factors that minimize deflection and weight.
A retrospective study of deep learning generalization across two centers and multiple models of X-ray devices using COVID-19 chest-X rays
Authors: Fernández-Miranda Pablo Menéndez , Fraguela Enrique Marqués , de Linera-Alperi Marta Álvarez , Cobo Miriam , del Barrio Amaia Pérez , González David Rodríguez , Vega José A. , Iglesias Lara Lloret
Published: June 2024

Generalization of deep learning (DL) algorithms is critical for the secure implementation of computer-aided diagnosis systems in clinical practice. However, broad generalization remains to be a challenge in machine learning. This research aims to identify and study potential factors that can affect the internal validation and generalization of DL networks, namely the institution where the images come from, the image processing applied by the X-ray device, and the type of response function of the X-ray device. For these purposes, a pre-trained convolutional neural network (CNN) (VGG16) was trained three times for classifying COVID-19 and control chest radiographs with the same hyperparameters, but using different combinations of data acquired in two institutions by three different X-ray device manufacturers. Regarding internal validation, the addition of images from an external institution to the training set did not modify the algorithm’s internal performance, however, the inclusion of images acquired by a device from a different manufacturer decreased the performance up to 8% (p < 0.05). In contrast, generalization across institutions and X-ray devices with the same type of response function was achieved. Nonetheless, generalization was not observed across devices with different types of response function. This factor was the key impediment to achieving broad generalization in our research, followed by the device’s image-processing and the inter-institutional differences, which both reduced generalization performance to 18.9% (p < 0.05), and 9.8% (p < 0.05), respectively. Finally, clustering analysis with features extracted by the CNN was performed, revealing a substantial dependence of feature values extracted by the pre-trained CNN on the X-ray device which acquired the images.
Seismic hazard prediction of the Hunhe Fault in the Shen-Fu New District
Authors: Wang Zijun , Zhao Boming , Wan Bo
Published: June 2024

Earthquake prevention and disaster mitigation are crucial aspects of social welfare that significantly impact national public security. This paper presents a seismic risk assessment and hazard prediction of the Hunhe Fault in the Shengyang-Fushun (Shen-Fu) New District. The target area is at risk of seismic damage due to two major branch ruptures, namely, F9 and F1; these ruptures have the potential to generate maximum earthquakes with a magnitude of 6.0 in the next 50 to 100 years. A three-dimensional underground velocity structure and asperity source model were established for the target faults. Subsequently, a hybrid technique combining deterministic and empirical approaches was employed to simulate the broadband strong ground motion of the target region in anticipation of the occurrence of expected scenario earthquakes. The distributions of peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD) for the area are provided, and the results indicate that densely populated urban areas could experience PGA values close to 280 cm/s2 along the fault traces. This study provides a reliable basis for engineering construction and urban planning in the Shen-Fu New District.
Rapid entomological assessment in eight high malaria endemic regencies in Papua Province revealed the presence of indoor and outdoor malaria transmissions
Authors: Rozi Ismail E. , Permana Dendi H. , Syahrani Lepa , Asih Puji B. S. , Zubaidah Siti , Risandi Rifqi , Wangsamuda Suradi , Dewayanti Farahana K. , Demetouw Michael R. , Mabui Silas , Robaha Marthen M. F. , Sumiwi Maria E. , Bangs Michael J. , Lobo Neil F. , Hawley William A. , Syafruddin Din
Published: June 2024

Malaria in eastern Indonesia remains high despite significant reduction and elimination in other parts of the country. A rapid entomological assessment was conducted in eight high malaria endemic regencies of Papua Province, Indonesia, to expedite malaria elimination efforts in this region. This study aims to characterize specific, actionable endpoints toward understanding where and when malaria transmission is happening, where interventions may function best, and identify gaps in protection that result in continued transmission. The entomological assessment included identifying potential vectors through human landing catch (HLC), indoor morning and night resting collections, identification of larval sites through surveillance of water bodies, and vector incrimination toward understanding exposure to malaria transmission. Human landing catches (HLCs) and larval collections identified 10 Anopheles species, namely Anopheles koliensis, Anopheles punctulatus, Anopheles farauti, Anopheles hinesorum, Anopheles longirostris, Anopheles peditaeniatus, Anopheles tesselatus, Anopheles vagus, Anopheles subpictus and Anopheles kochi. The most common and abundant species found overall were An. koliensis and An. punctulatus, while An. farauti was found in large numbers in the coastal areas of Mimika and Sarmi Regencies. Vector incrimination on Anopheles collected from HLCs and night indoor resting demonstrated that An. koliensis and An. punctulatus carried Plasmodium in Keerom, Jayapura, and Sarmi Regencies. Analysis of HLCs for the most common species revealed that the An. koliensis and An. punctulatus, bite indoors and outdoors at equal rates, while An. farauti predominantly bite outdoors. Larval surveillance demonstrated that most water bodies in and surrounding residential areas contained Anopheles larvae. This study demonstrated indoor and outdoor exposure to mosquito bites and gaps in protection, enabling exposure to infectious bites in all regencies. This explains why current malaria control efforts focusing on indoor protection have failed to substantially reduce malaria incidence in the region. Optimization of insecticide-treated bed nets (ITNs), as well as installment of mosquito screens in houses, may further reduce indoor transmission. For outdoor transmission, the use of community-centric approaches to reduce or eliminate larval sources within and surrounding the village through the guidance of locally stationed entomologists, along with Social and Behavior Change mediated health education towards the local adoption of mosquito protection tools during outdoor activities, may reduce malaria transmission.