Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of T2 inflammation biomarkers in severe asthma

Purpose of review Severe asthma is a heterogeneous syndrome. Classification of asthma into phenotypes and endotypes can improve understanding and treatment of the disease. Identification and utilization of biomarkers, particularly those linked to T2 inflammation, can help group patients into phenotypes, predict those who will respond to a specific therapy, and assess the response to treatment. Recent findings Biomarkers are present in sputum, exhaled breath, and blood of patients with asthma. These include sputum eosinophils and neutrophils, fractional excretion of nitric oxide, blood eosinophilia, IgE, and periostin. Many of these biomarkers are associated with eosinophilic inflammation propagated mainly by T2 cytokines such as IL-5 and IL-13, which are released from Th2 cells and Type 2 innate lymphoid cells. Biomarkers have been utilized in recent trials of novel biologic agents targeted at T2 inflammation and may contribute to the defining population who would respond to these therapies. Summary Despite advances in the identification and utilization of asthma biomarkers, further studies are needed to better clarify the role of biomarkers, individually or in combination, in the diagnosis and treatment of severe asthma. Future therapeutic trials should include the use of biomarkers in their design, which may lead to a more personalized approach to therapy and improved outcomes.
AMD codes based on wavelet transform

This article presents the new construction of security-oriented codes that provides protection of device against algebraic manipulations. New construction of algebraic manipulation detection codes based on wavelet decomposition. The proposed error-detecting schemes can significantly improves the reliability of storage systems and channels of information transmission. The presented code constructions provides a significant gain in the systems that are already has wavelet transformation. In these systems the coefficients of scaling functions are already calculated and it can be used in the proposed constructions, it gives the gain in rate of information processing and have lower maximum of error masking probability.
Multi-Component Flame Retardant Coatings

This chapter describes traditional and novel processes for the surface modification of natural and synthetic fibres and fabrics, with the aim of improving their flame retardancy properties. The formation of novel and smart flame retardant coatings onto the surface of fibres (or fabrics) could make them suitable for high performance applications. Back-coating, a traditional approach still used in the field of textiles for imparting flame retardancy to fibres and fabrics, is described and compared to such novel approaches as sol-gel processes, nanoparticle impregnation/exhausting, layer-by-layer assembly and plasma deposition. The advantages of such approaches in terms of the flame retardancy properties achieved are highlighted together with the possibility of conferring other novel effects such as hydrophobicity, soil release, self-cleaning and bioactivity
Average modeling of an alternating aerated activated sludge process for nitrogen removal

Abstract In this paper we present a novel average model for alternating activated sludge models. This kind of processes, where nitrification-denitrification operations occur, are represented by two consecutive dynamics: an aerobic phase followed by an anaerobic one. In particular, we study a reduced AS model for nitrogen removal. The resulting average continuous model captures the governing (average) behavior of switching dynamics, without considering the switching behavior. The continuous nonlinear AS models thus obtained can be used straightforwardly for analysis, control design and estimation purposes.
Expression of miR-205 in renal cell carcinoma and its association with clinicopathological features and prognosis.

OBJECTIVE The aim of the present study was to examine the expression of miR-205 in renal cell carcinoma (RCC) tissue and carcinoma cells; also, we aimed to determine the association of miR-205 expression with the clinicopathological features and prognosis of RCC, and to explore the mechanism of miR-205. PATIENTS AND METHODS Carcinoma tissue and adjacent normal tissue were collected from 60 patients with RCC, and the expression of miR-205 was determined by semi-quantitative PCR, followed by correlation analysis of miR-205 with clinicopathological features and prognosis. Subsequently, the human RCC line, ACHN, was transfected with miR-205, and the effect of miR-205 overexpression on the growth of RCC was examined by MTT assay. Moreover, the effect of miR-205 on the migration of colon cancer cells was studied by transwell assay. Additionally, immunohistochemistry and Western blot were used to investigate the epithelial-mesenchymal transition in renal cancer tissue. RESULTS The expression of miR-205 was downregulated in RCC tissue compared with adjacent non-cancerous tissue (p < 0.01). The expression of miR-205 was closely related to the infiltration and recurrence of tumors (p < 0.01), but was not correlated with a pathological grade or clinical stage (p > 0.05). We also found that overexpression of miR-205 in RCC significantly inhibited the growth of cancer cells (p < 0.01) and significantly reduced the migration ability (p < 0.01). The epithelial-mesenchymal transition occurs in RCC, and miR-205 might inhibit cell proliferation and migration by blocking the epithelial-mesenchymal transition. CONCLUSIONS The expression of miR-205 is low in RCC, and may play an important role throughout the progression of RCC. Further study of miR-205 may promote the development of a novel therapeutic approach for the treatment of RCC.
Logically centralized?: state distribution trade-offs in software defined networks

Software Defined Networks (SDN) give network designers freedom to refactor the network control plane. One core benefit of SDN is that it enables the network control logic to be designed and operated on a global network view, as though it were a centralized application, rather than a distributed system - logically centralized. Regardless of this abstraction, control plane state and logic must inevitably be physically distributed to achieve responsiveness, reliability, and scalability goals. Consequently, we ask: "How does distributed SDN state impact the performance of a logically centralized control application? " Motivated by this question, we characterize the state exchange points in a distributed SDN control plane and identify two key state distribution trade-offs. We simulate these exchange points in the context of an existing SDN load balancer application. We evaluate the impact of inconsistent global network view on load balancer performance and compare different state management approaches. Our results suggest that SDN control state inconsistency significantly degrades performance of logically centralized control applications agnostic to the underlying state distribution.
Pharmacokinetic–pharmacodynamic modelling of neutrophil response to G‐CSF in healthy subjects and patients with chemotherapy‐induced neutropenia

AIM The objective of the present study was to use pharmacokinetic-pharmacodynamic modelling to characterize the effects of chemotherapy on the granulopoietic system and to predict the absolute neutrophil counts (ANCs) for patients with chemotherapy-induced neutropenia treated with filgrastim and pegfilgrastim. METHODS Data were extracted from 10 phase I-III studies conducted in 110 healthy adults, and 618 adult and 52 paediatric patients on chemotherapy following administration of filgrastim or pegfilgrastim. The structural model accounted for ANC dynamics and the effects of filgrastim and pegfilgrastim, chemotherapy and corticosteroids. The impact of neutrophils on drug disposition was based on a drug receptor-binding model that assumed quasi-equilibrium and stimulation of the production and maturation of neutrophils upon treatment. The chemotherapy and corticosteroid effects were represented by kinetic-pharmacodynamic-type models, where chemotherapy stimulated elimination of neutrophil precursors at the mitotic stage, and corticosteroids stimulated neutrophil production. RESULTS The systemic half-lives of filgrastim (2.6 h) and pegfilgrastim (10.1 h) were as expected. The effective half-life of chemotherapy was 9.6 h, with a 2-day killing effect. The rate of receptor elimination from mitotic compartments exhibited extreme interindividual variability (% coefficient of variation >200), suggesting marked differences in sensitivity to chemotherapy effects on ANCs. The stimulatory effects of pegfilgrastim were significantly greater than those of filgrastim. Model qualification confirmed the predictive capability of this model. CONCLUSION This qualified model simulates the time course of ANC in the absence or presence of chemotherapy and predicts nadir, time to nadir and time of recovery from different grades of neutropenia upon treatment with filgrastim and pegfilgrastim.
Predicting Alignment Risk to Prevent Localization Failure

During localization and mapping the success of point cloud registration can be compromised when there is an absence of geometric features or constraints in corridors or across doorways, or when the volumes scanned only partly overlap, due to occlusions or constrictions between subsequent observations. This work proposes a strategy to predict and prevent laser-based localization failure. Our solution relies on explicit analysis of the point cloud content prior to registration. A model predicting the risk of a failed alignment is learned by analysing the degree of spatial overlap between two input point clouds and the geometric constraints available within the region of overlap. We define a novel measure of alignability for these constraints. The method is evaluated against three real-world datasets and compared to baseline approaches. The experiments demonstrate how our approach can help improve the reliability of laser-based localization during exploration of unknown and cluttered man-made environments.
Nursing Student Birth Doulas’ Influence On the Childbearing Outcomes of Vulnerable Populations

ABSTRACT Of 1,511 women served by nursing student birth doulas (Birth Companions) between 1998 and 2014, 34.5% were identified as vulnerable (refugees, non-English speakers, teens, low income, low education). This retrospective evaluation of the Birth Companions Program showed that vulnerable mothers had more epidurals and smaller babies, and attempted breastfeeding less frequently than nonvulnerable. There was no difference in the frequency of caesarean births, pitocin induction/augmentation, low birth weight, or preterm newborns among the vulnerable women. Birth Companion interventions may have a role in influencing these outcomes. The Birth Companions program will use this analysis to identify additional services for these populations.
How Many Types of Dystonia? Pathophysiological Considerations

Dystonia can be seen in a number of different phenotypes that may arise from different etiologies. The pathophysiological substrate of dystonia is related to three lines of research. The first postulate a loss of inhibition which may account for the excess of movement and for the overflow phenomena. A second abnormality is sensory dysfunction which is related to the mild sensory complaints in patients with focal dystonias and may be responsible for some of the motor dysfunction. Finally, there are strong pieces of evidence from animal and human studies suggesting that alterations of synaptic plasticity characterized by a disruption of homeostatic plasticity, with a prevailing facilitation of synaptic potentiation may play a pivotal role in primary dystonia. These working hypotheses have been generalized in all form of dystonia. On the other hand, several pieces of evidence now suggest that the pathophysiology may be slightly different in the different types of dystonia. Therefore, in the present review, we would like to discuss the neural mechanisms underlying the different forms of dystonia to disentangle the different weight and role of environmental and predisposing factors.
Sightseeing plan recommendation system using sequential pattern mining based on adjacent activities

This study proposes the novel sightseeing plan recommendation system based on the ad jacency of the activities in the plans (e.g., a shopping is selected after a lunch), and aims at verifying its effectiveness through the subject experiments. The proposed system recommends the sightseeing plans according to the adjacent relationship of the activities in the plans given by the group of other users whose preference are similar to the user. The intensive subject experiments have revealed that our proposed system can recommend the plans that contains the appropriate order of activities for the sightseeing plan recommendation in comparison with ones recommended by the conventional system.
Visualizing the impact of time series data for predicting user interactions

In recent years the importance of user interactions has been recognized in a variety of research contexts. There is a variety of algorithms for modeling these in social graphs; in particular, we distinguish static and dynamic relations. In contrast to static graphs in which the networks do not change over time, the underlying relation is changing frequently in various contexts. This should be reflected by a time dependent social neighborhood of users. In this paper, we present a new and intuitive visualization concept for the histories of user interactions. We derive association rules and visualize these using heatmaps. We demonstrate the impact of the presented approach by several examples utilizing real-world data - using the well known twitter dump of 2009.
A High-Speed Target-Free Vision-Based Sensor for Bus Rapid Transit Viaduct Vibration Measurements Using CMT and ORB Algorithms

Bus Rapid Transit (BRT) has become an increasing source of concern for public transportation of modern cities. Traditional contact sensing techniques during the process of health monitoring of BRT viaducts cannot overcome the deficiency that the normal free-flow of traffic would be blocked. Advances in computer vision technology provide a new line of thought for solving this problem. In this study, a high-speed target-free vision-based sensor is proposed to measure the vibration of structures without interrupting traffic. An improved keypoints matching algorithm based on consensus-based matching and tracking (CMT) object tracking algorithm is adopted and further developed together with oriented brief (ORB) keypoints detection algorithm for practicable and effective tracking of objects. Moreover, by synthesizing the existing scaling factor calculation methods, more rational approaches to reducing errors are implemented. The performance of the vision-based sensor is evaluated through a series of laboratory tests. Experimental tests with different target types, frequencies, amplitudes and motion patterns are conducted. The performance of the method is satisfactory, which indicates that the vision sensor can extract accurate structure vibration signals by tracking either artificial or natural targets. Field tests further demonstrate that the vision sensor is both practicable and reliable.
Effects of Task Difficulty and Display Format on Automation Usage Strategy: A Workload Capacity Analysis

Objective An experiment used workload capacity analysis to quantify automation usage strategy across different task difficulty and display format types in a speeded task. Background Workload capacity measures the efficiency of concurrent information processing and can serve as a gauge of automation usage strategy in speeded decision tasks. The present study used workload capacity analysis to investigate automation usage strategy while information display format and task difficulty were manipulated. Method Subjects performed a speeded judgment task assisted by an automated aid that issued decision cues at varying onset times. Response time distributions were converted to measures of workload capacity. Results Two variants of a workload capacity measure, CzOR and CzAND, gave evidence that operators moderated their own decision times both in anticipation of and following the arrival of the aid’s diagnosis under difficult task conditions regardless of display format. Conclusion Assistance from an automated decision aid may cause operators to delay their own responses in a speeded decision task, producing joint response time distributions that are slower than optimal. Application Even when it renders its own judgments quickly and with high accuracy, an automated decision aid may slow responses from a user. Automation designers should consider the relative costs and benefits of response accuracy and time when choosing whether and how to implement an automated decision aid.
On bandlimited fading channels at high SNR

This paper provides an overview of results on the capacity of noncoherent, multiple-input multiple-output (MIMO) flat-fading channels with a bandlimited power spectral density. The focus is on results that concern the capacity at high signal-to-noise ratio (SNR). In particular, the capacity pre-log, defined as the limiting ratio of the capacity to the logarithm of the SNR as the SNR tends to infinity, is studied. It is observed that the capacity pre-log is a function of the number of antennas as well as of the bandwidth of the fading channel's power spectral density. It is further observed that the capacity pre-log can be achieved with a simple communication system where the data detection and the channel estimation are performed separately.
Laccase GhLac1 Modulates Broad-Spectrum Biotic Stress Tolerance via Manipulating Phenylpropanoid Pathway and Jasmonic Acid Synthesis1[OPEN]

Modification of GhLac1 expression leads to redirection of phenylpropanoid metabolism and alteration of JA synthesis to confer broad spectrum resistance to both pathogens and pests. Plants are constantly challenged by a multitude of pathogens and pests, which causes massive yield and quality losses annually. A promising approach to reduce such losses is to enhance the immune system of plants through genetic engineering. Previous work has shown that laccases (p-diphenol:dioxygen oxidoreductase, EC 1.10.3.2) function as lignin polymerization enzymes. Here we demonstrate that transgenic manipulation of the expression of the laccase gene GhLac1 in cotton (Gossypium hirsutum) can confer an enhanced defense response to both pathogens and pests. Overexpression of GhLac1 leads to increased lignification, associated with increased tolerance to the fungal pathogen Verticillium dahliae and to the insect pests cotton bollworm (Helicoverpa armigera) and cotton aphid (Aphis gosypii). Suppression of GhLac1 expression leads to a redirection of metabolic flux in the phenylpropanoid pathway, causing the accumulation of JA and secondary metabolites that confer resistance to V. dahliae and cotton bollworm; it also leads to increased susceptibility to cotton aphid. Plant laccases therefore provide a new molecular tool to engineer pest and pathogen resistance in crops.
Methods in Bioengineering: Biomicrofabrication and Biomicrofluidics

Microfabrication Techniques for Microfluidic Devices. Micropumping and Microvalving. Micromixing and Microfluidic Devices. On-Chip Electrophoresis and Isoelectric Focusing Methods for Quantitative Biology. Electrowetting. Dieletrophoresis for Particle and Cell Manipulations. Optical Microfluidics for Molecular Diagnostics. Neutrophil Chemotaxis Assay from Whole Blood Samples. Microfluidics Immunoassays. Droplet-Based Microfluidics by Shear-Driven Microemulsions. MicroFACS Systems. Optical Flow Characterization - Micro Particle Image Velocimetry (uPIV). Microtubule Motors in Microfluidics.
Camera-based vehicle velocity estimation from monocular video

This paper documents the winning entry at the CVPR2017 vehicle velocity estimation challenge. Velocity estimation is an emerging task in autonomous driving which has not yet been thoroughly explored. The goal is to estimate the relative velocity of a specific vehicle from a sequence of images. In this paper, we present a light-weight approach for directly regressing vehicle velocities from their trajectories using a multilayer perceptron. Another contribution is an explorative study of features for monocular vehicle velocity estimation. We find that light-weight trajectory based features outperform depth and motion cues extracted from deep ConvNets, especially for far-distance predictions where current disparity and optical flow estimators are challenged significantly. Our light-weight approach is real-time capable on a single CPU and outperforms all competing entries in the velocity estimation challenge. On the test set, we report an average error of 1.12 m/s which is comparable to a (ground-truth) system that combines LiDAR and radar techniques to achieve an error of around 0.71 m/s.
Quantized state feedback stabilization with signal-to-noise ratio constraints

This paper deals with the problem of state feedback stabilization for single-input discrete-time systems over a communication channel, where both logarithmic quantization error and white noise are included. The logarithmic quantizer is characterized by a received signal-to-error ratio (R-SER) model and the white noise is modelled by additive white Gaussian noise (AWGN) channel where a signal-to-noise constraint is imposed. The desired control law is aimed to stabilize the system in the presence of quantized error and to satisfy some pre-specified power constraint, simultaneously. A solvability condition is derived in terms of Mahler measure of the plant and the desired feedback controller is obtained through solving an algebraic Riccati equation. An example is included to illustrate the current results.
Prediction of the Optimal Dosing Regimen Using a Mathematical Model of Tumor Uptake for Immunocytokine-Based Cancer Immunotherapy

Purpose: Optimal dosing is critical for immunocytokine-based cancer immunotherapy to maximize efficacy and minimize toxicity. Cergutuzumab amunaleukin (CEA-IL2v) is a novel CEA-targeted immunocytokine. We set out to develop a mathematical model to predict intratumoral CEA-IL2v concentrations following various systemic dosing intensities. Experimental Design: Sequential measurements of CEA-IL2v plasma concentrations in 74 patients with solid tumors were applied in a series of differential equations to devise a model that also incorporates the peripheral concentrations of IL2 receptor–positive cell populations (i.e., CD8+, CD4+, NK, and B cells), which affect tumor bioavailability of CEA-IL2v. Imaging data from a subset of 14 patients were subsequently utilized to additionally predict antibody uptake in tumor tissues. Results: We created a pharmacokinetic/pharmacodynamic mathematical model that incorporates the expansion of IL2R-positive target cells at multiple dose levels and different schedules of CEA-IL2v. Model-based prediction of drug levels correlated with the concentration of IL2R-positive cells in the peripheral blood of patients. The pharmacokinetic model was further refined and extended by adding a model of antibody uptake, which is based on drug dose and the biological properties of the tumor. In silico predictions of our model correlated with imaging data and demonstrated that a dose-dense schedule comprising escalating doses and shortened intervals of drug administration can improve intratumoral drug uptake and overcome consumption of CEA-IL2v by the expanding population of IL2R-positive cells. Conclusions: The model presented here allows simulation of individualized treatment plans for optimal dosing and scheduling of immunocytokines for anticancer immunotherapy. Clin Cancer Res; 24(14); 3325–33. ©2018 AACR. See related commentary by Ruiz-Cerdá et al., p. 3236