Miracle-dz/UniFAR-FTUs huggingface dataset:
Intradural Extramedullary Spinal Ependymoma: A Case Report of Malignant Transformation Occurring

Intradural extramedullary spinal ependymomas are extremely rare. Herein, we describe a lesion-type spinal ependymoma that followed a malignant course, and discuss its clinical presentation, etiopathogenesis, and treatment. We present a patient who was diagnosed with an intradural extramedullary spinal tumor at T4-T6. The patient underwent gross total resection of the tumor without damage to the spinal cord. Histological examination, classified the lesion as a World Health Organization (WHO)-grade 2 ependymoma. One and a half years later, magnetic resonance imaging detected a recurring tumor at T4-T5. The tumor was removed and classified as a WHO-grade 3 anaplastic ependymoma. The patient was started on a course of regional spinal cord radiotherapy. The patient achieved tumoral control and clinical stabilization after the recurrence. We must consider the differential diagnosis of intradural extramedullary spinal tumors. The best treatment for this lesion is gross total resection and adjunctive radiotherapy is necessary in cases of malignant-change.
Goal Directed Worry Rules Are Associated with Distinct Patterns of Amygdala Functional Connectivity and Vagal Modulation during Perseverative Cognition

Excessive and uncontrollable worry is a defining feature of Generalized Anxiety Disorder (GAD). An important endeavor in the treatment of pathological worry is to understand why some people are unable to stop worrying once they have started. Worry perseveration is associated with a tendency to deploy goal-directed worry rules (known as “as many as can” worry rules; AMA). These require attention to the goal of the worry task and continuation of worry until the aims of the “worry bout” are achieved. This study examined the association between the tendency to use AMA worry rules and neural and autonomic responses to a perseverative cognition induction. To differentiate processes underlying the AMA worry rule use from trait worry, we also examined the relationship between scores on the Penn State Worry Questionnaire (PSWQ) and neural and autonomic responses following the same induction. We used resting-state functional magnetic resonance brain imaging (fMRI) while measuring emotional bodily arousal from heart rate variability (where decreased HRV indicates stress-related parasympathetic withdrawal) in 19 patients with GAD and 21 control participants. Seed-based analyses were conducted to quantify brain changes in functional connectivity (FC) with the amygdala. The tendency to adopt an AMA worry rule was associated with validated measures of worry, anxiety, depression and rumination. AMA worry rule endorsement predicted a stronger decrease in HRV and was positively associated with increased connectivity between right amygdala and locus coeruleus (LC), a brainstem noradrenergic projection nucleus. Higher AMA scores were also associated with increased connectivity between amygdala and rostral superior frontal gyrus. Higher PSWQ scores amplified decreases in FC between right amygdala and subcallosal cortex, bilateral inferior frontal gyrus, middle frontal gyrus, and areas of parietal cortex. Our results identify neural mechanisms underlying the deployment of AMA worry rules. We propose that the relationship between AMA worry rules and increased connectivity between the amygdala and prefrontal cortex (PFC) represents attempts by high worriers to maintain arousal and distress levels in order to feel prepared for future threats. Furthermore, we suggest that neural mechanisms associated with the PSWQ represent effortful inhibitory control during worry. These findings provide unique information about the neurobiological processes that underpin worry perseveration.
Mortality of silver eels migrating through different types of hydropower turbines in Lithuania

Hydropower plants (HPP) are considered to be one of the major threats to the survival of European eels when they migrate downstream along inland water bodies during the early part of their annual journey to the spawning area in the Sargasso Sea. There are 98 HPPs in Lithuania and thousands throughout Europe. Numerous studies describe HPP induced mortality rates among European eels as variable depending on local, environmental, and technical factors. This heterogeneity in effect complicates theoretical extrapolation to eel mortality arising from specific types of HPP, necessary for effective management of local stocks. Silver eel mortality was estimated for 4 different HPPs in Lithuania. Mortality was estimated using RFID (Radio Frequency Identification, passive integrated transponders) tags and acoustic telemetry in a large HPP (>100 MW) with Kaplan turbines, a small HPP (<1 MW) with a Kaplan turbine and a fish passage, and for the first time in two small HPPs (<1 MW) with CINK turbines. The results supported a hypothesis that the mortality rate of migrating eels depends mainly on the type and size of the turbine. HPP induced mortality varied from 100% in a small CINK turbine down to 25% in the large HPP with Kaplan turbines. The importance of simple mitigation measures was highlighted by 34% of all tagged eels bypassing one of the HPP via an adjacent fish passage constructed for upstream migration of salmonids. The observed differences in mortality provide essential information for long term strategies designed to restore depleted eel populations in Lithuania and other European countries.
Evaluation of Methods for Preserving PTEN Antigenicity in Stored Paraffin Sections

Reduced phosphatase and tensin homolog (PTEN) expression in breast cancer confers susceptibility to phosphatidylinositol 3 kinase/Akt pathway inhibitors. As inhibitors of this pathway are in development for treatment of breast and other cancers, it is of interest to minimize the frequency at which PTEN signal is reduced because of poor antigen preservation, rather than to decreased protein expression. Methods for preserving antigenicity include coating sections in paraffin, refrigerated storage, desiccation, and storage in nitrogen. This study examined various combinations of the first 2 methods with respect to PTEN immunoreactivity. This was assessed semiquantitatively in freshly cut sections of paraffin blocks from 26 breast cancer specimens. The histoscore was compared with that observed in cut sections stored for 2 to 6 months with or without paraffin coating at room temperature (RT; 20 to 25°C) or at 2 to 8°C. At 2 months, the histoscore was strongest in freshly cut sections. Among the other treatments, refrigerated paraffin-coated sections had the highest histoscore followed by refrigerated uncoated sections. It was lower in RT paraffin-coated sections. Uncoated sections stored at RT showed the greatest signal loss. The greatest magnitude of changes between the various storage conditions and freshly cut slides ranged from 15% for refrigerated paraffin-coated slides to 75% for RT uncoated slides at 4 months. Although freshly cut paraffin-embedded sections were optimal, sections can be stored with adequate antigen preservation for 2 months using refrigeration with or without paraffin coating, and for 4 months using refrigeration with paraffin coating.
Blood Pressure Change in Normotensive, Gestational Hypertensive, Preeclamptic, and Essential Hypertensive Pregnancies

We compared patterns of blood pressure (BP) change among normotensive women, women who developed gestational hypertension or preeclampsia, and women who had essential hypertension to examine how distinct these conditions are and whether rates of BP change may help to identify women at risk for hypertensive disorders. We used antenatal clinic BP measurements (median, 14 per woman) of 13016 women from the Avon Longitudinal Study of Parents and Children who had a singleton or twin live birth surviving until ≥1 year. Linear spline models were used to describe changes in systolic and diastolic BPs in different periods of pregnancy (8–18, 18–30, 30–36, and ≥36 weeks' gestation). Women who had essential hypertension and those who developed gestational hypertension or preeclampsia had higher BP at 8 weeks' gestation (baseline) compared with normotensive women. The decrease in BP until 18 weeks was smaller in gestational hypertensive compared with normotensive pregnancies. BP rose more rapidly from 18 weeks onward in gestational hypertensive and preeclamptic pregnancies and from 30 weeks onward in essential hypertensive compared with normotensive pregnancies. Women who developed preeclampsia had a more rapid increase in BP from 30 weeks onward than those who developed gestational hypertension or had essential hypertension. Our findings indicate notable patterns of BP change that distinguish women with essential hypertension, gestational hypertension, and preeclampsia from each other and from normotensive women, even from early pregnancy. These distinct patterns may be useful for identifying women at risk of developing a hypertensive disorder later in pregnancy.
Hard real-time scheduling for low-energy using stochastic data and DVS processors

Addresses scheduling for reduced energy of hard real-time tasks with fixed priorities assigned in a rate monotonic or deadline monotonic manner. The approach described can be exclusively implemented in the RTOS. It targets energy consumption reduction by using both on-line and off-line decisions, taken both at task level and at task-set level. We consider sets of independent tasks running on processors with dynamic voltage supplies (DVS). Taking into account the real behavior of a realtime system, which is often better than the worst case, our methods employ stochastic data to derive energy efficient schedules. The experimental results show that our approach achieves more important energy reductions than other policies from the same class.
A Methodology for Predicting Application-Specific Achievable Memory Bandwidth for HW/SW-Codesign

The trend of using heterogeneous computing and HW/SW-Codesign approaches allows increasing performance significantly while reducing power consumption. One of the main challenges when combining multiple processing devices is the communication, as an inefficient communication configu-ration can pose a bottleneck to the overall system performance. To address this problem, we present a methodology that assists the designer in making good design decisions for systems using shared DDR memory for communication. Our methodology analyzes a software implementation of the application and subsequently predicts the memory accesses of a functionallyequivalent hardware implementation of the selected function. We furthermore propose an IP core that can perform these predicted memory accesses to estimate the achievable memory bandwidth between a functionally equivalent hardware implementation and shared memory. The resulting achievable memory bandwidth estimations differ by less than 2% from the actual achievable memory bandwidth of a functionally equivalent hardware implementation, demonstrating the feasibility of the presented methodology.
Reusable cryptographic fuzzy extractors

We show that a number of recent definitions and constructions of fuzzy extractors are not adequate for multiple uses of the same fuzzy secret---a major shortcoming in the case of biometric applications. We propose two particularly stringent security models that specifically address the case of fuzzy secret reuse, respectively from an outsider and an insider perspective, in what we call a chosen perturbation attack. We characterize the conditions that fuzzy extractors need to satisfy to be secure, and present generic constructions from ordinary building blocks. As an illustration, we demonstrate how to use a biometric secret in a remote fuzzy authentication protocol that does not require any storage on the client's side.
Cloning and expression of the Legionella micdadei“common antigen” in Escherichia coli

To study individual Legionella antigens, a Legionella micdadei genomic library in Escherichia coli SC181 was established. Partially Sau3A digested L. micdadei DNA fragments (15–25 kilobase pairs (kb)) were cloned into the tetracycline resistance gene of the cosmid vector pHC79. Four thousand ampicillin resistant recombinants were obtained; seven hundred were screened for expression of Legionella antigens in Western blot analysis with a polyspecific E. coli ‐absorbed anti‐L. micdadei rabbit antibody. One of the positive clones expressed a 60 kilodalton (K) antigen, which reacted strongly with a monospecific rabbit antiserum raised against L. micdadei“common antigen” (CA), and an additional 13 K L. micdadei protein. The region encoding these two proteins from the 17 kb recombinant plasmid (pBA 2) was subcloned in pBGS 18+. The DNA sequence of the CA encoding region in the 2.7 kb subcloned fragment will provide important information with respect to genetic vs. antigenic relatedness among Legionellae and other Gram‐negative species, as well as to CA structure and possible function.
Optimization and Simulation of Resource Constrained Scheduling Problem Using Genetic Algorithm

Due to the development of management idea and the scarcity of some resources, the lean management has become the necessary request to implement effective control of resource constrained project. Resource constrained project scheduling is the significant guarantee to attain the lean management. The resource constrained project scheduling problem (RCPSP), with the objective of minimizing project duration and with the precedence relations described by an activity-on-arrow (AOA) network, is formulated as a combination optimization problem and solved using the priority-based genetic algorithm (GA). The activity priorities are represented by chromosome and serial scheduling scheme (SSS) and parallel scheduling scheme (PSS) are developed and utilized to transform chromosome-represented priorities to an active schedule subject to the logic and resource constraints so that project duration corresponding to each chromosome can be evaluated. The overall framework of the GA for the RCPSP is developed and the basic components of the algorithm are designed. Simulation is provided so as to investigate the performance of the priority-based GA with SSS and PSS as decoding method, respectively. The optimal solution to a small-sized resource constrained benchmark instance is scheduled to find the shortest project duration. Comparative simulation results demonstrate not only the effectiveness and efficiency of GA with SSS or PSS as decoding methods in solution to RCPSP with precedence relation of activities diagramed as an AOA network but also the effect of different evolution parameter settings on solution quality of the problem.
Application of support vector machines to reliability-based automatic repeat request for brain-computer interfaces

A brain-computer interface (BCI) is a system that could enable patients like those with amyotrophic lateral sclerosis to control some equipment and to communicate with other people, and has been anticipated to be achieved. One of the problems in BCI research is a trade-off between speed and accuracy, and it is practically important to adjust those two performance measures effectively. So far the authors have considered BCIs as communications between users and computers, and have proposed an error control method, reliability-based automatic repeat request (RB-ARQ). It has been shown that, with linear discriminant analysis (LDA) as a classifier, RB-ARQ is more effective than other error control methods. In this paper, support vector machines (SVMs), one of the most popular classifiers, are applied to RB-ARQ. A quantitative comparison showed that there was no significant difference between LDA and SVM. Also, it was demonstrated that RB-ARQ improved the accuracy from the one acquired by the top ranked methods in the BCI competition to 100 percents, with less loss of the speed.
Learning Controllers for Complex Behavioral Systems.

Abstract : Biological control systems routinely guide complex dynamical systems through complicated tasks such as running or diving. Conventional control techniques, however, stumble with these problems, which have complex dynamics, many degrees of freedom, and a task which is often only partially specified. To address problems like these, we are using a biologically inspired, hierarchical control structure, in which controllers composed of radial basis function networks learn the controls required at each level of the hierarchy. Through learning and proper encoding of behaviors and controls, some of these difficulties in controlling complex systems can be overcome.
Multi-product inventory modeling with demand forecasting and Bayesian optimization

espanolLa complejidad de las cadenas de suministro exige mejores metodos para programar los inventarios de una empresa. En este trabajo se presenta una comparacion entre modelos de pronosticos de demanda de multiples productos, eligiendo el mejor entre: ARIMA, Suavizacion exponencial, Regresion Lineal Bayesiana y un Modelo Lineal Dinamico Bayesiano. Para ello, primero se realiza una simulacion de casos donde no hay una Distribucion Normal en las series de tiempo, segundo, se estiman las predicciones de ventas de tres productos de una estacion de servicios de gasolina con los cuatro modelos, encontrando los mejores resultados para la Regresion Lineal Bayesiana. Seguido a esto, se presenta la optimizacion de un Modelo de Inventarios Multi-Producto. Para definir la politica de pedidos, inventarios, costos y ganancias, se utiliza una busqueda bayesiana, que integra elementos de busqueda Tabu para mejorar la solucion. Dicha Optimizacion del Modelo de Inventarios se aplica a un caso de una estacion de combustibles en Colombia. EnglishThe complexity of supply chains requires advanced methods to schedule companies’ inventories. This paper presents a comparison of model forecasts of demand for multiple products, choosing the best among the following: autoregressive integrated moving average (ARIMA), exponential smoothing (ES), a Bayesian regression model (BRM), and a Bayesian dynamic linear model (BDLM). To this end, cases in which the time series is normally distributed are first simulated. Second, sales predictions for three products of a gas service station are estimated using the four models, revealing the BRM to be the best model. Subsequently, the multi-product inventory model is optimized. To define the policies for ordering, inventory, costs, and profits, a Bayesian search integrating elements of a Tabu search is used to improve the solution. This inventory model optimization process is then applied to the case of a gas service station in Colombia.
Gross morphological and sex wise morphometrical studies on the first, second and third thoracic vertebrae of blue bull (Boselaphus tragocamelus)

The present study was carried out on the first, second and third thoracic vertebrae of six specimens of adult Blue bull (Boselaphus tragocamelus) of either sex. The first, second and third thoracic vertebrae were characterized by a long supraspinous process, cylindrical, but shorter centrum. The arch presented shallow notches and was perforated by intervertebral foramina at its caudal aspect. They also presented cranial and caudal facets on their bodies. The length and breadth of supra spinous processes was observed to increase up to T3. The transverse process was reported to be thick, strong that presented a rounded non-articular mammillary process and a facet ventrally that articulated with the facet of the tubercle of the corresponding rib. The dorsal supraspinous process presented two surfaces, two borders and a summit. The costal facets were placed on either side at the end of the articular extremities of the centrum. The cranial articular processes were represented by oval facets on the anterior part of the arch and faced upwards, whereas the caudal ones spring from the base of the dorsal supraspinous process. The Biometrical observations on different parameters of first, second and third thoracic vertebrae reflected significance (P<0.05) differences between the sexes of this species.
CS reconstruction of the speech and musical signals

The application of Compressive sensing approach to the speech and musical signals is considered in this paper. Compressive sensing (CS) is a new approach to the signal sampling that allows signal reconstruction from a small set of randomly acquired samples. This method is developed for the signals that exhibit the sparsity in a certain domain. Here we have observed two sparsity domains: discrete Fourier and discrete cosine transform domain. Furthermore, two different types of audio signals are analyzed in terms of sparsity and CS performance - musical and speech signals. Comparative analysis of the CS reconstruction using different number of signal samples is performed in the two domains of sparsity. It is shown that the CS can be successfully applied to both, musical and speech signals, but the speech signals are more demanding in terms of the number of observations. Also, our results show that discrete cosine transform domain allows better reconstruction using lower number of observations, compared to the Fourier transform domain, for both types of signals.
A new machine learning algorithm for neoposy: coining new parts of speech

Unsupervised Natural Language Learning, the use of machine learning algorithms to extract linguistic patterns from raw, un-annotated text, is a growing research subfield; for examples, see Proceedings of annual conferences of CoNLL: Computational Natural Language Learning, or the membership list of ACLSIGNLL, the Association for Computational Linguistics – Special Interest Group in Natural Language Learning. Corpora, especially tagged and parsed corpora, can be used to train `machine learning’ or computational language learning models of complex sequence data. (Jurafsky and Martin 2000) divide Machine Learning systems into Supervised and Unsupervised approaches (p118):
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces

We formulate a general energy and method for segmentation that is designed to have preference for segmenting the coarse structure over the fine structure of the data, without smoothing across boundaries of regions. The energy is formulated by considering data terms at a continuum of scales from the scale space computed from the Heat Equation within regions, and integrating these terms over all time. We show that the energy may be approximately optimized without solving for the entire scale space, but rather solving time-independent linear equations at the native scale of the image, making the method computationally feasible. We provide a multi-region scheme, and apply our method to motion segmentation. Experiments on a benchmark dataset shows that our method is less sensitive to clutter or other undesirable fine-scale structure, and leads to better performance in motion segmentation.
Intelligent online BGP-4 analyzer

Border Gateway Protocol 4 (BGP-4) is one of the widely used routing protocols. This paper presents the design and implementation of an intelligent BGP-4 analyzer working online. Serving in the study and test of BGP-4, the analyzer can capture all the BGP-4 traffic and further exploit "state synchronization" algorithm to analyze the behavior of the BGP-4 state machine by observing the interaction between BGP-4 peers. As to the routing information manipulation, the analyzer can also verify its correctness and efficiency through internal process simulation and statistical computation. And the interested sessions and routes are traced and analyzed. This analyzer possesses many other beneficial features and plays an important role both in the study and test of BGP-4 and the network management.