Miracle-dz/UniFAR-FTUs huggingface dataset:
Level of cytokines in patients with pulmonary drug susceptible and resistant tuberculosis

Background: Cytokines are the regulators of the immune response in tuberculosis: TNF-alpha and CXCL8 (IL-8) are involved in the granuloma formation, IL-10 inhibits the inflammation; some chemokines increase the liver production of the acute phase proteins (APPs). The aim of the research was to assess the serum level of IL-8, TNF-alpha, IL-10, C-reactive protein (CRP), ceruloplasmin and fibrinogen in patients with drug-sensitive and multidrug resistant tuberculosis (MDR-TB). Material and methods: A prospective case-control study, which included 51 patients, distributed in 2 groups: the 1 st study group (N=24 new cases with drug-sensitive TB) and the 2 nd study group (N=27 new cases with MDR-TB) according to sex and age were compared with the control group (N=36 healthy individuals). Results: Serum concentration of IL-8 was elevated up to 13 times, TNF-alpha up to 4 times and IL-10 up to 2 times in study groups, compared with the reference value of the control group. Fibrinogen concentration was elevated up to 2 times in study groups compared with the control group and CRP up to 3 times compared with conventional value. Ceruloplasmin was statistically higher in the drug-sensitive TB and mildly elevated in MDR-TB group. Conclusions: Proinflammatory biomarkers are more elevated than the anti-inflammatory response, without differences among groups regarding drug sensitiveness.
Desquamative Gingivitis: Protocols, Procedures and Critical Issues

Desquamative gingivitis (DG) isn’t a specific disorder; it simply represents the gingival manifestation associated with some heterogeneous mucocutaneous disorders, such as oral lichen planus (OLP), mucous membrane pemphigoid (MMP), pemphigus vulgaris (PV), plasma cell gingivitis (PCG) and few others. [. ]
Enhancement of Atmospheric Stability by Anomalous Elevated Aerosols During Winter in China

Changes in anthropogenic aerosols with a low level of scientific cognition affect the atmospheric environment and radiative balance of the Earth. We determine anomalous elevated aerosol layers (EALs) and their source and effects on regional atmospheric stability in China by using 2007–2016 data from multiple satellite observations and models. Anomalous EALs occur during winter over Eastern China, and they are mainly distributed above 2 km and can even spread up to 4–5 km despite the disadvantageous atmospheric convection conditions during winter. Anthropogenic pollutants that mainly originate from local‐range transport with enhanced atmospheric updraft play a dominant role (about 60%) in this situation. Furthermore, anomalous EALs result in considerable warming in the upper atmosphere (+0.32 K/day) and cooling on the surface (−0.04 K/day) over Eastern China. They decrease surface incident radiation by an average of 3.4 W/m2. The opposite heating rate change in the upper atmosphere and surface caused by anomalous EALs consequently increases the low atmospheric stability by an average of 0.39 K, and this increment weakens the dissipation of atmospheric dispersion. Related studies have indicated that increased atmospheric stability can severely exacerbate atmospheric pollution near the surface.
Overinterpretation reveals image classification model pathologies

Image classifiers are typically scored on their test set accuracy, but high accuracy can mask a subtle type of model failure. We find that high scoring convolutional neural networks (CNN) exhibit troubling pathologies that allow them to display high accuracy even in the absence of semantically salient features. When a model provides a high-confidence decision without salient supporting input features we say that the classifier has overinterpreted its input, finding too much class-evidence in patterns that appear nonsensical to humans. Here, we demonstrate that state of the art neural networks for CIFAR-10 and ImageNet suffer from overinterpretation, and find CIFAR-10 trained models make confident predictions even when 95% of an input image has been masked and humans are unable to discern salient features in the remaining pixel subset. Although these patterns portend potential model fragility in real-world deployment, they are in fact valid statistical patterns of the image classification benchmark that alone suffice to attain high test accuracy. We find that ensembling strategies can help mitigate model overinterpretation, and classifiers which rely on more semantically meaningful features can improve accuracy over both the test set and out-of-distribution images from a different source than the training data.
SPATIAL MODELING OF ENVIRONMENTAL-BASED RISK FACTORS OF TUBERCULOSIS IN BALI PROVINCE: AN ECOLOGICAL STUDY

Background: Indonesia is one of countries with a quite high incidence of tuberculosis. One of the regions which has issue of tuberculosis incidence is Bali Province with a case notification rate that tends to increase in the last three years so that it has an impact on increasing the risk of disease transmission. Purpose: This research aims to identify the risk factor based on the environment/spatial incidence of the tuberculosis in Bali Province. Methods: This research used ecological study design through secondary data obtained from the Health Office of Bali Province, Indonesian Statistics of Bali Province, and Environmental Office of Bali Province. The dependent variable was tuberculosis incidence, while the independent variable was the level of PM 10 , population density, poverty percentage, healthy house percentage, percentage of Clean and Healthy Lifestyle (PHBS), ratio of healthcare facilities with the community. Results: The statistical model was obtained in the form of Spatial Error Model (SEM) with model ŷ i =1612,57+ 0,96 * level of PM 10 + 0,04 * population density - 2,56 * poverty - 0,58 * Healthy house - 3,099 * PHBS - 0,006 * health care facility, where μ i = 0,90 . Conclusion: The risk factor spatially affected the tuberculosis incidence in Bali Province, which were the factors of level of PM 10 , population density, poverty percentage, healthy house percentage, percentage of PHBS, and ratio of healthcare facilities with the community.
Postspinal analgesic effect of transdermal fentanyl patch (2.5 mg and 5 mg) in abdominal hysterectomy: A randomized double-blind control study

Background and Aims: The use of transdermal patches to deliver drugs systemically for postoperative analgesia offers lots of pharmacological and nonpharmacological advantages over the conventional enteral and parenteral drug therapies. Aim of this study was to assess the difference in mean duration and quality of postspinal analgesia in patients underwent elective abdominal hysterectomy. Methods: Sixty patients of American Society of Anesthesiologists Grade I and II undergoing elective abdominal hysterectomy were randomized into three groups of 20 each using chit in box method (Group I control, Group II 2.5 mg fentanyl patch, and Group III 5 mg fentanyl patch). The ANOVA test and post hoc test were used for continuous data and Chi-square test was used for count data. Results: The mean difference of visual analog scale score were significantly low in Group III and Group II (P = 0.00). The mean total duration of analgesia was more in Group III and Group II (P = 0.00). The requirement of rescue analgesic dose was significantly low in Group II and no rescue analgesic needed in Group III (P = 0.00). No significant side effects were noted. Conclusions: Transdermal fentanyl patch (TFP) (50 mcg/h) provides better postoperative analgesia than TFP (25 mcg/h) and placebo patch with significant reduction of requirement of rescue analgesic dose.
Pre-service secondary teachers’ knowledge of the function concept: A cluster analysis approach

Teacher training is a key aspect for the success of any educational system and it is a prior challenge to facein-depth in Zambia. This study explores some dimensions of prospective secondary mathematics teachers’ subject-matter knowledge and pedagogical content knowledge restricted to the topic of the function concept. Data were collected by means of a questionnaire that was administered to a sample of 150 prospective teachers enrolled attwo public universities inCopperbelt, Zambia. A two-step cluster analysis was used to reveal natural grouping within the datasetobtained. As a result, a two-cluster solution was revealedas the solution that best profiled the data, with participants within both clusters scoringlow in the knowledge domains under consideration. To further understand the results a one-way between-subjects multivariate analysis of variance (MANOVA) and a follow-up discriminant function analysis (DFA) were also conducted. Common content knowledge was shown to be the most important factor in discriminating between prospective teachers in Cluster 1 and those in Cluster 2. The knowledge inadequacies identified are certainly worrisome and are likely to be transferred to young learners at secondary schools. Consequently, it is necessary to address with some urgency certain reforms in educational policies for teacher training programs in Zambia.
Adjusting to Indonesia’s Culture: The Case of Expatriates in the Education Industry

A bstract Globalization has resulted in an increased number of foreign workers in Indonesia. Nevertheless, not all foreign workers can easily adapt to the culture, business, and social situation of the host country. According to cross-cultural literature, the cultural adjustment has been considered as the prerequisite for the success of the expatriates overseas. This study aims to investigate factors that affect the success of the international assignment. Using a qualitative case study approach, this study interviewed five expatriates working in the education industry in Central Java based on the U-curve cultural adjustment theory. The result of this study demonstrates that the inability of an expatriate to adjust with the host country was due to the lack of preparation toward the culture of the host country, resistance with the new culture, conflict-avoiding behavior, and the lack of two-way communication between the expatriate and the local staffs. Keywords : Cross-cultural adjustment, cross-cultural management, expatriate, international assignment, U-curve.
Systematic Bias in the Prediction of Warm‐Rain Hydrometeors in the WDM6 Microphysics Scheme and Modifications

Modifications are made to the calculation of warm‐rain hydrometeor number concentrations of the Weather Research and Forecasting double‐moment six‐class (WDM6) microphysics scheme. These modifications are related with cloud condensation nuclei activation, cloud droplet evaporation, and snow and graupel melting. The effects of the modifications are tested in a North China heavy rain event (Case01) as well as a short‐duration rain event in Hebei Province of China (Case02). The results show that the WDM6 scheme underestimates the cloud mixing ratios (Qc) and number concentrations (Nc), while it overestimates the rain mixing ratios (Qr) and number concentrations (Nr). Meanwhile, the WDM6 scheme overestimates the mass mean diameter of cloud droplets (Dmc) and underestimates that of raindrops (Dmr). Through microphysical budget analysis, it is found that the inappropriate calculations of cloud condensation nuclei activation and cloud evaporation are responsible for the underestimation of Nc and larger Dmc. The overestimation of the autoconversion rate resulting from larger Dmc plays a decisive role in the overestimation of Qr and Nr, as well as in the underestimation of Qc. The overestimation of raindrops produced by the melting of snow and graupel is another reason for higher Qr and Nr. By comparing with the airborne observations in Case02, it is found that the Nc and Dmc simulation are notably improved with the direct modifications of Nc. The Nr simulation is closer to the observation after the melting of snow and graupel are modified.
Adalimumab Modulates Angiogenesis in Psoriatic Skin

Angiogenesis plays a key role in the pathogenesis of psoriasis. New blood vessel formation occurs early during plaque lesion development in psoriatic skin, and sometimes precedes disease recurrence. TNF-α, a well established mediator of inflammation in psoriasis, up-regulates the transcription of several pro-angiogenic chemokines that are over-expressed in psoriatic skin and serum, promoting microvascular modifications in psoriatic plaque. Adalimumab is a fully human monoclonal antibody that blocks the interaction between TNF-α and its cell surface receptor, thus inhibiting the TNF-α dependent inflammatory cascade. The aims of this study were to investigate several angiogenic parameters involved in the pathogenesis of psoriasis, and to evaluate the ability of adalimumab to modulate them. Fifteen patients affected by psoriasis received Adalimumab 40 mg EOW for twelve weeks and were evaluated at baseline (T0) and after treatment (T12) for the following parameters: i) new blood vessels formation in lesional skin assessed by intra-vital video-capillaroscopy analysis (IVCP) and histology; ii) VEGF and Factor VIII expression in lesional skin detected by immunohistochemistry; iii) serological levels of several angiogenic chemokines detected by luminex assay. At baseline, newly formed capillaries in psoriatic plaque correlated with skin expression of VEGF and factor VIII and with serum levels of angiopoietin-2, IL-8, PDGF-BB, VEGF, but not with serum levels of follistatin, TNF-α, G-CSF, HGF, FGF, PECAM, IFN-γ, and TGF-α. All patients responded to adalimumab, reached PASI 50, and 70% achieved PASI 75 after twelve weeks of treatment. Although adalimumab administration for twelve weeks caused a dramatic decrease of new blood vessel formation, confirmed by IVCP (p<0.05), histology and immunohistochemical (p<0.05) analysis, we did not observe a parallel significant reduction of angiogenic chemokines in the serum. However, a positive correlation between the density of newly formed blood vessels in lesional skin and the serum levels of angiopoietin-2, IL-8, PDGF-BB, and VEGF, was still persisting after treatment.
Provably Constant-Time Planning and Re-planning for Real-time Grasping Objects off a Conveyor

In warehousing and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick and place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This brings the requirement for fast and reliable motion planners that could provide provable real-time planning guarantees, which the existing algorithms do not provide. Besides the planning efficiency, the success of manipulation tasks relies heavily on the accuracy of the perception system which often is noisy, especially if the target objects are perceived from a distance. For fast moving conveyor belts, the robot cannot wait for a perfect estimate before it starts execution. In order to be able to reach the object in time it must start moving early on (relying on the initial noisy estimates) and adjust its motion on-the-fly in response to the pose updates from perception. We propose an approach that meets these requirements by providing provable constant-time planning and replanning guarantees. We present it, give its analytical properties and show experimental analysis in simulation and on a real robot.
Efficient Predictive Model for Determining Critical Factors Affecting Commodity Price: The Case of Coffee in Ethiopian Commodity Exchange (ECX)

In this paper, we have focused on the data mining technique on market data to establish meaningful relationships or patterns to determine the determinate critical factors of commodity price. The data is taken from Ethiopia commodity exchange and 18141 data sets were used. The dataset contains all main information. The hybrid methodology is followed to explore the application of data mining on the market dataset. Data cleaning and data transformation were used for preprocessing the data. WEKA 3.8. 1 data mining tool, classification algorithms are applied as a means to address the research problem. The classification task was made using J48 decision tree classification algorithms, and different experimentations were conducted. The experiments have been done using pruning and unpruning for all attributes. The developed models were evaluated using the standard metrics of accuracy, ROC area. The most effective model to determine the determinate critical factors for the commodity has an accuracy of 88.35% and this result is a good experiment result. The output of this study is helpful to support decisionmaking activities in the area of the Ethiopia Commodity Exchange. The study support commodity suppliers to take care of the determinant factors work towards maintaining quality. Ethiopia Commodity Exchange (ECX), as the main facilitator of commodity exchanges, can also use the model for setting price ranges and regulations.
Understanding the adsorption performance of two glycine derivatives as novel and environmentally safe anti-corrosion agents for copper in chloride solutions: experimental, DFT, and MC studies

The inhibition impacts of two non-toxic glycine derivatives, namely, bicine (N,N-bis(2-hydroxyethyl)glycine) and tricine (N-(tri(hydroxymethyl)methyl) glycine) on copper corrosion were investigated in 3.5% NaCl solutions. Surprisingly, there is no report on using bicine and/or tricine as corrosion inhibitors for Cu and its alloys in a seawater-like environment. The effects of bicine and tricine on the corrosion behavior of Cu in 3.5% NaCl were examined using the open circuit potential, Tafel polarization, and AC spectroscopy (EIS) techniques. The corrosion rate decreased as a function of the inhibitor dose. The Tafel and EIS parameters showed that the inhibitors decreased both the anodic and cathodic corrosion currents and inhibited the charge transfer process by adsorption on the Cu surface. The inhibition property was attributed to the adsorption of inhibitor molecules with the Langmuir model. Tricine showed a superior inhibition performance of more than 98% at a concentration of ∼5 mmol L−1. The free energy of adsorption data revealed physical adsorption. The outcomes of Monte Carlo simulations and theoretical studies well supported the experimental data.
The issue of ‘proudliness’: Primary students’ motivation towards mathematics

In this paper, we study year 2 and year 5 students’ expressed motivations for doing mathematics. The responses were analysed using thematic analysis; first with a deductive approach using themes from previous research, and then an additional inductive analysis searching for new themes. The results show that the children express both intrinsic motivation (cognitive-oriented and emotional-oriented), as well as extrinsic motivation (including outward and compensation). Two new categories of cognitive intrinsic motivation were found—normative and personal. The results also indicated an interplay not only between the different categories but also within categories, signalling that expressed motivation is double-layered. Some implications are discussed.
ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection

Most advances in medical lesion detection network are limited to subtle modification on the conventional detection network designed for natural images. However, there exists a vast domain gap between medical images and natural images where the medical image detection often suffers from several domain-specific challenges, such as high lesion/background similarity, dominant tiny lesions, and severe class imbalance. Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain? Is there more powerful operations, filters, and sub-networks that better fit the medical lesion detection problem to be discovered? In this paper, we introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporates relation-aware operations among region proposals, and leads to more suitable and efficient classification and localization. 3) Relation transfer module incorporates the semantic relationship and transfers the relevant contextual information with an interpretable graph, thus alleviates the problem of lack of annotations for all types of lesions. Experiments on DeepLesion and Kits19 prove the effectiveness of ElixirNet, achieving improvement of both sensitivity and precision over FPN with fewer parameters.
Teacher-Student chain for efficient semi-supervised histology image classification

Deep learning shows great potential for the domain of digital pathology. An automated digital pathology system could serve as a second reader, perform initial triage in large screening studies, or assist in reporting. However, it is expensive to exhaustively annotate large histology image databases, since medical specialists are a scarce resource. In this paper, we apply the semi-supervised teacher-student knowledge distillation technique proposed by Yalniz et al. (2019) to the task of quantifying prognostic features in colorectal cancer. We obtain accuracy improvements through extending this approach to a chain of students, where each student's predictions are used to train the next student i.e. the student becomes the teacher. Using the chain approach, and only 0.5% labelled data (the remaining 99.5% in the unlabelled pool), we match the accuracy of training on 100% labelled data. At lower percentages of labelled data, similar gains in accuracy are seen, allowing some recovery of accuracy even from a poor initial choice of labelled training set. In conclusion, this approach shows promise for reducing the annotation burden, thus increasing the affordability of automated digital pathology systems.
Gamma-related Ornstein–Uhlenbeck processes and their simulation*

ABSTRACT We investigate the distributional properties of two Lévy-driven Ornstein–Uhlenbeck (OU) processes whose stationary distributions are the gamma law and the bilateral gamma law, respectively. The said distributions turn out to be related to the self-decomposable gamma and bilateral gamma laws, and their densities and characteristic functions are here given in closed form. Algorithms for the exact generation of such processes are accordingly derived with the advantage of being significantly faster than those available in the literature and therefore suitable for real-time simulations.
Time-Varying Formation Tracking of Uncertain Nonaffine Nonlinear Multiagent Systems With Communication Delays

This article addresses the time-varying formation tracking issue for a class of uncertain nonaffine nonlinear multiagent systems (MASs) with communication delays. The agents are connected though a directed topology, and the nonaffine problem is transformed into an affine problem using the idea of active disturbance rejection control. The uncertainties of individual followers in the MAS are approximated via the extended state observers and are compensated in real time. Tracking differentiators are introduced to overcome the explosion of complexity in the derivatives of the desired signal and the virtual control variables. Local information from neighboring followers are utilized, and the Lyapunov–Krasovskii function is employed to analyze the stability theoretically. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control strategy.