Miracle-dz/UniFAR-FTUs huggingface dataset:
Microsatellite Interruptions Stabilize Primate Genomes and Exist as Population-Specific Single Nucleotide Polymorphisms within Individual Human Genomes

Interruptions of microsatellite sequences impact genome evolution and can alter disease manifestation. However, human polymorphism levels at interrupted microsatellites (iMSs) are not known at a genome-wide scale, and the pathways for gaining interruptions are poorly understood. Using the 1000 Genomes Phase-1 variant call set, we interrogated mono-, di-, tri-, and tetranucleotide repeats up to 10 units in length. We detected ∼26,000-40,000 iMSs within each of four human population groups (African, European, East Asian, and American). We identified population-specific iMSs within exonic regions, and discovered that known disease-associated iMSs contain alleles present at differing frequencies among the populations. By analyzing longer microsatellites in primate genomes, we demonstrate that single interruptions result in a genome-wide average two- to six-fold reduction in microsatellite mutability, as compared with perfect microsatellites. Centrally located interruptions lowered mutability dramatically, by two to three orders of magnitude. Using a biochemical approach, we tested directly whether the mutability of a specific iMS is lower because of decreased DNA polymerase strand slippage errors. Modeling the adenomatous polyposis coli tumor suppressor gene sequence, we observed that a single base substitution interruption reduced strand slippage error rates five- to 50-fold, relative to a perfect repeat, during synthesis by DNA polymerases α, β, or η. Computationally, we demonstrate that iMSs arise primarily by base substitution mutations within individual human genomes. Our biochemical survey of human DNA polymerase α, β, δ, κ, and η error rates within certain microsatellites suggests that interruptions are created most frequently by low fidelity polymerases. Our combined computational and biochemical results demonstrate that iMSs are abundant in human genomes and are sources of population-specific genetic variation that may affect genome stability. The genome-wide identification of iMSs in human populations presented here has important implications for current models describing the impact of microsatellite polymorphisms on gene expression.
Developments in human growth hormone preparations: sustained-release, prolonged half-life, novel injection devices, and alternative delivery routes

Since the availability of recombinant human growth hormone (rhGH) enabled the application of human growth hormone both in clinical and research use in the 1980s, millions of patients were prescribed a daily injection of rhGH, but noncompliance rates were high. To address the problem of noncompliance, numerous studies have been carried out, involving: sustained-release preparations, prolonged half-life derivatives, new injectors that cause less pain, and other noninvasive delivery methods such as intranasal, pulmonary and transdermal deliveries. Some accomplishments have been made and launched already, such as the Nutropin Depot® microsphere and injectors (Zomajet®, Serojet®, and NordiFlex®). Here, we provide a review of the different technologies and illustrate the key points of these studies to achieve an improved rhGH product.
A Simple Strategy for the Qualitative Assessment of Learning Capacity Of Clients with Mental Retardation or Other Severe Cognitive Deficits

A simple procedure for evaluating the learning capacities of individuals with severe cognitive deficits is described. The procedure is based theoretically on a modification of the learning hierarchy described by Thomas (1980; 1993; 1996), and it examines simple observational learning, simple instrumental conditioning, learning a concurrent object discrimination, and mastering a reversal or several reversals of the object discrimination. It is argued that information gleaned from such an assessment is valuable in describing: (1) a client’s potential to profit from various behavior modification, education, and training strategies, (2) possible reasons that a particular client failed to show progress in a particular training program, and (3) neuropsychological conditions such as frontal lobe conditions or dementia that limit the adaptive capacity of an individual and, therefore, can be responsible for lack of progress in behavior therapy or training. The maximal level of learning attained was examined with a sample of 65 individuals diagnosed as being either moderately, severely, or profoundly mentally retarded. Consistent with expectations, nonparametric statistical analyses revealed that individuals with moderate mental retardation achieved a significantly higher level on the test than individuals diagnosed with severe mental retardation. Likewise, individuals with severe mental retardation performed at a higher level than individuals with profound mental retardation. Nonparametric analyses also revealed statistically significant correlations between the maximal level of learning achieved and Global Memory Composite scores and functional age equivalents as determined through administration of the Vineland Adaptive Behavior Scales. The diagnostic utility of the learning screen is demonstrated in sample cases where this procedure resulted in meaningful information.
Fracture model with variable range of interaction.

We introduce a fiber bundle model where the interaction among fibers is modeled by an adjustable stress-transfer function that can interpolate between the two limiting cases of load redistribution, i.e., the global and the local load sharing schemes. By varying the range of interaction, several features of the model are numerically studied and a crossover from mean-field to short-range behavior is obtained. The properties of the two regimes and the emergence of the crossover in between are explored by numerically studying the dependence of the ultimate strength of the material on the system size, the distribution of avalanches of breakings, and of the cluster sizes of broken fibers. Finally, we analyze the moments of the cluster size distributions to accurately determine the value at which the crossover is observed.
Effective Implementation of Agile Practices Ingenious and Organized Theoretical Framework

Delivering software in traditional ways is challenged by agile software development to provide a very different approach to software development. Agile methods aim at fast, light and efficient than any other vigorous method to develop and support customers business without being chaotic. Agile software development methods claim to be people-oriented rather than process-oriented and adaptive rather than predictive. Solid Determination and Dedicated efforts are required in agile development to overcome the disadvantages of predefined set of steps and changing requirements to see the desirable outcome and to avoid the predictable results. These methods reach the target promptly by linking developers and stakeholders. The focus of this research paper is two fold. The first part is to study different agile methodologies, find out the levelheaded difficulties in agile software development and suggests possible solutions with a collaborative and innovative framework. The second part of the research paper concentrates on the importance of handling traceability in agile software development and finally proposes an ingenious and organized theoretical framework with a systematic approach to agile software development.
Speech Enhancement Using a Multidimensional Mixture-Maximum Model

We present a single-microphone speech enhancement algorithm that models the log-spectrum of the noise-free speech signal by a multidimensional Gaussian mixture. The proposed estimator is based on an earlier study which uses the single-dimensional mixture-maximum (MIXMAX) model for the speech signal. The experimental study shows that there is only a marginal difference between the proposed extension and the original algorithm in terms of both objective and subjective performance measures.
Generation of Certifiably Correct Programs from Formal Models

Application of formal notations and verifications techniques helps to deliver systems that are free from engineering defects. A code generator is an essential tool for formal development of real-world systems; it transforms models into runnable software quickly, consistently and reproducibly. Commonly, a code generator is a program constructed informally and producing an output that is not formally traced to an input. Industrial standards to the development of safety-critical systems, such as IEC 61508, require a justification for any tool used in a development: extensive prior experience or a formal certification. An extensive experience is often not an option as there are very few sufficiently mature modelling toolsets. The certification of a code generator is a major effort increasing costs and development time. We propose an approach where a modeller places no trust whatsoever in the code generation stage but rather obtains software that is certifiable without any further effort. The essence of the approach is in the transformation of a formal model into runnable software that is demonstratively correct in respect to a given set of verification criteria, coming from a requirements document. A Hoare logic is used to embedd correctness criteria into the resultant program; the approach supports design-by-contract annotations to allow developer to mix formal and informal parts with a fair degree of rigour.
Pose graph optimization with hierarchical conditionally independent graph partitioning

In this paper, we propose a hierarchical pose graph optimization algorithm, which hierarchically divides a large pose graph into subgraphs and solves the optimization problem for each subgraph independently. Applying a modified graph partitioning algorithm, normalized cut, the original graph could be partitioned into subgraphs, which are conditionally independent on a set of key nodes. A modified normalized cut algorithm is applied to automatically partition a pose graph into several subgraphs, which are independent on each other when conditioned on a small number of keynodes. Preserving keynodes only, a simplified upper level graph is generated by a pose graph sparsification algorithm. Given the optimization results of all keynodes, which are obtained by solving the upper level graph, each subgraph in the lower level can be solved efficiently without concerning other subgraphs. The scale of each optimization is limited during graph partitioning. Therefore, the efficiency of the optimization is improved. Experiments with both public standard datasets and a dataset collected by an autonomous guided vehicle (AGV) system are conducted to test our algorithm.
Prevención de paciente con problemas de sedentarismo cardiovascular

El sedentarismo es en la actualidad una de las principales fuentes de amenaza para la salud de las personas como efecto de la inactividad física provocando los problemas cardiovasculares, la prevención, tratamiento, observación y recuperación a causa de las enfermedades crónicas no transmisibles; es así que los especialistas en ciencias médicas y del deporte y consecuentemente las autoridades sanitarias recomiendan que toda persona con o sin problemas cardiovasculares incluyan en el desempeño de su vida cotidiana, tanto en el trabajo como en el hogar desarrollen actividad física de forma regular, ya sea para recuperarse o mantener su salud. Por lo tanto, es de considerar los beneficios de la actividad física como herramienta clave para solucionar los problemas propios del sedentarismo causando los problemas cardiovasculares, las nuevas recomendaciones de actividad física para la salud establecen con la aprobación de la OMS, destacan que al menos 30 minutos diarios, por lo menos cinco días de la semana, de forma continua o en sesiones acumulativas de 10 o 15 minutos de una actividad de intensidad moderada.
Coinfection with Haemophilus influenzae promotes pneumococcal biofilm formation during experimental otitis media and impedes the progression of pneumococcal disease.

BACKGROUND Otitis media is an extremely common pediatric infection and is mostly caused by bacteria that are carried within the nasopharyngeal microbiota. It is clear that most otitis media cases involve simultaneous infection with multiple agents. METHODS Chinchillas were infected with nontypeable Haemophilus influenzae, Streptococcus pneumoniae, or a combination of both organisms, and the course of disease was compared. In vitro experiments were also performed to address how coinfection impacts biofilm formation. RESULTS The incidence of systemic disease was reduced in coinfected animals, compared with those infected with pneumococcus alone. Pneumococci were present within surface-attached biofilms in coinfected animals, and a greater proportion of translucent colony type was observed in the coinfected animals. Because this colony type has been associated with pneumococcal biofilms, the impact of coinfection on pneumococcal biofilm formation was investigated. The results clearly show enhanced biofilm formation in vitro by pneumococci in the presence of H. influenzae. CONCLUSIONS Based on these data, we conclude that coinfection with H. influenzae facilitates pneumococcal biofilm formation and persistence on the middle ear mucosal surface. This enhanced biofilm persistence correlates with delayed emergence of opaque colony variants within the bacterial population and a resulting decrease in systemic infection.
Efficient Scheme for Channel Estimation in OFDM System

The choice of channel estimation is of fundamental importance in OFDM receiver designs, which further involves a trade-off between complexity and estimation accuracy. In pilot-symbol-aided OFDM system, the Least Square (LS) estimator suffers from inherent additive Gaussian noise and Inter Carrier Interference (ICI), although the estimator exhibits lower complexity and requires implicit knowledge of the channel. In comparison, the Minimum Mean-Square-Error (MMSE) estimator shows much better performance than the LS. However, a major drawback of the MMSE is its higher computational overhead, which further grows with increasing the number of pilots. Accordingly, the optimal design of channel estimators has remained an area of ongoing research. This study performs modifications to both existing LS and MMSE algorithms, followed by proposing two new channel estimators, namely Simplified Least Square (SLS) and Simplified Minimum Mean Square Error (SMMSE). Mathematical analyses and simulation results show that the proposed SLS method is more robust against the additive Gaussian noise and outperforms the original LS estimator. Moreover, this method can perform almost similar to the MMSE for a range of SNRs. Meanwhile, in our study the proposed SMMSE method requires only a minimum knowledge of the Channel Impulse Response (CIR) to efficiently estimate the channel. Additionally, the SMMSE exhibits computational complexity to be significantly lower than that of the original MMSE estimator
Performance Evaluation of IEEE 802.11 Infrastructure Mode with Intra-Cell UDP Traffic

IEEE 802.11-based Wireless LANs have become ubiquitous in coffee shops, office buildings, and hundreds of millions of residential homes. Access points, or APs, have been playing an important role in infrastructure Wireless LANs. An AP provides lots of services including cell identification, synchronization, authentication, distribution service, etc. Another important functionality of AP is to relay messages among wireless stations inside the same wireless cell. In current implementations, an AP needs to compete for the wireless channel with all other wireless stations using DCF protocol. Our objective in this paper is to design systematic and reproducible experiments to show that, with uncontrolled UDP traffic in the network, the AP becomes the system bottleneck and the system goodput could drop to an unacceptable level, mainly due to buffer overflow at the AP. E.g., in an 802.11g wireless network operating at 54 Mbps, the saturation UDP goodput can be as low as only several Mbps, and TCP connections can be easily choked by UDP traffic for a long duration. We think this observation is important because UDP traffic volume is growing rapidly with the widely-deployed Voice over WiFi, wireless surveillance system, digital games, multimedia streaming applications, etc.
CSLDA and LDA fusion based face recognition

Face recognition has great demands and become one of the most important research area of pattern recognition but there are several issues involved in it. Unsupervised statistical methods i.e. PCA, LDA, ICA are the most popular algorithms in face recognition that finds the set of basis images and represents faces as linear combination of those images. This paper presents a novel layered face recognition method based on CSLDA and LDA. The basic aim is to decrease FAR by reducing the face dataset to very small size through layered linear discriminant analysis. Although the computational complexity at the time of recognition is much higher than conventional PCA and LDA because weights are computed for small subspace at time of recognition but it provide a good results especially for large dataset. CSLDA of LDA is insensitive to large dataset and also small sample size and it provided 84% accuracy on Banca face database. The proposed approach is also applicable on other applications and recognition methods i.e. PCA, KDA, DLDA etc. Streszczenie. Rozpoznawanie twarzy jest jedną z bardziej waznych metod graficznego rozpoznawania wzorow. Najbardziej popularnymi metodami są tu PCA, LDA, ICA gdzie twarz jest reprezentowana jako liniowa kombinacja bazowych komponentow. Artykul prezentuje inną metode bazującą na CSLDA i LDA. Glownym celem jest zmniejszenie FAR przez zredukowanie bazy danych do bardzo malych rozmiarow przez warstwową liniową dyskryminacje. Zlozonośc komputerowa metody jest nieco wieksza ale otrzymane rezultaty, glownie zmniejszenie bledu są zachecające. (Rozpoznawanie twarzy przez fuzje metod CSLDA i LDA).
James D. Meernik, Acting Dean of the Toulouse Graduate School INCREMENTAL LEARNING WITH LARGE DATASETS Balathasan Giritharan Dissertation Prepared for the Degree of DOCTOR OF PHILOSOPHY UNIVERSITY OF NORTH TEXAS

This dissertation focuses on the novel learning strategy based on geometric support vector machines to address the difficulties of processing immense data set. Support vector machines find the hyper-plane that maximizes the margin between two classes, and the decision boundary is represented with a few training samples it becomes a favorable choice for incremental learning. The dissertation presents a novel method Geometric Incremental Support Vector Machines (GISVMs) to address both efficiency and accuracy issues in handling massive data sets. In GISVM, skin of convex hulls is defined and an efficient method is designed to find the best skin approximation given available examples. The set of extreme points are found by recursively searching along the direction defined by a pair of known extreme points. By identifying the skin of the convex hulls, the incremental learning will only employ a much smaller number of samples with comparable or even better accuracy. When additional samples are provided, they will be used together with the skin of the convex hull constructed from previous dataset. This results in a small number of instances used in incremental steps of the training process. Based on the experimental results with synthetic data sets, public benchmark data sets from UCI and endoscopy videos, it is evident that the GISVM achieved satisfactory classifiers that closely model the underlying data distribution. GISVM improves the performance in sensitivity in the incremental steps, significantly reduced the demand for memory space, and demonstrates the ability of recovery from temporary performance degradation.
Optimization of a Multiproduct CONWIP-based Manufacturing System using Artificial Bee Colony Approach

In this paper, A Mixed Integer Non-linear Programming Model (MINLP) is developed to simultaneously generate an optimal sequence of jobs and WIP level in a serial CONWIP production line in order to minimize the overall completion time. Artificial bee colony algorithm , a novel heuristic optimization approach, is proposed to solve this model. Unlike many existing approaches, which are based on deterministic search algorithms such as nonlinear programming and mixed integer programming, the proposed method does not use a linearized or simplified model of the system. A numerical example is used to test the developed model and algorithm. Computational results validate the modeling and computational efficiency of the solution method.
Frying stability of sunflower oil blended with jujube (Ziziphus mauritiana Lam.) leaf extract

Abstract The aim of present study was to compare the effects of ultrasound‐assisted and microwave‐assisted extraction with solvent extraction method on antioxidant activities of jujube (Ziziphus mauritiana Lam.) leaf extracts in stability of sunflower oil during deep frying. The antioxidant activities of the extracts were evaluated by using 2, 2‐diphenyl‐1‐picrylhydrazyl (DPPH˙) radical scavenging and β‐carotene bleaching assays. Ultrasound‐assisted extraction was the most effective method on antioxidant activities of extracts and extraction yield of phenolic compounds compared to other extraction techniques. Protective effect of methanol–water extract of jujube leaf obtained with ultrasound‐assisted extraction (ULMW) at 500 and 700 ppm in stability of sunflower oil was compared to synthetic antioxidants by measuring total polar compounds (TPC), carbonyl value (CV), peroxide value (PV), free fatty acids (FFA), oxidative stability index (OSI), conjugated dienes (CD), and trienes values (CT). Results showed ULMW at 700 ppm had higher stabilization efficiency than synthetic antioxidants.