Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution of dendritic cells to stimulation of the murine syngeneic mixed leukocyte reaction

We have studied the proliferative response of unprimed T cells to syngeneic dendritic cells (DC) (syngeneic mixed leukocyte reaction [SMLR]) in cultures of mouse spleen and lymph node. T cells purified by passage over nylon wool contain few DC and exhibits little proliferative activity during several days of culture. Addition of small numbers of purified syngeneic DC induces substantial, dose- dependent, T cell-proliferative responses that peak at day 4-5. B cells purified on anti-Ig-coated plates do not respond to DC at all doses tested. DC culture medium does not induce proliferation, and coculture of DC and T cells is required. Purified mouse B and T lymphocytes stimulate SMLR weakly if at all. Likewise, peritoneal and spleen macrophages are weak or inactive. Therefore, DC are potent and possibly unique primary cells for stimulating the SMLR in mice. sIg- spleen and lymph node cells show extensive background proliferative responses in vitro, and fail to respond to small numbers of purified DC. If the sIg- cells are treated with anti-Ia and complement, or passed over nylon wool, DC are removed and proliferative activity falls. Proliferative activity is restored by adding back DC at levels similar to those present in sIg- cells (1-2%). Thus, DC-dependent, T cell proliferation probably occurs in all spleen and lymph node cultures. As expected from previous work (6), DC are also potent inducers of allogeneic MLR. On a per DC basis, the syngeneic response is 10 times weaker than the allogeneic MLR, and it is not accompanied by the development of cytotoxic lymphocytes. The magnitude of the SMLR was not altered by antigen priming, and DC maintained in isologous rather than fetal calf serum were active stimulators. Therefore, syngeneic stimulation appears to be an intrinsic property of DC, and modification by exogenous agents does not seem to be required. Coculture of DC and T cells results in the development of cell clusters that can be isolated and characterized directly. The clusters account for 10-20% of the viable cells in the culture, but contain greater than 80% of the responding T cells and stimulating DC by morphologic and surface-marker criteria. The efficient physical association of DC and responding T cells implies specific cell-cell recognition. We conclude that the SMLR reflects the ability of T cells, or some subpopulation of T cells, to interact with and proliferate in response to small numbers of DC.
Linked within-host and between-host models and data for infectious diseases: a systematic review

The observed dynamics of infectious diseases are driven by processes across multiple scales. Here we focus on two: within-host, that is, how an infection progresses inside a single individual (for instance viral and immune dynamics), and between-host, that is, how the infection is transmitted between multiple individuals of a host population. The dynamics of each of these may be influenced by the other, particularly across evolutionary time. Thus understanding each of these scales, and the links between them, is necessary for a holistic understanding of the spread of infectious diseases. One approach to combining these scales is through mathematical modeling. We conducted a systematic review of the published literature on multi-scale mathematical models of disease transmission (as defined by combining within-host and between-host scales) to determine the extent to which mathematical models are being used to understand across-scale transmission, and the extent to which these models are being confronted with data. Following the PRISMA guidelines for systematic reviews, we identified 24 of 197 qualifying papers across 30 years that include both linked models at the within and between host scales and that used data to parameterize/calibrate models. We find that the approach that incorporates both modeling with data is under-utilized, if increasing. This highlights the need for better communication and collaboration between modelers and empiricists to build well-calibrated models that both improve understanding and may be used for prediction.
Peripheral Circadian Oscillators

Circadian rhythms are ~24-hour cycles of physiology and behavior that are synchronized to environmental cycles, such as the light-dark cycle. During the 20th century, most research focused on establishing the fundamental properties of circadian rhythms and discovering circadian pacemakers that were believed to reside in the nervous system of animals. During this time, studies that suggested the existence of circadian oscillators in peripheral organs in mammals were largely dismissed. The discovery of a single-locus circadian pacemaker in the nervous system of several animals affirmed the single-oscillator model of the circadian system. However, the discovery of the genes that constituted the molecular timekeeping system provided the tools for demonstrating the existence of bona fide circadian oscillators in nearly every peripheral tissue in animals, including rodents, in the late 1990s and early 2000s. These studies led to our current understanding that the circadian system in animals is a hierarchical multi-oscillatory network, composed of master pacemaker(s) in the brain and oscillators in peripheral organs. Further studies showed that altering the temporal relationship between these oscillators by simulating jet-lag and metabolic challenges in rodents caused adverse physiological outcomes. Herein we review the studies that led to our current understanding of the function and pathology of the hierarchical multi-oscillator circadian system.
Do Disruptions in the Circadian Timing System Contribute to Autonomic Dysfunction in Huntington’s Disease?

Huntington’s disease (HD) patients suffer from a progressive neurodegenerative disorder that inflicts both motor and non-motor symptoms. HD is caused by a CAG repeat expansion within the first exon of the huntingtin (HTT) gene that produces a polyglutamine repeat that leads to protein misfolding, soluble aggregates, and inclusion bodies detected throughout the body. Both clinical and preclinical research indicate that cardiovascular dysfunction should be considered a core symptom in at least a subset of HD patients. There is strong evidence for dysautonomia (dysfunctional autonomic nervous system, ANS) in HD patients that can be detected early in the disease progression. The temporal patterning of ANS function is controlled by the circadian timing system based in the anterior hypothalamus. Patients with neurodegenerative diseases including HD exhibit disrupted sleep/wake cycle and, in preclinical models, there is compelling evidence that the circadian timing system is compromised early in the disease process. Here we review data from preclinical models of HD that explore the intersection between disruption of circadian rhythms and dysautonomia. This work will lead to new therapeutic strategies and standards of care for HD and other neurodegenerative diseases.
Dehazing of Natural Images Using Volterra Based Non-Linear Filter

When outdoor images are captured by a camera in bad weather conditions which yield poor visibility conditions, where noise is also present due to low contrast of an image, color variation which is a major issue for most computer vision applications. The hazy weather conditions, it blocks the dispersal of haze, smoke and other pollutions in the environment which leads to harm invisibility and respiratory health problems. In this paper, we proposed a Volterra based Non-Linear filters for image enhancement and image restoration in bad weather conditions. The linear filters help to reduce noise but it causes blurriness in the edges of the image. In case of Non-linear filters are dealing with impulsive noise which helps to preserve the edges of the image. So, the Volterra filter is designed to enhance the compact between unwanted noise reduction and edge preservation.
Automatic recognition of physical exercises performed by stroke survivors to improve remote rehabilitation

Strokes are the second cause of death and the third cause of disability in the world. Currently, there is not an actual cure for stroke victims, but physiotherapy can be used to restore as much mobility as possible until a plateau is reached again. However, performing these exercises implies that both patient and therapist are together in the same place, so that the latter can guide the former through a correct execution of physical exercises. This raises additional difficulties when it comes to continuously monitoring the recovery of stroke patients, due to the economic costs involved and the requirements of both geographical and temporal availability. These issues can be addressed by leveraging technology, specifically computer vision-based assistive systems and remote rehabilitation tools, so as to the affected person can check whether the exercises are being performed correctly. This paper is focused on the automatic classification of exercises, within the context of a gamification-based remote rehabilitation tool used to automatically assess the performance of stroke patients when making physical rehabilitation. To this end, we use the DTW algorithm for analyzing and comparing open-ended motion curves, so that the exercises do not have to be fully performed until existing candidates can be matched. This increases the system flexibility and offers an interaction mechanism much more simple, which is usually a needed requirement by stroke patients.
Application of Chaos Synchronization Technique and Pattern Clustering for Diagnosis Analysis of Partial Discharge in Power Cables

This paper primarily discusses the measurement of partial discharge (PD) phenomena and clustering in the defect pattern of a cross-linked polyethylene power cable joint. First, a high-speed data acquisition and pretreatment were performed for PD electrical signals at a sampling rate of 20 MS/s. The crucial characteristic signals were reversed to reduce the calculated amount of noise. A characteristic matrix was created according to the resulting dynamic error of chaos synchronization. The characteristic parameters were extracted using the fractal theory. Finally, the extension theory was used to develop a diagnostic system and anti-interference test. A comparison with the existing Hilbert–Huang transform (HHT) method revealed that the two characteristics extracted from the chaos synchronization results using the fractal theory were recognized at a higher pattern recognition rate by employing the extension theory. The proposed method can extract crucial information concerning PD as a defect in power cable joints.
Predicting Quality of Experience of Popular Mobile Applications from a Living Lab Study

In this paper, we present a hybrid method (qualitative and quantitative) to model and predict the Quality of Experience (QoE) of mobile applications used on WiFi or cellular network. Our 33 living lab participants rated their mobile applications’ QoE in various contexts for four weeks resulting in a total of 5663 QoE ratings. At the same time, our smartphone logger (mQoL-Log) collected background information such as network information, user activity, battery statistics and more. We focused this study on frequently used and highly interactive applications including Google Chrome, Google Maps, Spotify, Instagram, Facebook, Facebook Messenger and WhatsApp. After pre-processing the dataset, we used classical machine learning techniques and algorithms (Extreme Gradient Boosting) to predict the QoE of the application usage. The results showed that our model can predict the user QoE with 94 0.77 accuracy. Surprisingly, after the following top three features:± session length, battery level and network QoS, the user activity (e.g., if walking) and intended action to accomplish with the app were the most predictive features. Longer application use sessions often have worse QoE than shorter sessions.
IL-1 in osteoarthritis: time for a critical review of the literature

The concept of interleukin-1 (IL-1) as a target in osteoarthritis (OA) has been an attractive one for many years. It is a highly potent inducer of cartilage degradation, causing the induction of mRNA and controlling the bioavailability of disease-relevant proteases such as ADAMTS5 and MMP13. It drives synovitis and can induce other disease-relevant genes such as nerve growth factor, a key pain sensitiser in OA. However, the quality of evidence for its involvement in disease is modest. Descriptive studies have demonstrated expression of IL-1α and β in OA cartilage and elevated levels in the synovial fluid of some patients. Agnostic transcriptomic and genomic analyses do not identify IL-1 as a key pathway. In vivo models show a conflicting role for this molecule; early studies using therapeutic approaches in large animal models show a benefit, but most murine studies fail to demonstrate protection where the ligands (IL-1α/β), the cytokine activator (IL-1–converting enzyme), or the receptor (IL-1R) have been knocked out. Recently, a number of large double-blind randomised controlled clinical studies targeting IL-1 have failed. Enthusiasm for IL-1 as a target in OA is rapidly dwindling.
A Review of its Pharmacodynamic and Pharmacokinetic Properties , and Therapeutic Potential in Cancer Chemotherapy

Various sections of the manuscript reviewed by: C. Bonadonna, Istituto Nazionale per 10 Studio e la Cura dei Tumori, Milan, Italy; H. W. Bruckner, Department of Medicine, Mount Sinai School of Medicine, New York, New York, USA; J. Dubois, Institut de Pharmacie, Universite Libre de Bruxelles, Brussels, Belgium; P.J. Hoskins, British Columbia Cancer Agency, Vancouver, British Columbia, Canada; 1.R. Judson, Institute of Cancer Research, Royal Cancer Hospital, Sutton, England; S. Kohnoe, National Kyushu Cancer Center, Fukuoka, Japan; JA. Ledermann, Department of Oncology, University College London Medical School, London, England; EM. Muggia, Department of Medical Oncology, University of Southern California, Los Angeles, California, USA; J.P. Neijt, Department of Internal Medicine, Utrecht University Hospital, Utrecht, The Netherlands; M. Ogawa, Aichi Cancer Center, Nagoya, Japan; J.L. Pater, National Cancer Institute of Canada, Queen's University, Kingston, Ontario, Canada; C. Rustin, Department of Medical Oncology, Charing Cross Hospital, London, England; C. Sessa, Department of Oncology, Ospedale San Giovanni, Bellinzona, Switzerland; J.T. Thigpen, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA; 1. Vergote, Universitaire Zeikenhuizen Leuven, Leuven, Belgium; R.B. Weiss, Walter Reed Army Medical Center, Washington, District of Columbia, USA; P.H. Wiernik, Albert Einstein Cancer Center, New York, New York, USA
Sensor fusion of odometry and a single beacon distance measurement

The pose estimation of a differential drive robot from noisy odometry and a noisy beacon distance measurement is studied. It is shown that the problem is a state estimation problem with unknown input, which under some assumptions regarding the noise on the state, can be rewritten in a state estimation problem. An heuristic sensor fusion algorithm is proposed and compared with the extended Kalman filter and the particle filter in a simulation experiment.
A Controller Architecture With Anti-Windup

This letter presents a framework for anti-windup controllers based on the Youla–Jabr–Bongiorno–Kucera (YJBK) parameterization. Applying this architecture gives an additional YJBK matrix transfer function related to the input saturation. This additional YJBK transfer function can be applied for optimizing the feedback loop around the input saturation. Further, the connection with other anti-windup controller architectures is also considered in this letter.
Smart Relay Architecture for Over-the-Horizon High Quality Communications With Unmanned Aerial Vehicles

This paper presents a system architecture to accomplish over-the-horizon communications with medium-sized unmanned aerial vehicles (UAV) in order to enhance its autonomy and maximum range. This architecture is based on using a second UAV as a communication relay with a compact active circular array with separated transmitting and receiving elements at C-band. The designed active feeding network provides switching capabilities in order to selectively activate these elements depending on the desired direction of the antenna beam, achieving maximum equivalent isotropic radiated power (EIRP) in transmission and maximum antenna gain in reception. In this way, a bidirectional long-range link for telemetry, telecommand, and high-resolution video can be deployed between UAVs providing high quality of service, reliability, and moderate data throughput at an affordable cost. Based on the measurements of a manufactured prototype, the maximum range of the deployed air–air link using the designed system is estimated in 20 km, considerably increasing the performance of current systems based on the use of omnidirectional antennas onboard UAVs.
FPGA Implementation of Telecommand System for Nano-satellite

In the context of satellite communication, telecommand is a message from the ground station to the satellite that is used to control its activities. This paper contains a description of the telecommand system’s architecture for satellite communication. The telecommand system is being designed for RSAT, a Nano Satellite meant for fleet detection to monitor India’s coast line. At the ground station, the telecommand generation is done using Python and its counterpart is implemented at the receiver side on an FPGA using the Verilog HDL. The correctness of this design is verified as the simulation results and real time results are identical.
A Rule Framed SVM Model for Classification of Various DDOS Attack in Distributed Network

Centralized communication analysis is required for intrusion detection on any transaction server or database server. In this work, a probabilistic and neural specification based model is defined to identify different forms of DDOS (Distributed Denial of Service) Attack. In the early phase of this work, the associative analysis among different attributes is evaluated under probabilistic measures. Based on this analysis, the attribute strength, criticality and role is identified. Later on the selected weighted attributes are applied under SVM model for attack detection and classification. The experimentation is applied against Neural Network and SVM models which signify that the presented work provided more accurate attack classification.
Experiment Strategy and Parameter Evaluation at Fuzzy Measurement of Input Variables

The measurement of input and output variables in the control objects under industrial conditions is characterized by significant errors, explained by a stochastic nature of indicator measurement (it manifests itself in the environment spatial heterogeneity, noises, etc.) and not only by the control tool metrological characteristics. The formal application of the least squares estimate method at the ill-posed assumption (in most cases it is ill-posed) leads to failures and is the main cause of the doubts engineers and researchers have in terms of the method possibilities. On the basis of the aforementioned one can make a conclusion that the considered methods are characteristic for the experiment planning as well and can be used simultaneously.
Classical Multidimensional Scaling: A Subspace Perspective, Over-Denoising, and Outlier Detection

The classical multidimensional scaling (cMDS) has become a cornerstone for analyzing metric dissimilarity data due to its simplicity in derivation, low computational complexity and its nice interpretation via the principle component analysis. This paper focuses on its capability of denoising and outlier detection. Our new interpretation shows that cMDS always overly denoises a sparsely perturbed data by subtracting a fully dense denoising matrix in a subspace from the given data matrix. This leads us to consider two types of sparsity-driven models: subspace sparse MDS and full-space sparse MDS, which respectively uses the $\ell _1$ and $\ell _{1-2}$ regularization to induce sparsity. We then develop fast majorization algorithms for both models and establish their convergence. In particular, we are able to control the sparsity level at every iterate provided that the sparsity control parameter is above a computable threshold. This is a desirable property that has not been enjoyed by any of existing sparse MDS methods. Our numerical experiments on both artificial and real data demonstrates that cMDS with appropriate regularization can perform the tasks of denoising and outlier detection, and inherits the efficiency of cMDS in comparison with several state-of-the-art sparsity-driven MDS methods.
Dynamic Weighted Majority Approach for Detecting Malicious Crowd Workers

Crowdsourcing is a paradigm that utilizes human intelligence to solve problems that computers cannot yet solve. However, the introduction of human intelligence into computations has also resulted in new challenges in quality control. These challenges originate from the malicious behaviors of crowd workers. Malicious workers are workers with hidden motives, who either simply sabotage a task or provide arbitrary responses to attain some monetary compensation. Recently, many studies have tried to detect and reduce the impact of malicious workers. The mechanisms vary from using ground truth to peer review by experts. Although the use of such mechanisms may increase the overall quality of outputs, it also imposes overhead costs in terms of money and/or time, with such costs being often remarkable and contradictory to the philosophy of crowdsourcing. In this paper, a novel dynamic weighted majority method is introduced to detect malicious workers based on a new malicious metric. Effectiveness of the proposed methodology is then showed by presenting the experimental results.
Robust Waveform Design and Analysis of Active Radar for the Detection of Target

This paper illustrates how, the evaluation of robust properties for transmitted waveform by worst case detection analysis and performance for target detection in the existence of clutter and noise. Here, Stepped FM waveform is considered for waveform design and analysis. Its design and analysis can be achieved through simulink blocks and these blocks contains various numerical parameters through which the waveform can be generated. Evaluation of the robustness of the waveform can be determined using Waveform Co-variance Matrices(WCM) and Interference Covariance Matrix(ICM) over a uncertainty region. Derivation of this , will technically intimate the property of robustness for the generated waveform structure. These techniques involves certain parameters such as SINR(Signal to Noise Ratio), probability of detection and transmitted energy to address the optimal evaluation of receiver operating characteristics by representing the curves. The waveform's robustivity can be observed through these curves. Based on this, the Constant false alarm rate(CFAR) can be determined, which is the application of sequential target detection and estimation.
Power Factor Correction Converters with Continuous Input and Output Currents for Battery Chargers in Data Centers: Performance Assessment

In this paper, the performance of two types of DC-DC converters namely Ćuk and Boost-Buck converters is assessed for battery charging systems. Operation with power factor correction for the input side AC current, and continuous input and output currents at the DC side of the converter are considered. The aforementioned system can be employed for many applications such as electric vehicles, data centers, etc. This paper focuses typically on data centers. Matlab/Simulink models for the two converters have been built to verify the claimed contributions.