Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomarkers of inflammatory and auto-immune central nervous system disorders

Purpose of review Inflammatory and auto-immune disorders of the central nervous system are a heterogeneous group of disorders. Many of these disorders are potentially treatable with immune therapies that can reduce disability or prevent death. We review the clinical value of biomarkers which can aid in the diagnosis of paediatric inflammatory and auto-immune central nervous system (CNS) disorders. Recent findings This review will first describe the clinical usefulness of nonspecific biomarkers of CNS inflammation such as cerebrospinal fluid neopterin and oligoclonal bands. Neopterin is produced by immune and neuronal cells after stimulation by interferon species and is increased in a broad range of inflammatory and auto-immune CNS disorders. Oligoclonal bands represent clonal production of immunoglobulin G in the CNS and are present in demyelinating, auto-immune, and infectious CNS disorders. In addition, we will review new advances in the immunogenetic investigation of familial auto-inflammatory disorders such as Aicardi-Goutières syndrome and Chronic Infantile Neurologic Cutaneous Articular syndrome. Finally, we will review the clinical utility of auto-antibodies in CNS disorders, with specific focus on auto-antibodies that bind to cell surface proteins such as N-methyl-D-asparate receptor, voltage-gated potassium channels, myelin oligodendrocyte glycoprotein, and aquaporin-4. Summary These biomarkers are increasingly important in the recognition and treatment of inflammatory and auto-immune CNS disorders. Like many biomarkers in paediatric practice, it is essential to interpret the findings in the context of the patient history and examination.
Abstract unordered and ordered trees CRDT

Trees are fundamental data structure for many areas of computer science and system engineering. In this report, we show how to ensure eventual consistency of optimistically replicated trees. In optimistic replication, the different replicas of a distributed system are allowed to diverge but should eventually reach the same value if no more mutations occur. A new method to ensure eventual consistency is to design Conflict-free Replicated Data Types (CRDT). In this report, we design a collection of tree CRDT using existing set CRDTs. The remaining concurrency problems particular to tree data structure are resolved using one or two layers of correction algorithm. For each of these layer, we propose different and independent policies. Any combination of set CRDT and policies can be constructed, giving to the distributed application programmer the entire control of the behavior of the shared data in face of concurrent mutations. We also propose to order these trees by adding a positioning layer which is also independent to obtain a collection of ordered tree CRDTs.
Different ratios of lactide and glycolide in PLGA affect the surface property and protein delivery characteristics of the PLGA microspheres with hydrophobic additives

This study investigated whether the surface quantities of the additives and protein delivery characteristics for protein-loaded PLGA 85 (85 : 15), PLGA75 (75 : 25) and PLGA 50 (50 : 50) microspheres with hydrophobic additives, phosphatidylcholine (PC) as a model, are affected by the different ratios of lactide and glycolide in PLGA polymers. By applying phospholipids analysis, surface PC quantities of the microspheres are 2.1 ± 1.2, 1.2 ± 0.3 and 1.0 ± 0.7 µg (n = 3) per mg of PLGA 85, PLGA 75 and PLGA 50 microspheres with PC addition, respectively, that are affected by the ratio of lactide and glycolide in PLGA. The addition of PC causes the decreased encapsulation efficiency (E.E.) of albumin of the microspheres and the decreased percentages of E.E. are negative correspondent to ratio of lactide and glycolide in PLGA (e.g. −13.5, −20.2 and −24.5% for PLGA 85, PLGA 75 and PLGA 50, respectively) compared with those without addition. The cumulative releases of albumin were influenced by the ratio of lactide and glycolide in PLGA for both PC addition and non-addition microspheres. Moreover, the additions of PC reduce the initial burst and cumulative releases of albumin of the microspheres compared with those of non-added ones. Further investigation suggests that resistant water uptake of PC added microspheres may play a role on affecting those reduced protein release behaviours. In conclusion, the different ratios of lactide and glycolide in PLGA affect the surface quantities of PC, E.E. and cumulative release profiles of albumin for the PLGA microspheres with PC addition. The results of the study may help the better practical applications for protein delivery of the microspheres with hydrophobic additives.
Time Dependent On-line Boosting for Robust Background Modeling

In modern video surveillance systems change and outlier detection is of highest interest. Most of these systems are based on standard pixel-by-pixel background modeling approaches. In this paper, we propose a novel robust blockbased background model that is suitable for outlier detection using an extension to on-line boosting for feature selection. In order to be robust our system incorporates several novelties for previous proposed on-line boosting algorithms and classifier-based background modeling systems. We introduce time-dependency and control for on-line boosting. Our system allows for automatically adjusting its temporal behavior to the underlying scene by using a control system which regulates the model parameters. The benefits of our approach are illustrated on several experiments on challenging standard datasets.
Challenges in the development of chronic pulmonary hypertension models in large animals

Pulmonary hypertension (PH) results in significant morbidity and mortality. Chronic PH animal models may advance the study of PH’s mechanisms, evolution, and therapy. In this report, we describe the challenges and successes in developing three models of chronic PH in large animals: two models (one canine and one swine) utilized repeated infusions of ceramic microspheres into the pulmonary vascular bed, and the third model employed a surgical aorto-pulmonary shunt. In the canine model, seven dogs underwent microsphere infusions that resulted in progressive elevation of pulmonary arterial pressure over a few months. In this model, pulmonary endoarterial tissue was obtained for histology. In the aorto-pulmonary shunt swine model, 17 pigs developed systemic level pulmonary pressures after 2–3 months. In this model, pulmonary endoarterial tissue was sequentially obtained to assess for changes in gene and microRNA expression. In the swine microsphere infusion model, three pigs developed only a modest chronic increase in pulmonary arterial pressure, despite repeated infusions of microspheres (up to 40 in one animal). The main purpose of this model was for vasodilator testing, which was performed successfully immediately after acute microsphere infusions. Chronic PH in large animal models can be successfully created; however, a model’s characteristics need to match the investigational goals.
A Domain-Specific Language For Simulation Composition

The SmartGrid, the power grid of the future, is comprised of a large number of distributed, partially controllable energy consumers and producers that need to be matched to ensure that generation and consumption is balanced. Modelling & Simulation is a powerful approach for analysing and testing such complex systems. In this paper a scenario specification based on a domain-specific language (DSL) is presented which can be used to formally describe simulations and scenarios using these simulations. This formal description, in combination with a simulation framework that is able to interpret the description, allows the automatic composition of the simulations.The DSL has been tested with a lightweight simulation framework developed for a current project that analyses the impact of electric vehicles on the distribution grid. Although this work focuses on the domain of SmartGrids, the DSL-based approach can of course also be applied to other domains.
Continuous fragmented skylines over distributed streams

Distributed skyline computation is important for a wide range of application domains, from distributed and web-based systems to ISP-network monitoring and distributed databases. The problem is particularly challenging in dynamic distributed settings, where the goal is to efficiently monitor a continuous skyline query over a collection of distributed streams. All existing work relies on the assumption of a single point of reference for object attributes/dimensions, i.e., objects may be vertically or horizontally partitioned, but the accurate value of each dimension for each object is always maintained by a single site. This assumption is unrealistic for several distributed monitoring applications, where object information is fragmented over a set of distributed streams (each monitored by a different site) and needs to be aggregated (e.g., averaged) across several sites. Furthermore, it is frequently useful to define skyline dimensions through complex functions over the aggregated objects, which raises further challenges for dealing with object fragmentation. In this paper, we present the first known distributed approach for continuous fragmented skylines, namely distributed monitoring of skylines over complex functions of fragmented multi-dimensional objects. We also propose several optimizations, including a new technique based on random-walk models for adaptively determining the most efficient monitoring strategy for each object. A thorough experimental study with synthetic and real-life data sets verifies the effectiveness of our approach, demonstrating order-of-magnitude improvements in communication costs compared to the only available centralized solution.
Dendrimer stabilized silver particles for the antimicrobial finishing of textiles

Herein a simple approach to fabricate hybrid nanomaterials based on dendrimers as polymeric stabilizers for the preparation of silver nanoparticles in aqueous medium were presented. Solutions of these functional nanomaterials were used as finishing agents to produce antimicrobial textiles. The concept was verified on different generations of hydroxyl-terminated dendrimers with increasing size from first to fifth generation. The application was tested on two different fabrics types made of cotton and polyester. The thermal fixation was performed at 120 or 160°C. The antimicrobial properties of the treated fabrics were investigated using the bacteria Escherichia coli and Staphylococcus aureus. The antimicrobial effect of the treated fabrics was found which significant correlates not only with the amount of the silver content in the finishing agent but also with size of the generated Ag-nanoparticles. Furthermore, the results confirmed that the antimicrobial effect rises with increasing the dendrimers’ generations suggested due to decreasing the size of the formed silver particles. By changing both parameters thermal fixation and dendrimers’ generations, the strength of the antimicrobial effect can be controlled in a wide manner. This could be advantageous for applications demanding a textile with a certain antimicrobial effect.
Persistency of Analyticity for Nonlinear Wave Equations: An Energy-like Approach

Author(s): Guo, Yanqiu; Titi, Edriss S | Abstract: We study the persistence of the Gevrey class regularity of solutions to nonlinear wave equations with real analytic nonlinearity. Specifically, it is proven that the solution remains in a Gevrey class, with respect to some of its spatial variables, during its whole life-span, provided the initial data is from the same Gevrey class with respect to these spatial variables. In addition, for the special Gevrey class of analytic functions, we find a lower bound for the radius of the spatial analyticity of the solution that might shrink either algebraically or exponentially, in time, depending on the structure of the nonlinearity. The standard $L^2$ theory for the Gevrey class regularity is employed; we also employ energy-like methods for a generalized version of Gevrey classes based on the $\ell^1$ norm of Fourier transforms (Wiener algebra). After careful comparisons, we observe an indication that the $\ell^1$ approach provides a better lower bound for the radius of analyticity of the solutions than the $L^2$ approach. We present our results in the case of period boundary conditions, however, by employing exactly the same tools and proofs one can obtain similar results for the nonlinear wave equations and the nonlinear Schr quot;odinger equation, with real analytic nonlinearity, in certain domains and manifolds without physical boundaries, such as the whole space $\mathbb{R}^n$, or on the sphere $\mathbb{S}^{n-1}$.
Improved contrast sensitivity DVS and its application to event-driven stereo vision

This paper presents a new DVS sensor with one order of magnitude improved contrast sensitivity over previous reported DVSs. This sensor has been applied to a bio-inspired event-based binocular system that performs 3D event-driven reconstruction of a scene. Events from two DVS sensors are matched by using precise timing information of their ocurrence. To improve matching reliability, satisfaction of epipolar geometry constraint is required, and simultaneously available information on the orientation is used as an additional matching constraint.
Theoretical and Laboratory Studies on the Interaction of Cosmic-Ray Particles with Interstellar Ices. II. Formation of Atomic and Molecular Hydrogen in Frozen Organic Molecules

Methane ices are irradiated at 4 × 10-10 mbar at temperatures between 10 and 50 K with 9.0 MeV α-particles and 7.3 MeV protons to elucidate the formation of atomic as well as molecular hydrogen via interaction of Galactic cosmic-ray particles with extraterrestrial organic ices. Theoretical calculations focus on computer simulations of ion-induced collision cascades in irradiated targets. Our data reveal that more than 99% of the energy is transferred via inelastic interactions to the electronic system of the target to form electronically excited CH4 molecules decomposing to a CH3--H radical pair. Two H atoms recombine in a diffusion limited step to H2. Further, secondary dissociation of CH3 to H and CH2 contributes to H production. To a minor amount, implanted ions generate C and H knock-on atoms via elastic encounters which abstract hydrogen atoms or insert into chemical bonds (carbon atoms only). Fourier transform infrared spectroscopy (FTIR) and quadrupole mass spectrometry (QMS) analyses indicate that if these energy-loss processes accumulate up to 6 ± 3% H atoms in the CH4 target, more than 90% of the ice is released in an explosive ejection into the gas phase. This mechanism represents a powerful pathway to supply newly formed molecules from interstellar grains back to the gas phase of the interstellar medium even at temperatures as low as 10 K.
Bioguided Fractionation Shows Cassia alata Extract to Inhibit Staphylococcus epidermidis and Pseudomonas aeruginosa Growth and Biofilm Formation

Plant extracts have a long history to be used in folk medicine. Cassia alata extracts are known to exert antibacterial activity but details on compounds and mechanism of action remain poorly explored. We purified and concentrated the aqueous leaf extract of C. alata by reverse phase-solid phase extraction and screened the resulting CaRP extract for antimicrobial activity. CaRP extract exhibited antimicrobial activity for Pseudomonas aeruginosa, Staphylococcus epidermidis, S. aureus, and Bacillus subtilis. CaRP also inhibited biofilm formation of S. epidermidis and P. aeruginosa. Several bacterial growth-inhibiting compounds were detected when CaRP extract was fractionated by TLC chromatography coupled to bioautography agar overlay technique. HPLC chromatography of CaRP extract yielded 20 subfractions that were tested by bioautography for antimicrobial activity against S. aureus and S. epidermidis. Five bioactive fractions were detected and chemically characterized, using high-resolution mass spectrometry (qTOF-MS/MS). Six compounds from four fractions could be characterized as kaempferol, kaempferol-O-diglucoside, kaempferol-O-glucoside, quercetin-O-glucoside, rhein, and danthron. In the Salmonella/microsome assay CaRP showed weak mutagenicity (MI < 3) only in strain TA98, pointing to a frameshift mutation activity. These results indicate that C. alata leaf extract contains a minimum of 7 compounds with antimicrobial activity and that these together or as single substance are active in preventing formation of bacterial biofilm, indicating potential for therapeutic applications.
Exploiting Spatial Structure from Parts for Adaptive Kernelized Correlation Filter Tracker

Decomposing target into several parts may improve the capability of tracking algorithm to deal with appearance variations such as occlusion and deformation. In this letter, we propose a part-based appearance model by exploiting spatial structure from parts. The model minimizes appearance and deformation cost simultaneously to predict the new position of object. Then, the optimization problem is divided into two parts. Kernelized correlation filter (KCF) is used for tracking the appearance of parts separately to speed up the proposed tracker. Meanwhile, the deformation cost is minimized by structural learning schema, which can reduce the label noise that caused by inaccuracy bounding box. Finally, minimum spanning tree and dynamic programming are employed to combine the score map of the appearance and deformation of parts, and to detect best new position of target. Experimental results on several challenge sequences show the efficiency and effectiveness of the proposed tracking algorithm.
Scaling Hard Vertical Surfaces with Compliant Microspine Arrays

A new approach for climbing hard vertical surfaces has been developed that allows a robot to scale concrete, stucco, brick and masonry walls without using suction or adhesives.The approach is inspired by the mechanisms observed in some climbing insects and spiders and involves arrays of microspines that catch on surface asperities. The arrays are located on the toes of the robot and consist of a tuned, multi-link compliant suspension. The fundamental issues of spine allometric scaling versus surface roughness are discussed and the interaction between spines and surfaces is modeled. The toe suspension properties needed to maximize the probability that each spine will find a useable surface irregularity and to distribute climbing loads among many spines are detailed. The principles are demonstrated with a new climbing robot, SpinybotII, that can scale a wide range of flat exterior walls, carry a payload equal to its own weight, and cling without consuming power. The paper also reports how toe parameters scale with robot mass and how spines have also been used successfully on the larger RiSE robot.
Efetividade de antieméticos no controle da emese induzida pela quimioterapia antineoplásica, em domicílio

Objective Evaluating if antiemetics are effective in the prevention or treatment at home, of chemotherapy-induced emesis. Methods In total, were included 42 women with breast cancer in moderately emetogenic chemotherapy, using dexamethasone/ondansetron before each cycle. The frequency of nausea and vomiting was obtained by applying the instrument in the pre-chemotherapy period, and 24h, 48h, 72h and 96h after chemotherapy. The use of antiemetics was considered in accordance with adherence to medical prescription. Results All patients (n = 42, 100%) reported emesis at some point. Only five cases (11.9%) were anticipatory. In the first 24 hours (acute emesis), 38 (90.5%)ayed), emesis was reported by all despite the regular use (n = 20, 47.6%) or not (n = 22, 52.4%) of antiemetics (ondansetron, dexamethasone and metoclopramide/or dimenhydrinate). Conclusion Antiemetics were not effective in the prevention or treatment at home, of chemotherapy-induced emesis.
Comparative mRNA and microRNA Expression Profiling of Three Genitourinary Cancers Reveals Common Hallmarks and Cancer-Specific Molecular Events

Background Genome-wide gene expression profile using deep sequencing technologies can drive the discovery of cancer biomarkers and therapeutic targets. Such efforts are often limited to profiling the expression signature of either mRNA or microRNA (miRNA) in a single type of cancer. Methodology Here we provided an integrated analysis of the genome-wide mRNA and miRNA expression profiles of three different genitourinary cancers: carcinomas of the bladder, kidney and testis. Principal Findings Our results highlight the general or cancer-specific roles of several genes and miRNAs that may serve as candidate oncogenes or suppressors of tumor development. Further comparative analyses at the systems level revealed that significant aberrations of the cell adhesion process, p53 signaling, calcium signaling, the ECM-receptor and cell cycle pathways, the DNA repair and replication processes and the immune and inflammatory response processes were the common hallmarks of human cancers. Gene sets showing testicular cancer-specific deregulation patterns were mainly implicated in processes related to male reproductive function, and general disruptions of multiple metabolic pathways and processes related to cell migration were the characteristic molecular events for renal and bladder cancer, respectively. Furthermore, we also demonstrated that tumors with the same histological origins and genes with similar functions tended to group together in a clustering analysis. By assessing the correlation between the expression of each miRNA and its targets, we determined that deregulation of ‘key’ miRNAs may result in the global aberration of one or more pathways or processes as a whole. Conclusions This systematic analysis deciphered the molecular phenotypes of three genitourinary cancers and investigated their variations at the miRNA level simultaneously. Our results provided a valuable source for future studies and highlighted some promising genes, miRNAs, pathways and processes that may be useful for diagnostic or therapeutic applications.
Semantic Parsing with Structured SVM Ensemble Classification Models

We present a learning framework for structured support vector models in which boosting and bagging methods are used to construct ensemble models. We also propose a selection method which is based on a switching model among a set of outputs of individual classifiers when dealing with natural language parsing problems. The switching model uses subtrees mined from the corpus and a boosting-based algorithm to select the most appropriate output. The application of the proposed framework on the domain of semantic parsing shows advantages in comparison with the original large margin methods.
Event-based optimization for dispatching policies in material handling systems of general assembly lines

A material handling (MH) system of a general assembly line dispatching parts from inventory to working buffers could be complicated and costly to operate. Generally it is extremely difficult to find the optimal dispatching policy due to the complicated system dynamics and the large problem size. In this paper, we formulate the dispatching problem as a Markov decision process (MDP), and use event-based optimization framework to overcome the difficulty caused by problem dimensionality and size. By exploiting the problem structures, we focus on responding to certain events instead of all state transitions, so that the number of aggregated potential function (i.e., value function) is scaled to the square of the system size despite of the exponential growth of the state space. This effectively reduces the computational requirements to a level that is acceptable in practice. We then develop a sample path based algorithm to estimate the potentials, and implement a gradient-based policy optimization procedure. Numerical results demonstrate that the policies obtained by the event-based optimization approach significantly outperform the current dispatching method in production.