Miracle-dz/UniFAR-FTUs huggingface dataset:
Excitatory actions of GABA in the intact neonatal rodent hippocampus in vitro

The excitatory action of gamma-aminobutyric acid (GABA) is considered to be a hallmark of the developing nervous system. However, in immature brain slices, excitatory GABA actions may be secondary to neuronal injury during slice preparation. Here, we explored GABA actions in the rodent intact hippocampal preparations and at different depths of hippocampal slices during the early post-natal period [post-natal days (P) 1–7]. We found that in the intact hippocampus at P1–3: (i) GABA exerts depolarizing action as seen in cell-attached single GABA(A) channel recordings; (ii) GABA(A) receptor (GABA(A)-R) agonist isoguvacine and synaptic activation of the GABA(A)-Rs increase the frequency of multiple unit activity and the frequency of the network-driven giant depolarizing potentials (GDPs); and that (iii) Na+–K+–2Cl- cotransporter (NKCC1) antagonist bumetanide suppresses GDPs and the excitatory actions of isoguvacine. In the hippocampal slices at P2–5, isoguvacine and synaptic activation of GABA(A)-Rs-evoked excitatory responses at all slice depths, including surface and core. Thus, GABA exerts excitatory actions in the intact hippocampus (P1–3) and at all depths of hippocampal slices (P2–5). Therefore, the excitatory actions of GABA in hippocampal slices during the first post-natal days are not due to neuronal injury during slice preparation, and the trauma-related excitatory GABA actions at the slice surface are a fundamentally different phenomenon observed during the second post-natal week.
Scalable innovation coding

Low-complexity scalable methods are of importance to achieve multi-rate and variable rate speech coding. Adopting the CELP concept, we focus on the innovation coding, suggesting an adaptive companding VQ. We present a scheme having essentially no increase in complexity as the rate is increased, yet having a competitive distortion performance. This is acheived by avoiding an explicit perceptual filtering step in the coding, still utilizing close to optimal VQ techniques in a perceptual domain. Subjective and objective distortion performance is better than, or in parity with, that of conventional white noise excitation methods or multipulse structures.
Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)

Missing values, irregularly collected samples, and multi-resolution signals commonly occur in multivariate time series data, making predictive tasks difficult. These challenges are especially prevalent in the healthcare domain, where patients' vital signs and electronic records are collected at different frequencies and have occasionally missing information due to the imperfections in equipment or patient circumstances. Researchers have handled each of these issues differently, often handling missing data through mean value imputation and then using sequence models over the multivariate signals while ignoring the different resolution of signals. We propose a unified model named Multi-resolution Flexible Irregular Time series Network (Multi-FIT). The building block for Multi-FIT is the FIT network. The FIT network creates an informative dense representation at each time step using signal information such as last observed value, time difference since the last observed time stamp and overall mean for the signal. Vertical FIT (FIT-V) is a variant of FIT which also models the relationship between different temporal signals while creating the informative dense representations for the signal. The multi-FIT model uses multiple FIT networks for sets of signals with different resolutions, further facilitating the construction of flexible representations. Our model has three main contributions: a. ) it does not impute values but rather creates informative representations to provide flexibility to the model for creating task-specific representations b. ) it models the relationship between different signals in the form of support signals c. ) it models different resolutions in parallel before merging them for the final prediction task. The FIT, FIT-V and Multi-FIT networks improve upon the state-of-the-art models for three predictive tasks, including the forecasting of patient survival.
Expectation-Maximization for Inverse Reinforcement Learning with Hidden Data

We consider the problem of performing inverse reinforcement learning when the trajectory of the agent being observed is partially occluded from view. Motivated by robotic scenarios in which limited sensor data is available to a learner, we treat the missing information as hidden variables and present an algorithm based on expectation-maximization to solve the non-linear, non-convex problem. Previous work in this area simply removed the occluded portions from consideration when computing feature expectations; in contrast our technique takes expectations over the missing values, enabling learning even in the presence of dynamic occlusion. We evaluate our new algorithm in a simulated reconnaissance scenario in which the visible portion of the state space varies. Finally, we show our approach enables apprenticeship learning by observing a human performing a sorting task in spite of key information missing from observations.
Positron Emission Tomography with [18F]FLT Revealed Sevoflurane-Induced Inhibition of Neural Progenitor Cell Expansion in vivo

Neural progenitor cell expansion is critical for normal brain development and an appropriate response to injury. During the brain growth spurt, exposures to general anesthetics, which either block the N-methyl-d-aspartate receptor or enhance the γ-aminobutyric acid receptor type A can disturb neuronal transduction. This effect can be detrimental to brain development. Until now, the effects of anesthetic exposure on neural progenitor cell expansion in vivo had seldom been reported. Here, minimally invasive micro positron emission tomography (microPET) coupled with 3′-deoxy-3′ [18F] fluoro-l-thymidine ([18F]FLT) was utilized to assess the effects of sevoflurane exposure on neural progenitor cell proliferation. FLT, a thymidine analog, is taken up by proliferating cells and phosphorylated in the cytoplasm, leading to its intracellular trapping. Intracellular retention of [18F]FLT, thus, represents an observable in vivo marker of cell proliferation. Here, postnatal day 7 rats (n = 11/group) were exposed to 2.5% sevoflurane or room air for 9 h. For up to 2 weeks following the exposure, standard uptake values (SUVs) for [18F]-FLT in the hippocampal formation were significantly attenuated in the sevoflurane-exposed rats (p < 0.0001), suggesting decreased uptake and retention of [18F]FLT (decreased proliferation) in these regions. Four weeks following exposure, SUVs for [18F]FLT were comparable in the sevoflurane-exposed rats and in controls. Co-administration of 7-nitroindazole (30 mg/kg, n = 5), a selective inhibitor of neuronal nitric oxide synthase, significantly attenuated the SUVs for [18F]FLT in both the air-exposed (p = 0.00006) and sevoflurane-exposed rats (p = 0.0427) in the first week following the exposure. These findings suggested that microPET in couple with [18F]FLT as cell proliferation marker could be used as a non-invasive modality to monitor the sevoflurane-induced inhibition of neural progenitor cell proliferation in vivo.
The Effect of Childhood Trauma, Personal Wellness, Supervisory Working Alliance, and Organizational Factors on Vicarious Traumatization

Scholars have identified vicarious traumatization (VT) as one of the most extreme effects of working with traumatized clients; however, not all mental health therapists develop VT (Pearlman & Saakvitne, 1995). Path analytic procedures were used to assess a comprehensive theoretical VT model based on constructivist self-development theory (CSDT). The model explained 46% of the variance in VT in mental health therapists surveyed.
Evaluation of seven Cholestech L.D.X analyzers for total cholesterol determinations.

We assessed the performance of seven Cholestech L.D. X lipid analyzers under tightly controlled laboratory conditions for accuracy and precision in accordance with analytical guidelines of the National Cholesterol Education Program (NCEP). Venous heparinized whole blood (VB) and plasma (VP), venous serum (VS), and capillary fingerstick whole blood (FB) were collected from 18 individuals. Total cholesterol (TC) concentration was measured in VB, VP, and VS on all seven instruments. Three instruments were used for TC measurements of FB. Reference cholesterol values for each individual were generated in the same laboratory with a standardized method. The within-run coefficients of variation (CVs) for all instruments with a Level I pool (1560 mg/L, n = 10) ranged from 1.3% to 1.8% (mean = 1.59%). The between-run CVs with the same pool ranged from 2.2% to 3.4% (mean = 2.84%, n = 10). Correlation coefficients derived from comparison of total cholesterol values generated by the instruments for each specimen type vs the reference cholesterol values were all > 0.97. The average bias for all instruments for each sample type was 1.9% (FB), 4.3% (VB), 6.6% (VP), and 7.0% (VS). Predicted cholesterol concentration for each sample type from regression curves for total cholesterol at the suggested NCEP clinical decision cutoff values of 2000 and 2400 mg/L, respectively, were 2049 and 2431 mg/L for FB, 2081 and 2469 mg/L for VB, 2122 and 2522 mg/L for VP, and 2121 and 2521 mg/L for VS.
Influence of cell morphology in a computational model of ON and OFF retinal ganglion cells

We developed anatomically and biophysically detailed ionic models to understand how cell morphology contributes to the unique firing patterns of ON and OFF retinal ganglion cells (RGCs). With identical voltage-gated channel kinetics and distribution, cell morphology alone is sufficient to generate quantitatively distinct electrophysiological responses. Notably, recent experimental observations from ON and OFF RGCs can be closely reproduced by the variations in their cell morphologies alone. Our results suggest that RGC morphology in conjunction with biophysical properties and network connectivity are able to produce the diverse response repertoire of RGCs.
Noising versus Smoothing for Vertex Identification in Unknown Shapes

A method for identifying shape features of local nature on the shape's boundary, in a way that is facilitated by the presence of noise is presented. The boundary is seen as a real function. A study of a certain distance function reveals, almost counter-intuitively, that vertices can be defined and localized better in the presence of noise, thus the concept of noising, as opposed to smoothing, is conceived and presented. The method works on both smooth and noisy shapes, the presence of noise having an effect of improving on the results of the smoothed version. Experiments with noise and a comparison to state of the art validate the method.
Laboratory Apparatus for the Demonstration of Quicksand

The use of quicksand as a convenient plot device in television and movies often leads to misconceptions, even among students taking introductory earth science courses. Because quicksand is a familiar natural phenomenon, exploring the underlying mechanisms provides an exceptional opportunity for student learning. Studying quicksand is facilitated by an easily constructed experimental apparatus and illustrative exercises, both of which are detailed here.
Dynamics on Networks: The Role of Local Dynamics and Global Networks on the Emergence of Hypersynchronous Neural Activity

Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3–6 Hz) and low-alpha (6–9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80 predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.
ENO-Wavelet Transforms for Piecewise Smooth Functions

We have designed an adaptive essentially nonoscillatory (ENO)-wavelet transform for approximating discontinuous functions without oscillations near the discontinuities. Our approach is to apply the main idea from ENO schemes for numerical shock capturing to standard wavelet transforms. The crucial point is that the wavelet coefficients are computed without differencing function values across jumps. However, we accomplish this in a different way than in the standard ENO schemes. Whereas in the standard ENO schemes the stencils are adaptively chosen, in the ENO-wavelet transforms we adaptively change the function and use the same uniform stencils. The ENO-wavelet transform retains the essential properties and advantages of standard wavelet transforms such as concentrating the energy to the low frequencies, obtaining maximum accuracy, maintained up to the discontinuities, and having a multiresolution framework and fast algorithms, all without any edge artifacts. We have obtained a rigorous approximation error bound which shows that the error in the ENO-wavelet approximation depends only on the size of the derivative of the function away from the discontinuities. We will show some numerical examples to illustrate this error estimate.
Comprehensive Peroxidase-Based Hematologic Profiling for the Prediction of 1-Year Myocardial Infarction and Death

Background— Recognition of biological patterns holds promise for improved identification of patients at risk for myocardial infarction (MI) and death. We hypothesized that identifying high- and low-risk patterns from a broad spectrum of hematologic phenotypic data related to leukocyte peroxidase-, erythrocyte- and platelet-related parameters may better predict future cardiovascular risk in stable cardiac patients than traditional risk factors alone. Methods and Results— Stable patients (n=7369) undergoing elective cardiac evaluation at a tertiary care center were enrolled. A model (PEROX) that predicts incident 1-year death and MI was derived from standard clinical data combined with information captured by a high-throughput peroxidase-based hematology analyzer during performance of a complete blood count with differential. The PEROX model was developed using a random sampling of subjects in a derivation cohort (n=5895) and then independently validated in a nonoverlapping validation cohort (n=1474). Twenty-three high-risk (observed in ≥10% of subjects with events) and 24 low-risk (observed in ≥10% of subjects without events) patterns were identified in the derivation cohort. Erythrocyte- and leukocyte (peroxidase)-derived parameters dominated the variables predicting risk of death, whereas variables in MI risk patterns included traditional cardiac risk factors and elements from all blood cell lineages. Within the validation cohort, the PEROX model demonstrated superior prognostic accuracy (78%) for 1-year risk of death or MI compared with traditional risk factors alone (67%). Furthermore, the PEROX model reclassified 23.5% (P<0.001) of patients to different risk categories for death/MI when added to traditional risk factors. Conclusion— Comprehensive pattern recognition of high- and low-risk clusters of clinical, biochemical, and hematologic parameters provided incremental prognostic value in stable patients having elective diagnostic cardiac catheterization for 1-year risks of death and MI.
Bottleneck Genes and Community Structure in the Cell Cycle Network of S. pombe

The identification of cell cycle–related genes is still a difficult task, even for organisms with relatively few genes such as the fission yeast. Several gene expression studies have been published on S. pombe showing similarities but also discrepancies in their results. We introduce a network in which the weight of each link is a function of the phase difference between the expression peaks of two genes. The analysis of the stability of the clustering through the computation of an entropy parameter reveals a structure made of four clusters, the first one corresponding to a robustly connected M–G1 component, the second to genes in the S phase, and the third and fourth to two G2 components. They are separated by bottleneck structures that appear to correspond to cell cycle checkpoints. We identify a number of genes that are located on these bottlenecks. They represent a novel group of cell cycle regulatory genes. They all show interesting functions, and they are supposed to be involved in the regulation of the transition from one phase to the next. We therefore present a comparison of the available studies on the fission yeast cell cycle and a general statistical bioinformatics methodology to find bottlenecks and gene community structures based on recent developments in network theory.
Auditory Behaviors and Auditory Brainstem Responses of Infants with Hypogenesis of Cerebral Hemispheres

Three infants with an almost complete absence of the cerebral hemispheres as a result of brain anomalies were studied both audiologically and neurologically. The brain anomalies were diagnosed by means of MRI and CT scans. Behavioral audiometry revealed reactions only to loud sound stimulations but auditory brainstem responses showed wave configurations and thresholds compatible with the ages of the infants. There were significant differences in the thresholds obtained by behavioral audiometry and auditory brainstem responses. It can be considered that these auditorily stimulated behavioral responses are evoked by auditory motor reflexes originating in the brainstem, but not by auditory perception.
A Survey on Mining Implicit Interest by Clustering and Collaborative Filtering for the Recommender System in Big

In today's world Internet plays an vital role, in which number of services are increasing, with which service computing and cloud computing. In this paper deals with the service relevant issues from which time efficient will be more and sparsity problem will be reduced along with implicit user interest will be extracted. To resolve the problem of time n sparsity, using agglomerative hierarchical clustering and collaborative filtering for recommender system in big data application. Certainly this can't be processed with traditional
Rumor Detection over Varying Time Windows

This study determines the major difference between rumors and non-rumors and explores rumor classification performance levels over varying time windows—from the first three days to nearly two months. A comprehensive set of user, structural, linguistic, and temporal features was examined and their relative strength was compared from near-complete date of Twitter. Our contribution is at providing deep insight into the cumulative spreading patterns of rumors over time as well as at tracking the precise changes in predictive powers across rumor features. Statistical analysis finds that structural and temporal features distinguish rumors from non-rumors over a long-term window, yet they are not available during the initial propagation phase. In contrast, user and linguistic features are readily available and act as a good indicator during the initial propagation phase. Based on these findings, we suggest a new rumor classification algorithm that achieves competitive accuracy over both short and long time windows. These findings provide new insights for explaining rumor mechanism theories and for identifying features of early rumor detection.
Posterior structural brain volumes differ in maltreated youth with and without chronic posttraumatic stress disorder

Abstract Magnetic resonance imaging studies of maltreated children with posttraumatic stress disorder (PTSD) suggest that maltreatment-related PTSD is associated with adverse brain development. Maltreated youth resilient to chronic PTSD were not previously investigated and may elucidate neuromechanisms of the stress diathesis that leads to resilience to chronic PTSD. In this cross-sectional study, anatomical volumetric and corpus callosum diffusion tensor imaging measures were examined using magnetic resonance imaging in maltreated youth with chronic PTSD (N = 38), without PTSD (N = 35), and nonmaltreated participants (n = 59). Groups were sociodemographically similar. Participants underwent assessments for strict inclusion/exclusion criteria and psychopathology. Maltreated youth with PTSD were psychobiologically different from maltreated youth without PTSD and nonmaltreated controls. Maltreated youth with PTSD had smaller posterior cerebral and cerebellar gray matter volumes than did maltreated youth without PTSD and nonmaltreated participants. Cerebral and cerebellar gray matter volumes inversely correlated with PTSD symptoms. Posterior corpus callosum microstructure in pediatric maltreatment-related PTSD differed compared to maltreated youth without PTSD and controls. The group differences remained significant when controlling for psychopathology, numbers of Axis I disorders, and trauma load. Alterations of these posterior brain structures may result from a shared trauma-related mechanism or an inherent vulnerability that mediates the pathway from chronic PTSD to comorbidity.
Variance-Based Rewards for Approximate Bayesian Reinforcement Learning

The explore{exploit dilemma is one of the central challenges in Reinforcement Learning (RL). Bayesian RL solves the dilemma by providing the agent with information in the form of a prior distribution over environments; however, full Bayesian planning is intractable. Planning with the mean MDP is a common myopic approximation of Bayesian planning. We derive a novel reward bonus that is a function of the posterior distribution over environments, which, when added to the reward in planning with the mean MDP, results in an agent which explores efficiently and effectively. Although our method is similar to existing methods when given an uninformative or unstructured prior, unlike existing methods, our method can exploit structured priors. We prove that our method results in a polynomial sample complexity and empirically demonstrate its advantages in a structured exploration task.