bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of Patient and Surgery Characteristics on Persistent Postoperative Pain
Authors: Mi Xinlei , Zou Baiming , Rashidi Parisa , Baharloo Raheleh , Fillingim Roger B. , Wallace Margaret R. , Crispen Paul L. , Parvataneni Hari K. , Prieto Hernan A. , Gray Chancellor F. , Machuca Tiago N. , Hughes Steven J. , Murad Gregory J.A. , Thomas Elizabeth , Iqbal Atif , Tighe Patrick J. 
Published: September 2021

Objective:
Acute postoperative pain intensity is associated with persistent postsurgical pain (PPP) risk. However, it remains unclear whether acute postoperative pain intensity mediates the relationship between clinical factors and persistent pain.

Materials and Methods:
Participants from a mixed surgical population completed the Brief Pain Inventory and Pain Catastrophizing Scale before surgery, and the Brief Pain Inventory daily after surgery for 7 days and at 30 and 90 days after surgery. We considered mediation models using the mean of the worst pain intensities collected daily on each of postoperative days (PODs) 1 to 7 against outcomes of worst pain intensity at the surgical site endpoints reflecting PPP (POD 90) and subacute pain (POD 30).

Results:
The analyzed cohort included 284 participants for the POD 90 outcome. For every unit increase of maximum acute postoperative pain intensity through PODs 1 to 7, there was a statistically significant increase of mean POD 90 pain intensity by 0.287 after controlling for confounding effects. The effects of female versus male sex (m=0.212, P=0.034), pancreatic/biliary versus colorectal surgery (m=0.459, P=0.012), thoracic cardiovascular versus colorectal surgery (m=0.31, P=0.038), every minute increase of anesthesia time (m=0.001, P=0.038), every unit increase of preoperative average pain score (m=0.012, P=0.015), and every unit increase of catastrophizing (m=0.044, P=0.042) on POD 90 pain intensity were mediated through acute PODs 1 to 7 postoperative pain intensity.

Discussion:
Our results suggest the mediating relationship of acute postoperative pain on PPP may be predicated on select patient and surgical factors.
Moderate to severe obstructive sleep apnea is independently associated with inter-arm systolic blood pressure difference: Tokyo Sleep Heart Study
Authors: Shiina Kazuki , Takata Yoshifumi , Nakano Hiroki , Fujii Masatsune , Iwasaki Yoichi , Kumai Kento , Matsumoto Chisa , Chikamori Taishiro , Tomiyama Hirofumi 
Published: September 2021

Objective:
Obstructive sleep apnea (OSA) is recognized as an independent risk factor for cardiovascular disease. On the other hand, inter-arm systolic blood pressure difference (IAD), inter-ankle systolic blood pressure difference (IAND), and ankle-brachial index (ABI) are all known predictors of cardiovascular events. The aim of the present study was to investigate the association between OSA and four-limb blood pressure differences.

Methods:
We conducted this cross-sectional study in a large sleep cohort from Tokyo Sleep Heart Study. In 2643 consecutive patients who visited our sleep clinic for polysomnography between 2005 and 2017, all the patients underwent blood pressure measurement simultaneously in all the four limbs by oscillometric methods.

Results:
The prevalence rate of IAD ≥10 mmHg was significantly higher in the moderate OSA (15 ≤ apnea-hypopnea index [AHI] < 30) group (4.2%) and severe OSA (AHI ≥ 30) group (4.6%) than that in the no/mild (AHI < 15) OSA group (1.4%). Multivariate logistic regression analysis also identified moderate to severe OSA as being significantly associated with IAD ≥10 mmHg, even after adjustments for confounding variables (moderate OSA: odds ratio [OR], 4.869; 95% confidence interval [CI], 1.080–21.956; P = 0.039; severe OSA: OR, 5.301; 95% CI, 1.226–22.924; P = 0.026). However, there were no significant associations of the OSA severity with IAND ≥15 mmHg or ABI <0.9.

Conclusions:
Moderate to severe OSA was independently associated with the IAD, not but with the IAND or ABI.
Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS
Authors: Ghahramani Gregory , Brendel Matthew , Lin Mingquan , Chen Qingyu , Keenan Tiarnan , Chen Kun , Chew Emily , Lu Zhiyong , Peng Yifan , Wang Fei 
Published: September 2021

AbstractAge-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a rapid pace. Physicians analyze time-of-visit fundus photographs to predict patient risk of developing late-AMD, the most severe form of AMD. Our study hypothesizes that 1) incorporating historical data improves predictive strength of developing late-AMD and 2) state-of-the-art deep-learning techniques extract more predictive image features than clinicians do. We incorporate longitudinal data from the Age-Related Eye Disease Studies and deep-learning extracted image features in survival settings to predict development of late-AMD. To extract image features, we used multi-task learning frameworks to train convolutional neural networks. Our findings show 1) incorporating longitudinal data improves prediction of late-AMD for clinical standard features, but only the current visit is informative when using complex features and 2) “deep-features” are more informative than clinician derived features. We make codes publicly available athttps://github.com/bionlplab/AMD_prognosis_amia2021.
Mathematical modeling and adequate environmental sampling plans are essential for the public health assessment of COVID-19 pandemics : development of a monitoring indicator for SARS-CoV-2 in wastewater
Authors: Cluzel Nicolas , Courbariaux Marie , Wang Siyun , Moulin Laurent , Wurtzer Sébastien , Bertrand Isabelle , Laurent Karine , Monfort Patrick ,   Obépine consortium, Le Guyader Soizick , Boni Mickaël , Mouchel Jean-Marie , Maréchal Vincent , Nuel Grégory , Maday Yvon 
Published: September 2021

AbstractSince many infected people experience no or few symptoms, the SARS-CoV-2 epidemic is frequently monitored through massive virus testing of the population, an approach that may be biased and may be difficult to sustain in low-income countries. Since SARS-CoV-2 RNA can be detected in stool samples, quantifying SARS-CoV-2 genome by RT-qPCR in WWTPs1 has been proposed as an alternative tool to monitor virus circulation among human populations. However, measuring SARS-CoV-2 viral load in WWTPs can be affected by many experimental and environmental factors. To circumvent these limits, we propose here a novel indicator WWI2 that partly reduces and corrects the noise associated with the SARS-CoV-2 genome quantification in wastewater. This method has been successfully applied in the context of Obepine, a French national network that has been quantifying SARS-CoV-2 genome in a representative sample of French WWTPs since March 5th 2020. On August 26th, 2021, 168 WWTPs were monitored twice a week in the metropolitan and overseas territories of France. We detail the process of elaboration of this indicator, show that it is strongly correlated to the incidence rate and that the optimal time lag between these two signals is only a few days, making our indicator an efficient complement or even a credible alternative to the incidence rate. This alternative approach may be especially important to evaluate SARS-CoV-2 dynamics in human populations when the testing rate is low.Figure 1:Graphical abstract.
Colorectal cancer and microbiota modulation for clinical use. A systematic review
Authors: Madrigal-Matute Julio , Bañón Escandell Sara 
Published: September 2021

AbstractColorectal cancer is one of the top contributors to the global burden of cancer incidence and mortality with genetic and environmental factors contributing to its etiology. Modifiable or environmental factors can be the cause of up to 60% of the risk of developing colorectal cancer. Hence, there is a growing interest in specifically defining what can be improved in our lifestyle to reduce this risk, improve the effectiveness of treatments, reduce side effects, and decrease the risk of recurrence. One of the elements directly related to lifestyle is gut microbiota. The microbial ecosystem has a vital role in colorectal cancer prevention and antitumoral response through modulation of the immune system and production of short-chain fatty acids. Numerous approaches have been used to identify healthy microbiota that can reduce the risk of cancer development, improve treatment efficacy, and reduce side effects.Scientific literature in this subject is growing exponentially and, therefore, systematic reviews and meta-analysis are required to ensure that appropriate recommendations are given to patients.This work aimed to perform a systematic analysis of the published literature to elucidate whether microbiota modulation through pre-, pro-, symbiotic treatment and/or nutritional intervention can be beneficial for patients diagnosed with colorectal cancer.Detailed analysis of published studies shows that some prebiotics, such as inulin and resistant starch, probiotics such as lactic strains producers of short-chain fatty acids, and consumption of unprocessed plant products, can be effective recommendations for patients diagnosed with colorectal cancer. This advice should always be individually tailored and followed up by a healthcare professional with expertise in the field.
Integrated Omics Analysis Reveals Sirtuin Signaling is Central to Hepatic Response to a High Fructose Diet
Authors: Cox Laura A. , Chan Jeannie , Rao Prahlad , Hamid Zeeshan , Glenn Jeremy P. , Jadhav Avinash , Das Vivek , Karere Genesio M. , Quillen Ellen , Kavanagh Kylie , Olivier Michael 
Published: September 2021

AbstractBackgroundDietary high fructose (HFr) is a known metabolic disruptor contributing to development of obesity and diabetes in Western societies. Initial molecular changes from exposure to HFr on liver metabolism may be essential to understand the perturbations leading to insulin resistance and abnormalities in lipid and carbohydrate metabolism. We studied vervet monkeys (Clorocebus aethiops sabaeus) fed a HFr (n=5) or chow diet (n=5) for 6 weeks, and obtained clinical measures of liver function, blood insulin, cholesterol and triglycerides. In addition, we performed untargeted global transcriptomics, proteomics, and metabolomics analyses on liver biopsies to determine the molecular impact of a HFr diet on coordinated pathways and networks that differed by diet.ResultsWe show that integration of omics data sets improved statistical significance for some pathways and networks, and decreased significance for others, suggesting that multiple omics datasets enhance confidence in relevant pathway and network identification. Specifically, we found that sirtuin signaling and a peroxisome proliferator activated receptor alpha (PPARA) regulatory network were significantly altered in hepatic response to HFr. Integration of metabolomics and miRNAs data further strengthened our findings.ConclusionsOur integrated analysis of three types of omics data with pathway and regulatory network analysis demonstrates the usefulness of this approach for discovery of molecular networks central to a biological response. In addition, metabolites aspartic acid and docosahexaenoic acid (DHA), protein ATG3, and genesATG7, HMGCS2link sirtuin signaling and the PPARA network suggesting molecular mechanisms for altered hepatic gluconeogenesis from consumption of a HFr diet.
Metaproteomics as a tool for studying the protein landscape of human-gut bacterial species
Authors: Stamboulian Moses , Canderan Jamie , Ye Yuzhen 
Published: September 2021

AbstractHost-microbiome interactions and the microbial community have broad impact in human health and diseases. Most microbiome based studies are performed at the genome level based on next-generation sequencing techniques, but metaproteomics is emerging as a powerful technique to study microbiome functional activity by characterizing the complex and dynamic composition of microbial proteins. We conducted a large-scale survey of human gut microbiome metaproteomic data to identify generalist species that are ubiquitously expressed across all samples and specialists that are highly expressed in a small subset of samples associated with a certain phenotype. We were able to utilize the metaproteomic mass spectrometry data to reveal the protein landscapes of these species, which enables the characterization of the expression levels of proteins of different functions and underlying regulatory mechanisms, such as operons. Finally, we were able to recover a large number of open reading frames (ORFs) with spectral support, which were missed by de novo protein-coding gene predictors. We showed that a majority of the rescued ORFs overlapped with de novo predicted proteincoding genes, but on opposite strands or on different frames. Together, these demonstrate applications of metaproteomics for the characterization of important gut bacterial species. Results are available for public access at https://omics.informatics.indiana.edu/GutBac.Author summaryMany reference genomes for studying human gut microbiome are available, but knowledge about how microbial organisms work is limited. Identification of proteins at individual species or community level provides direct insight into the functionality of microbial organisms. By analyzing more than a thousand metaproteomics datasets, we examined protein landscapes of more than two thousands of microbial species that may be important to human health and diseases. This work demonstrated new applications of metaproteomic datasets for studying individual genomes. We made the analysis results available through the GutBac website, which we believe will become a resource for studying microbial species important for human health and diseases.
Human spindle variability
Authors: Gonzalez Christopher , Jiang Xi , Gonzalez-Martinez Jorge , Halgren Eric 
Published: September 2021

AbstractIn humans, sleep spindles are 10-16 Hz oscillations lasting approximately 0.5-2 s. Spindles, along with cortical slow oscillations, facilitate memory consolidation by enabling synaptic plasticity. Early recordings of spindles at the scalp found anterior channels had overall slower frequency than central-posterior channels. This robust, topographical finding led to dichotomizing spindles as ‘slow’ versus ‘fast’, modelled as two distinct spindle generators in frontal versus posterior cortex. Using a large dataset of intracranial sEEG recordings (n=20, 365 bipolar recordings), we show that the difference in spindle frequency between frontal and parietal channels is comparable to the variability in spindle frequency within the course of individual spindles, across different spindles recorded by a given site, and across sites within a given region. Thus, fast and slow spindles only capture average differences that obscure a much larger underlying overlap in frequency. Furthermore, differences in mean frequency are only one of several ways that spindles differ. For example, compared to parietal, frontal spindles are smaller, tend to occur after parietal when both are engaged, and show a larger decrease in frequency within-spindles. However, frontal and parietal spindles are similar in being longer, less variable, and more widespread than occipital, temporal, and Rolandic spindles. These characteristics are accentuated in spindles which are highly phase-locked to posterior hippocampal spindles. We propose that rather than a strict parietal-fast/frontal-slow dichotomy, spindles differ continuously and quasi-independently in multiple dimensions, with variability due about equally to within-spindle, within-region and between-region factors.Significance StatementSleep spindles are 10-16 Hz neural oscillations generated by cortico-thalamic circuits that promote memory consolidation. Spindles are often dichotomized into slow-anterior and fast-posterior categories for cognitive and clinical studies. Here, we show that the anterior-posterior difference in spindle frequency is comparable to that observed between different cycles of individual spindles, between spindles from a given site, or from different sites within a region. Further, we show that spindles vary on other dimensions such as duration, amplitude, spread, primacy and consistency, and that these multiple dimensions vary continuously and largely independently across cortical regions. These findings suggest that multiple continuous variables rather than a strict frequency dichotomy may be more useful biomarkers for memory consolidation or psychiatric disorders.
KappaBle fluorescent reporter mice enable dynamic and low-background single-cell detection of NF-κB activityin vivo
Authors: Tortola Luigi , Ampenberger Franziska , Rosenwald Esther , Heer Sebastian , Rülicke Thomas , Kisielow Jan , Kopf Manfred 
Published: September 2021

AbstractNuclear factor-κB (NF-κB) is a transcription factor with a key role in a great variety of cellular processes from embryonic development to immunity, the outcome of which depends on the fine-tuning of NF-κB activity. The development of sensitive and faithful reporter systems to accurately monitor the activation status of this transcription factor is therefore desirable. To address this need, over the years a number of different approaches have been used to generate NF-κB reporter mice, which can be broadly subdivided into bioluminescence- and fluorescence-based systems. While the former enables whole-body visualization of the activation status of NF-κB, the latter have the potential to allow the analysis of NF-κB activity at single cell level. However, fluorescence-based reporters frequently show poor sensitivity and excessive background or are incompatible with high-throughput flow cytometric analysis. In this work we describe the generation and analysis of ROSA26 knockin NF-κB reporter (KappaBle) mice containing a destabilized EGFP, which showed sensitive, dynamic, and faithful monitoring of NF-κB activity at the single-cell level of various cell types during inflammatory and infectious diseases.
A Compressive Sensing Approach for Inferring Cognitive Representations with Reverse Correlation
Authors: Roop Benjamin W. , Parrell Benjamin , Lammert Adam C. 
Published: September 2021

AbstractUncovering high-level cognitive representations of categories such as faces is an elusive goal that has been frequently sought using reverse correlation, a technique employed in fields ranging from neurophysiology to cognitive psychology. In reverse correlation, subjects are asked to make perceptual judgments (e.g., “do you see a face?”) about richly varying stimuli (e.g., white noise), and observed responses are then regressed against stimuli to yield reconstructions of the underlying cognitive representation. However, many thousands of stimulus-response pairs are frequently required, which severely limits the breadth of studies that are feasible using this powerful method. Techniques that are currently employed to improve efficiency, such as filtering the reconstruction, nevertheless bias the outcome. Here, we show that an advanced signal processing technique for improving sampling efficiency – compressive sensing – is directly compatible with reverse correlation. A trio of simulations are performed to demonstrate that compressive sensing can reduce the required stimulus-response pairs by up to 90% without biasing the reconstruction or can retrospectively improve the accuracy of the reconstructions on existing data. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the field of neuroscience and beyond.Significance StatementUncovering cognitive representations is an elusive goal that is increasingly pursued using the reverse correlation method, wherein human subjects make judgments about vague stimuli. Employing reverse correlation often entails collecting thousands of stimulus-response pairs, which severely limits the breadth of studies that are feasible using the method. Here we show that this methodological barrier can be overcome using compressive sensing, an advanced signal processing technique designed to improve sampling efficiency. Three sets of simulations are performed to demonstrate that compressive sensing can improve the accuracy of reconstructed cognitive representations and dramatically reduce the required number of stimulus-response pairs. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the field of neuroscience and beyond.
XSTREME: Comprehensive motif analysis of biological sequence datasets
Authors: Grant Charles E. , Bailey Timothy L. 
Published: September 2021

AbstractXSTREME is a web-based tool for performing comprehensive motif discovery and analysis in DNA, RNA or protein sequences, as well as in sequences in user-defined alphabets. It is designed for both very large and very small datasets. XSTREME is similar to the MEME-ChIP tool, but expands upon its capabilities in several ways. Like MEME-ChIP, XSTREME performs two types of de novo motif discovery, and also performs motif enrichment analysis of the input sequences using databases of known motifs. Unlike MEME-ChIP, which ranks motifs based on their enrichment in the centers of the input sequences, XSTREME uses enrichment anywhere in the sequences for this purpose. Consequently, XSTREME is more appropriate for motif-based analysis of sequences regardless of how the motifs are distributed within the sequences. XSTREME uses the MEME and STREME algorithms for motif discovery, and the recently developed SEA algorithm for motif enrichment analysis. The interactive HTML output produced by XSTREME includes highly accurate motif significance estimates, plots of the positional distribution of each motif, and histograms of the number of motif matches in each sequences. XSTREME is easy to use via its web server at https://meme-suite.org, and is fully integrated with the widely-used MEME Suite of sequence analysis tools, which can be freely downloaded at the same web site for non-commercial use.
Macrophages in the uterus are functionally specialised and continually replenished from the circulation
Authors: Scott Nicholas A. , Mohiyiddeen Lamiya , Mariano Livia Lacerda , Ruane Peter T. , Aplin John D. , Moran Olivia C. , Brison Daniel R. , Ingersoll Molly A. , Mann Elizabeth R. 
Published: September 2021

ABSTRACTMacrophages are innate immune cells that fight infection but also regulate tissue regeneration and remodelling. In the uterus, although tissue remodelling is essential for establishment and maintenance of pregnancy, the specialisation of macrophages is not well characterised compared to other mucosal tissues. Here we show that uterine macrophages are functionally specialised, expressing multiple markers of alternative activation associated with tissue remodelling and repair, and responding more highly to the type 2 cytokine IL-4 than other mucosal tissue macrophages. Uterine macrophages were continuously replenished from circulating bone marrow-derived CCR2+monocytes that fluctuated dramatically in number throughout the reproductive cycle, and had properties distinct from the macrophages that they became, including differential responses to microbial stimulation. Importantly, many of these properties of uterine monocytes and macrophages were conserved between mice and humans. These findings further our understanding of immune regulation of uterine tissue integrity and have important implications for differences in immune responses to infections at different phases of the reproductive cycle.SUMMARYUterine macrophages are specialised, alternatively activated cells that are replenished from circulating bone marrow-derived monocytes. Monocyte and macrophage properties fluctuate markedly throughout the reproductive cycle, with many features conserved between mice and humans, and exhibiting differential responses to microbial stimulation.