Miracle-dz/UniFAR-FTUs huggingface dataset:
Rab proteins implicated in lipid storage and mobilization

Abstract Abnormal intracellular accumulation or transport of lipids contributes greatly to the pathogenesis of human diseases. In the liver, excess accumulation of triacylglycerol (TG) leads to fatty liver disease encompassing steatosis, steatohepatitis and fibrosis. This places individuals at risk of developing cirrhosis, hepatocellular carcinoma or hepatic decompensation and also contributes to the emergence of insulin resistance and dyslipidemias affecting many other organs. Excessive accumulation of TG in adipose tissue contributes to insulin resistance as well as to the release of cytokines attracting leucocytes leading to a pro-inflammatory state. Pathological accumulation of cholesteryl ester (CE) in macrophages in the arterial wall is the progenitor of atherosclerotic plaques and heart disease. Overconsumption of dietary fat, cholesterol and carbohydrates explains why these diseases are on the increase yet offers few clues for how to prevent or treat individuals. Dietary regimes have proven futile and barring surgery, no realistic alternatives are at hand as effective drugs are few and not without side effects. Overweight and obesity-related diseases are no longer restricted to the developed world and as such, constitute a global problem. Development of new drugs and treatment strategies are a priority yet requires as a first step, elucidation of the molecular pathophysiology underlying each associated disease state. The lipid droplet (LD), an up to now overlooked intracellular organelle, appears at the heart of each pathophysiology linking key regulatory and metabolic processes as well as constituting the site of storage of both TGs and CEs. As the molecular machinery and mechanisms of LDs of each cell type are being elucidated, regulatory proteins used to control various cellular processes are emerging. Of these and the subject of this review, small GTPases belonging to the Rab protein family appear as important molecular switches used in the regulation of the intracellular trafficking and storage of lipids.
Evaluation of Extractants and Precipitants in Tear Film Proteomic Analyses

Purpose. To determine the efficiency of several protein extraction or precipitation treatments used in proteomic analyses. Methods. Tear samples were taken from each eye of 40 normal subjects using glass microcapillaries. Tear volume was measured followed by storage at −86°C. Lotrafilcon B contact lenses were fitted and worn for 14 days, followed by removal and storage at −86°C. Tear samples from each eye within a subject were randomly assigned to either one of four chemical treatments (acetone, trichloroacetic acid, urea, and trifluoroacetic acid/acetonitrile [TFA/ACN]) or no chemical treatment in groups of 10. Contact lens samples were subjected to the same treatments as tear samples for each subject, with a second treatment preceding the first. Protein concentrations were quantified by Bradford assay. Results. For tear samples, a significant reduction in total protein was observed when subjected to any of the four treatments studied compared with those samples left untreated. A positive relationship was noted between protein concentration and tear volume for treated, untreated, and combined tear samples. For contact lens samples, there was a significant reduction in the amount of deposited protein removed when comparing acetone, trichloroacetic acid, and urea with TFA/ACN. A second extraction from contact lenses assigned to the urea and TFA/ACN groups yielded a significant amount of additional protein compared with the amount removed initially. Conclusions. Tear samples subjected to any of the evaluated chemical treatments provided significantly less protein than untreated samples. For contact lenses, TFA/ACN extraction provided the highest yield of available protein out of the four treatments evaluated.
A Border Irregularity Measure Using Hidden Markov Models as a Malignant Melanoma Predictor

Malignant melanoma, a skin cancer, manifests itself as a dark lesion, most often with an irregular boundary. The degree of irregularity is an important diagnostic indicator. This paper presents a new measure of irregularity using Hidden Markov Models (HMMs) based on the Weibull probability distribution. The measure was tested on 98 skin lesions of which 16 were malignant melanoma. The ROC analysis showed that the measure is 82% sensitive and 82% specific in discriminating the malignant and benign lesions. These results compare favourably with other measures and indicate that HMM captures some distinguishing features in the boundary of malignant lesions.
Voltage-dependent block of cardiac inward-rectifying potassium current by monovalent cations

The inward-rectifying K+ current (IK1) in cat ventricular myocytes, like inward-rectifying K+ currents in many other preparations, exhibited a negative slope conductance region at hyperpolarized membrane potentials that was time-dependent. This was evident as an inactivation of inward current elicited by hyperpolarizing voltage- clamp pulses resulting in a negative slope region of the steady-state current-voltage relationship at potentials negative to -140 mV. Removing extracellular Na+ prevented the development of the negative slope in this voltage region, suggesting that Na+ can block IK1 channels in a time- and voltage-dependent manner. The time and voltage dependence of Cs+-induced block of IK1 was also examined. Cs+ blocked inward current in a manner similar to that of Na+, but the former was much more potent. The fraction of current blocked by Cs+ in the presence of Na+ was reduced in a time- and voltage-dependent manner, which suggested that these blocking ions compete for a common or at least similar site of action. In the absence of Na+, inactivation of IK1 could also be induced by both Cs+ and Li+. However, Li+ was less potent than Na+ in this respect. Calculation of the voltage sensitivity of current block by each of these ions suggests that the mechanism of block by each is similar.
A survey and experimental evaluation of proximity sensors for space robotics

This paper provides an overview of the authors' selection process for proximity sensors for manipulator collision avoidance. Five categories of sensors have been considered for this use in space operations: intensity of reflection, triangulation, time of flight, capacitive, and inductive. From these categories, the most promising commercial and mature laboratory prototype sensors have been selected and tested. After reviewing the selection process and the experimental results, conclusions are drawn about which sensors are best and why. <<ETX>>
INVITED PAPER Special Section on Recent Advances in Machine Learning for Spoken Language Processing Sensitivity-Characterised Activity Neurogram ( SCAN ) for Visualising and Understanding the Inner Workings of Deep Neural Network

Deep Neural Network (DNN) is a powerful machine learning model that has been successfully applied to a wide range of pattern classification tasks. Due to the great ability of the DNNs in learning complex mapping functions, it has been possible to train and deploy DNNs pretty much as a black box without the need to have an in-depth understanding of the inner workings of the model. However, this often leads to solutions and systems that achieve great performance, but offer very little in terms of how and why they work. This paper introduces Sensitivity-characterised Activity Neorogram (SCAN), a novel approach for understanding the inner workings of a DNN by analysing and visualising the sensitivity patterns of the neuron activities. SCAN constructs a lowdimensional visualisation space for the neurons so that the neuron activities can be visualised in a meaningful and interpretable way. The embedding of the neurons within this visualisation space can be used to compare the neurons, both within the same DNN and across different DNNs trained for the same task. This paper will present the observations from using SCAN to analyse DNN acoustic models for automatic speech recognition. key words: deep neural network, visualisation, interpretability
Joint attention training for children with autism using behavior modification procedures.

BACKGROUND Deficits in joint attention are considered by many researchers to be an early predictor of childhood autism (e.g., Osterling & Dawson, 1994) and are considered to be pivotal to deficits in language, play, and social development in this population (Mundy, 1995). Although many researchers have noted the importance of joint attention deficits in the development of children with autism (e.g., Mundy, Sigman, & Kasari, 1994) and have called for intervention strategies (e.g., Mundy & Crowson, 1997), few studies have attempted to target joint attention. In this study, joint attention behaviors were taught to children with autism using a behavior modification procedure. METHODS A multiple-baseline design was implemented to evaluate intervention effects. The following target behaviors were included in the intervention: 1) Responding to showing, pointing, and gaze shifting of adult; 2) Coordinated gaze shifting (i.e., coordinated joint attention); and 3) Pointing (with the purpose of sharing, not requesting). Generalization to setting and parent, follow-up sessions, and social validation measures were also analyzed. RESULTS Joint attention behaviors were effectively trained and targeted behaviors generalized to other settings. In addition, positive changes were noted by naive observers using social validation measures. CONCLUSIONS Integrating joint attention training into existing interventions may be important for children with autism. In addition, training parents in these techniques may help to maintain joint attention skills outside of the treatment setting.
SINR Balancing with Coordinated Multi-Cell Transmission

Coordinated multi-cell processing facilitates multi-user precoding techniques across distributed base station (BS) antenna heads. Hence, it can efficiently exploit the available spatial degrees of freedom in a multi-user multiple-input multiple-output (MIMO) channel. A generalised method for joint design of linear transceivers with coordinated multi-cell processing subject to per-BS/antenna power constraints is proposed. The system optimisation objective is to balance the weighted SINR across all the transmitted data streams. The method can accommodate a variety of scenarios from coherent multi-cell beamforming across a large virtual MIMO channel to single-cell beamforming with inter-cell interference coordination and beam allocation. The performance of different coherent/non-coherent and coordinated/noncoordinated multi-cell transmission methods with optimal and heuristic beam allocation algorithms is numerically compared in different scenarios with varying inter-cell interference. The coherent multi-cell beamforming greatly outperforms the non-coherent cases, especially at the cell edge and with a full spatial load. However, the coordinated single-cell transmission with interference avoidance and dynamic beam allocation performs considerably well with a partial spatial loading.
Interleukin-17A Is Dispensable for Myocarditis but Essential for the Progression to Dilated Cardiomyopathy

Rationale: One-third of myocarditis cases progresses to dilated cardiomyopathy (DCM), but the mechanisms controlling this process are largely unknown. CD4+ T helper (Th)17 cells have been implicated in the pathogenesis of autoimmune diseases, but the role of Th17-produced cytokines during inflammation-induced cardiac remodeling has not been previously studied. Objective: We examined the importance of interleukin (IL)-17A in the progression of myocarditis to DCM using a mouse model. Methods and Results: Immunization of mice with myocarditogenic peptide in complete Freund's adjuvant induced the infiltration of IL-17A–producing Th17 cells into the inflamed heart. Unexpectedly, IL-17A–deficient mice developed myocarditis with similar incidence and severity compared to wild-type mice. Additionally, IL-17A deficiency did not ameliorate the severe myocarditis of interferon (IFN)&ggr;-deficient mice, suggesting that IL-17A plays a minimal role during acute myocarditis. In contrast, IL-17A–deficient mice were protected from postmyocarditis remodeling and did not develop DCM. Flow cytometric and cytokine analysis revealed an important role for IL-17A in heart-specific upregulation of IL-6, TNF&agr;, and IL-1&bgr; and the recruitment of CD11b+ monocyte and Gr1+ granulocyte populations into the heart. Furthermore, IL-17A–deficient mice had reduced interstitial myocardial fibrosis, downregulated expression of matrix metalloproteinase-2 and -9 and decreased gelatinase activity. Treatment of BALB/c mice with anti–IL-17A monoclonal antibody administered after the onset of myocarditis abrogated myocarditis-induced cardiac fibrosis and preserved ventricular function. Conclusions: Our findings reveal a critical role for IL-17A in postmyocarditis cardiac remodeling and the progression to DCM. Targeting IL-17A may be an attractive therapy for patients with inflammatory dilated cardiomyopathy.
DECARBOXYLATION STUDIES OF GLUTAMATE, GLUTAMINE, AND ASPARTATE FROM BRAIN LABELLED WITH [1‐14C] ACETATE, l‐[U‐14C]‐ASPARTATE, AND l‐[U‐14C]GLUTAMATE

Studies in vivo and in vitro of the distribution of label in C‐1 of glutamate and glutamine and C‐4 of aspartate in the free amino acids of brain were carried out. [1‐14C]‐Acetate was used both in vivo and in vitro and l‐[U‐14C]aspartate and l‐[U‐14C]glutamate were used in vitro.
Modeling Athlete Performance Using Clustering Techniques

This paper focused on using clustering techniques to analyze sport physiological data collected during incremental tests to support the planning of training sessions, to provide a tool for athlete self-evaluation. Modeling athlete performance to analyze the progress of a test session, automatically assign the tested athlete to a group of athletes which are similar to him with respect to physical parameters and development of the test,, and evaluate these groups with respect to two quality indexes of the performance of the athlete, whose real value is known only at the end of the test. It provides a continuous characterization of the progress of the test. Index Terms—data mining, clustering techniques, athlete performance, physiological data
Exploiting Spatial Separations in CDMA Systems With Correlation Constrained Sets of Hadamard Matrices

Spreading codes for code-division multiple-access (CDMA) systems are constructed based on Hadamard matrices and almost-bent functions. These codes can be assigned to a tessellation of hexagonal cells in such a way that codewords assigned to the same cell or to adjacent cells have zero cross-correlation. The nonzero cross-correlations in the code only apply to pairs of codewords assigned to nonadjacent cells. The codes have four times as many codewords as codes constructed from a single Hadamard matrix, which leads to significantly increased codeword re-use distances in comparison with the conventional use of a single Hadamard matrix with the same number of codewords per cell.
Is there an ideal digital aerial camera

The IGN digital camera project was set up in the early 90's and the first research surveys were carried out in 1996. The digital camera was first used in production in 2000 when the DMC and ADS40 systems appeared on the market at the beginning of the 21 st century. Since then, two medium format systems (Applanix DSS322 and DiMAC system) and a third large-format system (Vexcel UltraCamD) appeared on the surveying market. Today, IGN's system could appear very underperforming in terms of possible swaths and frame rates compared with the ones offered by large format cameras; and parts of our electronics have become obsolete. This is the reason why we recently reanalyzed the needs of IGN in term of digital imagery and confronted them to the main characteristics and capabilities of the different existing commercial systems. Considering that Bayer acquisition without a coupled panchromatic acquisition does not allow an image reconstruction of good quality and considering that an acquisition system based on CCD area sensors must be equipped with a Forward Motion Compensation (FMC) able to compensate dozens of pixels, we only focused on the three large-format systems that are: Intergraph's DMC, Vexcel's UltracamD, and Leica Geosystems' ADS40. Each of these three systems has its pros and cons and no one seems to bring an answer to all our needs.
Training a Probabilistic Graphical Model With Resistive Switching Electronic Synapses

Current large-scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. New memory technologies, such as nanoscale two-terminal resistive switching memory devices, offer a compact, scalable, and low-power alternative that permits on-chip colocated processing and memory in fine-grain distributed parallel architecture. Here, we report the first use of resistive memory devices for implementing and training a restricted Boltzmann machine (RBM), a generative probabilistic graphical model as a key component for unsupervised learning in deep networks. We experimentally demonstrate a 45-synapse RBM realized with 90 resistive phase change memory (PCM) elements trained with a bioinspired variant of the contrastive divergence algorithm, implementing Hebbian and anti-Hebbian weight updates. The resistive PCM devices show a twofold to tenfold reduction in error rate in a missing pixel pattern completion task trained over 30 epochs, compared with untrained case. Measured programming energy consumption is 6.1 nJ per epoch with the PCM devices, a factor of ~ 150 times lower than the conventional processor-memory systems. We analyze and discuss the dependence of learning performance on cycle-to-cycle variations and number of gradual levels in the PCM analog memory devices.
Stacked Integral Image

Filtering in digital images via Integral Image yields fast computation times for uniform filtering. The extension by Heckbert [1] to perform filtering via repeated integration provides a way for non-uniform filtering, but has its own limitations. A recent method by Hussein et al. [2] provides non-uniform filtering by Euler expansion of filtering kernels, and is called kernel integral method. We propose a simplification for non-uniform filtering by stacking of box filters from a single integral image.1 Results show speedups of as much as 40∶1, similar to run time performance gains in kernel integral method, when comparing to naïve nonuniform filtering, while at the same time reducing the setup time drastically.
AI4FM: A new project seeking challenges!

The “proof obligations” generated from many formal methods tend to be simple and can often be discharged by modern automatic theorem provers or SMT systems. However, those proof tasks that need hand –or interactive– intervention present a barrier to the use of formal methods. Theorem proving was one of the earliest challenges addressed by researchers in the area of Artificial Intelligence and enormous progress has been made in the provision of general purpose heuristics. The approach in the recently started AI4FM project is different: we hope to devise a system that will learn from an expert user how they tackle one interactive proof and then apply the discovered high-level strategy to other related proof tasks. We are fortunate in having access to many such problems through the DEPLOY project but are aware of the dangers of devising an overly specific approach. This short paper appeals for challenge problems from other sources.
Structured sparse coding via lateral inhibition

This work describes a conceptually simple method for structured sparse coding and dictionary design. Supposing a dictionary with K atoms, we introduce a structure as a set of penalties or interactions between every pair of atoms. We describe modifications of standard sparse coding algorithms for inference in this setting, and describe experiments showing that these algorithms are efficient. We show that interesting dictionaries can be learned for interactions that encode tree structures or locally connected structures. Finally, we show that our framework allows us to learn the values of the interactions from the data, rather than having them pre-specified.
Resonant Relaxation near a Massive Black Hole: The Stellar Distribution and Gravitational Wave Sources

Resonant relaxation (RR) of orbital angular momenta occurs near massive black holes (MBHs) where the potential is spherical and stellar orbits are nearly Keplerian and so do not precess significantly. The resulting coherent torques efficiently change the magnitude of the angular momenta and rotate the orbital inclination in all directions. As a result, many of the tightly bound stars very near the MBH are rapidly destroyed by falling into the MBH on low angular momentum orbits, while the orbits of the remaining stars are efficiently randomized. We solve numerically the Fokker-Planck equation in energy for the steady state distribution of a single-mass population with an RR sink term. We find that the steady state current of stars, which sustains the accelerated drainage close to the MBH, can be ≲10 larger than that due to noncoherent two-body relaxation alone. RR mostly affects tightly bound stars, and so it increases only moderately the total tidal disruption rate, which is dominated by stars originating from less bound orbits farther away. We show that the event rate of gravitational wave (GW) emission from inspiraling stars, originating much closer to the MBH, is dominated by RR dynamics. The GW event rate depends on the uncertain efficiency of RR. The efficiency indicated by the few available simulations implies rates ≲10 times higher than those predicted by two-body relaxation, which would improve the prospects of detecting such events by future GW detectors, such as LISA. However, a higher, but still plausible, RR efficiency can lead to the drainage of all tightly bound stars and strong suppression of GW events from inspiraling stars. We apply our results to the Galactic MBH and show that the observed dynamical properties of stars there are consistent with RR.
Copper and Cadmium Tolerance, Uptake and Effect on Chloroplast Ultrastructure. Studies on Salix purpurea and Phragmites australis

We have compared the effect of toxic Cu and Cd concentrations on growth, metal accumulation, and chloroplast ultrastructure of willow (Salix purpurea L.) and reed [Phragmites australis (Cav.) Trin. ex Steud. ]. After a 10-day treatment, both species have tolerated to some extent the lowest concentration of both metals; however, plant growth was strongly reduced at the highest Cu and Cd concentrations. These plants could be described as Cutolerant at the lowest concentration tested, showing a higher tolerance index in reed than in willow; in contrast, willow exhibited higher tolerance against Cd. Both plants appeared to be moderate root accumulators of Cu and Cd. Ultrastructural studies revealed special features that can provide some protection against heavy metals stress, such as ferritin aggregates in the stroma. In addition, Cu and Cd induced distortion of thylakoids, reduction of grana stacks, as well as an increased number and size of plastoglobuli and peripheral vesicles.