Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulating molecular cloud regulated star formation in Galaxies

This thesis is primarily concerned with understanding the process of galaxy formation via the simulation of the interstellar medium, star formation and supernova feedback. In order to probe galaxy formation it is necessary that we first obtain a basic knowledge of the cosmological framework in which we are working. Therefore in chapter 1 we provide a brief overview of the salient features of the current cosmological paradigm in addition to discussing in some detail the physics of the interstellar medium. In chapter 2 we focus on the numerical methods necessary to perform accurate cosmological simulations. We begin by providing an overview of the different simulation techniques currently in use in the field before performing comparisons of two simulation codes that work via two completely different methods. We then introduce a code for generating high-resolution initial conditions for the simulation of individual objects and investigate the numerical effects of mass resolution in cosmological simulation. In chapter 3 we describe a statistical model of the interstellar medium, in which the conversion of cooling gas to stars in the multiphase interstellar medium is governed by the rate at which molecular clouds are formed and destroyed. In the model, clouds form from thermally unstable ambient gas and get destroyed by star formation, feedback and thermal conduction. In chapter 4 this statistical model is applied to the simulation of isolated disk galaxies. We show that it naturally produces a multiphase medium with cold clouds, a warm disk and hot supernova bubbles. We illustrate this by evolving an isolated Milky Way like galaxy. Many observed properties of disk galaxies are reproduced well, including the molecular cloud mass spectrum, the molecular fraction as a function of radius, the Schmidt law, the stellar density profile and the appearance of a galactic fountain. Finally in chapter 5 we perform an investigation into more dynamic situations, namely the evolution of gravitationally interacting disk galaxies and the formation of a galaxy in a fully cosmological simulation. It is found that the sticky particle model does a good job of reproducing many of the observed properties of interacting galaxies, including the properties of the ISM in the resulting elliptical galaxy.
A review on hotspots of input-output model’s applications in the energy and environment fields based on co-words network analysis

Leontief’s input-output model (IOM) is a widely applied method for tracing energy or emissions embodied in economic activities. The economic input-output model used in environmental science has aroused broad concerns from both economists and environmentalists. The aim of this study is to review the hotspots of application of IOM in the energy and environmental science fields based on a bibliometric method by using co-words network analysis. All 4938 publications in this study were retrieved from Science Citation Index (SCI), Social Science Citation Index (SSCI), Conference Proceedings Citation Index-Science (CPCI-S) and Conference Proceedings Citation Index-Social Science & Humanities (CPCI-SSH). The keywords and frequently cited articles were studied in this paper to reveal the evolution of hotspots related to IOM applications in the field of energy and environment from 1998 to 2016. The features of the co-words network analysis of keywords were analyzed by four network indicators, including modularity, number of clusters, closeness coefficient and average path length. The results showed that “Energy”, “CO 2 emissions”, “GHG”, “LCA”, “Industrial ecology”, “Carbon footprint”, “China” and “International trade” were the major application fields of IOM. In different stages the boundary of hotspots became overlapped and the whole network tightness became stronger. According to the analysis of frequently cited articles, we found those articles on CO 2 or GHG emissions embodied in trade had been the most frequently cited articles since 2007 with negotiations on climate change. Based on our findings, using IOM to analyze important environmental problems is the key point to popularize IOM applications. Future research opportunities exist to apply IOM to wider environment issues, such as combined emissions and resources.
Phytochemical Screening, Anti-Inflammatory, and Antidiabetic Activities of Different Extracts from Caralluma edulis Plant

The plant Caralluma edulis is traditionally used against diabetes and inflammatory conditions in Pakistan. This study was designed to provide scientific validation of the traditional use of Caralluma edulis. Phytochemicals were extracted from the plant by different solvents (distilled water, methanol, ethanol, and acetone) using the Soxhlet’s extraction method. Bioactive compounds were detected by gas chromatography–mass spectrometry (GC-MS). The in vitro anti-inflammatory activities (albumin denaturation, membrane stabilization, and proteinase inhibition) and antioxidant capacity (DPPH scavenging activity, FRAP reducing activity) of different extracts from Caralluma edulis were assessed. The antidiabetic potential of Caralluma edulis plant extracts was determined in acute and subacute diabetic rabbit models. Oxidative stress and enzymatic antioxidant status were also estimated in MDA, CAT, and SOD levels. Results showed that the methanol extract yielded the highest contents of phenolics, flavonoids, alkaloids, and terpenoids. The in vitro anti-inflammatory activity and antioxidant potential of the methanol extract were the highest among the tested solvents. The tested extracts did not show any remarkable antidiabetic activity in the acute diabetic model. However, all tested extracts demonstrated antidiabetic potential in the subacute diabetic model. No adverse effect was observed at the tested dose (200 mg/kg) of Caralluma edulis extracts in experimental animals. It is concluded that methanol is the key solvent for extracting bioactive compounds from Caralluma edulis. The plant can be used against inflammatory disorders and may prove a potential candidate for drug development. Long-term use of Caralluma edulis at the tested dose (200 mg/kg) showed antidiabetic properties in the animal model.
Azithromycin and Ceftriaxone Differentially Activate NLRP3 in LPS Primed Cancer Cells

Background: Cancer patients are prescribed antibiotics, such as macrolides and lactamides, for infection treatment. However, the effect of these antibiotics on NLRP3 activation remains largely unknown. Method: Lung cancer (A549) and prostate cancer (PC3) cell lines were primed with lipopolysaccharide (LPS) to activate NLRP3 transcription. Cells were then treated with azithromycin (Az) or ceftriaxone (Cf). NLRP3 activation was analyzed by qPCR, Western blot, and ELISA. Cell growth and viability were assessed by real-time cell analysis and Annexin V expression. Levels of 41 cytokines were also analyzed using a multiplex assay. Results: LPS-Az activated transcription of NLRP3, Pro-CASP-1, and Pro-IL-1β in A549 cells, while failing to upregulate NLRP3 and Pro-IL-1β in PC3 cells. LPS-Az decreased the secretion of pro-inflammatory cytokines while it induced the pro-angiogenic factors in A549 and PC3 cells. In contrast, LPS-Cf suppressed the expression of NLRP3-associated genes, NLRP3 protein expression, the inflammatory cytokine secretion in A549 and PC3 cells. LPS-Az and LPS-Cf had a limited effect on cell growth and viability. Discussion: Our data suggest that Cf could suppress LPS induced NLRP3, which should be considered when selecting antibiotics for cancer treatment. In contrast, the effect of Az on LPS primed NLRP3 and the inflammatory cytokines production appears to depend on the cancer cell origin. Therefore, these data indicate that considerations are required when selecting Az for the treatment of cancer patients.
Using phonics as a method of teaching reading in basic schools

This study examines the use of phonics as a method of teaching reading in basic schools within the framework of generative phonology proposed by Chomsky and Halle (1968) in the Sound Pattern of English (SPE) as discussed in Hawkins (1992). The study aims at describing the patterns of phonic sounds with the application of phonological rules. It describes naturally occurring phenomena (phonemes) without experimental manipulation. The data which is collected from some existing data onphonic teaching and learning and used in this paper are from both verbal and written sources. Participants selected include teachers (T), teacher-trainees (TT) and pupils (P). The study shows the relationship between phonic symbols and letters of thealphabet in English. It attempts to present consonants and vowels occurring in wordinitial, word-medial and word-final positions in English as the major sounds in the English language. Based on the synchronic data available, the paper hypothesizesthat the voiceless dental fricative /0/ and the voiced dental fricative /6/ are the difficult sounds that are not easily pronounced in English words by the LI learner in Ghana. The study, therefore, suggests that both teachers and teacher-trainees shouldhave some knowledge in phonic sounds, and that an introduction to phonetics and phonology should be considered as a course in the Colleges of Education.
The Impact of Smartphone Addiction on Chinese University Students’ Physical Activity: Exploring the Role of Motivation and Self-Efficacy

Background Previous studies showed that smartphone addiction (SA) can lead to reduced physical activity (PA), but only a few studies have explored the impact of SA from psychological perspective closely related to PA. This study aimed to examine the extrinsic and partial psychological factors leading to decrease in PA using structural equation modelling analysis. Methods We conducted an online survey on 628 males and 1159 female students from 10 universities in Henan Province, China, through a questionnaire survey application “Questionnaire Star”. This study used three models to test the mediating effects of three types of motives (intrinsic motives, body-related motives, and social motives) and self-efficacy, respectively, in the relationship between smartphone addiction and physical activities. Results Our result confirmed that smartphone addiction leads to lower physical activities. Secondly, self-efficacy mediates smartphone addiction and physical activities, but the mediating effect of all three types of motivation is not significant. Thirdly, smartphone addiction did not affect intrinsic motivation and body-related motivation, but positively affects social motivation. Finally, as the motivation type changes from internal to external, the mediating effect of self-efficacy becomes stronger. Conclusion This study showed that smartphone addiction lead to increase social motivation and decreased self-efficacy, and is a potential barrier to personal participation in physical activities. Our findings provide a new perspective for future design physical activities interventions in China and worldwide especially among university students where smartphone addiction is a problem.
Identifying FCN2 as a Potential Biomarker for Hepatocellular Carcinoma Using a Novel Supervised Classification Method

Hepatocellular carcinoma (HCC) is the most prevalent form of liver cancer of which most patients show no symptoms of the condition until the ailment is beyond the curable stage. Computational analysis using data mining methods were employed to analyze gene expression profiles on microarray chips from 511 samples (268 HCC tissues and 243 adjacent non-HCC tissues) to identify highly differentially expressed genes. Of 23,000 genes available in the platform, 500 genes (~2%) were preliminary identified by Relief algorithm based on the high ranking scores of their expression levels between HCC tissues and normal tissues. Then, the intensive search for differentially expressed genes were performed by machine learning methods; namely Support Vector Machines (SVM), K-Nearest Neighbour (KNN) and Naïve Bayes Classification (NBC) algorithms, by attributing pool sweep process. Results showed that several highly differentially expressed genes were detected having relatively high classification accuracy with Ficolin-2 acting as a common detected gene.
Bayesian Inference for a Mixture Moddel using the Gibbs Sampler

The Bayesian approach to cluster analysis is presented. We assume that all data stem from a finite mixture model, where each component corresponds to one cluster and is given by a multivariate normal distribution with unknown mean and variance. The method produces posterior distributions of all cluster parameters and proportions as well as associated cluster probabilities for all objects. We extend this method in several directions to some common but non-standard situations. The first extension covers the case with a few deviant observations not belonging to one of the normal clusters. An extra component/cluster is created for them, which has a larger variance or a different distribution, e.g. is uniform over the whole range. The second extension is clustering of longitudinal data. All units are clustered at all time points separately and the movements between time points are modeled by Markov transition matrices. This means that the clustering at one time point will be affected by what happens at the neighbouring time points. The third extension handles datasets with missing data, e.g. item non-response. We impute the missing values iteratively in an extra step of the Gibbs sampler estimation algorithm. The Bayesian inference of mixture models has many advantages over the classical approach. However, it is not without computational difficulties. A software package, written in Matlab for Bayesian inference of mixture models is introduced. The programs of the package handle the basic cases of clustering data that are assumed to arise from mixture models of multivariate normal distributions, as well as the non-standard situations.
A Novel Defined RAS-Related Gene Signature for Predicting the Prognosis and Characterization of Biological Function in Osteosarcoma

Background Osteosarcoma (OS) is the most common primary bone malignancy in children and adolescents with a high incidence and poor prognosis. Activation of the RAS pathway promotes progression and metastasis of osteosarcoma. RAS has been studied in many different tumors; however, the prognostic value of RAS-associated genes in OS remains unclear. On this basis, we investigated the RAS-related gene signature and explored the intrinsic biological features of OS. Methods We obtained RNA transcriptome sequencing data and clinical information of osteosarcoma patients from the TARGET database. RAS pathway-related genes were obtained from the KEGG pathway database. Molecular subgroups and risk models were developed using consensus clustering and least absolute shrinkage and selection operator (LASSO) regression, respectively. ESTIMATE algorithm and ssGSEA analysis were used to assess the tumor microenvironment and immune penetrance between the two groups. A comprehensive review of gene ontology (GO) and KEGG analyses revealed inherent biological functional differences between the two groups. Results The consistent clustering showed stratification of osteosarcoma patients into two subtypes based on RAS-associated genes and provided a robust prediction of prognosis. A risk model further confirmed that RAS-related genes are the best prognostic indicators for OS patients. GO analysis showed that GDP/GTP binding, focal adhesion, cytoskeletal motor activity, and cell-matrix junctions were associated with the RAS-related model group. Furthermore, RAS signaling in osteosarcoma based on KEGG analysis was significantly associated with cancer progression, with immune function and tumor microenvironment particularly affected. Conclusion We constructed a prognostic model founded on RAS-related gene and demonstrated its predictive ability. Then, furtherly exploration of the molecular mechanisms and immune characteristics proved the role of RAS-related gene in the dysregulation in OS.
Covertness and Secrecy Study in Untrusted Relay-Assisted D2D Networks

This article investigates the covertness and secrecy of wireless communications in an untrusted relay-assisted device-to-device (D2D) network consisting of a full-duplex base station (BS), a user equipment (UE), and an untrusted relay ${R}$ . For the covertness, we attempt to prevent Willie from detecting the very existence of communications via a D2D link from UE to R and cellular link from R to BS, while for the secrecy, we aim to prevent the untrusted relay from eavesdropping the UE message. To explore the fundamental covertness and secrecy in such a network, we first provide theoretical modelings for the average minimum detection error rate of Willie, and the average covert/secrecy rate from UE to BS under the underlay and overlay modes, respectively. Based on these models, th we further explore the optimal power control at UE, R, and BS to achieve the average covert rate maximization (MCR) for UE with the constraints of covertness and security requirements under the underlay mode. We also identify the optimal transmit powers and the optimal spectrum partition factor for MCR under the overlay mode. Finally, the exhaust searching method is adopted to solve the MCR problems, and extensive numerical and simulation results are presented to validate our theoretical analysis and to illustrate the average covert rate and secrecy rate of UE under various scenarios.
Scaling Multimodal Planning: Using Experience and Informing Discrete Search

Robotic manipulation is inherently continuous, but typically has an underlying discrete structure, such as if an object is grasped. Many problems like these are multimodal, such as pick-and-place tasks where every object grasp and placement is a mode. Multimodal problems require finding a sequence of transitions between modes—for example, a particular sequence of object picks and placements. However, many multimodal planners fail to scale when motion planning is difficult (e.g., in clutter) or the task has a long horizon (e.g., rearrangement). This work presents solutions for multimodal scalability in both these areas. For motion planning, we present an experience-based planning framework alef which reuses experience from similar modes both online and from training data. For task satisfaction, we present a layered planning approach that uses a discrete lead to bias search toward useful mode transitions, informed by weights over mode transitions. Together, these contributions enable multimodal planners to tackle complex manipulation tasks that were previously infeasible or inefficient, and provide significant improvements in scenes with high-dimensional robots.
3‐13 GHz Q‐enhanced low noise amplifier with optimized inductors utilizing in‐phase current returning path technique

In this letter, a 3‐13 GHz UWB low noise amplifier (LNA) with high power gain is reported using a standard 130‐nm CMOS. In the first stage, Q‐enhanced inductors are proposed by using an in‐phase current returning path (CRP) technique. Compared to traditional ground ring CRP and anti‐phase CRP, the quality factor of the inductor is improved by decreasing the mutual inductance between the spiral coil and CRP. As a result, the proposed LNA achieves a power gain (S21) of 22.5 ± 1.5 dB from 3.4 to 13.2 GHz while combining with the inductive peaking technology. In addition, an average noise figure of 3 dB is achieved within an ultra‐wide band up to 17 GHz. The UWB amplifier also achieves an in‐band IIP3 of −16.8 dBm and a DC power of 20.5 mW at 1.2 V supply. It occupies an area of 0.82 × 0.71 mm2 including pads.
Facts of Vertigo in Adolescents: Controversies and Challenges – A Narrative Review

Vertigo is a common complaint in the general population affecting 5% of adults in one year. At least 29.5% of adults have referred vertigo during life. Even though the prevalence of vertigo is well known in adults the epidemiologic data in adolescents is sparse. To date, it is known that adolescent females are usually affected by vertigo and some conditions such as depression and anxiety are found in this population. However, the lack of information about the prevalence of most common types of vertigo in adolescents, predisposing factors, challenges, and controversies in clinics in the literature, present a challenge for clinicians regarding the approach and follow-up of this population. Herein, we performed a literature review including data about the prevalence, most common types of vertigo and controversial events in the approach of vertigo in adolescents over the last two decades.
Gray Matter Abnormalities in Patients with Complex Regional Pain Syndrome: A Systematic Review and Meta-Analysis of Voxel-Based Morphometry Studies

Current findings on brain structural alterations in complex regional pain syndrome (CRPS) are heterogenous and controversial. This study aimed to perform a systematic review and meta-analysis to explore the significant gray matter volume (GMV) abnormalities between patients with CRPS and healthy controls (HCs). A systematic search of the PubMed, Web of Science, and MEDLINE databases was performed, updated through 27 January 2022. A total of five studies (93 CRPS patients and 106 HCs) were included. Peak coordinates and effect sizes were extracted and meta-analyzed by anisotropic effect size–signed differential mapping (AES-SDM). Heterogeneity, sensitivity, and publication bias of the main results were checked by the Q test, jackknife analysis, and the Egger test, respectively. Meta-regression analysis was performed to explore the potential impact of risk factors on GMV alterations in patients with CRPS. The main analysis exhibited that patients with CRPS had increased GMV in the left medial superior frontal gyrus (SFGmedial.L), left striatum, and an undefined area (2, 0, −8) that may be in hypothalamus, as well as decreased GMV in the corpus callosum (CC) (extending to right supplementary motor area (SMA.R), right median cingulate/paracingulate gyri (MCC.R)), and an undefined area (extending to the right caudate nucleus (CAU.R), and right thalamus (THA.R)). Meta-regression analysis showed a negative relationship between increased GMV in the SFGmedial.L and disease duration, and the percentage of female patients with CRPS. Brain structure abnormalities in the sensorimotor regions (e.g., SFGmedial.L, SMA.R, CAU.R, MCC.R, and THA.R) may be susceptible in patients with CRPS. Additionally, sex differences and disease duration may have a negative effect on the increased GMV in SFGmedial.L.