Miracle-dz/UniFAR-FTUs huggingface dataset:
Biophysical characterization of the structure of a SARS-CoV-2 self-amplifying RNA (saRNA) vaccine

The current SARS-Covid-2 pandemic has led to an acceleration of messenger – ribonucleic acid (mRNA) vaccine technology. The development of production processes for these large mRNA molecules, especially self-amplifying mRNA (saRNA) has required concomitant development of analytical characterisation techniques. Characterising the purity, shape and structure of these biomolecules is key to their successful performance as drug products. This paper describes the biophysical characterisation of the Imperial College London Self-amplifying viral RNA vaccine (IMP-1) developed for SARS-CoV-2. A variety of analytical techniques have been used to characterise the IMP-1 RNA molecule. In this paper we use UV spectroscopy, dynamic light scattering (DLS), size-exclusion chromatography small angle scattering (SEC-SAXS) and circular dichroism (CD) to determine key biophysical attributes of IMP-1. Each technique provides important information about the concentration, size, shape, structure and purity of the molecule. Statement of significance This paper is highly significant as it provides a prescient biophysical characterisation of an efficacious Sars-Cov-2 vaccine self-amplifying (sa)RNA molecule. RNA vaccines have been a major scientific breakthrough of the Covid-19 pandemic. saRNA is a further development of conventional mRNA vaccines, amplifying the RNA of interest in the cell, allowing the vaccine to be administered at lower dosages. These new biologics are distinct from previous biologics and have required distinct analytical characterisation. The analytics described herein provide detailed information on the size, shape, and structure of the RNA molecule. This paper is therefore an important step in characterising large saRNA biological relevant molecules.
Predator-mediated interactions through changes in predator home range size can lead to local prey exclusion

The effects of indirect biotic interactions on species occurrence are difficult to quantify in the wild. In theory, the exclusion of a prey species can occur through the numerical and functional responses of a predator to another prey. Few studies assessed the relative effects of these responses on the net interaction strength between multiple prey sharing common predators, in part because empirically based multi-species functional response models are very rare. To investigate whether the presence of a prey species affects predation rates and population growth rate of another prey species, we used a multi-prey mechanistic model of predation along with a population matrix model. The predation model was parameterized using a combination of behavioral, demographic, and experimental data acquired in an arctic vertebrate community. It includes the arctic fox (Vulpes lagopus), a predator feeding primarily on small mammals as well as eggs of various bird species such as sandpipers and colonial nesting geese. Our results showed that the positive effects of the presence of a goose colony on sandpiper nesting success (due to the handling time of goose eggs by the predator) were outweighed by the negative effect of an increase in fox density. The numerical response of the arctic fox was driven by a reduction in home range size in the goose colony. As a result, the net interaction from the presence of geese was negative. Our results also showed that this interaction could lead to local exclusion of sandpipers over a range of adult sandpiper annual survival observed in the wild, which is coherent with previous observations of their co-distribution. Our approach takes into account diverse proximate mechanisms underpinning interaction strengths in a multi-prey system and generates novel insights on some of the predator behavioral responses that may influence prey coexistence (and the lack of) in vertebrate communities.
Complete controllability of finite quantum systems with twofold energy level degeneracy

Complete controllability of finite-dimensional quantum systems with energy level degeneracy is investigated using two different approaches. One approach is to apply a weak constant field to eliminate the degeneracy and then control it using techniques developed for non-degenerate quantum systems. Conditions for the elimination of degeneracy are found and the issues of influence of relaxation time of a constant external field on the target state are addressed through the fidelity. Another approach is to control the degenerate system by a single control field directly. It is found that the system with twofold degenerate excited states and non-degenerate ground state is completely controllable except for the two-level system. Conditions of complete controllability are found for both systems with different energy gaps and with equal energy gaps.
Microbial communities from weathered outcrops of a sulfide-rich ultramafic intrusion, and implications for mine waste management

The Duluth Complex, Northeastern Minnesota, contains sulfide-rich magmatic intrusions that, collectively, represent one of the world’s largest known economic deposits of copper, nickel, and platinum group elements (Cu-Ni-PGEs). Previous work showed that microbial communities associated with experimentally-weathered Duluth Complex waste rock and tailings were dominated by uncultivated taxa and other populations not typically associated with mine waste. However, those experiments were designed for kinetic testing and do not necessarily represent the conditions expected for reclaimed mine waste or long-term weathering in the environment. We therefore used 16S rRNA gene methods to characterize the microbial communities present on the surfaces of naturally-weathered and historically disturbed outcrops of Duluth Complex material, as well as a circumneutral seep draining a reclaimed blast pit. Rock surfaces were dominated by diverse uncultured Ktedonobacteria, Acetobacteria, and Actinobacteria while seeps were dominated by Proteobacteria, including Leptothrix spp. and Methylovulum spp. All samples had abundant algae and other phototrophs. These communities were distinct from previously-described microbial assemblages from experimentally-weathered Duluth Complex rocks, suggested different energy and nutrient resources in the reclaimed rocks, outcrops, and seeps. Sulfide mineral incubations performed with and without algae showed that photosynthetic microorganisms could have an inhibitory effect on some of the autotrophic populations from the site, resulting in slightly lower sulfate release and differences in the dominant microorganisms. The microbial assemblages from these weathered outcrops show how communities are expected to develop during natural weathering of sulfide-rich Duluth Complex rocks, and represent baseline data that could be used to evaluate the effectiveness of future reclamation of tailings and waste rock produced by large scale mining operations.
From uncertainty to adaptivity : multiscale edge detection and image segmentation

This thesis presents the research on two different tasks in computer vision: edge detection and image segmentation (including texture segmentation and motion field segmentation). The central issue of this thesis is the uncertainty of the joint space-frequency image analysis, which motivates the design of the adaptive multiscale/multiresolution schemes for edge detection and image segmentation. Edge detectors capture most of the local features in an image, including the object boundaries and the details of surface textures. Apart from these edge features, the region properties of surface textures and motion fields are also important for segmenting an image into disjoint regions. The major theoretical achievements of this thesis are twofold. First, a scale parameter for the local processing of an image (e.g. edge detection) is proposed. The corresponding edge behaviour in the scale space, referred to as Bounded Diffusion, is the basis of a multiscale edge detector where the scale is adjusted adaptively according to the local noise level. Second, an adaptive multiresolution clustering scheme is proposed for texture segmentation (referred to as Texture Focusing) and motion field segmentation. In this scheme, the central regions of homogeneous textures (motion fields) are analysed using coarse resolutions so as to achieve a better estimation of the textural content (optical flow), and the border region of a texture (motion field) is analysed using fine resolutions so as to achieve a better estimation of the boundary between textures (moving objects). Both of the above two achievements are the logical consequences of the uncertainty principle. Four algorithms, including a roof edge detector, a multiscale step edge detector, a texture segmentation scheme and a motion field segmentation scheme are proposed to address various aspects of edge detection and image segmentation. These algorithms have been implemented and extensively evaluated.
Comparison between ribosomal assembly and machine learning tools for microbial identification of organisms with different characteristics

Genome assembly tools are used to reconstruct genomic sequences from raw sequencing data, which are then used for identifying the organisms present in a metagenomic sample. More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification. We start out by evaluating several commonly used metagenomic assembly tools, including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, and PathRacer, and compare them against state-of-the art deep learning-based machine learning classification approaches represented by DNABERT and DeLUCS, in the context of two synthetic mock community datasets. Our analysis focuses on determining whether ensembling metagenome assembly tools with machine learning tools has the potential to improve identification performance relative to using the tools individually. We find that this is indeed the case, and analyze the level of effectiveness of potential tool ensembling for organisms with different characteristics (based on factors such as repetitiveness, genome size, and GC content). Author Summary Metagenomic studies focus on the challenging problem of identifying the presence and abundance of different species in a sample. This process typically involves the creation of digital reads from the sample which correspond to small parts of the genome sequence, and then have to be assembled together by a genome assembly tool. More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification, and how they might complement traditional bioinformatics approaches. We conduct experiments with two representative state-of-the-art machine learning approaches and six metagenomic assembly tools in the context of two synthetic datasets. We find that for organisms with certain characteristics (levels of repetitiveness, GC content, and genome size), ensembling metagenome assembly tools with machine learning tools has the potential to improve species identification performance relative to using the tools individually.
Biodiversity–ecosystem function relationships change in sign and magnitude across the Hill diversity spectrum

Motivated by accelerating anthropogenic extinctions, decades of biodiversity–ecosystem function (BEF) experiments show that ecosystem function declines with species loss from local communities. Yet, at the local scale, changes in species' total and relative abundances are more common than species loss. The consensus best biodiversity measures are Hill numbers, which use a scaling parameter, ℓ, to emphasize rarer versus more common species. Shifting that emphasis captures distinct, function-relevant biodiversity gradients beyond species richness. Here, we hypothesized that Hill numbers that emphasize rare species more than richness does may distinguish large, complex and presumably higher-functioning assemblages from smaller and simpler ones. In this study, we tested which values of ℓ produce the strongest BEF relationships in community datasets of ecosystem functions provided by wild, free-living organisms. We found that ℓ values that emphasized rare species more than richness does most often correlated most strongly with ecosystem functions. As emphasis shifted to more common species, BEF correlations were often weak and/or negative. We argue that unconventional Hill diversities that shift emphasis towards rarer species may be useful for describing biodiversity change, and that employing a wide spectrum of Hill numbers can clarify mechanisms underlying BEF relationships. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’.
Common brain areas for processing physiologically and socially ‘needed’ stimuli

We looked at the overlap between brain areas related to perception of physiologically and socially (non-physiological) needed stimuli and how they might regulate serotonin levels. First, we conducted separate ALE meta-analyses on published results pertaining to brain activation patterns when participants perceived food while hungry or water while thirsty, and social interactions while being excluded. This allowed us to identify common consistent brain activation patterns for physiological and social needed stimuli. We also looked at significant spatial association between the common network and serotonin receptor distribution. We found that regions within the mid-posterior insula, the anterior cingulate cortex and the caudate are at the intersection of physiological (hunger and thirst) and social (exclusion) aspects of ‘needing’. Furthermore, we found a significant positive spatial correlation between that common network and 5HT4 receptor among serotonin receptors. While this was the highest for serotonin receptors, it was not the highest of all receptors. Our study suggests there is a common brain pattern during the processing of physiologically and socially needed stimuli, and discusses their spatial association with serotonin receptors and its possible implication.
Cluster analysis of the signal curves in perfusion DCE-MRI datasets

Pathological studies show that tumors consist of different sub-regions with more homogeneous vascular properties during their growth. In addition, destroying tumor's blood supply is the target of most cancer therapies. Finding the sub-regions in the tissue of interest with similar perfusion patterns provides us with valuable information about tissue structure and angiogenesis. This information on cancer therapy, for example, can be used in monitoring the response of the cancer treatment to the drug. Cluster analysis of perfusion curves assays to find sub-regions with a similar perfusion pattern. The present work focuses on the cluster analysis of perfusion curves, measured by dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). The study, besides searching for the proper clustering method, follows two other major topics, the choice of an appropriate similarity measure, and determining the number of clusters. These three subjects are connected to each other in such a way that success in one direction will help solving the other problems. This work introduces a new similarity measure, parallelism measure (PM), for comparing the parallelism in the washout phase of the signal curves. Most of the previous works used the Euclidean distance as the measure of dissimilarity. However, the Euclidean distance does not take the patterns of the signal curves into account and therefore for comparing the signal curves is not sufficient. To combine the advantages of both measures a two-steps clustering is developed. The two-steps clustering uses two different similarity measures, the introduced PM measure and Euclidean distance in two consecutive steps. The results of two-steps clustering are compared with the results of other clustering methods. The two-steps clustering besides good performance has some other advantages. The granularity and the number of clusters are controlled by thresholds defined by considering the noise in signal curves. The method is easy to implement and is robust against noise. The focus of the work is mainly the cluster analysis of breast tumors in DCE-MRI datasets. The possibility to adopt the method for liver datasets is studied as well.
Pomegranate Flower Extract—The Health-Promoting Properties Optimized by Application of the Box–Behnken Design

Herbal raw materials with antidiabetic activity can be a valuable support to therapy. An optimized extraction process allows for the best possible health-promoting effect. Box–Behnken design was employed to optimize the content of methanol used in the extraction mixture, its time, and temperature. The aim of this study was to enhance the efficiency of the pomegranate flowers extraction process in order to obtain extracts with the highest enzyme inhibition power (α-amylase and α-glucosidase), which is important for the antidiabetic effect and the highest antioxidant activity (DPPH assay). In the Box–Behnken design model, the content of pelargonidin-3,5-glucoside–anthocyanin compound that is associated with antidiabetic activity was also optimized as a variable associated with the action profile of pomegranate flower extracts. The process optimization carried out in this study provides a basis for further research using the pomegranate flower extract with the most potent desired properties, essential for supporting diabetes treatment based on pomegranate flowers.
Acculturation, Work-Related Stressors, and Respective Coping Strategies among Male Indonesian Migrant Workers in the Manufacturing Industry in Taiwan: A Post-COVID Investigation

Globalization has prompted cross-cultural migration in search of employment opportunities, and poor adaptation during acculturation is widely known to cause additional psychosocial stress. Hence, the objective of this study was to investigate migrant workers’ perceptions of acculturation, identify work-related stressors, and understand the respective coping strategies among male Indonesians in the manufacturing industry, particularly during the COVID-19 pandemic. Fifteen workers were recruited and interviewed on their acculturation experiences. We found that the workers were susceptible to forced adaptation to stressful conditions relating to work organization, economic distress, interaction with the manpower agencies, language barriers, and so on. During the pandemic, workers experienced overload, economic hardship, suspended home visits, isolation, discrimination, and fear of cluster infection in the crowded dormitory. We also found that workers were able to adopt coping strategies by capitalizing on resources at the individual, institutional, and governmental policy levels to actively solve problems, increase emotional support, and fortify self-appraisals. The identified coping strategies could inform policy development to assist with positive adaptation and promote the well-being of the migrant worker population.
Navigating Work Career through Locus of Control and Job Satisfaction: The Mediation Role of Work Values Ethic

This study examines navigating work careers through self-concept (locus of control and work values ethic) and job satisfaction within the postindustrial work environment of the 21st century. Career construction theory conceptualizes one’s career as a process of responding to a changing environment through self-concepts to more actively construct their careers. The SARS-CoV-2 (COVID-19) pandemic further highlights the importance of individuals’ self-leading their work journeys. The study indicates that work values ethic is an important variable in further explaining the relationship between locus of control and job satisfaction in our sample. When the effect of work values ethic is removed, the association between locus of control and job satisfaction is insignificant. We expect this research to spur further efforts by individuals to improve their understanding of the intricacies among their intrapersonal traits, needs, and abilities to better navigate their work careers with application to newly defined workplaces as a result of COVID-19. Our results also inform the practice of career education and counseling.
Brillouin cooling in a linear waveguide

Brillouin scattering is not usually considered as a mechanism that can cause cooling of a material due to the thermodynamic dominance of Stokes scattering in most practical systems. However, it has been shown in experiments on resonators that net phonon annihilation through anti-Stokes Brillouin scattering can be enabled by means of a suitable set of optical and acoustic states. The cooling of traveling phonons in a linear waveguide, on the other hand, could lead to the exciting future prospect of manipulating unidirectional phonon fluxes and even the nonreciprocal transport of quantum information via phonons. In this work, we present an analysis of the conditions under which Brillouin cooling of phonons of both low and high group velocities may be achieved in a linear waveguide. We analyze the three-wave mixing interaction between the optical and traveling acoustic modes that participate in forward Brillouin scattering, and reveal the key regimes of operation for the process. Our calculations indicate that measurable cooling may occur in a system having phonons with spatial loss rate that is of the same order as the spatial optical loss rate. If the Brillouin gain in such a waveguide reaches the order of 105 m−1 W−1, appreciable cooling of phonon modes may be observed with modest pump power of a few mW.