Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid diagnosis of drug resistant tuberculosis: current perspectives and challenges

Tuberculosis (TB) caused by Mycobacterium tuberculosis complex remains one of the major public health problems, especially in developing countries. The emergence of drug resistant tuberculosis (both multidrug resistant and extensively drug resistant tuberculosis) is widely considered a serious threat to global TB control. Rapid diagnosis of drug resistant tuberculosis is one of the cornerstones for global TB control as it allows early epidemiological and therapeutic interventions. The present article provides an overview of the various diagnostic options available for drug resistant tuberculosis, including rapid conventional tools and newer molecular methods. Newly developed rapid phenotypic tests include automated liquid based culture and susceptibility tests, thin layer agar cultures, TK medium, microscopic-observation drug susceptibility assay and phage-based assay. Among newly developed molecular methods, real-time polymerase chain reaction (RT-PCR) and line probe assays (LPAs) have been commercialised and widely used in clinical laboratories. To effectively address the threats of drug resistant tuberculosis, global initiatives are required to scale-up culture and drug susceptibility testing capacities. In parallel efforts are needed to expand the use of novel and emerging molecular technologies for rapid diagnosis of drug resistance.
DISGENESIA GONADAL MIXTA CON FÓRMULA CROMOSÓMICA 45,X/46,X, (MAR). PRESENTACIÓN DE UNA PACIENTE

Se presento una paciente con estigmas turnerianos y cariotipo 45,X/46,XX con diagnostico inicial de sindrome de Turner a la que se le realizo clitoridectomia por hipertrofia del clitoris a los 8 meses de edad. Se reevaluo a los 6 anos de edad y se le realizo cariotipo con tecnicas de bandas G (GTG) con formula cromosomica de 45,X/46,X, (cromosoma marcador -mar); dicho marcador dio la impresion de una delecion del cromosoma X desde Xq13 ®Xq ter y Xp22 ®Xp ter. Se completo dicho estudio con tecnica molecular de reaccion en cadena de la polimerasa (PCR) y se identifico el gen SRY en el cromosoma marcador. Se realizo intervencion quirurgica por minimo acceso y se comprobo ausencia de utero asi como trompa en el lado derecho con ausencia de gonada y en el lado opuesto, testiculo rudimentario; se planteo el diagnostico de disgenesia gonadal mixta.
Higher‐Order Correlations in a Turbulent Field

Higher‐order time‐correlations and the associated skewnesses and flatnesses were measured in a turbulent field downstream of a grid using high‐speed computing techniques. The results were obtained using samples of 160 020 digitized data recorded at time intervals of 1/12 800 sec during time periods of approximately 12.5 sec. Comparison is made between the measured correlations and the higher‐order correlation curves corresponding to a Gaussian probability density distribution of turbulent velocities. The departures from Gaussianity are shown, and non‐Gaussian probability distributions are proposed which correspond considerably better to experimental reality. Several relations between correlation coefficients of different orders are obtained for the non‐Gaussian probability distributions and confirmed by comparison with the measured correlations, skewnesses, and flatnesses.
A multicenter, randomized trial of treatment for mild gestational diabetes.

BACKGROUND It is uncertain whether treatment of mild gestational diabetes mellitus improves pregnancy outcomes. METHODS Women who were in the 24th to 31st week of gestation and who met the criteria for mild gestational diabetes mellitus (i.e., an abnormal result on an oral glucose-tolerance test but a fasting glucose level below 95 mg per deciliter [5.3 mmol per liter]) were randomly assigned to usual prenatal care (control group) or dietary intervention, self-monitoring of blood glucose, and insulin therapy, if necessary (treatment group). The primary outcome was a composite of stillbirth or perinatal death and neonatal complications, including hyperbilirubinemia, hypoglycemia, hyperinsulinemia, and birth trauma. RESULTS A total of 958 women were randomly assigned to a study group--485 to the treatment group and 473 to the control group. We observed no significant difference between groups in the frequency of the composite outcome (32.4% and 37.0% in the treatment and control groups, respectively; P=0.14). There were no perinatal deaths. However, there were significant reductions with treatment as compared with usual care in several prespecified secondary outcomes, including mean birth weight (3302 vs. 3408 g), neonatal fat mass (427 vs. 464 g), the frequency of large-for-gestational-age infants (7.1% vs. 14.5%), birth weight greater than 4000 g (5.9% vs. 14.3%), shoulder dystocia (1.5% vs. 4.0%), and cesarean delivery (26.9% vs. 33.8%). Treatment of gestational diabetes mellitus, as compared with usual care, was also associated with reduced rates of preeclampsia and gestational hypertension (combined rates for the two conditions, 8.6% vs. 13.6%; P=0.01). CONCLUSIONS Although treatment of mild gestational diabetes mellitus did not significantly reduce the frequency of a composite outcome that included stillbirth or perinatal death and several neonatal complications, it did reduce the risks of fetal overgrowth, shoulder dystocia, cesarean delivery, and hypertensive disorders. (ClinicalTrials.gov number, NCT00069576.)
Principles of Plasma Diagnostics: Frontmatter

Preface to first edition Preface to second edition 1. Plasma diagnostics 2. Magnetic diagnostics 3. Plasma particle flux 4. Refractive-index measurements 5. Electromagnetic emission by free electrons 6. Electromagnetic radiation from bound electrons 7. Scattering of electromagnetic radiation 8. Neutral atom diagnostics 9. Fast ions and fusion products Appendices Glossary.
Chronic Lithium Use As a Rare Cause of Chronic Renal Failure

Lithium is the treatment of choice in bipolar disorders. Long-term use has important side effects such as nephrotoxicity. A 37-year-old female patient on lithium with proteinuria and renal failure was diagnosed as having chronic tubulointerstistial nephritis and secondary focal segmental glomerulosclerosis at renal biopsy. These findings were linked to lithium use. We present this case as it is a rare event. key words: Lithium, Focal segmental glomerulosclerosis, Chronic renal failure doi: 10.5262/tndt.2012. 1003.23 Yazisma Adresi: Ozkan GunGOR Ege Universitesi, Tip Fakultesi, Nefroloji Bilim Dali, Izmir, Turkiye Gsm : 0 506 664 80 54 E-posta : ozkangungor@yahoo.com. tr Gelis Tarihi : 10.02. 2012 Kabul Tarihi : 27.04. 2012 yan etkileridir (2). Lityumun bobrek fonksiyonlari uzerine de olumsuz etkileri vardir. Nefrojenik diyabetes insipitus, tubulointerstisyel nefrit ve nefrotik sendrom nadir gorulen ancak onemli yan etkileri arasindadir (3). Biz, bipolar bozukluk nedeniyle uzun sure lityum tedavisi alan hastada proteinuri ve bobrek yetmezligi gelismesi uzerine bobrek biyopsisi yaptik; kronik tubulointerstisyel nefrit ve FSGS saptadik. Olguyu literaturde nadir olmasi nedeniyle sunuyoruz.
Efficient `1=`q Norm Regularization

Sparse learning has recently received increasing attentio n in many areas including machine learning, statistics, and applied mathematics. The mixed-norm regularization based on the `1=`q norm withq > 1 is attractive in many applications of regression and classification in that it facilitates group s parsity in the model. The resulting optimization problem is, however, challenging to solve due to the structure of the `1=`q-regularization. Existing work deals with special cases in cluding q = 2;1 , and they can not be easily extended to the general case. In this paper, we propose an efficient algorithm based on the accelerated grad ient method for solving the `1=`q-regularized problem, which is applicable for all values of q larger than 1, thus significantly extending existing work. One key buildi ng block of the proposed algorithm is the `1=`q-regularized Euclidean projection (EP1q). Our theoretical analysis reveals the key properties of EP1q and illustrates why EP1q for the general q is significantly more challenging to solve than the special c ases. Based on our theoretical analysis, we develop an efficient algorit hm for EP1q by solving two zero finding problems. Experimental results demonstrat e the efficiency of the proposed algorithm.
Forced Convective Boiling of a Fluorocarbon Liquid in Reduced Size Channels--an Experimental Study

The present paper reports the results of an experimental study of forced convection boiling heat transfer from a 2 cm x 2 cm simulated chip to a fluorocarbon coolant (FX-3250) in a parallel-plate channel. The experimental parameters were the channel height (1 and 5 mm), the surface geometry of the simulated chip (flat and microfinned), and the coolant velocity (0.125-4 m/s). Longitudinal microfins (0.5 mm high x 0.5 mm wide) on the finned chip increased the actual surface area to about twice that of the flat surface. Attention was focused on the data of the 1-mm high channel. When compared with the flat chip data on the footprint area basis, the microfins produced 40% of heat transfer enhancement at coolant velocities less than 1 m/s; however, the enhancement diminishes at higher velocities. The boiling curves of the finned chip, based on the actual area, seem to reflect that there is an effect due to the bypassing of flow through the cross-section over the fins. For both the flat chip and the finned chip.
Exponential bounds for inhomogeneous random graphs with finite fourth moment

Rank 1 inhomogeneous random graphs are a natural generalization of Erd\H{o}s R\'enyi random graphs. In this generalization each node is given a weight. Then the probability that an edge is present depends on the product of the weights of the nodes it is connecting. In this article, we give precise and uniform exponential bounds on the size, weight and surplus of rank 1 inhomogeneous random graphs where the weights of the nodes behave like a random variable with finite fourth moment. We focus on the case where the mean degree of a random node is slightly larger than 1, we call that case the barely supercritical regime. These bounds will be used in follow up articles to study a general class of random minimum spanning trees. They are also of independent interest since they show that these inhomogeneous random graphs behave like Erd\H{o}s R\'enyi random graphs even in a barely supercritical regime. The proof relies on novel concentration bounds for sampling without replacement and a careful study of the exploration process.
Unsupervised Online Feature Selection for Cost-Sensitive Medical Diagnosis

In medical diagnosis, physicians predict the state of a patient by checking measurements (features) obtained from a sequence of tests, e.g., blood test, urine test, followed by invasive tests. As tests are often costly, one would like to obtain only those features (tests) that can establish the presence or absence of the state conclusively. Another aspect of medical diagnosis is that we are often faced with unsupervised prediction tasks as the true state of the patients may not be known. Motivated by such medical diagnosis problems, we consider a Cost-Sensitive Medical Diagnosis (CSMD) problem, where the true state of patients is unknown. We formulate the CSMD problem as a feature selection problem where each test gives a feature that can be used in a prediction model. Our objective is to learn strategies for selecting the features that give the best trade-off between accuracy and costs. We exploit the ‘Weak Dominance’ property of problem to develop online algorithms that identify a set of features which provides an ‘optimal’ trade-off between cost and accuracy of prediction without requiring to know the true state of the medical condition. Our empirical results validate the performance of our algorithms on problem instances generated from real-world datasets.
A compendium of distances to molecular clouds in the Star Formation Handbook

Accurate distances to local molecular clouds are critical for understanding the star and planet formation process, yet distance measurements are often obtained inhomogeneously on a cloud-by-cloud basis. We have recently developed a method that combines stellar photometric data with Gaia DR2 parallax measurements in a Bayesian framework to infer the distances of nearby dust clouds to a typical accuracy of ∼5%. After refining the technique to target lower latitudes and incorporating deep optical data from DECam in the southern Galactic plane, we have derived a catalog of distances to molecular clouds in Reipurth (2008, Star Formation Handbook, Vols. I and II) which contains a large fraction of the molecular material in the solar neighborhood. Comparison with distances derived from maser parallax measurements towards the same clouds shows our method produces consistent distances with ≲10% scatter for clouds across our entire distance spectrum (150 pc−2.5 kpc). We hope this catalog of homogeneous distances will serve as a baseline for future work.
Resilient Primal–Dual Optimization Algorithms for Distributed Resource Allocation

Distributed algorithms for multiagent resource allocation can provide privacy and scalability over centralized algorithms in many cyber-physical systems. However, the distributed nature of these algorithms can render these systems vulnerable to man-in-the-middle attacks that can lead to nonconvergence and infeasibility of resource allocation schemes. In this article, we propose attack-resilient distributed algorithms based on primal–dual optimization when Byzantine attackers are present in the system. In particular, we design attack-resilient primal–dual algorithms for static and dynamic impersonation attacks by means of robust statistics. For static impersonation attacks, we formulate a robustified optimization model and show that our algorithm guarantees convergence to a neighborhood of the optimal solution of the robustified problem. On the other hand, a robust optimization model is not required for the dynamic impersonation attack scenario and we are able to design an algorithm that is shown to converge to a near-optimal solution of the original problem. We analyze the performances of our algorithms through both theoretical and computational studies.
Equal cost of computation for truth and falsity of experimental quantum propositions necessitates quantum parallel computing

Notwithstanding interest and excitement building around quantum computing in the last decades, a concise statement saying where this computing can truly help is still missing. As it is shown in the present paper, equal cost of computation for truth and falsity of experimental quantum propositions (required in order to infer a conclusion from a premise) cannot be achieved with classical computing. On the other hand, this equality might be realized with quantum parallel computing provided that the efficiency of such computing can be greater than 1.
Event-By-Event Fluctuation of Maximum Particle Density in Narrow Pseudo-Rapidity Interval at a Few AGeV/c

A detailed study of event by event fluctuation of maximum particle density of the produced particles in narrow pseudo-rapidity interval in terms of the scaled variance {\omega} has been carried out for 16O-AgBr, 28Si-AgBr and 32S-AgBr interactions at an incident momentum of 4.5 AGeV/c. For all the interactions the values of scaled variance are found to be greater than zero indicating the presence of strong event by event fluctuation of maximum particle density values in the multiparticle production process. The event by event fluctuations are found to decrease with the increase of pseudo-rapidity interval. Experimental analysis has been compared with the results obtained from the analysis of events simulated by the Ultra Relativistic Quantum Molecular Dynamics (UrQMD) model. UrQMD model could not replicate the experimental results.
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning

Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable and task boundaries are known during training. However, if our goal is to develop an algorithm that learns as humans do, this setting is far from realistic and it is essential to develop a methodology that works in a task-free manner. Meanwhile, among several branches of continual learning, expansion-based methods have the advantage of eliminating catastrophic forgetting by allocating new resource to learn new data. In this work, we propose an expansion-based approach for task-free continual learning for the first time. Our model, named Continual Neural Dirichlet Process Mixture (CN-DPM), consists of a set of neural network experts that are in charge of a subset of the data. CN-DPM expands the number of experts in a principled way under the Bayesian nonparametric framework. With extensive experiments, we show that our model successfully performs task-free continual learning for both discriminative and generative tasks such as image classification and image generation.
A Proximal Linearization-based Decentralized Method for Nonconvex Problems with Nonlinear Constraints

Decentralized optimization for non-convex problems are now demanding by many emerging applications (e.g., smart grids, smart building, etc.). Though dramatic progress has been achieved in convex problems, the results for non-convex cases, especially with non-linear constraints, are still largely unexplored. This is mainly due to the challenges imposed by the non-linearity and non-convexity, which makes establishing the convergence conditions bewildered. This paper investigates decentralized optimization for a class of structured non-convex problems characterized by: (i) nonconvex global objective function (possibly nonsmooth) and (ii) coupled nonlinear constraints and local bounded convex constraints w.r. t. the agents. For such problems, a decentralized approach called Proximal Linearizationbased Decentralized Method (PLDM) is proposed. Different from the traditional (augmented) Lagrangian-based methods which usually require the exact (local) optima at each iteration, the proposed method leverages a proximal linearization-based technique to update the decision variables iteratively, which makes it computationally efficient and viable for the non-linear cases. Under some standard conditions, the PLDM global convergence and local convergence rate to the epsilon-critical points are studied based on the Kurdyka-Lojasiewicz property which holds for most analytical functions. Finally, the performance and efficacy of the method are illustrated through a numerical example and an application to multi-zone heating, ventilation and air-conditioning (HVAC) control.
Modeling the Strongest Silicate Emission Features of Local Type 1 AGNs

We measure the 10 and 18 μm silicate features in a sample of 67 local (z < 0.1) type 1 active galactic nuclei (AGN) with available Spitzer spectra dominated by nonstellar processes. We find that the 10 μm silicate feature peaks at with a strength (Sip = ln fp(spectrum)/fp(continuum)) of , while the 18 μm one peaks at with a strength of . We select from this sample sources with the strongest 10 μm silicate strength ( , 10 objects). We carry out a detailed modeling of the infrared spectrometer/Spitzer spectra by comparing several models that assume different geometries and dust composition: a smooth torus model, two clumpy torus models, a two-phase medium torus model, and a disk+outflow clumpy model. We find that the silicate features are well modeled by the clumpy model of Nenkova et al., and among all models, those including outflows and complex dust composition are the best. We note that even in AGN-dominated galaxies, it is usually necessary to add stellar contributions to reproduce the emission at the shortest wavelengths.
Ultra-Narrow Spectral Response of a Hybrid Plasmonic-Grating Sensor

We configure and analyze a nanostructured device that hybridizes grating modes and surface plasmon resonances. The model uses an effective index of refraction that considers the volume fraction of the involved materials, and the propagation depth of the plasmon through the structure. Our geometry is an extruded low-order diffraction grating made of dielectric nano-triangles. Surface plasmon resonances are excited at a metal/dielectric interface, which is separated from the analyte by a high-index dielectric layer. The optical performance of the refractometric sensor is highly competitive in sensitivity and figure of merit (FOM) because of the the ultra-narrow spectral response (below 0.1 nm). Moreover, it is operative within a wide range of the index of refraction (from 1.3 till 1.56), and also works under normal incidence conditions.
Deep Unsupervised Common Representation Learning for LiDAR and Camera Data using Double Siamese Networks

Domain gaps of sensor modalities pose a challenge for the design of autonomous robots. Taking a step towards closing this gap, we propose two unsupervised training frameworks for finding a common representation of LiDAR and camera data. The first method utilizes a double Siamese training structure to ensure consistency in the results. The second method uses a Canny edge image guiding the networks towards a desired representation. All networks are trained in an unsupervised manner, leaving room for scalability. The results are evaluated using common computer vision applications, and the limitations of the proposed approaches are outlined.
Hard 3-CNF-SAT problems are in P - A first step in proving NP=P

The relationship between the complexity classes $P$ and $NP$ is an unsolved question in the field of theoretical computer science. In the first part of this paper, a lattice framework is proposed to handle the 3-CNF-SAT problems, known to be in $NP$. In the second section, we define a multi-linear descriptor function ${\cal H}_\varphi$ for any 3-CNF-SAT problem $\varphi$ of size $n$, in the sense that ${\cal H}_\varphi : \{0,1\}^n \rightarrow \{0,1\}^n$ is such that $Im \; {\cal H}_\varphi$ is the set of all the solutions of $\varphi$. A new merge operation ${\cal H}_\varphi \bigwedge {\cal H}_{\psi}$ is defined, where $\psi$ is a single 3-CNF clause. Given ${\cal H}_\varphi$ [but this can be of exponential complexity], the complexity needed for the computation of $Im \; {\cal H}_\varphi$, the set of all solutions, is shown to be polynomial for hard 3-CNF-SAT problems, i.e. the one with few ($\leq 2^k$) or no solutions. The third part uses the relation between ${\cal H}_\varphi$ and the indicator function $\mathbb{1}_{{\cal S}_\varphi}$ for the set of solutions, to develop a greedy polynomial algorithm to solve hard 3-CNF-SAT problems.