Miracle-dz/UniFAR-FTUs huggingface dataset:
Microwave Radiometric Signal from the Sea Surface in the Presence of the Currents

A model of microwave thermal radiation from the sea surface is considered. Variable current and wind fields are accounted by solving wave action balance equation. Energy sources and sinks are modeled similar to (1) with some modifications, which we believe make the model better suited to radiometric applications. The spreading functions of the spectrum and wind growth rate function were corrected. The model accounts for the most important for the microwave radiometry features of ocean surface: parasitic ripple generation and wave breaking statistics. Observations of the internal waves during COPE'95 experiment appeared to be in a good agreement with the model prediction. According to the model the most important contribution to the brightness temperature variations comes from modulation of the wave breaking frequency and consequently foam coverage. Keywords-microwave radiometry; sea surface electromagnetic scattering
Detecting activities at metro stations using smartphone sensors

Our paper aims to build a classification-model which delineates the typical motion-related activities performed at a metro station using smart phone sensors. We focus on typical movements, such as climbing the stairs or moving in the lift, waiting at security, waiting at the turnstile to check out and, moving on platform while waiting for a train. Such a classifier estimates crowd levels in a metro-station (and metro trains in an indirect sense), thereby adding towards the vision of efficient metro travel. However, building such a classification-model is challenging due to non-trivial decision boundaries among the classes of interest. Our experiments revealed that the best accuracy that a traditional multi-class classifier could obtain was 0.58, for a four-class classifier. To this end, we proposed a hierarchical approach of classification which divides the multi-class problem at hand into a set of manageable two-class classification problems. These two-class classifiers are then put together, in a hierarchy, to give an end-to-end solution which takes sensors values from the phone and predicts the class of motion being observed. Our model obtained an accuracy of around 0.75, a significantly higher value, on the real-data collected at Delhi Metro stations. The same classifiers can potentially be applied to detect crowd levels at train stations and bus depots, which will make transportation efficient in smart cities.
Real-Time Divisible Load Scheduling for Cluster Computing

Cluster computing has emerged as a new paradigm for solving large-scale problems. To enhance QoS and provide performance guarantees in cluster computing environments, various real-time scheduling algorithms and workload models have been investigated. Computational loads that can be arbitrarily divided into independent pieces represent many real-world applications. Divisible load theory (DLT) provides insight into distribution strategies for such computations. However, the problem of providing performance guarantees to divisible load applications has not yet been systematically studied. This paper investigates such algorithms for a cluster environment. Design parameters that affect the performance of these algorithms and scenarios when the choice of these parameters have significant effects are studied. A novel algorithmic approach integrating DLT and EDF (earliest deadline first) scheduling is proposed. For comparison, we also propose a heuristic algorithm. Intensive experimental results show that the application of DLT to real-time cluster-based scheduling leads to significantly better scheduling approaches
Using Prior Knowledge to Improve Reinforcement Learning in Mobile Robotics

Reinforcement learning (RL) is thought to be an appropriate paradigm for acquiring control policies in mobile robotics. However, in its standard formulation (tabula rasa) RL must explore and learn everything from scratch, which is neither realistic nor effective in real-world tasks. In this article we propose a new strategy, called Supervised Reinforcement Learning (SRL), for taking advantage of external knowledge within this type of learning and validate it in a wall-following behaviour.
Declines in Connected Language Are Associated with Very Early Mild Cognitive Impairment: Results from the Wisconsin Registry for Alzheimer’s Prevention

Changes to everyday spoken language (“connected language”) are evident in persons with AD dementia, yet little is known about when these changes are first detectable on the continuum of cognitive decline. The aim of this study was to determine if participants with very early, subclinical memory declines were also showing declines in connected language. We analyzed connected language samples obtained from a simple picture description task at two time points in 264 participants from the Wisconsin Registry for Alzheimer’s Prevention (WRAP). In parallel, participants were classified as either “Cognitively Healthy” or “Early Mild Cognitive Impairment” based on longitudinal neuropsychological test performance. Linear mixed effects models were used to analyze language parameters that were extracted from the connected language samples using automated feature extraction. Participants with eMCI status declined faster in features of speech fluency and semantic content than those who were cognitively stable. Measures of lexical diversity and grammatical complexity were not associated with eMCI status in this group. These findings provide novel insights about the relationship between cognitive decline and everyday language, using a quick, inexpensive, and performance-based method.
Design and Selection of a Camelid Single-Chain Antibody Yeast Two-Hybrid Library Produced De Novo for the Cap Protein of Porcine Circovirus Type 2 (PCV2)

Nanobodies (or variable domain of the heavy chain of the heavy-chain antibodies, VHHs) are single-domain antigen-binding fragments derived from camelid heavy chain antibodies. Their comparatively small size, monomeric behavior, high stability, high solubility, and ability to bind epitopes inaccessible to conventional antibodies make them especially suitable for many therapeutic and biotechnological applications. In this paper, for the first time, we created the immunized Camelus Bactrianus VHH yeast two-hybrid (Y2H) library according to the Clontech Mate & Plate library construction system. The transformation efficiency and titer of the VHH Y2H library were 7.26×106 cfu/3 µg and 2×109 cfu/ml, which met the demand for Y2H library screening. Using as an example the porcine circovirus type 2 (PCV2) Cap protein as bait, we screened 21 positive Cap-specific VHH sequences. Among these sequences, 7 of 9 randomly selected clones were strongly positive as indicated by enzyme-linked immunosorbent assay, either using PCV2 viral lysis or purified Cap protein as coated antigen. Additionally, the immunocytochemistry results further indicated that the screened VHHs could specifically detected PCV2 in the infected cells. All this suggests the feasibility of in vivo VHH throughput screening based on Y2H strategy.
Hybrid surgery versus percutaneous mechanical thrombectomy for the thrombosed hemodialysis autogenous arteriovenous fistulas

Purpose For the successful treatment of thrombosed autogenous arteriovenous fistula (AVF), we designed and performed a hybrid surgery. Its clinical outcomes were compared with those of percutaneous mechanical thrombectomy, retrospectively. Methods Forty cases of thrombosed autogenous AVFs underwent hybrid surgery, whereas 19 cases received percutaneous mechanical thrombectomy. Hybrid surgery consisted of surgical thrombectomy, balloon angioplasty and/or additional surgical angioplasty. Percutaneous mechanical thrombectomy included catheter-introduced thrombus aspiration, balloon angioplasty and/or stenting. Procedure related outcomes such as technical success rates and primary patency rates were analyzed, retrospectively. Results There were no statistically significant differences between the two groups in terms of demographic data of the patients including age, gender, diabetes status, and frequency of antiplatelet use, as well as the characteristics of thrombosed autogenous AVFs such as access age, site, type, and length of time between thrombosis and AVF creation (P > 0.05). Technical success rates (92.5% vs. 68.4%, P = 0.005, respectively) and primary patency rates (85.9% vs. 36.8% at 6 months, 81.1% vs. 26.3% at 12 months, 81.1% vs.21.1% at 18 and 24 months respectively, log-rank test, (P < 0.001) were significantly higher in the hybrid surgery group. In terms of cost analysis, supply cost was not different (P = 0.065), but total cost was statistically lower in the hybrid surgery group (P = 0.019). Conclusion Hybrid surgery showed better technical success rates and patency rates in the salvaging of thrombosed autogenous AVFs than in percutaneous mechanical thrombectomy.
Summary of SLAC'S SEY Measurement On Flat Accelerator Wall Materials

The electron cloud effect (ECE) causes beam instabilities in accelerator structures with intense positively charged bunched beams. Reduction of the secondary electron yield (SEY) of the beam pipe inner wall is effective in controlling cloud formation. We summarize SEY results obtained from flat TiN, TiZrV and Al surfaces carried out in a laboratory environment. SEY was measured after thermal conditioning, as well as after low energy, less than 300 eV, particle exposure.
Concept Learning and the Problem of Small Disjuncts

Ideally, definitions induced from examples should consist of all, and only, disjuncts that are meaningful (e.g., as measured by a statistical significance test) and have a low error rate. Existing inductive systems create definitions that are ideal with regard to large disjuncts, but far from ideal with regard to small disjuncts, where a small (large) disjunct is one that correctly classifies few (many) training examples. The problem with small disjuncts is that many of them have high rates of misclassification, and it is difficult to eliminate the errorprone small disjuncts from a definition without adversely affecting other disjuncts in the definition. Various approaches to this problem are evaluated, including the novel approach of using a bias different than the "maximum generality" bias. This approach, and some others, prove partly successful, but the problem of small disjuncts remains open.
Alcohol and the Intestine

Alcohol abuse is a significant contributor to the global burden of disease and can lead to tissue damage and organ dysfunction in a subset of alcoholics. However, a subset of alcoholics without any of these predisposing factors can develop alcohol-mediated organ injury. The gastrointestinal tract (GI) could be an important source of inflammation in alcohol-mediated organ damage. The purpose of review was to evaluate mechanisms of alcohol-induced endotoxemia (including dysbiosis and gut leakiness), and highlight the predisposing factors for alcohol-induced dysbiosis and gut leakiness to endotoxins. Barriers, including immunologic, physical, and biochemical can regulate the passage of toxins into the portal and systemic circulation. In addition, a host of environmental interactions including those influenced by circadian rhythms can impact alcohol-induced organ pathology. There appears to be a role for therapeutic measures to mitigate alcohol-induced organ damage by normalizing intestinal dysbiosis and/or improving intestinal barrier integrity. Ultimately, the inflammatory process that drives progression into organ damage from alcohol appears to be multifactorial. Understanding the role of the intestine in the pathogenesis of alcoholic liver disease can pose further avenues for pathogenic and treatment approaches.
A Comparative In Vitro Study of the Effects of Separate and Combined Products of Citrus e fructibus and Cydonia e fructibus on Immunological Parameters of Seasonal Allergic Rhinitis

This paper examined the effects of the combined product, Citrus e fructibus/Cydonia e fructibus (Citrus/Cydonia; Citrus and Cydonia: each 0.01 g/mL), and separate products of Citrus (0.01 g/mL) and Cydonia (0.01 g/mL) on the immunological pathways involved in seasonal allergic rhinitis (SAR). Peripheral blood mononuclear cells (PBMCs) from five healthy and five grass pollen-allergic donors were isolated and analyzed in vitro after polyclonal and allergen-specific stimulation of T cells in the presence of the three extracts. The analyses demonstrated acceptable cell survival with no signs of toxicity. Citrus mainly had a selective effect on reducing allergen-specific chronic inflammatory (TNF-α; Citrus compared to Cydonia and Citrus/Cydonia: −87.4 (P < 0.001) and −68.0 (P < 0.05), resp. ) and Th2 pathway activity (IL-5; Citrus compared to Cydonia: −217.8 (P < 0.01); while, both Cydonia and Citrus/Cydonia mainly affected the induction of the allergen-specific Th1 pathway (IFN-γ; Cydonia and Citrus/Cydonia compared to Citrus: 3.8 (P < 0.01) and 3.0 (P < 0.01), resp. ). Citrus and Cydonia demonstrated different working mechanisms in the treatment of SAR and the combination product did not demonstrate larger effects than the separate preparations. Further effectiveness and efficacy studies comparing the effects of the products on SAR in vivo are indicated.
Sodium Nitroprusside: Twenty Years and Counting

SNP remains an effective, reliable, and commonly used drug for the rapid reduction of significant arterial hypertension regardless of the etiology, for afterload reduction in the face of low CO when blood volume is normal or increased, and for intraoperative induced hypotension. After establishing indwelling arterial monitoring, an initial infusion rate of 0.3-0.5 micrograms.kg-1.min-1 is begun with titration as needed up to 2.0 micrograms.kg-1.min-1. Higher rates for brief periods of time (10 min) are acceptable. The use of alternative drugs to reduce the dose or shorten the duration of infusion should be considered when the 2.0 micrograms.kg-1.min-1 range is exceeded (Table 1). SNP should not be used by individuals unfamiliar with its potency and metabolic pathways, as the many reports of adverse reactions testify. Careful attention to infusion rates, particularly in patients at risk for depleted thiosulfate stores, is mandatory, and the use of other drugs in conjunction with or instead of SNP should always be considered. As with many therapeutic interventions, SNP requires careful administration to appropriately selected patients by a clinician who knows its inherent hazards. Despite its toxicity, SNP is popular because it is often the most (in some cases, the only) effective drug in some difficult clinical circumstances.
Adaptive product normalization: using online learning for record linkage in comparison shopping

The problem of record linkage focuses on determining whether two object descriptions refer to the same underlying entity. Addressing this problem effectively has many practical applications, e.g., elimination of duplicate records in databases and citation matching for scholarly articles. In this paper, we consider a new domain where the record linkage problem is manifested: Internet comparison shopping. We address the resulting linkage setting that requires learning a similarity function between record pairs from streaming data. The learned similarity function is subsequently used in clustering to determine which records are co-referent and should be linked. We present an online machine learning method for addressing this problem, where a composite similarity function based on a linear combination of basis functions is learned incrementally. We illustrate the efficacy of this approach on several real-world datasets from an Internet comparison shopping site, and show that our method is able to effectively learn various distance functions for product data with differing characteristics. We also provide experimental results that show the importance of considering multiple performance measures in record linkage evaluation.
A Point Mutation in Translation Initiation Factor eIF2B Leads to Function- and Time-Specific Changes in Brain Gene Expression

Background Mutations in eukaryotic translation initiation factor 2B (eIF2B) cause Childhood Ataxia with CNS Hypomyelination (CACH), also known as Vanishing White Matter disease (VWM), which is associated with a clinical pathology of brain myelin loss upon physiological stress. eIF2B is the guanine nucleotide exchange factor (GEF) of eIF2, which delivers the initiator tRNAMet to the ribosome. We recently reported that a R132H mutation in the catalytic subunit of this GEF, causing a 20% reduction in its activity, leads under normal conditions to delayed brain development in a mouse model for CACH/VWM. To further explore the effect of the mutation on global gene expression in the brain, we conducted a wide-scale transcriptome analysis of the first three critical postnatal weeks. Methodology/Principal Findings Genome-wide mRNA expression of wild-type and mutant mice was profiled at postnatal (P) days 1, 18 and 21 to reflect the early proliferative stage prior to white matter establishment (P1) and the peak of oligodendrocye differentiation and myelin synthesis (P18 and P21). At each developmental stage, between 441 and 818 genes were differentially expressed in the mutant brain with minimal overlap, generating unique time point-specific gene expression signatures. Conclusions The current study demonstrates that a point mutation in eIF2B, a key translation initiation factor, has a massive effect on global gene expression in the brain. The overall changes in expression patterns reflect multiple layers of indirect effects that accumulate as the brain develops and matures. The differentially expressed genes seem to reflect delayed waves of gene expression as well as an adaptation process to cope with hypersensitivity to cellular stress.
Non-data-aided carrier offset estimators for OFDM with null subcarriers: identifiability, algorithms, and performance

The ability of orthogonal frequency-division multiplexing systems to mitigate frequency-selective channels is impaired by the presence of carrier frequency offsets (CFOs). In this paper, we investigate identifiability issues involving high-resolution techniques that have been proposed for blind CFO estimation based on null subcarriers. We propose new approaches that do not suffer from the lack of identifiability and adopt adaptive algorithms that are computationally feasible. The performance of these techniques in relation to the location of the null subcarriers is also investigated via computer simulations and compared with the modified Cramer-Rao bound.
Ventricular perfusion in cats with kaolin-induced hydrocephalus.

✓ Cats were made hydrocephalic by cisternal instillation of kaolin. Three to 8 weeks later it was found by perfusion between the ventricular system and the spinal subarachnoid space that communication had been reestablished through a demonstrably dilated central canal of the spinal cord. Absorption of fluid from the ventricular system, measured both by ventriculospinal perfusion and, after ligation of the spinal cord, by perfusion between the lateral ventricles, was found to be indistinguishable from zero over a wide range of ventricular pressure.
Directional Migration of Recirculating Lymphocytes through Lymph Nodes via Random Walks

Naive T lymphocytes exhibit extensive antigen-independent recirculation between blood and lymph nodes, where they may encounter dendritic cells carrying cognate antigen. We examine how long different T cells may spend in an individual lymph node by examining data from long term cannulation of blood and efferent lymphatics of a single lymph node in the sheep. We determine empirically the distribution of transit times of migrating T cells by applying the Least Absolute Shrinkage & Selection Operator () or regularised to fit experimental data describing the proportion of labelled infused cells in blood and efferent lymphatics over time. The optimal inferred solution reveals a distribution with high variance and strong skew. The mode transit time is typically between 10 and 20 hours, but a significant number of cells spend more than 70 hours before exiting. We complement the empirical machine learning based approach by modelling lymphocyte passage through the lymph node . On the basis of previous two photon analysis of lymphocyte movement, we optimised distributions which describe the transit times (first passage times) of discrete one dimensional and continuous (Brownian) three dimensional random walks with drift. The optimal fit is obtained when drift is small, i.e. the ratio of probabilities of migrating forward and backward within the node is close to one. These distributions are qualitatively similar to the inferred empirical distribution, with high variance and strong skew. In contrast, an optimised normal distribution of transit times (symmetrical around mean) fitted the data poorly. The results demonstrate that the rapid recirculation of lymphocytes observed at a macro level is compatible with predominantly randomised movement within lymph nodes, and significant probabilities of long transit times. We discuss how this pattern of migration may contribute to facilitating interactions between low frequency T cells and antigen presenting cells carrying cognate antigen.
Transient-based identification of wireless sensor nodes

Identification of wireless sensor nodes based on the characteristics of their radio transmissions can provide an additional layer of security in all-wireless multi-hop sensor networks. Reliable identification can be means for the detection and/or prevention of wormhole, Sybil and replication attacks, and can complement cryptographic message authentication protocols. In this paper, we investigate the feasibility of transient-based identification of CC2420 wireless sensor nodes. We propose a new technique for transient-based identification and show that it enables reliable and accurate sensor node recognition with an Equal Error Rate as low as 0.0024 (0.24%). We investigate the performance of our technique in terms of parameters such as distance, antenna polarization and voltage and analyze how these parameters affect the recognition accuracy. Finally, we study the feasibility of certain types of impersonation attacks on the proposed technique.
The use of allograft shell with intramedullary vascularized fibula graft for intercalary reconstruction after diaphyseal resection for lower extremity bony malignancy

Reconstruction after excision of the femur and tibia malignancy is a challenging issue for the reconstructive surgeon. The combined use of a vascularized fibular flap and allograft can provide a reliable reconstructive option. This article describes the authors' experience with this technique for the treatment of large‐segmental bone defects after intercalary resection of lower extremity malignancy.
A frequency analysis of light transport

We present a signal-processing framework for light transport. We study the frequency content of radiance and how it is altered by phenomena such as shading, occlusion, and transport. This extends previous work that considered either spatial or angular dimensions, and it offers a comprehensive treatment of both space and angle.We show that occlusion, a multiplication in the primal, amounts in the Fourier domain to a convolution by the spectrum of the blocker. Propagation corresponds to a shear in the space-angle frequency domain, while reflection on curved objects performs a different shear along the angular frequency axis. As shown by previous work, reflection is a convolution in the primal and therefore a multiplication in the Fourier domain. Our work shows how the spatial components of lighting are affected by this angular convolution.Our framework predicts the characteristics of interactions such as caustics and the disappearance of the shadows of small features. Predictions on the frequency content can then be used to control sampling rates for rendering. Other potential applications include precomputed radiance transfer and inverse rendering.