Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mycobacterium tuberculosis sRNA F6 Modifies Expression of Essential Chaperonins, GroEL2 and GroES

Control of gene expression via small regulatory RNAs (sRNAs) is poorly understood in one of the most successful pathogens, Mycobacterium tuberculosis. Here, we present an in-depth characterization of the sRNA F6, including its expression in different infection models and the differential gene expression observed upon deletion of the sRNA. ABSTRACT Almost 140 years after the identification of Mycobacterium tuberculosis as the etiological agent of tuberculosis, important aspects of its biology remain poorly described. Little is known about the role of posttranscriptional control of gene expression and RNA biology, including the role of most of the small RNAs (sRNAs) identified to date. We have carried out a detailed investigation of the M. tuberculosis sRNA F6 and shown it to be dependent on SigF for expression and significantly induced in starvation conditions in vitro and in a mouse model of infection. Further exploration of F6 using an in vitro starvation model of infection indicates that F6 affects the expression of the essential chaperonins GroEL2 and GroES. Our results point toward a role for F6 during periods of low metabolic activity typically associated with long-term survival of M. tuberculosis in human granulomas. IMPORTANCE Control of gene expression via small regulatory RNAs (sRNAs) is poorly understood in one of the most successful pathogens, Mycobacterium tuberculosis. Here, we present an in-depth characterization of the sRNA F6, including its expression in different infection models and the differential gene expression observed upon deletion of the sRNA. Our results demonstrate that deletion of F6 leads to dysregulation of the two essential chaperonins GroEL2 and GroES and, moreover, indicate a role for F6 in the long-term survival and persistence of M. tuberculosis in the human host.
Estimation of the balance of metabolizable lysine and methionine for grazing dairy cows

Objective. The aim of this study was to determine in grazing cows, the reproducibility and accuracy of the balance between predicted values, when compared with the observed values for the most limiting amino acids in milk protein synthesis, the methionine (Met) and Lysine (Lys). Materials and methods. Twelve lactating cows were used for a 20-day experimental period. Control: animals grazing and supplemented with balanced food; Met-Lys: same as control and supplemented with adjusted rumen protected Met and Lys. For Met and Lys supply predicted values, it was taking in account the average of the dry matter intake (DMI) of the herd, based in offer and foraging control and values of microbial protein data. Observed values were determined based in the individual DMI intake, using external and internal markers and the individual microbial protein production. A t-paired-sample test was performed and the efficiency of the model’s prediction was determined using the mean square prediction error (MSPE) and the concordance coefficient (CCC). Results. Significant differences were found between the predicted and observed values for DMI forage, digestible microbial protein and microbial protein production, supply and the balance of Lys and Met. The CCC for Lys and Met balance were low (0.10), the MSPE was high except for the total DMI with a moderate concordance (0.63) and low MSPE (4.42). Conclusions. These results indicate a lack of precision of the tools, which underestimates the supply and balance of amino acids in individual grazing cows.
Cell Surface Phenotypic Diversity and Flocculation Gene Variability in Contaminant Industrial Fuel-Ethanol Yeast Strains Exhibiting Highly Foaming Phenotypes

Many contaminant yeast strains able to survive inside fuel ethanol industrial vats show detrimental cell surface phenotypes, such as filamentation, invasive growth, flocculation, biofilm formation and excessive foam production. Previous studies have linked some of these phenotypes to the expression of FLO genes, and the presence of gene length polymorphisms causing the expansion of FLO gene size appears to result in stronger flocculation and biofilm formation phenotypes. We have performed here a molecular analysis of FLO1 and FLO11 gene polymorphisms present in contaminant strains of S. cerevisae from Brazilian fuel ethanol distilleries showing strong foaming phenotypes during fermentation. The size variability of these genes was correlated with cellular hydrophobicity, flocculation and highly foaming phenotypes in these yeast strains. Our results also show that deleting the major activator of FLO genes (the FLO8 gene) from the genome of a contaminant and highly foaming industrial strain avoids problematic foam formation, flocculation, invasive growth and biofilm production by the engineered (flo8∆::BleR / flo8Δ::kanMX) yeast strain. Thus, the characterization of highly foaming yeasts and the influence of FLO8 in this phenotype opens new perspectives for yeast strain engineering and optimization in the sugarcane fuel-ethanol industry.
DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition

We propose a novel approach for large-scale nonlinear least squares problems based on deep learning frameworks. Nonlinear least squares are commonly solved with the Levenberg-Marquardt (LM) algorithm for fast convergence. We implement a general and efficient LM solver on a deep learning framework by designing a new backward jacobian network to enable automatic sparse jacobian matrix computation. Furthermore, we introduce a stochastic domain decomposition approach that enables batched optimization and preserves convergence for large problems. We evaluate our method by solving bundle adjustment as a fundamental problem. Experiments show that our optimizer significantly outperforms the state-of-the-art solutions and existing deep learning solvers considering quality, efficiency, and memory. Our stochastic domain decomposition enables distributed optimization, consumes little memory and time, and achieves similar quality compared to a global solver. As a result, our solver effectively solves nonlinear least squares on an extremely large scale. Our code will be available based on Pytorch1 and Mindspore2.
Object Handover between Humans and Robots in Microgravity

The collaborative space robots field is an emerging research field with high impact as robots perform collaborative missions with high precision during lengthy tasks, on an accelerated schedule and do not require spacial suits, infrastructures such as life support or to return to Earth, making them efficient and economically viable. However, human-robot collaboration in space is still a challenge concerning key issues such as manipulation of weightless objects and mobility due to the peculiar motion dynamics of the robot and the manipulator on a microgravity environment. Therefore, this dissertation aims to formulate an algorithm that enables a free-flyer robot equipped with a manipulator to perform a successful, fluent and dynamic robot-to-human and human-to-robot object handover. Additionally, a systematic user study is proposed with the goal of analysing the user’s preferences between a rigid and compliant impedance robot behavior during the object handover and understanding the impact of those behaviors on the success of the task. The proposed algorithm is formulated with resource to a finite-state machine that encompasses a designed impedance controller for the transfer phase enabling a dynamic interaction between both task participants. Following, the algorithm is implemented and validated on the simulator of NASA’s Astrobee free-flyer robot and the two impedance behaviors, rigid and compliant, are successfully studied. Furthermore, an user interaction interface is developed and includes an user simulated hand model controlled via a Leap Motion device. This interface generates a virtual reality perception environment that enables an accurate interaction with the robot on the proposed tasks. Lastly, the proposed systematic user study was conducted. Results showed that the rigid behavior was overall more preferable and registered higher transfer success during the tasks.
Viruses and Type 1 Diabetes: From Enteroviruses to the Virome

For over a century, viruses have left a long trail of evidence implicating them as frequent suspects in the development of type 1 diabetes. Through vigorous interrogation of viral infections in individuals with islet autoimmunity and type 1 diabetes using serological and molecular virus detection methods, as well as mechanistic studies of virus-infected human pancreatic β-cells, the prime suspects have been narrowed down to predominantly human enteroviruses. Here, we provide a comprehensive overview of evidence supporting the hypothesised role of enteroviruses in the development of islet autoimmunity and type 1 diabetes. We also discuss concerns over the historical focus and investigation bias toward enteroviruses and summarise current unbiased efforts aimed at characterising the complete population of viruses (the “virome”) contributing early in life to the development of islet autoimmunity and type 1 diabetes. Finally, we review the range of vaccine and antiviral drug candidates currently being evaluated in clinical trials for the prevention and potential treatment of type 1 diabetes.
PNAS201715227 1..8

Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, RI 02906; Department of Psychiatry, The University of Texas at Austin, Austin, TX 78712; Department of Psychology, New York University, New York, NY 10003; Department of Psychology, Princeton University, Princeton, NJ; Center for Neural Science, New York University, New York, NY 10003; and Emotional Brain Institute, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962
Learning Tensor Low-Rank Prior for Hyperspectral Image Reconstruction

Snapshot hyperspectral imaging has been developed to capture the spectral information of dynamic scenes. In this paper, we propose a deep neural network by learning the tensor low-rank prior of hyperspectral images (HSI) in the feature domain to promote the reconstruction quality. Our method is inspired by the canonical-polyadic (CP) decomposition theory, where a low-rank tensor can be expressed as a weight summation of several rank-1 component tensors. Specifically, we first learn the tensor low-rank prior of the image features with two steps: (a) we generate rank-1 tensors with discriminative components to collect the contextual information from both spatial and channel dimensions of the image features; (b) we aggregate those rank-1 tensors into a low-rank tensor as a 3D attention map to exploit the global correlation and refine the image features. Then, we integrate the learned tensor low-rank prior into an iterative optimization algorithm to obtain an end-to-end HSI reconstruction. Experiments on both synthetic and real data demonstrate the superiority of our method.
People detection using IR camera on a drone for more effective rescue operations

This bachelor’s thesis investigates the usage of object detection algorithms on images captured by an infrared camera placed on a drone. The solution will help to automate the analysis of captured data, targeting to increase the effectiveness of rescue operations. During the completion of the task, I developed a Python desktop application, that realizes chosen detection methods. The methods selection was based on an analysis of current approaches and take advantage of the existing detection systems. The application was used to measure the accuracy and performance of these approaches on the dataset created as a part of the thesis. In the end, the conclusion evaluates the possibility to use image detection on a thermogram, in a real-world application. The single-stage Region Proposal Convolutional Network showed the best result and was chosen for future development.
Improved Method for Neural Spike Alignment: The Centroid Filter

Background: This paper describes a new method to calculate the positions of action potentials (APs) in extracellular neural recordings in real-time. Extracellular recordings made using various types of electrode structures are a key tool in experimental electrophysiology and involve the identification of signals from individual neurons, a process known by the generic name spike sorting . An essential feature of the process is the alignment of individual APs (‘spikes’) in time. Given the recordings are invariably made in noisy environments there is a tendency to timing jitter leading to alignment errors. This is especially true if single point methods of AP location are employed. New Method: The problems of single point AP location in the presence of noise are significantly reduced by the use of the centroid as a measure of position, which is based on a time average of all the points in the AP. The method is described and analysed in detail and it is shown that a very economical hardware realisation is possible. Results: The new methods are investigated by simulation using deterministic models of nerve signals (i.e. APs with added noise; both correlated and uncorrelated noise models are considered) as well as results measured from a hardware implementation. A power efficient realisation of the centroid filter is demonstrated using a complex programmable logic device (CPLD). Comparison with Existing Method: Detailed simulations demonstrate the superiority of the centroid method compared to several standard, single point metrics. Conclusions: A new method has been suggested that significantly improves one of the key issues in neural spike sorting. The technique has the potential to influence significantly the design of electrophysiological recording systems in the future
Integrating XPath with SQL

The leading candidate for searching and retrieving fragments of XML text is XQuery. It is proposed that minor extensions to the SQL language might instead be employed to facilitate searching and retrieval of structured texts. This alternative approach has numerous advantages, since it builds upon existing database technology, and allows a tight integration of structured and semi-structured data, under a powerful and well-established query language. Enhancements to the XPath pattern matching language are proposed, that facilitate the melding of relational database and structural queries, while offering the potential of being rapidly resolved by existing text engines.
Normal Integration via Inverse Plane Fitting with Minimum Point-to-Plane Distance

This paper presents a surface normal integration method that solves an inverse problem of local plane fitting. Surface reconstruction from normal maps is essential in photometric shape reconstruction. To this end, we formulate normal integration in the camera coordinates and jointly solve for 3D point positions and local plane displacements. Unlike existing methods that consider the vertical distances between 3D points, we minimize the sum of squared point-to-plane distances. Our method can deal with both orthographic or perspective normal maps with arbitrary boundaries. Compared to existing normal integration methods, our method avoids the checkerboard artifact and performs more robustly against natural boundaries, sharp features, and outliers. We further provide a geometric analysis of the source of artifacts that appear in previous methods based on our plane fitting formulation. Experimental results on analytically computed, synthetic, and real-world surfaces show that our method yields accurate and stable reconstruction for both orthographic and perspective normal maps1.
Running title: ANTIMICROBIAL MOLECULES PRODUCED BY P. polymyxa Academy Section: CELLULAR AND MOLECULAR BIOLOGY

An endophytic bacterium inhibiting pathogenic bacteria was isolated and the strain was genetically identifi ed as Paenibacillus polymyxa. Biochemical characterization of fermentation broth indicated the presence of peptidic antimicrobial molecules. Liquid-liquid partition resulted in an organic fraction (OF) and an aqueous fraction (AF). OF presented a broad spectrum of activity against a panel of pathogenic bacteria and a fungus whereas the AF was active only against Gram-negative bacteria. AF was sequentially submitted to ion-exchange, desalting and reverse phase (RP) chromatography. A molecule with an RT of 2.45 min exhibited activity against all Gram-negative pathogenic strains tested beside P. mirabilis. The primary structure of the molecule, named AMP-Pp, was determined as Gly-Glu-Hyp-Gly-Ala by N-terminal sequencing. The molecular mass and amino acid sequence were confi rmed by MS/MS. With a molecular mass of 463 Da, AMP-Pp is one of the smallest active natural peptides reported, yet. RP chromatography of OF resulted in four peaks. The fi rst three peaks corresponded to known antimicrobials. MS analysis of peak 4 revealed the presence of an ion with m/z 3,376.4 Da, whose proposed molecular formula is C182H321N29O29. The compound, named polycerradin, showed a spectrum of activity against Gram-positive bacteria, Gram-negative bacteria (beside P. mirabilis) and a fungus.
Digital Phenotyping Analysis

The main goal of this bachelor thesis is to examine digital phenotyping and provide an analysis of this phenomenon to evaluate the importance of digital phenotyping for future medical use. The first part of this thesis describes the theoretical part focusing on people’s data collected from smart devices, mainly from smartphones, and simultaneously related computer science focusing on mental health using these data is introduced. Consequently, thorough concepts and terms used in this area are explained, and state of the art is described. Further, data collection platforms for smartphones are described and detailly compared by running a small pilot study. Then another pilot study is deployed to collect passive and active data from smartphones and actigraphy data from the wristband. These data entries are evaluated, visualised and assessed. Moreover, the GPS sensor, power state and actigraphy data are preprocessed, and meaningful features are calculated using these data types. The second part focuses on the exploratory analysis of the data set gathered in the pilot study. The data are assessed as a whole unit and simultaneously individually by a person. Moreover, the classification of people’s emotional states developed from the circumplex model of affect is introduced. This classification is done using a random forest algorithm based on the calculated features, and its ultimate goal is to distinguish between a person’s emotional state.
Magnetic Circuit Study of a Permanent Magnet Linear Generator for Wave Energy Recovery

The developments on wave energy conversion have been hindered by the high costs associated to the operation and maintenance of the converters, lagging considerably behind the other renewable energy sources. As the production costs of rare-earth magnets that are both light and offer a high energy density have been reducing over the last few years, permanent magnet linear generators are now becoming a viable option in what concerns wave energy conversion with several researchers aiming to solve their major problems with new state-of-the-art generators. Throughout this dissertation, a permanent magnet linear generator, previously developed in Instituto Superior Téecnico is studied and analyzed in its magnetic dimensions. The main objective is to study the generators magnetic circuit in order to improve, if possible, the high magnetic flux dispersion and electromagnetic forces experienced in the translator along its normal operation. To do that, state-of-the-art generators were analyzed and different translator topologies were conceived recurring to finite element analysis. The variations comprised different magnet arrangements, magnetization directions and different magnet shaping studies.
Optimization of Operations in the Surgical Service: Surgery Scheduling in a Hospital Context

The Operating Room (OR) holds a significant amount of a hospital’s total costs and revenues. Thus, an efficient management, easily translates into an efficient and profitable health facility and improves the healthcare provided there. Scheduling is a central activity in an OR, as it determines how the OR will function, for a certain planning period. In the case study hospital, a new and improved OR scheduling technique is expressed by the staff, as essential currently. With that in mind, in this work, a deterministic Integer Linear Programming (ILP) model is developed, to optimize the OR scheduling of elective surgeries, aiming at maximizing the number of surgeries scheduled, the profit and the balance between the MSS and the surgeon’s preferences. The model considers the room capacity, human resources capacity (nurses and anesthesiologists), type of rooms and downstream unit capacity. This study contributes to the literature with the integration between following tactical decisions made by the hospital and attending the surgeon’s preferences. The introduction of the surgeon’s preferences as an objective and the inclusion of day, hour, nursing team and anesthesiologist in these, represents a novel feature. The exact solution obtained, using real data, successfully integrates the surgeon’s preferences and the MSS, in the schedule. The results also highlight the main management changes necessary in the service, which also constrained the application of the model to real data.
Dissolution Pat- terns Prediction in Carbonate System

Acidizing is a widely used technique for intensification of geothermal wells. At certain conditions, acid injection results in a formation of highly conductive channels, e.g. wormholes. The associated rock matrix dissolution is highly nonlinear and may influence the fluid flow as well as rock properties. Wormhole patterns and breakthrough time are mainly controlled by acid strength and injection rate. Due to high complexity and, therefore, high computational cost, existing conventional simulators usually do not include complex chemical reactions or use weak (sequential) coupling of flow with reactive transport. Although the sequential approach is good for various applications, it is limited by the CFL condition and might suffer from poor convergence. In this project, we investigate the wormhole phenomenon to provide a stable fully implicit method (FIM) to numerically solve the reactive flow and transport problem coupled with equilibrium and kinetic reactions associated with the acid injection. The developed framework is aimed to predict a pattern of dissolution as well as wormhole breakthrough time. For an accurate description of species interactions, we directly connect PHREEQC equilibrium computation with FIM framework for kinetic dissolution using the Element Balance approach. This coupling was achieved with an application of a recently introduced Operator-Based Linearization scheme utilized within a Delft Advanced Research Terra Simulator (DARTS).