bluuebunny/crossref_metadata_2025_split huggingface dataset:
The EnigmaticCulex pipiens(Diptera: Culicidae) Species Complex: Phylogenetic Challenges and Opportunities From a Notoriously Tricky Mosquito Group
Authors: Aardema Matthew L , Olatunji Sarah K , Fonseca Dina M 
Published: September 2021

AbstractOur understanding of how natural selection and demographic processes produce and maintain biological diversity remains limited. However, developments in high-throughput genomic sequencing coupled with new analytical tools and phylogenetic methods now allow detailed analyses of evolutionary patterns in genes and genomes responding to specific demographic events, ecological changes, or other selection pressures. Here, we propose that the mosquitoes in the Culex pipiens complex, which include taxa of significant medical importance, provide an exceptional system for examining the mechanisms underlying speciation and taxonomic radiation. Furthermore, these insects may shed light on the influences that historical and contemporary admixture have on taxonomic integrity. Such studies will have specific importance for mitigating the disease and nuisance burdens caused by these mosquitoes. More broadly, they could inform predictions about future evolutionary trajectories in response to changing environments and patterns of evolution in other cosmopolitan and invasive species that have developed recent associations with humans.
Losing an Archive: Doing Place-Based History in the Age of the Anthropocene
Authors: Dunlop Catherine Tatiana 
Published: September 2021

Abstract
How will climate change affect place-based historical research? In this personal essay, author Catherine Dunlop reflects on her experience of researching historical attitudes toward France’s mistral wind while simultaneously feeling the wind’s powerful gusts firsthand. Unsatisfied with the French state’s indoor weather archives, Dunlop develops her research project around the outdoor archives that she encounters on a daily basis. Through her sensory experiences of the mistral, both at sea and on land, Dunlop hones her research questions and ultimately deepens her understanding of the mistral’s role in modern French history. Even as she acknowledges the benefits that came from her firsthand encounters with the mistral, Dunlop recognizes the fragile and tenuous aspects of place-based historical research. Places like Provence, and their distinctive winds, are changing. The Anthropocene, she argues, will alter historians’ ability to connect with and learn from the landscape heritage of the places that they visit. Losing our outdoor archives to climate change will impact historical research in ways that we are just beginning to realize.
Anatomy of a Cyberattack
Authors: Stowman Anne M , Cacciatore Lori S , Cortright Valerie , McConnell John , Wilburn Clayton , Bryant Bronwyn , Frisch Nora , Kalof Alexandra N 
Published: September 2021

Abstract

Objectives
Our institution was subject to a multi-institutional, systemwide cyberattack that led to a complete shutdown of multiple major patient care, operational, and communication systems for more than 25 days. The electronic health record computer system was taken offline, as was the hospital email and authentication systems, internet access, and the laboratory information system. The impact on the hospital and patient care was substantial, and our laboratories were crippled.

Methods
Our laboratory endured challenges in communication because of the loss of connectivity and difficulties in laboratory management, and we recognized a need to restructure leadership to maintain operations during the crisis. As an academic institution, residents and trainees were also significantly affected by the disaster.

Results
We developed an incident command team (ICT), alternative methods of communication, and innovative management strategies to remain operational. Trainees were incorporated into the disaster-relief efforts, with negative impacts on resident education.

Conclusions
This paper focuses on the challenges in communication and lab management as well as the need for an alternative leadership structure during the crisis. We also highlight the unique experience of our trainees during this prolonged downtime, underscoring the importance of incorporating resident trainees into the daily ICT’s administrative activities as an invaluable lab management experience.
Bone Marrow Collection: Comparison of Unassisted vs Assisted Bedside Collections by a Laboratory Technologist
Authors: Ali Saba F , Cloe Adam , Siaghani Parwiz J , Himchak Evan , Cantu David , Gaal Karl , Kim Young S , Afkhami Michelle , Pillai Raju , Chan Wanda , Quirk Elizabeth , Weisenburger Dennis D , Aoun Patricia , Song Joo Y 
Published: September 2021

Abstract

Objectives
Bone marrow collections are often difficult, and creating quality smears and touch preparations at the bedside can prove challenging. The objective of this study is to compare the quality of bone marrow specimens between unassisted and assisted bone marrow collections by a bone marrow technologist.

Methods
Data for this study were collected from 422 hematopathology reports over 14 months. We recorded the bone marrow quality of the different parts (aspirate smears, touch imprints, core biopsy, and clot/particle sections) as adequate, suboptimal, or inadequate. Student t test statistical analysis was performed between the corresponding parts in the two groups.

Results
Our results demonstrate that the quality of assisted bone marrow specimens is significantly better compared with unassisted specimens, particularly for the aspirate smears (P &lt; .0001) and touch imprints (P &lt; .0001). Notably, the quality of aspirate smears was improved, which is important for cytologic evaluation.

Conclusions
We conclude that assistance by a bone marrow technologist resulted in a significant improvement in the quality of bone marrow collection.
Potential for Process Improvement of Clinical Flow Cytometry by Incorporating Real-Time Automated Screening of Data to Expedite Addition of Antibody Panels
Authors: Simonson Paul D , Lee Aaron Y , Wu David 
Published: September 2021

AbstractObjectivesWe desired an automated approach to expedite ordering additional antibody panels in our clinical flow cytometry lab. This addition could improve turnaround times, decrease time spent revisiting cases, and improve consistency.MethodsWe trained a machine learning classifier to use our screening B-cell panel to predict whether we should order an additional panel to distinguish chronic lymphocytic lymphoma from mantle cell lymphoma. We used data from 2016 to 2018 for training and validation, and cases were restricted to the first case per patient (9,635 cases, 887 with the additional panel). We applied the model in real time over approximately 2.5 months in 2020 to 376 sequential cases, with automated email notifications for positive predictions.ResultsUsing 80% of the data from 2016 to 2018 to train and 20% for validation, we achieved 95% area under the receiving operating characteristic curve (AUROC) and 94% accuracy in the validation set. Applying the classifier in real time achieved 89% AUROC and 94% real-time prediction accuracy (precision [positive predictive value] = 51%, recall [sensitivity] = 78%, and F1 score = 0.62). Fourteen of the 17 false positives had prior diagnoses to which the algorithm was not privy.ConclusionsAs an observational, not interventional study, our system performed well on testing within our laboratory for identifying cases to be flagged but cannot be used without laboratory-specific modifications.
Insulin-Like Growth Factor 1 in the Early Postoperative Assessment of Acromegaly
Authors: Donegan Diane , Algeciras-Schimnich Alicia , Ashrafzadeh-Kian Susan , Erickson Dana 
Published: September 2021

Abstract

Objectives
Assessment of surgical outcome in acromegaly is typically recommended at 3 to 6 months following surgery. The purpose of this study was to determine if insulin-like growth factor 1 (IGF-1) concentrations at 6 weeks were equally predictive of surgical outcomes compared with IGF-1 concentrations at 3 to 6 months postoperatively applying newer IGF-1 assays.

Methods
Retrospective review of patients with newly diagnosed acromegaly who had surgery between 2013 and 2020 and had postoperative IGF-1 measured by 6 weeks and 3 to 6 months.

Results
At 6 weeks, 20 (35%) of the total 57 had normal IGF-1 and became abnormal in 1 at 3 to 6 months, whereas 37 (65%) of 57 had abnormal IGF-1 concentrations at 6 weeks, which normalized in 1 patient by 3 to 6 months. In patients who changed clinical status, IGF-1 at 6 weeks was within ±0.1-fold of normal. Although a difference was seen between median IGF-1 concentrations (286 vs 267 ng/mL, P = .009) at 6 weeks and 3 to 6 months, the mean reduction was small (–19.9 ng/mL).

Conclusions
Compared with 3 to 6 months, use of IGF-1 at 6 weeks was associated with a change in clinical status in 3.5% of patients. Therefore, in most patients, IGF-1 at 6 weeks can be used to assess clinical outcome via newer assays.
Distinct Mutation Landscapes Between Acute Myeloid Leukemia With Myelodysplasia-Related Changes and De Novo Acute Myeloid Leukemia
Authors: Gao Yajuan , Jia Mingnan , Mao Yueying , Cai Hao , Jiang Xianyong , Cao Xinxin , Zhou Daobin , Li Jian 
Published: September 2021

Abstract

Objectives
To explore the distinct mutation profiles between acute myeloid leukemia with myelodysplasia-related changes (AML-MRC) and de novo AML and their relationships with prognosis.

Methods
Next-generation sequencing of 42 myeloid neoplasm-related genes in 293 newly diagnosed patients with AML.

Results
Eighty-four patients had AML-MRC, and 161 patients had de novo AML. The mutation rates of ASXL1 (25% vs 8.7%, P = .001), NRAS (17.9% vs 8.1%, P = .022), PTPN11 (11.9% vs 5%, P = .048), SETBP1 (6% vs 0.6%, P = .033), SRSF2 (11.9% vs 5.6%, P = .08), TP53 (16.7% vs 1.2%, P &lt; .001), and U2AF1 (17.9% vs 7.5%, P = .014) in AML-MRC were higher than those in de novo AML, while the rates of FLT3-ITD (3.6% vs 15.5%, P = .005), KIT (0% vs 6.2%, P = .046), WT1 (3.6% vs 9.9%, P = .077), NPM1 (1.2% vs 21.7%, P &lt; .001), and CEBPA (4.8% vs 24.2%, P &lt; .001) mutation were lower. The appearance of ASXL1, TP53, U2AF1, SRSF2, and SETBP1 mutation could predict AML-MRC–like features in de novo AML, which was related to older age (60 vs 51 years, P = .001), low WBC counts (4.7 × 109/L vs 11.6 × 109/L, P = .001), and inferior outcomes (median overall survival, 15 months vs not reached, P = .003).

Conclusions
The presence of AML-MRC–related mutations can reveal a subset of patients with de novo AML similar to patients with AML-MRC.
Gabapentin Reduces Alcohol Intake in Rats by Regulating NF-κB Signaling Pathway Via PPAR γ
Authors: Li Jing , Xu Kewei , Ding Hao , Xi Qiaozhen 
Published: September 2021

AbstractAimsIncreasing preclinical and clinical reports have demonstrated the efficacy of gabapentin (GBP) in treating alcohol use disorder (AUD). However, the mechanism of the effects of GBP in AUD is largely unknown. Herein, we sought to investigate the effect of GBP in a rat model of AUD and explore the underlying mechanism.MethodsThe intermittent access to 20% ethanol in a 2-bottle choice (IA2BC) procedure was exploited to induce high voluntary ethanol consumption in rats. The rats were treated daily for 20 days with different doses of GBP, simultaneously recording ethanol/water intake. The locomotor activity and grooming behavior of rats were also tested to evaluate the potential effects of GBP on confounding motor in rats. The levels of IL-1β and TNF-α in serum and hippocampus homogenate from the rats were detected by using ELISA. The expressions of peroxisome proliferator-activated-receptor γ (PPAR-γ) and nuclear factor-κB (NF-κB) in the hippocampus were determined by immunofluorescence and western blot.ResultsGBP reduced alcohol consumption, whereas increased water consumption and locomotor activity of rats. GBP was also able to decrease the levels of IL-1β and TNF-α in both serum and hippocampus, in addition to the expression of NF-κB in the hippocampus. Furthermore, these effects attributed to GBP were observed to disappear in the presence of bisphenol A diglycidyl ether (BADGE), a specific inhibitor of PPAR-γ.ConclusionsOur findings revealed that GBP could activate PPAR-γ to suppress the NF-κB signaling pathway, contributing to the decrease of ethanol consumption and ethanol-induced neuroimmune responses.
Racialized Trajectories to Catalan Higher Education: Language, Anti-Racism and the ‘Politics of Listening’
Authors: Khan Kamran , Gallego-Balsà LíDIA 
Published: September 2021

AbstractThis paper examines two forms of listening in relation to racism and the minoritylanguage context in Catalonia. The research is based on a collaboration between twoacademics and RC (Racialised Catalans) which started with initial interviews on theireducational experiences and trajectories in Catalonia. From there, a collaboration betweenthe academics and a inter-cultural, anti-racism group (Junts a pseudonym) emerged .First, we analyse the role of the white listening subject in which RC are discriminatedagainst for a perceived lack of Catalan. Second, we describe how the group made otherslisten through their work. Thus, listening results in a form of 'undecideability' which requireswider society for the possibilities of racial equality.
Faux Pas Recognition and Executive Processes in Patients with Alcohol Use Disorder: Toward an Investigation of Interindividual Heterogeneity
Authors: Schmid Franca , Moreau Franklin , Benzerouk Farid , Raucher-Chéné Delphine , Kaladjian Arthur , Gierski Fabien , Henry Audrey 
Published: September 2021

Abstract

Objective
Impairment of executive functions (EF) has been documented for decades in patients with alcohol use disorders (AUD), while more recent studies have also reported impaired theory of mind. Both have been associated with negative outcomes, particularly a high risk of relapse. However, the interrelatedness of EF and theory of mind impairments remains subject to debate.

Method
About 19 AUD outpatients and 20 healthy controls (HC) were asked to complete measures of motor inhibition, mental flexibility, and updating to assess EF, and the faux pas test to assess theory of mind.

Results
As expected, patients’ mean performances on EF and faux pas measures were poorer than those of HC. Correlational analyses revealed that executive processes were differentially related to faux pas subscores. Additional single-case analyses corroborated the strong association between EF and faux pas interpretation, as patients with AUD mostly had congruent performances (i.e., both EF and faux pas impaired or both EF and faux pas preserved).

Conclusions
This study highlights the interrelatedness of EF and faux pas performances in AUD, but also emphasizes the incomplete overlap of the cognitive processes involved in these tasks, with heterogeneous patterns of association. Based on these findings, tailored cognitive rehabilitation programs that simultaneously target EF and faux pas recognition could be developed to favor patients’ social inclusion and reduce the risk of relapse. Results also argue in favor of systematic screening for EF and theory of mind impairments among AUD patients.
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
Authors: Bommert Andrea , Welchowski Thomas , Schmid Matthias , Rahnenführer Jörg 
Published: September 2021

Abstract
Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies for data with a survival outcome are rare. We compare 14 filter methods for feature selection based on 11 high-dimensional gene expression survival data sets. The aim is to provide guidance on the choice of filter methods for other researchers and practitioners. We analyze the accuracy of predictive models that employ the features selected by the filter methods. Also, we consider the run time, the number of selected features for fitting models with high predictive accuracy as well as the feature selection stability. We conclude that the simple variance filter outperforms all other considered filter methods. This filter selects the features with the largest variance and does not take into account the survival outcome. Also, we identify the correlation-adjusted regression scores filter as a more elaborate alternative that allows fitting models with similar predictive accuracy. Additionally, we investigate the filter methods based on feature rankings, finding groups of similar filters.
Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power
Authors: Yu Tzu-Hui , Su Bo-Han , Battalora Leo Chander , Liu Sin , Tseng Yufeng Jane 
Published: September 2021

Abstract
The trade-off between a machine learning (ML) and deep learning (DL) model’s predictability and its interpretability has been a rising concern in central nervous system-related quantitative structure–activity relationship (CNS-QSAR) analysis. Many state-of-the-art predictive modeling failed to provide structural insights due to their black box-like nature. Lack of interpretability and further to provide easy simple rules would be challenging for CNS-QSAR models. To address these issues, we develop a protocol to combine the power of ML and DL to generate a set of simple rules that are easy to interpret with high prediction power. A data set of 940 market drugs (315 CNS-active, 625 CNS-inactive) with support vector machine and graph convolutional network algorithms were used. Individual ML/DL modeling methods were also constructed for comparison. The performance of these models was evaluated using an additional external dataset of 117 market drugs (42 CNS-active, 75 CNS-inactive). Fingerprint-split validation was adopted to ensure model stringency and generalizability. The resulting novel hybrid ensemble model outperformed other constituent traditional QSAR models with an accuracy of 0.96 and an F1 score of 0.95. With the power of the interpretability provided with this protocol, our model laid down a set of simple physicochemical rules to determine whether a compound can be a CNS drug using six sub-structural features. These rules displayed higher classification ability than classical guidelines, with higher specificity and more mechanistic insights than just for blood–brain barrier permeability. This hybrid protocol can potentially be used for other drug property predictions.
How much can deep learning improve prediction of the responses to drugs in cancer cell lines?
Authors: Chen Yurui , Zhang Louxin 
Published: September 2021

Abstract
The drug response prediction problem arises from personalized medicine and drug discovery. Deep neural networks have been applied to the multi-omics data being available for over 1000 cancer cell lines and tissues for better drug response prediction. We summarize and examine state-of-the-art deep learning methods that have been published recently. Although significant progresses have been made in deep learning approach in drug response prediction, deep learning methods show their weakness for predicting the response of a drug that does not appear in the training dataset. In particular, all the five evaluated deep learning methods performed worst than the similarity-regularized matrix factorization (SRMF) method in our drug blind test. We outline the challenges in applying deep learning approach to drug response prediction and suggest unique opportunities for deep learning integrated with established bioinformatics analyses to overcome some of these challenges.
Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing
Authors: Wang Ting , Lu Haojie , Zeng Ping 
Published: September 2021

AbstractPleiotropy has important implication on genetic connection among complex phenotypes and facilitates our understanding of disease etiology. Genome-wide association studies provide an unprecedented opportunity to detect pleiotropic associations; however, efficient pleiotropy test methods are still lacking. We here consider pleiotropy identification from a methodological perspective of high-dimensional composite null hypothesis and propose a powerful gene-based method called MAIUP. MAIUP is constructed based on the traditional intersection–union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework. The key improvement of MAIUP is that it takes the composite null nature of pleiotropy test into account by fitting a three-component mixture null distribution, which can ultimately generate well-calibrated P-values for effective control of family-wise error rate and false discover rate. Another attractive advantage of MAIUP is its ability to effectively address the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate that compared with other methods, only MAIUP can maintain correct type I error control and has higher power across a wide range of scenarios. We apply MAIUP to detect shared associated genes among 14 psychiatric disorders with summary statistics and discover many new pleiotropic genes that are otherwise not identified if failing to account for the issue of composite null hypothesis testing. Functional and enrichment analyses offer additional evidence supporting the validity of these identified pleiotropic genes associated with psychiatric disorders. Overall, MAIUP represents an efficient method for pleiotropy identification.
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations
Authors: Wang Jinxian , Liu Xuejun , Shen Siyuan , Deng Lei , Liu Hui 
Published: September 2021

Abstract

Motivation
Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound–protein interactions, but it has not been applied to the screening of drug combinations.

Results
In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug–Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation.

Availability and implementation
Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master