bluuebunny/crossref_metadata_2025_split huggingface dataset:
Two new species of Euphaea Selys, 1840 (Odonata: Zygoptera: Euphaeidae) from northern Western Ghats, India
Authors: Bhakare Shriram Dinkar , Nair Vinayan P. , Pawar Pratima Ashok , Bhoite Sunil Hanmant , Sadasivan Kalesh 
Published: April 2021

Two new species of the damselfly genus Euphaea Selys, 1840 (Odonata: Euphaeidae) are described from the Western Ghats of Satara District, Maharashtra, distinguished by their distinct morphology and coloration. E. thosegharensis Sadasivan & Bhakare sp. nov. is similar to E. cardinalis (Fraser, 1924), but is distinguished by the extensor and flexor surface of all femora black while all femora bright red in E. cardinalis; apical fourth of Hw black while apical half of Hw black in E. cardinalis; genae reddish-orange, black in E. cardinalis; a tuft of sparse stub black hair on either side of tergite of S9 while both S8 and S9 with tufts of long ventral hairs in E. cardinalis. Male genital vesicle matt black, with distal border rounded angles, while vesicle black and hexagonal in shape with rounded angles in E. cardinalis and S9 twice the length of S10, while S9 and S10 of equal length in E. cardinalis. E. pseudodispar Sadasivan & Bhakare sp. nov., is very close to E. dispar (Rambur, 1842), but is differentiated easily by the absence of yellow patch on legs as in E. dispar; only apical fifth of Hw black; genae being yellowish-white, while black in E. dispar; male genital vesicle brownish-black & rhomboid-shaped and with no transverse rugosities while black with distal border rounded and with fine transverse rugosities in E. dispar; penis with single seta on each side while E. dispar has three pairs; sternite of S9 very prominently extending ventrally like a beak in comparison with E. dispar. We have identified additional morphological characters useful in taxonomy of Euphaea of the Western Ghats for example, tufts of ventral hairs on terminal abdominal segments genital vesicle, penile structure of males and sternite of S9 in the males, and vulvar scales of females. A taxonomic key to all known species of genus Euphaea of the Western Ghats is also provided.
New records of six termite (Blattodea: Termitidae) species from Kerala, India
Authors: Amina Poovoli , Rajmohana K. 
Published: April 2021

The present paper reports new records of six species of termites (Blattodea: Termitidae), viz., Angulitermes acutus Mathur & Sen-Sarma, Dicuspiditermes obtusus (Silvestri), Pericapritermes dunensis (Roonwal & Sen-Sarma), Pericapritermes topslipensis Thakur, Nasutitermes cherraensis Roonwal & Chhotani, and Nasutitermes kali Roonwal & Chhotani from Kerala. Three species P. dunensis, N. cherraensis and N. kali are reported for the first time from the Western Ghats. The details of materials collected, diagnostic characters and distribution data in India are provided.
A checklist of fishes of Telangana State, India
Authors: Prasad Kante Krishna , Srinivasulu Chelmala 
Published: April 2021

A checklist of freshwater fishes of Telangana State, India including accepted common name, vernacular name, IUCN status, and endemicity is presented. Freshwater fish diversity of Telangana State is represented by 143 species belonging to 14 orders and 34 families. Two species, Rita bakalu and Indoreonectes telanganaensis are endemic to Telangana State.
Stroke Care in the United Kingdom During the COVID-19 Pandemic
Authors: Douiri Abdel , Muruet Walter , Bhalla Ajay , James Martin , Paley Lizz , Stanley Kaili , Rudd Anthony G. , Wolfe Charles D.A. , Bray Benjamin D. ,   on behalf of the SSNAP Collaboration
Published: April 2021

Background and Purpose:
The coronavirus disease 2019 (COVID-19) pandemic has potentially caused indirect harm to patients with other conditions via reduced access to health care services. We aimed to describe the impact of the initial wave of the pandemic on admissions, care quality, and outcomes in patients with acute stroke in the United Kingdom.

Methods:
Registry-based cohort study of patients with acute stroke admitted to hospital in England, Wales, and Northern Ireland between October 1, 2019, and April 30, 2020, and equivalent periods in the 3 prior years.

Results:

One hundred fourteen hospitals provided data for a study cohort of 184 017 patients. During the lockdown period (March 23 to April 30), there was a 12% reduction (6923 versus 7902) in the number of admissions compared with the same period in the 3 previous years. Admissions fell more for ischemic than hemorrhagic stroke, for older patients, and for patients with less severe strokes. Quality of care was preserved for all measures and in some domains improved during lockdown (direct access to stroke unit care, 1-hour brain imaging, and swallow screening). Although there was no change in the proportion of patients discharged with good outcome (modified Rankin Scale score, ≤2; 48% versus 48%), 7-day inpatient case fatality increased from 6.9% to 9.4% (
P
<0.001) and was 22.0% in patients with confirmed or suspected COVID-19 (adjusted rate ratio, 1.41 [1.11–1.80]).

Conclusions:
Assuming that the true incidence of acute stroke did not change markedly during the pandemic, hospital avoidance may have created a cohort of untreated stroke patients at risk of poorer outcomes or recurrent events. Unanticipated improvements in stroke care quality should be used as an opportunity for quality improvement and to learn about how to develop resilient health care systems.
B-cell receptor signaling and genetic lesions in TP53 and CDKN2A/CDKN2B cooperate in Richter transformation
Authors: Chakraborty Supriya , Martines Claudio , Porro Fabiola , Fortunati Ilaria , Bonato Alice , Dimishkovska Marija , Piazza Silvano , Yadav Brijesh S. , Innocenti Idanna , Fazio Rosa , Vaisitti Tiziana , Deaglio Silvia , Zamò Alberto , Dimovski Aleksandar J. , Laurenti Luca , Efremov Dimitar G. 
Published: April 2021

AbstractB-cell receptor (BCR) signals play a critical role in the pathogenesis of chronic lymphocytic leukemia (CLL), but their role in regulating CLL cell proliferation has still not been firmly established. Unlike normal B cells, CLL cells do not proliferate in vitro upon engagement of the BCR, suggesting that CLL cell proliferation is regulated by other signals from the microenvironment, such as those provided by Toll-like receptors or T cells. Here, we report that BCR engagement of human and murine CLL cells induces several positive regulators of the cell cycle, but simultaneously induces the negative regulators CDKN1A, CDKN2A, and CDKN2B, which block cell-cycle progression. We further show that introduction of genetic lesions that downregulate these cell-cycle inhibitors, such as inactivating lesions in CDKN2A, CDKN2B, and the CDKN1A regulator TP53, leads to more aggressive disease in a murine in vivo CLL model and spontaneous proliferation in vitro that is BCR dependent but independent of costimulatory signals. Importantly, inactivating lesions in CDKN2A, CDKN2B, and TP53 frequently co-occur in Richter syndrome (RS), and BCR stimulation of human RS cells with such lesions is sufficient to induce proliferation. We also show that tumor cells with combined TP53 and CDKN2A/2B abnormalities remain sensitive to BCR-inhibitor treatment and are synergistically sensitive to the combination of a BCR and cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitor both in vitro and in vivo. These data provide evidence that BCR signals are directly involved in driving CLL cell proliferation and reveal a novel mechanism of Richter transformation.
MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction
Authors: Han Changhee , Rundo Leonardo , Murao Kohei , Noguchi Tomoyuki , Shimahara Yuki , Milacski Zoltán Ádám , Koshino Saori , Sala Evis , Nakayama Hideki , Satoh Shin’ichi 
Published: April 2021

AbstractBackgroundUnsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer’s disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans.ResultsWe propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100$$\ell _1$$ℓ1loss—trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones—reconstructs unseen healthy/abnormal scans; (Diagnosis) Average$$\ell _2$$ℓ2loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921.ConclusionsSimilar to physicians’ way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans.
Paired-end small RNA sequencing reveals a possible overestimation in the isomiR sequence repertoire previously reported from conventional single read data analysis
Authors: Sanchez Herrero Jose Francisco , Pluvinet Raquel , Luna de Haro Antonio , Sumoy Lauro 
Published: April 2021

Abstract
Background
Next generation sequencing has allowed the discovery of miRNA isoforms, termed isomiRs. Some isomiRs are derived from imprecise processing of pre-miRNA precursors, leading to length variants. Additional variability is introduced by non-templated addition of bases at the ends or editing of internal bases, resulting in base differences relative to the template DNA sequence. We hypothesized that some component of the isomiR variation reported so far could be due to systematic technical noise and not real.

Results
We have developed the XICRA pipeline to analyze small RNA sequencing data at the isomiR level. We exploited its ability to use single or merged reads to compare isomiR results derived from paired-end (PE) reads with those from single reads (SR) to address whether detectable sequence differences relative to canonical miRNAs found in isomiRs are true biological variations or the result of errors in sequencing. We have detected non-negligible systematic differences between SR and PE data which primarily affect putative internally edited isomiRs, and at a much smaller frequency terminal length changing isomiRs. This is relevant for the identification of true isomiRs in small RNA sequencing datasets.

Conclusions
We conclude that potential artifacts derived from sequencing errors and/or data processing could result in an overestimation of abundance and diversity of miRNA isoforms. Efforts in annotating the isomiRnome should take this into account.
CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques
Authors: Zuo Yun , Lin Jianyuan , Zeng Xiangxiang , Zou Quan , Liu Xiangrong 
Published: April 2021

Abstract
Background
Carbonylation is a non-enzymatic irreversible protein post-translational modification, and refers to the side chain of amino acid residues being attacked by reactive oxygen species and finally converted into carbonyl products. Studies have shown that protein carbonylation caused by reactive oxygen species is involved in the etiology and pathophysiological processes of aging, neurodegenerative diseases, inflammation, diabetes, amyotrophic lateral sclerosis, Huntington’s disease, and tumor. Current experimental approaches used to predict carbonylation sites are expensive, time-consuming, and limited in protein processing abilities. Computational prediction of the carbonylation residue location in protein post-translational modifications enhances the functional characterization of proteins.

Results
In this study, an integrated classifier algorithm, CarSite-II, was developed to identify K, P, R, and T carbonylated sites. The resampling method K-means similarity-based undersampling and the synthetic minority oversampling technique (SMOTE-KSU) were incorporated to balance the proportions of K, P, R, and T carbonylated training samples. Next, the integrated classifier system Rotation Forest uses “support vector machine” subclassifications to divide three types of feature spaces into several subsets. CarSite-II gained Matthew’s correlation coefficient (MCC) values of 0.2287/0.3125/0.2787/0.2814, False Positive rate values of 0.2628/0.1084/0.1383/0.1313, False Negative rate values of 0.2252/0.0205/0.0976/0.0608 for K/P/R/T carbonylation sites by tenfold cross-validation, respectively. On our independent test dataset, CarSite-II yield MCC values of 0.6358/0.2910/0.4629/0.3685, False Positive rate values of 0.0165/0.0203/0.0188/0.0094, False Negative rate values of 0.1026/0.1875/0.2037/0.3333 for K/P/R/T carbonylation sites. The results show that CarSite-II achieves remarkably better performance than all currently available prediction tools.

Conclusion
The related results revealed that CarSite-II achieved better performance than the currently available five programs, and revealed the usefulness of the SMOTE-KSU resampling approach and integration algorithm. For the convenience of experimental scientists, the web tool of CarSite-II is available in http://47.100.136.41:8081/
Genetic diversity structure of western-type carrots
Authors: Stelmach Katarzyna , Macko-Podgórni Alicja , Allender Charlotte , Grzebelus Dariusz 
Published: April 2021

AbstractBackgroundCarrot is a crop with a wide range of phenotypic and molecular diversity. Within cultivated carrots, the western gene pool comprises types characterized by different storage root morphology. First western carrot cultivars originated from broad-based populations. It was followed by intercrosses among plants representing early open-pollinated cultivars, combined with mass phenotypic selection for traits of interest. Selective breeding improved root uniformity and led to the development of a range of cultivars differing in root shape and size. Based on the root shape and the market use of cultivars, a dozen of market types have been distinguished. Despite their apparent phenotypic variability, several studies have suggested that western cultivated carrot germplasm was genetically non-structured.ResultsNinety-threeDcS-ILP markers and 2354 SNP markers were used to evaluate the structure of genetic diversity in the collection of 78 western type open-pollinated carrot cultivars, each represented by five plants. The mean percentage of polymorphic loci segregating within a cultivar varied from 31.18 to 89.25% forDcS-ILP markers and from 45.11 to 91.29% for SNP markers, revealing high levels of intra-cultivar heterogeneity, in contrast to its apparent phenotypic stability. Average inbreeding coefficient for all cultivars was negative for bothDcS-ILP and SNP, whereas the overall genetic differentiation across all market classes, as measured by FST, was comparable for both marker systems. ForDcS-ILPs 90–92% of total genetic variation could be attributed to the differences within the inferred clusters, whereas for SNPs the values ranged between 91 to 93%. Discriminant Analysis of Principal Components enabled the separation of eight groups cultivars depending mostly on their market type affiliation. Three groups of cultivars, i.e. Amsterdam, Chantenay and Imperator, were characterized by high homogeneity regardless of the marker system used for genotyping.ConclusionsBoth marker systems used in the study enabled detection of substantial variation among carrot plants of different market types, therefore can be used in germplasm characterization and analysis of genome relationships. The presented results likely reveal the actual genetic diversity structure within the western carrot gene pool and point at possible discrepancies within the cultivars’ passport data.
Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta
Authors: Jiang Shan , Warren Joshua L. , Scovronick Noah , Moss Shannon E. , Darrow Lyndsey A. , Strickland Matthew J. , Newman Andrew J. , Chen Yong , Ebelt Stefanie T. , Chang Howard H. 
Published: April 2021

AbstractBackgroundShort-term associations between extreme heat events and adverse health outcomes are well-established in epidemiologic studies. However, the use of different exposure definitions across studies has limited our understanding of extreme heat characteristics that are most important for specific health outcomes or subpopulations.MethodsLogic regression is a statistical learning method for constructing decision trees based on Boolean combinations of binary predictors. We describe how logic regression can be utilized as a data-driven approach to identify extreme heat exposure definitions using health outcome data. We evaluated the performance of the proposed algorithm in a simulation study, as well as in a 20-year time-series analysis of extreme heat and emergency department visits for 12 outcomes in the Atlanta metropolitan area.ResultsFor the Atlanta case study, our novel application of logic regression identified extreme heat exposure definitions that were associated with several heat-sensitive disease outcomes (e.g., fluid and electrolyte imbalance, renal diseases, ischemic stroke, and hypertension). Exposures were often characterized by extreme apparent minimum temperature or maximum temperature over multiple days. The simulation study also demonstrated that logic regression can successfully identify exposures of different lags and duration structures when statistical power is sufficient.ConclusionLogic regression is a useful tool for identifying important characteristics of extreme heat exposures for adverse health outcomes, which may help improve future heat warning systems and response plans.
Epidemiology, clinical characteristics, and treatment outcomes of patients with COVID-19 at Thailand’s university-based referral hospital
Authors: Sirijatuphat Rujipas , Suputtamongkol Yupin , Angkasekwinai Nasikarn , Horthongkham Navin , Chayakulkeeree Methee , Rattanaumpawan Pinyo , Koomanachai Pornpan , Assanasen Susan , Rongrungruang Yong , Chierakul Nitipatana , Ratanarat Ranistha , Jitmuang Anupop , Wangchinda Walaiporn , Kantakamalakul Wannee 
Published: April 2021

Abstract
Background
The epidemiology and outcomes of COVID-19 patients in Thailand are scarce.

Methods
This retrospective cohort study included adult hospitalized patients who were diagnosed with COVID-19 at Siriraj Hospital during February 2020 to April 2020.

Results
The prevalence of COVID-19 was 7.5% (107 COVID-19 patients) among 1409 patients who underwent RT-PCR for SARS-CoV-2 detection at our hospital during the outbreak period. Patients with COVID-19 presented with symptoms in 94.4%. Among the 104 patients who were treated with antiviral medications, 78 (75%) received 2-drug regimen (lopinavir/ritonavir or darunavir/ritonavir plus chloroquine or hydroxychloroquine), and 26 (25%) received a 3-drug regimen with favipiravir added to the 2-drug regimen. Disease progression was observed in 18 patients (16.8%). All patients with COVID-19 were discharged alive.

Conclusions
The prevalence of COVID-19 was 7.5% among patients who underwent RT-PCR testing, and 10% among those having risk factors for COVID-19 acquisition. Combination antiviral therapies for COVID-19 patients were well-tolerated and produced a favorable outcome.
Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer
Authors: Liu Nannan , Wu Yun , Cheng Weipeng , Wu Yuxuan , Wang Liguo , Zhuang Liwei 
Published: April 2021

AbstractBackgroundGastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics.MethodsWe integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation.ResultsWe comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration ofWASHC5(also known asKIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value.ConclusionsThese findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC.