Miracle-dz/UniFAR-FTUs huggingface dataset:
Critical points for an accurate human genome analysis

Next‐generation sequencing is radically changing how DNA diagnostic laboratories operate. What started as a single‐gene profession is now developing into gene panel sequencing and whole‐exome and whole‐genome sequencing (WES/WGS) analyses. With further advances in sequencing technology and concomitant price reductions, WGS will soon become the standard and be routinely offered. Here, we focus on the critical steps involved in performing WGS, with a particular emphasis on points where WGS differs from WES, the important variables that should be taken into account, and the quality control measures that can be taken to monitor the process. The points discussed here, combined with recent publications on guidelines for reporting variants, will facilitate the routine implementation of WGS into a diagnostic setting.
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

A long-standing problem at the interface of artificial intelligence and applied mathematics is to devise an algorithm capable of achieving human level or even superhuman proficiency in transforming observed data into predictive mathematical models of the physical world. In the current era of abundance of data and advanced machine learning capabilities, the natural question arises: How can we automatically uncover the underlying laws of physics from high-dimensional data generated from experiments? In this work, we put forth a deep learning approach for discovering nonlinear partial differential equations from scattered and potentially noisy observations in space and time. Specifically, we approximate the unknown solution as well as the nonlinear dynamics by two deep neural networks. The first network acts as a prior on the unknown solution and essentially enables us to avoid numerical differentiations which are inherently ill-conditioned and unstable. The second network represents the nonlinear dynamics and helps us distill the mechanisms that govern the evolution of a given spatiotemporal data-set. We test the effectiveness of our approach for several benchmark problems spanning a number of scientific domains and demonstrate how the proposed framework can help us accurately learn the underlying dynamics and forecast future states of the system. In particular, we study the Burgers', Korteweg-de Vries (KdV), Kuramoto-Sivashinsky, nonlinear Schr\"{o}dinger, and Navier-Stokes equations.
Membranous Organelles in Bacteria

organisms whose cells lack a cell nucleus (karyon) or any other membrane-bounded organelles’. In the same online compendium under ‘Organelle’, one can read: ‘whilst prokaryotes do not possess organelles per se, some do contain protein-based microcompartments’. Proteinaceous microcompartments will be the subject of a forthcoming Journal of Molecular Microbiology and Biotechnology written symposium, but this one will show that these generalizations, suggesting a lack of subcellular compartmentalization in prokaryotes, are blatantly in error [Murat et al., 2010a].
Cholinergic innervation of mossy cells in the rat fascia dentata

Hilar mossy cells are the main cells of origin of the commissural/associational projection to the inner molecular layer of the rat fascia dentata. In order to analyze the cholinergic innervation of hilar mossy cells, a light and electron microscopic double‐labeling technique was used. Immunolabeling for calcitonin gene‐related peptide (CGRP) was employed to identify mossy cells and immunocytochemistry for choline acetyltransferase (ChAT) was used to label cholinergic septohippocampal fibers. Cholinergic boutons were abundant around mossy cell somata and on their proximal dendrites. Electron microscopy confirmed that many of these boutons formed synapses with the CGRP‐positive mossy cells. These data demonstrate a direct innervation of hilar mossy cells by cholinergic septohippocampal afferents. This connectivity could contribute to the electrophysiological behavior of mossy cells during theta oscillations. Hippocampus 1999;9:314–320. © 1999 Wiley‐Liss, Inc.
Journal of Geophysical Research: Solid Earth An Improved Method to Determine Coda- Q , Earthquake Magnitude, and Site Ampliﬁcation: Theory and Application to Southern California

Seismic coda waves can be used to constrain attenuation, estimate earthquake magnitudes, and determine site ampliﬁcation factors. We have developed a new multistation and multievent method to determine these three important seismic parameters simultaneously. We analyze 642 representative local ( ≤ 100 km) and shallow ( ≤ 20 km) earthquakes with catalog magnitudes between 1.8 and 5.4 in southern California at multiple frequency bands centered at 1.5, 3, 6, and 12 Hz. We ﬁnd that the length of the moving average time window can aﬀect the measurement of coda attenuation Q C , but our tests indicate that the optimal window length is about 15 times the dominant data period. We use linear regression to ﬁt each coda section and use only those portions that agree with the model decay rate with a correlation coeﬃcient larger than 0.9. For a frequency-dependent coda- Q C model ( Q C = Q 0 f n ) at 1-Hz reference frequency, our results yield estimates for Q 0 and n of 107–288 and 0.42–1.14, respectively. Our coda magnitude estimates are linearly correlated with catalog magnitudes, and our observed lateral variations in coda- Q C and our site ampliﬁcation factors are in general agreement with previous results, although there are notable diﬀerences at some locations. This approach provides a uniﬁed, accurate, and stable method to measure coda- Q C , earthquake magnitude, and site ampliﬁcation using coda waves of locally recorded earthquakes.
Nonnegative Least-Mean-Square Algorithm

Dynamic system modeling plays a crucial role in the development of techniques for stationary and nonstationary signal processing. Due to the inherent physical characteristics of systems under investigation, nonnegativity is a desired constraint that can usually be imposed on the parameters to estimate. In this paper, we propose a general method for system identification under nonnegativity constraints. We derive the so-called nonnegative least-mean-square algorithm (NNLMS) based on stochastic gradient descent, and we analyze its convergence. Experiments are conducted to illustrate the performance of this approach and consistency with the analysis.
Spatio-temporal variation of fractional vegetation coverage and response to climatic factors in Three Gorges Reservoir area from 2010 to 2014

Current study investigated the spatio-temporal distribution of fractional vegetation coverage (FVC) and climatic factors response in Three Gorges Reservoir area from 2000 to 2014 using a dimidiate pixel model. Several data sets were used including the MODIS NDVI data, land use/cover change (LUCC) data and past 15 years of FVC data. Our results suggested that, except 2006 the annual maximum FVC were stable from 2000 to 2014. Extremely arid climate in 2006 influences FVC of Three Gorges area. The annual average FVC of the study area was 91.28%. The monthly mean FVC showed a regular fluctuation, where mean higher FVC values were found from May to August and from December to March of the following years FVC was lower. Overall study area showed an upward trend of FVC from 2000 to 2014, basic invariant areas, slight increased areas and obvious increased areas were accounted for 84.70%, 8.75%, and 0.78% respectively, whereas significantly reduced areas and mild reduction areas were accounted for 1.52% and 4.25% FVC, respectively. The FVC growth areas were mainly distributed in the eastern part of the Three Gorges Reservoir area whereas degraded areas were mainly distributed in the hilly areas of the West and middle of the Three Gorges area. Greater than 2000 meters high mountains the FVC value reached 98.47%, and the slope greater than 25° the regional FVC value reached 92.60%. Strong correlations were observed between monthly mean temperature and FVC (R=0.88), precipitation and FVC (R=0.77), and growth season lag 1 month of FVC values and temperature (R=0.85).
THE TIRRAWARRA OIL FIELD: FIELD REVITALISATION THROUGH RESERVOIR DESCRIPTION AND CHARACTERISATION

The need for an improved model of the Tirrawarra Oil Field reservoir led to a characterisation project jointly undertaken by SANTOS Ltd and the Bureau of Economic Geology. The objectives of the project were to describe and characterise the geological complexity of the field and within this framework to quantify the residency of the original and remaining oil resource. A detailed facies based flow-unit model was developed, within which petrophysical parameters and pay distribution have been mapped. Four principal facies were identified from which six flow units were characterised in detail. The environments of deposition were shown to control the detailed fabric of the rock, which in turn controls reservoir properties and productivity. The reservoir is interpreted to have been deposited as a braid delta, which has been reworked by a lacustrine shoreface transgressing and regressing across the area in response to rapid and frequent changes in lake level. Consequently, the two main flow units are relatively uniform in character, with much of the production-reducing shale being removed. The new reservoir model provides a greatly improved explanation of field performance, particularly that which was previously considered anomalous. This attests to the reliability of the model for use in reservoir engineering studies and the identification of opportunities for incremental development of the large oil resource remaining in the field. Under-developed portions of the field are now being assessed for miscible flood expansion with the aim of future growth in both oil production and reserves.
Convergence of Type-Symmetric and Cut-Balanced Consensus Seeking Systems

We consider continuous-time consensus seeking systems whose time-dependent interactions are cut-balanced, in the following sense: if a group of agents influences the remaining ones, the former group is also influenced by the remaining ones by at least a proportional amount. Models involving symmetric interconnections and models in which a weighted average of the agent values is conserved are special cases. We prove that such systems always converge. We give a sufficient condition on the evolving interaction topology for the limit values of two agents to be the same. Conversely, we show that if our condition is not satisfied, then these limits are generically different. These results allow treating systems where the agent interactions are a priori unknown, being for example random or determined endogenously by the agent values.
Visual resolution of macaque retinal ganglion cells.

1. The visual resolving ability of different types of macaque retinal ganglion cells was estimated at different retinal eccentricities, by measuring the amplitude of modulated responses to black‐white gratings of spatial frequencies near the resolution limit for each cell. 2. The resolving ability of tonic, spectrally opponent ganglion cells was usually similar to that of phasic, non‐opponent ganglion cells at similar eccentricities, except that at eccentricities greater than 10 deg some tonic ganglion cells with remarkably high resolution (up to ca. 15 cycles/deg) were found. Our cell sample was limited within the central 2 deg of the visual field, however. 3. Only a small proportion of phasic ganglion cells showed an increase of mean firing level to gratings near the resolution limit. The maintained firing of tonic ganglion cells was higher than that of phasic ganglion cells. 4. With red‐black or green‐black gratings, the resolution of phasic ganglion cells was unaffected. For red or green on‐centre ganglion cells, a marked deterioration of resolving ability occurred when the grating was of a colour to which a cell responded poorly (green‐black gratings for red on‐centre cells, and red‐black gratings for green on‐centre cells). A slight improvement in resolving ability occurred when the grating was of an excitatory colour. 5. For a sub‐sample of cells, we compared resolution limit with centre size as determined from area‐threshold curves. For both phasic and tonic ganglion cells, resolution limit (the period length just resolved) was about half the centre diameter, as is the case for cat ganglion cells. This implies that the centre sizes of phasic and tonic monkey ganglion cells are similar at most eccentricities. 6. We attempt to relate these results to primate retinal anatomy and visual resolution, determined behaviourally.
LifeCLEF Bird Identification Task 2015

The LifeCLEF bird identiﬁcation task provides a testbed for a system-oriented evaluation of 999 bird species identiﬁcation. The main originality of this data is that it was speciﬁcally built through a citizen science initiative conducted by Xeno-Canto, an international social network of amateur and expert ornithologists. This makes the task closer to the conditions of a real-world application than previous, similar initiatives. This overview presents the resources and the assessments of the task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results.
THE ROLE OF NEUREGULIN-1 IN THE MATURE CENTRAL NERVOUS SYSTEM

During the past 50 years, growth and differentiation molecules have been identified and shown to contribute importantly to many different biological functions. One of these, the recently discovered neuregulin (NRG) and the associated erbB receptors were first found to be important for differentiation of the peripheral nervous system, where they direct the differentiation of Schwann cells from neural crest precursors and contribute importantly to the formation of nerve-muscle synapses. More recently, these molecules have been identified as important contributors to the development of the brain. Our studies demonstrate that NRG-1 and the three erbB subunits that form functional NRG-1 receptors are present in high quantities in the brains of mammals, and they are differentially distributed. These anatomical data suggest that this signaling system is important in the mature brain. Studies by our group and others indicate that in the mature hippocampus both sustained synaptic excitation and neuronal damage increase the expression of NRG and erbB4 receptor subunits. Second, both NRG-1 and erbB4 are overexpressed in glia that surrounds βamyloid plaques in the brains of patients with Alzheimer’s disease and in a double transgenic murine model of the disease. This accumulation appears to parallel the degree of pathogenesis. Third, application of NRG-1 to differentiated hippocampal neurons induces dendritic outgrowth and elaboration and increases the expression of several neurotransmission-related genes. Fourth, in vivo microinjection of NRG-1 in the mature hippocampus enhances synaptic transmission. Together, these data indicate that NRG-1 is a pluripotent signaling system in the mature brain.
Metabolite fingerprinting of urine suggests breed-specific dietary metabolism differences in domestic dogs

Selective breeding of dogs has culminated in a large number of modern breeds distinctive in terms of size, shape and behaviour. Inadvertently, a range of breed-specific genetic disorders have become fixed in some pure-bred populations. Several inherited conditions confer chronic metabolic defects that are influenced strongly by diet, but it is likely that many less obvious breed-specific differences in physiology exist. Using Labrador retrievers and miniature Schnauzers maintained in a simulated domestic setting on a controlled diet, an experimental design was validated in relation to husbandry, sampling and sample processing for metabolomics. Metabolite fingerprints were generated from ‘spot’ urine samples using flow injection electrospray MS (FIE-MS). With class based on breed, urine chemical fingerprints were modelled using Random Forest (a supervised data classification technique), and metabolite features (m/z) explanatory of breed-specific differences were putatively annotated using the ARMeC database (http://www.armec.org). GC-MS profiling to confirm FIE-MS predictions indicated major breed-specific differences centred on the metabolism of diet-related polyphenols. Metabolism of further diet components, including potentially prebiotic oligosaccharides, animal-derived fats and glycerol, appeared significantly different between the two breeds. Analysis of the urinary metabolome of young male dogs representative of a wider range of breeds from animals maintained under domestic conditions on unknown diets provided preliminary evidence that many breeds may indeed have distinctive metabolic differences, with significant differences particularly apparent in comparisons between large and smaller breeds.
Annotating gigapixel images

Panning and zooming interfaces for exploring very large images containing billions of pixels (gigapixel images) have recently appeared on the internet. This paper addresses issues that arise when creating and rendering auditory and textual annotations for such images. In particular, we define a distance metric between each annotation and any view resulting from panning and zooming on the image. The distance then informs the rendering of audio annotations and text labels. We demonstrate the annotation system on a number of panoramic images.
Evidence of Biomass Smoke Exposure as a Causative Factor for the Development of COPD

Chronic obstructive pulmonary disease (COPD) is a progressive disease of the lungs characterised by chronic inflammation, obstruction of airways, and destruction of the parenchyma (emphysema). These changes gradually impair lung function and prevent normal breathing. In 2002, COPD was the fifth leading cause of death, and is estimated by the World Health Organisation (WHO) to become the third by 2020. Cigarette smokers are thought to be the most at risk of developing COPD. However, recent studies have shown that people with life-long exposure to biomass smoke are also at high risk of developing COPD. Most common in developing countries, biomass fuels such as wood and coal are used for cooking and heating indoors on a daily basis. Women and children have the highest amounts of exposures and are therefore more likely to develop the disease. Despite epidemiological studies providing evidence of the causative relationship between biomass smoke and COPD, there are still limited mechanistic studies on how biomass smoke causes, and contributes to the progression of COPD. This review will focus upon why biomass fuels are used, and their relationship to COPD. It will also suggest methodological approaches to model biomass exposure in vitro and in vivo.
DNA Markers for Food Products Authentication

Media constantly refer of unscrupulous producers that adulterate, alter or replace premium products in food chains with the goal to maximize illegally profits. Food traceability is a central issue for the identification of improper labeling of processed food and feed and there are rules aimed to protect consumers and producers against fraudulent substitution of quality products in food chain, but the tools available are not always appropriate. DNA-based markers proved very effective for fresh and processed food molecular authentication. In this review, we illustrate potential and limits of different DNA markers focusing on low, medium and high-throughput markers, in order to monitor the genetic identity of food components in meat, fish and plants net-chains.
Combined treatments for major depression

Major depression is a chronic disorder with a high morbidity and mortality. Approved treatment for major depression at present includes monotherapy with antidepressants of different pharmacologic classes. There is increasingly widespread use of two other options: augmentation, the addition to an antidepressant of a second compound that is not an antidepressant when used alone; and combination, which is the use of two antidepressants concurrently to enhance or accelerate response. This review focuses on the data available to support these various augmentation and combination treatments.
Comparison of Anesthetic Depth Indexes Based on Thalamocortical Local Field Potentials in Rats

Background:Local field potentials may allow a more precise analysis of the brain electrical activity than the electroencephalogram. In this study, local field potentials were recorded in the thalamocortical axis of rats to (i) compare the performance of several indexes of anesthetic depth and (ii) investigate the existence of thalamocortical correlated or disrupted activity during isoflurane steady-state anesthesia. Methods:Five rats chronically implanted with microelectrodes were used to record local field potentials in the primary somatosensory cortex and ventroposterolateral thalamic nuclei at six periods: before induction of anesthesia; in the last 5 min of randomized 20-min steady-state end-tidal 0.8, 1.1, 1.4, and 1.7% isoflurane concentrations; and after recovery. The approximate entropy, the index of consciousness, the spectral edge frequency, and the permutation entropy were estimated using epochs of 8 s. A correction factor for burst suppression was applied to the spectral edge frequency and to the permutation entropy. The correlation between the derived indexes and the end-tidal isoflurane was calculated and compared for the two studied brain regions indexes. Coherence analysis was also performed. Results:The burst suppression–corrected permutation entropy showed the highest correlation with the end-tidal isoflurane concentration, and a high coherence was obtained between the two studied areas. Conclusions:The permutation entropy corrected with the classic burst suppression ratio is a promising alternative to other indexes of anesthetic depth. Furthermore, high coherence level of activity exists between the somatosensory cortical and thalamic regions, even at deep isoflurane stages.
Multi-task learning of system dynamics with maximum information gain

This paper introduces a new approach to adaptively learn the dynamics of a robotic system. The methodology is based on maximizing the information gain from new observations while modeling the dynamics with a Multiple Output Gaussian Process (MOGP). High-dimensional state-action spaces with unknown dependencies between inputs and outputs can be highly computationally expensive to learn. Gaussian process modeling is a Bayesian technique that naturally overcomes one of the most difficult problems in machine learning known as over-fitting. This makes it very appealing for on-line problems where testing multiple hypothesis is difficult. The computational cost of the learning task is reduced by having a smaller dataset of informative training points. Therefore we introduce a learning strategy capable of determining the most informative training set for the MOGP. This method can be implemented for learning the behavior of dynamic systems where due to their complexity and disturbances are infeasible to be analytically defined. The benefits of our approach are verified in two experiments: learning the dynamics of a cart-pole system in simulation and the dynamics of a robotic blimp.