bluuebunny/crossref_metadata_2025_split huggingface dataset:
Financial Burden of Traumatic Injury Amongst the Privately Insured
Authors: Fu Sue J. , Arnow Katherine , Trickey Amber , Spain David A. , Morris Arden , Knowlton Lisa 
Published: October 2021

Objective:
We sought to evaluate the overall financial burden associated with traumatic injury amongst patients with private insurance and assess the effect of high deductible plans on out-of-pocket costs (OOPCs).

Summary of Background Data:
Traumatic injury can be a source of unexpected financial burden for households. However, the effect of increasing participation in higher cost-sharing private health insurance plans remains unknown.

Methods:
We conducted a retrospective cohort observational study, using the Clinformatics Data Mart Database, a nationwide single-payer administrative claims database to identify US adults who required emergency department services or hospital admission for single traumatic injury from 2008 to 2018. A 2-part model using a logistic regression and a generalized linear model with gamma distribution and log link was used to evaluate 12-month OOPCs after traumatic injury. Multivariable logistic regression was used to evaluate the likelihood of catastrophic health expenditure (CHE) after injury.

Results:
Of 426,945 included patients, 53% were male, 71% were white, and median age was 42 years. Patients faced monthly OOPC of $660 at the time of their injury. High deductible plan enrollment was associated with an increase of $1703 in 12-month OOPC after trauma, compared to those covered by traditional health plans. In addition to high deductible health plan enrollment, worsening injury severity and longer hospital stays were also associated with increased 12-month OOPC after trauma. Non-white minorities paid less 12-month OOPC after trauma compared to non-Hispanic white patients, but also used fewer services. Overall, the incidence of CHE was 5%; however high-deductible health plan enrollees faced a 13% chance of CHE.

Conclusions:
Privately insured trauma patients face substantial OOPCs at the time of their injuries. High-deductible health plans are associated with increased financial vulnerability after trauma.
Postpancreatectomy Acute Pancreatitis (PPAP)
Authors: Marchegiani Giovanni , Barreto Savio George , Bannone Elisa , Sarr Michael , Vollmer Charles M. , Connor Saxon , Falconi Massimo , Besselink Marc G. , Salvia Roberto , Wolfgang Christopher L. , Zyromski Nicholas J. , Yeo Charles J. , Adham Mustapha , Siriwardena Ajith K. , Takaori Kyoichi , Hilal Mohammad Abu , Loos Martin , Probst Pascal , Hackert Thilo , Strobel Oliver , Busch Olivier R. C. , Lillemoe Keith D. , Miao Yi , Halloran Christopher M. , Werner Jens , Friess Helmut , Izbicki Jakob R. , Bockhorn Maximillian , Vashist Yogesh K. , Conlon Kevin , Passas Ioannis , Gianotti Luca , Del Chiaro Marco , Schulick Richard D. , Montorsi Marco , Oláh Attila , Fusai Giuseppe Kito , Serrablo Alejandro , Zerbi Alessandro , Fingerhut Abe , Andersson Roland , Padbury Robert , Dervenis Christos , Neoptolemos John P. , Bassi Claudio , Büchler Markus W. , Shrikhande Shailesh V. ,   on behalf of the International Study Group for Pancreatic Surgery
Published: October 2021

Objective:
The ISGPS aimed to develop a universally accepted definition for PPAP for standardized reporting and outcome comparison.

Background: :
PPAP is an increasingly recognized complication after partial pancreatic resections, but its incidence and clinical impact, and even its existence are variable because an internationally accepted consensus definition and grading system are lacking.

Methods:
The ISGPS developed a consensus definition and grading of PPAP with its members after an evidence review and after a series of discussions and multiple revisions from April 2020 to May 2021.

Results:
We defined PPAP as an acute inflammatory condition of the pancreatic remnant beginning within the first 3 postoperative days after a partial pancreatic resection. The diagnosis requires (1) a sustained postoperative serum hyperamylasemia (POH) greater than the institutional upper limit of normal for at least the first 48 hours postoperatively, (2) associated with clinically relevant features, and (3) radiologic alterations consistent with PPAP. Three different PPAP grades were defined based on the clinical impact: (1) grade postoperative hyperamylasemia, biochemical changes only; (2) grade B, mild or moderate complications; and (3) grade C, severe life-threatening complications.

Discussions:
The present definition and grading scale of PPAP, based on biochemical, radiologic, and clinical criteria, are instrumental for a better understanding of PPAP and the spectrum of postoperative complications related to this emerging entity. The current terminology will serve as a reference point for standard assessment and lend itself to developing specific treatments and prevention strategies.
Transthoracic Robotic First and Cervical Rib Resection for Thoracic Outlet Syndrome
Authors: Palivela Nihanth , Burt Bryan M. 
Published: October 2021

Introduction:
Neurogenic thoracic outlet syndrome (TOS) is a musculoskeletal condition in which the brachial plexus is dynamically compressed within the scalene triangle, an anatomic space bordered by the anterior and middle scalene muscles and the first rib. In some cases, an offending cervical rib is present. Traditional surgical approaches to first rib resection and scalenectomy are limited by exposure, require retraction of neurovascular structures, and result in morbidity.

Methods:
We describe a novel transthoracic robotic approach to first/cervical rib resection that overcomes these limitations, and we review its early clinical outcomes.

Results:
Robotic first rib resection (FRR) is crystallized into 12 distinct steps, each with detailed video commentary, and nuances specific to neurogenic and venous TOS cases are provided. Published data supports decreased surgical morbidity of robotic FRR compared with open cases.

Conclusions:
Robotic FRR offers advantages over traditional operative approaches including improved exposure and elimination of retraction of neurovascular structures, which result in improved safety.
Combined neuromodulatory approaches in the central nervous system for treatment of spinal cord injury
Authors: Noga Brian R. , Guest James D. 
Published: October 2021

Purpose of review
To report progress in neuromodulation following spinal cord injury (SCI) using combined brain and spinal neuromodulation.
Neuromodulation refers to alterations in neuronal activity for therapeutic purposes. Beneficial effects are established in disease states such as Parkinson's Disease (PD), chronic pain, epilepsy, and SCI. The repertoire of neuromodulation and bioelectric medicine is rapidly expanding. After SCI, cohort studies have reported the benefits of epidural stimulation (ES) combined with training. Recently, we have explored combining ES with deep brain stimulation (DBS) to increase activation of descending motor systems to address limitations of ES in severe SCI. In this review, we describe the types of applied neuromodulation that could be combined in SCI to amplify efficacy to enable movement. These include ES, mesencephalic locomotor region (MLR) - DBS, noninvasive transcutaneous stimulation, transcranial magnetic stimulation, paired-pulse paradigms, and neuromodulatory drugs. We examine immediate and longer-term effects and what is known about: (1) induced neuroplastic changes, (2) potential safety concerns; (3) relevant outcome measures; (4) optimization of stimulation; (5) therapeutic limitations and prospects to overcome these.

Recent findings
DBS of the mesencephalic locomotor region is emerging as a potential clinical target to amplify supraspinal command circuits for locomotion.

Summary
Combinations of neuromodulatory methods may have additive value for restoration of function after spinal cord injury.
Inevitability of Red Queen evolution driven by organismic complexity and simple feedback via environmental modification
Authors: Fisher Daniel S. 
Published: October 2021

AbstractEvolution in complex high-dimensional phenotype spaces can be very different than the caricature of uphill evolutionary trajectories in a low-dimensional fitness landscape. And slight modifications of the environment can have large consequences for the future evolution. Here, the simplest approximation of evolution, an almost-always clonal population evolving by small effect mutations under deterministic “adaptive” dynamics, is studied. The complexities of organisms and their interactions with their environments are caricatured by population growth rates being smoothly varying random functions in very high dimensional phenotype spaces. In a fixed environment, there are huge numbers of fitness maxima, yet evolutionary trajectories wander around amongst saddles, gradually slowing down but still wandering widely and without committing to any maximum. But with even very small changes of the environment caused by the phenotypic changes, after an initial transient the evolution continues forever without further slowing down. In this Red Queen “phase” the apparent rate of increase of the fitness saturates (at a feedback strength-dependent rate) and the trajectories perpetually wander over large phenotypic distances. Organismic complexities, caricatured by a large number of constraints on the molecular-level phenotype, together with the simplest possible interactions of the organisms with their environment, are shown to be sufficient to cause such Red Queen dynamics. Arguments are made for the ubiquity of such behavior.
Here’s the twist: How the brain updates the representations of naturalistic events as our understanding of the past changes
Authors: Zadbood Asieh , Nastase Samuel A. , Chen Janice , Norman Kenneth A. , Hasson Uri 
Published: October 2021

AbstractThe brain actively reshapes our understanding of past events in light of new incoming information. In the current study, we ask how the brain supports this updating process during the encoding and recall of naturalistic stimuli. One group of participants watched a movie (“The Sixth Sense”) with a cinematic “twist” at the end that dramatically changed the interpretation of previous events. Next, participants were asked to verbally recall the movie events, taking into account the new “twist” information. Most participants updated their recall to incorporate the twist. Two additional groups recalled the movie without having to update their memories during recall: one group never saw the twist; another group was exposed to the twist prior to the beginning of the movie, and thus the twist information was incorporated both during encoding and recall. We found that providing participants with information about the twist beforehand altered neural response patterns during movie-viewing in the default mode network (DMN). Moreover, presenting participants with the twist at the end of the movie changed the neural representation of the previously-encoded information during recall in a subset of DMN regions. Further evidence for this transformation was obtained by comparing the neural activation patterns during encoding and recall and correlating them with behavioral signatures of memory updating. Our results demonstrate that neural representations of past events encoded in the DMN are dynamically integrated with new information that reshapes our understanding in natural contexts.
Spectral graph theory of brain oscillations – revisited and improved
Authors: Verma Parul , Nagarajan Srikantan , Raj Ashish 
Published: October 2021

AbstractMathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso- (regional population ensemble) and macroscale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately.In this work, we demonstrate an improved hierarchical, linearized, and analytic spectral graph theorybased model that can capture the frequency spectra obtained from magnetoencephalography recordings of resting healthy subjects. We reformulated the spectral graph theory model in line with classical neural mass models, therefore providing more biologically interpretable parameters, especially at the local scale. We demonstrated that this model performs better than the original model when comparing the spectral correlation of modeled frequency spectra and that obtained from the magnetoencephalography recordings. This model also performs equally well in predicting the spatial patterns of the empirical alpha and beta frequency bands.HighlightsWe show an improved hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography recordingsThis model also accurately captures the spatial patterns of the empirical alpha and beta frequency bands, requiring only 5-10 graph eigenmodes to do so
16S rRNA gene sequences of Candidatus Methylumidiphilus (Methylococcales), a putative methanotrophic genus in lakes and ponds
Authors: Rissanen Antti J , Buck Moritz , Peura Sari 
Published: October 2021

AbstractA putative novel methanotrophic genus, Candidatus Methylumidiphilus (Methylococcales), was recently shown to be ubiquitous and one of the most abundant methanotrophic genera in water columns of oxygen-stratified lakes and ponds of boreal and subarctic area. However, it has probably escaped detection in many previous studies using 16S rRNA gene amplicon sequencing due to insufficient database coverage, which is because Ca. Methylumidiphilus lacks cultured representatives and previously analysed metagenome assembled genomes (MAGs) affiliated with it do not contain 16S rRNA genes. Therefore, we screened MAGs affiliated with the genus for their 16S rRNA gene sequences in a recently published lake and pond MAG dataset. Among 66 MAGs classified as Ca. Methylumidiphilus (with completeness over 40% and contamination less than 5%) originating from lakes in Finland, Sweden and Switzerland as well as from ponds in Canada, we could find 5 MAGs each containing one 1532 bp long sequence spanning the V1-V9 regions of the 16S rRNA gene. After removal of sequence redundancy, this resulted in two unique 16S rRNA gene sequences. These sequences represented two different putative species, i.e. Ca. Methylumidiphilus alinenensis (Genbank accession: OK236221) as well as another so far unnamed species of Ca. Methylumidiphilus (Genbank accession: OK236220). We suggest that including these two sequences in reference databases will enhance 16S rRNA gene - based detection of members of this genus from environmental samples.
Accelerating Identification of Chromatin Accessibility from noisy ATAC-seq Data using Modern CPUs
Authors: Chaudhary Narendra , Misra Sanchit , Kalamkar Dhiraj , Heinecke Alexander , Georganas Evangelos , Ziv Barukh , Adelman Menachem , Kaul Bharat 
Published: October 2021

AbstractIdentifying accessible chromatin regions is a fundamental problem in epigenomics with ATAC-seq being a commonly used assay. Exponential rise in single cell ATAC-seq experiments has made it critical to accelerate processing of ATAC-seq data. ATAC-seq data can have a low signal-to-noise ratio for various reasons including low coverage or low cell count. To denoise and identify accessible chromatin regions from noisy ATAC-seq data, use of deep learning on 1D data – using large filter sizes, long tensor widths, and/or dilation - has recently been proposed. Here, we present ways to accelerate the end-to-end training performance of these deep learning based methods using CPUs. We evaluate our approach on the recently released AtacWorks toolkit. Compared to an Nvidia DGX-1 box with 8 V100 GPUs, we get up to 2.27× speedup using just 16 CPU sockets. To achieve this, we build an efficient 1D dilated convolution layer and demonstrate reduced precision (BFloat16) training.
Direct cryopreservation of poultry/avian embryonic reproductive cells: A low-tech, cost-effective and efficient method for safeguarding genetic diversity
Authors: Hu Tuanjun , Taylor Lorna , Sherman Adrian , Tiambo Christian K. , Kemp Stephen J. , Whitelaw Bruce W. , Hawken Rachel J. , Djikeng Appolinaire , McGrew Mike J. 
Published: October 2021

AbstractChickens are an important resource for smallholder farmers who raise locally adapted, genetically distinct breeds for eggs and meat. The development of efficient reproductive technologies to conserve and regenerate chicken breeds safeguards existing biodiversity and secures poultry genetic resources for climate resilience, biosecurity, and future food production. The majority of the over 1600 breeds of chicken are raised in low and lower to middle income countries (LMICs) under resource limited, small scale production systems, which necessitates a low tech, cost effective means of conserving diversity is needed. Here, we validate a simple biobanking technique using cryopreserved embryonic chicken gonads. The gonads are quickly isolated, visually sexed, pooled by sex, and cryopreserved. Subsequently, the stored material is thawed and dissociated before injection into sterile host chicken embryos. By using pooled GFP and RFP-labelled donor gonadal cells and Sire Dam Surrogate (SDS) mating, we demonstrate that chicks deriving entirely from male and female donor germ cells are hatched. This technology will enable ongoing efforts to conserve chicken genetic diversity for both commercial and small holder farmers, and to preserve existing genetic resources at poultry research facilities.
Fine scale population structure and extensive gene flow within an Eastern Nearctic snake complex (Pituophis melanoleucus)
Authors: Nikolakis Zachary L. , Orton Richard W. , Crother Brian I. 
Published: October 2021

AbstractUnderstanding the processes and mechanisms that promote lineage divergence is a central goal in evolutionary biology. For instance, studies investigating the spatial distribution of genomic variation often highlight biogeographic barriers underpinning geographic isolation, as well as patterns of isolation by environment and isolation by distance that can also lead to lineage divergence. However, the patterns and processes that shape genomic variation and drive lineage divergence may be taxa-specific, even across closely related taxa co-occurring within the same biogeographic region. Here, we use molecular data in the form of ultra-conserved elements (UCEs) to infer the evolutionary relationships and population genomic structure of the Eastern Pinesnake complex (Pituophis melanoleucus) – a polytypic wide-ranging species that occupies much of the Eastern Nearctic. In addition to inferring evolutionary relationships, population genomic structure, and gene flow, we also test relationships between genomic diversity and putative barriers to dispersal, environmental variation, and geographic distance. We present results that reveal shallow population genomic structure and ongoing gene flow, despite an extensive geographic range that transcends geographic features found to reduce gene flow among many taxa, including other squamate reptiles within the Eastern Nearctic. Further, our results indicate that the observed genomic diversity is spatially distributed as a pattern of isolation by distance and suggest that the current subspecific taxonomy do not adhere to independent lineages, but rather, show a significant amount of admixture across the entire P. melanoleucus range.
Real-world size of objects serves as an axis of object space
Authors: Huang Taicheng , Song Yiying , Liu Jia 
Published: October 2021

AbstractOur mind can represent various objects from the physical world metaphorically into an abstract and complex high-dimensional object space, with a finite number of orthogonal axes encoding critical object features. Previous fMRI studies have shown that the middle fusiform sulcus in the ventral temporal cortex separates the real-world small-size map from the large-size map. Here we asked whether the feature of objects’ real-world size constructed an axis of object space with deep convolutional neural networks (DCNNs) based on three criteria of sensitivity, independence and necessity that are impractical to be examined altogether with traditional approaches. A principal component analysis on features extracted by the DCNNs showed that objects’ real-world size was encoded by an independent component, and the removal of this component significantly impaired DCNN’s performance in recognizing objects. By manipulating stimuli, we found that the shape and texture of objects, rather than retina size, co-occurrence and task demands, accounted for the representation of the real-world size in the DCNNs. A follow-up fMRI experiment on humans further demonstrated that the shape, but not the texture, was used to infer the real-world size of objects in humans. In short, with both computational modeling and empirical human experiments, our study provided the first evidence supporting the feature of objects’ real-world size as an axis of object space, and devised a novel paradigm for future exploring the structure of object space.TeaserThis work provides the first evidence illuminating the feature of objects’ real-world size as an axis of the object space for object recognition with a mutually-inspired paradigm of computational modelling and biological observation.