bluuebunny/crossref_metadata_2025_split huggingface dataset:
The complexity of atrial fibrillation newly diagnosed after ischemic stroke and transient ischemic attack: advances and uncertainties
Authors: Cerasuolo Joshua O. , Cipriano Lauren E. , Sposato Luciano A. 
Published: December 2016

Purpose of review
Atrial fibrillation is being increasingly diagnosed after ischemic stroke and transient ischemic attack (TIA). Patient characteristics, frequency and duration of paroxysms, and the risk of recurrent ischemic stroke associated with atrial fibrillation detected after stroke and TIA (AFDAS) may differ from atrial fibrillation already known before stroke occurrence. We aim to summarize major recent advances in the field, in the context of prior evidence, and to identify areas of uncertainty to be addressed in future research.

Recent findings
Half of all atrial fibrillations in ischemic stroke and TIA patients are AFDAS, and most of them are asymptomatic. Over 50% of AFDAS paroxysms last less than 30 s. The rapid initiation of cardiac monitoring and its duration are crucial for its timely and effective detection. AFDAS comprises a heterogeneous mix of atrial fibrillation, possibly including cardiogenic and neurogenic types, and a mix of both. Over 25 single markers and at least 10 scores have been proposed as predictors of AFDAS. However, there are considerable inconsistencies across studies. The role of AFDAS burden and its associated risk of stroke recurrence have not yet been investigated.

Summary
AFDAS may differ from atrial fibrillation known before stroke in several clinical dimensions, which are important for optimal patient care strategies. Many questions remain unanswered. Neurogenic and cardiogenic AFDAS need to be characterized, as it may be possible to avoid some neurogenic cases by initiating timely preventive treatments. AFDAS burden may differ in ischemic stroke and TIA patients, with distinctive diagnostic and treatment implications. The prognosis of AFDAS and its risk of recurrent stroke are still unknown; therefore, it is uncertain whether AFDAS patients should be treated with oral anticoagulants.
Preliminary Predictors of Initial Attendance, Symptom Burden, and Motor Subtype in a US Functional Neurological Disorders Clinic Population
Authors: Perez David L. , Young Sigrid S. , King Julie N. , Guarino Anthony J. , Dworetzky Barbara A. , Flaherty Alice , Chemali Zeina , Caplan David , Dickerson Bradford C. 
Published: December 2016

Background and Objective:
Although many patients present with functional neurological symptoms (FNS), few US clinics offer specialized FNS care, and data on clinic attendees remain limited. We determined predictors of initial attendance, symptom burden, and FNS subtype in the first patients referred to our Functional Neurological Disorders Clinic for suspected FNS.

Methods:
We reviewed the charts of 62 consecutive patients (46 women, 16 men). Regression analyses investigated predictors of keeping the first scheduled clinic appointment. For the 49 patients who did keep that appointment, regression analyses examined neuropsychiatric factors associated with symptom burden and motor FNS subtypes.

Results:
The odds of not keeping the first appointment were 10.4 times greater for patients referred from the emergency department than from other sources. The patients who kept their appointment reported a symptom burden that was significantly associated with a past FNS-related emergency department visit and a diagnosis of another medically unexplained somatic syndrome. The number of FNS findings on neurological examination also correlated with a history of an FNS-related emergency department visit. Patients with psychogenic non-epileptic seizures reported cognitive complaints and prior psychiatric hospitalizations significantly more often than did patients with other FNS. One fourth of all patients had two or more motor FNS.

Conclusions:
In our FNS cohort, patients were less likely to keep an initial clinic appointment if they were referred from the emergency department than from other sources. Patients with psychogenic non-epileptic seizures were more likely to report cognitive symptoms and past psychiatric hospitalizations than patients with other FNS.
Short Follow-up Bias Confounds Estimates of the “Typical” Clinical Course of Susac Syndrome
Authors: Vodopivec Ivana , Prasad Sashank 
Published: December 2016

Background:
To evaluate the validity of the prevailing concept that Susac syndrome (SS), a rare microvasculopathy of the brain, retina, and inner ear, is a self-limiting disease.

Methods:
We performed a literature search to identify all cases of SS reported between 1973 and October 2015. If available, we determined their demographics, duration of follow-up, and the clinical course that was labeled as monocyclic or polycyclic. We attempted to determine the number of relapses and the relapse rate in patients with polycyclic disease.

Results:
Our literature search yielded 185 relevant publications reporting 405 cases of SS. The duration of follow-up could be determined in 247/405 cases, with a range 0.5–312 months. The mean was 41 months but the distribution was skewed, with a median of 24 months. Defining the clinical course as monocyclic or polycyclic was possible in 102 patients who were followed for greater than 24 months; 53 were identified as having a polycyclic course. Patients labeled polycyclic were followed longer than those labeled monocyclic (median 62 vs 42 months, P < 0.001). The number or frequency of attacks per patient could not be determined.

Conclusions:
The follow-up of published cases of SS is short, creating an inherent bias toward the impression that the disease is self-limiting. Our findings suggest that stratification of SS into monocyclic, polycyclic, and chronic continuous courses may oversimplify the phenotype of SS; instead, the possibility of a relapsing-remitting course must be considered in all patients with this disorder.
Reversible Charles Bonnet Syndrome After Oculoplastic Procedures
Authors: Beaulieu Robert A. , Tamboli Diana A. , Armstrong Blair K. , Hogan R. Nick , Mancini Ronald 
Published: December 2016

Abstract:Individuals with Charles Bonnet syndrome (CBS) typically have severe visual loss and experience visual hallucinations yet have no psychiatric disease. Visual impairment often is due to end-stage glaucoma or macular degeneration. We report 3 cases of CBS in patients who underwent an oculoplastic surgical procedure. One patient experienced binocular visual distortion due to excessive topical ophthalmic ointment, and 2 patients experienced monocular visual impairment from patching. Visual hallucinations resolved once vision returned to baseline. We highlight the possibility of transient CBS in postoperative patients who have temporary iatrogenic vision impairment in one or both eyes.
Creating a universal SNP and small indel variant caller with deep neural networks
Authors: Poplin Ryan , Chang Pi-Chuan , Alexander David , Schwartz Scott , Colthurst Thomas , Ku Alexander , Newburger Dan , Dijamco Jojo , Nguyen Nam , Afshar Pegah T. , Gross Sam S. , Dorfman Lizzie , McLean Cory Y. , DePristo Mark A. 
Published: December 2016

AbstractNext-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an individual’s genome1by calling genetic variants present in an individual using billions of short, errorful sequence reads2. Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome3,4. Here we show that a deep convolutional neural network5can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls. This approach, called DeepVariant, outperforms existing tools, even winning the “highest performance” award for SNPs in a FDA-administered variant calling challenge. The learned model generalizes across genome builds and even to other mammalian species, allowing non-human sequencing projects to benefit from the wealth of human ground truth data. We further show that, unlike existing tools which perform well on only a specific technology, DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, from deep whole genomes from 10X Genomics to Ion Ampliseq exomes. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.
Ancient recombination events between human herpes simplex viruses
Authors: Burrel Sonia , Boutolleau David , Ryu Diane , Agut Henri , Merkel Kevin , Leendertz Fabian H. , Calvignac-Spencer Sébastien 
Published: December 2016

AbstractHerpes simplex viruses 1 and 2 (HSV-1 and HSV-2) are seen as close relatives but also unambiguously considered as evolutionary independent units. Here, we sequenced the genomes of 18 HSV-2 isolates characterized by divergent UL30 gene sequences to further elucidate the evolutionary history of this virus. Surprisingly, genome-wide recombination analyses showed that all HSV-2 genomes sequenced to date comprise HSV-1 fragments. Using phylogenomic analyses, we could also show that two main HSV-2 lineages exist. One lineage is mostly restricted to sub-Saharan Africa while the other has reached a global distribution. Interestingly, only the worldwide lineage is characterized by ancient recombination events with HSV-1. Our findings highlight the complexity of HSV-2 evolution, a virus of likely zoonotic origin which later recombined with its human-adapted relative. They also suggest that co-infections with HSV-1 and 2 may have genomic and potentially functional consequences and should therefore be monitored more closely.
Cortical excitability controls the strength of mental imagery
Authors: Keogh Rebecca , Bergmann Johanna , Pearson Joel 
Published: December 2016

AbstractMental imagery provides an essential simulation tool for remembering the past and planning the future, with its strength affecting both cognition and mental health. Research suggests that neural activity spanning prefrontal, parietal, temporal, and visual areas supports the generation of mental images. Exactly how this network controls the strength of visual imagery remains unknown. Here, brain imaging and transcranial magnetic phosphene data show that lower resting activity and excitability levels in early visual cortex (V1-V3) predict stronger sensory imagery. Electrically decreasing visual cortex excitability using tDCS increases imagery strength, demonstrating a causative role of visual cortex excitability in controlling visual imagery. These data suggest a neurophysiological mechanism of cortical excitability involved in controlling the strength of mental images.
A global co-expression network approach for connecting genes to specialized metabolic pathways in plants
Authors: Wisecaver Jennifer H. , Borowsky Alexander T. , Tzin Vered , Jander Georg , Kliebenstein Daniel J. , Rokas Antonis 
Published: December 2016

AbstractPlants produce a tremendous diversity of specialized metabolites (SMs) to interact with and manage their environment. A major challenge hindering efforts to tap this seemingly boundless source of pharmacopeia is the identification of SM pathways and their constituent genes. Given the well-established observation that the genes comprising a SM pathway are co-regulated in response to specific environmental conditions, we hypothesized that genes from a given SM pathway would form tight associations (modules) with each other in gene co-expression networks, facilitating their identification. To evaluate this hypothesis, we used 10 global co-expression datasets—each a meta-analysis of hundreds to thousands of expression experiments—across eight plant model organisms to identify hundreds of modules of co-expressed genes for each species. In support of our hypothesis, 15.3-52.6% of modules contained two or more known SM biosynthetic genes (e.g., cytochrome P450s, terpene synthases, and chalcone synthases), and module genes were enriched in SM functions (e.g., glucoside and flavonoid biosynthesis). Moreover, modules recovered many experimentally validated SM pathways in these plants, including all six known to form biosynthetic gene clusters (BGCs). In contrast, genes predicted based on physical proximity on a chromosome to form plant BGCs were no more co-expressed than the null distribution for neighboring genes. These results not only suggest that most predicted plant BGCs do not represent genuine SM pathways but also argue that BGCs are unlikely to be a hallmark of plant specialized metabolism. We submit that global gene co-expression is a rich, but largely untapped, data source for discovering the genetic basis and architecture of plant natural products, which can be applied even without knowledge of the genome sequence.
Localizing hidden regularities with known temporal structure in the EEG evoked response data
Authors: Kuznetsova Aleksandra , Krugliakova Elena , Ossadtchi Alexei 
Published: December 2016

AbstractIn this paper we describe a novel data driven spatial filtering technique that can be applied to the ERP analysis in order to find statistically significant hidden differential activations in the EEG data. The technique is based on the known morphological characteristics of the response. Underlying optimization problem is formulated as a generalized Rayleigh quotient maximization problem. We supply our tech-nique with a relevant randomization-based statistical test to assess the significance of the discovered phenomenon. Furthermore, we describe an application of the proposed method to the EEG data acquired in the study devoted to the analysis of the auditory neuroplasticity. We show how the mismatch negativity component, a tiny and short-lasting negative response that hallmarks the novel stimuli activating primary error-detection mechanisms, can be detected after filtration.
First record of double-edged sword effect of caterpillar-induced plant volatiles in nature
Authors: El-Sayed Ashraf M. , Suckling David M. 
Published: December 2016

AbstractPlants release volatiles in response to caterpillar feeding that attract natural enemies of the herbivores, a tri-trophic interaction which has been considered an indirect plant defence against herbivores. The caterpillar-induced plant volatiles have been reported to repel or attract conspecific adult herbivores. Apple seedlings infested withPandemis pyrusanalarvae uniquely release five compounds (benzyl alcohol, benzyl nitrile, phenylacetaldehyde, indole, and (E)-nerolidol). These compounds and other known caterpillar-induced plant volatiles were tested to investigate the response of both herbivores and natural enemies. In field tests, binary blends of benzyl nitrile and acetic acid or 2-phenylethanol and acetic acid attracted a large number of conspecific male and female adult moths. On the other hand, a ternary blend of benzyl nitrile, 2-phenylethanol and acetic acid attracted the largest numbers of the general predator, the common green lacewing,Chrysoperla carnea.This study provides the first record of caterpillar-induced plant volatile attraction to conspecific adult herbivores as well as predators under natural conditions.
HICL table can manipulate all proteins in human complete proteome
Authors: Xie Zhenhua 
Published: December 2016

AbstractBackgroundThe data of human complete proteome in the databases of Universal Protein Resource (UniProt) or National Center for Biotechnology Information(NCBI) were disorderly organized and hardly handled by an ordinary biologist.ResultsThe HICL table enable an ordinary biologist efficiently to handle the human complete proteome with 67911 entries, to get an overview on the distribution of the physicochemical features of all proteins in the human complete proteome, to perceive the details of the distribution patterns of the physicochemical features in some protein family members and protein variants, to find some particular proteins.Moreover, two discoveries were made via the HICL table: (1) The amino aicds(Asp,Glu) have symmetrical trend of the distributions versus pI, but the amino aicds(Arg, Lys) have local asymmetrical trend of the distributions versus pI in human complete proteome. (2) Protein sequence, besides amino acid properties, can in theory influence the modal distribution of protein isoelectric points.ConclusionI has created the HICL table as a robust tool for orderly managing 67911 proteins in human complete proteome by their physicochemical features, the names and sequences. Any proteins with the particular physicochemical features can be screened out from the human complete proteome via the HICL table. In addition, the unbalanced distribution of the amino aicds(Arg, Lys) in high pI proteins of human complete proteome and the effect of protein sequence on modal distribution of protein isoelectric points have been discovered through the HICL table.
Machine-learning annotation of human splicing branchpoints
Authors: Signal Bethany , Gloss Brian S , Dinger Marcel E , Mercer Timothy R 
Published: December 2016

ABSTRACTBackgroundThe branchpoint element is required for the first lariat-forming reaction in splicing. However due to difficulty in experimentally mapping at a genome-wide scale, current catalogues are incomplete.ResultsWe have developed a machine-learning algorithm trained with empirical human branchpoint annotations to identify branchpoint elements from primary genome sequence alone. Using this approach, we can accurately locate branchpoints elements in 85% of introns in current gene annotations. Consistent with branchpoints as basal genetic elements, we find our annotation is unbiased towards gene type and expression levels. A major fraction of introns was found to encode multiple branchpoints raising the prospect that mutational redundancy is encoded in key genes. We also confirmed all deleterious branchpoint mutations annotated in clinical variant databases, and further identified thousands of clinical and common genetic variants with similar predicted effects.ConclusionsWe propose the broad annotation of branchpoints constitutes a valuable resource for further investigations into the genetic encoding of splicing patterns, and interpreting the impact of common- and disease-causing human genetic variation on gene splicing.
Searching for Principles of Brain Computation
Authors: Maass Wolfgang 
Published: December 2016

HighlightsHints for computational principles from experimental dataComputational role of diverse network componentsEmergence and computational role of assembliesProbabilistic inference through stochastic network dynamicsOngoing network rewiring and compensation through synaptic samplingAbstractExperimental methods in neuroscience, such as calcium-imaging and recordings with multielectrode arrays, are advancing at a rapid pace. They produce insight into the simultaneous activity of large numbers of neurons, and into plasticity processes in the brains of awake and behaving animals. These new data constrain models for neural computation and network plasticity that underlie perception, cognition, behavior, and learning. I will discuss in this short article four such constraints: Inherent recurrent network activity and heterogeneous dynamic properties of neurons and synapses, stereotypical spatio-temporal activity patterns in networks of neurons, high trial-to-trial variability of network responses, and functional stability in spite of permanently ongoing changes in the network. I am proposing that these constraints provide hints to underlying principles of brain computation and learning.
The contribution of area MT to visual motion perception depends on training
Authors: Liu Liu D. , Pack Christopher C. 
Published: December 2016

SummaryPerceptual decisions require the transformation of raw sensory inputs into cortical representations suitable for stimulus discrimination. One of the best-known examples of this transformation involves the middle temporal area (MT) of the primate visual cortex. Area MT provides a robust representation of stimulus motion, and previous work has shown that it contributes causally to performance on motion discrimination tasks. Here we report that the strength of this contribution can be highly plastic: Depending on the recent training history, pharmacological inactivation of MT can severely impair motion discrimination, or it can have little detectable influence. Similarly, depending on training, microstimulation can bias motion perception or simply introduce noise. Further analysis of neural and behavioral data suggests that training shifts the readout of motion information between MT and lower-level cortical areas. These results show that the contribution of individual brain regions to conscious perception can shift flexibly depending on sensory experience.
A systematic cell size screen uncovers coupling of growth to division by the p38/HOG network inCandida albicans
Authors: Sellam Adnane , Chaillot Julien , Mallick Jaideep , Tebbji Faiza , Richard Albert Julien , Cook Michael A. , Tyers Mike 
Published: December 2016

AbstractCell size is a complex trait that responds to developmental and environmental cues. Quantitative analysis of the size phenome in the pathogenic yeastCandida albicansuncovered 195 genes that markedly altered cell size, few of which overlapped with known size genes in other yeast species. A potent size regulator specific toC. albicanswas the conserved p38/HOG MAPK module that mediates the osmotic stress response. Basal HOG activity inhibited the SBF G1/S transcription factor complex in a stress-independent fashion to delay the G1/S transition. The HOG network also governed ribosome biogenesis through the master transcriptional regulator Sfp1. Hog1 bound to the promoters and cognate transcription factors for both the G1/S and ribosome biogenesis regulons and thereby directly linked cell growth and division. These results illuminate the evolutionary plasticity of size control and identify the HOG module as a nexus of cell cycle and growth regulation.
The value of prior knowledge in machine learning of complex network systems
Authors: Craft David , Ferranti Dana , Krane David 
Published: December 2016

AbstractOur overall goal is to develop machine learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing, and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available. The simulations are based on Boolean networks – directed graphs with 0/1 node states and logical node update rules – which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics, and as such provide a useful framework for developing machine learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine learning algorithms to predict network steady state node values (“phenotypes”) and perturbation responses (“drug effects”).