Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of P2X-mediated calcium signaling in microglia

Microglia function is orchestrated through highly-coupled signaling pathways that depend on calcium (Ca2+). In response to extracellular adenosine triphosphate (ATP), transient increases in intracellular Ca2+ driven through the activation of purinergic receptors, P2X and P2Y, are sufficient to promote cytokine synthesis and potentially their release. While steps comprising the pathways bridging purinergic receptor activation with transcriptional responses have been probed in great detail, a quantitative model for how these steps collectively control cytokine production has not been established. Here we developed a minimal computational model that quantitatively links extracellular stimulation of two prominent ionotropic puriner-gic receptors, P2X4 and P2X7, with the graded production of a gene product, namely the tumor necrosis factor α (TNFα) cytokine. In addition to Ca2+ handling mechanisms common to eukaryotic cells, our model includes microglia-specific processes including ATP-dependent P2X4 and P2X7 activation, activation of NFAT transcription factors, and TNFα production. Parameters for this model were optimized to reproduce published data for these processes, where available. With this model, we determined the propensity for TNFα production in microglia, subject to a wide range of ATP exposure amplitudes, frequencies and durations that the cells could encounter in vivo. Furthermore, we have investigated the extent to which modulation of the signal transduction pathways influence TNFα production. Our key findings are that TNFα production via P2X4 is maximized at low ATP when subject to high frequency ATP stimulation, whereas P2X7 contributes most significantly at millimolar ATPranges. Given that Ca2+ homeostasis in microglia is profoundly important to its function, this computational model provides a quantitative framework to explore hypotheses pertaining to microglial physiology.
Iodine supplementation activates folliculogenesis in rabbit ovary.

The aim of this study was to assess the biological effect of the product Jodis Concentrate (JC) on the rabbit ovaries by evaluating the folliculogenesis and expression of oocyte-specific growth differentiation factor 9 (GDF9). The experiment was conducted with 30 female two month old New Zealand rabbits that were the F1 offspring born to mothers differently treated with Jodis concentrate. The control group (n=10), consisted of F1 offspring born to mothers without iodine treatment, and was not supple- mented with JC. The first experimental group (n=10), consisted of F1 offspring born to mothers treated with JC during pregnancy and the suckling period, and was supplemented with JC daily at a dose of 2 ml/L drinking. The second experimental group (n=10), consisted of F1 offspring born to mothers without iodine treatment, and was also supplemented daily with the same dose of JC - 2 ml/L drinking. All groups were fed with total mixed ration for growing rabbits. The trial lasted 48 days. The ovaries were weighed and prepared for histological examination. The GDF9 protein expression in the ovary was determined by immunohistochemical analysis. The addition of JC to the drinking water of female rabbits led to more active development of the ovarian follicles from primordial to tertiary stage in both experimental groups. More intensive GDF9 protein expression in the oocytes and cumulus cells of rabbits, supplemented with JC was observed.
Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

In cloud radio access networks (C-RAN), more accurate prediction of the number of virtual machines (VMs) one server can support would improve network capacity and energy efficiency (EE). In this paper, the problem of allocating an optimal number of VMs to the cloud server is introduced. Monte Carlo-based evolutionary algorithm [particle swarm optimization (PSO), quantum PSO, or genetic algorithm] are used to find the suboptimal number of VMs that optimizes the EE of C-RAN. To enable such evaluation, a power model is proposed to evaluate the power consumption of each unit within a virtualized server. This evaluation occurs under the circumstances of increased number of hosted VMs, and processed resource blocks (RBs) at each VM. Moreover, power allocation methods are proposed to transmit the power from base band unit pool to the remote radio heads (RRHs), and from RRHs to the users (UEs). This allocation is based on the combination of one or more of RRH distance, RRH channel gain, UE distance, UE channel gain, and UE path loss. The EE problem was constrained to crucial quality of service indicators, including minimum UE data rate, number of allocated RBs, and latency imposed due to virtualization.
Handwritten Digits Recognition Based on Deep Learning4j

Over the past few decades, Optical Character Recognition (OCR), particularly handwriting recognition, has received much attention. Handwritten Digits Recognition (HDR) means, receive and comprehend handwriting inputs from different sources for example pictures, touch screens, paper documents, and other devices. The field of HDR has witnessed rapid progress owing to the concurrent availability of cheap and well-assembled computers, advancements in learning algorithms, and availability of large databases. In recent years, HDR has received much attention due to ambiguity in learning methods. The aim of the current study was to explore the potential of Deeplearnig4j (DL4J) framework for HDR. DL4J offers the most appropriate framework for the identification of handwritten digits. To execute the task of HDR, Convolutional Neural Network (CNN) is implemented. This study measures the strength and productivity of DL4J for the aforementioned tasks of recognition and attempts to upgrade the procedure. Results obtained shows significant improvement in the recognition rates of hand-typed digits. Though the accuracy and error rates obtained through our proposed system (CNN-DL4J) show variations, on average the accuracy rate remained at 97 %. The aim of the proposed endeavor was to make the path towards digitalization clearer. Though the purpose was only to identify the digits, we can extend it to deal with digits having different sizes, different languages (Urdu, Arabic, Persian), letters, and the task of detecting multi-digit person's handwriting. Hence, it could reduce the typing need to an extent that people will be able to convert their handwritten materials into digital form in one click on its picture. Altogether, this investigation combines CNN with the DL4J framework and took MNIST as a standard dataset to accomplish the task of digit recognition. In addition, the test framework can be assessed in the future for the prospects of image classification and such other pattern recognition tasks.
Recommending Users: Whom to Follow on Federated Social Networks

To foster an active and engaged community, social networks employ recommendation algorithms that filter large amounts of contents and provide a user with personalized views of the network. Popular social networks such as Facebook and Twitter generate follow recommendations by listing profiles a user may be interested to connect with. Federated social networks aim to resolve issues associated with the popular social networks - such as large-scale user-surveillance and the miss-use of user data to manipulate elections - by decentralizing authority and promoting privacy. Due to their recent emergence, recommender systems do not exist for federated social networks, yet. To make these networks more attractive and promote community building, we investigate how recommendation algorithms can be applied to decentralized social networks. We present an offline and online evaluation of two recommendation strategies: a collaborative filtering recommender based on BM25 and a topology-based recommender using personalized PageRank. Our experiments on a large unbiased sample of the federated social network Mastodon shows that collaborative filtering approaches outperform a topology-based approach, whereas both approaches significantly outperform a random recommender. A subsequent live user experiment on Mastodon using balanced interleaving shows that the collaborative filtering recommender performs on par with the topology-based recommender.
libmpk: Software Abstraction for Intel Memory Protection Keys (Intel MPK)

Intel Memory Protection Keys (MPK) is a new hardware primitive to support thread-local permission control on groups of pages without requiring modification of page tables. Unfortunately, its current hardware implementation and software support suffer from security, scalability, and semantic problems: (1) vulnerable to protection-key-use-after-free; (2) providing the limited number of protection keys; and (3) incompatible with mprotect()’s process-based permission model. In this paper, we propose libmpk, a software abstraction for MPK. It virtualizes the hardware protection keys to eliminate the protection-key-use-after-free problem while providing accesses to an unlimited number of virtualized keys. To support legacy applications, it also provides a lazy inter-thread key synchronization. To enhance the security of MPK itself, libmpk restricts unauthorized writes to its metadata. We apply libmpk to three real-world applications: OpenSSL, JavaScript JIT compiler, and Memcached for memory protection and isolation. Our evaluation shows that it introduces negligible performance overhead (
Integrating Sentiment Analysis and Topic Detection in Financial News for Stock Movement Prediction

Media-expressed information in financial news are critical for stock market prediction. Nevertheless, researchers have primarily focused on the role of sentiment analysis in predicting stock returns and volatility. Here we show that topics discussed in the financial news may carry additional important information. We use a combination of sentiment analysis (using finance-specific dictionary-based approach) and topic detection (using latent dirichlet allocation) to predict one-day-ahead stock movements of major US companies. The proposed system employs a deep neural network to model complex stock market relations. We demonstrate the effectiveness of this approach compared to baselines, such as support vector machines and sentiment- and topic-based models used separately.
High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures

We tackle the problem of protein secondary structure prediction using a common task framework. This lead to the introduction of multiple ideas for neural architectures based on state of the art building blocks, used in this task for the first time. We take a principled machine learning approach, which provides genuine, unbiased performance measures, correcting longstanding errors in the application domain. We focus on the Q8 resolution of secondary structure, an active area for continuously improving methods. We use an ensemble of strong predictors to achieve accuracy of 70.7% (on the CB513 test set using the CB6133filtered training set). These results are statistically indistinguishable from those of the top existing predictors. In the spirit of reproducible research we make our data, models and code available, aiming to set a gold standard for purity of training and testing sets. Such good practices lower entry barriers to this domain and facilitate reproducible, extendable research.
Selective BTK inhibition improves bendamustine therapy response and normalizes immune effector functions in chronic lymphocytic leukemia

The Bruton's tyrosine kinase (BTK) inhibitor ibrutinib has been shown to be highly effective in patients with chronic lymphocytic leukemia (CLL) and is approved for CLL treatment. Unfortunately, resistance and intolerance to ibrutinib has been observed in several studies, opening the door for more specific BTK inhibitors. CC‐292 (spebrutinib) is a BTK inhibitor with increased specificity for BTK and less inhibition of other kinases. Our in vitro studies showed that CC‐292 potently inhibited B‐cell receptor signaling, activation, proliferation and chemotaxis of CLL cells. In in vivo studies using the adoptive transfer TCL1 mouse model of CLL, CC‐292 reduced tumor load and normalized tumor‐associated expansion of T cells and monocytes, while not affecting T cell function. Importantly, the combination of CC‐292 and bendamustine impaired CLL cell proliferation in vivo and enhanced the control of CLL progression. Our results demonstrate that CC‐292 is a specific BTK inhibitor with promising performance in combination with bendamustine in CLL. Further clinical trials are warranted to investigate the therapeutic efficacy of this combination regimen.
Open-vocabulary Phrase Detection

Most existing work that grounds natural language phrases in images starts with the assumption that the phrase in question is relevant to the image. In this paper we address a more realistic version of the natural language grounding task where we must both identify whether the phrase is relevant to an image and localize the phrase. This can also be viewed as a generalization of object detection to an open-ended vocabulary, essentially introducing elements of few- and zero-shot detection. We propose a Phrase R-CNN network for this task that extends Faster R-CNN to relate image regions and phrases. By carefully initializing the classification layers of our network using canonical correlation analysis (CCA), we encourage a solution that is more discerning when reasoning between similar phrases, resulting in over double the performance compared to a naive adaptation on two popular phrase grounding datasets, Flickr30K Entities and ReferIt Game, with test-time phrase vocabulary sizes of 5K and 39K, respectively.
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization

Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization. However, learning complicated image representations requires compute-intense models parametrized by a huge number of weights, which in turn requires large datasets to make learning successful. Non-parametric exemplar-based generation is a technique that works well to reproduce style from small datasets, but is also compute-intensive. These aspects are a drawback for the practice of digital AI artists: typically one wants to use a small set of stylization images, and needs a fast flexible model in order to experiment with it. With this motivation, our work has these contributions: (i) a novel stylization method called Fully Adversarial Mosaics (FAMOS) that combines the strengths of both parametric and non-parametric approaches; (ii) multiple ablations and image examples that analyze the method and show its capabilities; (iii) source code that will empower artists and machine learning researchers to use and modify FAMOS.
Fibrous dysplasia of the middle nasal turbinate: imaging and clinical significance

Two cases of fibrous dysplasia involving the middle nasal turbinate are presented. Fibrous dysplasia is a common benign fibro-osseous disease involving the flat bones, often affecting the bony structures of the skull and facial skeleton. Primary occurrence or secondary involvement of the nasal turbinate is not a common manifestation of the disease. Involvement of the inferior turbinate generally does not have specific management-related issues; however, involvement of the middle turbinate, especially the lateral lamella, can predispose to surgical morbidity during endoscopic surgical management. Clinical presentation, management and features of the disease on CT imaging are presented.
Indoor Air Quality and Potential Health Risk Impacts of Exposure to Antibiotic Resistant Bacteria in an Office Rooms in Southern Poland

The aims of this article are to characterize: the quantity of culturable bacterial aerosol (QCBA) and the quality of culturable bacterial aerosol (QlCBA) in an office building in Southern Poland during the spring. The average concentration of culturable bacterial aerosol (CCBA) in this building ranged from 424 CFU m−3 to 821 CFU m−3, below Polish proposals for threshold limit values. Size distributions were unimodal, with a peak of particle bacterial aerodynamic diameters less than 3.3 μm, increasing potentially adverse health effects due to their inhalation. The spring office exposure dose (SPED) of bacterial aerosol was estimated. The highest value of SPED was in April (218 CFU kg−1), whereas the lowest was in June (113 CFU kg−1). Analysis was undertaken to determine the antibiotic resistance of isolated strains and their ability to form biofilms, which may facilitate the spread of antibiotic resistance genes. In the course of the study, it was found that Staphylococcus xylosus had the greatest ability to form biofilms, while the strains with the highest antibiotic resistance were Micrococcus luteus D and Macrococcus equipercicus. Given that mainly antibiotic-sensitive bacteria from bioaerosol were isolated, which transfers resistance genes to their plasmids, this shows the need for increased monitoring of indoor air quality in workplaces.
Learning Face Deblurring Fast and Wide

Portrait images and photos containing faces are ubiquitous on the web and the predominant subject of images shared via social media. Especially selfie images taken with lightweight smartphone cameras are susceptible to camera shake. Despite significant progress in the field of image deblurring over the last decade, the performance of state-of-the-art deblurring methods on blurry face images is still limited. In this work, we present a novel deep learning architecture that is designed to be computationally fast and exploits a very wide receptive field to return sharp face images even in challenging scenarios. Our network features an effective resampling convolution operation that ensures a wide receptive field from the very first layers, while at the same time being highly computationally efficient. We also show that batch normalization prevents networks from yielding high-quality image results and introduce instance normalization instead. We demonstrate our architecture on face deblurring as well as other more general scenes. Extensive experiments with state-of-the-art methods demonstrate the effectiveness of our proposed network, in terms of run-time, accuracy, and robustness to ISO levels as well as gamma correction.
Molecular Factors of Hypochlorite Tolerance in the Hypersaline Archaeon Haloferax volcanii

Halophilic archaea thrive in hypersaline conditions associated with desiccation, ultraviolet (UV) irradiation and redox active compounds, and thus are naturally tolerant to a variety of stresses. Here, we identified mutations that promote enhanced tolerance of halophilic archaea to redox-active compounds using Haloferax volcanii as a model organism. The strains were isolated from a library of random transposon mutants for growth on high doses of sodium hypochlorite (NaOCl), an agent that forms hypochlorous acid (HOCl) and other redox acid compounds common to aqueous environments of high concentrations of chloride. The transposon insertion site in each of twenty isolated clones was mapped using the following: (i) inverse nested two-step PCR (INT-PCR) and (ii) semi-random two-step PCR (ST-PCR). Genes that were found to be disrupted in hypertolerant strains were associated with lysine deacetylation, proteasomes, transporters, polyamine biosynthesis, electron transfer, and other cellular processes. Further analysis revealed a ΔpsmA1 (α1) markerless deletion strain that produces only the α2 and β proteins of 20S proteasomes was hypertolerant to hypochlorite stress compared with wild type, which produces α1, α2, and β proteins. The results of this study provide new insights into archaeal tolerance of redox active compounds such as hypochlorite.
Functional cardiac orexin receptors: role of orexin-B/orexin 2 receptor in myocardial protection

Orexins/hypocretins exert cardiovascular effects which are centrally mediated. In the present study, we tested whether orexins and their receptors may also act in an autocrine/paracrine manner in the heart exerting direct effects. Quantitative reverse transcription-PCR (RT-PCR), immunohistochemical and Western blot analyses revealed that the rat heart expresses orexins and orexin receptors (OXR). In isolated rat cardiomyocytes, only orexin-B (OR-B) caused an increase in contractile shortening, independent of diastolic or systolic calcium levels. A specific orexin receptor-2 (OX2R) agonist ([Ala11, d-Leu15]-Orexin B) exerted similar effects as OR-B, whereas a specific orexin receptor-1 (OX1R) antagonist (SB-408124) did not alter the responsiveness of OR-B. Treatment of the same model with OR-B resulted in a dose-dependent increase in myosin light chain and troponin-I (TnI) phosphorylation. Following ischaemia/reperfusion in the isolated Langendorff perfused rat heart model, OR-B, but not OR-A, exerts a cardioprotective effect; mirrored in an in vivo model as well. Unlike OR-A, OR-B was also able to induce extracellular signal-regulated kinase (ERK) 1/2 (ERK1/2) and Akt phosphorylation in rat myocardial tissue and ERK1/2 phosphorylation in human heart samples. These findings were further corroborated in an in vivo rat model. In human subjects with heart failure, there is a significant negative correlation between the expression of OX2R and the severity of the disease clinical symptoms, as assessed by the New York Heart Association (NYHA) functional classification. Collectively, we provide evidence of a distinct orexin system in the heart that exerts a cardioprotective role via an OR-B/OX2R pathway.
Human Activity Recognition Based on Symbolic Representation Algorithms for Inertial Sensors

Mobile sensing has allowed the emergence of a variety of solutions related to the monitoring and recognition of human activities (HAR). Such solutions have been implemented in smartphones for the purpose of better understanding human behavior. However, such solutions still suffer from the limitations of the computing resources found on smartphones. In this sense, the HAR area has focused on the development of solutions of low computational cost. In general, the strategies used in the solutions are based on shallow and deep learning algorithms. The problem is that not all of these strategies are feasible for implementation in smartphones due to the high computational cost required, mainly, by the steps of data preparation and the training of classification models. In this context, this article evaluates a new set of alternative strategies based on Symbolic Aggregate Approximation (SAX) and Symbolic Fourier Approximation (SFA) algorithms with the purpose of developing solutions with low computational cost in terms of memory and processing. In addition, this article also evaluates some classification algorithms adapted to manipulate symbolic data, such as SAX-VSM, BOSS, BOSS-VS and WEASEL. Experiments were performed on the UCI-HAR, SHOAIB and WISDM databases commonly used in the literature to validate HAR solutions based on smartphones. The results show that the symbolic representation algorithms are faster in the feature extraction phase, on average, by 84.81%, and reduce the consumption of memory space, on average, by 94.48%, and they have accuracy rates equivalent to conventional algorithms.