Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterodonty and ontogenetic shift dynamics in the dentition of the tiger shark Galeocerdo cuvier (Chondrichthyes, Galeocerdidae)

The lifelong tooth replacement in elasmobranch fishes (sharks, rays and skates) has led to the assemblage of a great number of teeth from fossil and extant species, rendering tooth morphology an important character for taxonomic descriptions, analysing phylogenetic interrelationships and deciphering their evolutionary history (e.g. origination, divergence, extinction). Heterodonty (exhibition of different tooth morphologies) occurs in most elasmobranch species and has proven to be one of the main challenges for these analyses. Although numerous shark species are discovered and described every year, detailed descriptions of tooth morphologies and heterodonty patterns are lacking or are only insufficiently known for most species. Here, we use landmark‐based 2D geometric morphometrics on teeth of the tiger shark Galeocerdo cuvier to analyse and describe dental heterodonties among four different ontogenetic stages ranging from embryo to adult. Our results reveal rather gradual and subtle ontogenetic shape changes, mostly characterized by increasing size and complexity of the teeth. We furthermore provide the first comprehensive description of embryonic dental morphologies in tiger sharks. Also, tooth shapes of tiger sharks in different ontogenetic stages are re‐assessed and depicted in detail. Finally, multiple cases of tooth file reversal are described. This study, therefore, contributes to our knowledge of dental traits across ontogeny in the extant tiger shark G. cuvier and provides a baseline for further morphological and genetic studies on the dental variation in sharks. Therefore, it has the potential to assist elucidating the underlying developmental and evolutionary processes behind the vast dental diversity observed in elasmobranch fishes today and in deep time.
Exploring complex water stress–gross primary production relationships: Impact of climatic drivers, main effects, and interactive effects

The dominance of vapor pressure deficit (VPD) and soil water content (SWC) for plant water stress is still under debate. These two variables are strongly coupled and influenced by climatic drivers. The impacts of climatic drivers on the relationships between gross primary production (GPP) and water stress from VPD/SWC and the interaction between VPD and SWC are not fully understood. Here, applying statistical methods and extreme gradient boosting models—Shapley additive explanations framework to eddy‐covariance observations from the global FLUXNET2015 data set, we found that the VPD‐GPP relationship was strongly influenced by climatic interactions and that VPD was more important for plant water stress than SWC across most plant functional types when we removed the effect of main climatic drivers, e.g. air temperature, incoming shortwave radiation and wind speed. However, we found no evidence for a significant influence of elevated CO2 on stress alleviation, possibly because of the short duration of the records (approximately one decade). Additionally, the interactive effect between VPD and SWC differed from their individual effect. When SWC was high, the SHAP interaction value of SWC and VPD on GPP was decreased with increasing VPD, but when SWC was low, the trend was the opposite. Additionally, we revealed a threshold effect for VPD stress on GPP loss; above the threshold value, the stress on GPP was flattened off. Our results have important implications for independently identifying VPD and SWC limitations on plant productivity, which is meaningful for capturing the magnitude of ecosystem responses to water stress in dynamic global vegetation models.
The impact of COVID-19 lockdown on men having sex with men (MSM)

ABSTRACT Social distancing including lockdowns are acceptable measures to cope with the COVID-19 pandemic. In this cross sectional study, we surveyed the impact of these measurements on sexual behavior and pre-exposure prophylaxis for HIV (PrEP) use among MSM. A digital questionnaire was distributed via social media and geographically based meeting applications after the first lockdown (March–April 2020). 1194 MSM responded, 91.8% were HIV negative, 19.4% of them used PrEP regularly, and 8.2% were people living with HIV (PLWH). Median age was 34.5 years. 54.4% reported that they kept strictly social distancing guidelines. Low education, hazardous sexual behavior, moderate/severe, and depression predicted low compliance with social distancing guidelines. 66.7% reported a significant decrease in the number of casual sex partners. 55% of those who took PrEP reduced their PrEP intake. Many of the PLWH and PrEP users reduced their medical follow-up. In light of the continuous outbreak and the risk of further outbreaks in the future consideration should be given to provide continuing access to HIV and sexual health clinics.
Semiparametric empirical likelihood inference for abundance from one‐inflated capture–recapture data

Abundance estimation from capture–recapture data is of great importance in many disciplines. Analysis of capture–recapture data is often complicated by the existence of one‐inflation and heterogeneity problems. Simultaneously taking these issues into account, existing abundance estimation methods are usually constructed on the basis of conditional likelihood under one‐inflated zero‐truncated count models. However, the resulting Horvitz–Thompson‐type estimators may be unstable, and the resulting Wald‐type confidence intervals may exhibit severe undercoverage. In this paper, we propose a semiparametric empirical likelihood (EL) approach to abundance estimation under one‐inflated binomial and Poisson regression models. To facilitate the computation of the EL method, we develop an expectation‐maximization algorithm. We also propose a new score test for the existence of one‐inflation and prove its asymptotic normality. Our simulation studies indicate that compared with existing estimators, the proposed score test is more powerful and the maximum EL estimator has a smaller mean square error. The advantages of our approaches are further demonstrated by analyses of prinia data from Hong Kong and drug user data from Bangkok.
The story of rRNA expansion segments: Finding functionality amidst diversity

Expansion segments (ESs) are multinucleotide insertions present across phyla at specific conserved positions in eukaryotic rRNAs. ESs are generally absent in bacterial rRNAs with some exceptions, while the archaeal rRNAs have microexpansions at regions that coincide with those of eukaryotic ESs. Although there is an increasing prominence of ribosomes, especially the ribosomal proteins, in fine‐tuning gene expression through translation regulation, the role of rRNA ESs is relatively underexplored. While rRNAs have been established as the major catalytic hub in ribosome function, the presence of ESs widens their scope as a species‐specific regulatory hub of protein synthesis. In this comprehensive review, we have elaborately discussed the current understanding of the functional aspects of rRNA ESs of cytoplasmic eukaryotic ribosomes and discuss their past, present, and future.
Glucose-PEG2000-DSPE modified carbamazepine nano system alleviated cell apoptosis and oxidative stress in epilepsy

Abstract Investigations on the effects of newly constructed glucose-PEG2000-DSPE modified carbamazepine nano system on oxidative stress damage and cell apoptosis in epilepsy. The nano system was constructed by egg yolk lecithin, cholesterol, GLUPEG2000-DSPE, and carbamazepine as per the molar ratio of 95:20:5:6.35. The particle size, zeta potential, and release rate of carbamazepine was determined using a microscope and a microplate reader. The cells toxicity was detected for determine the optimal concentration of carbamazepine nano system. Cell uptake, cell apoptosis ratio and ROS level was determined by flow cytometry analysis. The in vivo studies were performed using male Wistar rats. H&E staining was employed for histological evaluation. Immunofluorescence was utilized for measure the expression level of GLUT1. ELISA assay was obtained for detecting the levels of SOD, GSH-Px and MDA. The results shown the average particle size was 108.57 ± 3.42 nm, and the mean zeta potential was −52.75 ± 1.48 mV. The modified carbamazepine liposomes exhibited higher release rate. Cell uptake indicated that carbamazepine nano system could be successfully internalized into cells. Flow cytometry analysis revealed the carbamazepine nano system dramatically decreased cell apoptosis rate and downregulated ROS level. Moreover, carbamazepine nano system improved histological status, increased GLUT1 expression and decreased oxidative stress in vivo. In conclusion, glucose-PEG2000-DSPE modified carbamazepine nano system ameliorated cell apoptosis and oxidative stress damage in epilepsy.
One-year surveillance of SARS-CoV-2 transmission of the ELISA cohort: A model for population-based monitoring of infection risk

With newly rising coronavirus disease 2019 (COVID-19) cases, important data gaps remain on (i) long-term dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rates in fixed cohorts (ii) identification of risk factors, and (iii) establishment of effective surveillance strategies. By polymerase chain reaction and antibody testing of 1% of the local population and >90,000 app-based datasets, the present study surveilled a catchment area of 300,000 inhabitants from March 2020 to February 2021. Cohort (56% female; mean age, 45.6 years) retention was 75 to 98%. Increased risk for seropositivity was detected in several high-exposure groups, especially nurses. Unreported infections dropped from 92 to 29% during the study. “Contact to COVID-19–affected” was the strongest risk factor, whereas public transportation, having children in school, or tourism did not affect infection rates. With the first SARS-CoV-2 cohort study, we provide a transferable model for effective surveillance, enabling monitoring of reinfection rates and increased preparedness for future pandemics.
Multiple Modal Constructions in Rural Chesapeake English and Mandarin Chinese

Multi-modal constructions, (also called `modal stacking') appear in a number of natural languages; most notably in the literature are Southern (or Rural) American English, Scots English, Caribbean Creoles, German, Dutch, and the Chinese languages. Cross-linguistically there is considerable variation in the number of modals allowable in the syntax, from simple double-modal constructions in Southern American English (Labov, 1972; Di Paolo, 1989; Hasty, 2012; Ellison, 2007 inter alia), to up to four projections in languages like Mandarin Chinese (Lin, 2012). The present study focusses on Southern English and Mandarin to answer long-standing theoretic questions about how multiple-modal constructions are derived, adopting a feature-geometric approach (Harley, 1994; Cowper, 2005 inter alia) to Distributed Morphology (Harley & Noyer, 2003). I posit that there are two distinct types of languages which exhibit multiple modality, exemplied by Mandarin and Southern English (respectively): those in which multiple-modality is a product of a recursive Modal Phrase, and those which are produced from a post-syntactic split of the INFL domain.
Exploring data practices of the earthquake engineering community

There is a need to compare and contrast data practices of different disciplines and groups. This study explores data practices in earthquake engineering (EE), an interdisciplinary field with a variety of research activities and dynamic data types and forms. Findings identify the activities of typical EE research projects, the types and forms of data produced and used in those activities, the project roles played by EE researchers in connection with data practices, the tools used to manage data in those activities, the types and sources of data quality problems in EE, and the perceptions of data quality in EE. A strong relation exists among these factors, with a stronger role for test specimens and high quality documentation and more blurring of project roles than in other fields. Suggestions are provided for resolving contradictions impeding EE researchers’ curation and archiving activities and for future research on data practices.
Building Information Modelling Maturity Matrix

Building Information Modelling (BIM) is an expanding collection of concepts and tools which have been attributed with transformative capabilities within the Architecture, Engineering, Construction and Operations (AECO) industry. BIM discussions have grown to accommodate increasing software capabilities, infinitely varied deliverables, and competing standards emanating from an abundance of overlapping definitions attempting to delineate the BIM term. This chapter will steer away from providing its own definition of BIM yet concurs with those identifying it as a catalyst for change (Bernstein, 2005) poised to reduce industry‟s fragmentation (CWIC, 2004), improve its efficiency (Hampson & Brandon, 2004) and lower its high costs of inadequate interoperability (NIST, 2004). In essence, BIM represents an array of possibilities and challenges which need to be understood and met respectively through a measurable and repeatable approach. This chapter briefly explores the multi-dimensional nature of the BIM domain and then introduces a knowledge tool to assist individuals, organisations and project teams to assess their BIM capability, maturity and improve their performance (Figure 1). The first section introduces BIM Fields and Stages which lay the foundations for measuring capability and maturity. Section 2 introduces BIM Competencies which can be used as active implementation steps or as performance assessment areas. Section 3 introduces an Organisational Hierarchy/Scale suitable for tailoring capability and maturity assessments according to markets, industries, disciplines and organisational sizes. Section 4 explores the concepts behind „capability maturity models‟ and then adopts a five-level BIMspecific Maturity Index (BIMMI). Section 5 introduces the BIM Maturity Matrix (BIm3), a performance measurement and improvement tool which identifies the correlation between BIM Stages, Competency Sets, Maturity Levels and Organisational Scales. Finally, Section 6 introduces a Competency Granularity Filter which enables the tailoring of BIM tools, guides and reports according to four different levels of assessment granularity. i Email: bsuccar@changeagents.com. au
Investigating the Impact of Using Cooperative Learning Strategies on Promoting Students’ Science Learning in Private Schools in UAE

I DEDICATION III ACKNOWLEDGEMENTS IV TABLE OF CONTENTS V CHAPTER ONE (INTRODUCTION) 1 1.1 Cooperative Learning (CL) 1 1.2 The Effects of Cooperative Learning Strategies in Science Education 3 1.3 Background of the Research 5 1.4 Statement of the Problem 7 1.5 Purpose of the Study 9 1.6 Scope of Work 10 1.7 Structure of Dissertation 10 CHAPTER TWO (LITERATURE REVIEW) 11 2.1 Theoretical Framework 11 2.2 The Pedagogy of Cooperative Learning 14 2.3 The Influences of Cooperative Learning on Effective Study Learning 16 2.4 The Benefits of Using Cooperative Learning Strategies 19
The Determinants of Lateness: Evidence from British Workers

AbstractUsing a sample of male and female workers from the 1992 Employment in Britainsurvey, we estimate a generalised grouped zero-inﬂated Poisson regression modelof employees’ self-reported lateness. Lateness is higher for males, private sectorworkers and in service industries. Reﬂecting theoretical predictions from bothpsychology and economics, we model lateness as a function of incentives, themonitoring of, and sanctions for, lateness within the workplace, job satisfaction andattitudes to work. Various aspects of workplace incentive and disciplinary policiesturn out to affect lateness; however, controlling for these, an important role for jobsatisfaction remains.I IntroductionLabour productivity depends in part on the commitment of workers.Commitment, or the lack thereof, drives worker behaviour in ways that canaffect output and hence ﬁrm proﬁtability. Recent research on worker behaviourhas studied absenteeism (Barmby et al., 1995), the intensity of work effort in anyhour on the job (Green, 2001) and quits (Clark, 2001). A relatively neglectedaspect of worker commitment is punctuality, yet it is clear that late arrival atwork imposes direct and indirect costs on the employer. In addition to the directcost of lost output and the knock-on effects of lateness in integrated productionsystems, late arrival can be a correlate of, or precursor to, shirking, absenteeismor turnover. Just as employers invest in personnel policies intended to reduceabsenteeism and turnover or to elicit additional worker effort, they will ﬁnd itproﬁtable to monitor and control employee lateness.In this paper, we provide statistical evidence from a large, representativesample of British employees, the Employment in Britain survey, on the deter-minants of late arrival at work. Our empirical model includes variables reﬂectingboth economic and psychological motivations to be late. In economics, thepreferences of individuals and the incentives and constraints they face governhow individuals use their time. Lateness is therefore expected to respond to
Valorization of Liquor Waste Derived Spent Coffee Grains for the Development of Injection-Molded Polylactide Pieces of Interest as Disposable Food Packaging and Serving Materials

The present work puts the Circular Bioeconomy’s concept into action, originally valorizing residues of spent coffee grains from the beverage liquor coffee industry to develop green composite pieces of polylactide (PLA). The as-received spent coffee grains were first milled to obtain the so-called spent coffee grounds (SCGs) that were, thereafter, incorporated at 20 wt. % into PLA by extrusion. Finally, the resultant green composite pellets were shaped into pieces by injection molding. Moreover, two oligomers of lactic acid (OLAs), namely OLA2 and OLA2mal, the latter being functionalized with maleic anhydride (MAH), were added with SCGs during the extrusion process at 10 wt. %. The results show that, opposite to most claims published in the literature of green composites of PLA, the incorporation of the liquor waste derived SCGs increased the ductility of the pieces by approximately 280% mainly due to their high lipid content. Moreover, the simultaneous addition of OLA2 and OLA2mal further contributed to improve the tensile strength of the green composite pieces by nearly 36% and 60%, respectively. The higher performance of OLA2mal was ascribed to the chemical interaction achieved between the biopolyester and the lignocellulosic fillers by the MAH groups. The resultant green composite pieces are very promising as disposable food-serving utensils and tableware.
Distributional Reinforcement Learning for Optimal Execution

When trading a ﬁnancial asset, large orders will often incur higher execution costs as the trader uses up the available liquidity. To reduce this eﬀect, orders are split and executed over a short period of time. Theoretical solutions for how to optimally split orders rely on models of market environments. These fail to take into account market idiosyncrasies and tend to oversimplify a complex optimisation problem. Deep Q learning provides a set of methodologies for learning an optimal solution from real experience. Successful application would allow models of the trading environment to be sidestepped in favour of direct interaction with the ﬁnancial markets. Deep Q learning has previously been applied to the problem of optimal execution and has shown promise, both in simulated environments and on historical data. In the last few years many improvements have been suggested for the vanilla deep Q learning algorithm. Distributional reinforcement learning in particular has shown to outperform value based deep Q learning on a selection of Atari games. Given the highly stochastic nature of the trading environment it is reasonable to assume that it would perform well for the problem of optimal execution.Inthefollowing work we will outline the principles behind distributional reinforcement learning and show that it can outperform value based deep Q learning for optimal execution. To the best of our knowledge this is the ﬁrst time distributional reinforcement learning has been used for optimal execution.
Potential Use of Edible Insects in Complementary Foods for Children: A Literature Review

Background: Childhood malnutrition is an important public health problem. Animal protein provides essential amino acids in a more adequate pattern than plant-based protein. However, the production of sufficient animal-sourced protein to feed the growing world population is a serious challenge. This review aims to explore the evidence on the use of edible insects as an alternative source of protein and micronutrients in complementary foods for children and their potential to address childhood malnutrition. Methods: Searches were conducted in two electronic databases PubMed and Cochrane. The reference lists of included studies were also searched. Results: Twelve studies were included in this review. All insect-enriched formulations (e.g., biscuits, cereals, porridge, paste, etc.) exceeded the daily recommended amount of protein and fat for children’s complementary foods and showed good acceptability. Only two studies assessed the efficacy of insect-enriched foods on nutritional indicators and found no effect on the reduction of stunting and wasting. However, one study found improvements in the haemoglobin levels and fewer cases of anaemia in the intervention group. Conclusions: Insect-enriched complementary foods for children are safe, acceptable and have the potential to tackle micronutrient deficiencies. More studies are needed to examine their effect on nutritional status in children.