bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adherence to EAT-Lancet dietary recommendations for health and sustainability in the Gambia
Authors: Ali Zakari , Scheelbeek Pauline F D , Felix Jyoti , Jallow Bakary , Palazzo Amanda , Segnon Alcade C , Havlík Petr , Prentice Andrew M , Green Rosemary 
Published: September 2022

Abstract
Facilitating dietary change is pivotal to improving population health, increasing food system resilience, and minimizing adverse impacts on the environment, but assessment of the current ‘status-quo’ and identification of bottlenecks for improvement has been lacking to date. We assessed deviation of the Gambian diet from the EAT-Lancet guidelines for healthy and sustainable diets and identified leverage points to improve nutritional and planetary health. We analysed the 2015/16 Gambian Integrated Household Survey dataset comprising food consumption data from 12 713 households. Consumption of different food groups was compared against the EAT-Lancet reference diet targets to assess deviation from the guidelines. We computed a ‘sustainable and healthy diet index (SHDI)’ based on deviation of different food groups from the EAT-Lancet recommendations and modelled the socio-economic and geographic determinants of households that achieved higher scores on this index, using multivariable mixed effects regression. The average Gambian diet had very low adherence to EAT-Lancet recommendations. The diet was dominated by refined grains and added sugars which exceeded the recommendations. SHDI scores for nutritionally important food groups such as fruits, vegetables, nuts, dairy, poultry, and beef and lamb were low. Household characteristics associated with higher SHDI scores included: being a female-headed household, having a relatively small household size, having a schooled head of the household, having a high wealth index, and residing in an urban settlement. Furthermore, diets reported in the dry season and households with high crop production diversity showed increased adherence to the targets. While average Gambian diets include lower amounts of food groups with harmful environmental footprint, they are also inadequate in healthy food groups and are high in sugar. There are opportunities to improve diets without increasing their environmental footprint by focusing on the substitution of refined grains by wholegrains, reducing sugar and increasing fruit and vegetables consumption.
Modulation of ENSO teleconnections over North America by the Pacific decadal oscillation
Authors: Maher Nicola , Kay Jennifer E , Capotondi Antonietta 
Published: September 2022

Abstract
In this study, we investigate whether the Pacific decadal oscillation (PDO) can enhance or diminish El Niño Southern Oscillation (ENSO) temperature and precipitation teleconnections over North America using five single model initial-condition large ensembles (SMILEs). The use of SMILEs facilitates a statistically robust comparison of ENSO events that occur during different phases of the PDO. We find that a positive PDO enhances winter and spring El Niño temperature and precipitation teleconnections and diminishes La Niña teleconnections. A negative PDO has the opposite effect. The modulation of ENSO by the PDO is mediated by differences in the location and strength of the Aleutian Low and Pacific Jet during ENSO events under different phases of the PDO. This modulation is a simple combination of the individual effects of the PDO and ENSO over North America. Finally, we show that ENSO and the PDO can be used to evaluate the likelihood of the occurrence of temperature and precipitation anomalies in different regions, but cannot be used as a deterministic predictor of these anomalies due to the large variability between individual events.
Regime shifts of the wet and dry seasons in the tropics under global warming
Authors: Guo Jinyuan , Hu Shujuan , Guan Yuping 
Published: September 2022

Abstract
The main seasonal characteristics in the tropics include both spatial patterns and temporal parameters of onset, cessation, duration, and the number of wet and dry seasons. Previous studies showed that wet seasons shortened and dry seasons extended with global warming, but the changes in spatial distribution and the number of wet and dry seasons are still unclear. Here, we analyze the climatic characteristics of once wet and dry season a year (annual regime) and twice wet and dry seasons a year (biannual regime), and find that regimes of wet and dry seasons have changed from 1935 to 2014. Across the equator and the Tropic of Cancer and Capricorn, some regions where there used to be an annual regime have become a biannual regime; instead, other regions have shifted from a biannual regime into an annual regime. With seasonal regimes shifting, areas of the biannual regime have expanded at a rate of 31 000 km2/decade. Meanwhile, in annual regime regions, wet seasons have been shortened in 60.3% of regions, with an average of 7 d; the onset dates of wet seasons have been delayed in 64.8%, with an average of 6 d. Besides, wet seasons have become wetter in 51.1% of regions, and dry seasons have become drier in 59.9%. In biannual regime regions, the shortened wet seasons have occurred in 83.7% of regions, with an average shortening of 8 d, and precipitation has decreased in both wet and dry seasons. Moreover, the shorter wet seasons will amplify further by the end of the 21st century. The continuous seasonal changes will threaten agricultural, ecological security, and even human well-being.
Linga’s Dream?
Authors: Janzen Philip 
Published: September 2022

Abstract
This article is about a young farmer named Linga and his encounter with a water spirit in Oubangui-Chari (present-day Central African Republic) in 1930. It is also about the telling and re-telling of Linga’s encounter as the story was transcribed into Banda, translated into French, and rewritten as an ethnographic folktale. Responsible for these transformations were two Banda interpreters and a colonial administrator from the Caribbean. In the last two decades, historians of colonialism have turned increasingly to interpreters and other intermediaries. Often missing from this scholarship, however, are analyses of the granular details—of how colonial intermediaries used transcription, translation, and other methods to create new words, texts, and ideas. Linguistic anthropologists refer to such interventions as “entextualization,” the process by which fragments of discourse are extracted from their immediate settings and rendered into detached texts. Drawing on this scholarship, I argue that tracing the granular processes of entextualization opens up new vantage points from which to study the role of colonial intermediaries in the production of knowledge. The article also illuminates a methodology for mapping otherwise invisible constellations of language, meaning, and colonial power.
Urdu Ethics Literature and the Diversity of Muslim Thought in Colonial India
Authors: Mir Farina 
Published: September 2022

Abstract
This essay discusses Urdu-language akhlaq (ethics) texts produced and circulated in late-colonial India (1858–1947), situating them both within a classical genre of Islamic ethics and in the context of Indian vernacular print culture. It focuses on three akhlaq texts published between the 1870s and 1930s to consider contemporary Muslim ethical concerns and schema, arguing that the genre points to a widespread, everyday, and unexceptional Muslim subjectivity that placed a high value on ethical striving. The essay offers new perspectives on the history of Muslims in late-colonial India by highlighting a non-institutional, diffuse, and capacious intellectual formation whose ideas were disseminated through print in a commercial market. It also expands existing notions of Muslim authority from individuals—such as the ulama (Muslim clerical class)—and institutions—such as madrasas (religious schools and seminaries)—to include literary genres themselves. Finally, the article broadens the range of Muslim subject-positions represented in scholarship on Islam in colonial India.
Improved NSGA-II algorithms for multi-objective biomarker discovery
Authors: Cattelani Luca , Fortino Vittorio 
Published: September 2022

AbstractMotivationIn modern translational research, the development of biomarkers heavily relies on use of omics technologies, but implementations with basic data mining algorithms frequently lead to false positives. Non-dominated Sorting Genetic Algorithm II (NSGA2) is an extremely effective algorithm for biomarker discovery but has been rarely evaluated against large-scale datasets. The exploration of the feature search space is the key to NSGA2 success but in specific cases NSGA2 expresses a shallow exploration of the space of possible feature combinations, possibly leading to models with low predictive performances.ResultsWe propose two improved NSGA2 algorithms for finding subsets of biomarkers exhibiting different trade-offs between accuracy and feature number. The performances are investigated on gene expression data of breast cancer patients. The results are compared with NSGA2 and LASSO. The benchmarking dataset includes internal and external validation sets. The results show that the proposed algorithms generate a better approximation of the optimal trade-offs between accuracy and set size. Moreover, validation and test accuracies are better than those provided by NSGA2 and LASSO. Remarkably, the GA-based methods provide biomarkers that achieve a very high prediction accuracy (&gt;80%) with a small number of features (&lt;10), representing a valid alternative to known biomarker models, such as Pam50 and MammaPrint.Availability and implementationThe software is publicly available on GitHub at github.com/UEFBiomedicalInformaticsLab/BIODAI/tree/main/MOO.Supplementary informationSupplementary data are available at Bioinformatics online.
CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection
Authors: Mitrofanov Alexander , Ziemann Marcus , Alkhnbashi Omer S , Hess Wolfgang R , Backofen Rolf 
Published: September 2022

AbstractMotivationThe CRISPR-Cas9 system is a Type II CRISPR system that has rapidly become the most versatile and widespread tool for genome engineering. It consists of two components, the Cas9 effector protein, and a single guide RNA that combines the spacer (for identifying the target) with the tracrRNA, a trans-activating small RNA required for both crRNA maturation and interference. While there are well-established methods for screening Cas effector proteins and CRISPR arrays, the detection of tracrRNA remains the bottleneck in detecting Class 2 CRISPR systems.ResultsWe introduce a new pipeline CRISPRtracrRNA for screening and evaluation of tracrRNA candidates in genomes. This pipeline combines evidence from different components of the Cas9-sgRNA complex. The core is a newly developed structural model via covariance models from a sequence-structure alignment of experimentally validated tracrRNAs. As additional evidence, we determine the terminator signal (required for the tracrRNA transcription) and the RNA–RNA interaction between the CRISPR array repeat and the 5′-part of the tracrRNA. Repeats are detected via an ML-based approach (CRISPRidenify). Providing further evidence, we detect the cassette containing the Cas9 (Type II CRISPR systems) and Cas12 (Type V CRISPR systems) effector protein. Our tool is the first for detecting tracrRNA for Type V systems.Availability and implementationThe implementation of the CRISPRtracrRNA is available on GitHub upon requesting the access permission, (https://github.com/BackofenLab/CRISPRtracrRNA). Data generated in this study can be obtained upon request to the corresponding person: Rolf Backofen (backofen@informatik.uni-freiburg.de).Supplementary informationSupplementary data are available at Bioinformatics online.
DeepZF: improved DNA-binding prediction of C2H2-zinc-finger proteins by deep transfer learning
Authors: Aizenshtein-Gazit Sofia , Orenstein Yaron 
Published: September 2022

AbstractMotivationCys2His2 zinc-finger (C2H2-ZF) proteins are the largest class of human transcription factors and hence play central roles in gene regulation and cell function. C2H2-ZF proteins are characterized by a DNA-binding domain containing multiple ZFs. A subset of the ZFs bind diverse DNA triplets. Despite their central roles, little is known about which of their ZFs are binding and how the DNA-binding preferences are encoded in the amino acid sequence of each ZF.ResultsWe present DeepZF, a deep-learning-based pipeline for predicting binding ZFs and their DNA-binding preferences given only the amino acid sequence of a C2H2-ZF protein. To the best of our knowledge, we compiled the first in vivo dataset of binding and non-binding ZFs for training the first ZF-binding classifier. Our classifier, which is based on a novel protein transformer, achieved an average AUROC of 0.71. Moreover, we took advantage of both in vivo and in vitro datasets to learn the recognition code of ZF-DNA binding through transfer learning. Our newly developed model, which is the first to utilize deep learning for the task, achieved an average Pearson correlation greater than 0.94 over each of the three DNA binding positions. Together, DeepZF outperformed extant methods in the task of C2H2-ZF protein DNA-binding preferences prediction: it achieved an average Pearson correlation of 0.42 in motif similarity compared with an average correlation smaller than 0.1 achieved by extant methods. By applying established interpretability techniques, we show that DeepZF inferred biologically relevant binding principles, such as the effect of amino acid residue positions on ZF DNA-binding potential.Availability and implementationDeepZF code, model, and results are available via github.com/OrensteinLab/DeepZF.Supplementary informationSupplementary data are available at Bioinformatics online.
Cross-modality and self-supervised protein embedding for compound–protein affinity and contact prediction
Authors: You Yuning , Shen Yang 
Published: September 2022

Abstract

Motivation
Computational methods for compound–protein affinity and contact (CPAC) prediction aim at facilitating rational drug discovery by simultaneous prediction of the strength and the pattern of compound–protein interactions. Although the desired outputs are highly structure-dependent, the lack of protein structures often makes structure-free methods rely on protein sequence inputs alone. The scarcity of compound–protein pairs with affinity and contact labels further limits the accuracy and the generalizability of CPAC models.

Results
To overcome the aforementioned challenges of structure naivety and labeled-data scarcity, we introduce cross-modality and self-supervised learning, respectively, for structure-aware and task-relevant protein embedding. Specifically, protein data are available in both modalities of 1D amino-acid sequences and predicted 2D contact maps that are separately embedded with recurrent and graph neural networks, respectively, as well as jointly embedded with two cross-modality schemes. Furthermore, both protein modalities are pre-trained under various self-supervised learning strategies, by leveraging massive amount of unlabeled protein data. Our results indicate that individual protein modalities differ in their strengths of predicting affinities or contacts. Proper cross-modality protein embedding combined with self-supervised learning improves model generalizability when predicting both affinities and contacts for unseen proteins.

Availability and implementation
Data and source codes are available at https://github.com/Shen-Lab/CPAC.

Supplementary information
Supplementary data are available at Bioinformatics online.
Linking cells across single-cell modalities by synergistic matching of neighborhood structure
Authors: Hristov Borislav H , Bilmes Jeffrey A , Noble William Stafford 
Published: September 2022

AbstractMotivationA wide variety of experimental methods are available to characterize different properties of single cells in a complex biosample. However, because these measurement techniques are typically destructive, researchers are often presented with complementary measurements from disjoint subsets of cells, providing a fragmented view of the cell’s biological processes. This creates a need for computational tools capable of integrating disjoint multi-omics data. Because different measurements typically do not share any features, the problem requires the integration to be done in unsupervised fashion. Recently, several methods have been proposed that project the cell measurements into a common latent space and attempt to align the corresponding low-dimensional manifolds.ResultsIn this study, we present an approach, Synmatch, which produces a direct matching of the cells between modalities by exploiting information about neighborhood structure in each modality. Synmatch relies on the intuition that cells which are close in one measurement space should be close in the other as well. This allows us to formulate the matching problem as a constrained supermodular optimization problem over neighborhood structures that can be solved efficiently. We show that our approach successfully matches cells in small real multi-omics datasets and performs favorably when compared with recently published state-of-the-art methods. Further, we demonstrate that Synmatch is capable of scaling to large datasets of thousands of cells.Availability and implementationThe Synmatch code and data used in this manuscript are available at https://github.com/Noble-Lab/synmatch.
Insights into performance evaluation of compound–protein interaction prediction methods
Authors: Yaseen Adiba , Amin Imran , Akhter Naeem , Ben-Hur Asa , Minhas Fayyaz 
Published: September 2022

AbstractMotivationMachine-learning-based prediction of compound–protein interactions (CPIs) is important for drug design, screening and repurposing. Despite numerous recent publication with increasing methodological sophistication claiming consistent improvements in predictive accuracy, we have observed a number of fundamental issues in experiment design that produce overoptimistic estimates of model performance.ResultsWe systematically analyze the impact of several factors affecting generalization performance of CPI predictors that are overlooked in existing work: (i) similarity between training and test examples in cross-validation; (ii) synthesizing negative examples in absence of experimentally verified negative examples and (iii) alignment of evaluation protocol and performance metrics with real-world use of CPI predictors in screening large compound libraries. Using both state-of-the-art approaches by other researchers as well as a simple kernel-based baseline, we have found that effective assessment of generalization performance of CPI predictors requires careful control over similarity between training and test examples. We show that, under stringent performance assessment protocols, a simple kernel-based approach can exceed the predictive performance of existing state-of-the-art methods. We also show that random pairing for generating synthetic negative examples for training and performance evaluation results in models with better generalization in comparison to more sophisticated strategies used in existing studies. Our analyses indicate that using proposed experiment design strategies can offer significant improvements for CPI prediction leading to effective target compound screening for drug repurposing and discovery of putative chemical ligands of SARS-CoV-2-Spike and Human-ACE2 proteins.Availability and implementationCode and supplementary material available at https://github.com/adibayaseen/HKRCPI.Supplementary informationSupplementary data are available at Bioinformatics online.
Highly significant improvement of protein sequence alignments with AlphaFold2
Authors: Baltzis Athanasios , Mansouri Leila , Jin Suzanne , Langer Björn E , Erb Ionas , Notredame Cedric 
Published: September 2022

Abstract

Motivation
Protein sequence alignments are essential to structural, evolutionary and functional analysis, but their accuracy is often limited by sequence similarity unless molecular structures are available. Protein structures predicted at experimental grade accuracy, as achieved by AlphaFold2, could therefore have a major impact on sequence analysis.

Results
Here, we find that multiple sequence alignments estimated on AlphaFold2 predictions are almost as accurate as alignments estimated on experimental structures and significantly closer to the structural reference than sequence-based alignments. We also show that AlphaFold2 structural models of relatively low quality can be used to obtain highly accurate alignments. These results suggest that, besides structure modeling, AlphaFold2 encodes higher-order dependencies that can be exploited for sequence analysis.

Availability and implementation
All data, analyses and results are available on Zenodo (https://doi.org/10.5281/zenodo.7031286). The code and scripts have been deposited in GitHub (https://github.com/cbcrg/msa-af2-nf) and the various containers in (https://cloud.sylabs.io/library/athbaltzis/af2/alphafold, https://hub.docker.com/r/athbaltzis/pred).

Supplementary information
Supplementary data are available at Bioinformatics online.
Comparative Effectiveness of BNT162b2 and mRNA-1273 Booster Dose After BNT162b2 Primary Vaccination Against the Omicron Variants: A Retrospective Cohort Study Using Large-Scale Population-Based Registries in Japan
Authors: Ono Sachiko , Michihata Nobuaki , Yamana Hayato , Uemura Kohei , Ono Yosuke , Jo Taisuke , Yasunaga Hideo 
Published: September 2022

Abstract

Background
Direct comparative effectiveness of booster doses of BNT162b2 and mRNA-1273 after BNT162b2 primary vaccination is unknown.

Methods
We investigated comparative effectiveness of BNT162b2 and mRNA-1273 booster dose using data from registry systems for vaccination and coronavirus disease 2019 (COVID-19) infection in a local city in Japan. We followed participants aged ≥16 years who completed the BNT162b2 primary vaccination between 22 November 2021, and 15 April 2022. We collected information on age, sex, vaccination status, vaccine type, and infection status. Age was categorized as 16–44, 45–64, 65–84, and ≥85 years. Vaccine effectiveness for mRNA-1273 and no booster vaccination against BNT162b2 was estimated using age-stratified Cox regression adjusted for age, sex, and days since the second vaccination. The estimated hazard ratios for mRNA-1273 and no booster vaccinations were integrated separately using random effects meta-analyses.

Results
During the study period, we identified 62 586 (40.4%), 51 490 (33.2%), and 40 849 (26.4%) participants who received BNT162b2, mRNA-1273, and no booster dose, respectively. The median age was 69, 71, and 47 years for BNT162b2, mRNA-1273, and no booster dose, respectively. The integrated hazard ratio with reference to BNT162b2 was 1.72 for no booster vaccination and 0.62 for mRNA-1273. The comparative effectiveness of mRNA-1273 was similar across age categories.

Conclusions
Both homologous and heterologous vaccinations are effective against Omicron variants. In the head-to-head comparison, the effect was stronger in people who received heterologous vaccination than in those who received homologous vaccination. These findings may help improve logistics and decision making in future vaccination programs.