Miracle-dz/UniFAR-FTUs huggingface dataset:
Recombinant expression and characterisation of a lipase from the Antarctic zooplankton Salpa thompsoni

Cold marine environments are abundant on earth and represent a rich resource for low temperature enzymes. Here we apply in silico bioprospecting methods followed by in vitro expression and biochemical analyses to characterise a novel low temperature lipase from the Antarctic tunicate Salpa thompsoni. A 586 amino acid pancreatic lipase-like gene was identified from S. thompsoni transcriptomic data, expressed as a hexahistadine fusion protein in Escherichia coli at 10°C and purified by affinity chromatography. Hydrolysis of the synthetic substrate ρ-nitrophenyl butyrate (PNPB) showed that this recombinant protein has optimal activity at 20 °C and pH 7, and a specific activity of 3.16 U/mg under this condition. Over 60% of enzyme activity was maintained between 15 to 25 °C, with a sharp decrease outside this range. These results are indicative of cold active psychrophilic enzyme activity. A meta-analysis of lipase activities towards PNPB showed that the novel S. thompsoni lipase displays a higher activity at lower temperatures relative to previously characterised enzymes. The work demonstrates a methodology for conversion of transcriptomic to in vitro expression data for the discovery of new cold-active biocatalysts from marine organisms.
CAGEE: Computational Analysis of Gene Expression Evolution

Despite the increasing abundance of whole transcriptome data, few methods are available to analyze global gene expression across phylogenies. Here, we present a new software package (CAGEE) for inferring patterns of increases and decreases in gene expression across a phylogenetic tree, as well as the rate at which these changes occur. In contrast to previous methods that treat each gene independently, CAGEE can calculate genome-wide rates of gene expression, along with ancestral states for each gene. The statistical approach developed here makes it possible to infer lineage-specific shifts in rates of evolution across the genome, in addition to possible differences in rates among multiple tissues sampled from the same species. We demonstrate the accuracy and robustness of our method on simulated data, and apply it to a dataset of ovule gene expression collected from multiple self-compatible and self-incompatible species in the genus Solanum to test hypotheses about the evolutionary forces acting during mating system shifts. These comparisons allow us to highlight the power of CAGEE, demonstrating its utility for use in any empirical system and for the analysis of most morphological traits. Our software is available at https://github.com/hahnlab/CAGEE/.
Sounding rocket instrument development at Univ. of Alabama in Huntsville/NASA MSFC

We present an overview of solar sounding rocket instruments developed jointly by NASA Marshall Space Flight Center and the University of Alabama in Huntsville. The High Resolution Coronal Imager (Hi-C) is an EUV (19.3 nm) imaging telescope which was flown successfully in July 2012. The Chromospheric Lyman-Alpha SpectroPolarimeter (CLASP) is a Lyman Alpha (121.6 nm) spectropolarimeter developed jointly with the National Astronomical Observatory of Japan and scheduled for launch in 2015. The Marshall Grazing Incidence X-ray Spectrograph is a soft X-ray (0.5-1.2 keV) stigmatic spectrograph designed to achieve 5 arcsecond spatial resolution along the slit.
Polymorphic regions in BA.2.12.1, BA.4 and BA.5 likely implicated in immunological evasion of Omicron subvariant BQ.1.1

In this work, 45 Spike glycoprotein Chain B polypeptides were used in the subvariants BA.2. 12.1, BA.4 and BA.5 were recovered from GENBANK. All sequences were publicly available on the National Biotechnology Information Center (NCBI) platform. The results indicate the existence of informative polymorphic and parsimony sites that may be implicated in the level of diversity of the studied strains, as well as reflect the immunological evasion potential of the subvariant BQ1.1. of the variant Ômicron d and SARS-CoV-2. The results also suggest the formation of ancestral polymorphism with slight retention, and the probable is responsible the diversity of the whole studied set.
Hungry for compliments? Ghrelin is not associated with neural responses to social rewards or their pleasantness

The stomach-derived hormone ghrelin motivates food search and stimulates food consumption, with highest plasma concentrations before a meal and lowest shortly after. However, ghrelin also appears to affect the value of non-food rewards such as interaction with rat conspecifics, and monetary rewards in humans. The present pre-registered study investigated how nutritional state and ghrelin concentrations are related to the subjective and neural responses to social and non-social rewards. In a cross-over feed-and-fast design, 67 healthy volunteers (20 women) underwent functional magnetic resonance imaging (fMRI) in a hungry state and after a meal with repeated plasma ghrelin measurements. In task 1, participants received social rewards in the form of approving expert feedback, or non-social computer reward. In task 2, participants rated the pleasantness of compliments and neutral statements. Nutritional state and ghrelin concentrations did not affect the response to social reward in task 1. In contrast, ventromedial prefrontal cortical activation to non-social rewards was reduced when the meal strongly suppressed ghrelin. In task 2, fasting increased activation in the right ventral striatum during all statements, but ghrelin concentrations were neither associated with brain activation nor with experienced pleasantness. Complementary Bayesian analyses provided moderate evidence for a lack of correlation between ghrelin concentrations and behavioral and neural responses to social rewards, but moderate evidence for an association between ghrelin and non-social rewards. This suggests that ghrelin’s influence may be restricted to non-social rewards. Social rewards implemented via social recognition and affirmation may be too abstract and complex to be susceptible to ghrelin’s influence. In contrast, the non-social reward was associated with the expectation of a material object that was handed out after the experiment. This may indicate that ghrelin might be involved in anticipatory rather than consummatory phases of reward.
Carousel: Improving the Accuracy of Virtual Reality Assessments for Inspection Training Tasks

Training simulations in virtual reality (VR) have become a focal point of both research and development due to allowing users to familiarize themselves with procedures and tasks without needing physical objects to interact with or needing to be physically present. However, the increasing popularity of VR training paradigms raises the question: Are VR-based training assessments accurate? Many VR training programs, particularly those focused on inspection tasks, employ simple pass or fail assessments. However, these types of assessments do not necessarily reflect the user’s knowledge. In this paper, we present Carousel, a novel VR-based assessment method that requires users to actively employ their training knowledge by considering all relevant scenarios during assessments. We also present a within-subject user study that compares the accuracy of our new Carousel method to a conventional pass or fail method for a series of virtual object inspection tasks involving shapes and colors. The results of our study indicate that the Carousel method affords significantly more-accurate assessments of a user’s knowledge than the binary-choice method.
Comprehension of Antimicrobial Peptides Modulation of the Type VI Secretion System in Vibrio cholerae

The Type VI secretion System (T6SS) is a versatile weapon used by bacteria for virulence, resistance to grazing and competition with other bacteria. We previously demonstrated that the role of the T6SS in interbacterial competition and in resistance to grazing is enhanced in Vibrio cholerae in the presence of subinhibitory concentrations of polymyxin B (PmB). In this study, we performed a global quantitative proteomic analysis by liquid chromatography coupled to mass spectrometry and a transcriptomic analysis by quantitative PCR of the T6SS known regulators in V. cholerae grown with and without PmB. The proteome of V. cholerae is greatly modified in the presence of PmB at subinhibitory concentrations with more than 39 % of the identified cellular proteins displaying a difference in their abundance, including T6SS-related proteins (Hcp, VasC, TsaB and ClpV). We identified a regulator whose abundance and expression are increased in the presence of PmB, vxrB, the response regulator of the two-component system VxrAB. In a vxrAB deficient mutant, the expression of hcp measured by quantitative PCR, although globally reduced, was not modified in the presence of PmB, confirming its role in hcp upregulation with PmB. The upregulation of the T6SS in the presence of PmB appears to be, at least in part, due to the two-component system VxrAB. Importance The type VI secretion system is important for bacterial competition, virulence and resistance to grazing by predators. In this study, we investigated the regulation leading to the type VI secretion system activation in the presence of polymyxin B (PmB), an antimicrobial used in veterinary and human health to treat infection caused by multi-resistant Gram-negative bacteria, in V. cholerae. In addition to making an overall portrait of the modifications to the proteome, we identified the VxrAB two-component system as the main regulator responsible for this activation. Our results provide evidence that subinhibitory concentrations of antimicrobials are responsible for important modifications of the proteome of pathogenic bacteria, inducing the production of proteins involved in virulence, host colonisation, resistance and environmental survival.
Estimation of Discrete Distributions with a Class of Simplex Constraints

Abstract Simplex constraints, such as monotonicity and convexity or concavity on the probabilities of a set of discrete distributions, are useful for modeling and analyzing discrete data. This article considers both maximum likelihood estimation and Bayesian estimation of discrete distribution with a class of simplex constraints using the Expectation-Maximization (EM) algorithm and the data augmentation (DA) algorithm. The formulation and implementation of EM and DA for binomial, Poisson, hierarchical Poisson-binomial, multinomial, and hierarchical multinomial distributions are considered in detail and illustrated with examples.
VCPoser: Interactive Pose Generation of Virtual Characters Corresponding to Human Pose Input

Virtual characters (VCs) play a significant role in the entertainment industry, and AI-driven VCs are being developed to enable interaction with users. People are attracted to these VCs, resulting in a demand for them to co-exist in the same world. An approach to allow recording of the memories with the VCs is to capture videos or photos with them, where users are usually required to adapt their poses to the pre-rendered VC’s action. To allow a more seamless collaboration with VCs in photography scenarios, we propose VCPoser, which enables VCs to adapt their pose to the pose of the user. We created a deep neural network-based system that predicts a VC’s pose using the user’s pose data as input by learning the paired pose data. Our quantitative evaluations and user studies demonstrate that our system can predict and generate poses of VCs and allow them to be combined next to the posing user in a photo. We also provide an analysis of the human mindsets of paired poses for a better understanding of them and to share insights for aesthetic pose design.
Using Virtual Reality Food Environments to Study Individual Food Consumer Behavior in an Urban Food Environment

The objective of this research was to explore whether virtual reality can be used to study individual food consumer decision-making and behavior through a public health lens by developing a simulation of an urban food environment that included a street-level scene and three prototypical stores. Twelve participants completed the simulation and a survey. Preliminary results showed that 72.7% of participants bought food from the green grocer, 18.2% from the fast food store, and 9.1% from the supermarket. The mean presence score was 38.9 out of 49 and the mean usability score was 85.9 out of 100. This experiment demonstrates that virtual reality should be further considered as a tool for studying food consumer behavior within a food environment.
Left Main Stenting Induced Flow Disturbances on Ascending Aorta and Aortic Arch

Abstract Background and Objective Ostial LM stenting potentially induces turbulence in the aortic wall near the LM ostium, which might be correlated with aorta dilation and dissection. We investigated through a computational fluid dynamic analysis (CFD), the presence and potential consequences of flow turbulences both in the ascending aorta and arch after a stenting left main (LM) mid shaft or distal disease. Methods The model of the ascending aorta and left coronary artery was reconstructed reviewing both angiographic and echocardiographic measurements of 80 consecutive patients (43 males, mean age 75.1 ± 6.2 years) with significant LM mid shaft or distal disease treated in our institution. For stent simulation, a third-generation everolimus-eluting stent was reconstructed. Two stenting procedures (lesion 1:1 or ostial coverage) were investigated. Results The net area averaged WSS of the model resulted higher when the stent covered the lesion 1:1 compared to the ostial coverage (3.68 vs. 2.06 Pa, P=0.01 and 3.97 vs. 1.98 Pa, P < 0.001, respectively). LM ostial coverage generates more turbulences in the LM itself, in the aortic wall at ostium level, and at the sino-tubular junction compared with the stenting of the lesion 1:1. Conversely, in the ascending aorta, the WSS appears lower when stenting the lesion 1:1. Conclusion Extending the stent coverage up to the ostium, when the ostial region is not diseased, might induce unfavorable alterations of flow; not only both at the level of the LM lesion and ostium sites, but also in the ascending aorta and aortic arch, potentially predisposing the aortic wall to long-term damage.
XGBoost Prediction of Infection of Leukemia Patients with Fever of Unknown Origin

Discovering the source of a patient's fever without clinically localised signs can be a daunting task for doctors. In particular for leukaemia patients with fever of unknown origin, fast discovering the source of the fever is a formidable challenge, as this population has the potential to lead to fever in many different situations. In this paper, we applied XGBoost algorithm to predict the pathogenic infections from a big data repository of leukemia patients with fever of unknown origin (FUO) and compared the performance with other machine learning algorithms. Our results illustrates that those machine learning algorithms achieves good performance. In particular, the XGBoost obtains the best performance with an area under receiving-operating-characteristics curve (AUC) of 0.8376 and F1-score of 0.7034. Compared with existing literature, our experiment provides new insights for doctors to determine the cause of fever in leukemia patients.
LPR: learning-based page replacement scheme for scientific applications

Recent advances in machine learning techniques open up new opportunities for solving problems in other domains. One of these problems, the page replacement system, attempts to use machine learning techniques since they have a significant impact on application performance. Specifically, scientific applications show certain data access patterns, such as iterative memory access through loops or linked lists, while performing arithmetic operations. For such applications, providing self-tunable page replacement systems can improve application performance. In this paper, we present a Learning-based Page Replacement (LPR) scheme for scientific applications. We propose a model that learns the memory reference patterns of a given application and determines the best-fit page replacement policy online. Using two least/most-recently used (LRU/MRU)-based replacement policies, LPR gives a reward or penalty to each policy according to its previous decisions. LPR evolves its own page replacement policy that can minimize cumulative regrets for each decision. Our scheme provides efficient memory management without explicitly detecting application-specific memory access patterns through self-learning. The experimental results show that our scheme properly detects the changes in memory access patterns and handles page replacement online using the best-fit policy with little overhead.
FastAddr: real-time abnormal address detection via contrastive augmentation for location-based services

An address, a textual description of a physical location, plays an important role in location-based services such as on-demand delivery and e-commerce. However, abnormal addresses (i.e., an address without detailed information representing a spatial location) have led to significant costs. In real-world settings like e-commerce, abnormal address detection is not trivial because it needs to be completed in real-time to support massive online queries. In this study, we design FastAddr, a fast abnormal address detection framework, which detects abnormal addresses among millions of addresses in a short time. By investigating and modeling the hierarchical structure of address data, we first design a novel contrastive address augmentation approach to generate training data via learning the entity transition probability matrix. We further design a lightweight multi-head attention model for learning compact address representation by modeling the address characteristics. We conduct a comprehensive three-phase evaluation. (i) We evaluate FastAddr on a real-world dataset and it yields the average F1 of 85.7% in 0.058 milliseconds, which outperforms the state-of-the-art models by 47.4% with similar detection time. (ii) An offline A/B test shows that FastAddr outperforms the previous deployed model significantly. (iii) We also conduct an online A/B test to compare FastAddr with the deployed model, which shows an improvement of F1 by more than 20%. Moreover, a real-world case study demonstrates both the efficiency and effectiveness of FastAddr.
A novel framework for handling sparse data in traffic forecast

The ever increasing amount of GPS-equipped vehicles provides in real-time valuable traffic information for the roads traversed by the moving vehicles. In this way, a set of sparse and time evolving traffic reports is generated for each road. These time series are a valuable asset in order to forecast the future traffic condition. In this paper we present a deep learning framework that encodes the sparse recent traffic information and forecasts the future traffic condition. Our framework consists of a recurrent part and a decoder. The recurrent part employs an attention mechanism that encodes the traffic reports that are available at a particular time window. The decoder is responsible to forecast the future traffic condition.
Preliminary ECG Cloud-based Telecardiology System for Rural Areas

According to the Ministry of Health of Republic Indonesia, cardiovascular disease the number of sufferers continues to increase every and ranks the highest cause of death in Indonesia. The most common CVD manifestation is atrial fibrillation. The CVD prevalence in rural areas of Indonesia is 1.3% and only one-third of CVD patients received preventive treatment. The low number of patients receiving preventive treatment is due to a shortage of active cardiologists and an unoptimized health care facility integration system in Indonesia. Therefore, a virtual access system equipped with a computer-aided diagnosis that connects a public health center in a rural area with cardiologists in a district hospital for better health care service performance. In this research, the virtual access system was developed by using Amazon Web Services along with ESP32 and micro-SD for simulating the ECG data transmission. The developed system can be accessed publicly and able to do cloud computation to detect atrial fibrillation. Furthermore, the developed system data transmission with the buffer method showed a result of 0% data loss in 1 minute, 5 minutes, 60 hours; and 0.0014% of extra data for 24 hours of transmission. Thus, the developed system has a high possibility for Holter monitoring and its implementation is not limited to ECG signal data transmission.
Exploring multilevel regularity in human mobility patterns using a feature engineering approach: a case study in chicago

Fine-grained trajectory data offers the opportunity to advance our understanding of regularity in individual mobility choices. Existing research on regular location-based mobility patterns cannot fully capture the complexity of day-to-day individual trajectories that are critical for downstream predictive models. To bridge the gap, we construct a comprehensive mobility profile using interpretable features to quantify the regularity of human mobility from three levels, e.g. locations, daily itineraries, and routes. An empirical study uses over 93k trips from 776 car drivers in the Chicago metropolitan area validates the routinely regular patterns in users' mobility choices. A feature engineering approach is then designed for user segmentation. Six user clusters are discovered from the users' mobility profiles. The clusters exhibit heterogeneous commuting behavior and preference for motif choices. The improved multilevel understanding of repeated travel behavior can further assist transportation modeling and planning.
Model-Based Deep Portrait Relighting

Like most computer vision problems the relighting of portrait face images is more and more being entirely formulated as a deep learning problem. However, data-driven approaches need a detailed and exhaustive database to work on and the creation of ground truth data is tedious and oftentimes technically complex. At the same time, networks get bigger and deeper. Knowledge about the problem statement, scene structure, and physical laws are often neglected. In this paper, we propose to encompass prior knowledge for relighting directly in the network learning process, adding model-based building blocks to the training. Thereby, we improve the learning speed and effectiveness of the network, thus performing better even with a restricted dataset. We demonstrate through an ablation study that the proposed model-based building blocks improve the network’s training and enhance the generated images compared with the naive approach.
Motorch: an on-device trajectory data management system during a pandemic

In this paper, we would like to demonstrate a personal trajectory management system called Motorch, which allows users to manage their trajectories and enables risk analytics based on a lightweight similarity measure called LCTS. At the back end, a web crawler collects the desensitized COVID-19 cases information from data sources (news, social media, etc.) and pushes them to Elasticsearch for storage after data cleaning. At the front end, Motorch implements a set of operations including data collection, data preprocessing, indexing, storage, and visualization in a mobile application. Motorch aims to help individuals manipulate their data and evaluate personal risk without uploading data to a server.