Miracle-dz/UniFAR-FTUs huggingface dataset:
The BRCA1 c.5434C→G (p.Pro1812Ala) variant induces a deleterious exon 23 skipping by affecting exonic splicing regulatory elements

Background A large fraction of the sequence variants of unknown significance or unclassified variants (UVs) could be pathogenic by affecting mRNA splicing. The breast and ovarian cancer susceptibility gene BRCA1 exhibits a large spectrum of sequence variation but only two variants, both located in exon 18, have been shown experimentally to affect splicing regulatory elements. The present study investigated the impact on splicing of the variant BRCA1 c.5434C→G (p.Pro1812Ala), identified in an ovarian cancer patient. This variant has previously been studied at the protein level with inconclusive results concerning its pathogenic role. Methods Analysis of RNA from patient peripheral blood was performed by RT-PCR. The effect of the variant was tested by using splicing reporter hybrid minigene assays. Results Using patient RNA analyses and hybrid minigene assays, we showed that this variant induces a major splicing defect, with skipping of exon 23, resulting in frameshift and predicted protein termination within the second BRCT domain. Moreover, we showed that the segment c.5420–5449 of BRCA1, in the centre of exon 23, exhibits splicing enhancer properties. This enhancement is abolished by the c.5434C→G mutation, indicating that the nucleotide change, in this highly conserved region, affects a splicing regulatory element. Bioinformatics analyses predict that the mutation c.5434C→G creates an hnRNPA1 dependent splicing silencer. Conclusion These data, together with segregation data, argue for the classification of BRCA1 c.5434C→G as a pathogenic splicing mutation. These results also suggest that UVs in highly conserved nucleotide sequences of short exons may be good candidates for detecting functionally relevant splicing regulatory elements.
Configurable compressive sensing and processing using tracking information for delay-Doppler radar applications

Compressive sensing and processing (CSP) acquires and processes waveforms at a rate much lower than that of Nyquist sensing and processing (NSP). However, the use of CSP in estimation problems increases estimation error compared to NSP. To improve estimation performance an adaptive compressive sensing and processing (ACSP) method is proposed that creates a dictionary containing delay-Doppler shifts likely to be found in the radar return waveform according to tracking information. The proposed ACSP method improves tracking performance by adapting the size and content of the dictionary compared to non-adaptive CSP and a fixed sized dictionary ACSP method.
Heterothermy in large mammals: inevitable or implemented?

Advances in biologging techniques over the past 20 years have allowed for the remote and continuous measurement of body temperatures in free‐living mammals. While there is an abundance of literature on heterothermy in small mammals, fewer studies have investigated the daily variability of body core temperature in larger mammals. Here we review measures of heterothermy and the factors that influence heterothermy in large mammals in their natural habitats, focussing on large mammalian herbivores. The mean 24 h body core temperatures for 17 species of large mammalian herbivores (>10 kg) decreased by ∼1.3°C for each 10‐fold increase in body mass, a relationship that remained significant following phylogenetic correction. The degree of heterothermy, as measured by the 24 h amplitude of body core temperature rhythm, was independent of body mass and appeared to be driven primarily by energy and water limitations. When faced with the competing demands of osmoregulation, energy acquisition and water or energy use for thermoregulation, large mammalian herbivores appear to relax the precision of thermoregulation thereby conserving body water and energy. Such relaxation may entail a cost in that an animal moves closer to its thermal limits for performance. Maintaining homeostasis requires trade‐offs between regulated systems, and homeothermy apparently is not accorded the highest priority; large mammals are able to maintain optimal homeothermy only if they are well nourished, hydrated, and not compromised energetically. We propose that the amplitude of the 24 h rhythm of body core temperature provides a useful index of any compromise experienced by a free‐living large mammal and may predict the performance and fitness of an animal.
Thiocyanate insecticides; the relation between knockdown activity and chemical constitution.

The results of an investigation into the relation between chemical constitution and the knock-down activity and toxicity of thiocyanate insecticides are recorded. Linkage of the toxic thiocyano radical through a ketomethylene group to a lipoid soluble hydrocarbon residue gives rise to knock-down activity. The most active α-thiocyanoketones R.CO.CH2.SCN and thiocyanoacetates R. O. CO. CH2. SCN are too irritant to the eyes and nose for inclusion in domestic fly-sprays. Thiocyanoacetates derived from saturated 10-carbon alcohols, however, are non-irritant to the mucous membranes while possessing moderately high activity. The mode of action of these compounds is discussed.
Media Mix Optimization - Applying a Quadratic Knapsack Model

In this contribution we present an optimization model for deciding on the best selection of advertising media to be used in a promotional campaign. The effect of each single medium and each pair of media is estimated from the evaluation data of past campaigns taking into account a similarity measure between the attributes and goals of campaigns. The resulting discrete optimization model is a Quadratic Knapsack Problem which we solve by a genetic algorithm. Then campaign budget is assigned to each selected advertising medium based on a statistical estimation from previous campaigns. Our optimization tool is integrated in the marketing management software solution MARMIND.
Exact fuzzy k-nearest neighbor classification for big datasets

The k-Nearest Neighbors (kNN) classifier is one of the most effective methods in supervised learning problems. It classifies unseen cases comparing their similarity with the training data. Nevertheless, it gives to each labeled sample the same importance to classify. There are several approaches to enhance its precision, with the Fuzzy k-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. Fuzzy-kNN computes a fuzzy degree of membership of each instance to the classes of the problem. As a result, it generates smoother borders between classes. Apart from the existing kNN approach to handle big datasets, there is not a fuzzy variant to manage that volume of data. Nevertheless, calculating this class membership adds an extra computational cost becoming even less scalable to tackle large datasets because of memory needs and high runtime. In this work, we present an exact and distributed approach to run the Fuzzy-kNN classifier on big datasets based on Spark, which provides the same precision than the original algorithm. It presents two separately stages. The first stage transforms the training set adding the class membership degrees. The second stage classifies with the kNN algorithm the test set using the class membership computed previously. In our experiments, we study the scaling-up capabilities of the proposed approach with datasets up to 11 million instances, showing promising results.
The end of the Ottoman Empire as reflected in the İstanbul bourse

ABSTRACT The Ottoman Empire faced catastrophic events during its period of dissolution which started with the First World War. At the end of this war, the Ottoman lands were shared by the Allied forces. As a reaction to this occupation, the Turkish War of Independence started in 1919 and finished in 1923. The authors aim to examine how these events were reflected in the İstanbul bourse, using Ottoman government bond prices data between 1918 and 1925. Econometric methodology of Carla Inclan and George Tiao (1994) identifies several break points, which imply a lower risk for the repayment of the bonds during the end of the First World War and the Turkish War of Independence.
A new algorithm for training sparse autoencoders

Data representation plays an important role in performance of machine learning algorithms. Since data usually lacks the desired quality, many efforts have been made to provide a more desirable representation of data. Among many different approaches, sparse data representation has gained popularity in recent years. In this paper, we propose a new sparse autoencoder by imposing the power two of smoothed L0 norm of data representation on the hidden layer of regular autoencoder. The square of smoothed L0 norm increases the tendency that each data representation is "individually" sparse. Moreover, by using the proposed sparse autoencoder, once the model parameters are learned, the sparse representation of any new data is obtained simply by a matrix-vector multiplication without performing any optimization. When applied to the MNIST, CIFAR-10, and OPTDIGITS datasets, the results show that the proposed model guarantees a sparse representation for each input data which leads to better classification results.
Heterogeneity-aware scheduler for stream processing frameworks

This article discusses problems and decisions related to scheduling of stream processing applications in heterogeneous clusters. An overview of the current state of the art of the stream processing on heterogeneous clusters with a focus on resource allocation and scheduling is presented first. Then, common scheduling approaches of various stream processing frameworks are discussed and their limited applicability in the heterogeneous environment is demonstrated on a simple stream application. Finally, the article presents a novel heterogeneity-aware scheduler for the stream processing frameworks based on design-time knowledge as well as benchmarking techniques. It is shown that the scheduler overcomes alternatives in resource-aware deployment over cluster nodes and thus it leads to a better utilisation of the clusters.
Correlated functional and morphometric study of thyroid hyperplasia induced by iodine deficiency.

The effect of feeding a low iodine diet (LID) on the thyroids of mice of both sexes for 1–8 weeks was investigated. Morphometric, autoradiographic, and chemical methods were used. Morphologically, the thyroid response to LID could be divided into two successive phases. During the first 4 weeks, the glandular weight remained constant, but changes in the relative volumes of various tissue components occurred, i.e. an increase of the blood vessels and the follicular epithelium, contrasting with a decrease of the colloid. The epithelial height almost doubled. [3H]Thymidine incorporation indexes (TII) increased by the first week in endothelial cells, earlier than in the follicular cells. At all times studied, more than 40% of the TII was related to nonfollicular cells. During the second period (5–8 weeks), the glandular weight increased, but the relative volumes of all of the tissue components, the epithelial height, and the TII were almost stabilized; all of the glandular components grew at a similar rate. Fu.
Solid Lipid Nanoparticles: A Potential Approach for Dermal Drug Delivery

Solid lipid nanoparticles (SLNs) have attracted increasing attention during recent years. Due to their unique size dependent properties, lipid nanoparticles offer possibilities to develop new therapeutics. The ability to incorporate drugs into nanoparticles offers a new prototype in drug delivery thus realizing the dual goal of both controlled release and site-specific drug delivery. Drug delivery to the skin is widely used for local and systemic delivery and has potential to be improved by application of nanoparticulate formulations. If investigated appropriately, solid lipid nanoparticles may open new opportunities in therapy of complex diseases which is difficult to treat.
A 1.2V 246uW CMOS latched comparator with neutralization technique for reducing Kickback Noise

A low power CMOS latched comparator has been designed in TSMC 0.18um employ neutralization technique for reducing Kickback Noise. The simulation results demonstrate that it can work at 1GHz suitable for high speed applications. Measurement results prove that the latched comparator consumes 246uW with a power supply of 1.2v at 10MHz. A simulation method for accurately determining dynamic offset in latched comparator is presented.
Highly Efficient Single-Step Enrichment of Low Abundance Phosphopeptides from Plant Membrane Preparations

Mass spectrometry (MS)-based large scale phosphoproteomics has facilitated the investigation of plant phosphorylation dynamics on a system-wide scale. However, generating large scale data sets for membrane phosphoproteins usually requires fractionation of samples and extended hands-on laboratory time. To overcome these limitations, we developed “ShortPhos,” an efficient and simple phosphoproteomics protocol optimized for research on plant membrane proteins. The optimized workflow allows fast and efficient identification and quantification of phosphopeptides, even from small amounts of starting plant materials. “ShortPhos” can produce label-free datasets with a high quantitative reproducibility. In addition, the “ShortPhos” protocol recovered more phosphorylation sites from membrane proteins, especially plasma membrane and vacuolar proteins, when compared to our previous workflow and other membrane-based data in the PhosPhAt 4.0 database. We applied “ShortPhos” to study kinase-substrate relationships within a nitrate-induction experiment on Arabidopsis roots. The “ShortPhos” identified significantly more known kinase-substrate relationships compared to previous phosphoproteomics workflows, producing new insights into nitrate-induced signaling pathways.
Entropy-Based Sparse Trajectories Prediction Enhanced by Matrix Factorization

Existing moving object’s trajectory prediction algorithms suffer from the data sparsity problem, which affects the accuracy of the trajectory prediction. Aiming to the problem, we present an Entropy-based Sparse Trajectories Prediction method enhanced by Matrix Factorization (ESTP-MF). Firstly, we do trajectory synthesis based on trajectory entropy and put synthesized trajectories into the trajectory space. It can resolve the sparse problem of trajectory data and make the new trajectory space more reliable. Secondly, under the new trajectory space, we introduce matrix factorization into Markov models to improve the sparse trajectory prediction. It uses matrix factorization to infer transition probabilities of the missing regions in terms of corresponding existing elements in the transition probability matrix. It aims to further solve the problem of data sparsity. Experiments with a real trajectory dataset show that ESTP-MF generally improves prediction accuracy by as much as 6% and 4% compared to the SubSyn algorithm and STP-EE algorithm respectively. key words: trajectory prediction, data sparsity, entropy estimation, matrix factorization
Gambogic acid inhibits spinal cord injury and inflammation through suppressing the p38 and Akt signaling pathways.

Gamboge is the dry resin secreted by Garcinia hanburyi Hook.f, with the function of promoting blood circulation, detoxification, hemostasis and killing insects, used for the treatment of cancer, brain edema and other diseases. Gambogic acid is the main effective constituent of Gamboge. The present study investigated the protective effects of gambogic acid on spinal cord injury (SCI) and its anti‑inflammatory mechanism in an SCI model in vivo. Basso, Beattie and Bresnahan (BBB) testing was used to detect the protective effects of gambogic acid on nerve function of SCI rats. The water content of the spinal cord was used to analyze the protective effects of gambogic acid on the damage of SCI. Treatment with gambogic acid effectively improved BBB scores and inhibited water content of the spinal cord in SCI rats. Also, gambogic acid significantly reduced inflammatory cytokines levels of [tumor necrosis factor‑α, interleukin (IL)‑6, IL‑12 and IL‑1β] and oxidative stress (malondialdehyde, superoxide dismutase, glutathione and glutathione‑peroxidase) factors, and suppressed receptor activator of nuclear factor κB ligand, phosphorylated p38 protein expression and toll‑like receptor 4/nuclear factor‑κB pathway activation, and increased phosphatidylinositol 3‑kinase/protein kinase B (Akt) pathway activation in SCI rats. These results provide evidence that gambogic acid inhibits SCI and inflammation through suppressing the p38 and Akt signaling pathways.
Intentional Inferences Are Not More Likely Than Unintentional Ones: Some Evidence Against the Intentionality Bias Hypothesis

ABSTRACT We conducted a study to test the hypothesis that inferences about intentionality are biased toward an intentional interpretation. Contrary to previous research, participants were no more likely to judge ambiguous actions as intentional in a speeded compared to an unspeeded condition. Further, participants were faster to respond and more consistent in responding to unintentional rather than intentional actions.
Learning by Demonstration for a Dancing Robot within a Computational Creativity Framework

The paper presents a system that learns a set of movements for a creative dancing robot. A human user only dances in front of an 3D camera, and automatically the acquisition system segments the acquired sequence of postures depending on the detected music beat and rhythm. A clustering phase allows the system to group the identified actions in 20 classes, defining the set of movements that is typical of a given person. Analysis of the k-mean algorithm outcomes using different distances is reported. The human postures are translated in the corresponding robot joints configurations and are used to compose dance choreographies creatively. A cognitive architecture developed in previous works drives the process of dance creation. Experimentation shows the sets of movements derived from human users with different dance skills. Audience evaluates the robot performances based on these sets, and results are coherent with the quality and richness of the acquired movements.
Development of a High-Definition and Multispectral Image Capturing System for Digital Archiving of Early Modern Tapestries of Kyoto Gion Festival

We developed a two-shot 6-band image capturing system consisting of a large-format camera, a customized interference filter, and a scanning digital back to capture a 185-M-pixel images. The interference filter is set in front of the camera lens to obtain a 6-band image, that is, two 3-band images, one taken with the filter and the other without it. After correction of optical aberrations caused by the interference filter as well as system arrangement errors, the two images are combined into a 6-band image. The 6-band image was converted into a color-managed RGB image embedded ICC profile. In experiments, object images were captured as several divided parts and synthesized as almost 500-M-pixel image by using an image stitching technique. Resolution of the captured images is 0.02 mm/pixel. This paper discusses the camera system with its focus on some early modern tapestries used in the Kyoto Gion Festival. After the experiments, we interviewed a craftsman to assess the image’s importance in archiving and analyzing fabric structures.
CHARACTERIZATION OF ANAEROBIC BIOMASS IMMOBILIZED IN POLYURETHANE FOAM MATRICES FROM HAIS REACTOR BY SCANNING ELECTRON MICROSCOPY

This paper reports on the characterization of immobilized anaerobic biomass by scanning electron microscopy (SEM) using the hexamethyldesilazane (HMDS) technique. Polyurethane foam matrices containing anaerobic sludge were taken from a horizontal-flow anaerobic immobilized sludge (HAIS) reactor treating glucose based substrate operating with bed porosities (ε) of 0.24 and 0.4. After three weeks of operation with ε of 0.24, different kind of bacteria were found. Rods, coccus and Methanothrix-like morphologies appeared into the support as small granules (micro-granules) or attached to the matrix as disperse cells. Such micro-granules were attached to or entrapment in the matrices. Micro-granules with 409±15 μm in size were observed in the polyurethane foam matrices taken from HAIS reactor operating with ε of 0.4. The bacterial morphologies were similar to those found in samples taken during the operation with ε of 0.24, but acidogenic fat rods were predominant in the first HAIS reactor’s segment. The intense acidogenic activity was verified by the excess of volatile fatty acids measured in this part of the reactor. Microscopy observations permitted to conclude that anaerobic microrganisms were effectively immobilized into the polyurethane foam support.
Contrasting strategies for UV-B screening in sub-Arctic dwarf shrubs.

The content and distribution of UV-absorbing phenolic compounds was investigated in leaves of three species of Vaccinium co-existing at a site in north Sweden. Vaccinium myrtillus L. , Vaccinium vitis-idaea L. , and Vaccinium uliginosum L. exhibit markedly different strategies, in terms of localization and content of leaf phenolics and in their responses to UV-B enhancement. Plants were exposed to either ambient radiation or to enhancement of UV-B corresponding to 15% (clear sky) depletion of stratospheric ozone for approximately 10 years prior to commencement of this study. Vaccinium myrtillus contained the highest concentration of methanol-extractable UV-B-absorbing compounds, which was elevated in plants exposed to enhanced UV-B. Fluorescence and confocal laser scanning microscopy showed that these compounds were distributed throughout the leaf, and were particularly concentrated in chlorophyll-containing cells. In V. vitis-idaea, most phenolic compounds were cell wall-bound and concentrated in the walls of the epidermis; this pool increased in response to UV-B enhancement. It is suggested that these two plants represent extreme forms of two divergent strategies for UV-B screening, the different responses possibly being related to leaf longevity in the two species. The response of V. uliginosum was intermediate between the other two, with high concentrations of cell wall-bound phenolics in the epidermis but with this pool decreasing, and the methanol-soluble pool tending to increase, after exposure to enhanced UV-B. One explanation for this response is that this plant is deciduous, like V. myrtillus, but has leaves that are structurally similar to those of V. vitis-idaea.
The Use of a Mobile Laser Scanning System for Mapping Large Forest Plots

Terrestrial laser scanning (TLS) has been demonstrated to be an efficient measurement method in plot-level forest inventories. A permanent sample plot in national forest inventories is typically a small area of forest with a radius of approximately 10 m. In practice, whether reference data can be automatically and accurately collected for larger plot sizes is of great interest. It is expensive to collect references in large areas utilizing conventional measurement tools. The application of static TLS is a possible choice but is very challenging due to its lack of mobility. In this letter, a mobile laser scanning (MLS) system was tested and its implications for forest inventories were discussed. The system is composed of a high performance laser scanner, a navigation unit, and a six-wheeled all-terrain vehicle. In this experiment, about 0.4 ha forest area was mapped utilizing the MLS system. The stem mapping accuracy was 87.5%; the root mean square errors of the estimations of the diameter at breast height and the location were 2.36 cm and 0.28 m, respectively. These results indicate that the MLS system has the potential to accurately map large forest plots and further research on mapping accuracy and cost-benefit analyses is needed.