bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reintervention After Antireflux Surgery for Gastroesophageal Reflux Disease in England
Authors: Markar Sheraz R. , Arhi Chanpreet , Wiggins Tom , Vidal-Diez Alberto , Karthikesalingam Alan , Darzi Ara , Lagergren Jesper , Hanna George B. 
Published: November 2018

Background:
After antireflux surgery, highly variable rates of recurrent gastroesophageal reflux disease (GERD) have been reported.

Objective:
To identify the occurrence and risk factors of recurrent GERD requiring surgical reintervention or medication.

Methods:
The Hospital Episode Statistics database was used to identify adults in England receiving primary antireflux surgery for GERD in 2000 to 2012 with follow-up through 2014, and the outcome was surgical reintervention. In a subset of participants, the Clinical Practice Research Datalink was additionally used to assess proton pump inhibitor therapy for at least 6 months (medical reintervention). Risk factors were assessed using multivariable Cox regression providing adjusted hazard ratios (HRs) with 95% confidence intervals (95% CIs).

Results:
Among 22,377 patients who underwent primary antireflux surgery in the Hospital Episode Statistics dataset, 811 (3.6%) had surgical reintervention, with risk factors being age 41 to 60 years (HR = 1.22, 95% CI 1.03–1.44), female sex (HR = 1.5; 95% CI 1.3–1.74), white ethnicity (HR = 1.71, 95% CI 1.06–2.77), and low hospital annual volume of antireflux surgery (HR = 1.32, 95% CI 1.04–1.67). Among 2005 patients who underwent primary antireflux surgery in the Clinical Practice Research Datalink dataset, 189 (9.4%) had surgical reintervention and 1192 (59.5%) used proton pump inhibitor therapy, with risk factors for the combined outcome being age >60 years (HR = 2.38, 95% CI 1.81–3.13) and preoperative psychiatric morbidity (HR = 1.58, 95% CI 1.25–1.99).

Conclusion:
At least 3.6% of patients may require surgical reintervention and 59.5% medical therapy following antireflux surgery in England. The influence of patient characteristics and hospital volume highlights the need for patient selection and surgical experience in successful antireflux surgery.
Surgical Treatment of Spontaneous Pneumothorax: Pleural Abrasion or Pleurectomy?
Authors: Ocakcioglu Ilhan , Kupeli Mustafa 
Published: November 2018

Purpose:
A primary spontaneous pneumothorax is a condition that occurs predominantly in young and thin male individuals who do not have any history of underlying lung disease. Various techniques such as pleural abrasion and pleurectomy are used to reduce the recurrence rate, but there exists no consensus among surgeons on which surgical technique offers the lowest risk of recurrence.
We aimed to compare the efficiency and recurrence risk of pleural abrasion and pleurectomy methods in patients with primary spontaneous pneumothorax undergoing a wedge resection for the bulleous part of the paranchyme.

Materials and Methods:
Statistically compared variables between the 2 groups were age, sex, symptoms, smoking status, number of previous pneumothorax episodes, surgical indication, bleb number, estimated blood loss, intraoperative complication, duration of the surgery, conversion to open surgery, postoperative drainage amount, chest tube removal time, length of hospital stay, morbidity, mortality, duration of narcotic analgesic usage, pain according to Visual Analog Scale evaluation, follow-up period, and postoperative recurrence.

Results:
The medical records of 88 patients undergoing surgery between 2013 and 2017 were reviewed retrospectively. The pleural abrasion group was superior to the pleurectomy group in terms of operation time, drainage volume, time to drain removal, hospital stay, and Visual Analog Scale pain score on postoperative day 0. Statistically, there was no difference between the pleural abrasion group and the pleurectomy group in the recurrence rates after operation (2.0% vs. 2.5%, respectively; P=0.89).

Conclusions:
Thoracoscopic pleural abrasion is safer than apical pleurectomy and is associated with the same pneumothorax recurrence rate.
Current State of Renal Regenerative Therapies
Authors: Shankar Anusha Shweta , Hoorn Ewout J. , Gribnau Joost , Baan Carla C. , Hoogduijn Martin J. 
Published: November 2018

Abstract
The worldwide increase in the number of patients with end-stage renal disease leads to a growing waiting list for kidney transplantation resulting from the scarcity of kidney donors. Therefore, alternative treatment options for patients with end-stage renal disease are being sought. In vitro differentiation of stem cells into renal tissue is a promising approach to repair nonfunctional kidney tissue. Impressive headway has been made in the use of stem cells with the use of adult renal progenitor cells, embryonic stem cells, and induced pluripotent stem cells for the development toward primitive kidney structures. Currently, efforts are directed at improving long-term maintenance and stability of the cells. This review aims to provide a comprehensive overview of the cell sources used for the generation of kidney cells and strategies used for transplantation in in vivo models. Furthermore, it provides a perspective on stability and safety during future clinical application of in vitro generated kidney cells.
Internal representation of hippocampal neuronal population span a time-distance continuum
Authors: Haimerl Caroline , Angulo-Garcia David , Villette Vincent , Reichinnek Susanne. , Torcini Alessandro , Cossart Rosa , Malvache Arnaud 
Published: November 2018

AbstractThe hippocampus plays a critical role in episodic memory: the sequential representation of visited places and experienced events. This function is mirrored by hippocampal activity that self organizes into sequences of neuronal activation that integrate spatio-temporal information. What are the underlying mechanisms of such integration is still unknown. Single cell activity was recently shown to combine time and distance information; however, it remains unknown whether a degree of tuning between space and time can be defined at the network level. Here, combining daily calcium imaging of CA1 sequence dynamics in running head-fixed mice and network modeling, we show that CA1 network activity tends to represent a specific combination of space and time at any given moment, and that the degree of tuning can shift within a continuum from one day to the next. Our computational model shows that this shift in tuning can happen under the control of the external drive power. We propose that extrinsic global inputs shape the nature of spatio-temporal integration in the hippocampus at the population level depending on the task at hand, a hypothesis which may guide future experimental studies.Significance StatementThe hippocampus organizes experience in sequences of events that form episodic memory. How are time and space internally computed in the hippocampus in the absence of sequential external inputs? Here we show that time and space are integrated together within the hippocampal network with different degrees of tuning across days. This was found by recording the activity of hundreds of pyramidal cells for several days. We also propose a mechanism supporting such spatio-temporal integration based on a ring attractor network model: the degree of tuning between space and time can be adjusted by modulating the power of a non-sequential external excitatory drive. In this way, the hippocampus is able to generate a spatio-temporal representation tuned to the task at hand.
Parameter Identifiability and Non-Uniqueness In Connectome Based Neural Mass Models
Authors: Xie X. , Kuceyeski A. , Shah S.A. , Schiff N.D. , Nagarajan S.S. , Raj A. 
Published: November 2018

AbstractThe spatial-temporal patterns of neuronal dynamics emerge from the network of coordinated brain regions, this structure-function relationship of the brain can be described mathematically by biophysical models of coupled brain regions connected by white matter tractography. Implementations of such models have focused on reproducing functional connectivity extracted from functional magnetic resonance imaging (fMRI), but these efforts are limited by the temporal resolution of fMRI data and the reduction of time course recordings into phenomenological functional connectivity maps. Here, we optimize parameters of a neural mass model (NMM) to best fit region-wise power spectra across the whole brain estimated from source localized electroencephalography (EEG). NMM models with global parameters were not able to fully reproduce region-wise power spectra, with or without the inclusion of structural connectivity information. In contrast, without the inclusion of structural connectivity information, independent oscillators at each brain region are able to reproduce region-wise power spectra. But the addition of structural connectivity and transmission delays to the NMM does not improve overall power spectra fit. Connectome-based NMM implementations with regional parameters lead to high dimensional network models that produce non-unique results. Inherent parameter identifiability problem in network models poses challenges for using such models as diagnostic tools for neurological diseases.
Bacterial diversity and population dynamics during the fermentation of palm wine from Guerrero Mexico
Authors: Astudillo-Melgar Fernando , Ochoa-Leyva Adrián , Utrilla José , Huerta-Beristain Gerardo 
Published: November 2018

AbstractPalm wine is obtained by fermentation of palm tree sap. In the Pacific coast of Mexico, palm wine is called Tuba and it is consumed as a traditional fermented beverage. Tuba has empirical applications such as an auxiliary in gastrointestinal diseases and a good source of nutrients. In the present study, a next-generation sequencing of the V3-V4 regions of the 16S rRNA gene was employed to analyze bacterial diversity and population dynamics during the fermentation process of Tuba, both in laboratory controlled conditions and in commercial samples from local vendors. Taxonomic identification showed that Fructobacillus was the main genus in all the samples, following by Leuconostoc, Gluconacetobacter, Sphingomonas, and Vibrio. Alpha diversity analysis demonstrated variability between all the samples. Beta diversity clustered the bacterial population according to the collection origin of the sample. Metabolic functional profile inference showed that the members of the bacterial communities may present the vitamin, antibiotic and antioxidant biosynthesis genes. Additionally, we further investigated the correlation between the predominant genera and some composition parameters of this beverage. This study provides the basis of the bacterial community composition and functionality of the fermented beverage.
Neural Correlates of Optimal Multisensory Decision Making
Authors: Hou Han , Zheng Qihao , Zhao Yuchen , Pouget Alexandre , Gu Yong 
Published: November 2018

AbstractPerceptual decisions are often based on multiple sensory inputs whose reliabilities rapidly vary over time, yet little is known about how our brain integrates these inputs to optimize behavior. Here we show multisensory evidence with time-varying reliability can be accumulated near optimally, in a Bayesian sense, by simply taking time-invariant linear combinations of neural activity across time and modalities, as long as the neural code for the sensory inputs is close to an invariant linear probabilistic population code (ilPPC). Recordings in the lateral intraparietal area (LIP) while macaques optimally performed a vestibular-visual multisensory decision-making task revealed that LIP population activity reflects an integration process consistent with the ilPPC theory. Moreover, LIP accumulates momentary evidence proportional to vestibular acceleration and visual velocity which are encoded in sensory areas with a close approximation to ilPPCs. Together, these results provide a remarkably simple and biologically plausible solution to optimal multisensory decision making.
Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA
Authors: Xu Jin , Nuno Kevin , Litzenburger Ulrike M. , Qi Yanyan , Corces M Ryan , Majeti Ravindra , Chang Howard Y. 
Published: November 2018

AbstractSimultaneous measurement of cell lineage and cell fates is a longstanding goal in biomedicine. Here we describe EMBLEM, a strategy to track cell lineage using endogenous mitochondrial DNA variants in ATAC-seq data. We show that somatic mutations in mitochondrial DNA can reconstruct cell lineage relationships at single cell resolution with high sensitivity and specificity. Using EMBLEM, we define the genetic and epigenomic clonal evolution of hematopoietic stem cells and their progenies in patients with acute myeloid leukemia. EMBLEM extends lineage tracing to any eukaryotic organism without genetic engineering.
Action perception recruits the cerebellum and is impaired in spinocerebellar ataxia patients
Authors: Abdelgabar Abdel R. , Suttrup Judith , Broersen Robin , Bhandari Ritu , Picard Samuel , Keysers Christian , Zeeuw Chris I. De , Gazzola Valeria 
Published: November 2018

AbstractOur cerebellum has been proposed to generate prediction signals that may help us plan and execute our motor programs. However, to what extent our cerebellum is also actively involved in perceiving the action of others remains to be elucidated. Using fMRI, we show here that observing goal-directed hand actions of others bilaterally recruits cerebellar Lobules VI, VIIb and VIIIa. Moreover, whereas healthy subjects (n=31) were found to be able to discriminate subtle differences in the kinematics of observed limb movements of others, patients suffering from spinocerebellar ataxia type 6 (SCA6; n=21) were severely impaired in performing such tasks. Our data suggest that the human cerebellum is actively involved in perceiving the kinematics of the hand actions of others and that SCA6 patients’ deficits include a difficulty in perceiving the actions of other individuals. This finding alerts us to the fact that cerebellar disorders can alter social cognition. Given that impairments in social cognition have been reported to be one of the most debilitating consequences of neurological disorders, this finding may be relevant to improving the quality of life of patients and their families.
Genetic effect of type 2 Diabetes to the progression of Neurological Diseases
Authors: Rahman Md Habibur , Peng Silong , Chen Chen , Lio’ Pietro , Moni Mohammad Ali 
Published: November 2018

AbstractNeurological Diseases (NDs) are progressive disorder often advances with age and comorbidities of Type 2 diabetes (T2D). Epidemiological, clinical and neuropathological evidence advocate that patients with T2D are at an increased risk of getting NDs. However, it is very little known how T2D affects the risk and severity of NDs.To tackle these problems, we employed a transcriptional analysis of affected tissues using agnostic approaches to identify overlapping cellular functions. In this study, we examined gene expression microarray human datasets along with control and disease-affected individuals. Differentially expressed genes (DEG) were identified for both T2D and NDs that includes Alzheimer Disease (AD), Parkinson Disease (PD), Amyotrophic Lateral Sclerosis (ALS), Epilepsy Disease (ED), Huntington Disease (HD), Cerebral Palsy (CP) and Multiple Sclerosis Disease (MSD).We have developed genetic association and diseasome network of T2D and NDs based on the neighborhood-based benchmarking and multilayer network topology approaches. Overlapping DEG sets go through protein-protein interaction for hub protein identification and gene enrichment using pathway analysis and gene ontology methods that enhance our understanding of the fundamental molecular procedure of NDs progression.Gene expression analysis platforms have been extensively used to investigate altered pathways and to identify potential biomarkers and drug targets. Finally, we validated our identified biomarkers using the gold benchmark datasets which identified the corresponding relationship of T2D and NDs. Therapeutic targets aimed at attenuating identified altered pathway could ameliorate neurological dysfunction in a T2D patient.
Action planning modulates the representation of object features in human fronto-parietal and occipital cortex
Authors: Velji-Ibrahim Jena , Crawford J. Douglas , Cattaneo Luigi , Monaco Simona 
Published: November 2018

AbstractThe role of the visual cortex has been extensively studied to determine its role in object recognition but to a lesser degree to determine how action planning influences the representation of objects’ features. We used functional MRI and pattern classification methods to determine if during action planning, object features (orientation and location) could be decoded in an action-dependent way. Sixteen human participants used their right dominant hand to perform movements (Align or Open reach) towards one of two 3D-real oriented objects that were simultaneously presented and placed on either side of a fixation cross. While both movements required aiming toward target location, Align but not Open reach movements required participants to precisely adjust hand orientation. Therefore, we hypothesized that if the representation of object features is modulated by the upcoming action, pre-movement activity pattern would allow more accurate dissociation between object features in Align than Open reach tasks. We found such dissociation in the anterior and posterior parietal cortex, as well as in the dorsal premotor cortex, suggesting that visuomotor processing is modulated by the upcoming task. The early visual cortex showed significant decoding accuracy for the dissociation between object features in the Align but not Open reach task. However, there was no significant difference between the decoding accuracy in the two tasks. These results demonstrate that movement-specific preparatory signals modulate object representation in the frontal and parietal cortex, and to a lesser extent in the early visual cortex, likely through feedback functional connections.
Novel Insight into the Aetiology of Autism Spectrum Disorder Gained by Integrating Expression Data with Genome-wide Association Statistics
Authors: Pain Oliver , Pocklington Andrew J. , Holmans Peter A. , Bray Nicholas J. , O’Brian Heath E. , Hall Lynsey S. , Pardiñas Antonio F. , O’Donovan Michael C. , Owen Michael J. , Anney Richard 
Published: November 2018

AbstractBackgroundA recent genome-wide association study (GWAS) of autism spectrum disorders (ASD) (Ncases=18,381, Ncontrols=27,969) has provided novel opportunities for investigating the aetiology of ASD. Here, we integrate the ASD GWAS summary statistics with summary-level gene expression data to infer differential gene expression in ASD, an approach called transcriptome-wide association study (TWAS).MethodsUsing FUSION software, ASD GWAS summary statistics were integrated with predictors of gene expression from 16 human datasets, including adult and fetal brain. A novel adaptation of established statistical methods was then used to test for enrichment within candidate pathways, specific tissues, and at different stages of brain development. The proportion of ASD heritability explained by predicted expression of genes in the TWAS was estimated using stratified linkage disequilibrium-score regression.ResultsThis study identified 14 genes as significantly differentially expressed in ASD, 13 of which were outside of known genome-wide significant loci (±500kb). XRN2, a gene proximal to an ASD GWAS locus, was inferred to be significantly upregulated in ASD, providing insight into functional consequence of this associated locus. One novel transcriptome-wide significant association from this study is the downregulation of PDIA6, which showed minimal evidence of association in the GWAS, and in gene-based analysis using MAGMA. Predicted gene expression in this study accounted for 13.0% of the total ASD SNP-heritability.ConclusionThis study has implicated several genes as significantly up-/down-regulated in ASD providing novel and useful information for subsequent functional studies. This study also explores the utility of TWAS-based enrichment analysis and compares TWAS results with a functionally agnostic approach.
FastqPuri: high-performance preprocessing of RNA-seq data
Authors: Pérez-Rubio Paula , Lottaz Claudio , Engelmann Julia C 
Published: November 2018

AbstractBackgroundRNA sequencing (RNA-seq) has become the standard means of analyzing gene and transcript expression in high-throughput. While previously sequence alignment was a time demanding step, fast alignment methods and even more so transcript counting methods which avoid mapping and quantify gene and transcript expression by evaluating whether a read is compatible with a transcript, have led to significant speed-ups in data analysis. Now, the most time demanding step in the analysis of RNA-seq data is preprocessing the raw sequence data, such as running quality control and adapter, contamination and quality filtering before transcript or gene quantification. To do so, many researchers chain different tools, but a comprehensive, flexible and fast software that covers all preprocessing steps is currently missing.ResultsWe here present FastqPuri, a light-weight and highly efficient preprocessing tool for fastq data. FastqPuri provides sequence quality reports on the sample and dataset level with new plots which facilitate decision making for subsequent quality filtering. Moreover, FastqPuri efficiently removes adapter sequences and sequences from biological contamination from the data. It accepts both single- and paired-end data in uncompressed or compressed fastq files. FastqPuri can be run stand-alone and is suitable to be run within pipelines. We benchmarked FastqPuri against existing tools and found that FastqPuri is superior in terms of speed, memory usage, versatility and comprehensiveness. Conclusions: FastqPuri is a new tool which covers all aspects of short read sequence data preprocessing. It was designed for RNA-seq data to meet the needs for fast preprocessing of fastq data to allow transcript and gene counting, but it is suitable to process any short read sequencing data of which high sequence quality is needed, such as for genome assembly or SNV (single nucleotide variant) detection. FastqPuri is most flexible in filtering undesired biological sequences by offering two approaches to optimize speed and memory usage dependent on the total size of the potential contaminating sequences. FastqPuri is available at https://github.com/jengelmann/FastqPuri. It is implemented in C and R and licensed under GPL v3.