Miracle-dz/UniFAR-FTUs huggingface dataset:
A social trust based friend recommender for online communities “invited paper”

Recommendations to connect like-minded people can result in increased engagement amongst members of online communities, thus playing an important role in their sustainability. We have developed a suite of algorithms for friend recommendations using a social trust model called STrust. In STrust, the social trust of individual members is derived from their behaviours in the community. The unique features of our friend recommendation algorithms are that they capture different behaviours by (a) distinguishing between passive and active behaviours, (b) classifying behaviours as contributing to users' popularity or engagement and (c) considering different member activities in a variety of contexts. In this paper, we present our social trust based recommendation algorithms and evaluate them against algorithms based on the social graph (such as Friends-Of-A-Friend). We use data collected from the online CSIRO Total Wellbeing Diet portal which has been trialled by over 5,000 Australians over a 12 week period. Our results show that social trust based recommendation algorithms outperform social graph based algorithms.
The First-aid Advice and Safety Training (FAST) parents programme for the prevention of unintentional injuries in preschool children: a protocol

Background Unintentional injury is the leading cause of preventable death in children in the UK, and 0–4-year-olds frequently attend emergency departments following injuries in the home. Parenting programmes designed to support parents, promote behaviour change and enhance parent–child relationships have been shown to improve health outcomes in children. It is not known whether group-based parenting programmes have the potential to prevent unintentional injuries in preschool children. Methods A study to develop a group-based parenting programme to prevent unintentional home injuries in preschool children, and assess the feasibility of evaluation through a cluster-randomised controlled trial. The intervention, designed for parents of children who have sustained a medically attended injury, will be developed with two voluntary sector organisations. The feasibility study will assess ability to recruit parents, deliver the programme and follow-up participants. Participants will complete questionnaires at baseline, 3 months and 6 months, and report injuries in their preschool children using a tool designed and validated for this study. Qualitative methods will assess user and deliverer perceptions of the programme. Discussion This study will develop the first group-based parenting programme to prevent injuries in preschool children, and design tools for parent-reported injury outcomes. A key challenge will be to recruit parents to participate in a manner that is non-stigmatising, and does not result in feelings of guilt or belief that they are perceived to be a bad parent. The findings will be used to prepare a trial to assess the effectiveness and cost-effectiveness of the intervention.
Fast Rates for Empirical Risk Minimization of Strict Saddle Problems

We derive bounds on the sample complexity of empirical risk minimization (ERM) in the context of minimizing non-convex risks that admit the strict saddle property. Recent progress in non-convex optimization has yielded efficient algorithms for minimizing such functions. Our results imply that these efficient algorithms are statistically stable and also generalize well. In particular, we derive fast rates which resemble the bounds that are often attained in the strongly convex setting. We specify our bounds to Principal Component Analysis and Independent Component Analysis. Our results and techniques may pave the way for statistical analyses of additional strict saddle problems.
A novel method for assessing arterial stiffness by a hydrostatic approach

Arterial stiffness is an important index for cardiovascular events. The objective of this study is to examine possible parameters related to arterial stiffness that can be estimated during simple arm movements. An experiment was conducted on 32 subjects divided into two groups, one with an age of 26±4 years old, and the other 61±9. The pulse transit time measured from electrocardiogram to finger photoplethysmogram (PPG) and the amplitude of PPG were calculated beat-to-beat for the subjects while they had their arms lowered. The results of the study showed that the ratio between percentage changes in PTT and finger height are significantly different for the two groups of subjects with different age and health conditions, indicating that parameters can be potentially extracted from this procedure to represent the difference in arterial stiffness of the two groups of subjects.
MASSIF-1 : a beamline dedicated to the fully automatic characterization and data collection from crystals of biological macromolecules

MASSIF-1 (ID30A-1) is an ESRF undulator beamline operating at a fixed wavelength of 0.969 Å (12.8 keV) that is dedicated to the completely automatic characterization of and data collection from crystals of biological macromolecules. The first of the ESRF Upgrade MASSIF beamlines to be commissioned, it has been open since September 2014, providing a unique automated data collection service to academic and industrial users. Here, the beamline characteristics and details of the new service are outlined.
Selecting antagonistic antibodies that control differentiation through inducible expression in embryonic stem cells

Significance The ability to control and direct differentiation of stem cells for research or therapeutic applications relies on the availability of ligands that control specific signaling pathways. Natural ligands exhibit promiscuous interactions and have limited availability because of their poor expression/stability profiles. In contrast, antibodies exhibit exquisite specificity and have optimal expression properties. Antibodies that block or activate receptor signaling have great potential for controlling differentiation, but their identification is laborious. Herein we describe an innovative system for identifying functional antibodies by introducing antibody gene populations into ES cells. Antibody-expressing ES clones with altered differentiation outcomes can be readily identified using lineage-specific gene-expression markers. The antibody gene can then be recovered for antibody production and use. Antibodies that modulate receptor function have great untapped potential in the control of stem cell differentiation. In contrast to many natural ligands, antibodies are stable, exquisitely specific, and are unaffected by the regulatory mechanisms that act on natural ligands. Here we describe an innovative system for identifying such antibodies by introducing and expressing antibody gene populations in ES cells. Following induced antibody expression and secretion, changes in differentiation outcomes of individual antibody-expressing ES clones are monitored using lineage-specific gene expression to identify clones that encode and express signal-modifying antibodies. This in-cell expression and reporting system was exemplified by generating blocking antibodies to FGF4 and its receptor FGFR1β, identified through delayed onset of ES cell differentiation. Functionality of the selected antibodies was confirmed by addition of exogenous antibodies to three different ES reporter cell lines, where retained expression of pluripotency markers Oct4, Nanog, and Rex1 was observed. This work demonstrates the potential for discovery and utility of functional antibodies in stem cell differentiation. This work is also unique in constituting an example of ES cells carrying an inducible antibody that causes a functional protein “knock-down” and allows temporal control of stable signaling components at the protein level.
Role of Glomerular Proteoglycans in IgA Nephropathy

Mesangial matrix expansion is a prominent feature of the most common form of glomerulonephritis, IgA nephropathy (IgAN). To find molecular markers and improve the understanding of the disease, the gene and protein expression of proteoglycans were investigated in biopsies from IgAN patients and correlated to clinical and morphological data. We collected and microdissected renal biopsies from IgAN patients (n = 19) and from healthy kidney donors (n = 14). Patients were followed for an average time of 4 years and blood pressure was according to target guidelines. Distinct patterns of gene expression were seen in glomerular and tubulo-interstitial cells. Three of the proteoglycans investigated were found to be of special interest and upregulated in glomeruli: perlecan, decorin and biglycan. Perlecan gene expression negatively correlated to albumin excretion and progress of the disease. Abundant decorin protein expression was found in sclerotic glomeruli, but not in unaffected glomeruli from IgAN patients or in controls. Transforming growth factor beta (TGF-β), known to interact with perlecan, decorin and biglycan, were upregulated both on gene and protein level in the glomeruli. This study provides further insight into the molecular mechanisms involved in mesangial matrix expansion in IgAN. We conclude that perlecan is a possible prognostic marker for patients with IgAN. In addition, the up-regulation of biglycan and decorin, as well as TGF-β itself, indicate that regulation of TGF-β, and other profibrotic markers plays a role in IgAN pathology.
Dyna-like reinforcement learning based on accumulative and average rewards

An approach to accelerating the learning process of the actor-critic learning algorithm for reinforcement learning is presented. The algorithm was derived from principles based on the prediction of average rewards and temporal difference (TD) learning with averaged and discounted rewards. The derived algorithm was applied to neural networks, demonstrating their effective operation in nonlinear control problems. The motivation of the proposed algorithm was to elaborate how a learning scheme, implemented by artificial neural networks (ANNs), can speed up learning processes based on an arrangement akin to the Dyna-Q learning, where a simulative model of the controlled plant is established for virtual learning between two control cycles. Instead of modeling the complicated plant, the approach just introduced a simple predictor of rewards for virtual learning in simulation mode. Two TD learning methods based discounted and averaged rewards respectively, are used alternatively in the control and simulation mode to facilitate the derived algorithm. The proposed Alternative Learning Critic (ALC) algorithm consists of two sub-systems: one is Evaluation Predictor (EP), which performs an approximation of a long-term evaluation function, and the other is an immediate action selector, which is composed of two ANNs: Action Controller (AC) and Reinforcement Predictor (RP). The proposed learning scheme is then applied to control a pendulum system for tracking a desired trajectory to demonstrate its applausive performance and robustness. Through reinforcement signals from the environment, the system takes an appropriate action to a plant with unknown dynamics so the actual output of the plant can track the desired one concisely within a short learning cycles. Further, ALC is used as the compensator of a PI controller, which is actually only working well on a linear system, to control that pendulum system. The results show the affined system, trained ALC and the PI controller can manipulate together on a nonlinear system with unknown dynamics.
The Grid: Stronger, Bigger, Smarter?: Presenting a Conceptual Framework of Power System Resilience

Increasing the resilience of critical power infrastructures to high-impact, low-probability events, such as extreme weather phenomena driven by climate change, is of key importance for keeping the lights on. However, what does resilience really mean? Should we build a stronger and bigger grid or a smarter one? This article discusses a conceptual framework of power system resilience, its key features, and potential enhancement measures.
Crystal structures of open and closed forms of binary and ternary complexes of the large fragment of Thermus aquaticus DNA polymerase I: structural basis for nucleotide incorporation

The crystal structures of two ternary complexes of the large fragment of Thermus aquaticus DNA polymerase I (Klentaq1) with a primer/template DNA and dideoxycytidine triphosphate, and that of a binary complex of the same enzyme with a primer/template DNA, were determined to a resolution of 2.3, 2.3 and 2.5 Å, respectively. One ternary complex structure differs markedly from the two other structures by a large reorientation of the tip of the fingers domain. This structure, designated ‘closed’, represents the ternary polymerase complex caught in the act of incorporating a nucleotide. In the two other structures, the tip of the fingers domain is rotated outward by 46° (‘open’) in an orientation similar to that of the apo form of Klentaq1. These structures provide the first direct evidence in DNA polymerase I enzymes of a large conformational change responsible for assembling an active ternary complex.
Software Licensing Models: What's Out There?

Software licensing is changing how organization and individuals purchase and use software. For enterprises that rely on software to maintain a market share, the software licensing model can strongly influence the return on software investment. Yet choosing a model can be daunting, involving considerations from total licenses purchased to the resources available to install and set up licensing systems. Given the recent evolution of software licensing models, enterprises are finding it increasingly important to keep abreast of what's available and to understand how particular models are advantageous or detrimental matches. This article gives an overview of the choices, describes the resources needed to activate or protect software for the various models, and looks at how licensing systems can help enterprises better comply with licensing terms and agreements
An Interactive, Web-Based High Performance Modeling Environment for Computational Epidemiology

We present an integrated interactive modeling environment to support public health epidemiology. The environment combines a high resolution individual-based model with a user-friendly Web-based interface that allows analysts to access the models and the analytics backend remotely from a desktop or a mobile device. The environment is based on a loosely coupled service-oriented-architecture that allows analysts to explore various counterfactual scenarios. As the modeling tools for public health epidemiology are getting more sophisticated, it is becoming increasingly difficult for noncomputational scientists to effectively use the systems that incorporate such models. Thus an important design consideration for an integrated modeling environment is to improve ease of use such that experimental simulations can be driven by the users. This is achieved by designing intuitive and user-friendly interfaces that allow users to design and analyze a computational experiment and steer the experiment based on the state of the system. A key feature of a system that supports this design goal is the ability to start, stop, pause, and roll back the disease propagation and intervention application process interactively. An analyst can access the state of the system at any point in time and formulate dynamic interventions based on additional information obtained through state assessment. In addition, the environment provides automated services for experiment set-up and management, thus reducing the overall time for conducting end-to-end experimental studies. We illustrate the applicability of the system by describing computational experiments based on realistic pandemic planning scenarios. The experiments are designed to demonstrate the system’s capability and enhanced user productivity.
Verified heap theorem prover by paramodulation

We present VeriStar, a verified theorem prover for a decidable subset of separation logic. Together with VeriSmall [3], a proved-sound Smallfoot-style program analysis for C minor, VeriStar demonstrates that fully machine-checked static analyses equipped with efficient theorem provers are now within the reach of formal methods. As a pair, VeriStar and VeriSmall represent the first application of the Verified Software Toolchain [4], a tightly integrated collection of machine-verified program logics and compilers giving foundational correctness guarantees. VeriStar is (1) purely functional, (2) machine-checked, (3) end-to-end, (4) efficient and (5) modular. By purely functional, we mean it is implemented in Gallina, the pure functional programming language embedded in the Coq theorem prover. By machine-checked, we mean it has a proof in Coq that when the prover says "valid", the checked entailment holds in a proved-sound separation logic for C minor. By end-to-end, we mean that when the static analysis+theorem prover says a C minor program is safe, the program will be compiled to a semantically equivalent assembly program that runs on real hardware. By efficient, we mean that the prover implements a state-of-the-art algorithm for deciding heap entailments and uses highly tuned verified functional data structures. By modular, we mean that VeriStar can be retrofitted to other static analyses as a plug-compatible entailment checker and its soundness proof can easily be ported to other separation logics.
Participatory adaptive management leads to environmental learning outcomes extending beyond the sphere of science

Participatory active adaptive management leads to far-reaching environmental learning outcomes in stakeholders. Resolving uncertainties in managed social-ecological systems requires adaptive experimentation at whole-ecosystem levels. However, whether participatory adaptive management fosters ecological understanding among stakeholders beyond the sphere of science is unknown. We experimentally involved members of German angling clubs (n = 181 in workshops, n = 2483 in total) engaged in self-governance of freshwater fisheries resources in a large-scale ecological experiment of active adaptive management of fish stocking, which constitutes a controversial management practice for biodiversity and ecosystem functioning when conducted inappropriately. The collaborative ecological experiments spanned several years and manipulated fish densities in 24 lakes with two species. In parallel, we experimentally compared changes in ecological knowledge and antecedents of proenvironmental behavior in stakeholders and managers who were members of a participatory adaptive management treatment group, with those receiving only a standard lecture, relative to placebo controls. Using a within-subjects pretest-posttest control design, changes in ecological knowledge, environmental beliefs, attitudes, norms, and behavioral intentions were evaluated. Participants in adaptive management retained more knowledge of ecological topics after a period of 8 months compared to those receiving a standard lecture, both relative to controls. Involvement in adaptive management was also the only treatment that altered personal norms and beliefs related to stocking. Critically, only the stakeholders who participated in adaptive management reduced their behavioral intentions to engage in fish stocking in the future. Adaptive management is essential for robust ecological knowledge, and we show that involving stakeholders in adaptive management experiments is a powerful tool to enhance ecological literacy and build environmental capacity to move toward sustainability.
The Online Diagnosis System for sanger sequencing based genetic testing

Sanger sequencing based genetic testing has been well established for many genetic diseases. In these testings, DNA sequencers produce vast amounts of data that need to be interpreted by the clinicians within a short peiod of time. To achieve this goal, a bioinformatics platform that can automates this process is yet to be developed. To fulfill this gap, we developed Online Diagnosis System (ODS), which is implemented as a web server and supports the commonly used ABI trace file format generated from Sanger sequencing. ODS seamlessly integrates useful functions covering base calling, Small Nucleotide Variant (SNV) identification, and comprehensive SNV annotation. It also allows the clinicians to manually inspect the quality of the identified SNVs when generating the final report. ODS can therefore significantly reduce the data analysis time, allowing the completion of the genetic testing in a timely manner.
Sampled-Data Consensus of Linear Multi-agent Systems With Packet Losses

In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.
Some reminiscences on studies of age-dependent and activity-dependent degeneration of sensory and motor endings in mammalian skeletal muscle

I present here an overview of research on the biology of neuromuscular sensory and motor endings that was inspired and influenced partly by my educational experience in the Department of Zoology at the University of Durham, from 1971 to 1974. I allude briefly to neuromuscular synaptic structure and function in dystrophic mice, influences of activity on synapse elimination in development and regeneration, and activity‐dependent protection and degeneration of neuromuscular junctions in WldS mice.