Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating genetic and biophysical approaches to estimate connectivity in an isolated, insular system: case of the culturally important marine gastropod Monetaria caputdraconis

Abstract Population connectivity has a fundamental role in metapopulation dynamics with important implications for population persistence in space and time. Oceanic islands, such as Easter Island (EI) and the Salas & Gómez Island (SG), are ideal for the study of population connectivity because they are separated by 415 km and isolated from other islands in the Pacific Ocean by >2000 km. Considering that the dispersal process could play a critical role in the persistence of their populations, we evaluated the connectivity pattern of the endemic gastropod Monetaria caputdraconis between EI and SG using population genetics and biophysical modelling. Eleven microsatellite loci did not show differences in the allelic frequency of individuals located in EI and SG, suggesting the presence of one genetic population. Historical reciprocal migration implies that 0.49% of the recruits in EI come from SG and 0.37% in SG come from EI. Considering year-round larval release and a larval development of 2 weeks in the plankton, a Lagrangian experiment based on a regional oceanic simulation indicated a weak population connectivity with a high rate of self-recruitment. Interestingly, self-recruitment showed both monthly and interannual variation ranging from 1 to 45% of returned larvae, with lower values estimated in SG compared to EI. The results suggest that few larvae/individuals arrive at each other's island, possibly due to stochastic events, such as rafting. Overall, our results indicate that both islands maintain population connectivity despite their distance; these findings have implications for designing conservation strategies in this region.
Berberine suppressed sarcopenia insulin resistance through SIRT1-mediated mitophagy

Abstract Abnormal mitochondrial function resulting in inadequate energy supply leads to sarcopenia and IR, suggesting that maintaining mitochondrial homeostasis by regulating mitophagy may be a promising strategy for sarcopenia IR therapy. Herein, we constructed sarcopenia mice model, which was treated with berberine and/or SIRT1/mitophagy inhibitors, and the activity of SIRT1/mitophagy signaling pathway was identified. Then, muscle tissue, blood biochemical index, inflammatory factors, GTT, and ITT were detected. We found that berberine treatment increased the body weight and alleviated d-galactose-induced weight loss in mice. SIRT1/mitophagy inhibitors suppressed the effects of berberine in the treatment of sarcopenia. The effect of berberine on the increase of muscle tissue, improving metabolic disorders, reducing the expression of inflammatory factors, and suppressing sarcopenia insulin resistance (IR) were reversed by SIRT1/mitophagy inhibitors. Our study establishes proof-of-concept to distinct the effect of berberine in sarcopenia IR, and provides strong evidence to support the hypothesis that berberine-induced SIRT1 triggers mitochondrial autophagy pathway and suppresses IR in sarcopenia.
Parasitic gap patterns and hierarchy preservation in German

Abstract This paper discusses how German parasitic gap data from various earlier publications illustrate two patterns of systematic grammatical variation in the language, which have not been previously identified as such in the literature. I show how Heck and Himmelreich’s (Heck, Fabian & Anke Himmelreich. 2017. Opaque intervention. Linguistic Inquiry 48. 47–97) analysis for one pattern, although not able to currently capture both patterns, can be extended by allowing for variation in the positions targeted by scrambling along with the phrase markers that constitute domains for linearization. The resulting unifying analysis highlights how different grammatical mechanisms can in various ways (both local and global) have the effect of preserving the hierarchical relations involved in multiple movement dependencies.
A multilayered approach to the analysis of genetic data from individuals with suspected albinism

Albinism is a clinically and genetically heterogeneous group of conditions characterised by visual abnormalities and variable degrees of hypopigmentation. Multiple studies have demonstrated the clinical utility of genetic investigations in individuals with suspected albinism. Despite this, the variation in the provision of genetic testing for albinism remains significant. One key issue is the lack of a standardised approach to the analysis of genomic data from affected individuals. For example, there is variation in how different clinical genetic laboratories approach genotypes that involve incompletely penetrant alleles, including the common, ‘hypomorphic’ TYR c.1205G>A (p.Arg402Gln) [rs1126809] variant. Here, we discuss the value of genetic testing as a frontline diagnostic tool in individuals with features of albinism and propose a practice pattern for the analysis of genomic data from affected families.
On particle Gibbs samplers

Cet article analyse une classe de methodes de Monte Carlo avancees de type particulaire introduites par Andrieu, Doucet, et Holenstein (J. R. Stat. Soc. Ser. B Stat. Methodol. 72 (2010) 1–269). Nous presentons une interpretation naturelle de ces methodes en termes de mesures de Feynman–Kac particulaires non biaisees classiques et d’une nouvelle formule de dualite entre modeles de Feynman–Kac. Ce cadre d’etude apporte un nouvel eclairage sur les fondations et l’analyse mathematique de ces methodes. Une consequence importante est l’equivalence de ces dernieres avec la methode d’echantillonnage de Gibbs d’une distribution de Feynman–Kac multi-corps. Notre etude developpe aussi un nouveau calcul differentiel stochastique fonde sur des techniques geometriques et combinatoires. Ces techniques permettent d’obtenir des developpements non asymptotiques des semigroupes de modeles de Monte Carlo par Chaines de Markov particulaires autour de leur mesure invariante, en fonction de la taille des systemes de particules en interaction auxiliaires. Cette analyse conduit a des estimations quantitatives precises de la convergence a l’equilibre de ces modeles par rapport a l’horizon temporel et la taille des systemes. Nous illustrons ces resultats avec quelques implications directes, notamment l’estimation precise des coefficients de contraction et des exposants de Lyapunov de ces algorithmes de simulation, ainsi que l’estimation fine de l’erreur en norme $\mathbb{L}_{p}$ entre la loi des etats aleatoires de ces chaines de Markov et leur mesure d’equilibre. Le cadre abstrait de l’article permet d’elaborer et d’etendre de facon naturelle ces methodes a des classes d’algorithmes fondes sur des evolutions d’ilots particulaires (aussi connus sous le nom $\mathrm{SMC}^{2}$). Nous montrons enfin comment ce cadre general et les resultats de l’article s’appliquent a l’etude de problemes de filtrage non lineaire, l’estimation de parametres fixes dans des modeles de chaines de Markov cachees, et dans des problemes d’integration trajectorielle rencontres en physique quantique et en chimie moleculaire.
Single top-quark production cross-section measurement in association with Z boson and performance studies of the RPCs with the CMS detector using 8 TeV pp collisions at the LHC

In this thesis, the measurement of the tqZ production cross-section for both the inclusive and exclusive cases are reported. The top quark being the heaviest particle in the Standard Model (SM) has been discovered about two decades ago but many of its properties have still not been fully investigated. The production cross-section of single top quark in association with a Z boson is an unmeasured rare SM process and also an irreducible background to many important searches at the Large Hadron Collider (LHC). The process is studied by using the three nal state leptons (electrons, muons) for an integrated luminosity of 19.7 fb−1 recorded by the Compact Muon Solenoid (CMS) detector at the LHC using 8 TeV center-of-mass energy of proton-proton (pp) collisions. The predicted cross-section times branching ratio for this process is 0.008±0.001 pb at the Next-to-Leading Order (NLO) in Quantum Chromodynamics (QCD), which is comparable to the measured cross-section σtqZ = 0.018 (stat.) +0.011 −0.009 (Syst.) +0.004 −0.004 pb. The signi cance of the tqZ inclusive cross-section as well as the exclusive one for all the leptonic combinations is also presented. The observed signi cance of inclusive channel is reported to be 1.80, which is consistent to the predicted value. The Resistive Plate Chamber (RPC) is a dedicated trigger detector at the CMS experiment. The RPC has a signi cant contribution in the trigger decisions and reconstruction of muon trajectories in the detector. In this thesis, the stability and performance of the RPC detector is also discussed for the 2012 data-taking period. The detector stability is studied through the high voltage scans to determine the optimal operating working voltage for each individual RPC chamber installed in the CMS experiment. The RPC performance is found to be very stable and almost caused no interruption in the CMS data-taking during 8 TeV pp collisions. The CMS down time due to the RPC was less than 1.5 %, and the fraction of active channels was about 97.5% during the 2012 data-taking period. The background radiation measurements at the RPC remained within the expectation values and no signi cant issues were observed at the nominal luminosity. Over the last few years of the CMS data-taking, the performance of the RPC detectors both as a triggering and reconstruction system remained well within the CMS speci cation.
Adaptive fault‐tolerant control for a nonlinear flexible aircraft wing system

Fault‐tolerant control problems have been extensively studied in all kinds of control systems. However, there is little work on fault‐tolerant control for distributed parameter systems. In this paper, a novel adaptive fault‐tolerant boundary control scheme is proposed for a nonlinear flexible aircraft wing system against actuator faults. The whole system is regarded as a distributed parameter system, and the dynamic model of the flexible wing system is described by a set of partial differential equations (PDEs) and ordinary differential equations (ODEs). The proposed controller is designed by using the Lyapunov's direct method and adaptive control strategies. Based on the online estimation of actuator faults, the adaptive controller parameters can update automatically to compensate the actuator faults of the system. Besides, a fault‐tolerant controller is also developed for this system in the presence of external disturbances. Differing from existing works about adaptive fault‐tolerant control, the adaptive controller presented in this paper is designed for a distributed parameter system. Finally, numerical simulations are carried out to illustrate the effectiveness of the proposed control scheme.
“We will not be afraid to share who we are”: Black Teachers’ Experiences with Antiblackness during a Global Pandemic

Abstract:This study shares the stories of Black Bay Area teachers during the 2021–2022 school year--the year of the return to in-person teaching. Drawing primarily on the analysis of semi-structured interviews with 30 teachers from across the Bay Area, I use Critical Race Theory and BlackCrit to examine Black teachers' experiences with antiblackness during the global pandemic. Analysis of the data suggests that the COVID-19 pandemic exacerbated issues of equitable access for their students and their job salary sustainability. However, teachers named that their continued experiences with antiblackness and their disproportionate workloads compared to their non-Black colleagues that they were experiencing before the pandemic as their most pressing concerns regarding teacher retention.
Impact that Transcends Proximity: Black College Students Traversing Multiple Pandemics

Abstract:While there have been several Black joys reflected in mainstream media (e. g., election of the first Black president and Black/Asian vice-president), Black college students also witness the mainstream visibility of anti-Black violence through police brutality, inequitable health outcomes, and political rhetoric denouncing racial/social justice movements (e.g., Black Lives Matter, overturning of Roe v. Wade). Within this qualitative study, 45 Black undergraduate and graduate students discussed how they made meaning of contemporary racial issues in U.S. society, particularly within the global COVID-19 health pandemic, and how they formed ideologies of their Black identity on their campuses. This study utilized Black Critical Theory (BlackCrit) to elucidate how anti-Blackness impacts institutional practices, policies, and actions.
Development and evaluation of a fast fabrication process-based 3D printed strain gauge sensors

Abstract The prevailing use of sensing technologies has facilitated advanced research on the fabrication of sensors for various applications. As the demand for sensor technology continues to rise, there is also an increased need for fabrication methods that are inexpensive and fit for use. Most conventional fabrication methods are not adaptive to rapid design and material change. Strain gauge sensors play a vital role in monitoring subtle mechanical deformations in rigid bodies. Additive manufacturing techniques can be utilized to easily prototype sensors that fit the application’s specific geometric and material needs. Also, modifications of design in terms of both shape and substrate can be implemented and tested using rapid prototyping. Within this study, the performance of 3D-printed meandering-line strain gauge sensors is investigated. A design of experiments (DoE) was developed, wherein the parameters of line width, thickness, and shape were varied and their impact on performance is analyzed.
Common Misspellings and Their Impact on Health Sciences Literature Search Results

Abstract This study examines the frequency of misspellings in health sciences literature and explores how they affect citation retrieval in multiple databases. Searches for commonly misspelled medical words were conducted in PubMed, CINAHL Complete, APA PsycArticles (ProQuest), APA PsycInfo, and ProQuest Psychology databases. Citations that would be retrieved using a word’s correct spelling were removed from the search results. Remaining results were citations that could only be retrieved if the word was misspelled in the search. Articles with clinical significance were targeted. The top five most commonly misspelled words were occurrence, ophthalmology, pruritus, sagittal, and resistance. Ophthalmology had the highest number of citations that contained at least one misspelling, with 57% of those citations “missing” when searched with the correct spelling of the word. The word with the highest percentage (82%) of missed citations was arrhythmia. The results of this study indicate that misspellings in scholarly literature are more prevalent than searchers might realize. The ability to retrieve citations is adversely affected by misspellings, which has the potential to affect patient care. Many opportunities exist in the editorial process to identify and correct misspellings before publication. This is less so once a journal is published. The implications for database searching and manuscript evaluation are discussed.
Dried Meat Products Obtained by Different Methods from Past to Present

ABSTRACT Dried meat products gain further attention worldwide thanks to features very similar to fresh meat products with a longer shelf life. Pastırma in Turkey, jerky in North America, carne-de-sol in Brazil, biltong in South Africa, kaddid in North Africa, and cecina in Spain exemplify dried meat products. In this work, the effect of various drying methods on drying characteristics and quality attributes of meat is summarized. The drying methods include sun or hot air drying, vacuum drying, ultrasonic drying, freeze-drying, cold drying, microwave drying, heat pump drying, pulsed electric field drying, and refractance window drying, etc. Meat drying through conventional approaches such as sun or hot air drying found a slow process and may lead to adverse effects, such as color darkening, shrinkage, loss of aroma and nutritional compounds, lipid oxidation together with a decrease in rehydration rate. In order to prevent these adverse effects, new and combined drying methods, oﬀering various superiorities over conventional ones, have been developed around the globe.
Turning Coffee Time into Teaching Moments Through Bite-Sized Learning for Adult Learners

Abstract The increasing number of adult learners in higher education is a trend that has been observed in recent years. An impetus for this trend is the need for upskilling and re-skilling in order to stay relevant and employable in today’s world of globalised knowledge. However, finding uninterrupted blocks of time to study in adult learners’ busy schedules may be challenging because of their many other commitments and responsibilities. Bite-sized learning, through the utilisation of micro-learning activities that learners can access anytime and anywhere on their mobile devices, is one method of helping adult learners better manage their studies and learn more effectively. Before adult educators put in the resources to develop bite-sized learning activities, it is nonetheless important to ascertain that adult learners would indeed utilise such provisions. This article discusses the results of a study conducted to investigate the utilisation of bite-sized learning by adult learners enrolled in a part-time undergraduate degree programme. Specifically, the study examined whether adult learners utilised bite-sized learning activities during pockets of “down time” in their busy schedules, especially during the daytime, since adult learners tend to study at night after work or over the weekends. The results showed that adult learners were keen to learn from bite-sized learning activities during these pockets of down time. Implications of the results and directions for future investigation, including the aptness of bite-sized learning in today’s COVID-19 situation, are also discussed.
Heading the orchestra of innovation: how firms align partners in ecosystems

ABSTRACT The ecosystem as a new and distinct organisational form is increasingly replacing the single firm as the unit of analysis and driving the way how firms innovate to the next level. The ultimate purpose of an ecosystem is the realisation of a joint, innovative, value proposition by the partners involved. For that, ecosystem partners need to be aligned towards the value proposition by the orchestrator. In order to do so, orchestrators are using inter-firm structures, the so-called alignment structure. Surprisingly, extant research falls short in showing how firms can use such structural approaches, which motivated our research question on how orchestrators can align the other ecosystem actors towards the joint value proposition by using inter-organisational structures. Our qualitative multi-case study with ten cases led to three distinct approaches for aligning ecosystem partners by using inter-firm structures. And this yields at least four main contributions: First, we show how ecosystems are structured and governed by illuminating how firms can use different structural approaches to align their partners. Second, we elucidate on how ecosystem orchestrators secure their position within the ecosystem and manage trade-offs between competition and collaboration as well as dependencies. We contribute to an understanding of how flows of knowledge and information are being managed within ecosystems. And, finally, we contribute to an understanding of how such aspects are being managed in a dynamic context over time.
Balancing the scales: Impact of irrigation and pathogen burden on potato blackleg disease and soil microbial communities

Understanding the interaction between environmental conditions, crop yields, and soil health is crucial for sustainable agriculture in a changing climate. Management practices to limit disease are a balancing act. For example, in potato production, dry conditions favour common scab (Streptomyces spp.) and wet conditions favour blackleg disease (Pectobacterium spp.). The exact mechanisms involved and how these link to changes in the soil microbiome is unclear. Our objectives were to test how irrigation management and bacterial pathogen load in potato seed stocks impact: i) crop yields; ii) disease development (blackleg/common scab); and iii) soil microbial community dynamics. We used stocks of seed potatoes with varying Pectobacterium levels (Jelly [high load], Jelly [low load], and Estima [Zero – no Pectobacterium]). Stocks were grown under four irrigation regimes that differed in the timing and level of watering. The soil microbial communities were profiled using amplicon sequencing at 50% plant emergence and harvest and advanced bioinformatic analyses were used to correlate microbes to treatments and disease symptoms. Irrigation increased blackleg symptoms in the plots planted with stocks with low and high levels of Pectobacterium (22-34%) but not in the zero stock (2-6%). Not irrigating increased common scab symptoms (2-5%) and reduced crop yields. Irrigation did not impact the composition of the soil microbiome, but planting stock with a high Pectobacterium burden resulted in an increased abundance of Planctomycetota, Anaerolinea, and Acidobacteria species within the microbiome. Ensemble quotient analysis highlighted Anaerolinea as highly associated with blackleg symptoms in the field. We conclude that Pectobacterium pathogen load within seed stocks could have more substantial effects on soil communities than irrigation regimes.
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments

Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the phylogenetic tree reconstruction based on the Maximum Likelihood method. In this study, we explored the utility of neural networks to predict the correct model of sequence evolution and the correct topology for four sequence alignments. We trained neural networks with different architectures using simulated nucleotide and amino acid sequence alignments for a wide range of evolutionary models, model parameters and branch lengths. By comparing the accuracy of model and topology prediction of the trained neural networks with Maximum Likelihood and Neighbour Joining methods, we show that for quartet trees, the neural network classifier outperforms the Neighbour Joining method and is in most cases as good as the Maximum Likelihood method to infer the best model of sequence evolution and the best tree topology. These results are consistent for nucleotide and amino acid sequence data. Furthermore, we found that neural network classifiers are much faster than the IQ-Tree implementation of the Maximum Likelihood method. Our results show that neural networks could become a true competitor for the Maximum Likelihood method in phylogenetic reconstructions.