Miracle-dz/UniFAR-FTUs huggingface dataset:
Leber's Hereditary Optic Neuropathy (LHON) Pathogenic Mutations Induce Mitochondrial-dependent Apoptotic Death in Transmitochondrial Cells Incubated with Galactose Medium*

Leber's hereditary optic neuropathy (LHON), a maternally inherited form of central vision loss, is associated with mitochondrial DNA pathogenic point mutations affecting different subunits of complex I. We here report that osteosarcoma-derived cytoplasmic hybrids (cybrid) cell lines harboring one of the three most frequent LHON pathogenic mutations, at positions 11778/ND4, 3460/ND1, and 14484/ND6, undergo cell death when galactose replaces glucose in the medium, contrary to control cybrids that maintain some growth capabilities. This is a well known way to produce a metabolic stress, forcing the cells to rely on the mitochondrial respiratory chain to produce ATP. We demonstrate that LHON cybrid cell death is apoptotic, showing chromatin condensation and nuclear DNA laddering. Moreover, we also document the mitochondrial involvement in the activation of the apoptotic cascade, as shown by the increased release of cytochromec into the cytosol in LHON cybrid cells as compared with controls. Cybrids bearing the 3460/ND1 and 14484/ND6 mutations seemed more readily prone to undergo apoptosis as compared with the 11778/ND4 mutation. In conclusion, LHON cybrid cells forced by the reduced rate of glycolytic flux to utilize oxidative metabolism are sensitized to an apoptotic death through a mechanism involving mitochondria.
Articulatory bases of sonority in English liquids

Sonority is generally considered to play a primary role in governing intrasyllabic phonotactics. In this chapter, we examine the phonotactic and articulatory properties of tautosyllabic vowel-liquid sequences in American English, and consider the implications for theories of sonority. Constraints on the distribution of vowels preceding liquid codas were first examined through lexical corpus analysis, revealing that fewer vowel contrasts occur before /o/ than /l/, with contrasts further reduced before complex codas compared to simple codas. To shed light on these patterns, we investigated liquid production in different syllabic contexts in American English using real-time structural MRI. For each speaker examined, /o/ showed more stability than /l/ in its lingual posture across contexts. For /l/, the tongue dorsum showed the greatest convergence across different vowel contexts in codas and complex syllable margins. In general, /o/ exhibited less coronal stricture than /l/, consistent with some phonetic characterizations of sonority, and with traditional sonority-based accounts of the sequencing of /o/ before /l/ in codas. Yet differences in stricture do not illuminate the observed restrictions on vowel-liquid sequences. Our data point to a greater encroachment on the vowel by a liquid in a complex coda, reducing the potential for contrast in pre-cluster nuclei; furthermore, the greater articulatory flexibility of /l/ renders it compatible with a larger range of vowels than /o/. We conclude that articulatory factors can have a fundamental influence on phonotactic constraints, and suggest that our understanding of sonority will be enriched by taking into account a wider scope of articulatory properties beyond stricture degree alone.
SPIKING NEURON MODELS Single Neurons , Populations , Plasticity

mathematical framework of the population equations. It will turn out that the results are – of course – consistent with those derived from the population equation. We study a homogeneous network of N identical neurons which are mutually coupled with strength wij = J0/N where J0 > 0 is a positive constant. In other words, the (excitatory) interaction is scaled with one over N so that the total input to a neuron i is of order one even if the number of neurons is large (N →∞). Since we are interested in synchrony we suppose that all neurons have fired simultaneously at t̂ = 0. When will the neurons fire again? Since all neurons are identical we expect that the next firing time will also be synchronous. Let us calculate the period T between one synchronous pulse and the next. We start from the firing condition of SRM0 neurons θ = ui(t) = η(t− t̂i) + ∑
SVM Candidates and Sparse Representation for Bird Identification

We present a description of our approach for the "Bird task Identifica- tion LifeCLEF 2014". Our approach consists of four stages: (1) a filtering stage for the filtering of audio bird recordings; (2) segmentation stage for the extraction of syllables; (3) a candidate generation based on HOG features from the syllables using SVM; and (4) a species identification using a Sparse Representation-based Classification of HOG and LBP features. Our approach ranked seventh team-wise in the challenge and showed a poor performance in the fourth stage.
SOM-based R*-tree for similarity retrieval

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e.g., documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors. A feature vector is a vector that represents a set of features, and are usually high-dimensional data. The performance of conventional multidimensional data structures (e.g., R-tree family K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R*-tree is the most successful variant of the R-tree. We propose a SOM-based R*-tree as a new indexing method for high-dimensional feature vectors. The SOM-based R*-tree combines SOM and R*-tree to achieve search performance more scalable to high dimensionalities. Self-organizing maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. We experimentally compare the retrieval time cost of a SOM-based R*-tree with that of an SOM and an R*-tree using color feature vectors extracted from 40,000 images.
A Cre knock‐in mouse line on the Sickle tail locus induces recombination in the notochord and intervertebral disks

Sickle tail (Skt) was originally identified by gene trap mutagenesis in mice, and the trapped gene is highly expressed in the notochord, intervertebral discs (IVD), and mesonephros. Here, we report the generation of Sktcre mice expressing Cre recombinase in the IVD due to target insertion of the cre gene into the Skt locus by recombinase‐mediated cassette exchange. Crossing a conditional lacZ Reporter (R26R), Cre expression from the Sktcre allele specifically activates β‐galactosidase expression in the whole notochord from E9.5 onwards. In E15.5 Sktcre;R26R embryos, reporter activity was detected in the nucleus pulposus and in a portion of the annulus fibrosus, resulting in expansion of Cre‐expressing cells in the adult IVD. Reporter activity was also seen in the Sktcre;R26R mesonephros at E15.5. These results suggest that Sktcre mice are useful for exploring the fate specification of notochordal cells and creating models for IVD‐related skeletal diseases. genesis 50:758–765, 2012. © 2012 Wiley Periodicals, Inc.
New Features of Automatic Meter Reading System: Based on Edge Computing

In this paper, we focus on edge computing in smart grid and have improved the automated meter reading (AMR) system. In addition, to achieve security processing of a large number of intelligent terminals and massive data in real time and efficient responses, this paper presents an open platform which is developed on the edge of the network close to the power terminal devices or data sources, and proposes a security protection framework of smart grid based on edge computing. As a result, this new AMR system can provide real-time services, possess abilities of efficient data processing and applied intelligence, and effectively resist attacks on smart grid. Introduction With the continuous deepening of the reform of the national power system, the power sector has adopted a high-efficiency, low-cost and highly practical meter reading system, namely low-voltage automated meter reading (AMR) system [1]. In recent years, with the advancement of the national “Energy Production and Consumption Revolution Strategy 2016-2030” [2], the sales side is liberalized and the smart power service is promoted. A large number of terminal devices and multiple users on the demand side access the power grid and participate in the interaction. In this context, data flow between electrical devices and control centers, between grid companies and power users in continues to rise [3]. Moreover, with the increasing application range of electrical devices, the time and space distribution of it is more dispersed than the traditional ones, which makes the data collection and analysis face a huge test. In terms of security, the multi-power end-users supply and demand for a variety of interactive electricity, bringing convenience while serious security and confidentiality issues [4]. Therefore, power terminal security protection is unprecedentedly challenged. Related Work Edge computing refers to providing an open platform for connection, computing, storage and application near the network edge of terminal devices or data sources, and providing edge intelligent services for the data of power sensor node devices [5]. Based on this, we deploy edge computing models for AMR system as showing in Figure 1, which uses the meter reading concentrator integrated edge computing module to bring some new features. It is geographically close to terminal devices and data sources, able to realize parallel processing and analysis of various acquisition terminals, intelligent devices and power user data in the smart grid at the edge of the information network. The distributed information computing service mode of edge computing with large data volume and fast response can meet the rapid response requirements of equipment and users in smart grid. According to the edge computing architecture [6], the edge security protection architecture is mainly from the aspects of device domain security, network domain security, data domain security, and application domain security. Security protection, connected by applying a domain cloud
Identification of Developmental and Behavioral Markers Associated With Genetic Abnormalities in Autism Spectrum Disorder.

OBJECTIVE Aside from features associated with risk of neurogenetic syndromes in general (e.g., cognitive impairment), limited progress has been made in identifying phenotype-genotype relationships in autism spectrum disorder (ASD). The objective of this study was to extend work in the Simons Simplex Collection by comparing the phenotypic profiles of ASD probands with or without identified de novo loss of function mutations or copy number variants in high-confidence ASD-associated genes or loci. METHOD Analyses preemptively accounted for documented differences in sex and IQ in affected individuals with de novo mutations by matching probands with and without these genetic events on sex, IQ, and age before comparing them on multiple behavioral domains. RESULTS Children with de novo mutations (N=112) had a greater likelihood of motor delay during early development (later age at walking), but they were less impaired on certain measures of ASD core symptoms (parent-rated social communication abnormalities and clinician-rated diagnostic certainty about ASD) in later childhood. These children also showed relative strengths in verbal and language abilities, including a smaller discrepancy between nonverbal and verbal IQ and a greater likelihood of having achieved fluent language (i.e., regular use of complex sentences). CONCLUSIONS Children with ASD with de novo mutations may exhibit a "muted" symptom profile with respect to social communication and language deficits relative to those with ASD with no identified genetic abnormalities. Such findings suggest that examining early milestone differences and standardized testing results may be helpful in etiologic efforts, and potentially in clinical differentiation of various subtypes of ASD, but only if developmental and demographic variables are properly accounted for first.
Gaussian Kernel in Quantum Paradigm

The Gaussian kernel is a very popular kernel function used in many machine-learning algorithms, especially in support vector machines (SVM). For nonlinear training instances in machine learning, it often outperforms polynomial kernels in model accuracy. We use Gaussian kernel profoundly in formulating nonlinear classical SVM. In the recent research, P. Rebentrost et.al. discuss a very elegant quantum version of least square support vector machine using the quantum version of polynomial kernel, which is exponentially faster than the classical counterparts. In this paper, we have demonstrated a quantum version of the Gaussian kernel and analyzed its complexity in the context of quantum SVM. Our analysis shows that the computational complexity of the quantum Gaussian kernel is O(\epsilon^(-1)logN) with N-dimensional instances and \epsilon with a Taylor remainder error term |R_m (\epsilon^(-1) logN)|.
Lifelong User Modeling and Meta-cognitive Scaffolding: Support Self Monitoring of Long Term Goals

It is becoming increasingly easy to capture lifelong data about a person. This creates the potential for valuable new ways for people to self-monitor diverse aspects their lives. If people are to do this effectively, we need to ensure that relevant aspects of lifelong user models are available to the user, in a form that is meaningful. Beyond this, current research indicates that most people need a meta-cognitive scaffolding to make more effective use of such information. This paper reviews work about such meta-cognitive scaffolding. Then it presents use cases illustrating how this scaffolding can help people achieve long term goals. We use these to identify requirements for the architecture and associated user interfaces for a lifelong user model with scaffolded self monitoring. Our key contribution is the requirements for a system that scaffolds self monitoring of long term goals, based on data stored in a lifelong user model.
Semi-spatiotemporal fMRI Brain Decoding

Functional behavior of the brain can be captured using functional Magnetic Resonance Imaging (fMRI). Even though fMRI signals have temporal and spatial structures, most studies have neglected the temporal structure when inferring mental states (brain decoding). This has two main side effects: 1. Degradation in brain decoding performance due to lack of temporal information in the model, 2. Inability to provide temporal interpretability. Few studies have targeted this issue but have had less success due to the burdening challenges related to high feature-to-instance ratio. In this study, a novel model for incorporating temporal information while maintaining a low feature-to-instance ratio, is proposed. Experimental results show the effectiveness of the model compared to recent state of the art approaches.
LO-PHI: Low-Observable Physical Host Instrumentation for Malware Analysis

—Dynamic-analysis techniques have become the linchpins of modern malware analysis. However, software-based methods have been shown to expose numerous artifacts, which can either be detected and subverted, or potentially interfere with the analysis altogether, making their results untrustworthy. The need for less-intrusive methods of analysis has led many researchers to utilize introspection in place of instrumenting the software itself. While most current introspection technologies have focused on virtual-machine introspection, we present a novel system, LO-PHI, which is capable of physical-machine introspection of both non-volatile and volatile memory, i.e., hard disk and system memory. We demonstrate that we are able to provide analysis capabilities comparable to existing solutions, whilst exposing zero software-based artifacts and minimal hardware artifacts. To demonstrate the usefulness of our system, we have developed a framework for performing automated binary analysis. We employ this framework to analyze numerous potentially malicious binaries using both traditional virtual-machine introspection and our new hardware-based instrumentation. Our results show that not only is our analysis on-par with existing software-based counterparts, but that our physical instrumentation is capable of successfully analyzing far more binaries, as it is not foiled by popular anti-analysis techniques.
Quality of Life after Epilepsy Surgery in Korea

Background and purpose Temporal changes in the quality of life (QOL) and the underlying factors after epilepsy surgery might be specific to Korea, where social stigma toward patients with epilepsy is still pronounced. Methods The seizure characteristics, number of antiepileptic drugs (AEDs), and the presence of stigma, anxiety, and depression were assessed before and after surgery (at 6 months and around 2 years) in 32 surgery patients and 32 nonsurgery patients. The QOL was compared between these groups using the Epilepsy Surgery Inventory-55 questionnaire. The factors affecting QOL were also evaluated. Results The scores in the mental, physical, and role-functioning domains were significantly higher at 6 months (all p<0.01) and around 2 years (all p<0.01) than at baseline in the surgery group but not in the nonsurgery group. The factors related to QOL differed at the two follow-up times, with seizure freedom being important at 6 months, and AEDs and depression being important at around 2 years. Conclusions A marked increase in QOL in our population was observed after epilepsy surgery. Although the small sample limits the interpretation of the results, the QOL change in our surgery patients shows similar trends to those reported in Western countries. A full understanding of underlying factors related to QOL might aid the development of optimal strategies for improving the long-term postsurgery QOL in this population.
Instant power spectrum analysis of heart rate variability during orthostatic tilt using a time-/frequency-domain method.

BACKGROUND Spectral analysis of heart rate (HR) variability (HRV) requires, as a rule, some level of stationarity and, as a result, is inadequate to quantify biological transients. A time-/frequency-domain method (TF) was developed to obtain an instant spectral power (SP) of HRV during tilt. METHODS AND RESULTS HR was recorded by Holter monitoring in volunteers and analyzed with a TF, the smoothed pseudo-Wigner-Ville transformation (SPWVT), with the table inclination randomly set or continuously increased while the table rotated in head-up position. (1) The SPWVT assesses, beat by beat, the instant center frequency (ICF) of the SP. ICF correlates better with instant HR than the ratio of low- (LF) to high-frequency (HF) oscillations. The transient effect of tilt is better characterized as a shift of SP toward lower frequencies than by changes in amplitudes. (2) The method evidences variations of HR from one second to another. During the passage to head-up position, the vagal withdrawal and the sympathetic activation occur nearly simultaneously, as indicated by the instant changes in both LF and HF amplitudes and ICF. (3) The averaged results of the SPWVT give results similar to those previously obtained with autoregressive algorithms. CONCLUSIONS The SPWVT is a new tool to explore HR transitions such as periods before episodes of arrhythmias on a time scale of one beat and allows quantification of an instant frequency index (ICF) that closely reflects the instantaneous relationship between sympathetic and vagal modulations.
Validation strategies for target prediction methods

Abstract Computational methods for target prediction, based on molecular similarity and network-based approaches, machine learning, docking and others, have evolved as valuable and powerful tools to aid the challenging task of mode of action identification for bioactive small molecules such as drugs and drug-like compounds. Critical to discerning the scope and limitations of a target prediction method is understanding how its performance was evaluated and reported. Ideally, large-scale prospective experiments are conducted to validate the performance of a model; however, this expensive and time-consuming endeavor is often not feasible. Therefore, to estimate the predictive power of a method, statistical validation based on retrospective knowledge is commonly used. There are multiple statistical validation techniques that vary in rigor. In this review we discuss the validation strategies employed, highlighting the usefulness and constraints of the validation schemes and metrics that are employed to measure and describe performance. We address the limitations of measuring only generalized performance, given that the underlying bioactivity and structural data are biased towards certain small-molecule scaffolds and target families, and suggest additional aspects of performance to consider in order to produce more detailed and realistic estimates of predictive power. Finally, we describe the validation strategies that were employed by some of the most thoroughly validated and accessible target prediction methods.
Deformable registration using shape statistics with applications in sinus surgery

Evaluating anatomical variations in structures like the nasal passage and sinuses is challenging because their complexity can often make it difficult to differentiate normal and abnormal anatomy. By statistically modeling these variations and estimating individual patient anatomy using these models, quantitative estimates of similarity or dissimilarity between the patient and the sample population can be made. In order to do this, a spatial alignment, or registration, between patient anatomy and the statistical model must first be computed. In this dissertation, a deformable most likely point paradigm is introduced that incorporates statistical variations into probabilistic feature-based registration algorithms. This paradigm is a variant of the most likely point paradigm, which incorporates feature uncertainty into the registration process. The deformable registration algorithms optimize the probability of feature alignment as well as the probability of model deformation allowing statistical models of anatomy to estimate, for instance, structures seen in endoscopic video without the need for patient specific computed tomography (CT) scans. The probabilistic framework also enables the algorithms to ii