Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancements of the Sequence Saturation Mutagenesis (SeSaM) Method to Efficiently Explore Protein Sequence Space

Elucidating structure-function relationships in proteins and adapting biocatalysts to non-conventional substrates and production conditions are of high academic and economical interest. Independent from any structural information, directed evolution can steer protein properties in iterative cycles of diversity generation on the gene level and screening for improved variants on the protein level. Homogeneous distribution of mutations, consecutive nucleotide exchanges and transversion mutations are the keys to achieve chemically diverse amino acid substitutions. However, commonly used random mutagenesis methods can only generate point mutations and achieve chemically conserved amino acid substitutions due to transition biased mutational spectra or cannot give homogeneous distribution of mutations thus severely hampering the diversity. Sequence Saturation Mutagenesis (SeSaM-Tv+) is a novel random mutagenesis method which can construct transversion biased mutant library and consecutive mutations. Four steps involved for construction of mutant libraries of the gene are (i) Generation of DNA fragments by random incorporation of cleavable dNTPαS in the gene by PCR followed by cleavage and purification of the fragments, (ii) Enzymatic addition of universal base/s, (iii) Elongation of fragments (iv) Replacement of universal bases by standard nucleotides. Choice of dNTPαS used to generate pool of DNA fragments and bias of replacing universal base is responsible for mutational flexibility in SeSaM and consecutive mutations can be created by addition of more than one universal base. SeSaM-Tv+ was reported to achieve enrichment of desired transversions.
Prognostic Value of the Intratumoral Lymphocyte to Monocyte Ratio and M0 Macrophage Enrichment in the Melanoma Immune Microenvironment

Background: Cutaneous melanoma (SKCM) is characterized by significant heterogeneity in its molecular, genomic, and immunologic characteristics. Methods: Whole transcriptome RNAseq data from The Cancer Genome Atlas of SKCM (n=328) was utilized. The immune microenvironment was characterized using CIBERSORTx to identify immune cell type composition. Unsupervised hierarchical clustering was performed based on immune cell type content. Samples were separated into those obtained from the primary tumor site and r egional skin or soft tissue (locoregional), or distant metastasis and regional lymph node (metastatic). Analysis of overall survival (OS) was performed using Kaplan-Meier estimates and Cox-regression multivariable analyses. Results: Four immune clusters were identified, largely defined by lymphocyte:monocyte (L:M) ratio, monocyte-enrichment, and M0-macrophage-enrichment (L:MLow, MonocyteHigh, M0High; L:MLow, MonocyteMid, M0Low; L:MMid, MonocyteLow, M0Low; L:MHigh, Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 11 September 2020 doi:10.20944/preprints202009.0239. v1 © 2020 by the author(s). Distributed under a Creative Commons CC BY license.
Tuberculosis versus COVID-19 Mortality: A New Evidence

BACKGROUND: Coronavirus current pandemic (COVID-19) is the striking subject worldwide hitting countries in an unexplained non-universal pattern. Bacillus Calmette–Guerin (BCG) vaccine was an adopted recent justification depending on its non-specific immune activation properties. Still the problem of post-vaccine short duration of protection needs to be solved. The same protective mechanism was identified in active or latent tuberculosis (TB). For each single patient of active TB, there are about nine cases of asymptomatic latent TB apparently normal individuals living within the community without restrictions carrying benefits of immune activation and involved in re-infection cycles in an excellent example of repeated immunity training sessions of the whole community. AIM: We aimed to asses the correlation between TB burden and COVID-19 mortality in all affected countries having different BCG vaccination policies. METHODS: Publicly available data were extracted for 191 countries including population size, TB estimations, national BCG vaccination policy, the World Health Organization regions and economic classification, and COVID-19 mortality and number of cases. The analysis was performed using Spearman’s correlation test. RESULTS: Significant large negative correlation (−0.539, p < 0.001) was found between TB prevalence and COVID-19 mortality rate worldwide. Medium negative significant correlations were found between TB cases and COVID-19 mortality in the high and lower middle-income countries, and those having current BCG vaccination programs (−0.395, p = 0.001, −0.365, p = 0.015, and −0.476, p < 0.001, respectively). CONCLUSION: Countries with high TB prevalence have higher chances of protection against COVID-19 mortality through the theory of widely distributed natural immune activation within community. Confounders should be assessed separately.
Contextual Information Retrieval within Recommender System: Case Study "E-learning System"

This paper focuses on monitoring and analyzing user activities on collaborative filtering -based recommender system in order to guess suitable and unsuitable items’ context information using rating matrix which makes more efficient adaptation task. An ontology-based user profile and rules-based context modeling for reasoning about context information is proposed in this research work, in addition to an investigation to apply Semantic Web technologies in user modeling and context reasoning. This proposal is applied in education field in which we have designed an authoring tool for learning objects within ubiquitous environment. This system aims to improve the learning object production task (creation, review, edition.) on behalf of technologies offered by collaborative filtering systems as well as user behaviors monitoring to improve the recommendation process.
Conformational dynamics of amyloid-β (16–22) peptide in aqueous ionic liquids

Molecular dynamics simulations of amyloid-β (16–22) peptide dimer in water as well as at two different experimentally studied concentrations of hydrated ionic liquids (ILs), ethylammonium mesylate (EAM), ethylammonium nitrate (EAN), and triethylammonium mesylate (TEAM), were carried out employing an umbrella sampling method. We used the average Ψ angle of the peptide backbone as the reaction coordinate to observe the conformational changes of a peptide dimer. Secondary structural element values were calculated for the peptide dimer along the reaction coordinate to see the transition of the peptide dimer between β-sheet and α-helix conformations. We observe the β-sheet conformation as the global minimum on the free energy surfaces in both EAM and EAN ILs at both the concentrations and at a low concentration of TEAM. However, we observe α-helix conformation as the global minimum at a high concentration of TEAM. Our results are in good correlation with the experimental findings. We calculated the average number of intramolecular and intermolecular hydrogen bonds of α-helix and β-sheet conformations in all solutions, and they are in correlation with the secondary structure element values. To understand the peptide–IL interactions, atom–atom radial distribution functions of cation, anion, and water around amide oxygen and hydrogen atoms were calculated. The solvent-accessible surface area of the peptide dimer was calculated to understand the exposure of the peptide towards the solvent during conformational changes. Finally, van der Waals (vdW) and Coulomb interaction energies were calculated between peptide–cation, peptide–anion, and peptide–water to understand the stability of conformations in different concentrations. We find that the TEA cation has more vdW interaction energy compared to Coulomb interaction energy with peptide in 70% (w/w) TEAM, which mimics a membrane-like environment to induce α-helix conformation rather than β-sheet conformation.
Cardiac manifestations of dengue fever in pediatrics age group

Background: Dengue infection is a serious public health concern in tropical and subtropical regions of the world. Cardiac involvement is a known but rare complication of dengue. Electrocardiogram (ECG) and 2D echocardiogram (ECHO) are preferred non invasive instant diagnostic tool to evaluate the status of heart in dengue infection. Objectives: The present study was carried out to confirm the cardiac manifestation in dengue fever, along with its correlation with the severity of the disease. Materials and Methods: A total of 60 subjects were included over a period of 1-year time from inpatient and outpatient departments presenting with clinical features suggestive of dengue and further confirmed by positive serology test of IgM and/or NS1 antigen by rapid and/or by ELISA method. Cardiac manifestations were analyzed with the help of ECG and 2D ECHO in all serologically confirmed cases. Results: 16 out of 60 cases had cardiac findings in the form of tachycardia, bradycardia, and myocarditis. ECG showed some nonspecific findings like sinus tachycardia followed by sinus bradycardia. Myocarditis diagnosed by ECHO as ejection fractions <50% was observed in four cases of dengue with warning sign and in one of severe dengue, while mild TR was seen in three cases of dengue with a warning sign, two cases of probable dengue, and one case of severe dengue. Conclusion: This study showed that cardiac involvement of dengue infections runs a benign course without any complications. Early diagnosis, strict monitoring, and prompt supportive management reduce the mortality rate in dengue.
How does the informed trading impact Bitcoin returns and volatility?

ABSTRACT This study employed an augmented AR-GJRGARCH model that incorporates an intraday-based buy-sell order size imbalance measure to explore how informed trading behaviour/activity impacted Bitcoin returns and volatility from January 2014 to February 2019. Our results show that the informed trading behaviour dominated by sell-order significantly led to decreased concurrent Bitcoin returns for alternative sample periods. However, the informed trading behaviour dominated by buy-order related positively to Bitcoin returns only for the full sample period. Moreover, the informed trading activity helped to reduce Bitcoin’s volatility, which is consistent with expectations based on dispersion of beliefs models. Finally, we uncovered a positive (inverted) asymmetric volatility effect for both the full and the rising sample periods, indicating the presence of the ‘fear of missing out’ psychological effect.
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance

3D dynamic point clouds provide a natural discrete representation of real-world objects or scenes in motion, with a wide range of applications in immersive telepresence, autonomous driving, surveillance, etc. Nevertheless, dynamic point clouds are often perturbed by noise due to hardware, software or other causes. While a plethora of methods have been proposed for static point cloud denoising, few efforts are made for the denoising of dynamic point clouds, which is quite challenging due to the irregular sampling patterns both spatially and temporally. In this paper, we represent dynamic point clouds naturally on spatial-temporal graphs, and exploit the temporal consistency with respect to the underlying surface (manifold). In particular, we define a manifold-to-manifold distance and its discrete counterpart on graphs to measure the variation-based intrinsic distance between surface patches in the temporal domain, provided that graph operators are discrete counterparts of functionals on Riemannian manifolds. Then, we construct the spatial-temporal graph connectivity between corresponding surface patches based on the temporal distance and between points in adjacent patches in the spatial domain. Leveraging the initial graph representation, we formulate dynamic point cloud denoising as the joint optimization of the desired point cloud and underlying graph representation, regularized by both spatial smoothness and temporal consistency. We reformulate the optimization and present an efficient algorithm. Experimental results show that the proposed method significantly outperforms independent denoising of each frame from state-of-the-art static point cloud denoising approaches, on both Gaussian noise and simulated LiDAR noise.
Régulation de l'expression fonctionnelle de transporteurs membranaires dans des cellules hépatocytaires et pulmonaires exposées aux extraits de particules diesel

Les transporteurs membranaires jouent un role primordial dans la pharmacocinetique de medicaments mais aussi dans le transport de composes endogenes. La maitrise de modele in vitro permettant d’evaluer leur implication dans des interactions medicamenteuses, notamment au niveau hepatique, est donc primordiale. L’utilisation de ces modeles permettra aussi de determiner l’impact potentiel de contaminants environnementaux, comme les particules diesel, sur l’expression fonctionnelle de transporteurs. Nos resultats demontrent la fiabilite de modeles hepatocytaires (hepatocytes et cellules d’hepatome HepaRG) en culture monocouche pour l’etude du transport membranaire de medicaments. Grâce a ces modeles, nous avons mis en evidence que des extraits de particules diesel (DEPe) peuvent modifier l’expression et/ou la fonction de transporteurs membranaires hepatiques, les Organic Anion Polypeptide Transporter (OATP) et les Multidrug Resistance associated Protein (MRP). Au niveau pulmonaire, les DEPe peuvent aussi augmenter l’expression du complexe LAT1/CD98hc, un complexe proteique de transport d’acides amines souvent associe a de mauvais pronostics dans les cas de cancer du poumon. En conclusion, nos resultats mettent en evidence que les DEPe peuvent intervenir dans la regulation de l’activite et de l’expression de transporteurs membranaires tant au niveau hepatique qu’au niveau pulmonaire.
Osteotomia segmentar da maxila em área estética para viabilizar reabilitação com implantes dentários: relato de caso

Atualmente o sucesso das reabilitações com implantes tem despertado nos pacientes o interesse e a esperança de sorrir novamente após terem sido submetidos a exodontias no passado, e pelo fato da não adaptação ao uso de próteses dentárias convencionais por muito tempo. O procedimento de osteotomia segmentar da maxila é uma proposta de tratamento viável para pacientes que apresentam diminuição da dimensão vertical anterior da maxila, devido ao processo reabsortivo crônico na região, possibilitando a reabilitação implantossuportada de forma mais satisfatória. Diante da necessidade de se corrigir as discrepâncias maxilares em um único tempo cirúrgico, alguns aspectos devem ser levados em consideração, como: seleção do paciente, planejamento cirúrgico e protético e a determinação de um tipo de tratamento mais previsível, dentro das perspectivas do paciente. O objetivo desse trabalho é relatar um caso clínico de uma paciente submetida a osteotomia segmentar em bloco da região anterior da maxila, prévia a reabilitação implantossuportada. Paciente do sexo feminino procurou o serviço queixando-se de desconforto por usar prótese dentária há muito tempo. Ao exame clínico e imaginológico inicial foi constatada a necessidade de aumentar a altura óssea da região anterior da maxila. Foi proposto para o caso a realização de uma osteotomia segmentar da região anterior da maxila para ganho de altura óssea na região que permitiu uma reabilitação funcional e estética para a paciente. Com este caso pode-se concluir que o procedimento osteotomia segmentar em bloco da região anterior da maxila para correção estética é eficaz e demanda um planejamento adequado para que se obtenha resultados estéticos satisfatórios.
Leucemia linfoblástica aguda pediátrica, evaluación de enfermedad mínima residual

Minimal residual disease (MRD) has proven to be an independent predictor for risk of relapse, allowing the adequacy of treatment according to risk groups.Between June 2010 and May 2019, 137 patients were evaluated with diagnosis of acute lymphoblastic leukemia treated using the ALLIC BFM-GATLA 2010 protocol. Evaluation at day 15 was used as indicator to stratify risk groups.The cut-off used for MRD on day 15 was 0.1%. Although MRD > 0.1% (87 patients, 63%) was associated with risk and relapse, neither overall survival (OS) nor disease-free survival (DFS) has shown a significance correlation. Moreover, MRD at day 33 > 0.05% (20 patients) was highly associated with risk, relapse, OS and DFS. OS at month 24 for MRD day 33 negative/day 72 negative was 88%, MRD at day 33 positive/at day 72 negative was 71% and MRD at day 33 positive/at day 72 positive was 40% (p= 0.001). DFS at month 24 for MRD at day 33 negative/at day 72 negative was 89%, MRD at day 33 positive/at day 72 negative was 61% and MRD at day 33 positive/at day 72 positive was 40% (p= 0.0001). The MRD at day 33 with 0.05% or higher was the only that shows association with relapse (p 0.02),hazard risk 4,1 (IC 95 1.72-10.055) which suggests that MRD day 33 positive increases 4 times the risk of relapse.
Comparison of different machine learning algorithms for prediction of wind evolution

Wind evolution, i.e. the evolution of turbulence structures over time, has become an increasingly interesting topic in recent years. Part of this increasing interest is resulted from the development of lidar-assisted wind turbine control, which requires accurate prediction of wind evolution to avoid unnecessary or even harmful control actions. Moreover, study of wind evolution can deepen our understanding of turbulence characteristics and, for example, could make 4D stochastic wind field simulations possible by integrating wind evolution into 3D simulations. This study compares different machine learning algorithms for the prediction of wind evolution, including three variants of Gaussian process regression, regression tree, support vector machine regression, and shallow neural network. The comparison is done using lidar data measured by a nacelle-mounted lidar, and it focuses on the prediction accuracy and the computational time of models, to provide some insights of the trade-off between these factors. In terms of computational time, both the training time of the final model and the time for hyperparameter optimization are taken into account. Moreover, up-scaling of the computational time with data size is also investigated. It is found that Gaussian process regression provides high prediction accuracy but needs long computational time, increasing significantly with data size. For cases where long computational time might be a critical problem, shallow neural network could be a good alternative thanks to its high training efficiency, although its prediction accuracy is lower than Gaussian process regression. The other methods have no advantages in wind evolution prediction compared to these two methods.
Improved Point Estimation for the Rayleigh Regression Model

The Rayleigh regression model was recently proposed for modeling amplitude values of synthetic aperture radar (SAR) image pixels. However, inferences from such model are based on the maximum-likelihood estimators, which can be biased for small-signal lengths. The Rayleigh regression model for SAR images often considers small pixel windows, which may lead to inaccurate results. In this letter, we introduce bias-adjusted estimators tailored for the Rayleigh regression model based on: 1) Cox and Snell’s method; 2) Firth’s scheme; and 3) the parametric bootstrap method. We present numerical experiments considering synthetic and actual SAR data sets. The bias-adjusted estimators yield nearly unbiased estimates and accurate modeling results.
Dry-Coupled Airborne Ultrasonic Inspection Using Coded Excitation

Unmanned Aerial Vehicles (UAVs) offer significant potential benefits to the inspection of large-scale facilities due to their ability to access areas where manual inspection is not practical. Ultrasonic inspections typically utilise acoustic couplant, placed between the specimen and transducer surfaces, to eliminate any air gap and enable acoustic energy propagation. Conventional ultrasonic inspection UAVs contain a mechanical system to deliver a small quantity of liquid couplant between the transducer and inspection surface. Such mechanisms increase the system payload, resulting in the reduction of UAV flight endurance and inspection efficiency. Any couplant remaining on the surface may also increase the risk of corrosion. Instead of a liquid couplant layer, dry-coupled ultrasonic transducers utilise a thin layer of rubberised material. However, the acoustic characteristics of the conformable materials typically result in dry-coupled transducers with a lower Signal-to-Noise Ratio (SNR) than liquid-coupled sensors. Coded excitation, a pulse compression technology, improves SNR without sacrificing the measurement acquisition rate, as is the case with signal averaging. This paper explores the potential for application of coded excitation to maintain the SNR aboard a UAV deploying a dry-coupled transducer.
Intraoperative Optical Coherence Tomography Imaging in Corneal Surgery: A Literature Review and Proposal of Novel Applications

Intraoperative optical coherence tomography (iOCT) is widely used in ophthalmic surgeries for cross-sectional imaging of ocular tissues. The greatest advantage of iOCT is its adjunct diagnostic efficacy, which facilitates to decision-making during surgery. Since the development of microscopic-integrated iOCT (MIOCT), it has been widely used mainly for vitreoretinal and anterior segment surgeries. In corneal transplantation, MIOCT allows surgeons to visualise structure underneath the turbid and distorted cornea, which are impossible to visualise with a usual microscope. Real-time visualisation of hard-to-see area reduces the operation time and leads to favorable surgical outcomes. The use of MIOCT is advantageous for a variety of corneal surgical procedures. Here, we have reviewed articles focusing on the utility of iOCT and MIOCT in penetrating keratoplasty, deep anterior lamellar keratoplasty, Descemet stripping automated endothelial keratoplasty, and Descemet membrane endothelial keratoplasty. The applications of MIOCT to corneal surgery in terms of surgical education for trainees, emergency surgery, and novel surgery are also discussed, with our cases performed using RESCAN® 700.
ANTIBACTERIAL AND ANTIOXIDANT ACTIVITIES OF FRACTIONATED PERSEA GRATISSIM SEED EXTRACTS

Fractionated methanol extracts of dried seeds of Persea gratissim were evaluated for the antibacterial and antioxidant activity. The extracts were tested for antibacterial activity by spread plate method against Bacillus subtilis and Streptococcus aureus. The antioxidant activity of these fractions were determined by DPPH [1, 1-Diphenyl-2-Picryl Hydrazyl] assay. Fraction III and IV showed a maximum inhibition zone against Bacillus subtilis and Staphylococcus aureus at 50 µl concentration. The antioxidant activity of the seed extracts were observed to scavenge 1,1-diphenyl,2-picrylhydrazyl(DPPH) in the order terpenoids > lipids > alkaloids > polysaccharide. Among the four fractions analyzed, maximum antioxidant activity was observed in fraction III and recorded 81.6% compared to standard antioxidants ascorbic acid [82.3%]. Moderate antioxidant was observed in fraction I,II and IV.
Montane regions shape patterns of diversification in small mammals and reptiles from Madagascar’s moist evergreen forest

Madagascar is renowned for its exceptional species diversity and endemism. The island's mountainous regions are thought to have played a role in lineage and species diversification, but this has yet to be explored across taxonomic groups and a temporal context has not yet been identified. We tested whether montane regions have promoted population divergence in Madagascar's vertebrate fauna and, if so, whether these divergence events were contemporaneous.