Miracle-dz/UniFAR-FTUs huggingface dataset:
CD19 Alterations Emerging after CD19-Directed Immunotherapy Cause Retention of the Misfolded Protein in the Endoplasmic Reticulum

We previously described a mechanism of acquired resistance of B-cell acute lymphoblastic leukemia to CD19-directed chimeric antigen receptor T-cell (CART) immunotherapy. It was based on in-frame insertions in or skipping of CD19 exon 2. ABSTRACT We previously described a mechanism of acquired resistance of B-cell acute lymphoblastic leukemia to CD19-directed chimeric antigen receptor T-cell (CART) immunotherapy. It was based on in-frame insertions in or skipping of CD19 exon 2. To distinguish between epitope loss and defects in surface localization, we used retroviral transduction and genome editing to generate cell lines expressing CD19 exon 2 variants (CD19ex2vs) bearing vesicular stomatitis virus G protein (VSVg) tags. These lines were negative by live-cell flow cytometry with an anti-VSVg antibody and resistant to killing by VSVg-directed antibody-drug conjugates (ADCs), suggestive of a defect in surface localization. Indeed, pulse-chase and α-mannosidase inhibitor assays showed that all CD19ex2vs acquired endoplasmic reticulum (ER)-specific high-mannose-type sugars but not complex-type glycans synthesized in the Golgi apparatus. When fused with green fluorescent protein (GFP), CD19ex2vs (including a mutant lacking the relevant disulfide bond) showed colocalization with ER markers, implying protein misfolding. Mass spectrometric profiling of CD19-interacting proteins demonstrated that CD19ex2vs fail to bind to the key tetraspanin CD81 and instead interact with ER-resident chaperones, such as calnexin, and ER transporters involved in antigen presentation. Thus, even the intact domains of CD19ex2vs cannot be easily targeted with ADCs or current CD19 CARTs but could serve as sources of peptides for major histocompatibility complex (MHC)-restricted presentation and T-cell receptor (TCR)-mediated killing.
Data-Based Detection and Diagnosis of Faults in Linear Actuators

Modern industrial facilities, as well as vehicles and many other assets, are becoming highly automated and instrumented. As a consequence, actuators are required to perform a wide variety of tasks, often for linear motion. However, the use of tools to monitor the condition of linear actuators is not widely extended in industrial applications. This paper presents a data-based method to monitor linear electro-mechanical actuators. The proposed algorithm makes use of features extracted from electric current and position measurements, typically available from the controller, to detect and diagnose mechanical faults. The features are selected to characterize the system dynamics during transient and steady-state operation and are then combined to produce a condition indicator. The main advantage of this approach is the independence from a need for a physical model or additional sensors. The capabilities of the method are assessed using a novel experimental linear actuator test rig specially designed to recreate fault scenarios under different operating conditions.
Threshold Effects of Inflation on Economic Growth in Africa : Evidence from a Dynamic Panel Threshold Regression Approach Arcade

This study examines nonlinearities in the inflation-growth nexus in Africa. A dynamic panel threshold regression is applied to account for the potential endogeneity bias in the model. The findings of this study confirm the existence of nonlinearities in the inflation-growth nexus. An inflation threshold of 6.7% is estimated for the whole sample, 9% for the sub-sample of low-income countries and 6.5% for middle-income countries. The findings suggest that low inflation is growthenhancing for the sub-sample of middleincome countries but neither affects economic growth for the whole sample nor for the sub-sample of low-income countries. However, inflation above the threshold is detrimental to economic growth for all the cases considered. The findings of this study may be useful to African monetary policymakers as they decide on inflation targets to adopt to avoid the detrimental effects of high inflation while reaping the growth benefits of low inflation.
A BAYESIAN MULTINOMIAL-POISSON SIMPLIFIED MODEL FOR NETWORK TRAFFIC INFERENCE BASED ON LINK COUNT DATA

Given a network where traffic counts are observed on a selected set of links, we consider the problem of estimating traffic intensities between origin-destination (OD) pairs in this network. We adopt a Bayesian approach and set independent Poisson prior distributions on the number of trips generated on a given time interval between each OD pair. Trips are further assigned to routes in the network according to a multinomial distribution where route choice proportions are defined by a SUE model. Given the complexity in the likelihood function, we derive a hybrid Markov chain Monte Carlo algorithm that samples asymptotically from the posterior distribution and thus enables the joint inference of mean OD pair and network flows. We discuss theoretical aspects of the model, including its relation to previously studied models and the adequacy of the assumed prior distribution, and practical issues such as computational efficiency. We illustrate our method on well known example networks, compare estimates with other methods, and finish by presenting directions for future research.
Verifying Fail-Free Declarative Programs

Failed computations are a frequent problem in software system development. Some failures have external reasons (e.g., missing files) that can be caught by exception handlers. Many other failures have internal reasons, such as calling a partially defined operation with unintended arguments. In order to avoid the latter kind of failures, one can try to analyze the program at compile time for potential occurrences of these failures at run time. In this paper we present an approach to verify the absence of such failures in functional logic programs. Since programming with failures is a typical technique in logic programming, we are not interested to abandon partially defined operations at all. Instead, we want to verify conditions which ensure that operations can be executed without running into a failure. For this purpose, we propose to annotate operations with non-fail conditions that are verified at compile time with an SMT solver. For successfully verified programs, it is ensured that computations never fail provided that the non-fail condition of the main operation is satisfied.
Minimum KS estimator using PH-transform technique

In this paper, we propose an improvement of the Minimum Kolmogorov-Smirnov (K-S) estimator using proportional hazards transform (PH-transform) technique. The data of experiment is 47 fire accidents data of an insurance company in Thailand. This experiment has two operations, the first operation, we minimize K-S statistic value using grid search technique for nine distributions; Rayleigh distribution, gamma distribution, Pareto distribution, log-logistic distribution, logistic distribution, normal distribution, Weibull distribution, lognormal distribution, and exponential distribution and the second operation, we improve K-S statistic using PHtransform. The result appears that PH-transform technique can improve the Minimum K-S estimator. The algorithms give better the Minimum K-S estimator for seven distributions; Rayleigh distribution, logistic distribution, gamma distribution, Pareto distribution, log-logistic distribution, normal distribution, Weibull distribution, log-normal distribution, and exponential distribution while the Minimum K-S estimators of normal distribution and logistic distribution are unchanged.
CDA & > ? E Tele-Healthcare Systems for End-of-Life Decision at Home

Super aging society such as Japan would need tele-healthcare systems for home medical care to suppress medical cost inflation. In this situation, we have developed a tele-healthcare system for end-of-life decision at home using a wireless electrocardiograph combined with a smartphone and a sphygmogram signal extracted from an ordinary video camera. The experimental results have indicated that long term monitoring through the smartphone and a server will provide rich information on the patient's terminal status for doctors in remote locations. However, it is necessary to collect many clinical data to predict the timing of end-of-life more precisely.
3 D-Modeling of the Santa Terezinha Coalfield , Brazil : Assessment of Coal Bed Methane Potential

In Brazil, economically important coal-measures occur in the southern Paraná Basin. Their proven reserves amount up to 32 billion tons. From all coal fields in the Paraná Basin, the Santa Terezinha field in Rio Grande do Sul state appears to have the highest CBM potential based on coal rank, coal distribution and depth of the coal seams. The coal measures dip at an angle of 3° to the NW. In addition to that, they are downthrown at a set of NW-SE striking normal faults towards the trough of a grabenlike structure ("Torres syncline"). The coal measures occur near surface and at depths down to 950 m. Coal seams area hosted in a succession of sandstones, mudstones, and shales of Early Permian age. Sequence stratigraphy was applied in order to subdivide this succession and to correlate the coal seams. Four coal bearing parasequences have been identified showing a cumulative coal thickness up to 10 m. The most important controls for the CBM potential are a) basin geology, i.e. reservoir size, depth and thickness of coal seams; b) coal characteristics, i.e. coal rank, coal type and coal grade; c) insitu methane content, measured by means of gas desorption tests and isotherm adsorption tests. To make an assessment of the CBM potential of the coal-bearing strata, computer-aided modeling of the Terezinha Coalfield was carried out using the Surpac system by Surpac Software International of Perth, Western Australia. Computerized modeling packages are mainly used on the scale of a single deposit. Here, we want to show that such systems are also powerful tools in large-scale exploration projects. The resulting model will provide a vital decision support for locating a first coalbed methane test well.
Segmentation technique of complex image scene for an automatic blood-cell-counting system

The paper presents a method for automatic localization and segmentation of white blood cells (WBCs) with color images to develop an efficient automated leukocyte counter by using pattern recognition-based slide readers. The segmentation techniques consist of the following steps. On the first a smear image acquired at the low magnification. The next is extraction of WBC nuclei by chromatic properties and image mapping. After this the cells clustered according to the distances between them and regions of interest (ROI) determined. Image of ROI captured at the high magnification and its validity checked. Then nucleus segments extracted and grouped into prospective cells. The detection of blood cells is based on the intensity of G image plane and the balance between G and B intensity of the nuclei. A cytoplasm region approximated by a circle area around the nucleus center. Finally, the cytoplasm area cleaned considering a priori knowledge of background color and possible cell occlusions. The result of the segmentation is presented in the form of a cell location list and image template in which every pixel is assigned to a label such as Background, Cytoplasm, Nucleus, Hole, etc. The proposed technique has yielded correct segmentation of complex image scenes for blood smears prepared by ordinary manual staining methods in 99% of tested images.
Shape from Textures Phenomenon for 3 D Modeling Applications with Big Data Images

This paper discusses the shape from textures phenomenon with proposed features and finds its application in the interactive 3D modeling works. Two different themes are discussed in this paper; the proposed algorithm and its integration with the 3D modeling environment. There are many 3D modeling tools which builds 3D model out of the 2D image collections, by requiring much of human interactions for the generation of the 3D objects. This paper applies the shape from textures phenomenon with additional features and propose an algorithm to simplify the process of the 3D planes generation. The proposed algorithm analyzes the textures in the single image and results the orientation and shape details for the corresponding textures. The result of the algorithm is integrated with the 3D modeling environment. There are mutual exchanges between the modeling works and the proposed shape from textures algorithm. The texture orientation details from the algorithm are complemented with the depth measures from the modeler. The algorithm statistically analyzes the collective orientation of the superpixels and suggests the user to go for the nearby matching templates. The proposed algorithm together with the 3D modeling measures provides the flexible environment for the big data image collections.
STRUCTURED MECHANICAL MODELS FOR EFFICIENT REINFORCEMENT LEARNING

Learning accurate dynamics models is necessary for optimal, compliant control of robotic systems. Model-based reinforcement learning attempts to learn accurate models by interacting with the environment, and the efficiency of doing so depends on how the model is parameterized. Current approaches to white-box modeling using analytic parameterizations, or black-box modeling using neural networks, can suffer from high bias or high variance. We address the need for a flexible, gray-box model of mechanical systems that can seamlessly incorporate prior knowledge where it is available, and train expressive function approximators where it is not. We propose to parameterize a mechanical system using neural networks to model its Lagrangian and the generalized forces that act on it. We test our method on a simulated, actuated double pendulum. We show that our method outperforms a black-box model in terms of data-efficiency, as well as performance in model-based reinforcement learning.
Formal Security Proof of CMAC and Its Variants

The CMAC standard, when initially proposed by Iwata and Kurosawa as OMAC1, was equipped with a complex game-based security proof. Following recent advances in formal verification for game-based security proofs, we formalize a proof of unforgeability for CMAC in EasyCrypt. A side effects of this proof are improvements of EasyCrypt libraries. This formal proof obtains security bounds very similar to Iwata and Kurosawa’s for CMAC, but also proves secure a certain number of intermediate constructions of independent interest, including ECBC, FCBC and XCBC. This work represents one more step in the direction of obtaining a reliable set of independently verifiable evidence for the security of international cryptographic standards.
Microbiological Decontamination of Water: Improving the Solar Disinfection Technique (SODIS) with the Use of Nontoxic Vital Dye Methylene Blue

Rational use of water is a major challenge for governments and global organizations, with easy and inexpensive interventions being sought by communities that are not supplied with drinking water. In this context, solar disinfection (SODIS) has shown great efficiency for water disinfection. To speed up the process and improve inactivation, we studied the effects of methylene blue (MB) as a photodynamic agent because of its ability to absorb visible light (red wavelength) and generate singlet oxygen as a reactive species, thereby inactivating bacteria and viruses present in water. In this study, samples of clean mineral water were artificially contaminated with Gram‐positive (Staphylococcus epidermidis or Deinococcus radiodurans) or with Gram‐negative strains (Escherichia coli or Salmonella typhimurium) and exposed to traditional SODIS or to MB‐SODIS. A lethal synergistic effect was observed when cultures were illuminated in the presence of MB. The obtained results indicate that bacterial inactivation can be achieved in a much shorter time when using MB associated with SODIS treatment. Therefore, this technique was able to provide safe water for consumption through the inactivation of microorganisms in general, including pathogens and some strains resistant to the traditional SODIS procedure, thus allowing its use in areas usually less exposed to sunlight.
DAFX-1 TOWARDS AN INVERTIBLE RHYTHM REPRESENTATION AggelosGkiokas and

This paper investigates the development of a rhythm representation of music audio signals, that (i) is able to tackle rhythm related tasks and, (ii) is invertible, i.e. is suitable to reconstruct audio from it with the corresponding rhythm content being preserved. A conventional front-end processing schema is applied to the audio signal to extract time varying characteristics (accent features) of the signal. Next, a periodicity analysis method is proposed that is capable of reconstructing the accent features. Afterwards, a network consisting of Restricted Boltzmann Machines is applied to the periodicity function to learn a latent representation. This latent representation is finally used to tackle two distinct rhythm tasks, namely dance style classification and meter estimation. The results are promising for both input signal reconstruction and rhythm classification performance. Moreover, the proposed method is extended to generate random samples from the corresponding classes.
A Memristor-CMOS Hybrid Circuit for Classical Conditioning Reflex

The imitation of classical conditioning reflex at circuit level is a significant procedure to achieve biology-like circuit systems. In order to realize the imitation, the important process are to imitate synaptic behaviors. As an emerging device, the memristor has some excellent properties like nonvolatility, adjustability, nanoscale. Therefore, it is an appropriate candidate to simulate the synaptic behavior in artificial neural network circuits. This paper presents a memristor-CMOS hybrid circuit to imitate classical conditioning reflex of aplysia californica. Besides, the proposed circuit can simulate additional forgetting stages that are activated by the single unconditional stimulus (US) or the single conditioned stimulus (CS) after the learning stage. The learning and forgetting stages can be described as: when US and CS are given to an aplysia californica together, it forms classical conditioning reflex through associative learning. Then, giving US or CS alone to the aplysia californica after the learning, it will forget the classical conditioning reflex gradually. The proposed circuit is simulated on PSPICE to demonstrate the effectiveness.
Foraging, feeding, and physiological stress responses of wild wood mice to increased illumination and common genet cues

Abstract In nature, animals are exposed to a broad range of threats imposed by predators, which may strongly influence the ecology of prey species directly or indirectly by affecting their behavior via fear of predation. Here, we studied wood mice Apodemus sylvaticus behavioral and physiological responses to simulated predation risk. Risk avoidance was analyzed by live trapping with control traps and traps treated with feces of common genet Genetta genetta (direct cue of risk) under new moon nights and following by simulated full moon conditions (indirect cue). The time devoted to foraging behavior and capture time were analyzed by video recording mice activity around traps. Food intake was calculated based on the amount of bait remaining in each trap. Fecal corticosterone metabolites (FCMs) were measured by enzyme-immunoassay as indicators of physiological stress responses. Fewer wood mice were captured during full moon, yet only non-breeding adult males clearly avoided common genet odor. Mice were captured sooner at night during the simulated full moon conditions and later in predator-treated traps. Foraging activity was lower when individuals faced predator’s feces, but neither food intake nor FCM levels were affected by predation risk cues. Direct and indirect cues of predation risk selectively affected wood mice behavior, although behavioral responses seem to be modulated by different costs–benefit balances related to the individual’s perception of risk. The lack of physiological responses to predation risk cues suggests that wood mice did not perceive them as reliable stressors or the response was too small or transient to be measured by FCM.
Adaptive Nonlinear Flight Control of STOL-Aircraft Based on Incremental Nonlinear Dynamic Inversion

In this paper an adaptive control system for a passenger aircraft with active high-lift system is presented. Failures in the high-lift system parts of such aircraft are critical and consequently need to be handled automatically. An adaptive controller is proposed which consists of incremental nonlinear dynamic inversion (INDI) with a reference model and linear controller. As the INDI is adaptive against uncertainties or system failures, no additional adaptive element like a neural network is needed. The implementation of the INDI requires a nonlinear system model which is permanently linearized during the runtime in order to obtain the current input matrix which here basically consists of the control surfaces effectiveness. It also requires feedback of the translational and rotational acceleration measurements which usually suffer from noise. In order to test the adaptivity of the INDI, a partial failure of the high-lift system during the landing approach is regarded. It shows that the INDI is capable of compensating the error by only using the conventional control surfaces.
Prediction of GO annotation by combining entity specific sentence sliding window profiles

Information extraction aims to derive from free text significant information related to a given query (Hobbs, 2002). The ”Critical assessment of text mining methods in molecular biology (BioCreative)” is a community wide text mining and information extraction contest. It is divided into two major tasks, each composed by further sub-tasks. While task 1 refers mainly to named entity extraction of gene and protein names, task 2 is concerned with functional annotation of gene products from free text and is organized into four distinct sub-tasks. The BioCreative sub-task 2.1, aims to extract protein annotations using full length biomedical articles and taking Gene Ontology (GO) as reference. This means that for a given protein and GO term, a text passage should be returned where a traceable association between them is provided. In order to participate in sub-task 2.1 we developed a method which returns text passages associating a given protein to a GOterm. It is based on a strong name identification system, a catalog of GO related terms and patterns, and statistics derived from a collection of sentences derived from information contained in the Gene Ontology Annotation (GOA) database. The system generates distinct sub-tag sets or word lists for the query entities (protein and GO term). Each sub-tag set has associated a characteristic sub-tag score. To extract the sentences participating in the annotation event we used a sentence sliding window approach applying previously calculated sub-tag scores for the protein and GO term sub-tag matches.