bluuebunny/crossref_metadata_2025_split huggingface dataset:
Medical cost of advanced illnesses in the last-year of life—retrospective database study
Authors: Kaur Palvinder , Wu Huei Yaw , Hum Allyn , Heng Bee Hoon , Tan Woan Shin 
Published: September 2021

AbstractObjectiveThis study aims to quantify medical care utilisation, and to describe the cost trajectories of individuals with advanced illnesses in the last-year of life, differentiated by advanced cancer, end-stage organ failure and progressive neurological disorders.MethodsThis retrospective database study included decedents who had previous inpatient or outpatient encounters at a public hospital in Singapore. Patients with advanced diseases were identified based on diagnostic codes and clinical criteria. Using a look-back approach, the amount of healthcare services utilised and the corresponding mean monthly and annual costs to the healthcare system in the last 12-months of life were quantified.ResultsThe last 12-months of life among 6,598 decedents was associated with £20,524 (95% confidence interval: £20,013–£21,036) in medical costs, of which 80% was accounted for by inpatient admissions. Costs increased sharply in the last 2-months of life, with a large proportion of monthly costs accounted for by inpatient admissions which rose rapidly from 61% at 12-months prior to death to 94% in the last-month of life. Compared to patients with cancer, individuals diagnosed with non-cancer advanced illnesses accumulated 1.6 times more healthcare costs in the last-year of life with significant differences across patients with end-stage organ failure and progressive neurological disorders.ConclusionHealthcare costs varied across disease conditions at the end-of-life. With advance care planning and close collaboration between the inpatient clinical team and the community providers, it may be possible to re-direct some of the hospitalisation costs to community-based palliative care services.
StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides
Authors: Singh Vishakha , Shrivastava Sameer , Kumar Singh Sanjay , Kumar Abhinav , Saxena Sonal 
Published: September 2021

AbstractDue to the rapid emergence of multi-drug resistant (MDR) bacteria, existing antibiotics are becoming ineffective. So, researchers are looking for alternatives in the form of antibacterial peptides (ABPs) based medicines. The discovery of novel ABPs using wet-lab experiments is time-consuming and expensive. Many machine learning models have been proposed to search for new ABPs, but there is still scope to develop a robust model that has high accuracy and precision. In this work, we present StaBle-ABPpred, a stacked ensemble technique-based deep learning classifier that uses bidirectional long-short term memory (biLSTM) and attention mechanism at base-level and an ensemble of random forest, gradient boosting and logistic regression at meta-level to classify peptides as antibacterial or otherwise. The performance of our model has been compared with several state-of-the-art classifiers, and results were subjected to analysis of variance (ANOVA) test and its post hoc analysis, which proves that our model performs better than existing classifiers. Furthermore, a web app has been developed and deployed at https://stable-abppred.anvil.app to identify novel ABPs in protein sequences. Using this app, we identified novel ABPs in all the proteins of the Streptococcus phage T12 genome. These ABPs have shown amino acid similarities with experimentally tested antimicrobial peptides (AMPs) of other organisms. Hence, they could be chemically synthesized and experimentally validated for their activity against different bacteria. The model and app developed in this work can be further utilized to explore the protein diversity for identifying novel ABPs with broad-spectrum activity, especially against MDR bacterial pathogens.
A survey of circular RNAs in complex diseases: databases, tools and computational methods
Authors: Xiao Qiu , Dai Jianhua , Luo Jiawei 
Published: September 2021

AbstractCircular RNAs (circRNAs) are a category of novelty discovered competing endogenous non-coding RNAs that have been proved to implicate many human complex diseases. A large number of circRNAs have been confirmed to be involved in cancer progression and are expected to become promising biomarkers for tumor diagnosis and targeted therapy. Deciphering the underlying relationships between circRNAs and diseases may provide new insights for us to understand the pathogenesis of complex diseases and further characterize the biological functions of circRNAs. As traditional experimental methods are usually time-consuming and laborious, computational models have made significant progress in systematically exploring potential circRNA-disease associations, which not only creates new opportunities for investigating pathogenic mechanisms at the level of circRNAs, but also helps to significantly improve the efficiency of clinical trials. In this review, we first summarize the functions and characteristics of circRNAs and introduce some representative circRNAs related to tumorigenesis. Then, we mainly investigate the available databases and tools dedicated to circRNA and disease studies. Next, we present a comprehensive review of computational methods for predicting circRNA-disease associations and classify them into five categories, including network propagating-based, path-based, matrix factorization-based, deep learning-based and other machine learning methods. Finally, we further discuss the challenges and future researches in this field.
iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization
Authors: Yu Yingying , He Wenjia , Jin Junru , Xiao Guobao , Cui Lizhen , Zeng Rao , Wei Leyi 
Published: September 2021

Abstract

Motivation
DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, identification of DNA methylation sites is critical for better understanding and revealing their functional mechanisms. To date, several machine learning and deep learning methods have been developed for the prediction of different DNA methylation types. However, they still highly rely on manual features, which can largely limit the high-latent information extraction. Moreover, most of them are designed for one specific DNA methylation type, and therefore cannot predict multiple methylation sites in multiple species simultaneously. In this study, we propose iDNA-ABT, an advanced deep learning model that utilizes adaptive embedding based on Bidirectional Encoder Representations from Transformers (BERT) together with transductive information maximization (TIM).

Results
Benchmark results show that our proposed iDNA-ABT can automatically and adaptively learn the distinguishing features of biological sequences from multiple species, and thus perform significantly better than the state-of-the-art methods in predicting three different DNA methylation types. In addition, TIM loss is proven to be effective in dichotomous tasks via the comparison experiment. Furthermore, we verify that our features have strong adaptability and robustness to different species through comparison of adaptive embedding and six handcrafted feature encodings. Importantly, our model shows great generalization ability in different species, demonstrating that our model can adaptively capture the cross-species differences and improve the predictive performance. For the convenient use of our method, we further established an online webserver as the implementation of the proposed iDNA-ABT.

Availability and implementation
Our proposed iDNA-ABT and data are freely accessible via http://server.wei-group.net/iDNA_ABT and our source codes are available for downloading in the GitHub repository (https://github.com/YUYING07/iDNA_ABT).

Supplementary information
Supplementary data are available at Bioinformatics online.
FLINO: a new method for immunofluorescence bioimage normalization
Authors: Graf John , Cho Sanghee , McDonough Elizabeth , Corwin Alex , Sood Anup , Lindner Andreas , Salvucci Manuela , Stachtea Xanthi , Van Schaeybroeck Sandra , Dunne Philip D , Laurent-Puig Pierre , Longley Daniel , Prehn Jochen H M , Ginty Fiona 
Published: September 2021

Abstract

Motivation
Multiplexed immunofluorescence bioimaging of single-cells and their spatial organization in tissue holds great promise to the development of future precision diagnostics and therapeutics. Current multiplexing pipelines typically involve multiple rounds of immunofluorescence staining across multiple tissue slides. This introduces experimental batch effects that can hide underlying biological signal. It is important to have robust algorithms that can correct for the batch effects while not introducing biases into the data. Performance of data normalization methods can vary among different assay pipelines. To evaluate differences, it is critical to have a ground truth dataset that is representative of the assay.

Results
A new immunoFLuorescence Image NOrmalization method is presented and evaluated against alternative methods and workflows. Multiround immunofluorescence staining of the same tissue with the nuclear dye DAPI was used to represent virtual slides and a ground truth. DAPI was restained on a given tissue slide producing multiple images of the same underlying structure but undergoing multiple representative tissue handling steps. This ground truth dataset was used to evaluate and compare multiple normalization methods including median, quantile, smooth quantile, median ratio normalization and trimmed mean of the M-values. These methods were applied in both an unbiased grid object and segmented cell object workflow to 24 multiplexed biomarkers. An upper quartile normalization of grid objects in log space was found to obtain almost equivalent performance to directly normalizing segmented cell objects by the middle quantile. The developed grid-based technique was then applied with on-slide controls for evaluation. Using five or fewer controls per slide can introduce biases into the data. Ten or more on-slide controls were able to robustly correct for batch effects.

Availability and implementation
The data underlying this article along with the FLINO R-scripts used to perform the evaluation of image normalizations methods and workflows can be downloaded from https://github.com/GE-Bio/FLINO.

Supplementary information
Supplementary data are available at Bioinformatics online.
A first-generation haplotype map (HapMap-1) of tea (Camellia sinensis L. O. Kuntz)
Authors: Singh Nisha , Rawal Hukam C , Angadi Ulavappa B , Sharma Tilak Raj , Singh Nagendra Kumar , Mondal Tapan Kumar 
Published: September 2021

Abstract

Motivation
Tea is a cross-pollinated woody perennial plant, which is why, application of conventional breeding is limited for its genetic improvement. However, lack of the genome-wide high-density SNP markers and genome-wide haplotype information has greatly hampered the utilization of tea genetic resources toward fast-track tea breeding programs. To address this challenge, we have generated a first-generation haplotype map of tea (Tea HapMap-1). Out-crossing and highly heterozygous nature of tea plants, make them more complicated for DNA-level variant discovery.

Results
In this study, whole genome re-sequencing data of 369 tea genotypes were used to generate 2,334,564 biallelic SNPs and 1,447,985 InDels. Around 2928.04 million paired-end reads were used with an average mapping depth of ∼0.31× per accession. Identified polymorphic sites in this study will be useful in mapping the genomic regions responsible for important traits of tea. These resources lay the foundation for future research to understand the genetic diversity within tea germplasm and utilize genes that determine tea quality. This will further facilitate the understanding of tea genome evolution and tea metabolite pathways thus, offers an effective germplasm utilization for breeding the tea varieties.

Supplementary information
Supplementary data are available at Bioinformatics online.
An Outcome-Oriented, Social–Ecological Framework for Assessing Protected Area Effectiveness
Authors: Ghoddousi Arash , Loos Jacqueline , Kuemmerle Tobias 
Published: September 2021

AbstractBoth the number and the extent of protected areas have grown considerably in recent years, but evaluations of their effectiveness remain partial and are hard to compare across cases. To overcome this situation, first, we suggest reserving the term effectiveness solely for assessing protected area outcomes, to clearly distinguish this from management assessments (e.g., sound planning). Second, we propose a multidimensional conceptual framework, rooted in social–ecological theory, to assess effectiveness along three complementary dimensions: ecological outcomes (e.g., biodiversity), social outcomes (e.g., well-being), and social–ecological interactions (e.g., reduced human pressures). Effectiveness indicators can subsequently be evaluated against contextual and management elements (e.g., design and planning) to shed light on management performance (e.g., cost-effectiveness). We summarize steps to operationalize our framework to foster more holistic effectiveness assessments while improving comparability across protected areas. All of this can ensure that protected areas make real contributions toward conservation and sustainability goals.
Biallelic variants in SLC38A3 encoding a glutamine transporter cause epileptic encephalopathy
Authors: Marafi Dana , Fatih Jawid M , Kaiyrzhanov Rauan , Ferla Matteo P , Gijavanekar Charul , Al-Maraghi Aljazi , Liu Ning , Sites Emily , Alsaif Hessa S , Al-Owain Mohammad , Zakkariah Mohamed , El-Anany Ehab , Guliyeva Ulviyya , Guliyeva Sughra , Gaba Colette , Haseeb Ateeq , Alhashem Amal M , Danish Enam , Karageorgou Vasiliki , Beetz Christian , Subhi Alaa A , Mullegama Sureni V , Torti Erin , Sebastin Monisha , Breilyn Margo Sheck , Duberstein Susan , Abdel-Hamid Mohamed S , Mitani Tadahiro , Du Haowei , Rosenfeld Jill A , Jhangiani Shalini N , Coban Akdemir Zeynep , Gibbs Richard A , Taylor Jenny C , Fakhro Khalid A , Hunter Jill V , Pehlivan Davut , Zaki Maha S , Gleeson Joseph G , Maroofian Reza , Houlden Henry , Posey Jennifer E , Sutton V Reid , Alkuraya Fowzan S , Elsea Sarah H , Lupski James R 
Published: September 2021

Abstract
The solute carrier (SLC) superfamily encompasses &gt;400 transmembrane transporters involved in the exchange of amino acids, nutrients, ions, metals, neurotransmitters and metabolites across biological membranes. SLCs are highly expressed in the mammalian brain; defects in nearly 100 unique SLC-encoding genes (OMIM: https://www.omim.org) are associated with rare Mendelian disorders including developmental and epileptic encephalopathy and severe neurodevelopmental disorders.
Exome sequencing and family-based rare variant analyses on a cohort with neurodevelopmental disorders identified two siblings with developmental and epileptic encephalopathy and a shared deleterious homozygous splicing variant in SLC38A3. The gene encodes SNAT3, a sodium-coupled neutral amino acid transporter and a principal transporter of the amino acids asparagine, histidine, and glutamine, the latter being the precursor for the neurotransmitters GABA and glutamate. Additional subjects with a similar developmental and epileptic encephalopathy phenotype and biallelic predicted-damaging SLC38A3 variants were ascertained through GeneMatcher and collaborations with research and clinical molecular diagnostic laboratories. Untargeted metabolomic analysis was performed to identify novel metabolic biomarkers.
Ten individuals from seven unrelated families from six different countries with deleterious biallelic variants in SLC38A3 were identified. Global developmental delay, intellectual disability, hypotonia, and absent speech were common features while microcephaly, epilepsy, and visual impairment were present in the majority. Epilepsy was drug-resistant in half. Metabolomic analysis revealed perturbations of glutamate, histidine, and nitrogen metabolism in plasma, urine, and CSF of selected subjects, potentially representing biomarkers of disease.
Our data support the contention that SLC38A3 is a novel disease gene for developmental and epileptic encephalopathy and illuminate the likely pathophysiology of the disease as perturbations in glutamine homeostasis.
Cholinergic white matter pathways in dementia with Lewy bodies and Alzheimer’s disease
Authors: Schumacher Julia , Ray Nicola J , Hamilton Calum A , Donaghy Paul C , Firbank Michael , Roberts Gemma , Allan Louise , Durcan Rory , Barnett Nicola , O’Brien John T , Taylor John-Paul , Thomas Alan J 
Published: September 2021

Abstract
Patients who have dementia with Lewy bodies and Alzheimer’s disease show early degeneration of the cholinergic nucleus basalis of Meynert. However, how white matter projections between the nucleus basalis of Meynert and the cortex are altered in neurodegenerative disease is unknown.
Tractography of white matter pathways originating from the nucleus basalis of Meynert was performed using diffusion-weighted imaging in 46 patients with Alzheimer’s disease dementia, 48 with dementia with Lewy bodies, 35 with mild cognitive impairment with Alzheimer’s disease, 38 with mild cognitive impairment with Lewy bodies and 71 control participants. Mean diffusivity of the resulting pathways was compared between groups and related to cognition, attention, functional EEG changes and dementia conversion in the mild cognitive impairment groups.
We successfully tracked a medial and a lateral pathway from the nucleus basalis of Meynert. Mean diffusivity of the lateral pathway was higher in both dementia and mild cognitive impairment groups than controls (all P &lt; 0.03). In the patient groups, increased mean diffusivity of this pathway was related to more impaired global cognition (β = −0.22, P = 0.06) and worse performance on an attention task (β = 0.30, P = 0.03). In patients with mild cognitive impairment, loss of integrity of both nucleus basalis of Meynert pathways was associated with increased risk of dementia progression [hazard ratio (95% confidence interval), medial pathway: 2.51 (1.24–5.09); lateral pathway: 2.54 (1.24–5.19)]. Nucleus basalis of Meynert volume was reduced in all clinical groups compared to controls (all P &lt; 0.001), but contributed less strongly to cognitive impairment and was not associated with attention or dementia conversion. EEG slowing in the patient groups as assessed by a decrease in dominant frequency was associated with smaller nucleus basalis of Meynert volumes (β = 0.22, P = 0.02) and increased mean diffusivity of the lateral pathway (β = −0.47, P = 0.003).
We show that degeneration of the cholinergic nucleus basalis of Meynert in Alzheimer’s disease and dementia with Lewy bodies is accompanied by an early reduction in integrity of white matter projections that originate from this structure. This is more strongly associated with cognition and attention than the volume of the nucleus basalis of Meynert itself and might be an early indicator of increased risk of dementia conversion in people with mild cognitive impairment.
Dopamine neurons exhibit emergent glutamatergic identity in Parkinson’s disease
Authors: Steinkellner Thomas , Conrad William S , Kovacs Imre , Rissman Robert A , Lee Edward B , Trojanowski John Q , Freyberg Zachary , Roy Subhojit , Luk Kelvin C , Lee Virginia M , Hnasko Thomas S 
Published: September 2021

Abstract
Loss of midbrain dopamine neurons causes the cardinal symptoms of Parkinson’s disease. However, not all dopamine neurons are equally vulnerable and a better understanding of the cell-type specific properties relating to selective dopamine neuron degeneration is needed. Most midbrain dopamine neurons express the vesicular glutamate transporter VGLUT2 during development and a subset continue to express low levels of VGLUT2 in adulthood, enabling the co-release of glutamate. Moreover, VGLUT2 expression in dopamine neurons can be neuroprotective since its genetic disruption was shown to sensitize dopamine neurons to neurotoxins. Here, we show that in response to toxic insult, and in two distinct models of alpha-synuclein stress, VGLUT2 dopamine neurons were resilient to degeneration. Dopamine neurons expressing VGLUT2 were enriched whether or not insult induced dopamine neuron loss, suggesting that while VGLUT2 dopamine neurons are more resilient, VGLUT2 expression can also be transcriptionally upregulated by injury. Finally, we observed that VGLUT2 expression was enhanced in surviving dopamine neurons from post-mortem Parkinson’s disease individuals. These data indicate that emergence of a glutamatergic identity in dopamine neurons may be part of a neuroprotective response in Parkinson’s disease.
Neurological complications induced by immune checkpoint inhibitors: a comprehensive descriptive case-series unravelling high risk of long-term sequelae
Authors: Plaçais Léo , Michot Jean-Marie , Champiat Stéphane , Romano-Martin Patricia , Baldini Capucine , Joao Maria Silva , Marabelle Aurélien , Voisin Anne-Laure , Not Adeline , Labeyrie Céline , Beaudonnet Guillemette , Laparra Ariane , Maria Alexandre T J , Masseau Agathe , Dehette Stéphanie , Deleporte Amélie , Echaniz-Laguna Andoni , Denier Christian , Adams David , Lambotte Olivier , Noel Nicolas , Cauquil Cécile 
Published: September 2021

Abstract
Neurological immune-related adverse events are complications of programmed-cell death 1 or programmed-cell death 1 ligand immunotherapies that can be life threatening and often lead to anticancer immunotherapy withdrawal. Scant clinical data are available that integrate the clinical presentation, therapeutic management and long-term outcome. All consecutive adult patients treated by programmed-cell death 1 or programmed-cell death 1 ligand immunotherapies, given alone or in combination with other treatment, who experienced a neurological immune-related adverse event with a severity grade ≥2 in Paris Saclay-University hospitals were investigated from June 2014 to February 2019. The frequency of neurological immune-related adverse events was calculated from the prospective Registre des Effets Indésirables Sévères des Anticorps Monoclonaux Immunomodulateurs en Cancérologie cohort. Forty patients presenting with 51 distinct neurological immune-related adverse events were included. The prevalence of grade ≥2 neurological immune-related adverse events was estimated to be 1.22% in the Registre des Effets Indésirables Sévères des Anticorps Monoclonaux Immunomodulateurs en Cancérologie cohort. Among 40 patients with neurological immune-related adverse events, 65% received programmed-cell death 1 or programmed-cell death 1 ligand monotherapy and 35% received a combination of programmed-cell death 1 plus anti-CTLA4 (Common Terminology Criteria for Adverse Events). Clinical neurological presentations were peripheral (48%), central (35%), or mixed (18%). The severity of neurological immune-related adverse events was grade 2 for 14 (35%) and ≥grade 3 for 26 patients (65%). The mortality rate related to neurological immune-related adverse events was 8%. Corticosteroid treatment led to neurological recovery in 74%. Long-term follow-up highlighted that 53% of patients experienced long-term neurological sequelae. Five patients were rechallenged by programmed-cell death 1 monotherapy without recurrence of their neurological immune-related adverse event(s). Neurological immune-related adverse events induced by programmed-cell death 1 or programmed-cell death 1 ligand are rare but are severe with a mortality rate of 8% and long-term sequelae for 53% of patients. Corticosteroids should be started when neurological immunological complications are identified to avoid long-term sequelae.