Miracle-dz/UniFAR-FTUs huggingface dataset:
Setting Priorities to Inform Assessment of Care Homes’ Readiness to Participate in Healthcare Innovation: A Systematic Mapping Review and Consensus Process

Organisational context is known to impact on the successful implementation of healthcare initiatives in care homes. We undertook a systematic mapping review to examine whether researchers have considered organisational context when planning, conducting, and reporting the implementation of healthcare innovations in care homes. Review data were mapped against the Alberta Context Tool, which was designed to assess organizational context in care homes. The review included 56 papers. No studies involved a systematic assessment of organisational context prior to implementation, but many provided post hoc explanations of how organisational context affected the success or otherwise of the innovation. Factors identified to explain a lack of success included poor senior staff engagement, non-alignment with care home culture, limited staff capacity to engage, and low levels of participation from health professionals such as general practitioners (GPs). Thirty-five stakeholders participated in workshops to discuss findings and develop questions for assessing care home readiness to participate in innovations. Ten questions were developed to initiate conversations between innovators and care home staff to support research and implementation. This framework can help researchers initiate discussions about health-related innovation. This will begin to address the gap between implementation theory and practice.
Numerical Simulation and Experimental Validation of Liquid Metal Droplet Formation in a Co-Flowing Capillary Microfluidic Device

A two-phase flow axisymmetric numerical model was proposed to understand liquid metal droplet formation in a co-flowing capillary microfluidics device based on a phase field model. The droplet detachment processes were observed in the experiment and are in good agreement with the simulation method. The effects of the viscosities and flowrates of the continuous phase fluid, interfacial tension as well as the wetting property of the metallic needle against the bulk liquid metal on the droplet formation and production rate were numerically investigated. It was found that the droplet diameter decreased with the increment of the viscosities and flowrates of the outer phase carrier fluid. The dispersed phase fluid with high interfacial tension tended to prolong the time for equilibrium between the viscous drag force and interfacial tension on the liquid–liquid fluid surface, delaying the droplet to be pinched off from the capillary orifice and causing large droplet diameter. Finally, the wetting performance of the metallic needle against the liquid metal was explored. The result indicate that the droplet diameter became less dependent on the contact angle while the size distribution of the liquid metal droplet was affected by their wetting performance. A more hydrophilic wetting performance were expected to prepare liquid metal droplet with more monodispersity. The numerical model and simulation results provide the feasibility of predicting the droplet formation with a high surface tension in a glass capillary microfluidic device.
Prevalence of Colistin-Resistant, Carbapenem-Hydrolyzing Proteobacteria in Hospital Water Bodies and Out-Falls of West Bengal, India

Indiscriminate use of antibiotics has resulted in a catastrophic increase in the levels of antibiotic resistance in India. Hospitals treat critical bacterial infections and thus can serve as reservoirs of multidrug resistant (MDR) bacteria. Hence, this study was conducted to gauge the prevalence patterns of MDR bacteria in hospital wastewater. Water samples collected from 11 hospitals and 4 environmental sources belonging to 5 most-densely populated districts of West Bengal, India were grown on MacConkey and Eosin Methylene Blue agar. A total of 84 (hospital-associated = 70, environmental water sources = 14) isolates were characterized. The predominant species found in water from hospital-associated areas (HAA) were Acinetobacter baumannii (22.9%), Escherichia coli (28.6 %), and Klebsiella pneumoniae (25.7%). Greater than 75% of the HAA isolates were found to be mcr-1 gene negative and colistinresistant. Meropenem non-susceptibility was also high among the HAA isolates at 58.6%, with the presence of the carbapenemase gene and blaNDM in 67.1% of the non-susceptible isolates. Among the three predominant species, significantly higher numbers of E. coli isolates were found to be non-susceptible to meropenem ((80%), p-value = 0.00432) and amikacin (AK (90%), p-value = 0.00037). This study provides evidence for the presence of high numbers of colistin-resistant and carbapenem-hydrolyzing Proteobacteriain hospital wastewater.
High-Resolution Mapping in Two RIL Populations Refines Major “QTL Hotspot” Regions for Seed Size and Shape in Soybean (Glycine max L.)

Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods; two of them were major (R2 > 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., “QTL Hotspot A”, “QTL Hotspot B”, “QTL Hotspot C”, and “QTL Hotspot D” located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four “QTL Hotspots” were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).
On the Microstructure and Isothermal Oxidation at 800 and 1200 °C of the Nb-24Ti-18Si-5Al-5Cr-5Ge-5Sn (at.%) Silicide-Based Alloy

The research presented in this paper aspired to understand how the simultaneous addition of Ge and Sn in an Hf-free Nb-silicide-based alloy affected its oxidation resistance. Results are presented for the Nb-24Ti-18Si-5Al-5Cr-5Ge-5Sn alloy (at.%) which was studied in the as-cast and heat-treated (1400 °C/100 h) conditions and after isothermal oxidation in air at 800 and 1200 °C. There was macrosegregation in the cast alloy, in which the Nbss formed at a low volume fraction and was not stable after heat treatment at 1400 °C. The βNb5Si3, A15-Nb3Sn, and C14-NbCr2 were stable phases. The alloy did not undergo pest oxidation at 800 °C, and there was no spallation of its scale at 1200 °C. There was enrichment in Ge and Sn in the substrate below the scale/substrate interface, where the compounds Nb3Sn, Nb5Sn2Si, (Ti,Nb)6Sn5, and Nb5Ge3 were formed. After the oxidation at 1200 °C, the solid solution in the bulk of the alloy was very Ti-rich (Ti,Nb)ss. Improvement of oxidation resistance at both temperatures was accompanied by a decrease and increase, respectively, of the alloy parameters VEC (valence electron concentration) and δ, in agreement with the alloy design methodology NICE (Niobium Intermetallic Composite Elaboration). The elimination of scale spallation at 1200 °C was attributed (a) to the formation of Ti-rich (Ti,Nb)ss solid solution and (Ti,Nb)6Sn5, respectively, in the bulk and below the scale, (b) to the low concentration of Cr in the scale, (c) to the absence of GeO2 in the scale, (d) to the formation of αAl2O3 in the scale, and (e) to the presence (i) of Nb5Ge3 below the scale/substrate interface and (ii) of oxides in the scale, namely, SiO2, Al2O3, TiO2, and SnO2, and Ti2Nb10O29,TiNb2O7, and AlNbO4, respectively, with a range of intrinsic thermal shock resistances and coefficient of thermal expansion (CTE) values that reduced stresses in the scale and the substrate below it.
The Added Value of Diagnostic and Theranostic PET Imaging for the Treatment of CNS Tumors

This review highlights the added value of PET imaging in Central Nervous System (CNS) tumors, which is a tool that has rapidly evolved from a merely diagnostic setting to multimodal molecular diagnostics and the guidance of targeted therapy. PET is the method of choice for studying target expression and target binding behind the assumedly intact blood–brain barrier. Today, a variety of diagnostic PET tracers can be used for the primary staging of CNS tumors and to determine the effect of therapy. Additionally, theranostic PET tracers are increasingly used in the context of pharmaceutical and radiopharmaceutical drug development and application. In this approach, a single targeted drug is used for PET diagnosis, upon the coupling of a PET radionuclide, as well as for targeted (nuclide) therapy. Theranostic PET tracers have the potential to serve as a non-invasive whole body navigator in the selection of the most effective drug candidates and their most optimal dose and administration route, together with the potential to serve as a predictive biomarker in the selection of patients who are most likely to benefit from treatment. PET imaging supports the transition from trial and error medicine to predictive, preventive, and personalized medicine, hopefully leading to improved quality of life for patients and more cost-effective care.
Leptin Signaling in Liver Tissue of a Transgenic Breast Cancer Mouse Model

Leptin, an adipocytokine, is secreted from various tissues including the liver. The roles of both leptin and leptin receptor (ObR) in numerous pathophysiological conditions including mammary tumor (MT) development have been reported. However, the roles of leptin signaling-related proteins in the liver have not been reported previously in MT development. The objective of this study was to examine the expression levels of leptin and ObR in liver tissue of a transgenic breast cancer mouse model to investigate whether the roles of leptin in MT development are systemic or local. MMTV-TGF-α transgenic female mice were fed ad-libitum from week 10 up to week 74. Protein expression levels of leptin and ObR were measured in liver tissues of 74-week-old MMTV-TGF-α mice with and without MT by western blot. Serum leptin and insulin levels were measured using a enzyme-linked immunosorbent assay. Protein expression levels of leptin and ObR were similar in mice with MT compared to the ones without MT. Serum leptin and insulin levels were also not significantly different between the two groups. These results indicate that the effects of leptin signaling in MT development might be important at a local tissue level, such as mammary fat pad, and not as important at a systemic level.
Intuitionistic Euler-Venn Diagrams (extended)

We present an intuitionistic interpretation of Euler-Venn diagrams with respect to Heyting algebras. In contrast to classical Euler-Venn diagrams, we treat shaded and missing zones differently, to have diagrammatic representations of conjunction, disjunction and intuitionistic implication. We present a cut-free sequent calculus for this language, and prove it to be sound and complete. Furthermore, we show that the rules of cut, weakening and contraction are admissible.
Geometric Dataset Distances via Optimal Transport

The notion of task similarity is at the core of various machine learning paradigms, such as domain adaptation and meta-learning. Current methods to quantify it are often heuristic, make strong assumptions on the label sets across the tasks, and many are architecture-dependent, relying on task-specific optimal parameters (e.g., require training a model on each dataset). In this work we propose an alternative notion of distance between datasets that (i) is model-agnostic, (ii) does not involve training, (iii) can compare datasets even if their label sets are completely disjoint and (iv) has solid theoretical footing. This distance relies on optimal transport, which provides it with rich geometry awareness, interpretable correspondences and well-understood properties. Our results show that this novel distance provides meaningful comparison of datasets, and correlates well with transfer learning hardness across various experimental settings and datasets.
I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents

Dialogue research tends to distinguish between chit-chat and goal-oriented tasks. While the former is arguably more naturalistic and has a wider use of language, the latter has clearer metrics and a straightforward learning signal. Humans effortlessly combine the two, for example engaging in chit-chat with the goal of exchanging information or eliciting a specific response. Here, we bridge the divide between these two domains in the setting of a rich multi-player text-based fantasy environment where agents and humans engage in both actions and dialogue. Specifically, we train a goal-oriented model with reinforcement learning against an imitation-learned ``chit-chat'' model with two approaches: the policy either learns to pick a topic or learns to pick an utterance given the top-K utterances from the chit-chat model. We show that both models outperform an inverse model baseline and can converse naturally with their dialogue partner in order to achieve goals.
Massless Phases for the Villain model in $d\geq 3$

We consider the classical Villain rotator model in $\mathbb{Z}^d, d\geq 3$ at sufficiently low temperature, and prove that the truncated two-point function decays asymptotically as $|x|^{2-d}$, with an algebraic rate of convergence. We also obtain the same asymptotic decay separately for the transversal two-point functions. This quantifies the spontaneous magnetization result for the Villain model at low temperature, and rigorously establishes the Gaussian spin-wave conjecture in dimension $d\ge 3$. We believe that our method extends to finite range interactions and to other abelian spin systems and abelian gauge theory in $d\geq 3$.
Detection of PAH Absorption and Determination of the Mid-infrared Diffuse Interstellar Extinction Curve from the Sight Line toward Cyg OB2-12

The sight line toward the luminous blue hypergiant Cyg OB2-12 is widely used to study interstellar dust on account of its large extinction (AV ≃ 10 mag) and the fact that this extinction appears to be dominated by dust typical of the diffuse interstellar medium. We present a new analysis of archival Infrared Space Observatory Short Wavelength Spectrometer and Spitzer IRS observations of Cyg OB2-12 using a model of the emission from the star and its stellar wind to determine the total extinction Aλ from 2.4 to 37 μm. In addition to the prominent 9.7 and 18 μm silicate features, we robustly detect absorption features associated with polycyclic aromatic hydrocarbons, including the first identification of the 7.7 μm feature in absorption. The 3.3 μm aromatic feature is found to be much broader in absorption than is typically seen in emission. The 3.4 and 6.85 μm aliphatic hydrocarbon features are observed with relative strengths that are consistent with observations of these features on sight lines toward the Galactic center. We identify and characterize more than 60 spectral lines in this wavelength range, which may be useful in constraining models of the star and its stellar wind. Based on this analysis, we present an extinction curve that extrapolates smoothly to determinations of the mean Galactic extinction curve at shorter wavelengths and to dust opacities inferred from emission at longer wavelengths, providing a new constraint on models of interstellar dust in the mid-infrared.
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization

We consider the problem of robust optimization within the well-established Bayesian optimization (BO) framework. While BO is intrinsically robust to noisy evaluations of the objective function, standard approaches do not consider the case of uncertainty about the input parameters. In this paper, we propose Noisy-Input Entropy Search (NES), a novel information-theoretic acquisition function that is designed to find robust optima for problems with both input and measurement noise. NES is based on the key insight that the robust objective in many cases can be modeled as a Gaussian process, however, it cannot be observed directly. We evaluate NES on several benchmark problems from the optimization literature and from engineering. The results show that NES reliably finds robust optima, outperforming existing methods from the literature on all benchmarks.
Unraveling the Nature of the Deeply Embedded Wolf–Rayet Star WR 121a

An X-ray study of the deeply embedded Wolf–Rayet star WR 121a has been carried out using long-term (spanning over ∼12 yr) archival observations from Chandra and XMM-Newton. For the first time, a periodic variation of 4.1 days has been detected in the X-ray light curve of WR 121a. No companion is seen in a merged and exposure-corrected Chandra X-ray image of WR 121a, as found in other previous observations in the J band. The X-ray spectrum of WR 121a is well explained by a thermal plasma emission model with temperatures of 0.98 ± 0.34 keV and 3.55 ± 0.69 keV for the cool and hot components, respectively, and nonsolar abundances. The present study indicates that WR 121a is an X-ray-bright massive binary with an X-ray luminosity of ∼1034 erg s−1, which can be explained by active wind collision between its components. Phase-locked modulations have been seen in the flux variation of WR 121a where the flux increases by a factor of ∼1.6 from minimum to maximum in the 0.3–10.0 keV energy band. These variations could be caused by the wind collision region being eclipsed by the secondary star in a binary orbit. The winds of both components of WR 121a appear to be radiative. Radiative inhibition and radiative braking are the most likely processes that are severely affecting the wind collision in this short-period massive binary system.
Convergence Rates of Accelerated Markov Gradient Descent with Applications in Reinforcement Learning

Motivated by broad applications in machine learning, we study the popular accelerated stochastic gradient descent (ASGD) algorithm for solving (possibly nonconvex) optimization problems. We characterize the finite-time performance of this method when the gradients are sampled from Markov processes, and hence biased and dependent from time step to time step; in contrast, the analysis in existing work relies heavily on the stochastic gradients being independent and sometimes unbiased. Our main contributions show that under certain (standard) assumptions on the underlying Markov chain generating the gradients, ASGD converges at the nearly the same rate with Markovian gradient samples as with independent gradient samples. The only difference is a logarithmic factor that accounts for the mixing time of the Markov chain. One of the key motivations for this study are complicated control problems that can be modeled by a Markov decision process and solved using reinforcement learning. We apply the accelerated method to several challenging problems in the OpenAI Gym and Mujoco, and show that acceleration can significantly improve the performance of the classic temporal difference learning and REINFORCE algorithms.
Self-Awareness-Based Resource Allocation Strategy for Containment of Epidemic Spreading

Resource support between individuals is of particular importance in controlling or mitigating epidemic spreading, especially during pandemics. However, there remains the question of how we can protect ourselves from being infected while helping others by donating resources in fighting against the epidemic. To answer the question, we propose a novel resource allocation model by considering the awareness of self-protection of individuals. In the model, a tuning parameter is introduced to quantify the reaction strength of individuals when they are aware of the disease. And then, a coupled model of resource allocation and disease spreading is proposed to study the impact of self-awareness on resource allocation and its impact on the dynamics of epidemic spreading. Through theoretical analysis and extensive Monte Carlo simulations, we find that in the stationary state, the system converges to two states: the whole healthy or the completely infected, which indicates an abrupt increase in the prevalence when there is a shortage of resources. More importantly, we find that too cautious and too selfless for the people during the outbreak of an epidemic are both not suitable for disease control. Through extensive simulations, we locate the optimal point, at which there is a maximum value of the epidemic threshold, and an outbreak can be delayed to the greatest extent. At last, we study further the effects of the network structure on the coupled dynamics. We find that the degree heterogeneity promotes the outbreak of disease, and the network structure does not alter the optimal phenomenon in behavior response. Based on the results of this study, a constructive suggestion is that in the face of a global pandemic, individuals or countries should strengthen mutual support and cooperation while doing their own prevention to suppress the epidemic optimally.
The Impact of Planetary Rotation Rate on the Reflectance and Thermal Emission Spectrum of Terrestrial Exoplanets around Sunlike Stars

Robust atmospheric and radiative transfer modeling will be required to properly interpret reflected-light and thermal emission spectra of terrestrial exoplanets. This will help break observational degeneracies between the numerous atmospheric, planetary, and stellar factors that drive planetary climate. Here, we simulate the climates of earthlike worlds around the Sun with increasingly slow rotation periods, from earthlike to fully Sun-synchronous, using the ROCKE-3D general circulation model. We then provide these results as input to the Spectral Planet Model, which employs the Spectral Mapping Atmospheric Radiative Transfer model to simulate the spectra of a planet as it would be observed from a future space-based telescope. We find that the primary observable effects of slowing planetary rotation rate are the altered cloud distributions, altitudes, and opacities that subsequently drive many changes to the spectra by altering the absorption band depths of biologically relevant gas species (e.g., , , and ). We also identify a potentially diagnostic feature of synchronously rotating worlds in mid-infrared absorption/emission lines.