Miracle-dz/UniFAR-FTUs huggingface dataset:
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN

In this paper, we propose a novel regularization method for Generative Adversarial Networks, which allows the model to learn discriminative yet compact binary representations of image patches (image descriptors). We employ the dimensionality reduction that takes place in the intermediate layers of the discriminator network and train binarized low-dimensional representation of the penultimate layer to mimic the distribution of the higher-dimensional preceding layers. To achieve this, we introduce two loss terms that aim at: (i) reducing the correlation between the dimensions of the binarized low-dimensional representation of the penultimate layer i. e. maximizing joint entropy) and (ii) propagating the relations between the dimensions in the high-dimensional space to the low-dimensional space. We evaluate the resulting binary image descriptors on two challenging applications, image matching and retrieval, and achieve state-of-the-art results.
Show, Attend, and Translate: Unsupervised Image Translation With Self-Regularization and Attention

Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain. It has been applied to numerous applications, such as data augmentation, domain adaptation, and unsupervised training. When paired training data is not accessible, image translation becomes an ill-posed problem. We constrain the problem with the assumption that the translated image needs to be perceptually similar to the original image and also appears to be drawn from the new domain, and propose a simple yet effective image translation model consisting of a single generator trained with a self-regularization term and an adversarial term. We further notice that the existing image translation techniques are agnostic to the subjects of interest and often introduce unwanted changes or artifacts to the input. Thus, we propose to add an attention module to predict an attention map to guide the image translation process. The module learns to attend to key parts of the image while keeping everything else unaltered, essentially avoiding undesired artifacts or changes. Extensive experiments and evaluations show that our model while being simpler, achieves significantly better performance than existing image translation methods.
Melanoma treatment in review

Melanoma represents the most aggressive and the deadliest form of skin cancer. Current therapeutic approaches include surgical resection, chemotherapy, photodynamic therapy, immunotherapy, biochemotherapy, and targeted therapy. The therapeutic strategy can include single agents or combined therapies, depending on the patient’s health, stage, and location of the tumor. The efficiency of these treatments can be decreased due to the development of diverse resistance mechanisms. New therapeutic targets have emerged from studies of the genetic profile of melanocytes and from the identification of molecular factors involved in the pathogenesis of the malignant transformation. In this review, we aim to survey therapies approved and under evaluation for melanoma treatment and relevant research on the molecular mechanisms underlying melanomagenesis.
Integrated Multi-Omic Analysis of Mycobacterium tuberculosis H37Ra Redefines Virulence Attributes

H37Ra is a virulence attenuated strain of Mycobacterium tuberculosis widely employed as a model to investigate virulence mechanisms. Comparative high-throughput studies have earlier correlated its avirulence to the presence of specific mutations or absence of certain proteins. However, a recent sequencing study of H37Ra, has disproved several genomic differences earlier reported to be associated with virulence. This warrants further investigations on the H37Ra proteome as well. In this study, we carried out an integrated analysis of the genome, transcriptome, and proteome of H37Ra. In addition to confirming single nucleotide variations (SNVs) and insertion-deletions that were reported earlier, our study provides novel insights into the mutation spectrum in the promoter regions of 7 genes. We also provide transcriptional and proteomic evidence for 3,900 genes representing ~80% of the total predicted gene count including 408 proteins that have not been identified previously. We identified 9 genes whose coding potential was hitherto reported to be absent in H37Ra. These include 2 putative virulence factors belonging to ESAT-6 like family of proteins. Furthermore, proteogenomic analysis enabled us to identify 63 novel proteins coding genes and correct 25 existing gene models in H37Ra genome. A majority of these were found to be conserved in the virulent strain H37Rv as well as in other mycobacterial species suggesting that the differences in the virulent and avirulent strains of M. tuberculosis are not entirely dependent on the expression of certain proteins or their absence but may possibly be ascertained to functional changes.
Environmental Exposure Associated with Oxidative Stress Biomarkers in Children and Adolescents Residents in Brazilian Western Amazon

Oxidative stress is a biological process that occurs in response to an imbalance between prooxidant and antioxidant substances and has been described in the pathophysiology of more than 200 clinical disorders. In this study, we evaluate the relationship between genetic, demographic, social, environmental, and health factors and redox imbalance biomarkers in a group of children and adolescents environmentally exposed to atmospheric pollutants and mercury in the Brazilian Western Amazon. This is a cross-sectional study of the relationship between demographic, genetic, and socioenvironmental factors and serum concentrations of redox imbalance biomarkers (thiol groups, malondialdehyde, and glutathione S-transferase [GST]) in children and adolescents living in the municipality of Porto Velho, Rondonia. The investigated factors were hierarchically organized into groups of variables and their relationship with redox imbalance biomarkers was estimated by a multiple linear regression model. Children and adolescents with asthma, with C-reactive protein values, with the polymorphic variant GSTP1, and exposed to indoor air pollution presented lower thiol serum concentrations when compared to those categorized in their respective reference groups. GST activity and malondialdehyde serum concentrations were positively related to weekly fish consumption and exposure to PM2.5. This study showed that enzymatic GST activity and malondialdehyde serum concentrations are positively associated with environmental factors, especially air pollution (β = 8.64 U/L for GST and β = 0.244 µmol/L for MDA in high exposure group; p-value < 0.01); while serum thiol concentrations presented an inversely proportional relationship with markers of general health status, such asthma (median: 0.45 mmol/L vs. 0.48 mmol/L; p-value < 0.05), acute inflammation (β = -0.25 mmol/L; p-value < 0.01), and positively with genetic factor (β = 0.12 mmol/L for Val/Val; p-value < 0.05).
A transformative route to nanoporous manganese oxides of controlled oxidation states with identical textural properties

Nanoporous nanocrystalline metal oxides with tunable oxidation states are crucial for controlling their catalytic, electronic, and optical properties. However, previous approaches to modulate oxidation states in nanoporous metal oxides commonly lead to the breakdown of the nanoporous structure as well as involve concomitant changes in their morphology, pore size, surface area, and nanocrystalline size. Herein, we present a transformative route to nanoporous metal oxides with various oxidation states using manganese oxides as model systems. Thermal conversion of Mn-based metal–organic frameworks (Mn-MOFs) at controlled temperature and atmosphere yielded a series of nanoporous manganese oxides with continuously tuned oxidation states: MnO, Mn3O4, Mn5O8, and Mn2O3. This transformation enabled the preparation of low-oxidation phase MnO and metastable intermediate phase Mn5O8 with nanoporous architectures, which were previously rarely accessible. Significantly, nanoporous MnO, Mn3O4, and Mn5O8 had a very similar morphology, surface area, and crystalline size. We investigated the electrocatalytic activity of nanoporous manganese oxides for oxygen reduction reaction (ORR) to identify the role of oxidation states, and observed oxidation state-dependent activity and kinetics for the ORR.
The Vadalog System: Datalog-based Reasoning for Knowledge Graphs

Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowledge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog+/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford's contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation.
Determination of the Phenolic Profile and Antioxidant Properties of Salvia viridis L. Shoots: A Comparison of Aqueous and Hydroethanolic Extracts

Salvia viridis L. is an annual herb used in Mediterranean medicine. The purpose of this study was to determine the polyphenol profile of aqueous (decoction and infusion) and hydroethanolic extracts of aerial parts of field-grown S. viridis and to evaluate their antioxidant activity. The polyphenol profiling was performed via UPLC-DAD/ESI-MS. Additionally, the total polyphenol content in extracts tested were determined by UV-Vis spectrophotometry using the Folin-Ciocalteu assay. The antioxidant effect was evaluated by the FRAP, DPPH, ABTS, O2•− scavenging and TBARS methods. The hydroethanolic extract gave the highest content of total phenolic compounds, followed by the infusion. The UPLC-DAD/ESI-MS analysis of extracts showed a total of 19 phenolic compounds identified as flavonoids (four compounds), phenylethanoids (eight compounds) and phenolic acids (seven compounds). Rosmarinic acid was the predominant phenolic acid, verbascoside was the predominant phenylethanoid, while apigenin glucuronide or methylluteolin glucuronide, depending on the sample, were the predominant flavonoids in the analyzed extracts. The presence of a high polyphenol level indicated a high antioxidant activity of both the infusion and the hydroalcoholic extract. These results indicate that S. viridis is a rich resource of phenolic compounds and can be used in dietary applications with the potential to reduce oxidative stress.
Optimization of Biogas Production from Organic Municipal Waste: Development of Activated Sludge as Digesters Inoculum

This study is a contribution to the optimization of organic fraction of municipal waste bioconversion into biomethane, by activated sludge production as inoculum for digesters. The wastewater (WW) and cow dung (CD) samples were taken from the slaughterhouse of Ouagadougou town, Burkina Faso. Different mixtures were made, enriched with mineral solution and cellulose at 5% (w/v) as: 10% CD + 90% WW (C7), 30% CD + 70% WW (C6), 50% CD + 50% WW (C5), 70% CD + 30% WW (C4), 90% CD + 10% WW (C3), 100% CD (C2) and 100% WW (C1). The pH evolution and biogas (CH4 and CO2) production were followed for 25 days. Cultures tend to acidify with increase in cow dung proportion. Biogas production was significantly higher (p < 0.05) in C5 (880.0 mL), C6 (862.0 10 mL) and C7 (772.0 mL). Mixture C5 had a highest level of CO2 production (40%). Also C7 and C6 retained in the experiment contained respectively organic matter, volatile fatty acids (VFA) and total alkalinity (TAC) as 41.06%, 47.02%, 1320 mg acetic acid/L, 3036 mg Acetic acid/L and 520 mg CaCO3/L, 1310 mg CaCO3/L. Mixture C6 was the best medium for microorganism proliferation stability with 3.5 × 105 UFC/ml of methanogens bacteria. It also possessed buffering capacity, which prevents acidification of medium during VFAs production.
Predicting User’s Conﬁdence During Visual Decision Making

People are not infallible consistent “oracles”: their conﬁdence in decision-making may vary signiﬁcantly between tasks and over time. We have previously reported the beneﬁts of using an interface and algorithms that explicitly captured and exploited users’ conﬁdence: error rates were reduced by up to 50% for an industrial multi-class learning problem; and the number of interactions required in a design optimisation context was reduced by 33%. Having access to users’ conﬁdence judgements could signiﬁcantly beneﬁt intelligent interactive systems in industry, in areas such as Intelligent Tutoring systems, and in healthcare. There are many reasons for wanting to capture information about conﬁdence implicitly . Some are ergonomic, but others are more ‘social’ - such as wishing to understand (and possibly take account of) users’ cognitive state without interrupting them. We investigate the hypothesis that users’ conﬁdence can be accurately predicted from measurements of their behaviour. Eye-tracking systems were used to capture users’ gaze patterns as they undertook a series of visual decision tasks, after each of which they reported their conﬁdence on a 5-point Likert scale. Subsequently, predictive models were built us-ing “conventional” Machine Learning approaches for numerical summary features derived from users’ behaviour. We also investigate the extent to which the deep learning paradigm can reduce the need to design features speciﬁc to each application, by creating “gazemaps” – visual representations of the trajectories and durations of users’ gaze ﬁxations – and then training deep convolutional networks on these images. Treating the prediction of user conﬁdence as a two-class problem (con-ﬁdent/not conﬁdent), we attained classiﬁcation accuracy of 88% for the scenario of new users on known tasks, and 87% for known users on new tasks. Considering the conﬁdence as an ordinal variable, we produced regression models with a mean absolute error of ≈ 0.7 in both cases. Capturing just a simple subset of non-task-speciﬁc numerical features gave slightly worse, but still quite high accuracy (eg. MAE ≈ 1.0). Results obtained with gazemaps and convolutional networks are competitive, despite not having access to longer-term information about users and tasks, which was vital for the ‘summary’ feature sets. This suggests that the gazemap-based approach forms a viable, transferable, alternative to hand-crafting features for each diﬀerent application. These results provide signiﬁcant evidence to conﬁrm our hypothesis, and oﬀer a way of substantially improving many interactive artiﬁcial intelligence applications via the addition of cheap non-intrusive hardware and computationally cheap prediction algorithms.
Classification of GABAergic neuron subtypes from the globus pallidus using wild‐type and transgenic mice

Classifying different subtypes of neurons in deep brain structures is a challenge and is crucial to better understand brain function. Understanding the diversity of neurons in the globus pallidus (GP), a brain region positioned to influence afferent and efferent information processing within basal ganglia, could help to explain a variety of brain functions. We present a classification of neurons from the GP using electrophysiological data from wild‐type mice and confirmation using transgenic mice. This work will help researchers to identify specific neuronal subsets in the GP of wild‐type mice when transgenic mice with labelled neurons are lacking.
LncRNA MALAT1 promotes epithelial-to-mesenchymal transition of esophageal cancer through Ezh2-Notch1 signaling pathway

To investigate effect of long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) on epithelial-to-mesenchymal transition (EMT) of esophageal cancer (EC) and role of enhancer of zeste homolog 2 (Ezh2)-Notch1 signaling pathway in the process. The expression of MALAT1 was determined in four EC cell lines by real-time PCR. TE-1 and EC109 cells were transfected with sh-MALAT1 to inhibit expression of MALAT1 or transfected with pcDNA3.1-Ezh2 to overexpress Ezh2. Invasion and migration assays were conducted to analyze cell metastasis, and expressions of Ezh2-Notch1 signaling-related proteins as well as EMT related proteins were determined using both real-time PCR and western blot. MALAT1 was significantly up-regulated in all EC cell lines compared with the normal cells. Silencing MALAT1 using shRNA could significantly inhibit cell viability (reduced almost 30% of cell viability compared with the control), invasion (reduced almost 30% of cell migration compared with the control), and migration (reduced almost 50% of cell migration compared with the control) of both TE-1 and EC109 cells (P<0.05). Meanwhile, expression of Ezh2, Notch1, Hes1, MMP-9, and Vimentin was significantly decreased and expression of E-cadherin was significantly increased when cells were transfected with sh-MALAT1 compared with the nontransfected cells (P<0.05). However, when cells were cotransfected with both sh-MALAT1 and pcDNA3.1-Ezh2, the protein expression changes induced by sh-MALAT1 were recovered. MALAT1 could affect EMT and metastasis of EC cells through Ezh2-Notch1 signaling pathway. This study can give deeper understandings of the role of MALAT1 in EC and may provide some new directions for treatment of patients with EC.
SpWA: An Efficient Sparse Winograd Convolutional Neural Networks Accelerator on FPGAs

FPGAs have been an efficient accelerator for CNN inference due to its high performance, flexibility, and energy-efficiency. To improve the performance of CNNs on FPGAs, fast algorithms and sparse methods emerge as the most attractive alternatives, which can effectively reduce the complexity of CNNs. Using fast algorithms, the feature maps are transformed to special domain to reduce the arithmetic complexity. On the other hand, compressing CNN models by pruning the unimportant connections reduces both storage and arithmetic complexity.In this paper, we introduce sparse Winograd convolution accelerator (SpWA) combining these two orthogonal approaches on FPGAs. First, we employ a novel dataflow by rearranging the filter layout in Winograd convolution. Then we design an efficient architecture to implement SpWA using line buffer design and Compress-Sparse-Column (CSC) format-based processing element. Finally, we propose an efficient algorithm based on dynamic programming to balance the computation among different processing elements. Experimental results on VGG16 and YOLO network show a 2.9x~3.1x speedup compared with state-of-the-art technique.
Looking Inward: Philosophical and Methodological Perspectives on Phenomenological Self-Reflection

Engaging in early and ongoing self-reflection during interpretive phenomenological research is critical for ensuring trustworthiness or rigor. However, the lack of guidelines and clarity about the role of self-reflection in this methodology creates both theoretical and procedural confusion. The purpose of this article is to describe key philosophical underpinnings, characteristics, and hallmarks of the process of self-reflection in interpretive phenomenological investigation and to provide a list of guidelines that facilitate this process. Excerpts from an interpretive phenomenological study are used to illustrate characteristics of quality self-reflection. The guidelines are intended to be particularly beneficial for novice researchers who may find self-reflective writing to be daunting and unclear. Facilitating use of self-reflection may strengthen both the interpretive phenomenological body of work as well as that of all qualitative research.
Prospective Creation and Validation of the PREVENTT (Prediction and Enaction of Prevention Treatments Trigger) Scale for Surgical Site Infections (SSIs) in Patients With Diverticulitis.

OBJECTIVE Create and validate diverticulitis surgical site infection prediction scale. BACKGROUND Surgical site infections cause significant morbidity after colorectal surgery. An infection prediction scale could target infection prevention bundles to high-risk patients. METHODS Prospectively collected National Surgical Quality Improvement Program and electronic medical record data obtained on diverticulitis colectomy patients across a Healthcare Network-wide Colorectal Surgery Collaborative (5 hospitals). Patients with and without surgical site infections were compared. Predictive variables were identified using logistic regression model; model estimates obtained through 1000 bootstrap replications for scale validation. RESULTS A total of 1737 colectomies were performed (2010-2016): mean age 59.9 years (SD 12.7), 56.4% female; 93.4% Caucasian; smokers 16.3%, diabetics 7.7%, steroid use 6.0%. Two hundred thirty-one (13.3%) were presented to operating room emergently and 138 (7.9%) with abscess at time of disease admission. Two hundred ninety-six patients underwent Hartman procedures, and 113 (6.5%) received diverted primary anastomosis. Average length of stay was 6.9 days (standard deviation 7.01), 30-day mortality was 1.5%, anastomotic leak rate was 3.1%. Twenty-one percent of patients (n = 366) developed a surgical site infection. Several predictors for infection were identified: obesity (body mass index >30), advanced age (>70 years), diabetes mellitus, preoperative abscess, open surgery, emergent operations, and prolonged operations (>3 h). Creation of protected anastomosis in emergent settings was associated with increased infection rates. Presence of more than 5 risk factors was associated with infection rates of 45.8% (c = 0.69). CONCLUSIONS Patients with diverticulitis have high surgical site infection rates due to nonmodifiable risk factors. Our Prediction and Enaction of Prevention Treatments Trigger scale can risk stratify patients for targeting surgical site infection prevention bundles and outcomes risk adjustments.