Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary material to "The pathway of aerosol direct effects impact on secondary inorganic aerosol formation"

Abstract. Airborne aerosols reduce surface solar radiation through light scattering and absorption (aerosol direct effects, ADE), influence regional meteorology, and further affect atmospheric chemical reactions and aerosol concentrations. Several studies have revealed that the inhibition of turbulence and the increase in atmospheric stability induced by ADE increases surface primary aerosol concentration, but the pathway of ADE impacts on secondary aerosol is still unclear. In this study, the two-way coupled WRF-CMAQ with integrated process analysis was applied to explore how ADE impacts secondary aerosol formation through changes in atmospheric dynamics and photolysis processes. Meteorological and air quality fields in Jing-Jin-Ji area (denoted JJJ, including Beijing, Tianjin and Hebei Province in China) in January and July 2013 were simulated to represent winter and summer conditions, respectively. Two pathways of ADE impacts on aerosol concentration, i.e., photolysis modification and atmospheric dynamics modification were estimated separately through scenario analysis. The results show that solar radiation is the restricting factor in winter, and the formation of sulfate is sensitive to the perturbation of solar radiation. While in summer, availability of gaseous precursors primarily dictates the levels of secondary aerosol concentrations. ADE through the attenuation of photolysis inhibits secondary aerosol formation during winter and promotes secondary aerosol formation during summer. The seasonal differences are attributed to change of effective actinic flux in winter and summer determined by aerosol optical depth, solar zenith angles, and single scattering albedo. ADE through dynamic processes is the dominant process influencing surface secondary aerosol formation due to the accumulation of gaseous precursors. Different from sulfate, the surface layer is a net-source of nitrate during winter but a sink during summer. Therefore, ADE promotes nitrate accumulation in winter and reduces nitrate accumulation in summer.
Teaching about decision trees for classification problems

In times of big data tree-based algorithms are an important method of machine learning which supports decision making, e.g., in medicine, finance, public policy and many more. Trees are a versatile method to represent decision processes that mirror human decision-making more closely than sophisticated traditional statistical methods like multivariate regression or neural networks. We introduce and illustrate the tool ARBOR, a digital learning tool which is a plug-in to the freely available data science education software CODAP. It is designed to critically appreciate and explore the steps of automatically generated decision trees.
Evaluation of serum concentrations of hs-CRP and Hb in varying severities of chronic periodontitis

Abstract Context Chronic periodontitis patients exhibit elevations in serum concentrations of c-reactive protein (CRP). Also, periodontitis can potentially contribute to reduced haemoglobin (Hb) levels leading to anaemia of chronic disease (ACD). Objective To determine whether disease severity affect CRP and Hb levels in patients with chronic periodontitis. Material and Methods Based on periodontitis severity, three groups of 50 subjects each were selected, who underwent screening for gingival index (GI), probing pocket depth (PPD), and clinical attachment level (CAL). Blood samples were taken to estimate the number of erythrocytes, mean corpuscular volume (MCV), packed cell volume (PCV), Hb concentration, mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), and high sensitivity C-reactive protein (hs-CRP) Results Mean hs-CRP levels of 0.796, 2.238, 2.886 were found in mild, moderate, and severe periodontitis groups respectively with significant intergroup differences. The differences in haematological parameters other than MCV were not significant between groups. Conclusion The possible systemic effects of periodontitis might be influenced by its severity, as evidenced by a corresponding change in the levels of systemic inflammatory markers like CRP.
Istradefylline: A novel agent in the treatment of “off” episodes associated with levodopa/carbidopa use in Parkinson disease

The current gold standard for treatment of Parkinson disease (PD) is levodopa/carbidopa (L/C), but long-term treatment frequently results in motor complications, such as wearing-off and motor fluctuations (eg, dyskinesia, “on-off” phenomenon). Istradefylline is a new drug with a unique pharmacologic profile that was approved by the FDA for use as adjunctive treatment to L/C in adult patients with PD experiencing “off” episodes. The drug was shown to reduce “off” time in 4 randomized, double-blind, placebo-controlled studies. The most common adverse effects are dyskinesia, dizziness, constipation, nausea, hallucinations, and insomnia. Unlike many drugs that treat PD, istradefylline is a nondopaminergic drug that exerts its effects via adenosine A2A receptor antagonism. The major drug interactions involve inhibitors or inducers of CYP3A4 as well as tobacco smoking via induction of CYP1A1. Istradefylline is taken once daily as a 20- or 40-mg dose, except in cases involving drug interactions or hepatic impairment. The cost of the drug is relatively expensive, which has implications for Medicare and private insurance coverage. Istradefylline is an alternative option to dopaminergic drugs such as dopamine agonists, monoamine oxidase B inhibitors, and catechol-O-methyltransferase inhibitors as an adjunct to L/C in patients with motor fluctuations, but clinical use will further define its role in treatment of PD.
Increased Activity of a NK-Specific CAR-NK Framework Targeting CD3 and CD5 for T-Cell Leukemias

Simple Summary Chimeric antigen receptors (CAR) can redirect the activity of NK cells to target T-cell malignancies. Our results identify that recognition of CD5 molecules in malignant T cells by the CAR leads to improved antitumor response compared to targeting CD3, due to strong downregulation of the CD3 antigen after CD3-CAR treatment. We have also identified that a specific CAR-NK framework has superior activity than a CAR-T framework on NK effector cells. Abstract NK effector cells expressing a CAR construct may be used to target T-lineage markers. In this work, we compared the activity of a NK-specific CAR-NK and a CAR-T framework when expressed on NK effector cells to target CD3 and CD5 in T-cell malignancies. Our results show that CD3-CAR-T is more active than CD5-CAR-T to eliminate malignant T cells in vitro, however, CD3-CAR-T were less efficient to eliminate tumor cells in vivo, while CD5-CAR-T had antitumor activity in a diffuse xenograft model. Lack of in vivo efficacy correlated with downregulation of CD3 levels in target T cells after coculture with CD3-CAR effector cells. The CAR-NK framework greatly improved the efficacy of CARs leading to increased degranulation, cytokine secretion and elimination of the tumor xenograft by CD5-CAR-NK effector cells. Finally, all CAR constructs were similarly effective to eliminate malignant T cells in vitro. Our results show that the NK-CAR framework improves the activity of CARs in NK cells and that CD5 would be a better target than CD3 for T-cell malignancies, as dynamic downregulation of target expression may affect in vivo efficacy.
Fake News Classification Based on Content Level Features

Due to the openness and easy accessibility of online social media (OSM), anyone can easily contribute a simple paragraph of text to express their opinion on an article that they have seen. Without access control mechanisms, it has been reported that there are many suspicious messages and accounts spreading across multiple platforms. Accordingly, identifying and labeling fake news is a demanding problem due to the massive amount of heterogeneous content. In essence, the functions of machine learning (ML) and natural language processing (NLP) are to enhance, speed up, and automate the analytical process. Therefore, this unstructured text can be transformed into meaningful data and insights. In this paper, the combination of ML and NLP are implemented to classify fake news based on an open, large and labeled corpus on Twitter. In this case, we compare several state-of-the-art ML and neural network models based on content-only features. To enhance classification performance, before the training process, the term frequency-inverse document frequency (TF-IDF) features were applied in ML training, while word embedding was utilized in neural network training. By implementing ML and NLP methods, all the traditional models have greater than 85% accuracy. All the neural network models have greater than 90% accuracy. From the experiments, we found that the neural network models outperform the traditional ML models by, on average, approximately 6% precision, with all neural network models reaching up to 90% accuracy.
Class-B Power Amplifier with Si-Based Double-Gate MOSFET: A Circuit Perspective

This research work designs a power amplifier with the use of Silicon-based Double-Gate (DG) MOSFET. It is a novel device used to amplify the input signal of an audio signal, etc. This research paper provides information on the problem identification in the existing models and its design objectives with its design constraints. It also reduces crossover distortion due to DG MOSFET instead of BJTs and MOSFETs in the class-B power amplifier. This is a low-power device for the mA range using SiO2 as a dielectric material.
The era of lenalidomide maintenance therapy in multiple myeloma: settings for achieving best outcomes

ABSTRACT Introduction For patients with multiple myeloma who are eligible for high-dose melphalan therapy and autologous stem cell transplant (ASCT), the strategy of maintenance with low-dose lenalidomide therapy has become the current standard of care. However, this strategy is not curative, and many unanswered questions remain regarding the optimization of lenalidomide-based maintenance therapy. Areas covered In this review, we evaluate the current data supporting the use of lenalidomide maintenance, either alone or in combination, following ASCT. We provide an overview of the management of lenalidomide-associated toxicities as well as address the unresolved topics of optimal treatment duration and use of minimal residual disease assessment. Expert opinion While single-agent lenalidomide maintenance is a current standard of care, a one-size-fits-all approach to maintenance therapy is not optimal. The rapidly evolving landscape of multiple myeloma therapy in conjunction with ongoing clinical trials should enable a future where an individualized approach based on disease characteristics, response to induction and ASCT (or even non-ASCT consolidation approaches such as CAR T-cell therapy or bispecific antibodies), as well as patient preferences will influence the use of lenalidomide maintenance.
Effects of Antibiotics on Growth Performance, Immune Response, and Intestinal Microflora of Broilers

Shahan Azeem1*, Muhammad Akram Muneer2, Liaquat Ahmad3, Sameera Akhtar1 and Talat Naseer Pasha4,5 1Institute of Microbiology, Faculty of Veterinary Science, University of Veterinary and Animal Sciences, Lahore 54000, Pakistan. 2Department of Microbiology, Riphah College of Veterinary Sciences, Riphah International University, Lahore, Pakistan. 3Department of Statistics and Computer Science, Faculty of Life Sciences Business Management, University of Veterinary and Animal Sciences, Lahore 54000, Pakistan. 4Department of Animal Nutrition, University of Veterinary and Animal Sciences, Lahore 54000, Pakistan 5University of Education, Lahore 54770, Pakistan Article Information Received 16 September 2021 Revised 06 October 2021 Accepted 27 October 2021 Available online 19 January 2022 (early access)
A 60 GHz reconfigurable active phase shifter based on a vector modulator in 65 nm CMOS technology

This paper presents a 60 GHz reconfigurable active phase shifter based on a vector modulator implemented in 65 nm complementary metal–oxide–semiconductor technology. This circuit is based on the recombination of two differential paths in quadrature. The proposed vector modulator allows us to generate a phase shift between 0° and 360°. The voltage gain varies between −13 and −9 dB in function of the phase shift generated with a static consumption between 26 and 63 mW depending on its configuration.
Investigating the Molecular Mechanism of Xijiao Dihuang Decoction for the Treatment of SLE Based on Network Pharmacology and Molecular Docking Analysis

Objective To elucidate the main mechanism of Xijiao Dihuang decoction (XJDHT) for the treatment of systemic lupus erythematosus (SLE). Methods TCMSP, BATMAN-TCM, ETCM, and TCMID databases and literature search were used to screen the potential active compounds of XJDHT, and TCMSP and SwissProt databases were searched to predict the targets of the compounds. The targets of SLE were obtained from Genegards, OMIM, and DisGeNET databases, and Venn online platform was used to obtain the intersection targets of XJDHT and SLE. Afterwards, the PPI network was constructed by using the STRING database, and the core targets were identified by network topology analysis. GO and KEGG enrichment analyses were performed through R software, and molecular docking of the top three core targets and their corresponding compounds were accomplished by Autodock Vina and Pymol softwares. Results There were 30 potential active ingredients, 289 potential targets, and 129 intersection targets screened from the above databases. Network topology analysis identified 23 core targets, such as AKT1, TNF, IL6, IL1B, and INS. GO enrichment analysis obtained 2555 terms and mainly clustering on the react to lipopolysaccharide, membrane raft, and ubiquitin-like protein ligase binding. KEGG enrichment analysis obtained 187 signaling pathways, mainly concentrating on the lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, fluid shear stress, and atherosclerosis. Molecular docking verified that the active compounds of XJDHT have the strong binding activity to the core targets. Conclusion This study preliminarily uncovers the mechanism of XJDHT acting on SLE through a “multicompound, multitarget, and multipathway” manner. XJDHT may achieve the treatment of SLE by inhibiting the proinflammatory factors, inflammatory signal cvtokines, proliferation, injury, and apoptosis processes. In summary, the present study would provide a promising theoretical basis for further clinical and experimental studies.
French Modern: Norms and Forms of the Social Environment by Paul Rabinow (review)

Paul Rabinow's study of space and society, power and knowledge in France from the 1830s through the 1930s uses tools from anthropology, philosophy, and cultural criticism to make fascinating connections between diverse protagonists and domains. In each of these domains - ranging from medicine to the layout of colonial cities - Rabin ow describes the creation of norms and the search for forms adequate for understanding and regulating what became known as modern society. He also focuses on an unexplored middle ground between the masters of high culture and the experiences of ordinary life, which he calls "middling modernism. "Paul Rabinow is Professor of Anthropology at the University of California at Berkeley. His most recent books include Michel Foucault: Beyond Structuralism and Hermeneutics (with Hubert Dreyfus) and The Foucault Reader.
Ultrahigh spatiotemporal resolution fluorescence molecular tomography with a sparsity constrained dimensional reduction reconstruction model

We present here a new fluorescence molecular tomographic model that can provide ultrahigh spatial and temporal resolution reconstruction through sparsity constrained dimensional reduction. The new method implements a novel sparsity function to asymptotically enforce the sparsest representation of fluorescent targets while reducing the problem dimension based correlation between sensing matrix and measurement. Parameterized temporal data (TD) 𝐿(𝑠), available from the Laplace transform, is used here as input to the inverse model for their computational efficiency and accuracy and robustness to noise. We use radiative transfer equation (RTE) as a light propagation model as it provides more accurate predictions of light propagation in small-volume tissue. The performance of this new method is evaluated through numerical phantoms, focusing on spatial resolution and computational speed. The results show that the sparsity constrained dimensional reduction inverse model can achieve near cellular resolution (~1mm spatial resolution) at depth of 70 mean free paths (MFPs) within ~25 milliseconds.
Domain-Specific Adaptation of CNN for Detecting Face Presentation Attacks in NIR

For the automotive industry moving towards personalized applications and experiences, the identification of the person inside vehicle is necessary; and it must be carried out in a secure manner. In this paper, we propose a unique face presentation attack detection (PAD) system for operation inside a passenger vehicle. A typical in-vehicular face PAD system is required to function with several constraints such as bounded sensing (imaging) capabilities, limited computing resources on embedded devices, real-time inference, and essentially, very high accuracy. In this work, we develop a face PAD system for automotive domain, relying on a single NIR camera, to continually verify whether the driver’s face is bona-fide or not. Our work has two main contributions: first, a lightweight face PAD framework has been developed using a 9-layer convolutional neural network (CNN). With its compact size and limited set of operators, it can be deployed in a resource constrained embedded device to achieve a near real-time inference. To alleviate the problem of limited training data (face PAD in NIR) for a given system, we develop an efficient mechanism to obtain this CNN through the combination of adaptation of domain-specific layers and task-specific fine-tuning of a base CNN. As the second contribution, we collect a large face PAD dataset with 5800+ videos, acquired in NIR (940 nm) illumination, for in-vehicular use-cases. This dataset, named VFPAD, captures several real-world variations in terms of environmental settings, illumination, subject’s pose, and appearances. Based on the VFPAD dataset, we demonstrate that the proposed face PAD method achieves very high performance (overall accuracy $\approx 98.0$ %), and also outperforms several baseline face PAD methods. The dataset will be shared with the wider scientific community for research purposes.
Exact Recovery of Two-Latent Variable Stochastic Block Model with Side Information

The two-latent variable stochastic block model is a new graph synthetic model making a connection between the conventional stochastic block model and real-world networks. In this model, each node contains two latent variables such that at least one of these two latent variables is unknown. Still, this model lonely is not able to model a real-world network. Side information is another component that sometimes exists beside a real-world network. In this paper, we will investigate the asymptotic behavior of the two-latent variable stochastic block model in the presence of side information. Two different types of side information are considered in this paper: noisy labels and partially revealed labels side information. For each case, the necessary and sufficient conditions for the exact recovery of the desired latent variable are obtained via semidefinite programming optimization. It is shown that these conditions are tight and create a phase transition for the exact recovery.
Literacy Concepts as an Intervention Strategy for Improving Fake News Knowledge, Detection Skills, and Curtailing the Tendency to Share Fake News in Nigeria

Abstract This study examined the role of literacy concepts (information, news, media, and digital literacies), as an intervention strategy, in improving fake news knowledge, detection skills, and curtailing the tendency to share fake news among social media users. In doing so, this study used the inoculation theory and message interpretation process (MIP) theory to provide a useful explanation for literacy concept intervention. An experiment was carried out to test the effects of literacy intervention on the treatment group which were later compared with the results deduced from the control group who did not receive any intervention. It was found that participants in the experimental group demonstrated a higher knowledge of fake news, better ability to detect fake news and shared more accurate news articles, as compared to their counterparts who were in the control group. Implications for research and practice were discussed.
Chaotic Analysis as a Tool for Sensing Long-Term Degradation of High Voltage Fibre Reinforced Plastic Core Used in Composite Insulators

The leakage current which flows on the surface of dielectric rods under high voltage involves a series of events which include partial arcing, discharges and corona, these phenomenon are highly non-linear. These sequence of events render the leakage current signal to be very much chaotic. In the present work, the process of delay-embedding is employed to study the phase-portrait of the signal, further Lyapunov Exponents are computed to characterize the chaotic nature of the leakage current. The main focus of the study is intended to understand the chaotic nature of leakage current behaviour over the ageing period and to determine the degree of degradation on the dielectric rod surface.
Phylogeny and disparate selection signatures suggest two genetically independent domestication events in pea (Pisum L.)

SUMMARY Domestication is considered a model of adaptation that can be used to draw conclusions about the modus operandi of selection in natural systems. Investigating domestication may give insights into how plants react to different intensities of human manipulation, which has direct implication for the continuing efforts of crop improvement. Therefore, scientists of various disciplines study domestication‐related questions to understand the biological and cultural bases of the domestication process. We employed restriction site‐associated DNA sequencing (RAD‐seq) of 494 Pisum sativum (pea) samples from all wild and domesticated groups to analyze the genetic structure of the collection. Patterns of ancient admixture were investigated by analysis of admixture graphs. We used two complementary approaches, one diversity based and one based on differentiation, to detect the selection signatures putatively associated with domestication. An analysis of the subpopulation structure of wild P. sativum revealed five distinct groups with a notable geographic pattern. Pisum abyssinicum clustered unequivocally within the P. sativum complex, without any indication of hybrid origin. We detected 32 genomic regions putatively subjected to selection: 29 in P. sativum ssp. sativum and three in P. abyssinicum. The two domesticated groups did not share regions under selection and did not display similar haplotype patterns within those regions. Wild P. sativum is structured into well‐diverged subgroups. Although Pisum sativum ssp. humile is not supported as a taxonomic entity, the so‐called 'southern humile' is a genuine wild group. Introgression did not shape the variation observed within the sampled germplasm. The two domesticated pea groups display distinct genetic bases of domestication, suggesting two genetically independent domestication events.