Miracle-dz/UniFAR-FTUs huggingface dataset:
Peer Support as Part of Scaling-Up Integrated Care in Patients with Type 2 Diabetes and Arterial Hypertension at the Primary Healthcare Level: A Study Protocol

ABSTRACT Introduction Type 2 diabetes (T2D) and arterial hypertension (AH) are among the greatest challenges facing health systems worldwide and require comprehensive patient-centred care. The key to successful management in chronic patients is self-management support, which was found to be only weakly implemented in Slovenia. The aim of the study is to develop an evidence-based model of peer support for people with T2D and AH at the primary healthcare level in Slovenia, which could represent a potential solution for upgrading integrated care for these patients. Methods A prospective interventional, mixed-methods pilot study will begin by recruiting approximately 40 eligible people with T2D and AH through purposive sampling. The participants will receive structured training, led by a specialist nurse, to become trained peer supporters. Each will voluntarily share their knowledge and experience at monthly group meetings with up to 10 people with T2D and AH over a three-month period in the local community. Data will be collected through interviews and focus groups and questionnaires about socio-demographic and clinical data, knowledge about T2D and AH, participants’ quality of life, level of empowerment and acceptability of the intervention. Expected results The study will provide an evidence-based model for integrating peer support into the local community. It is expected that the intervention will prove feasible and acceptable with educational, psychosocial and behavioural benefits. Conclusion Peer support through empowerment of people with T2D and AH, family members and other informal caregivers in the local community could scale-up the integrated care continuum and contribute to sustainability of the healthcare system.
Darwin's log: a toy model of speciation and extinction

Stochastic models for the origin and extinction of species have been rather neglected in applied probability. As an alternative to modelling speciation and extinction as intrinsically random, I shall describe and show simulations of a rulebased model. This involves mathematical representations of notions such as genetic type of species, environmental niche, fitness of a species in a niche, and adaptation. There are underlying random mechanisms for changes of niche sizes and for disconnection and reconnection of geographical regions, and these ultimately drive the evolution of species. Other approaches to mathematical modelling of evolution are briefly mentioned.
Spatial and temporal localization of ultrasonic heating effect using cylindrical standing waves

Abstract The aim of this work was to develop a new method alternative to high intensity focused ultrasound for spatial and temporal localization of ultrasonic heating effects using cylindrical standing waves. Computer simulations and experimental validation of the standing cylindrical waves generation and the viscoelastic media heating inside a cylindrical piezoelectric transducer were carried out.
Linking B-factor and temperature-induced conformational transition

The crystallographic B-factor, also called temperature factor or Debye-Waller factor, has long been used as a surrogate for local protein flexibility. However, the use of the absolute B-factor as a probe for protein motion requires reproducibility and intervalidation against chemical and physical variables. Here we report the investigation of the thermal dependence of the crystallographic B-factor and its correlation with protein conformational changes. We solved the B-factor reproducibility issue at high resolution (1.5 Å) over a broad temperature range (100 K to 325 K) by protecting crystals with hydrocarbon grease during data collection. We found that the crystallographic protein conformation varies as a function of temperature. Further, the demonstrated that the thermal dependence of B-factor as a function of temperature were similar for all atoms (Cα, N-amide and side chains), without local variations, indicating lack of correlation between temperature-dependent conformational change and the B-factor. These data indicate a linear correlation of B-factor with temperature due to global rigid body motion.
Shortest paths synthesis for a car-like robot

This paper deals with the complete characterization of the shortest paths for a car-like robot. Previous works have shown that the search for a shortest path may be limited to a simple family of trajectories. Our work completes this study by providing a way to select inside this family an optimal path to link any two configurations. We combine the necessary conditions given by Pontryagin's maximum principle with a geometric reasoning. This approach enables us to complete the local information with a global analysis of different wave fronts. We construct a partition of the configuration space in regions where the same kind of path is optimal to reach the origin. In other words, we determine a shortest path synthesis by providing, at each point, an optimal control law to steer the robot to the origin.
Cortical changes during the learning of sequences of simultaneous finger presses

The cortical alterations underpinning the acquisition of motor skills remain debated. In this longitudinal study in younger adults, we acquired performance and neuroimaging (7T MRI) measures weekly over the course of 6 weeks to investigate neural changes associated with learning sequences of simultaneous finger presses executed with the non-dominant hand. Both the intervention group (n = 33) and the control group (n = 30) showed general performance improvements, but performance improved more and became more consistent for sequences that were intensively trained by the intervention group, relative to those that were not. Brain activity for trained sequences decreased compared with untrained sequences in the bilateral parietal and premotor cortices. No training-related changes in the primary sensorimotor areas were detected. The similarity of activation patterns between trained and untrained sequences decreased in secondary, but not primary, sensorimotor areas, while the similarity of the activation patterns between different trained sequences did not show reliable changes. Neither the variability of activation patterns across trials, nor the estimates of brain structure displayed practice-related changes that reached statistical significance. Overall, the main correlate of learning configural sequences was a reduction in brain activity in secondary motor areas.
A rapid, multiplex digital PCR assay for EGFR, KRAS, BRAF, ERBB2 variants and ALK, RET, ROS1, NTRK1 gene fusions in non-small cell lung cancer

Digital PCR (dPCR) is emerging as an ideal platform for the detection and tracking of genomic variants in cancer due to its high sensitivity and simple workflow. The growing number of clinically-actionable cancer biomarkers creates a need for fast, accessible methods that allow for dense information content and high accuracy. Here, we describe a proof-of-concept amplitude modulation based multiplex dPCR assay capable of detecting 12 single nucleotide and indel variants in EGFR, KRAS, BRAF, and ERBB2, 14 gene fusions in ALK, RET, ROS1, NTRK1, and MET exon 14 skipping present in non-small cell lung cancer (NSCLC). We also demonstrate the use of multi-spectral target signal encoding to improve the specificity of variant detection by reducing background noise up to 11-fold. The assay reported an overall 100% PPA and 98.5% NPA compared to a sequencing-based assay in a cohort of 62 human FFPE samples. In addition, the dPCR assay rescued actionable information in 10 samples that failed to sequence, highlighting the utility of a multiplexed digital assay as a potential reflex solution for challenging NSCLC samples.
Conservation genetics of the white‐bellied pangolin in West Africa: A story of lineage admixture, declining demography, and wide sourcing by urban bushmeat markets

During the last 40 years, the volumes of African pangolins feeding the illegal wildlife trade have dramatically increased. We conducted a conservation genetics survey of the most traded African species, the white bellied pangolin (WBP; Phataginus tricuspis), across three West African countries including Guinea, Côte d’Ivoire and Ghana. Our study combining mitochondrial DNA (mtDNA) sequencing and microsatellites genotyping is the first to reveal a global pattern of admixture between two of the six mitochondrial lineages as previously delimited within WBP. We found a signature of isolation-by-distance and a lack of population genetic structuring, supporting the idea that WBP may have underestimated dispersal abilities. Levels of genetic diversity were low compared to central African lineages, reinforcing the picture of genetic pauperization shared by West African WBP. We observed a 85-98% decline in the effective population size of WBP occurring c. 3200 to 400 ya, with current numbers (520–590) at the lower end of the conservative thresholds for minimum viable population size. The microsatellites markers were powerful enough to differentiate between individuals and identify replicated samples, confirming the utility of this approach in tracing the pangolin trade. Genetic diversity estimates confirmed that Yopougon, the main bushmeat market from Abidjan (Côte d’Ivoire), was fed by a large trade network as confirmed by vendors reporting 10 different sources situated 62-459 km away from the market. We conclude that WBP distributed in the Upper Guinean Block should be considered a single management unit of high conservation concern, as impacted by genetic diversity erosion, drastic decline in effective population size and wide range sourcing for feeding urban bushmeat markets. Given the genetic admixture pattern detected within WBP from West Africa, we advocate for a multi-locus strategy to trace the international trade of the species.
Asymmetric oligomerization state and sequence patterning can tune multiphase condensate miscibility

Endogenous biomolecular condensates, comprised of a multitude of proteins and RNAs, can organize into multiphasic structures, with compositionally-distinct phases. This multiphasic organization is generally understood to be critical for facilitating their proper biological function. However, the biophysical principles driving multiphase formation are not completely understood. Here, we utilize in vivo condensate reconstitution experiments and coarse-grained molecular simulations to investigate how oligomerization and sequence interactions modulate multiphase organization in biomolecular condensates. We demonstrate that increasing the oligomerization state of an intrinsically disordered protein region (IDR) results in enhanced immiscibility and multiphase formation. Interestingly, we found that oligomerization tunes the miscibility of IDRs in an asymmetric manner, with the effect being more pronounced when the IDR exhibiting stronger homotypic IDR interactions is oligomerized. Our findings suggest that oligomerization is a flexible biophysical mechanism which cells can exploit to tune the internal organization of biomolecular condensates and their associated biological functions.
Inhibition of SARS-CoV-2 3CLpro in vitro by chemically modified tyrosinase from Agaricus bisporus

Antiviral compounds are crucial to controlling the SARS-CoV-2 pandemic. Approved drugs have been tested for their efficacy against COVID-19, and new pharmaceuticals are being developed as a complementary tool to vaccines However, there are not any effective treatment against this disease yet. In this work, a cheap and fast purification method of natural tyrosinase from Agaricus bisporus fresh mushrooms was developed in order to evaluate the potential of this enzyme as a therapeutic protein by the inhibition of SARS-CoV-2 3CLpro protease activity in vitro. Tyrosinase showed a mild inhibition of 3CLpro of around 15%. Thus, different variants of this protein were synthesized through chemical modifications, covalently binding different tailor-made glycans and peptides to the amino terminal groups of the protein. These new tyrosinase conjugates were purified and characterized by circular dichroism and fluorescence spectroscopy analyses, and their stability under different conditions. Then all these tyrosinase conjugates were tested in 3CLpro protease inhibition. From them, the conjugate between tyrosinase and dextran-aspartic acid (6kDa) polymer showed the highest inhibition, with an IC50 of 2.5 μg/ml and IC90 of 5 μg/ml, results that highlight the potential use of modified tyrosinase as a therapeutic protein and opens the possibility of developing this and other enzymes as pharmaceutical drugs against diseases.
Long-term climatic stability drives accumulation and maintenance of divergent lineages in a temperate biodiversity hotspot

Aim Anthropogenic climate change is forecast to drive regional climate disruption and instability across the globe. This issue is likely to be exacerbated within biodiversity hotspots, both due to the greater potential for species loss but also to the possibility that endemic lineages might not have experienced significant climatic variation in the past, limiting their evolutionary potential to respond to rapid climate change. We assessed the role of climatic stability on the accumulation and persistence of lineages in an obligate freshwater fish group endemic to a biodiversity hotspot. Location Southwest Western Australia (SWWA). Taxa Western pygmy perch (Nannoperca vittata) and little pygmy perch (Nannoperca pygmaea). Methods We sampled 33 individuals from nine populations spanning the range of both study taxa to explore their phylogeographic history. Using a combination of genomic (ddRAD-seq) and environmental approaches, we investigated population divergence and phylogenetic relationships, delimited species and estimated changes in species distributions since the Pliocene. Results We identified two deep phylogroups comprising three divergent clusters, which showed no historical connectivity since the Pliocene. We conservatively suggest these represent three isolated species with additional intraspecific structure within one widespread species. All lineages showed long-term patterns of isolation and persistence owing to climatic stability. Main conclusions Our results highlighted the role of climatic stability in allowing the persistence of isolated lineages in the SWWA. This biodiversity hotspot is under compounding threat from ongoing climate change and habitat modification, which may further threaten previously undetected cryptic diversity across the region.
Underwater image transmission and blurred image restoration

Because light is scattered and absorbed by seawater, the images obtained underwater are always blurry after transmitted through a certain distance. The point spread function (PSF) and modulation transfer function (MTF) are important properties of seawater to predict underwater light propagation and underwater image quality. We describe the laboratory facilities to measure the PSF and MTF of water body by the image transmission method and the restoration methods of under- water blurred images. The measured results are given and compared with the theories. The blurred images of a bar target transmitted through different water paths are restored with the measured MTF using a Wiener filter. © 2001 Society of Photo-Optical Instrumentation Engineers.
Ab-initio density-functional lattice-dynamics studies of ice

We present the results of first-principles computational studies of the dynamical properties of hexagonal ice using both the ab-initio pseudopotential method and the full-potential augmented plane-wave method. Properties obtained using both the generalized gradient approximation (GGA) and the meta-GGA in density-functional theory are compared. The lattice-dynamical properties of the structures are obtained using a finite-difference evaluation of the dynamical matrix and force-constant matrix from atomic forces. Phonon dispersion is evaluated by the direct determination of the force-constant matrix in supercells derived from the primitive molecule unit cells with the assumption that force constants are zero beyond the second molecular nearest neighbors. The k-dependent phonon frequencies are then obtained from the force-constant matrix and dispersion relations, and the Brillouin-zone integrated density of states is evaluated. The importance of phonon dispersion in the various regions of the phonon spectra is then assessed and compared to existing neutron-scattering data. Frozen-phonon calculations are used to compare phonon frequencies evaluated in both the GGA and meta-GGA.
Prediction of The Diabetics Using The Deep Learning Based Artificial Memory Vector Network

An rise in blood sugar or a lack of insulin causes diabetes, one of the most prevalent, severe, and expensive illnesses worldwide today. If diabetes is not detected in time, it might have serious consequences for the health of the patient. The rise of diabetes has become a major problem for health and government agencies. Taking care of people's health without sacrificing their comfort is crucial, and preventing illness is a top responsibility. In this investigation, an accurate diagnosis and prognosis for the onset of diabetes mellitus were achieved by using an optimization and deep learning based approach. In this first step, error is eliminated from the dataset's input data by replicative derivative error normalization. Using a multivariate salp swarm optimization (MSSO) strategy, unique features may be retrieved from an artificial bound co-occurrence matrix (ABCM). Finally, artificial memory vector network (AMVN) classifier based on deep learning is used to classify/predict diabetes risk as part of the classification technique. In this research, genuine data from the University of California's website on Indians with diabetes were used for experimental purposes. When compared to other current mechanisms, the findings demonstrate the superior performance of the suggested technique in illness diagnosis, which reaches 99.8% accuracy in this investigation.
A Segmentation of Brain Tissue Using Transfer Learning

Gliomas, the most widely recognized sort of threatening cerebrum growth, are on the ascent and are progressively being identified at standard specialist visits. Attractive Reverberation Imaging (X-ray) is regularly utilized in the discovery and conclusion of cerebrum growths. Consequently, in the clinical space, there is a requirement for mechanized and exact division methods to decrease the weight of time and intricacy of errands. To beat this trouble, various Profound Learning techniques have been presented, including Convolutional Brain Organizations (CNN) and Completely Associated Organizations (FCN), which have shown empowering division results on various datasets. Ongoing examination has shown that FCNs like U-Net can outflank cutting edge strategies in division errands and can be adjusted to address a great many spaces. Here, we propose a change to a current exchange learning technique and test it on the Cerebrum Growth Division (Whelps) 2020 dataset, where it performs hardly better compared to the pattern.
ProProv: A Language and Graphical Tool for Specifying Data Provenance Policies

The Function-as-a-Service cloud computing paradigm has made large-scale application development convenient and efficient as developers no longer need to deploy or manage the necessary infrastructure themselves. However, as a consequence of this abstraction, developers lose insight into how their code is executed and data is processed. Cloud providers currently offer little to no assurance of the integrity of customer data. One approach to robust data integrity verification is the analysis of data provenance—logs that describe the causal history of data, applications, users, and non-person entities. This paper introduces ProProv, a new domain-specific language and graphical user interface for specifying policies over provenance metadata to automate provenance analyses.To evaluate the convenience and usability of the new ProProv interface, 61 individuals were recruited to construct provenance policies using both ProProv and the popular, general-purpose policy specification language Rego—used as a baseline for comparison. We found that, compared to Rego, the ProProv interface greatly increased the number of policies successfully constructed, improved the time taken to construct those policies, and reduced the failed-attempt rate. Participants successfully constructed 73% of the requested policies using ProProv, compared to 41% using Rego. To further evaluate the usability of the tools, participants were given a 10-question questionnaire measured using the System Usability Scale (SUS). The median SUS score for the graphical ProProv interface was above average and fell into the “excellent” category, compared to below average and “OK” for Rego. These results highlight the impacts that graphical domain-specific tools can have on the accuracy and speed of policy construction.
Emotion Detection in Multimedia Data Using Convolution Neural Network

Emotions is the way of understanding the human action mainly those conveyed with facial expression. We understand only one third of other people feeling through their words and tone of voice and rest two third comes from the facial expression. Emotion detection is carried out using deep learning approaches. Surveying social media content through text, speech, and images and video should be essential in various uses. Emotion recognition can be applied to images, videos, text and written content. In this paper we have applied emotion recognition to video as it carries human face in motion and gives more information on human feelings. In this paper deep learning techniques are used to recognise human emotions in stored video. We have considered the Convolutional Neural Networks (CNN) to forecast the different emotions namely anger, surprise, happiness and neutral portrayed in a stored video. Convolution Neural Network applied to emotion detection has provided promising results. Emotion recognition technology can be applied to activities like market research and can be beneficial for security purpose utilizing CCTVs to catch the behavior of some unspecified person and forecast either he/she is suspicious or not.