Miracle-dz/UniFAR-FTUs huggingface dataset:
Radiographic signs and hip pain 5 years after THA with a cemented stem predict future revision for aseptic loosening: a prospective cohort study

Background and purpose We aimed to evaluate the long-term predictive value of radiographic abnormality and/or hip pain assessed 5 years following primary total hip arthroplasty (THA) and the occurrence of revision for aseptic loosening between 5 and 25 years postoperatively. Patients and methods We included all primary THAs performed between 1996 and 2011 (same uncemented cup, polyethylene-ceramic bearing, 28 mm head, cemented stem) and prospectively enrolled in the institutional registry, for whom baseline and follow-up radiographs were available. At 5 years radiographically we assessed femoral osteolysis and/or stem migration. Pain was evaluated with the Harris Hip pain subscore. Kaplan–Meier survival and Cox regression analyses were performed. Results 1,317 primary THAs were included. 25 THAs (2%) were revised for aseptic stem loosening. Any abnormal radiographic sign at 5 years was present in 191 THAs (14%). Occasional hip pain was reported by 20% and slight to severe pain by 12% of patients at 5 years. In patients < 60 years, 10 of the 12 later revised for aseptic stem loosening had abnormal radiographs at 5 years vs. 5 of the 13 later revised in those ≥ 60 years. Hazard ratios (HR) were 34 (95% confidence interval [CI] 7–155) in younger vs. 4 (CI 1–11) in the older group. HR for association of hip pain at 5 years with future revision was 3 (CI 1–5). Conclusion The presence of abnormal radiographic signs 5 years after THA was strongly associated with later revision for aseptic stem loosening, especially in patients < 60 years. The association between pain at 5 years and future revision was much weaker.
An overview of the nutritional status of childbearing age women, children and adolescents living in a rural area of Madagascar: preliminary results of the Tany Vao project

Objective: To describe the food consumption, nutrition knowledge and nutritional assessment of childbearing age women and their children, living in rural villages in Madagascar. The results presented are related to the Tany Vao research study. Design: A cross-sectional pilot study. Setting: The study was carried out in Ampanitosoha village on Nosy Mitsio island in Madagascar. Participants: 32 women (14–49 years) and 36 children and adolescents (2–17 years). Results: 70 % of the women lacked nutrition knowledge and did not reach the Minimum Dietary Diversity Index for Women cut-off. The median BMI was 21·1 kg/m2 but 55·2 % of the women exceeded the cut-off for waist-to-hip ratio, 51·7 % for waist-to-height ratio and 81·2 % for mid-upper arm circumference (MUAC). Almost all had adequate intake of energy, protein and carbohydrates, while 27·6 % had excessive fat intake and 75·9 % of added sugars. Over half of the women did not meet the micronutrients Reference Daily Intake (RDI). For children, the MUAC z-score was lower for boys than for girls (P-value = 0·041). Conclusions: These results underline the importance of increasing women’s nutritional knowledge to promote healthy pregnancy and lactation. Moreover, it is fundamental to provide people living in rural areas with sustainable tools to improve dietary diversity and support long-term health.
Psoriasis treatment patterns: a retrospective claims study

Abstract Objective: The objective was to characterize psoriasis treatment patterns, including estimating persistence and describing subsequent events (i.e. switching and restarting) for all systemic therapies. Methods: This retrospective cohort study utilized Truven MarketScan databases from 1 January 2014 to 31 December 2016 to investigate persistence, switching and restarting in new users of systemic psoriasis medications. Descriptive statistics, time-to-event analyses and a Cox proportional hazards regression were conducted. Results: A total of 5205 patients met inclusion criteria. Regardless of treatment type, >50% lost persistence by 12 months. Patients newly initiating acitretin or non-TNF biologic experienced the highest loss of persistence (85.2%, 73.8%, respectively). Patients initiating a TNF-α inhibitor or apremilast experienced the lowest loss (51.8%, 56.4% respectively). Treatment type had a statistically significant effect on persistence loss (adjusted hazard ratio: 0.86, 95% CI: 0.81, 0.91). Restarting was the most commonly observed event for patients on an oral or biologic (60.2%, 79.9%, respectively). The most common switch from an oral was to a TNF-α inhibitor, while apremilast often followed biologics. Conclusion: Most patients lost persistence on initial treatment by 12 months, and the majority restarted treatment. This may indicate poor compliance or the cyclical nature of psoriasis. More patients switched from an oral to biologic than vice versa, likely due to formulary design and preference for orals. Studies are needed to investigate underlying reasons and patient characteristics that differentiate treatment utilization.
Filling knowledge gaps in insect conservation by leveraging genetic data from public archives

Abstract Insect decline has become a growing concern in recent years, with studies showing alarming declines in populations of several taxa. Our knowledge about genetic spatial patterns and evolutionary history of insects still exhibits significant gaps hindering our ability to effectively conserve and manage insect populations and species. Genetic data may provide valuable insights into the diversity and the evolutionary relationships of insects’ species and populations. Public repositories, such as GenBank and BOLD, containing vast archives of genetic data with associated metadata, offer an irreplaceable resource for researchers contributing to our understanding of species diversity, population structure and evolutionary relationships. However, there are some issues in using these data, as they are often scattered and may lack accuracy due to inconsistent sampling protocols and incomplete information. In this paper we describe a curated georeferenced database of genetic data collected in GenBank and BOLD, for insects listed in the International Union for Conservation of Nature (IUCN) Italian Red Lists (dragonflies, bees, saproxylic beetles and butterflies). After querying these repositories, we performed quality control and data standardization steps. We created a dataset containing approximately 33 000 mitochondrial sequences and associated metadata about taxonomy, collection localities, geographic coordinates and IUCN Red List status for 1466 species across the four insect lists. We describe the current state of geographical metadata in queried repositories for species listed under different conservation status in the Italian Red Lists to quantify data gaps posing barriers to prioritization of conservation actions. Our curated dataset is available for data repurposing and analysis, enabling researchers to conduct comparative studies. We emphasize the importance of filling knowledge gaps in insect diversity and distribution and highlight the potential of this dataset for promoting other research fields like phylogeography, macrogenetics and conservation strategies. Our database can be downloaded through the Zenodo repository in SQL format. Database URL: https://zenodo.org/records/8375181
Effectiveness of an Educational Intervention on Clinical Competency among Mental Health Nurses Working at a Governmental Mental Health Hospital: A Quasi – Experimental Study

Mental health nurses play an important role in caring for patients with mental illnesses. In order to provide comprehensive mental health nursing care, it is important to establish periodically educational interventions about mental health and psychiatric nursing inside any mental health institution. The presence of frequent educational programs can lead to improvements in mental health nurses' clinical competency. Aim: To evaluate the effectiveness of an educational intervention on the clinical competency of mental health nurses. A quasi-experiment study pretest and posttest with one group study design was conducted among 80 nurses in a large mental health hospital in Tabuk city, Saudi Arabia. Data were collected by using the clinical competence evaluation in mental health nurses scale to evaluate the effectiveness of an educational intervention on the clinical competency of mental health nurses by comparing the result of the pretest and post-test scores. The total score for the pre-test was (74.83±14.25/Average), and then the score had increased dramatically for the post-test (96.18±26.53/Good) also, the Wilcoxon signed-rank test indicated that Post-test (Mean rank=102) significantly higher than pre-test (Mean rank=59) (U=1480, p<0.001). As a result of the current study, the program showed great effectiveness (p<0.001) with an advantage for post-test. The collected data analysis revealed a statistically significant positive effect of educational programs on improving mental health clinical competency hence, Healthcare organizations and senior decision-makers should develop well designed periodically mental health programs that involve nurses who have a master's and doctorate degree in mental health nursing to enhance mental health clinical competency.
Quantum Private Membership Aggregation

We consider the problem of private set membership aggregation of $N$ parties by using an entangled quantum state. In this setting, the $N$ parties, which share an entangled state, aim to \emph{privately} know the number of times each element (message) is repeated among the $N$ parties, with respect to a universal set $\mathcal{K}$. This problem has applications in private comparison, ranking, voting, etc. We propose an encoding algorithm that maps the classical information into distinguishable quantum states, along with a decoding algorithm that exploits the distinguishability of the mapped states. The proposed scheme can also be used to calculate the $N$ party private summation modulo $P$.
Spot the Error: Non-autoregressive Graphic Layout Generation with Wireframe Locator

Layout generation is a critical step in graphic design to achieve meaningful compositions of elements. Most previous works view it as a sequence generation problem by concatenating element attribute tokens (i.e., category, size, position). So far the autoregressive approach (AR) has achieved promising results, but is still limited in global context modeling and suffers from error propagation since it can only attend to the previously generated tokens. Recent non-autoregressive attempts (NAR) have shown competitive results, which provides a wider context range and the flexibility to refine with iterative decoding. However, current works only use simple heuristics to recognize erroneous tokens for refinement which is inaccurate. This paper first conducts an in-depth analysis to better understand the difference between the AR and NAR framework. Furthermore, based on our observation that pixel space is more sensitive in capturing spatial patterns of graphic layouts (e.g., overlap, alignment), we propose a learning-based locator to detect erroneous tokens which takes the wireframe image rendered from the generated layout sequence as input. We show that it serves as a complementary modality to the element sequence in object space and contributes greatly to the overall performance. Experiments on two public datasets show that our approach outperforms both AR and NAR baselines. Extensive studies further prove the effectiveness of different modules with interesting findings. Our code will be available at https://github.com/ffffatgoose/SpotError.
"Bacchus" Methodological approach for vineyard inventory and management. Chap.4: Textural and structural analysis

Ce chapitre presente les methodes qui ont ete developpees dans le projet Bacchus pour la detection et la caracterisation des parcelles de vigne en imagerie aerienne en se basant sur leur structure. Une analyse texturale est d'abord mise en oeuvre, et completee par l'introduction de contraintes de regularite des contours pour ameliorer la segmentation. Finalement, les parcelles issues de ces premieres etapes sont verifiees et caracterisees au moyen d'une analyse de leur spectre de Fourier. Les resultats obtenus sur diverses zones d'etude du projet Bacchus sont presentes et discutes. / This chapter presents the methodologies that have been developed during the Bacchus project concerning the automatic detection and characterisation of vineyard plots in satellite and aerial images, based on their structural properties. First, a textural analysis has been used. Then shape regularity constraints have been introduced to improve the image segmentation. Finally, the vineyard plots issued from these previous steps are checked and characterised using a Fourier spectrum analysis. Results on various study areas of the Bacchus project are presented and discussed.
Metabolic light absorption, scattering and emission (MetaLASE) microscopy

Optical imaging of metabolism can provide key information about health and disease progression in cells and tissues, however, current methods have lacked gold-standard information about histological structure. Conversely, histology and virtual histology methods have lacked metabolic contrast. Here we present a novel microscopy technology, Metabolic Light Absorption, Scattering and Emission (MetaLASE) microscopy, which rapidly provides a virtual histology and optical metabolic readout simultaneously. Photoacoustic remote sensing microscopy achieves nuclei contrast and hematoxylin contrast using ultraviolet absorption and an eosin contrast using the scattered ultraviolet light. The same ultraviolet source excites endogenous NADH, FAD and collagen autofluorescence allowing a measurable Optical Redox Ratios to see enhanced metabolism in areas of invasive carcinoma in breast and prostate tissues compared to benign regions. Benign chronic inflammation and glands also are seen to exhibit hypermetabolism. MetaLASE microscopy offers promise for future applications in intraoperative margin analysis, and in research applications where greater insights into metabolic activity should be correlated with cell and tissue types.
Dissecting endogeneous genetic circuits from first principles

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a “theory of the experiment” to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. Here, we develop a computational pipeline that makes it possible to systematically explore how each biological and experimental parameter controls measured MPRA data. Specifically, we use equilibrium statistical mechanics in conjunction with predictive base-pair resolution energy matrices to predict expression levels of genes with mutated regulatory sequences and subsequently use mutual information to interpret synthetic MPRA data including recovering the expected binding sites. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.
Competing magnetic states on the surface of multilayer ABC-stacked graphene

We study interaction-mediated magnetism on the surface of ABC-multilayer graphene driven by its zero-energy topological flat bands. Using the random-phase approximation we treat onsite Hubbard repulsion and find multiple competing magnetic states, due to both intra- and inter-valley scattering, with the latter causing an enlarged magnetic unit cell. At half-filling and when the Hubbard repulsion is weak, we observe two different ferromagnetic orders. Once the Hubbard repulsion becomes more realistic, new ferrimagnetic orders arise with distinct incommensurate intra- or inter-valley scattering vectors depending on interaction strength and doping, leading to a multitude of competing magnetic states.
AnisoGNN: graph neural networks generalizing to anisotropic properties of polycrystals

We present AnisoGNNs -- graph neural networks (GNNs) that generalize predictions of anisotropic properties of polycrystals in arbitrary testing directions without the need in excessive training data. To this end, we develop GNNs with a physics-inspired combination of node attributes and aggregation function. We demonstrate the excellent generalization capabilities of AnisoGNNs in predicting anisotropic elastic and inelastic properties of two alloys.
Rapidly rotating radiatively driven convection: experimental and numerical validation of the `geostrophic turbulence' scaling predictions

We experimentally and numerically characterize rapidly rotating radiatively driven thermal convection, beyond the sole heat transport measurements reported in Bouillaut et al. (2021). Based on a suite of direct numerical simulations (DNS) and additional processing of the experimental data collected by Bouillaut et al. (2021), we report the simultaneous validation of the scaling predictions of the `geostrophic turbulence' regime -- the diffusivity-free or `ultimate' regime of rapidly rotating convection -- for the heat transport, the temperature fluctuations, the flow speed and the flow structure. Radiatively driven convection thus appears as a versatile setup for the laboratory observation of the diffusivity-free regimes of various convective flows of geophysical and/or astrophysical interest.
A KDM-Based Approach for Architecture Conformance Checking in Adaptive Systems

Adaptive Systems (ASs) are capable to monitor their behavior and make adjustments when quality goals are not achieved through the MAPE-K, a widely recognized reference model that offers abstractions for designing ASs. By making these abstractions evident in the system structure, numerous benefits emerge, particularly in terms of enhancing the architecture's maintenance and comprehensibility. However, it is observed that many existing ASs are not designed in accordance with MAPE-K, causing these abstractions to remain hidden in their architecture. To address this issue, Architectural Conformance Checking (ACC) emerges as a valuable technique for verifying whether the current architecture (CA) of a system adheres to the rules prescribed by the planned architecture (PA) or a reference model, such as MAPE-K. In this paper, we present REMEDY, a domain-specific approach that encompasses the specification of the planned adaptive architecture based on the MAPE-K reference model, the recovery of the current adaptive architecture, the conformance checking process, and architecture visualizations. Furthermore, our approach is specifically tailored for ASs, incorporating well-known rules from the MAPE-K model. The evaluation of the REMEDY DSL involves a comparison with a general-purpose DSL, and the results demonstrate improvements in productivity. REMEDY facilitates the identification and correction of architectural non-conformance issues, thereby enhancing the overall quality of adaptive systems.
Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization

In this study, we address multi-robot localization issues, with a specific focus on cooperative localization and observability analysis of relative pose estimation. Cooperative localization involves enhancing each robot's information through a communication network and message passing. If odometry data from a target robot can be transmitted to the ego robot, observability of their relative pose estimation can be achieved through range-only or bearing-only measurements, provided both robots have non-zero linear velocities. In cases where odometry data from a target robot are not directly transmitted but estimated by the ego robot, both range and bearing measurements are necessary to ensure observability of relative pose estimation. For ROS/Gazebo simulations, we explore four sensing and communication structures. We compare extended Kalman filtering (EKF) and pose graph optimization (PGO) estimation using different robust loss functions (filtering and smoothing with varying batch sizes of sliding windows) in terms of estimation accuracy. In hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation, applying both EKF and PGO and comparing their performance.
Roq: Robust Query Optimization Based on a Risk-aware Learned Cost Model

Query optimizers in relational database management systems (RDBMSs) search for execution plans expected to be optimal for a given queries. They use parameter estimates, often inaccurate, and make assumptions that may not hold in practice. Consequently, they may select execution plans that are suboptimal at runtime, when these estimates and assumptions are not valid, which may result in poor query performance. Therefore, query optimizers do not sufficiently support robust query optimization. Recent years have seen a surge of interest in using machine learning (ML) to improve efficiency of data systems and reduce their maintenance overheads, with promising results obtained in the area of query optimization in particular. In this paper, inspired by these advancements, and based on several years of experience of IBM Db2 in this journey, we propose Robust Optimization of Queries, (Roq), a holistic framework that enables robust query optimization based on a risk-aware learning approach. Roq includes a novel formalization of the notion of robustness in the context of query optimization and a principled approach for its quantification and measurement based on approximate probabilistic ML. It also includes novel strategies and algorithms for query plan evaluation and selection. Roq also includes a novel learned cost model that is designed to predict query execution cost and the associated risks and performs query optimization accordingly. We demonstrate experimentally that Roq provides significant improvements to robust query optimization compared to the state-of-the-art.
Nobody Expects a Differential Equation: Minimum Energy-Per-Bit for the Gaussian Relay Channel with Rank-1 Linear Relaying

Motivated by the design of low-complexity low-power coding solutions for the Gaussian relay channel, this work presents an upper bound on the minimum energy-per-bit achievable on the Gaussian relay channel using rank-1 linear relaying. Our study addresses high-dimensional relay codes and presents bounds that outperform prior known bounds using 2-dimensional schemes. A novelty of our analysis ties the optimization problem at hand to the solution of a certain differential equation which, in turn, leads to a low energy-per-bit achievable scheme.