Miracle-dz/UniFAR-FTUs huggingface dataset:
Course of Infection with the Emergent Pathogen Brucella microti in Immunocompromised Mice

ABSTRACT A new Brucella species, Brucella microti, has been isolated from wild rodents and found to be pathogenic in mice. The biological relevance of this new mouse pathogen is clear, as it allows us to study Brucella infection in a species-specific model. The course of infection in wild-type (wt) and immunodeficient mice that lack B (Jh), T and B (SCID), or T, B, and NK (SCID.Beige) cells was analyzed over 3 weeks. wt mice completely cleared bacteria from the liver and spleen after that time. However, SCID mice showed a much higher bacterial load in the spleen and liver than wt and Jh mice after 1 week and maintained the same level during the next 2 weeks. All mice tested survived for the 3 weeks. In contrast, the bacterial levels in mice that lacked NK cell activity progressively increased and these mice succumbed to infection after 16 to 18 days. Histopathology analysis of infected mice showed extensive areas of necrotic tissue and thrombosis in liver after 1 week in all infected SCID.Beige mice but were not seen in either SCID or wt animals. These processes were dramatically increased after 21 days, corresponding with the death of SCID.Beige animals. Our results indicate that T and/or B cells are required for the control of infection with the mouse pathogen Brucella microti in liver and spleen but that NK cells are crucial for survival in the absence of B and T cells. In addition, they suggest that controlled granuloma formation is critical to clear this type of infection in wt mice.
Case-Based Preference Elicitation (Preliminary Report)

While decision theory provides an appealing normative framework for representing rich preference structures, eliciting utility or value functions typically incurs a large cost. For many applications involving interactive systems this overhead precludes the use of formal decision-theoretic models of preference. Instead of performing elicitation in a vacuum, it would be useful if we could augment directly elicited preferences with some appropriate default information. In this paper we propose a case-based approach to alleviating the preference elicitation bottleneck. Assuming the existence of a population of users from whom we have elicited complete or incomplete preference models, we propose eliciting the preference model of a new user interactively and incrementally, using the closest existing preference models as potential defaults. A notion of closeness demands a measure of distance among preference orders, which is formally defined and investigated.
An evolutionary approach to training feedforward and recurrent neural networks

This paper describes a method of utilising genetic algorithms to train fixed architecture feedforward and recurrent neural networks. The technique described uses the genetic algorithm to evolve changes to the weights and biases of the network rather than the weights and biases themselves. Results achieved by this technique indicate that for many problems it compares very favourably with the more common gradient descent techniques for training neural networks, and in some cases is superior. The technique is useful for those problem which are known to be difficult for the gradient descent techniques.
Proving threshold saturation for nonbinary SC-LDPC codes on the binary erasure channel

We analyze nonbinary spatially-coupled low-density parity-check (SC-LDPC) codes built on the general linear group for transmission over the binary erasure channel. We prove threshold saturation of the belief propagation decoding to the potential threshold, by generalizing the proof technique based on potential functions recently introduced by Yedla et al. The existence of the potential function is also discussed for a vector sparse system in the general case, and some existence conditions are developed. We finally give density evolution and simulation results for several nonbinary SC-LDPC code ensembles.
Effects of a high-molecular-weight cranberry fraction on growth, biofilm formation and adherence of Porphyromonas gingivalis.

BACKGROUND Porphyromonas gingivalis is a major aetiological agent of periodontitis, a destructive disease affecting the tooth-supporting tissues. Recent reports have indicated that high-molecular-weight molecules from cranberry juice concentrate can prevent the attachment of human pathogens to host tissues. OBJECTIVES The aim of the present study was to investigate the effect of non-dialysable material (NDM) prepared from cranberry juice concentrate on growth, biofilm formation and adherence properties of P. gingivalis. METHODS The effect of cranberry NDM on biofilm formation was studied using a polystyrene microplate assay and by scanning electron microscopy. The effect of cranberry NDM on the attachment properties of P. gingivalis was evaluated by a microplate assay in which mammalian proteins were immobilized into wells. RESULTS Our results indicated that cranberry NDM is a potent inhibitor of biofilm formation by P. gingivalis. However, it has no effect on growth and viability of bacteria. Cranberry NDM also prevented significantly the attachment of P. gingivalis to surfaces coated with type I collagen, fibrinogen or human serum. CONCLUSIONS Our data suggest that cranberry constituents may have a beneficial effect for the prevention and treatment of periodontitis by reducing the capacity of P. gingivalis to colonize periodontal sites.
Neurobiological basis of state-dependent control of motor behaviors

The importance of an appropriate combination of excitability for the higher (neocortex, basal ganglia, and limbic system) and lower (brainstem-spinal cord) motor systems could be necessary for normal behavior during wakefulness and REM sleep. Neurotransmitters such as acetylcholine, the monoamines, GABA, and the orexins regulate the background excitability of the higher and lower motor systems so that an interaction of these systems can be appropriately maintained. Pathophysiological mechanisms of sleep deficiency may be induced, not only by an organic disturbance of brain structures (hardware), but also by dysfunctioning of the neurotransmitter systems (software). From these considerations, this review provides a hypothetical model for “state-dependent interaction of the higher and lower motor systems” for understanding normal behavior and pathophysiological mechanisms of sleep-related disorders such as narcolepsy and the REM-sleep behavioral syndrome.
Artificial intelligence in nanotechnology

During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines.
Globally Convergent Edge-preserving Reconstruction with Contour-line Smoothing

The standard approach to image reconstruction is to stabilize the problem by including an edge-preserving roughness penalty in addition to faithfulness to the data. However, this methodology produces noisy object boundaries and creates a staircase effect. The existing attempts to favor the formation of smooth contour lines take the edge field explicitly into account; they either are computationally expensive or produce disappointing results. In this paper, we propose to incorporate the smoothness of the edge field in an implicit way by means of an additional penalty term defined in the wavelet domain. We also derive an efficient half-quadratic algorithm to solve the resulting optimization problem, including the case when the data fidelity term is non-quadratic and the cost function is nonconvex. Numerical experiments show that our technique preserves edge sharpness while smoothing contour lines; it produces visually pleasing reconstructions which are quantitatively better than those obtained without wavelet-domain constraints.
Single Object Long-term Tracker for Smart Control of a PTZ camera

In this paper, we present a single-object long-term tracker that supports high appearance changes in the tracked target, occlusions, and is also capable of recovering a target lost during the tracking process. The initial motivation was real time automatic speaker tracking by a static camera in order to control a PTZ camera capturing a lecture. The algorithm consists of a novel combination of state-of-the-art techniques. Subjective evaluation, over existing and newly recorded sequences, shows that the tracker is able to overcome the problems and difficulties of long-term tracking in a real lecture. Additionally, in order to further assess the performance of the proposed approach, a comparative evaluation over the VOT2013 dataset is presented.
Feature extraction and classification for detection malaria parasites in thin blood smear

Malaria is caused by Plasmodium parasites that are able to invade human red blood cell. Many researches have focused on improving the accuracy of the diagnosis. Image processing method is able to increase results of malaria parasite cell detection. This paper is developed based on the image processing technique to detect three stages of Plasmodium parasites while in human host, i.e. trophozoite, schizont, and gametocyte plasmodium falciparum. Feature extraction based on histogram-based texture is used to extract feature parasite cell. Multilayer perceptron backpropagation algorithm is used to classify all features. The results show that the proposed method achieves accuracy of 87.8%, sensitivity of 81.7%, and specificity of 90.8% for detecting infected red blood cells thus improving decision-making for malaria diagnosis.
Best-next-view algorithm for three-dimensional scene reconstruction using range images

The primary focus of the research detailed in this paper is to develop an intelligent sensing module capable of automatically determining the optimal next sensor position and orientation during scene reconstruction. To facilitate a solution to this problem, we have assembled a system for reconstructing a 3D model of an object or scene from a sequence of range images. Candidates for the best-next-view position are determined by detecting and measuring occlusions to the range camera's view in an image. Ultimately, the candidate which will reveal the greatest amount of unknown scene information is selected as the best-next-view position. Our algorithm uses ray tracing to determine how much new information a given sensor perspective will reveal. We have tested our algorithm successfully on several synthetic range data streams, and found the system's results to be consistent with an intuitive human search. The models recovered by our system from range data compared well with the ideal models. Essentially, we have proven that range information of physical objects can be employed to automatically reconstruct a satisfactory dynamic 3D computer model at a minimal computational expense. This has obvious implications in the contexts of robot navigation, manufacturing, and hazardous materials handling. The algorithm we developed takes advantage of no a priori information in finding the best-next-view position.
Adolescent Metabolic Syndrome Risk Is Increased with Higher Infancy Weight Gain and Decreased with Longer Breast Feeding

Background. Prevalence of the metabolic syndrome is increasing in pediatric age groups worldwide. Meeting the criteria for the metabolic syndrome puts children at risk for later cardiovascular and metabolic disease. Methods. Using linear regression, we examined the association between infant weight gain from birth to 3 months and risk for the metabolic syndrome among 16- to 17-year-old Chilean adolescents (n = 357), accounting for the extent of breastfeeding in infancy and known covariates including gender, birth weight, and socioeconomic status. Results. Participants were approximately half male (51%), born at 40 weeks of gestation weighing 3.5 kg, and 48% were exclusively breastfed for ≥90 days. Factors independently associated with increased risk of metabolic syndrome in adolescence were faster weight gain in the first 3 months of life (B = 0.16, P < 0.05) and male gender (B = 0.24, P < 0.05). Breastfeeding as the sole source of milk for ≥90 days was associated with significantly decreased risk of metabolic syndrome (B = −0.16). Conclusion. This study adds to current knowledge about early infant growth and breastfeeding and their long-term health effects.
Iterative Time-Variant Channel Estimation for 802.11p Using Generalized Discrete Prolate Spheroidal Sequences

This paper deals with channel estimation for orthogonal frequency-division multiplexing (OFDM) in time-variant wireless propagation channels. We particularly consider the challenges of the IEEE 802.11p standard, which is the worldwide dominant system for vehicular communications. For historic reasons, 802.11p uses a pilot pattern that is identical to the pattern used in 802.11a, which was initially designed for the estimation of indoor channels with little or no time variations. Therefore, this pilot pattern violates the sampling theorem for channels with both large delay spread and large Doppler spread, as often occurs in vehicular communications. To remedy this problem, we design a robust iterative channel estimator based on a 2-D subspace spanned by generalized discrete prolate spheroidal sequences. Due to the tight subspace design, the iterative receiver is able to converge to the same bit error rate (BER) as a receiver with perfect channel knowledge. Furthermore, we propose a backward compatible modification of the 802.11p pilot pattern such that the number of iterations sufficient for convergence can be reduced by a factor of 2-3, strongly reducing implementation complexity.
The influence of dynein processivity control, MAPs, and microtubule ends on directional movement of a localising mRNA

Many cellular constituents travel along microtubules in association with multiple copies of motor proteins. How the activity of these motors is regulated during cargo sorting is poorly understood. In this study, we address this issue using a novel in vitro assay for the motility of localising Drosophila mRNAs bound to native dynein-dynactin complexes. High precision tracking reveals that individual RNPs within a population undergo either diffusive, or highly processive, minus end-directed movements along microtubules. RNA localisation signals stimulate the processive movements, with regulation of dynein-dynactin’s activity rather than its total copy number per RNP, responsible for this effect. Our data support a novel mechanism for multi-motor translocation based on the regulation of dynein processivity by discrete cargo-associated features. Studying the in vitro responses of RNPs to microtubule-associated proteins (MAPs) and microtubule ends provides insights into how an RNA population could navigate the cytoskeletal network and become anchored at its destination in cells. DOI: http://dx.doi. org/10.7554/eLife.01596. 001
In-Execution Malware Detection Using Task Structures of Linux Processes

In this paper, we present a novel framework -- it uses the information in kernel structures of a process -- to do run-time analysis of the behavior of an executing program. Our analysis shows that classifying a process as malicious or benign -- using the information in kernel structures of a process -- is not only very accurate but also has very low processing overheads; as a result, this lightweight framework can be incorporated within operating system kernel. To provide a proof-of-concept of our thesis, we design and implement our system as a kernel module in Linux. We perform the time series analysis of 118 parameters of Linux task structures and pre-process them to come up with a minimal features' set of 11 features. Our analysis show that these features have remarkably different values for benign and malicious processes; as a result, a number of classifiers operating on these features provide 93% detection accuracy with 0% false alarm rate within 100 milliseconds. Last but not the least, we justify that it is very difficult for a crafty attacker to evade these low-level system specific features.
QoS-Aware Adaptive Routing in Multi-layer Hierarchical Software Defined Networks: A Reinforcement Learning Approach

Software-defined networks (SDNs) have been recognized as the next-generation networking paradigm that decouples the data forwarding from the centralized control. To realize the merits of dedicated QoS provisioning and fast route (re-)configuration services over the decoupled SDNs, various QoS requirements in packet delay, loss, and throughput should be supported by an efficient transportation with respect to each specific application. In this paper, a QoS-aware adaptive routing (QAR) is proposed in the designed multi-layer hierarchical SDNs. Specifically, the distributed hierarchical control plane architecture is employed to minimize signaling delay in large SDNs via three-levels design of controllers, i.e., the super, domain (or master), and slave controllers. Furthermore, QAR algorithm is proposed with the aid of reinforcement learning and QoS-aware reward function, achieving a time-efficient, adaptive, QoS-provisioning packet forwarding. Simulation results confirm that QAR outperforms the existing learning solution and provides fast convergence with QoS provisioning, facilitating the practical implementations in large-scale software service-defined networks.
Competition in Bank-Provided Payment Services

Banks supply payment services that underpin the smooth operation of the economy. To ensure an efficient payment system, it is important to maintain competition among payment service providers, but data available to gauge the degree of competition are quite limited. We propose and implement a frontier- based method to assess relative competition in bank-provided payment services. Billion dollar banks account for around 90 percent of assets in the U.S., and those with around $4 to $7 billion in assets turn out to be both the most and the least competitive in payment services, not the very largest banks.
Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints

In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated in this paper. The state constraints are frequently emerged in the real-life plants and how to avoid the violation of state constraints is an important task. By introducing a barrier Lyapunov function (BLF) to every step in a backstepping procedure, a novel adaptive backstepping design is well developed to ensure that the full-state constraints are not violated. At the same time, one remarkable feature is that the minimal learning parameters are employed in BLF backstepping design. By making use of Lyapunov analysis, we can prove that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output. Finally, a simulation is given to verify the effectiveness of the method.
Gene Function Prediction With Gene Interaction Networks: A Context Graph Kernel Approach

Predicting gene functions is a challenge for biologists in the postgenomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.
Rab11 is required for synchronous secretion of chondroitin proteoglycans after fertilization in Caenorhabditis elegans

We previously identified a novel type of caveolin-enriched secretory vesicle in Caenorhabditis elegans oocytes. These vesicles undergo synchronous fusion with the plasma membrane immediately after fertilization, suggesting that they could be cortical granules that have been described in diverse animal species. Here, we report that these vesicles are indeed cortical granules, delivering essential chondroitin proteoglycans and mucin-like glycoproteins to the early embryonic extracellular matrices (ECMs). Furthermore, we have found that the small GTPase RAB-11 and the target-SNARE SYN-4 are required for cortical granule excoytosis after fertilization. In oocytes, SYN-4 localizes mainly to the plasma membrane, whereas GFP::RAB-11 accumulates transiently on the cortical granules during ovulation, immediately prior to fertilization. Importantly, cytokinesis defects in early embryos are commonly observed after depletion of either rab-11 or syn-4, producing a phenotype very similar to that observed after blockade of chondroitin synthesis. Taken together, our results indicate that at least part of the essential role for RAB-11 and SYN-4 in early embryogenesis is in the targeting of cortical granules to the plasma membrane during the precisely regulated secretion of ECM components.