Miracle-dz/UniFAR-FTUs huggingface dataset:
The hierarchical cluster model for image region segmentation

The hierarchical cluster model (HCM), a neural network inspired by the human brain (see Sutton, J., Harvard Medical School, MIT, Neural Systems Group, Technical Report, 1995), is demonstrated for the purpose of region segmentation in digital images. Starting with an over segmented image, regions are merged based on evidence of a valid edge between the two regions. Unlike Sutton's work, in which the HCM is used to recall a set of pre-trained memory patterns, the HCM in our work demonstrates unsupervised decision making capabilities.
Estimation of human operational feeling level for a lever manipulation task using shoulder angle and manipulability

Currently, the concept of ergonomically designed workplace using 3D graphic mannequins is gaining wide acceptance among the design engineers. However, predicting the human operational feeling level (i.e., comfort or discomfort level) during an ergonomic analysis of a virtual work situation is still in immature level. The current study is an attempt in that direction. This paper presents an approach that estimates the degree of comfort or discomfort felt by an operator during a lever manipulation task by using limb anthropometric and kinematic data. In this study, a haptic lever has been developed that emulates a lock-ball type lever action. Experiments were carried out with healthy male subjects under two different torque characteristics of the haptic lever. Using multiple regression analysis it has been found that the subjects operational feeling level can be estimated by means of shoulder flexion-extension angle and manipulability measure.
Immune injury of mouse peritoneal mast cells by antibodies to mouse serum and mouse gamma-globulin.

In vitro degranulation of mouse peritoneal mast cells and release of histamine were induced by the addition at 37° of anti-mouse serum or antimouse γ-globulin rabbit antibody. The release of histamine does not depend upon complement, and seems to be due to the reaction of the antibody with the homologous γ-globulin present on the surface of the mast cells.
Isolation and characterization of Staphylococcus aureus from raw cow milk in Bangladesh

The study was intended for identification and characterization of Staphylococcus aureus isolated from raw cow milk. A total of 47 milk samples were collected from Sheshmore, Shutiakhali and Bangladesh Agricultural University Dairy Farm, Mymensingh. Using bacteriological, biochemical and PCR-based identification schemes, 12 (25.53%) isolates were confirmed as S. aureus. All the isolates showed β-hemolysis on 5% sheep blood agar. S. aureus specific nuc gene (target size 279-bp) was amplified in the cases of all isolates. The isolates were found as resistant to Penicillin (100%), Erythromycin (75%) and Amoxicillin (100%). On the other hand, the isolates were sensitive to Ciprofloxacin (83.33%), Oxacillin (100%), Cloxacillin (100%) and Neomycin (100%). The isolated S. aureus showed increased resistance to broad spectrum antibiotic (e.g., Ciprofloxacin). As many people have a tendency to drink raw milk and raw milk products, there is high risk of S. aureus infection in human.
Effect of Various Parameters of Carbon and Nitrogen Sources and Environmental Conditions on the Growth of Lactobacillus Casei in the Production of Lactic Acid

Lactic acid is widely used in food and pharmaceutical industries and is produced by two biological (fermentative processes) and synthetic methods. Due to the production capacity of isomer L(+), the highest global production of lactic acid is done by fermentation. Lactobacillus casei strain was used in this study that is the homofermentative bacteria producing L(+)-lactic acid. Two nitrogen sources of yeast extract and corn steep liquor and two sugar sources of sucrose and molasses at temperatures of 37 Co, 40 Co and 42 Co as well as two pHs 5 and 7 were used in the study. The results showed that higher lactic acid is produced by increasing the amount of yeast extract compared to the use of corn steep liquor. Moreover, Lactobacillus casei using sucrose produced more lactic acid than the molasses. The optimal temperature for lactic acid produced by the bacterium was 37 Co in these experiments. In addition, it was found that Lactobacillus casei could produce more lactic acid at pH = 5 than at pH = 7, and the production of the acid reached the maximum value at pH = 5. In optimum conditions, 60/90g/L Lactic acid was obtained after 24 hours incubation of samples (pH = 5 and 37 Co).
Researching the ‘forgotten sector’: Low and medium tech (LMT) innovation - present light on future trends

As a research topic and priority for policy formulation, resource allocation and strategy implementation, low-tech (LT) and low- and medium-tech (LMT) sectors have traditionally attracted lesser attention compared to high-tech sectors. This paper addresses questions concerning our current priorities in terms of research on innovation in LT/LMT sectors. A systematic literature review spanning the time period 1999 to 2010 has been conducted to address these questions. From the review, a research agenda is developed
Increased Susceptibility of Burned Rats to Pseudomonas aeruginosa∗

Summary The LD50 of Ps. aeruginosa for extensively burned rats 2 minutes after burning, injected intraperitoneally or subcutaneously in the burned or non-burned area, was less than 10 organisms. The period of increased susceptibility existed for at least 24 hours when organisms were injected subcutaneously in the burned area but was less than 24 hours when organisms were injected intraperitoneally or subcutaneously in non-burned areas.
An Efficient Privacy Preserving in MSN using Improved Decision Tree Algorithm

Here in this paper, efficient privacy preservation over Mobile Social Networks is implemented to secure attacks over Mobile Social Networks. The Existing methodology implemented for the Friending Mobile Social Networks is efficient which provides an efficient computation of Data and privacy from unauthorized users. Here an efficient Decision Tree based algorithm is implemented using Partition of Data using Some Partition based algorithm and then classify data using an ID3 algorithm. The Experimental results when performed on Social Network Dataset the proposed methodology gives efficient results in comparison.
An Adaptive H Controller Design for Bank-To-Turn Missiles Using Ridge Gaussian Neural Networks

A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with stabilization.
Microwave bandpass filters with multiple couplings, designed using electromagnetic simulations and linear circuit optimization

In this paper a bandpass planar filter, with cross-couplings and with a pair of attenuation poles, is investigated. The equi-ripple in-band response and the location of the poles on the frequency axis can be precisely controlled by using a new technique which combines accurate electromagnetic field simulations and fast linear circuit optimization, allowing the design of bandpass filters with improved performances. To illustrate the procedure, a microstrip bandpass filter was designed, verified by em-field simulation, fabricated and tested. The response of the designed filter is in good agreement with the specification, confirming the possibilities of realizing microwave bandpass filters with rigorously controlled characteristics, with reduced design time and non-prohibitive computational effort.
Space-Time Computing with Temporal Neural Networks

Understanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers. Not only are the brain's computational capabilities far beyond those of conventional computers, its energy efficiency is truly remarkable. This book, written from the perspective of a computer designer and targeted at computer researchers, is intended to give both background and lay out a course of action for studying the brain's computational paradigm. It contains a mix of concepts and ideas drawn from computational neuroscience, combined with those of the author. As background, relevant biological features are described in terms of their computational and communication properties. The brain's neocortex is constructed of massively interconnected neurons that compute and communicate via voltage spikes, and a strong argument can be made that precise spike timing is an essential element of the paradigm. Drawing from the biological features, a mathematics-based computational paradigm is constructed. The key feature is spiking neurons that perform communication and processing in space-time, with emphasis on time. In these paradigms, time is used as a freely available resource for both communication and computation. Neuron models are first discussed in general, and one is chosen for detailed development. Using the model, single-neuron computation is first explored. Neuron inputs are encoded as spike patterns, and the neuron is trained to identify input pattern similarities. Individual neurons are building blocks for constructing larger ensembles, referred to as "columns". These columns are trained in an unsupervised manner and operate collectively to perform the basic cognitive function of pattern clustering. Similar input patterns are mapped to a much smaller set of similar output patterns, thereby dividing the input patterns into identifiable clusters. Larger cognitive systems are formed by combining columns into a hierarchical architecture. These higher level architectures are the subject of ongoing study, and progress to date is described in detail in later chapters. Simulation plays a major role in model development, and the simulation infrastructure developed by the author is described.
Recurrent fuzzy neural network adaptive hybrid control for linearized multivariable systems

This paper develops a design method of recurrent fuzzy neural network RFNN based adaptive hybrid control for multi-input multi-output MIMO linearized dynamic systems. This hybrid control system consists a feedback controller and an adaptive RFNN controller. The feedback controller is used as a basic stabilizing controller to stabilize the nominal system and the adaptive RFNN controller is used to deal with unknown part of system dynamics. The adaptive laws of RFNN are derived based on the Lyapunov function so that the stability of the system can be guaranteed. Finally, the proposed control system is applied to an F-16 flight control system. Simulation results demonstrate that the developed control system can achieve favorable tracking performance even with some failure of the flight control system.
Observer-Based Output Feedback Containment Control for Multiple Euler-Lagrange Systems

This paper investigates output feedback containment control problem for multiple Euler-Lagrange systems based on finite-time observer. By considering the states of leaders are only available to part of followers and leaders are dynamic, generalized velocity observers and distributed adaptive control laws are designed for multiple Euler-Lagrange systems when the generalized velocity cannot be obtained directly. By using the graph theory and stability analysis, it is demonstrated that followers will converge asymptotically to the convex hull formed by the leaders. Finally, numerical simulations are given to illustrate the effectiveness of proposed observers and control schemes.
ENVOLVIMENTO DA FLORA INTESTINAL NA MODULAÇÃO DE DOENÇAS PSIQUIÁTRICAS

Nas ultimas decadas, o envolvimento da flora intestinal na regulacao de processos mediados pelo sistema nervoso central (SNC) tem despertado grande interesse de neurocientistas (um campo de pesquisa ate entao exclusivo dos microbiologistas). Nesse interim, estudos vem revelando como a composicao da flora intestinal pode participar da fisiologia normal do individuo, assim como a associacao entre alteracoes da composicao e diversidade da flora e o desenvolvimento de diversas doencas psiquiatricas. Esta comunicacao encefalo-intestinal tende a ocorrer utilizando diferentes vias, como a comunicacao por meio endocrino, imunologico e/ou neuronal. Esta troca de informacoes e diretamente influenciada pela composicao da flora intestinal. Estudos pre-clinicos em animais, onde a flora intestinal e alterada, sugerem que as bacterias intestinais regulam varios genes e neurotransmissores envolvidos na modulacao de doencas psiquiatricas, como a ansiedade, depressao e o autismo. Desta forma, o conceito de eixo encefalo-intestinal sugere que a modulacao da flora intestinal pode ser uma importante estrategia para o desenvolvimento de novas formas de prevencao e tratamento de doencas do SNC.
Effect of neurohypophyseal hormones on FFA mobilization from the adipose tissue in the dehydrated rat.

Effect of neurohypophyseal hormones on FFA mobilization from the adipose tissue was studied in the dehydrated rat. Despite the fact that Lys-vasopressin decreased plasma FFA concentration in the normal rat, this hormone did not produce any effect on plasma FFA in the dehydrated rat. In contrast, insulin decreased plasma FFA concentration in the rat under the same condition. Oxytocin administration did not affect on plasma FFA in the normal rat; however, it increased both plasma FFA concentration and FFA release from the parametrial adipose tissue in the female dehydrated rat. Furthermore, in vitro addition of oxytocin also stimulated FFA release from the adipose tissue of female dehydrated rat.
The bacterial lipopeptide surfactin targets the lipid fraction of the plant plasma membrane to trigger immune‐related defence responses

The lipopeptide surfactin secreted by plant‐beneficial bacilli has crucial biological functions among which the ability to stimulate immune‐related responses in host tissues. This phenomenon is important for biological control of plant diseases but its molecular basis is still poorly understood. In this work, we used various approaches to study the mechanism governing the perception of this biosurfactant at the plant cell surface. Combining data on oxidative burst induction in tobacco cells, structure/activity relationship, competitive inhibition, insertion kinetics within plant membranes and thermodynamic determination of binding parameters on model membranes globally indicates that surfactin perception relies on a lipid‐driven process at the plasma membrane level. Such a sensor role of the lipid bilayer is quite uncommon considering that plant basal immunity is usually triggered upon recognition of microbial molecular patterns by high‐affinity proteic receptors.
Citation function, polarity and influence classification

Abstract Current methods for assessing the impact of authors and scientific media employ tools such as H-Index, Co-Citation and PageRank. These tools are primarily based on citation counting, which considers all citations to be equal. This type of methods can produce perverse incentives to publish controversial or incomplete papers, as mixed or negative reviews often generate larger citation counts and better indexes, regardless of whether the citations were critical or exerted minimal influence on the citing document. Passing citations that are employed to establish background, which do not have a real impact on the citing paper, are common in scientific literature. However, these citations have equal weight in impact evaluations. Notable researchers have emphasized the need to correct this situation by developing estimation methods that consider the different roles of quotations in citing papers. To accomplish this type of evaluation, a context citation analysis should be applied to determine the nature of the citations. We propose that citations should be categorized using four dimensions – FUNCTION, POLARITY, ASPECTS and INFLUENCE – as these dimensions provide adequate information that can be employed toward the generation of a qualitative method to measure the impact of a given publication in a citing paper. In this paper, we used interchangeably the words influence and impact. We present a method for obtaining this information using our proposed classification scheme and manually annotated corpus, which is marked with meaningful keywords and labels to help identify the characteristics or properties that constitute what we call ASPECTS. We develop a classification scheme which considers purpose definition shared by previous works. Our contribution is to abstract purpose classes from several other schemes and divide a complex structure in more manageable parts, to attain a simple system that combines low granularity dimensions but nevertheless produces a fine-grained classification. For annotators, the classification process is simple because in a first step, the coders distinguish only four primary classes, and in a second pass, they add the information contained in ASPECTS keyword and labels to obtain the more specific functions. This way, we gain a high granularity labeling that gives enough information about the citations to characterize and classify them, and we achieve this detailed coding with a straightforward process where the level of human error could be minimized.
Immune Responses and Viral Phenotype: Do Replication Rate and Cytopathogenicity Influence Virus Load?

We use mathematical models to investigate the relationship between viral characteristics and virus load under the following immune responses: (a) CTL-mediated lysis, (b) CTL-mediated inhibition of virus entry into target cells, (c) CTL-mediated inhibition of virion production and (d) antibody responses. We find that the rate of virus entry into target cells may generally only have a weak influence on virus load. The rate of virion production by infected cells only has a weak effect on the equilibrium number of infected cells while strongly influencing the number of free virus particles. On the other hand, viral cytopathogenicity may be a major determinant of virus load under certain types of immune responses. If there is no immune response, or if immune mediators inhibit infection of target cells, non-cytopathic viruses may attain significantly higher abundances than cytopathic ones. On the other hand, immune mediators acting on infected cells control both types of viruses with similar efficiencies. These results are used to interpret data on perforin-knockout experiments in LCMV infection and provide the basis for understanding the suppression and rise of non-syncytium (NSI) and syncytium inducing (SI) HIV phenotypes during the disease process.
A comprehensive review of upper reference limits reported for (high-)sensitivity cardiac troponin assays: the challenges that lie ahead

Abstract Cardiac troponins (cTn) are the preferred markers for the diagnosis of acute myocardial infarction (AMI). The guidelines recommend the use of the 99th percentile upper reference concentration of a healthy population as the diagnostic cut-off for AMI. However, a broad range of upper reference limits is still employed, complicating the diagnosis of AMI. This overview is meant to assist laboratory specialists to define an appropriate cut-off value for the diagnosis of AMI. Therefore, we provide an overview of the analytical performance and upper reference limits of seven (high-)sensitivity cTn assays: Roche high-sensitivity cTnT and ADVIA Centaur, Stratus CS, Dimension Vista, Vitros ECi, Access and Architect cTnI assays. It is shown that none of the reference populations completely met the guidelines, including those in package inserts. Forty percent of the studies collected less than the advised minimum of 300 subjects. Many studies (50%) did not report their inclusion criteria, while lower 99th percentile limits were observed when more stringent selection criteria were applied. Higher troponin cut-offs were found in men and elderly subjects, suggesting sex- and age-specific cut-offs would be considered. Therefore, there is still need for a large, rigorously screened reference population to more accurately establish cTn upper reference limits.
Stress Induces Contextual Blindness in Lotteries and Coordination Games

In this paper, we study how stress affects risk taking in three tasks: individual lotteries, Stag Hunt (coordination) games, and Hawk-Dove (anti-coordination) games. Both control and stressed subjects take more risks in all three tasks when the value of the safe option is decreased and in lotteries when the expected gain is increased. Also, subjects take longer to take decisions when stakes are high, when the safe option is less attractive and in the conceptually more difficult Hawk-Dove game. Stress (weakly) increases reaction times in those cases. Finally, our main result is that the behavior of stressed subjects in lotteries, Stag Hunt and Hawk-Dove are all highly predictive of each other (p-value < 0.001 for all three pairwise correlations). Such strong relationship is not present in our control group. Our results illustrate a “contextual blindness” caused by stress. The mathematical and behavioral tensions of Stag Hunt and Hawk-Dove games are axiomatically different, and we should expect different behavior across these games, and also with respect to the individual task. A possible explanation for the highly significant connection across tasks in the stress condition is that stressed subjects habitually rely on one mechanism to make a decision in all contexts whereas unstressed subjects utilize a more cognitively flexible approach.
Honokiol inhibits U 87 MG human glioblastoma cell invasion through endothelial cells by regulating membrane permeability and the epithelial-mesenchymal transition

Glioblastoma is one of the most lethal and prevalent malignant human brain tumors, with aggressive proliferation and highly invasive properties. There is still no effective cure for patients with glioblastoma. Honokiol, derived from Magnolia officinalis, can cross the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier (BCSFB), making it a strong candidate for an effective drug for the treatment of brain tumors, including glioblastoma. In our previous study, we demonstrated that honokiol effectively induced apoptotic cell death in glioblastoma. Metastasis poses the largest problem to cancer treatment and is the primary cause of death in cancer patients. Thus, in this study, we investigated the effect of honokiol on the cell invasion process of U87MG human glioblastoma cells through brain microvascular endothelial cells (BMECs) and its possible mechanisms. Honokiol dose-dependently inhibited TNF-α-induced VCAM-1 expression in BMECs and adhesion of U87MG to BMECs. Moreover, honokiol effectively blocked U87MG invasion through BMEC-Matrigel-coated transwell membranes. Increased phosphorylation of VE-cadherin and membrane permeability by TNF-α were suppressed by honokiol in BMECs. Furthermore, we investigated the effect of honokiol on the epithelial-mesenchymal transition (EMT) in U87MG cells. Honokiol reduced the expression levels of Snail, N-cadherin and β-catenin, which are mesenchymal markers, but increased E-cadherin, an epithelial marker. In conclusion, these results suggest that honokiol inhibits metastasis by targeting the interaction between U87MG and BMECs, regulating the adhesion of U87MG to BMECs by inhibiting VCAM-1, and regulating the invasion of U87MG through BMECs by reducing membrane permeability and EMT processes of U87MG cells.