bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pattern-dependent resistivity variations in inkjet-printed conductors due to non-uniform ink drying
Authors: Ghalamboran Milad , Nazeri Mohammad , Grau Gerd 
Published: March 2024

Abstract
When fabricating inkjet-printed electronic devices and circuits, inkjet-printed conductive materials require drying and sintering to improve electrical conductivity. Electrical conductivity should be the same irrespective of pattern design, size, location, or density of adjacent patterns. However, we demonstrate that spatial variations in the drying process for inkjet-printed patterns with proximity to others cause resistivity variations. These resistivity variations are studied here experimentally for different circuit patterns and in arrays of inkjet-printed square electrodes. This variation depends not only on the location of each electrode in an array but also on the number of electrodes. This means that for the same drying temperature and duration, the array with a larger number of electrodes exhibits a larger resistivity variation. The sooner an electrode dries, the lower resistivity it achieves. The resistivity variation between an individual electrode and the center electrode in a 7 × 7 electrode array can be a factor of seven. This variation decreases for lower numbers of electrodes to a factor of three for a 3 × 3 array. Furthermore, x-ray photoelectron spectroscopy analyses provide evidence for the residual presence of carbon-based materials within electrodes after the drying process. These results confirm that the location of electrodes within an array significantly influences the amount of residual carbon-based materials, thereby contributing to resistivity variations. Although intense pulsed light sintering can decrease this variation, its optimal parameters depend on the printed designs, and our simulation results show a non-uniform temperature profile over the electrode arrays. Temperature increases more at the center of patterns than the corners, which can be useful in this case to improve resistivity uniformity. In this study, for the first time, we show how different printed shapes and designs can result in non-uniform resistivity after drying and sintering.
High flux strontium atom source
Authors: Feng C-H , Robert P , Bouyer P , Canuel B , Li J , Das S , Kwong C C , Wilkowski D , Prevedelli M , Bertoldi A 
Published: March 2024

Abstract
We present a novel cold strontium atom source designed for quantum sensors. We optimized the deceleration process to capture a large velocity class of atoms emitted from an oven and achieved a compact and low-power setup capable of generating a high atomic flux. Our approach involves velocity-dependent transverse capture of atoms using a two-dimensional magneto-optical trap. To enhance the atomic flux, we employ tailored magnetic fields that minimize radial beam expansion and incorporate a cascaded Zeeman-slowing configuration utilizing two optical frequencies. The performance is comparable to that of conventional Zeeman slower sources, and the scheme is applicable to other atomic species. Our results represent a significant advancement towards the deployment of portable and, possibly, space-based cold atom sensors.
Projections and patterns of heat-related mortality impacts from climate change in Southeast Asia
Authors: Amnuaylojaroen Teerachai , Parasin Nichapa , Limsakul Atsamon 
Published: March 2024

AbstractThis study aims to investigate the impact of climate change on heat-related mortality in Southeast Asia in the future. The ensemble mean from five General Circulation Models (GCMs) including the Flexible Global Ocean-Atmosphere-Land System Model: Grid-Point Version 3 (FGOALS-g3), Max Planck Institute Earth System Model Version 1.2 (MPI-ESM1-2-LR), EC-Earth3, The Meteorological Research Institute Earth System Model Version 2.0 (MRI-ESM2-0), and Geophysical Fluid Dynamics Laboratory Earth System Model Version 4 (GFDL-ESM4) was used to project severe temperatures and heat indices in Southeast Asia under the Coupled Model Intercomparison Projects Phase 6 (CMIP6). This data was used to correlate with mortality data from the Global Burden of Disease database to quantify heat-related mortality in the region. The ensemble mean results show a reasonable level of accuracy in capturing temperature patterns in the Southeast Asian region with an R2of 0.96, root mean square error (RMSE) of 0.84 and a standard deviation of residual (SDR) of 0.02. When compared to the baseline (1990–2019), temperature extreme indices are rising across all climatic scenarios, with a substantial increase in the SSP3–7.0 and SSP5–8.5 scenarios, ranging from 10% to 50% over the regions, with the heat index predicted to peak in the middle of the century. The two low-emission scenarios, SSP1-2.6 and SSP2-4.5, on the other hand, anticipate more moderate increases, indicating a potentially less severe impact on the region. As a result, under high-emission scenarios, there is expected to be a significant increase in heat-related mortality across Southeast Asia. The expected impact is estimated to affect between 200 and 300 people per 100,000 people from 2030 to 2079, accordingly. Our results highlight the critical need to address health-related impacts of climate change in this region.
Onshore methane emissions measurements from the oil and gas industry: a scoping review
Authors: Vollrath Coleman , Hugenholtz Chris H , Barchyn Thomas E 
Published: March 2024

Abstract
Research on methane (CH4) emissions from the oil and gas (O&G) industry informs policies, regulations, and international initiatives that target reductions. However, there has been little integration and synthesis of the literature to document the state of knowledge, identify gaps, and determine key insights that can guide research priorities and mitigation. To address this, we performed a scoping review of 237 English-language peer-reviewed articles on CH4 emissions from onshore O&G sources, charting data on five research themes: publication trends, geography, measurement levels and methods, emissions sources, and emissions rates. Almost all articles (98%) were published between 2012 and 2022 with an increasing publication rate, indicating a nascent and evolving understanding of the science. Most articles (72%) focused on CH4 emissions from the U.S. O&G industry and were written by U.S.-based authors (69%), while other major O&G-producing countries like Saudi Arabia, Russia, and China were under-represented. Upstream was the most frequently studied supply chain segment, where U.S.-focused articles accounted for 75% of the research. Nearly half the articles (43%) included in the review reported site-level measurements, limiting the identification of equipment- and component-level emissions sources and root cause. Articles that measured or identified equipment-level sources (18%) noted high emissions from tanks, unlit flares, and compressors. The most common stand-off measurement platforms were vehicles and aircraft, while the use of satellites increased in articles published since 2019. Reported emissions profiles were consistently heavy-tailed and indicate method-based and geographic differences in magnitude and skew. All articles (n = 26) that compared inventory- to measurement-based estimates of emissions found large discrepancies in that inventories under-estimated the latter by a factor of 1.2–10 times. We recommend future research focus on: (i) field-based emissions studies for under-represented regions and source categories, (ii) identifying root causes and linking measurements to mitigation, and (iii) multi-level measurement integration.
Interlinking electronic band properties in catalysts with electrochemical nitrogen reduction performance: a direct influence
Authors: Biswas Ashmita , Samui Surajit , Dey Ramendra Sundar 
Published: March 2024

Abstract
The wordwide energy demands and the surge towards a net-zero sustainable society let the researchers set a goal towards the end of carbon cycle. This has enormously exaggerated the electrocatalytic processes such as water splitting, CO2 capture and reduction and nitrogen reduction reaction (NRR) as a safe and green alternative as these involve the utilization of renewable green power. Interestingly, the NH3 produced from NRR has been realized as a future fuel in terms of safer green H2 storage and transportation. Nevertheless, to scale up the NH3 production electrochemically, a benevolent catalyst needs to be developed. More interestingly, the electronic features of the catalyst that actually contribute to the interaction and binding between the adsorbate and reaction intermediates should be analyzed such that these can be tuned based on our requirements to obtain the desired high-standard goals of NH3 synthesis. The current topical review aims to provide an illustrative understanding on the experimental and theoretical descriptors that are likely to influence the electronic structure of catalysts for NRR. We have widely covered a detailed explanation regarding work function, d-band center and electronic effect on the electronic structures of the catalysts. While summarizing the same, we realized that there are several discrepancies in this field, which have not been discussed and could be misleading for the newcomers in the field. Thus, we have briefed the limitations and diverging explanations and have provided a few directions that could be looked upon to overcome the issues.
A novel auxetic stator winding to improve the performance of permanent magnet synchronous electric motors
Authors: Ravanbod Mohammad , Montazeri Allahyar 
Published: March 2024

Abstract
High efficiency and torque density in permanent magnet synchronous motors (PMSMs) have contributed to their increasing popularity. Nonetheless, these advantages are compromised by higher vibration levels resulting from the torque ripple issue and magnetic flux density in the stator, causing magnetic forces on the stator surface. In this study, a new smart shape for the stator winding is proposed which reduces unwanted torque vibration and the overall magnetic flux density while keeping the same motor efficiency. The proposed windings shape is designed based on the auxetic principle and a locally resonant mechanism (LRM). Afterward, the proposed and original PMSM models are compared by looking at the average torque, total losses, torque ripple, flux density, output power, and motor efficiency under different speed operating conditions. In addition, the sensitivity analyses of the proposed model reveal the influence of auxetic structural parameters and initial mechanical angle on the system’s performance, which can be utilized to control the physical and mechanical properties of the system. According to the results, the designed model reduces torque ripple and magnetic flux density in the stator region by 41.38% and 4.70%, respectively, while the motor efficiency remains unaffected. The present work offers a potentially robust and affordable solution for regulating the vibration behavior of electric motors without impacting power efficiency.
Harlem, USSR: Black Modernism in the Soviet Union, 1923–1937
Authors: Karmanov Fedor 
Published: March 2024

Abstract
The following essay tracks the translation of African-American poetry in the first two decades of the Soviet Union. Because the concept of race had no direct correlate in the Soviet Union, this essay argues that the translation of African-American poetry allowed Russian translators to publish modernist poetry that challenged the aesthetic imperatives of the Bolshevik party. To that end, the translation of Black modernist poetry—work by Langston Hughes, Claude McKay, and others—maps on to a canon of “socialist modernism,” an assembly of work that was both aesthetically and politically radical and one that pushed back against the Soviet conceptualizations of “socialist realism” and “bourgeois modernism.”
nanoBERT: a deep learning model for gene agnostic navigation of the nanobody mutational space
Authors: Hadsund Johannes Thorling , Satława Tadeusz , Janusz Bartosz , Shan Lu , Zhou Li , Röttger Richard , Krawczyk Konrad 
Published: March 2024

Abstract

Motivation
Nanobodies are a subclass of immunoglobulins, whose binding site consists of only one peptide chain, bestowing favorable biophysical properties. Recently, the first nanobody therapy was approved, paving the way for further clinical applications of this antibody format. Further development of nanobody-based therapeutics could be streamlined by computational methods. One of such methods is infilling—positional prediction of biologically feasible mutations in nanobodies. Being able to identify possible positional substitutions based on sequence context, facilitates functional design of such molecules.

Results
Here we present nanoBERT, a nanobody-specific transformer to predict amino acids in a given position in a query sequence. We demonstrate the need to develop such machine-learning based protocol as opposed to gene-specific positional statistics since appropriate genetic reference is not available. We benchmark nanoBERT with respect to human-based language models and ESM-2, demonstrating the benefit for domain-specific language models. We also demonstrate the benefit of employing nanobody-specific predictions for fine-tuning on experimentally measured thermostability dataset. We hope that nanoBERT will help engineers in a range of predictive tasks for designing therapeutic nanobodies.

Availability and implementation
https://huggingface.co/NaturalAntibody/.
Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes
Authors: Gisdon Florian J , Zunker Mariella , Wolf Jan Niclas , Prüfer Kai , Ackermann Jörg , Welsch Christoph , Koch Ina 
Published: March 2024

Abstract

Motivation
The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes.

Results
We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain–chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments.

Availability and implementation
Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.
AbFlex: designing antibody complementarity determining regions with flexible CDR definition
Authors: Jeon Woosung , Kim Dongsup 
Published: March 2024

Abstract

Motivation
Antibodies are proteins that the immune system produces in response to foreign pathogens. Designing antibodies that specifically bind to antigens is a key step in developing antibody therapeutics. The complementarity determining regions (CDRs) of the antibody are mainly responsible for binding to the target antigen, and therefore must be designed to recognize the antigen.

Results
We develop an antibody design model, AbFlex, that exhibits state-of-the-art performance in terms of structure prediction accuracy and amino acid recovery rate. Furthermore, &gt;38% of newly designed antibody models are estimated to have better binding energies for their antigens than wild types. The effectiveness of the model is attributed to two different strategies that are developed to overcome the difficulty associated with the scarcity of antibody–antigen complex structure data. One strategy is to use an equivariant graph neural network model that is more data-efficient. More importantly, a new data augmentation strategy based on the flexible definition of CDRs significantly increases the performance of the CDR prediction model.

Availability and implementation
The source code and implementation are available at https://github.com/wsjeon92/AbFlex.
iNGNN-DTI: prediction of drug–target interaction with interpretable nested graph neural network and pretrained molecule models
Authors: Sun Yan , Li Yan Yi , Leung Carson K , Hu Pingzhao 
Published: March 2024

Abstract

Motivation
Drug–target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but challenges remain, making it an open question. Existing approaches encode drugs and targets into features using deep learning models, but they often lack explanations for underlying interactions. Moreover, limited labeled DTIs in the chemical space can hinder model generalization.

Results
We propose an interpretable nested graph neural network for DTI prediction (iNGNN-DTI) using pre-trained molecule and protein models. The analysis is conducted on graph data representing drugs and targets by using a specific type of nested graph neural network, in which the target graphs are created based on 3D structures using Alphafold2. This architecture is highly expressive in capturing substructures of the graph data. We use a cross-attention module to capture interaction information between the substructures of drugs and targets. To improve feature representations, we integrate features learned by models that are pre-trained on large unlabeled small molecule and protein datasets, respectively. We evaluate our model on three benchmark datasets, and it shows a consistent improvement on all baseline models in all datasets. We also run an experiment with previously unseen drugs or targets in the test set, and our model outperforms all of the baselines. Furthermore, the iNGNN-DTI can provide more insights into the interaction by visualizing the weights learned by the cross-attention module.

Availability and implementation
The source code of the algorithm is available at https://github.com/syan1992/iNGNN-DTI.
Rapid generation of high-quality structure figures for publication with PyMOL-PUB
Authors: Chen Yuting , Zhang Haoling , Wang Wen , Shen Yue , Ping Zhi 
Published: March 2024

Abstract

Motivation
The advancement of structural biology has increased the requirements for researchers to quickly and efficiently visualize molecular structures in silico. Meanwhile, it is also time-consuming for structural biologists to create publication-standard figures, as no useful tools can directly generate figures from structure data. Although manual editing can ensure that figures meet the standards required for publication, it requires a deep understanding of software operations and/or program call commands. Therefore, providing interfaces based on established software instead of manual editing becomes a significant necessity.

Results
We developed PyMOL-PUB, based on the original design of PyMOL, to effectively create publication-quality figures from molecular structure data. It provides functions including structural alignment methods, functional coloring schemes, conformation adjustments, and layout plotting strategies. These functions allow users to easily generate high-quality figures, demonstrate structural differences, illustrate inter-molecular interactions, and predict performances of biomacromolecules.

Availability and implementation
Our tool is publicly available at https://github.com/BGI-SynBio/PyMOL-PUB.
Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS
Authors: Trimbour Remi , Deutschmann Ina Maria , Cantini Laura 
Published: March 2024

Motivation
The molecular identity of a cell results from a complex interplay between heterogeneous molecular layers. Recent advances in single-cell sequencing technologies have opened the possibility to measure such molecular layers of regulation.

Results
Here, we present HuMMuS, a new method for inferring regulatory mechanisms from single-cell multi-omics data. Differently from the state-of-the-art, HuMMuS captures cooperation between biological macromolecules and can easily include additional layers of molecular regulation. We benchmarked HuMMuS with respect to the state-of-the-art on both paired and unpaired multi-omics datasets. Our results proved the improvements provided by HuMMuS in terms of transcription factor (TF) targets, TF binding motifs and regulatory regions prediction. Finally, once applied to snmC-seq, scATAC-seq and scRNA-seq data from mouse brain cortex, HuMMuS enabled to accurately cluster scRNA profiles and to identify potential driver TFs.

Availability and implementation
HuMMuS is available at https://github.com/cantinilab/HuMMuS.
Cerebellar degeneration in gluten ataxia is linked to microglial activation
Authors: Floare Mara-Luciana , Wharton Stephen B , Simpson Julie E , Aeschlimann Daniel , Hoggard Nigel , Hadjivassiliou Marios 
Published: March 2024

Abstract
Gluten sensitivity has long been recognized exclusively for its gastrointestinal involvement; however, more recent research provides evidence for the existence of neurological manifestations that can appear in combination with or independent of the small bowel manifestations. Amongst all neurological manifestations of gluten sensitivity, gluten ataxia is the most commonly occurring one, accounting for up to 40% of cases of idiopathic sporadic ataxia. However, despite its prevalence, its neuropathological basis is still poorly defined. Here, we provide a neuropathological characterization of gluten ataxia and compare the presence of neuroinflammatory markers glial fibrillary acidic protein, ionized calcium-binding adaptor molecule 1, major histocompatibility complex II and cluster of differentiation 68 in the central nervous system of four gluten ataxia cases to five ataxia controls and seven neurologically healthy controls. Our results demonstrate that severe cerebellar atrophy, cluster of differentiation 20+ and cluster of differentiation 8+ lymphocytic infiltration in the cerebellar grey and white matter and a significant upregulation of microglial immune activation in the cerebellar granular layer, molecular layer and cerebellar white matter are features of gluten ataxia, providing evidence for the involvement of both cellular and humoral immune-mediated processes in gluten ataxia pathogenesis.