bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genomic Profiling of Metastatic Uveal Melanoma Shows Frequent Coexisting BAP1 or SF3B1 and GNAQ/GNA11 Mutations and Correlation With Prognosis
Authors: Isaacson Alexandra L , Sompallae Ramakrishna R , Guseva Natalya V , Bellizzi Andrew M , Bossler Aaron D , Ma Deqin 
Published: January 2022

Abstract

Objectives
To identify therapeutic targets and correlate with clinical outcomes from mutation profiling of metastatic uveal melanoma (UM) using next-generation sequencing (NGS).

Methods
Melanoma cases that were tested using DNA-based NGS panels of 25 and/or 214 genes were evaluated retrospectively (263 cases) and identified 27 UM cases. BAP1 expression was examined by immunohistochemistry.

Results
Mutations in GNA11 (14) and GNAQ (12) were found in 96% (n = 27) of cases of UM, and most had coexisting BAP1 (17) or SF3B1 (4) mutations. Coexisting GNAQ/11-SF3B1 mutations correlated with a longer average time to first metastasis compared with GNAQ/11-BAP1 mutations (99.7 vs 38.5 months, P = .047). Three patients with BAP1 mutations received trametinib; two are still alive (15 months; 23 months), and one died (32 months). In non-UMs, only 4.2% (n = 236) had BAP1 and 3.8% had SF3B1 mutations; none had coexisting GNAQ/11 mutations.

Conclusions
Coexisting BAP1/SF3B1 and GNAQ/11 mutations were unique to UM. SF3B1 mutations were reported to be UM-specific in melanoma and associated with rare/no metastasis. The finding of mutated SF3B1 in 14.8% (n = 27) of UMs suggests its role should be further evaluated. The correlation of BAP1/SF3B1 mutation with survival also warrants investigation.
Effects of primary seed dormancy on lifetime fitness of Arabidopsis thaliana in the field
Authors: Postma Froukje M , Ågren Jon 
Published: January 2022

Abstract

Background and Aims
Seed dormancy determines the environmental niche of plants in seasonal environments, and has consequences for plant performance that potentially go far beyond the seed and seedling stages. In this study, we examined the cascading effects of seed dormancy on the expression of subsequent life-history traits and fitness in the annual herb Arabidopsis thaliana.

Methods
We planted seeds of &gt;200 recombinant inbred lines (RILs) derived from a cross between two locally adapted populations (Italy and Sweden), and both parental genotypes at the native site of the Swedish population in three consecutive years. We quantified the relationship between primary seed dormancy and the expression of subsequent life-history traits and fitness in the RIL population with path analysis. To examine the effects of differences in dormancy on the relative fitness of the two parental genotypes, we planted dormant seeds during the seed dispersal period and non-dormant seeds during the germination period of the local population.

Key Results
In the RIL population, strong primary dormancy was associated with high seedling survival, but with low adult survival and fecundity, and path analysis indicated that this could be explained by effects on germination timing, rosette size and flowering start. The relationship between primary seed dormancy and germination proportion varied among years, and this was associated with differences in seasonal changes in soil moisture. The planting of dormant and non-dormant seeds indicated that the lower primary dormancy of the local Swedish genotype contributed to its higher germination proportion in two years and to its higher fecundity in one year.

Conclusions
Our results show that seed dormancy affects trait expression and fitness components across the life cycle, and suggest that among-year variation in the incidence of drought during the germination period should be considered when predicting the consequences of climatic change for population growth and evolution.
SCAT5 Sex Differences: Normative Data, Clinical Thresholds, and Relevance for Identification of Concussion
Authors: Bailey Christopher , Meyer Jessica , Soden Daniel , Tangen Christopher , Deane Ryan , Briskin Susannah , Gunstad John , Fastenau Philip , Smith Paul , Solomon Mary , Kelly Amanda Weiss 
Published: January 2022

AbstractObjectiveThis study evaluated sex differences in performance on the Sport Concussion Assessment Tool–5 (SCAT5) Standardized Assessment of Concussion (SAC) and in baseline SCAT5 symptom reporting. It established clinically relevant cut points for low performance on the SAC based on both reliable chance indices (RCIs) and normative performance. This study also evaluated the diagnostic utility of the sex-adjusted SCAT5 SAC for identification of suspected concussion in collegiate athletes.MethodIn total, 671 uninjured collegiate athletes were administered the SCAT5 and 264 of these athletes also completed SCAT5 testing ~1 year later. Fifty-four athletes were administered the SCAT5 after being removed from play due to suspected concussion. Sex differences in cognitive performance and symptom reporting at baseline were evaluated and sex-specific clinically relevant cut points were provided. Chi square and logistic regression models were used to evaluate if SAC performance was a significant predictor of concussion status.ResultsFemale athletes outperformed male athletes on the SCAT 5 SAC and showed minimally higher symptom endorsement. Use of sex-corrected normative data improved performance of the SAC in identification of suspected concussion when a low score cut point was used. Logistic regression models showed that sex-corrected SAC change from baseline (RCI) improved the predictive value of the model after first accounting for other elements of the SCAT5.ConclusionsPresent results support the use of sex-specific normative data for the SCAT5 SAC, particularly if using low performance without comparison to a baseline; however, reliable change from a pre-injury baseline may have somewhat higher diagnostic utility.
MHCRoBERTa: pan-specific peptide–MHC class I binding prediction through transfer learning with label-agnostic protein sequences
Authors: Wang Fuxu , Wang Haoyan , Wang Lizhuang , Lu Haoyu , Qiu Shizheng , Zang Tianyi , Zhang Xinjun , Hu Yang 
Published: January 2022

Abstract
Predicting the binding of peptide and major histocompatibility complex (MHC) plays a vital role in immunotherapy for cancer. The success of Alphafold of applying natural language processing (NLP) algorithms in protein secondary struction prediction has inspired us to explore the possibility of NLP methods in predicting peptide–MHC class I binding. Based on the above motivations, we propose the MHCRoBERTa method, RoBERTa pre-training approach, for predicting the binding affinity between type I MHC and peptides. Analysis of the results on benchmark dataset demonstrates that MHCRoBERTa can outperform other state-of-art prediction methods with an increase of the Spearman rank correlation coefficient (SRCC) value. Notably, our model gave a significant improvement on IC50 value. Our method has achieved SRCC value and AUC value as 0.785 and 0.817, respectively. Our SRCC value is 14.3% higher than NetMHCpan3.0 (the second highest SRCC value on pan-specific) and is 3% higher than MHCflurry (the second highest SRCC value on all methods). The AUC value is also better than any other pan-specific methods. Moreover, we visualize the multi-head self-attention for the token representation across the layers and heads by this method. Through the analysis of the representation of each layer and head, we can show whether the model has learned the syntax and semantics necessary to perform the prediction task well. All these results demonstrate that our model can accurately predict the peptide–MHC class I binding affinity and that MHCRoBERTa is a powerful tool for screening potential neoantigens for cancer immunotherapy. MHCRoBERTa is available as an open source software at github (https://github.com/FuxuWang/MHCRoBERTa).
Deciphering the etiology and role in oncogenic transformation of the CpG island methylator phenotype: a pan-cancer analysis
Authors: Yates Josephine , Boeva Valentina 
Published: January 2022

AbstractNumerous cancer types have shown to present hypermethylation of CpG islands, also known as a CpG island methylator phenotype (CIMP), often associated with survival variation. Despite extensive research on CIMP, the etiology of this variability remains elusive, possibly due to lack of consistency in defining CIMP. In this work, we utilize a pan-cancer approach to further explore CIMP, focusing on 26 cancer types profiled in the Cancer Genome Atlas (TCGA). We defined CIMP systematically and agnostically, discarding any effects associated with age, gender or tumor purity. We then clustered samples based on their most variable DNA methylation values and analyzed resulting patient groups. Our results confirmed the existence of CIMP in 19 cancers, including gliomas and colorectal cancer. We further showed that CIMP was associated with survival differences in eight cancer types and, in five, represented a prognostic biomarker independent of clinical factors. By analyzing genetic and transcriptomic data, we further uncovered potential drivers of CIMP and classified them in four categories: mutations in genes directly involved in DNA demethylation; mutations in histone methyltransferases; mutations in genes not involved in methylation turnover, such as KRAS and BRAF; and microsatellite instability. Among the 19 CIMP-positive cancers, very few shared potential driver events, and those drivers were only IDH1 and SETD2 mutations. Finally, we found that CIMP was strongly correlated with tumor microenvironment characteristics, such as lymphocyte infiltration. Overall, our results indicate that CIMP does not exhibit a pan-cancer manifestation; rather, general dysregulation of CpG DNA methylation is caused by heterogeneous mechanisms.
Multi-variable AUC for sifting complementary features and its biomedical application
Authors: Su Yue , Du Keyu , Wang Jun , Wei Jin-mao , Liu Jian 
Published: January 2022

Abstract
Although sifting functional genes has been discussed for years, traditional selection methods tend to be ineffective in capturing potential specific genes. First, typical methods focus on finding features (genes) relevant to class while irrelevant to each other. However, the features that can offer rich discriminative information are more likely to be the complementary ones. Next, almost all existing methods assess feature relations in pairs, yielding an inaccurate local estimation and lacking a global exploration. In this paper, we introduce multi-variable Area Under the receiver operating characteristic Curve (AUC) to globally evaluate the complementarity among features by employing Area Above the receiver operating characteristic Curve (AAC). Due to AAC, the class-relevant information newly provided by a candidate feature and that preserved by the selected features can be achieved beyond pairwise computation. Furthermore, we propose an AAC-based feature selection algorithm, named Multi-variable AUC-based Combined Features Complementarity, to screen discriminative complementary feature combinations. Extensive experiments on public datasets demonstrate the effectiveness of the proposed approach. Besides, we provide a gene set about prostate cancer and discuss its potential biological significance from the machine learning aspect and based on the existing biomedical findings of some individual genes.
Shedding light on the hidden human proteome expands immunopeptidome in cancer
Authors: Li Yongsheng , Zhang Yunpeng , Pan Tao , Zhou Ping , Zhou Weiwei , Gao Yueying , Zheng Shaojiang , Xu Juan 
Published: January 2022

AbstractUnrestrained cellular growth and immune escape of a tumor are associated with the incidental errors of the genome and transcriptome. Advances in next-generation sequencing have identified thousands of genomic and transcriptomic aberrations that generate variant peptides that assemble the hidden proteome, further expanding the immunopeptidome. Emerging next-generation sequencing technologies and a number of computational methods estimated the abundance of immune infiltration from bulk transcriptome have advanced our understanding of tumor microenvironments. Here, we will characterize several major types of tumor-specific antigens arising from single-nucleotide variants, insertions and deletions, gene fusion, alternative splicing, RNA editing and non-coding RNAs. Finally, we summarize the current state-of-the-art computational and experimental approaches or resources and provide an integrative pipeline for the identification of candidate tumor antigens. Together, the systematic investigation of the hidden proteome in cancer will help facilitate the development of effective and durable immunotherapy targets for cancer.
GenomeTornadoPlot: a novel R package for CNV visualization and focality analysis
Authors: Hong Chen , Thiele Robin , Feuerbach Lars 
Published: January 2022

Abstract

Motivation
Analysis of focal copy number variations (CNVs) is highly relevant for cancer research, as they pinpoint driver genes. More specifically, due to selective pressure oncogenes and tumor suppressor genes are more often affected by these events than neighboring passengers. In cases where multiple candidates co-reside in a genomic locus, careful comparison is required to either identify multigenic minimally deleted regions of synergistic co-mutations, or the true single driver gene. The study of focal CNVs in large cancer genome cohorts requires specialized visualization and statistical analysis.

Results
We developed the GenomeTornadoPlot R-package which generates gene-centric visualizations of CNV types, locations and lengths from cohortwise NGS data. Furthermore, the software enables the pairwise comparison of proximate genes to identify co-mutation patterns or driver-passenger hierarchies. The visual examination provided by GenomeTornadoPlot is further supported by adaptable local and global focality scoring. Integrated into the GenomeTornadoPlot R-Package is the comprehensive PCAWG database of CNVs, comprising 2976 cancer genome entities from 46 cohorts of the Pan-cancer Analysis of Whole Genomes project. The GenomeTornadoPlot R-package can be used to perform exploratory or hypothesis-driven analyses on the basis of the PCAWG data or in combination with data provided by the user.

Availability and implementation
GenomeTornadoPlot is written in R script and released via github: &lt;https://github.com/chenhong-dkfz/GenomeTornadoPlot/&gt;. The package is under the license of GPL-3.0.
SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model
Authors: Singh Jaspreet , Litfin Thomas , Singh Jaswinder , Paliwal Kuldip , Zhou Yaoqi 
Published: January 2022

Abstract

Motivation
Accurate prediction of protein contact-map is essential for accurate protein structure and function prediction. As a result, many methods have been developed for protein contact map prediction. However, most methods rely on protein-sequence-evolutionary information, which may not exist for many proteins due to lack of naturally occurring homologous sequences. Moreover, generating evolutionary profiles is computationally intensive. Here, we developed a contact-map predictor utilizing the output of a pre-trained language model ESM-1b as an input along with a large training set and an ensemble of residual neural networks.

Results
We showed that the proposed method makes a significant improvement over a single-sequence-based predictor SSCpred with 15% improvement in the F1-score for the independent CASP14-FM test set. It also outperforms evolutionary-profile-based methods trRosetta and SPOT-Contact with 48.7% and 48.5% respective improvement in the F1-score on the proteins without homologs (Neff = 1) in the independent SPOT-2018 set. The new method provides a much faster and reasonably accurate alternative to evolution-based methods, useful for large-scale prediction.

Availability and implementation
Stand-alone-version of SPOT-Contact-LM is available at https://github.com/jas-preet/SPOT-Contact-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-contact-single. The datasets used in this research can also be downloaded from the GitHub.

Supplementary information
Supplementary data are available at Bioinformatics online.
Functional hybrid factor regression model for handling heterogeneity in imaging studies
Authors: Huang C , Zhu H 
Published: January 2022

Summary
This paper develops a functional hybrid factor regression modelling framework to handle the heterogeneity of many large-scale imaging studies, such as the Alzheimer’s disease neuroimaging initiative study. Despite the numerous successes of those imaging studies, such heterogeneity may be caused by the differences in study environment, population, design, protocols or other hidden factors, and it has posed major challenges in integrative analysis of imaging data collected from multicentres or multistudies. We propose both estimation and inference procedures for estimating unknown parameters and detecting unknown factors under our new model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed procedures is assessed by using Monte Carlo simulations and a real data example on hippocampal surface data from the Alzheimer’s disease study.
Postoperative mortality risk assessment in colorectal cancer: development and validation of a clinical prediction model using data from the Dutch ColoRectal Audit
Authors: de Nes Lindsey C. F. , Hannink Gerjon , ‘t Lam-Boer Jorine , Hugen Niek , Verhoeven Rob H. , de Wilt Johannes H. W. ,   Dutch Colorectal Audit Group
Published: January 2022

Abstract

Background
As the outcome of modern colorectal cancer (CRC) surgery has significantly improved over the years, however, renewed and adequate risk stratification for mortality is important to identify high-risk patients. This population-based study was conducted to analyse postoperative outcomes in patients with CRC and to create a risk model for 30-day mortality.

Methods
Data from the Dutch Colorectal Audit were used to assess differences in postoperative outcomes (30-day mortality, hospital stay, blood transfusion, postoperative complications) in patients with CRC treated from 2009 to 2017. Time trends were analysed. Clinical variables were retrieved (including stage, age, sex, BMI, ASA grade, tumour location, timing, surgical approach) and a prediction model with multivariable regression was computed for 30-day mortality using data from 2009 to 2014. The predictive performance of the model was tested among a validation cohort of patients treated between 2015 and 2017.

Results
The prediction model was obtained using data from 51 484 patients and the validation cohort consisted of 32 926 patients. Trends of decreased length of postoperative hospital stay and blood transfusions were found over the years. In stage I–III, postoperative complications declined from 34.3 per cent to 29.0 per cent (P &lt; 0.001) over time, whereas in stage IV complications increased from 35.6 per cent to 39.5 per cent (P = 0.010). Mortality decreased in stage I–III from 3.0 per cent to 1.4 per cent (P &lt; 0.001) and in stage IV from 7.6 per cent to 2.9 per cent (P &lt; 0.001). Eight factors, including stage, age, sex, BMI, ASA grade, tumour location, timing, and surgical approach were included in a 30-day mortality prediction model. The results on the validation cohort documented a concordance C statistic of 0.82 (95 per cent c.i. 0.80 to 0.83) for the prediction model, indicating good discriminative ability.

Conclusion
Postoperative outcome improved in all stages of CRC surgery in the Netherlands. The developed model accurately predicts postoperative mortality risk and is clinically valuable for decision-making.
Risk-adjusted mortality in severely injured adult trauma patients in Sweden
Authors: Strömmer Lovisa , Lundgren Fredrik , Ghorbani Poya , Troëng Thomas 
Published: January 2022

Abstract

Background
Risk-adjusted mortality (RAM) analysis and comparisons of clinically relevant subsets of trauma patients allow hospitals to assess performance in different processes of care. The aim of the study was to develop a RAM model and compare RAM ratio (RAMR) in subsets of severely injured adult patients treated in university hospitals (UHs) and emergency hospitals (EHs) in Sweden.

Methods
This was a retrospective study of the Swedish trauma registry data (2013 to 2017) comparing RAMR in patients (aged 15 years or older and New Injury Severity Score (NISS) of more than 15) in the total population (TP) and in multisystem blunt (MB), truncal penetrating (PEN), and severe traumatic brain injury (STBI) subsets treated in UHs and EHs. The RAM model included the variables age, NISS, ASA Physical Status Classification System Score, and physiology on arrival.

Results
In total, 6690 patients were included in the study (4485 from UHs and 2205 from EHs). The logistic regression model showed a good fit. RAMR was 4.0, 3.8, 7.4, and 8.5 percentage points lower in UH versus EH for TP (P &lt; 0.001), MB (P &lt; 0.001), PEN (P = 0.096), and STBI (P = 0.005), respectively. The TP and MB subsets were subgrouped in with (+) and without (−) traumatic brain injury (TBI). RAMR was 7.5 and 7.0, respectively, percentage points lower in UHs than in EHs in TP + TBI and MB + TBI (both P &lt; 0.001). In the TP–TBI (P = 0.027) and MB–TBI (P = 0.107) subsets the RAMR was 1.6 and 1.8 percentage points lower, respectively.

Conclusion
The lower RAMR in UHs versus EH were due to differences in TBI-related mortality. No evidence supported that Swedish EHs provide inferior quality of care for trauma patients without TBI or for patients with penetrating injuries.
Matrix-induced autologous chondrocyte implantation (mACI) versus autologous matrix-induced chondrogenesis (AMIC) for chondral defects of the knee: a systematic review
Authors: Migliorini Filippo , Eschweiler Jörg , Götze Christian , Driessen Arne , Tingart Markus , Maffulli Nicola 
Published: January 2022

Abstract

Introduction
Chondral defects of the knee are common and their treatment is challenging.

Source of data
PubMed, Google scholar, Embase and Scopus databases.

Areas of agreement
Both autologous matrix-induced chondrogenesis (AMIC) and membrane-induced autologous chondrocyte implantation (mACI) have been used to manage chondral defects of the knee.

Areas of controversy
It is debated whether AMIC and mACI provide equivalent outcomes for the management of chondral defects in the knee at midterm follow-up. Despite the large number of clinical studies, the optimal treatment is still controversial.

Growing points
To investigate whether AMIC provide superior outcomes than mACI at midterm follow-up.

Areas timely for developing research
AMIC may provide better outcomes than mACI for chondral defects of the knee. Further studies are required to verify these results in a clinical setting.