Miracle-dz/UniFAR-FTUs huggingface dataset:
Leishmania donovani: Immunostimulatory Cellular Responses of Membrane and Soluble Protein Fractions of Splenic Amastigotes in Cured Patient and Hamsters

Visceral leishmaniasis (VL), caused by the intracellular parasite Leishmania donovani, L. chagasi and L. infantum is characterized by defective cell-mediated immunity (CMI) and is usually fatal if not treated properly. An estimated 350 million people worldwide are at risk of acquiring infection with Leishmania parasites with approximately 500,000 cases of VL being reported each year. In the absence of an efficient and cost-effective antileishmanial drug, development of an appropriate long-lasting vaccine against VL is the need of the day. In VL, the development of a CMI, capable of mounting Th1-type of immune responses, play an important role as it correlate with recovery from and resistance to disease. Resolution of infection results in lifelong immunity against the disease which indicates towards the feasibility of a vaccine against the disease. Most of the vaccination studies in Leishmaniasis have been focused on promastigote- an infective stage of parasite with less exploration of pathogenic amastigote form, due to the cumbersome process of its purified isolation. In the present study, we have isolated and purified splenic amastigotes of L. donovani, following the traditional protocol with slight modification. These were fractionated into five membranous and soluble subfractions each i.e MAF1-5 and SAF1-5 and were subjected for evaluation of their ability to induce cellular responses. Out of five sub-fractions from each of membrane and soluble, only four viz. MAF2, MAF3, SAF2 and SAF3 were observed to stimulate remarkable lymphoproliferative, IFN-γ, IL-12 responses and Nitric Oxide production, in Leishmania-infected cured/exposed patients and hamsters. Results suggest the presence of Th-1 type immunostimulatory molecules in these sub-fractions which may further be exploited for developing a successful subunit vaccine from the less explored pathogenic stage against VL.
Processing Spatial Relations: A Meta-Analysis

The ability to reason about relations is relevant for many spa- tial cognitive processes. This can involve: (i) to represent spatial information mentally, (ii) to manipulate the spatial repre- sentation, and (iii) to infer new spatial information. Several cognitive theories make assumptions and predictions about the underlying processes. A detailed and systematic overview and analysis of ireliable effects across studies is missing. This ar- ticle presents a meta-analysis of 35 studies about spatial relational reasoning. Studies were classiﬁed according to different factors including the ambiguity of the spatial description , i.e., if it the description allows for more than one representation, the presentation of information , i.e., if the information has been presented auditorily or in a written form, and the task , i.e., if a conclusion or model of the premises needs to be generated or veriﬁed. Implications of the ﬁndings for the mental model theory and working memory are discussed.
Ambiguity and Overconfidence

There are two phenomena in behavioral finance and economics which are seemingly unrelated and have been studied separately; overconfidence and ambiguity aversion. In this paper we are trying to link these two phenomena providing a theoretical foundation supported by evidence from an experimental study. We derive a model, based on the max-min ambiguity framework that links overconfidence to ambiguity aversion. In the experimental study we find that overconfidence is decreasing in ambiguity, as predicted by our model.
Training Group Orthogonal Neural Networks with Privileged Information

Learning rich and diverse representations is critical for the performance of deep convolutional neural networks (CNNs). In this paper, we consider how to use privileged information to promote inherent diversity of a single CNN model such that the model can learn better representations and offer stronger generalization ability. To this end, we propose a novel group orthogonal convolutional neural network (GoCNN) that learns untangled representations within each layer by exploiting provided privileged information and enhances representation diversity effectively. We take image classification as an example where image segmentation annotations are used as privileged information during the training process. Experiments on two benchmark datasets -- ImageNet and PASCAL VOC -- clearly demonstrate the strong generalization ability of our proposed GoCNN model. On the ImageNet dataset, GoCNN improves the performance of state-of-the-art ResNet-152 model by absolute value of 1.2% while only uses privileged information of 10% of the training images, confirming effectiveness of GoCNN on utilizing available privileged knowledge to train better CNNs.
Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation

Prior art search or recommending citations for a patent application is a challenging task. Many approaches have been proposed and shown to be useful for prior art search. However, most of these methods do not consider the network structure for integrating and diffusion of different kinds of information present among tied patents in the citation network. In this paper, we propose a method based on a time-aware random walk on a weighted network of patent citations, the weights of which are characterized by contextual similarity relations between two nodes on the network. The goal of the random walker is to find influential documents in the citation network of a query patent, which can serve as candidates for drawing query terms and bigrams for query refinement. The experimental results on CLEF-IP datasets (CLEF-IP 2010 and CLEF-IP 2011) show the effectiveness of encoding contextual similarities (common classification codes, common inventor, and common applicant) between nodes in the citation network. Our proposed approach can achieve significantly better results in terms of recall and Mean Average Precision rates compared to strong baselines of prior art search.
Hybrid LISA Precoding for Multiuser Millimeter-Wave Communications

Millimeter-wave (mm-wave) communications plays an important role in future cellular networks because of the vast amount of spectrum available in the underutilized mm-wave frequency bands. To overcome the huge free space omnidirectional path loss in those frequency bands, the deployment of a very large number of antenna elements at the base station is crucial. The complexity, power consumption, and costs resulting from the large number of antenna elements can be reduced by limiting the number of RF chains. This leads to hybrid precoding and combining, which, in contrast to the traditional fully digital precoding and combining, moves a part of the signal processing from the digital to the analog domain. This paper proposes new algorithms for the design of hybrid precoders and combiners in a multiuser scenario. The algorithms are based on the previously proposed linear successive allocation method developed for the traditional fully digital version. It successively allocates data streams to users and suppresses the respective interstream interference in two stages, which perfectly matches the hybrid architecture. Furthermore, a low-complexity version is developed by exploiting the typical structure of mm-wave channels. The good performance of the proposed method and its low-complexity version is demonstrated by simulation results.
Brief Definitive Report

Medullary thymic epithelial cells (mTECs) contribute to self-tolerance through the ectopic expression of tissue-specific antigens (TSAs) in the the recognition of TSAs, developing autoreactive T cells are either negatively selected from the pool of developing thymocytes or recruited into the regulatory T (T reg) cell lineage (Liston et al.
Lack of Indinavir Effects on Methadone Disposition Despite Inhibition of Hepatic and Intestinal Cytochrome P4503A (CYP3A)

Background: Methadone disposition and pharmacodynamics are highly susceptible to interactions with antiretroviral drugs. Methadone clearance and drug interactions have been attributed to cytochrome P4503A4 (CYP3A4), but actual mechanisms are unknown. Drug interactions can be clinically and mechanistically informative. This investigation assessed effects of the protease inhibitor indinavir on methadone pharmacokinetics and pharmacodynamics, hepatic and intestinal CYP3A4/5 activity (using alfentanil), and intestinal transporter activity (using fexofenadine). Methods: Twelve healthy volunteers underwent a sequential crossover. On three consecutive days they received oral alfentanil plus fexofenadine, intravenous alfentanil, and intravenous plus oral (deuterium-labeled) methadone. This was repeated after 2 weeks of indinavir. Plasma and urine analytes were measured by mass spectrometry. Opioid effects were measured by miosis. Results: Indinavir significantly inhibited hepatic and first-pass CYP3A activity. Intravenous alfentanil systemic clearance and hepatic extraction were reduced to 40–50% of control, apparent oral clearance to 30% of control, and intestinal extraction decreased by half, indicating 50% and 70% inhibition of hepatic and first-pass CYP3A activity. Indinavir increased fexofenadine area under the plasma concentration-time curve 3-fold, suggesting significant P-glycoprotein inhibition. Indinavir had no significant effects on methadone plasma concentrations, methadone N-demethylation, systemic or apparent oral clearance, renal clearance, hepatic extraction or clearance, or bioavailability. Methadone plasma concentration-effect relationships were unaffected by indinavir. Conclusions: Despite significant inhibition of hepatic and intestinal CYP3A activity, indinavir had no effect on methadone N-demethylation and clearance, suggesting little or no role for CYP3A in clinical disposition of single-dose methadone. Inhibition of gastrointestinal transporter activity had no influence of methadone bioavailability.
Tourists' acceptance of advanced technology-based innovations for promoting arts and culture

Advanced technologies are able to influence tourists' decisions. They can be exploited for attracting touristic flows towards cultural destinations. In particular, virtual tours are new tools based on advanced 3D graphics that can be useful for promoting arts and culture. In this paper, we test users' acceptance of virtual tours as a tool supporting their decision to visit a touristic destination. A new virtual tour has been developed for promoting a site with a high archaeological value in the south of Italy. Acceptance of this system has been tested on a sample of 100 interviewees. Data were analysed using structural equation modelling (SEM). All the hypotheses were supported indicating the robustness of TAM model for explaining the intention to use virtual tours while deciding on touristic destinations. Results also showed the importance of enjoyment while using the tool to explain acceptance.
Reasoning about Entailment with Neural Attention

While most approaches to automatically recognizing entailment relations have used classifiers employing hand engineered features derived from complex natural language processing pipelines, in practice their performance has been only slightly better than bag-of-word pair classifiers using only lexical similarity. The only attempt so far to build an end-to-end differentiable neural network for entailment failed to outperform such a simple similarity classifier. In this paper, we propose a neural model that reads two sentences to determine entailment using long short-term memory units. We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases. Furthermore, we present a qualitative analysis of attention weights produced by this model, demonstrating such reasoning capabilities. On a large entailment dataset this model outperforms the previous best neural model and a classifier with engineered features by a substantial margin. It is the first generic end-to-end differentiable system that achieves state-of-the-art accuracy on a textual entailment dataset.
Phosphorus MR spectroscopy shows a tissue specific in vivo distribution of biochemical expression of the G3460A mutation in Leber's hereditary optic neuropathy

Occipital lobe and calf muscle energy metabolism were studied in vivo by magnetic resonance spectroscopy (31P-MRS) in four members of a family harbouring the mitochondrial DNA G3460A mutation causing Leber's hereditary optic neuropathy (LHON). Three siblings carried 100% mutated mitochondrial DNA (homoplasmy), while their mother had coexistence of mutated and wild-type mitochondrial DNA (heteroplasmy). Indices of brain energy metabolism on 31P-MRS were abnormal in all subjects examined, but the muscle oxidative phosphorylation rate was normal. These findings indicate a tissue specific distribution of the biochemical expression of the G3460A LHON mutation and suggest that extramitochondrial factors, such as nuclear genes, may influence expression of this mutation in vivo.
Plasma levels of advanced glycation end products during haemodialysis, haemodiafiltration and haemofiltration: potential importance of dialysate quality.

BACKGROUND Advanced glycation end products (AGEs) accumulate in patients with end-stage renal disease (ESRD). The aim of this study was to investigate the potential influence of different modalities of renal replacement therapies on plasma AGE levels. METHODS The removal of AGEs by high-flux haemodialysis (HD) using standard and ultrapure dialysis fluid (SDF and UDF), by haemodiafiltration (HDF) and by haemofiltration (HF) was studied by fluorescence spectroscopy and by a carboxymethyllysine (CML)-specific ELISA. In addition, molecular weight distribution of fluorescent AGE products in serum of several patients was analysed by gel filtration. RESULTS The highest AGE-typical fluorescence was found in the serum of patients on HD using SDF (114,667+/-18,967 arbitrary units (AU)), followed by patients on HDF (86,912+/-24,411 AU, P<0.005), by patients on HD using UDF (74,953+/-21,152 AU, P<0.0001) and by patients on HF (74 039+/-17 027 AU, P<0.0001). Similar results were found for serum CML levels with the highest values in HD patients on SDF (1609+/-504 ng/ml), followed by patients on HF (1354+/-614 ng/ml, P<0.001), then by HD patients on UDF (1310+/-403 ng/ml, P<0.001) and by patients on HDF (1132+/-338 ng/ml, P<0.001). The removal rate of AGEs, as evaluated by the determination of the pre-/post-dialysis AGE differences, was comparable across all groups. CONCLUSION These findings suggest that factors other than removal are responsible for the lower pre-dialysis AGE levels found in patients on convective dialysis as well as on HD with UDF. A role of water quality is assumed. This is corroborated by the finding that the high molecular weight AGE-fraction is preferentially lowered in comparison with patients on HD with SDF, as analysed by gel filtration chromatography. These findings could be best explained by a less severe oxidative stress (i.e. resulting in decreased AGE generation) with HF and HDF, as well as with ultrapure HD.
Glioma Survival Prediction with Combined Analysis of In Vivo 11C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning

Gliomas are the most common type of tumor in the brain. Although the definite diagnosis is routinely made ex vivo by histopathologic and molecular examination, diagnostic work-up of patients with suspected glioma is mainly done using MRI. Nevertheless, l-S-methyl-11C-methionine (11C-MET) PET holds great potential in the characterization of gliomas. The aim of this study was to establish machine-learning–driven survival models for glioma built on in vivo 11C-MET PET characteristics, ex vivo characteristics, and patient characteristics. Methods: The study included 70 patients with a treatment-naïve glioma that was 11C-MET–positive and had histopathology-derived ex vivo feature extraction, such as World Health Organization 2007 tumor grade, histology, and isocitrate dehydrogenase 1 R132H mutational status. The 11C-MET–positive primary tumors were delineated semiautomatically on PET images, followed by the extraction of tumor-to-background–based general and higher-order textural features by applying 5 different binning approaches. In vivo and ex vivo features, as well as patient characteristics (age, weight, height, body mass index, Karnofsky score), were merged to characterize the tumors. Machine-learning approaches were used to identify relevant in vivo, ex vivo, and patient features and their relative weights for predicting 36-mo survival. The resulting feature weights were used to establish 3 predictive models per binning configuration: one model based on a combination of in vivo, ex vivo, and clinical patient information (M36IEP); another based on in vivo and patient information only (M36IP); and a third based on in vivo information only (M36I). In addition, a binning-independent model based on ex vivo and patient information only (M36EP) was created. The established models were validated in a Monte Carlo cross-validation scheme. Results: The most prominent machine-learning–selected and –weighted features were patient-based and ex vivo–based, followed by in vivo–based. The highest areas under the curve for our models as revealed by the Monte Carlo cross-validation were 0.9 for M36IEP, 0.87 for M36EP, 0.77 for M36IP, and 0.72 for M36I. Conclusion: Prediction of survival in amino acid PET–positive glioma patients was highly accurate using computer-supported predictive models based on in vivo, ex vivo, and patient features.
Markov modeling of energy harvesting Body Sensor Networks

The emerging paradigm of wearable and implantable medical sensors has enabled continuous and unobtrusive monitoring for patients and human subjects, allowing them to continue their normal activities, and yet be assured of immediate response in case of a detected health emergency. Energy harvesting has been proposed as a viable scheme for powering such sensors as periodic retrievals for battery replacements may not be feasible. The current state of the art in energy harvesting allows tapping into several physical and naturally existing sources, such as solar, wind, vibration, RF scavenging, among others. However, there is a lack of theoretical models that can predict future consumption and residual availability of energy in a sensor node equipped with multiple boards that can simultaneously operate on different types of sources. In this paper, we propose MAKERS, a Markov model based method to capture the energy states of such sensors. MAKERS allows detailed prediction of the probability of a node failing to detect an event owing to lack of energy, which is a key design consideration for body sensor sensors.
An adaptive method for combined covariance estimation and classification

In this paper, a family of adaptive covariance estimators is proposed to mitigate the problem of limited training samples for application to hyperspectral data analysis in quadratic maximum likelihood classification. These estimators are the combination of adaptive classification procedures and regularized covariance estimators. In these proposed estimators, the semi-labeled samples (whose labels are determined by a decision rule) are incorporated in the process of determining the optimal regularized parameters and estimating those supportive covariance matrices that formulate final regularized covariance estimators. In all experiments with simulated and real remote sensing data, these proposed combined covariance estimators achieved significant improvement on statistics estimation and classification accuracy over conventional regularized covariance estimators and an adaptive maximum likelihood classifier (MLC). The degree of improvement increases with dimensions, especially for ill-posed or very ill-posed problems where the total number of training samples is smaller than the number of dimensions.
Study of integration of statistical model-based voice activity detection and noise suppression

This paper addresses robust front-end processing for automatic speech recognition (ASR) in noisy environments. To recognize the corrupted speech accurately, it is necessary to employ robust methods against various types of interference. Usually, noise suppression (NS) is used for the front-end processing of ASR in noise. Voice activity detection (VAD) is also used for front-end processing to reduce the redundant non-speech period. VAD and NS are typically combined as series processing. However, VAD and NS should not be assumed to be a separate technique, because the output information of these methods be mutually beneﬁcial. Thus, we investigate the integrated front-end processing of VAD and NS, which can utilize each others’ input-output information. The evaluation is carried out by using a concatenated speech corpus, CENSREC-1-C. In the evaluation, the proposed method improves ASR accuracy compared with conventional series combination.
Simultaneous Electromyography and Functional Magnetic Resonance Imaging of Skeletal Muscle

Two commonly used diagnostic techniques for examining muscle function in vivo are functional Magnetic Resonance Imaging (fMRI) and Electromyography (EMG). EMG allows for examination of the functional, electrical activity of muscle during force production. Comparatively, fMRI or more specifically Blood oxygen level dependant (BOLD) imaging can be applied to visualize muscle activation and recovery post-exercise. It is a combination of oxygenation, metabolism, blood flow and blood volume. The proposed method combines both techniques in simultaneous data acquisition to provide greater muscle physiological information during exercise. Additionally, both techniques are non-invasive making repeated measurements feasible. EMG hardware filtering was designed and constructed to facilitate EMG measurements alongside Magnetic Resonance Imaging (MRI) scans during simultaneous acquisition. Next, a complex artifact subtraction method called fMRI artifact slice template removal (FASTR) was implemented. With custom scripts and small adaptations to FASTR, it was modified for use with EMG/fMRI, specifically, with a Echo Planar Imaging (EPI) BOLD sequence. Several experiments were then performed to test it’s capabilities improving the SNR of the EMG data from 2.8 to 46 in one case. After EMG hardware and software were developed and implemented, a simple exercise protocol was developed to investigate changes in concurrent BOLD/EMG,
Syneresis and texture stability of hydrogel complexes containing konjac flour over multiple freezethaw cycles

Hydrogel complexes formed by a 1% mixture of konjac flour and secondary gums (kcarrageenan or xanthan) at different ratios between 70:30 and 30:70 were investigated for syneresis and texture stability under multiple freezethaw cycles. Increasing of freezethaw cycles caused the syneresis generated by all konjac/kcarrageenan gels significantly increased (p<0.05) while their peak force values decreased (p<0.05). As compared with the gels freshly prepared, significant differences (p<0.05) in syneresis and peak force values were found in all konjac/kcarrageenan gels after the first freezethaw cycle, in exception of the konjac/kcarrageenan (50:50) gel that showed the most stable gel texture until the end of the second cycle. Konjac/xanthan gels ranged from 70:30 to 50:50 demonstrated increasing syneresis but decreasing peak force values with increasing freezethaw cycles. Whilst, no syneresis were found (p>0.05) in the gels formed by konjac/xanthan blends at 40:60 and 30:70, which also maintained their gel texture over the fourth freezethaw cycle.