Miracle-dz/UniFAR-FTUs huggingface dataset:
Human-Associated Fecal Quantitative Polymerase Chain Reaction Measurements and Simulated Risk of Gastrointestinal Illness in Recreational Waters Contaminated with Raw Sewage

We used quantitative microbial risk assessment to simulate the risk of gastrointestinal (GI) illness associated with swimming in waters containing different concentrations of human-associated fecal markers from raw sewage, HF183 and HumM2. The volume/volume ratio of raw sewage to ambient water was determined by comparing marker concentrations in recreational water to concentrations in raw sewage from 54 geographic locations across the United States. Concentrations of reference GI pathogens in raw sewage, volumes ingested by swimmers, dose–response functions, and fractions of infected that become ill were adopted from previous studies. Simulated GI risk increased with concentration of the human quantitative polymerase chain reaction markers in recreational waters. A benchmark illness rate of 30 GI illnesses per 1000 swimmers occurred at median concentrations of 4200 copies of HF183 and 2800 copies of HumM2 per 100 mL of recreational water. This study establishes a risk-based approach for interpreting conce.
Validation and comparison of 2D grading scales and 3D volumetric measurements for outcome assessment of bone-grafted alveolar clefts in children

Abstract Background Several methods have been proposed to assess outcome of bone-grafted alveolar clefts on cone beam computed tomography (CBCT), but so far these methods have not been compared and clinically validated. Objectives To validate and compare methods for outcome assessment of bone-grafted clefts with CBCT and provide recommendations for follow-up. Methods In this observational follow-up study, two grading scales (Suomalainen; Liu) and the volumetric bone fill (BF) were used to assess the outcome of 23 autogenous bone-grafted unilateral alveolar clefts. The mean age at bone grafting was 9 years. The volumetric BF was assessed in five vertical sections. The bone-grafted cleft outcome was based on a binary coding (success or regraft) on a clinical multidisciplinary expert consensus meeting. Grading scales and volumetric assessment were compared in relation to the bone-grafted cleft outcome (success or regraft). Reliability for the different outcome variables was analyzed with intra-class correlation and by calculating kappa values. Limitations The study had a limited sample size. Clinical CBCT acquisitions had a varying tube current and exposure time. Results Volumetric 3D measurements allowed for outcome assessment of bone-grafted alveolar clefts with high reliability and validity. The two grading scales showed highly reliable outcomes, yet the validity was high for the Suomalainen grading scale but low for the Liu grading scale. Conclusions Volumetric 3D measurement as well as the Suomalainen grading can be recommended for outcome assessment of the bone-grafted cleft. Yet, one must always make a patient-specific assessment if there is a need to regraft.
Antagonistic activity of Phaeobacter piscinae against the emerging fish pathogen Vibrio crassostreae in aquaculture feed algae

ABSTRACT Aquaculture provides a rich resource of high-quality protein; however, the production is challenged by emerging pathogens such as Vibrio crassostreae. While probiotic bacteria have been proposed as a sustainable solution to reduce pathogen load in aquaculture, their application requires a comprehensive assessment across the aquaculture food chain. The purpose of this study was to determine the antagonistic effect of the potential probiotic bacterium Phaeobacter piscinae against the emerging fish pathogen V. crassostreae in aquaculture feed algae that can be an entry point for pathogens in fish and shellfish aquaculture. P. piscinae strain S26 produces the antibacterial compound tropodithietic acid (TDA). In a plate-based assay, P. piscinae S26 was equally to more effective than the well-studied Phaeobacter inhibens DSM17395 in its inhibition of the fish pathogens Vibrio anguillarum 90-11-286 and V. crassostreae DMC-1. When co-cultured with the microalgae Tetraselmis suecica and Isochrysis galbana, P. piscinae S26 reduced the maximum cell density of V. crassostreae DMC-1 by 2 log and 3–4 log fold, respectively. A TDA-deficient mutant of P. piscinae S26 inhibited V. crassostreae DMC-1 to a lesser extent than the wild type, suggesting that the antagonistic effect involves TDA and other factors. TDA is the prime antagonistic agent of the inhibition of V. anguillarum 90-11-286. Comparative genomics of V. anguillarum 90-11-286 and V. crassostreae DMC-1 revealed that V. crassostreae DMC-1 carries a greater arsenal of antibiotic resistance genes potentially contributing to the reduced effect of TDA. In conclusion, P. piscinae S26 is a promising new candidate for inhibition of emerging pathogens such as V. crassostreae DMC-1 in algal feed systems and could contribute to a more sustainable aquaculture industry. IMPORTANCE The globally important production of fish and shellfish in aquaculture is challenged by disease outbreaks caused by pathogens such as Vibrio crassostreae. These outbreaks not only lead to substantial economic loss and environmental damage, but treatment with antibiotics can also lead to antibiotic resistance affecting human health. Here, we evaluated the potential of probiotic bacteria, specifically the newly identified strain Phaeobacter piscinae S26, to counteract these threats in a sustainable manner. Through a systematic assessment of the antagonistic effect of P. piscinae S26 against V. crassostreae DMC-1, particularly within the context of algal feed systems, the study demonstrates the effectiveness of P. piscinae S26 as probiotic and thereby provides a strategic pathway for addressing disease outbreaks in aquaculture. This finding has the potential of significantly contributing to the long-term stability of the industry, highlighting the potential of probiotics as an efficient and environmentally conscious approach to safeguarding aquaculture productivity against the adverse impact of pathogens.
Prior dengue virus serotype 3 infection modulates subsequent plasmablast responses to Zika virus infection in rhesus macaques

ABSTRACT Immunodominant and highly conserved flavivirus envelope proteins can trigger cross-reactive IgG antibodies against related flaviviruses, which shapes subsequent protection or disease severity. This study examined how prior dengue serotype 3 (DENV-3) infection affects subsequent Zika virus (ZIKV) plasmablast responses in rhesus macaques (n = 4). We found that prior DENV-3 infection was not associated with diminished ZIKV-neutralizing antibodies or magnitude of plasmablast activation. Rather, characterization of 363 plasmablasts and their derivative 177 monoclonal antibody supernatants from acute ZIKV infection revealed that prior DENV-3 infection was associated with a differential isotype distribution toward IgG, lower somatic hypermutation, and lesser B cell receptor variable gene diversity as compared with repeat ZIKV challenge. We did not find long-lasting DENV-3 cross-reactive IgG after a ZIKV infection but did find persistent ZIKV-binding cross-reactive IgG after a DENV-3 infection, suggesting non-reciprocal cross-reactive immunity. Infection with ZIKV after DENV-3 boosted pre-existing DENV-3-neutralizing antibodies by two- to threefold, demonstrating immune imprinting. These findings suggest that the order of DENV and ZIKV infections has impact on the quality of early B cell immunity which has implications for optimal immunization strategies. IMPORTANCE The Zika virus epidemic of 2015–2016 in the Americas revealed that this mosquito-transmitted virus could be congenitally transmitted during pregnancy and cause birth defects in newborns. Currently, there are no interventions to mitigate this disease and Zika virus is likely to re-emerge. Understanding how protective antibody responses are generated against Zika virus can help in the development of a safe and effective vaccine. One main challenge is that Zika virus co-circulates with related viruses like dengue, such that prior exposure to one can generate cross-reactive antibodies against the other which may enhance infection and disease from the second virus. In this study, we sought to understand how prior dengue virus infection impacts subsequent immunity to Zika virus by single-cell sequencing of antibody producing cells in a second Zika virus infection. Identifying specific qualities of Zika virus immunity that are modulated by prior dengue virus immunity will enable optimal immunization strategies.
Gait Intention Prediction Using a Lower-Limb Musculoskeletal Model and Long Short-Term Memory Neural Networks

The prediction of gait motion intention is essential for achieving intuitive control of assistive devices and diagnosing gait disorders. To reduce the cost associated with using multimodal signals and signal processing, we proposed a novel method that integrates machine learning with musculoskeletal modelling techniques for the prediction of time-series joint angles, using only kinematic signals. Additionally, we hypothesised that a stacked long short-term memory (LSTM) neural network architecture can perform the task without relying on any ahead-of-motion features typically provided by electromyography signals. Optical cameras and inertial measurement unit (IMU) sensors were used to track level gait kinematics. Joint angles were modelled using the musculoskeletal model. The optimal LSTM architecture in fulfilling the prediction task was determined. Joint angle predictions were performed for joints on the sagittal plane, benefiting from joint angle modelling using signals from optical cameras and IMU sensors. Our proposed method predicted the upcoming joint angles in the prediction time of 10 ms, with an averaged root mean square error of 5.3° and a coefficient of determination of 0.81. Moreover, in support of our hypothesis, the recurrent stacked LSTM network demonstrated its ability to predict intended motion accurately and efficiently in gait, outperforming two other neural network architectures: a feedforward MLP and a hybrid LSTM-MLP. The method paves the way for the development of a cost-effective, single-modal control system for assistive devices in gait rehabilitation.
Simultaneous localization and mapping using single cluster probability hypothesis density filters

The majority of research in feature-based SLAM builds on the legacy of foundational work using the EKF, a single-object estimation technique. Because feature-based SLAM is an inherently multi-object problem, this has led to a number of suboptimalities in popular solutions. We develop an algorithm using the SC-PHD filter, a multi-object estimator modeled on cluster processes. This algorithm hosts capabilities not typically seen with feature-base SLAM solutions such as principled handling of clutter measurements and missed detections, and navigation with a mixture of stationary and moving landmarks. We present experiments with the SC-PHD SLAM algorithm on both synthetic and real datasets using an autonomous underwater vehicle. We compare our method to the RB-PHD SLAM, showing that it requires fewer approximations in its derivation and thus achieves superior performance. En aquesta tesis es desenvolupa aquest algoritme a partir d’un filtre PHD amb un unic grup (SC-PHD), una tecnica d’estimacio multi-objecte basat en processos d’agrupacio. Aquest algoritme te unes capacitats que normalment no es veuen en els algoritmes de SLAM basats en caracteristiques, ja que es capac de tractar falses caracteristiques, aixi com caracteristiques no detectades pels sensors del vehicle, a mes de navegar en un entorn amb la presencia de caracteristiques estatiques i caracteristiques en moviment de forma simultania. Es presenten els resultats experimentals de l’algoritme SC-PHD en entorns reals i simulats utilitzant un vehicle autonom submari. Els resultats son comparats amb l’algoritme de SLAM Rao-Blackwellized PHD (RB-PHD), demostrant que es requereixen menys aproximacions en la seva derivacio i en consequencia s’obte un rendiment superior.
Sexualities in/of the Global South

This paper scopes the contemporary literatures that examine sexualities in/of the Global South. Recognising the complexities of both ‘sexualities’ and ‘the Global South’, the paper outlines key areas that can be explored under these headings. It questions the conflation of sexualities with homosexualities and contests the assumptions that spatial analyses of the Global South are confined within the discipline of geography. Instead a broad range of material is drawn upon and pointed to highlighting the breadth and possibilities of this field. The paper concludes by considering the shifting and contingent power relations of authors writing the ‘Global South’ from positions of privilege in the Global North. Yet like many before us we call for a reflexive approach that is not afraid of researching these areas but that does recognise the importance of destabilising existing power relations both in terms of geopolitical interventions, and in interrogating academic hegemonies.
Revealing the Structure of 6-Aminopenillanic Acid: The Active Nucleus of Penicillins

6-Aminopenicillanic acid is a penicillanic acid compound and is the active nucleus common to all penicillins. Using laser ablation techniques, we transformed the solid into the gas phase and characterized its conformational panorama by combining supersonic expansions and Fourier transform microwave techniques. Five conformers were determined, adopting different spatial configurations. Among them, the axial and equatorial forms, which are biologically relevant, have been observed. The structural similarity to d-Ala-d-Ala and the detection of both axial and equatorial forms could explain its potential as a penicillin core and its capability as an antibiotic.
A Southern Hemispheric influence on the North Atlantic through a shallow atmospheric circulation response

Previous research has discussed the importance of meridional migrations of the North Atlantic Subtropical High (NASH) on U.S. precipitation patterns, but the mechanisms that control these meridionial migrations are virtually unknown. We have observed that, under certain conditions, a southward migration of the NASH is associated with deep tropical incursions of cold surges from the winter hemisphere over South America. When upper tropospheric winds are westerly over Amazonia, cold surges originating from extratropical South America can penetrate deep into the tropics and increase geopotential height over a broad region span from equatorial South America, across the intra-American seas, and into the subtropics of the Northern Atlantic, with anomalies exceeding +1 standard deviation to at least 18°N. The anomalous geopotential and temperature gradients associated with the South American cold surge induces a shallow tropical meridional circulation. The latter in turn increases the lower tropospheric geopotential height over the tropical to subtropical North Atlantic, leading to the
Equipping Saccharomyces cerevisiae with an Additional Redox Cofactor Allows F420-Dependent Bioconversions in Yeast

Industrial application of the natural deazaflavin cofactor F420 has high potential for the enzymatic synthesis of high value compounds. It can offer an additional range of chemistry to the use of well-explored redox cofactors such as FAD and their respective enzymes. Its limited access through organisms that are rather difficult to grow has urged research on the heterologous production of F420 using more industrially relevant microorganisms such as Escherichia coli. In this study, we demonstrate the possibility of producing this cofactor in a robust and widely used industrial organism, Saccharomyces cerevisiae, by the heterologous expression of the F420 pathway. Through careful selection of involved enzymes and some optimization, we achieved an F420 yield of ∼1.3 μmol/L, which is comparable to the yield of natural F420 producers. Furthermore, we showed the potential use of F420-producing S. cerevisiae for F420-dependent bioconversions by carrying out the whole-cell conversion of tetracycline. As the first demonstration of F420 synthesis and use for bioconversion in a eukaryotic organism, this study contributes to the development of versatile bioconversion platforms.
A hybrid, non-split, stiff/RKC, solver for advection–diffusion–reaction equations and its application to low-Mach number combustion

We present a new strategy to couple, in a non-split fashion, stiff integration schemes with explicit, extended-stability predictor-corrector methods. The approach is illustrated through the construction of a mixed scheme incorporating a stabilised second-order, Runge-Kutta-Chebyshev method and the CVODE stiff solver. The scheme is first applied to an idealised stiff reaction-diffusion problem that admits an analytical solution. Analysis of the computations reveals that as expected the scheme exhibits a second-order in time convergence, and that, compared to an operator-split construction, time integration errors are substantially reduced. The non-split scheme is then applied to model the transient evolution of a freely-propagating, 1D methane-air flame. A low-mach-number, detailed kinetics, combustion model, discretised in space using fourth-order differences, is used for this purpose. To assess the performance of the scheme, self-convergence tests are conducted, and the results are contrasted with computations performed using a Strang-split construction. Whereas both the split and non-split approaches exhibit second-order in time behaviour, it is seen that for the same value of the time step, the non-split approach exhibits significantly smaller time integration errors. On the other hand, the results also indicate that the application of the present non-split construction becomes attractive when large integration steps are used, due to numerical overhead.
The development of object oriented Bayesian networks to evaluate the social, economic and environmental impacts of solar PV

Domestic and community low carbon technologies are widely heralded as valuable means for delivering sustainability outcomes in the form of social, economic and environmental (SEE) policy objectives. To accelerate their diffusion they have benefited from a significant number and variety of subsidies worldwide. Considerable aleatory and epistemic uncertainties exist, however, both with regard to their net energy contribution and their SEE impacts. Furthermore the socio-economic contexts themselves exhibit enormous variability, and commensurate uncertainties in their parameterisation. This represents a significant risk for policy makers and technology adopters. This work describes an approach to these problems using Bayesian Network models. These are utilised to integrate extant knowledge from a variety of disciplines to quantify SEE impacts and endogenise uncertainties. A large-scale Object Oriented Bayesian network has been developed to model the specific case of solar photovoltaics (PV) installed on UK domestic roofs. Three specific model components have been developed. The PV component characterises the yield of UK systems, the building energy component characterises the energy consumption of the dwellings and their occupants and a third component characterises the building stock in four English urban communities. Three representative SEE indicators, fuel affordability, carbon emission reduction and discounted cash flow are integrated and used to test the model s ability to yield meaningful outputs in response to varying inputs. The variability in the percentage of the three indicators is highly responsive to the dwellings built form, age and orientation, but is not just due to building and solar physics but also to socio-economic factors. The model can accept observations or evidence in order to create scenarios which facilitate deliberative decision making. The BN methodology contributes to the synthesis of new knowledge from extant knowledge located between disciplines . As well as insights into the impacts of high PV penetration, an epistemic contribution has been made to transdisciplinary building energy modelling which can be replicated with a variety of low carbon interventions.
KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery

Abstract The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug–target interactions (DTIs) and drug–drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} in AUROC and 3.56\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} in AUPR for DTIs and 6.56\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} in AUROC and 8.17\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein–protein interactions prediction, ablation studies and case studies further validate its effectiveness.