Miracle-dz/UniFAR-FTUs huggingface dataset:
High Genetic Diversity Revealed by Variable-Number Tandem Repeat Genotyping and Analysis of hsp65 Gene Polymorphism in a Large Collection of “Mycobacterium canettii” Strains Indicates that the M. tuberculosis Complex Is a Recently Emerged Clone of “M. canettii”

ABSTRACT We have analyzed, using complementary molecular methods, the diversity of 43 strains of “Mycobacterium canettii” originating from the Republic of Djibouti, on the Horn of Africa, from 1998 to 2003. Genotyping by multiple-locus variable-number tandem repeat analysis shows that all the strains belong to a single but very distant group when compared to strains of the Mycobacterium tuberculosis complex (MTBC). Thirty-one strains cluster into one large group with little variability and five strains form another group, whereas the other seven are more diverged. In total, 14 genotypes are observed. The DR locus analysis reveals additional variability, some strains being devoid of a direct repeat locus and others having unique spacers. The hsp65 gene polymorphism was investigated by restriction enzyme analysis and sequencing of PCR amplicons. Four new single nucleotide polymorphisms were discovered. One strain was characterized by three nucleotide changes in 441 bp, creating new restriction enzyme polymorphisms. As no sequence variability was found for hsp65 in the whole MTBC, and as a single point mutation separates M. tuberculosis from the closest “M. canettii” strains, this diversity within “M. canettii” subspecies strongly suggests that it is the most probable source species of the MTBC rather than just another branch of the MTBC.
Structural Basis for the Specificity of Bipartite Nuclear Localization Sequence Binding by Importin-α*

Importin-α is the nuclear import receptor that recognizes cargo proteins carrying conventional basic monopartite and bipartite nuclear localization sequences (NLSs) and facilitates their transport into the nucleus. Bipartite NLSs contain two clusters of basic residues, connected by linkers of variable lengths. To determine the structural basis of the recognition of diverse bipartite NLSs by mammalian importin-α, we co-crystallized a non-autoinhibited mouse receptor protein with peptides corresponding to the NLSs from human retinoblastoma protein and Xenopus laevis phosphoprotein N1N2, containing diverse sequences and lengths of the linker. We show that the basic clusters interact analogously in both NLSs, but the linker sequences adopt different conformations, whereas both make specific contacts with the receptor. The available data allow us to draw general conclusions about the specificity of NLS binding by importin-α and facilitate an improved definition of the consensus sequence of a conventional basic/bipartite NLS (KRX10–12KRRK) that can be used to identify novel nuclear proteins.
Performance analysis of turbo coding with blind channel estimation based on subspace decomposition in a SIMO FIR channel

This paper addresses the issue of blind channel estimation (BCE) of multi channel FIR filters. This is an issue arising in e.g. mobile communication systems using digital signaling. It also arises in the presence of frequency selective fading, when training (reference/pilot) symbols are unavailable. In this contribution we analyze the performances of ½ and - rate turbo codes of various frame lengths using BCE. The BCE technique used here is the second order statistics algorithm based on subspace decomposition (SSD). It has been shown in earlier work that the SSD algorithm, which exploits the orthogonality property between the noise subspace and the channel matrix, can estimate the channel's impulse response up to a multiplicative constant. The turbo coding schemes were combined with this BCE technique in order to characterize a realistic scenario. Two types of blind equalizers were investigated; the zero forcing and the minimum mean squared error equalizers.
Identification of Wiener systems with monotonous nonlinearity

A Wiener system is composed of a linear dynamic subsystem followed by a static nonlinearity. It is well known in the literature that the identi cation of the linear subsystem of a Wiener system can be separated from that of the output nonlinearity, if the input signal is Gaussian. In order to deal with non Gaussian inputs, two new algorithms are proposed in this paper for direct identi cation of the linear susbsystem, regardless of any parameterization of the output nonlinearity. The essential assumption required in this paper is the strict monotonicity of the output nonlinearity.
A Tool for Visualizing and Exploring Relationships Among Cancer-Related Patents

In this paper, we present a prototype for an online interactive visualization tool for analyzing the semantic proximities of US patent documents that are related to cancer treatments. This tool allows the user to perform keyword searches and then presents visualizations of sets of relevant patent documents clustered by semantic similarity. Semantic similarity is calculated using a combination of word embeddings obtained using the skip-gram algorithm and the t-SNE dimensionality reduction algorithm. The user may then select individual points in the cluster to view more detailed patent information. This process allows the user to explore the connections between related patents and see more general trends in the semantic shape of the technological space. It is our hope that this tool may serve as one of the starting points for data analysis leading to future innovative approaches to cancer treat-
Sparse Canonical Correlation Analysis with Application to Genomic Data Integration

Large scale genomic studies with multiple phenotypic or genotypic measures may require the identification of complex multivariate relationships. In multivariate analysis a common way to inspect the relationship between two sets of variables based on their correlation is canonical correlation analysis, which determines linear combinations of all variables of each type with maximal correlation between the two linear combinations. However, in high dimensional data analysis, when the number of variables under consideration exceeds tens of thousands, linear combinations of the entire sets of features may lack biological plausibility and interpretability. In addition, insufficient sample size may lead to computational problems, inaccurate estimates of parameters and non-generalizable results. These problems may be solved by selecting sparse subsets of variables, i.e. obtaining sparse loadings in the linear combinations of variables of each type. In this paper we present Sparse Canonical Correlation Analysis (SCCA) which examines the relationships between two types of variables and provides sparse solutions that include only small subsets of variables of each type by maximizing the correlation between the subsets of variables of different types while performing variable selection. We also present an extension of SCCA - adaptive SCCA. We evaluate their properties using simulated data and illustrate practical use by applying both methods to the study of natural variation in human gene expression.
Discretized model of proteins

A discretized model of globular proteins is employed in a Monte Carlo study of the helix-coil transition of polyalanine and the collapse transition of polyvaline. The present lattice realization permits real protein crystal structures to be represented at the level of 1 A resolution. Furthermore, the Monte Carlo dynamic scheme is capable of moving elements of assembled secondary and supersecondary structure. The potentials of mean force for the interactions are constructed from the statistics of a set of high resolution x-ray structures of nonhomologous proteins. The cooperativity of formation of ordered structures is found to be larger when the major contributions to the conformational energy of the low temperature states come from hydrogen bonds and short range conformational propensities. The secondary structure seen in the folded state is the result of an interplay between the short and long range interactions. Compactness itself, driven by long range, nonspecific interactions, seems to be insufficient to generate any appreciable secondary structure. A detailed examination of the dynamics of highly helical model proteins demonstrates that all elements of secondary structure are mobile in the present algorithm, and thus the folding pathways do not depend on the use of a lattice approximation. Possible applications of the present model to the prediction of protein 3D structures are briefly discussed.
Position Measurement and Wireless Measurement System of Straightness Error Based on Two-Dimensional PSD

The wireless measurement system of straightness error based on two-dimensional PSD, C8051F330 and PTR4000 was designed. The principle of PSD, the signal processing circuit, and the wireless straightness error data transmission system were introduced in detail. An accuracy algorithm meeting the least condition which constructed the characteristic polygon and calculated the maximum intercepts of the measuring points in the characteristic polygon as straightness error was proposed. The system has features of high precision and fast speed. Combination with the two types of measurement methods of straightness error, the evaluation software of straightness error was designed. It can be used with other straightness measuring instruments such as level-meter, autocollimator.
A static approach for the frequency shift of parasitic excitations in MEMS gyroscopes with geometric nonlinear drive mode

We present a method to estimate the frequency shift of parasitic modes in MEMS gyroscopes with respect to the displacement of the drive mode. Our method is based on static FE simulations in ANSYS and thus it is computationally efficient and very fast. The method correctly reproduces the measurements of a benchmark MEMS structure.
Reduced-order ℓ2-ℓ∞ filtering for discrete-time T-S fuzzy systems with stochastic perturbation

This paper is concerned with the problem of ℓ2-ℓ∞ filtering for discrete-time Takagi-Sugeno (T-S) fuzzy systems with stochastic perturbation. Firstly, a basis-dependent existence condition of desirable ℓ2-ℓ∞ filters is proposed. Then by means of the convex linearisation technique, the derived condition is transformed into some strict linear matrix inequality (LMI) constraints, by which both full- and reduced-order filters can be designed. Moreover, for the reduced-order ℓ2-ℓ∞ filter design, a novel analysis and design method with the projection lemma is also provided. Finally, a numerical example is provided to illustrate the feasibility and effectiveness of the proposed full- and reduced-order ℓ2-ℓ∞ filter design methods.
Adaptation of universal dependencies to specific grammars: The case of Persian dependency grammar

Universal Dependencies is the latest standard annotation scheme available, aiming at cross-linguistically consistent treebank annotation. It has already been adapted to a large number of languages including Persian. The trend, however, has been to take into account the original principles of dependency grammar rather than a description of the specific language in dependency terms. This paper reports an attempt to adapt Universal Dependencies to form a scheme for annotation of Persian dependency structure based on a comprehensive description of Persian syntax according to a theory introduced as the Autonomous Phrases Theory. The main idea there is that the significance of phrases should be appreciated in dependency analyses due to their cognitive reality, and the notion of valency is also extended beyond verbs. On that basis, every dependent of whatever head type is classified as either a complement or an adjunct depending on whether or not it plays a role in the valency structure of the head. A tagset was proposed consisting of fifty-three dependency relations, including fifteen original labels and the rest borrowed from the universal dependencies.
Contribution of trends in survival and coronary-event rates to changes in coronary heart disease mortality: 10-year results from 37 WHO MONICA project populations. Monitoring trends and determinants in cardiovascular disease.

BACKGROUND The WHO MONICA (monitoring trends and determinants in cardiovascular disease) Project monitored, from the early 1980s, trends over 10 years in coronary heart disease (CHD) across 37 populations in 21 countries. We aimed to validate trends in mortality, partitioning responsibility between changing coronary-event rates and changing survival. METHODS Registers identified non-fatal definite myocardial infarction and definite, possible, or unclassifiable coronary deaths in men and women aged 35-64 years, followed up for 28 days in or out of hospital. We calculated rates from population denominators to estimate trends in age-standardised rates and case fatality (percentage of 28-day fatalities=[100-survival percentage]). FINDINGS During 371 population-years, 166,000 events were registered. Official CHD mortality rates, based on death certification, fell (annual changes: men -4.0% [range -10.8 to 3.2]; women -4.0% [-12.7 to 3.0]). By MONICA criteria, CHD mortality rates were higher, but fell less (-2.7% [-8.0 to 4.2] and -2.1% [-8.5 to 4.1]). Changes in non-fatal rates were smaller (-2.1%, [-6.9 to 2.8] and -0.8% [-9.8 to 6.8]). MONICA coronary-event rates (fatal and non-fatal combined) fell more (-2.1% [-6.5 to 2.8] and -1.4% [-6.7 to 2.8]) than case fatality (-0.6% [-4.2 to 3.1] and -0.8% [-4.8 to 2.9]). Contribution to changing CHD mortality varied, but in populations in which mortality decreased, coronary-event rates contributed two thirds and case fatality one third. INTERPRETATION Over the decade studied, the 37 populations in the WHO MONICA Project showed substantial contributions from changes in survival, but the major determinant of decline in CHD mortality is whatever drives changing coronary-event rates.
Digital Image Processing of Cephalometric Radiographs: A Preliminary Report

The principles of image capture, image storage and image processing in digital radiology are described. The enhancement of radiographic images using digital image processing techniques and its application to cephalometry is discussed. The results of a pilot study which compared some common cephalometric measurements made from manual point identification with those made by direct digitization of digital radiographic images from video monitors are presented. Although in an early stage of development, the results from the image processing system were comparable with those obtained by traditional methods.
The 20S proteasome isolated from Alzheimer’s disease brain shows post‐translational modifications but unchanged proteolytic activity

Chronic neurodegenerative diseases are characterized by the accumulation of aggregated protein species, and functional impairment of the ubiquitin proteasome system has been hypothesized to contribute to neuronal cell loss. Decreased proteolytic activity of the 20S proteasome has been shown postmortem in crude brain lysates from Alzheimer’s disease (AD) patients. In the present study, we demonstrate, however, that catalytic activity of the 20S proteasome increases during chromatographic purification from AD brains as compared with age‐matched controls. By two‐dimensional difference gel electrophoresis we detected pI shifts in several proteasome subunits in AD samples pointing to differential post‐translational modifications. Moreover, we identified N‐terminal acetylation and dephosphorylation of subunit α7 in AD by tandem mass spectrometry. Thus, reduced peptidase activity in AD brain extracts is not an intrinsic property of the 20S proteasome, but may be resulting from the presence of endogenous inhibitory proteins or substrates. Post‐translational modifications of non‐catalytic subunits in situ may contribute to the trend towards enhanced hydrolytic activity of the isolated 20S proteasome after removal of the endogenous inhibitors.
Characterization of Lactobacillus sake isolates from dry‐cured sausages by restriction fragment length polymorphism analysis of the 16S rRNA gene

Lactobacillus sake strains originally isolated from dry‐fermented sausages were characterized by phenotypic and genotypic methods, including DNA‐DNA hybridization, restriction fragment length polymorphism (RFLP), and 16S rDNA sequencing analysis, in order to establish their taxonomic position and relation to well defined reference species. Initially, isolates of Lact. sake showing a characteristic phenotype (melibiose‐positive, maltose‐ and arabinose‐negative) were identified by DNA‐DNA hybridization. Subsequently, RFLP studies using EcoRI and HindIII as restriction enzymes, and cDNA from Escherichia coli or 16S rDNA from Lact. sake strains as probes, showed distinct polymorphism levels. Thus, EcoRI‐digested DNA probed with cDNA from E. coli disclosed the presence of a unique cluster for the meat isolates tested, allowing their differentiation from the reference type strain. When HindIII‐digested DNA was hybridized with the cDNA probe, strain‐specific patterns were obtained, showing a higher discrimination power. Considerable strain differentiation was also observed when EcoRI and HindIII digests were hybridized with 16S rDNA probes. Finally, sequence analysis of the 16S rDNA from one isolate also revealed a certain degree of genetic variability with respect to the reference strain of Lact. sake.
Beta Subunits in Synechococcus sp . Strain PCC 7002 and Synechocystis sp . Strain PCC 6803 †

CpcM Posttranslationally Methylates Asparagine-71/72 of Phycobiliprotein Beta Subunits in Synechococcus sp. Strain PCC 7002 and Synechocystis sp. Strain PCC 6803 † Gaozhong Shen, Heidi S. Leonard, Wendy M. Schluchter, and Donald A. Bryant* Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, and Department of Biological Science, University of New Orleans, New Orleans, Louisiana 70148
A Real-Time Suspicious Stay Detection System Based on Face Detection and Tracking in Monitor Videos

This paper proposed a novel approach of a suspicious stay detection system based on face detection and tracking. Through the intelligent analysis of real-time monitor videos, we judge whether there is any suspicious stay in the monitoring area. In the proposed system, we firstly set the face detection and tracking area. Then we use the convolution neural network algorithm to detect the face and the Particle Filter to track the face. Finally, the time someone stays in the surveillance area is used to determine whether he is stuck suspiciously. Experimental results show that the proposed approach has a relatively high accuracy, and meets the real-time requirement.
T OWARDS SAFE DEEP LEARNING : UNSUPERVISED DEFENSE AGAINST GENERIC ADVERSARIAL ATTACKS A

Recent advances in adversarial Deep Learning (DL) have opened up a new and largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. We introduce a novel automated countermeasure called Parallel Checkpointing Learners (PCL) to thwart the potential adversarial attacks and significantly improve the reliability (safety) of a victim DL model. The proposed PCL methodology is unsupervised, meaning that no adversarial sample is leveraged to build/train parallel checkpointing learners. We formalize the goal of preventing adversarial attacks as an optimization problem to minimize the rarely observed regions in the latent feature space spanned by a DL network. To solve the aforementioned minimization problem, a set of complementary but disjoint checkpointing modules are trained and leveraged to validate the victim model execution in parallel. Each checkpointing learner explicitly characterizes the geometry of the input data and the corresponding high-level data abstractions within a particular DL layer. As such, the adversary is required to simultaneously deceive all the defender modules in order to succeed. We extensively evaluate the performance of the PCL methodology against the state-of-the-art attack scenarios, including Fast-Gradient-Sign (FGS), Jacobian Saliency Map Attack (JSMA), Deepfool, and Carlini&WagnerL2. Extensive proof-of-concept evaluations for analyzing various data collections including MNIST, CIFAR10, and ImageNet corroborate the effectiveness of our proposed defense mechanism against adversarial samples.
Field Testing of Moving Short‐baseline Navigation for Autonomous Underwater Vehicles using Synchronized Acoustic Messaging

This paper presents the results from field testing of a unique approach to the navigation of a fleet of autonomous underwater vehicles (AUVs) using only onboard sensors and information provided by a moving surface ship. The approach, considered moving short‐baseline (MSBL) navigation, uses two transponders mounted on a single surface ship that alternately broadcast acoustic messages containing one of the parameters of the kinematic state of the surface ship. The broadcasts are initiated according to a predefined schedule so that the one‐way travel time (OWTT) of the acoustic messages may be used to determine the range to the transponder. Each AUV in the fleet uses the surface ship state measurements and ranges provided by the acoustic messages in two extended Kalman filters (EKFs) for state estimation. The first EKF merges the intermittent surface ship state measurements with a kinematic model to estimate the state of the surface ship. This is necessary because the presented approach uses 13‐bit acoustic messages as opposed to the more commonly used 32‐byte messages, which allow the full state to be encoded in a single broadcast. The second EKF uses the current surface ship state estimate to properly interpret the acoustic ranges, combining them with a kinematic model to estimate the state of the AUV itself. Numerous MSBL navigation experiments were compared against a more traditional approach using a long‐baseline (LBL) array of transponders and OWTT acoustic ranging. The results of all tests were verified by independent LBL measures of position.
Infant feeding and childhood atopy: does early introduction of non‐milk fluids matter?

Studies on the role of non‐milk fluids in the development of child atopic disease are scarce. We had a unique opportunity to investigate prospective association between the introduction of fruit syrup, orange juice, sterilized water, vitamins and honey at 1 month and the development of child atopic disease. The exposure of interest was measured by parental report of non‐milk fluids introduction to infants aged 1 month at the Tasmanian Infant Health Survey, 1988–89, Tasmania. Data on the outcomes of interest (atopic sensitization, asthma, eczema and hay fever) were collected during the 1997 Childhood Allergy and Respiratory Health Study when children were 8 yr old. Relative risks were derived from generalized linear model with a log link function and binomial error structure. None of the non‐milk fluids appeared to be a significant predictor of atopic sensitization. Only sterilized water was a significant risk factor for asthma (adjusted relative risk = 1.59; 95% confidence intervals: 1.14–2.22), which may be partly because of associated overall better hygienic conditions and decreased exposure to early infections in the household. In summary, we were unable to find evidence for an association between introduction of non‐milk fluids in infancy and childhood atopic disease.