Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiotherapy private practitioners’ opinions regarding interprofessional collaborative practice: A qualitative study

ABSTRACT Physiotherapy private practitioners comprise a growing proportion of Australia’s primary care workforce, yet their views and experiences of interprofessional collaborative practice (IPCP) are poorly documented. The aim of this study was to explore Australian physiotherapy private practitioners’ opinions regarding IPCP. Twenty-eight semi-structured interviews were conducted with physiotherapists in 10 private practice sites in Queensland, Australia. Interviews were analyzed using reflexive thematic analysis. Data analysis produced five themes that characterized physiotherapists’ perceptions of IPCP: (a) quality of care considerations; (b) not a one-size-fits-all approach; (c) the need for effective interprofessional communication; (d) fostering a positive work culture; and (e) fear of losing clientele. The findings from this study suggest that physiotherapy private practitioners value IPCP because it can deliver superior client outcomes, can strengthen interprofessional relationships, and has the potential to enhance the professional reputation of the organizations within which they work. Physiotherapists also claimed that IPCP can contribute to poor client outcomes when performed inappropriately, while some reported approaching interprofessional referrals with caution following instances of lost clientele. The mixed views toward IPCP in this study highlight the need to explore the facilitators and barriers to IPCP in the Australian physiotherapy private practice setting.
La théorie du polynôme de Bernstein-Sato pour les algèbres de Tate et de Dwork-Monsky-Washnitzer

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Insights into the toxicity, biochemical activity, and molecular docking of Cymbopogon citratus essential oils and citral on Spodoptera littoralis (Lepidoptera: Noctuidae)

Insecticide resistance is a significant problem in insect management that can result from several processes including target-site change and increased activity of detoxifying enzymes. Spodoptera littoralis is one of the most resistant insect pests. For more effective insect management, alternatives to synthetic pesticides are encouraged. One of these alternatives is essential oils (EOs). Cymbopogon citratus EO and its main constituent citral were, therefore, considered in this study. The results revealed that C. citratus EO and citral exhibited significant larvicidal activity against S. littoralis, and the former was insignificantly more toxic than the latter. Additionally, treatments significantly affected the activity of detoxification enzymes. Cytochrome P-450 and glutathione-S-transferase were inhibited, while carboxylesterases, a-esterase and β-esterase, were induced. The molecular docking study indicated that citral bonded with the amino acids cysteine (CYS 345) and histidine (HIS 343) of cytochrome P-450. This result suggests that interaction with cytochrome P-450 enzyme is one key mechanism by which C. citratus EO and citral act in S. littoralis. The results of our study are hoped to contribute to a better understanding of the mechanism of action of essential oils at the biochemical and molecular levels and provide safer and more efficient pest management solutions for S. littoralis. Graphical Abstract
Quantitative nanopatterning of fg-scale liquids with dip-pen nanolithography

The ability to precisely pattern nanoscale amounts of liquids is essential for biotechnology and high-throughput chemistry, but controlling fluid flow on these scales is very challenging. Scanning probe lithography methods such as dip-pen nanolithography (DPN) provide a mechanism to write fluids at the nanoscale, but this is an open loop process as methods to provide feedback while patterning sub-pg features have yet to be reported. Here, we demonstrate a novel method for programmably nanopatterning liquid features at the fg-scale through a combination of ultrafast atomic force microscopy probes, the use of spherical tips, and inertial mass sensing. We begin by investigating the required probe properties that would provide sufficient mass responsivity to detect fg-scale mass changes and find ultrafast probes to be capable of this resolution. Further, we attach a spherical bead to the tip of an ultrafast probe as we hypothesize that the spherical tip could hold a drop at its apex which both facilitates interpretation of inertial sensing and maintains a consistent fluid environment for reliable patterning. We experimentally find that sphere-tipped ultrafast probes are capable of reliably patterning hundreds of features in a single experiment. Analyzing the changes in the vibrational resonance frequency during the patterning process, we find that drift in the resonance frequency complicates analysis, but that it can be removed through a systematic correction. Subsequently, we quantitatively study patterning using sphere-tipped ultrafast probes as a function of retraction speed and dwell time to find that the mass of fluid transferred can be modulated by greater than an order of magnitude and that liquid features as small as 6 fg can be patterned and resolved. Taken together, this work addresses a persistent concern in DPN by enabling quantitative feedback for nanopatterning of aL-scale features and lays the foundation for programmably nanopatterning fluids.
Application of metagenomic sequencing of drainage fluid in rapid and accurate diagnosis of postoperative intra-abdominal infection: a diagnostic study

Background: Postoperative intra-abdominal infection (PIAI) is one of the most serious complications of abdominal surgery, increasing the risk of postoperative morbidity and mortality and prolonging hospital stay. Rapid diagnosis of PIAI is of great clinical value. Unfortunately, the current diagnostic methods of PIAI are not fast and accurate enough. Methods: The authors performed an exploratory study to establish a rapid and accurate diagnostic method of PIAI. The authors explored the turnaround time and accuracy of metagenomic next-generation sequencing (mNGS) in diagnosing PIAI. Patients who underwent elective abdominal surgery and routine abdominal drainage with suspected PIAI were enroled in the study. The fresh midstream abdominal drainage fluid was collected for mNGS and culturing. Results: The authors found that the median sample-to-answer turnaround time of mNGS was dramatically decreased than that of culture-based methods (<24 h vs. 59.5–111 h). The detection coverage of mNGS was much broader than culture-based methods. The authors found 26 species from 15 genera could only be detected by mNGS. The accuracy of mNGS was not inferior to culture-based methods in the 8 most common pathogens detected from abdominal drainage fluid (sensitivity ranged from 75 to 100%, specificity ranged from 83.3 to 100%, and kappa values were higher than 0.5). Moreover, the composition of the microbial spectrum established by mNGS varied between upper and lower gastrointestinal surgery, enhancing the understanding of PIAI pathogenesis. Conclusion: This study preliminarily revealed the clinical value of mNGS in the rapid diagnosis of PIAI and provided a rationale for further research.
Fuzzy Prediction of Transmembrane Protein Topology

The prediction of the topology of transmembrane proteins is one of the open issues in bioinformatics. There are many methods proposed to predict transmembrane protein topology. However, these methods have some shortcomings to correctly predict on the boundary of the transmembrane region. In this paper, a new transmembrane prediction based on data fusion technology is proposed. The results of different prediction methods, represented as discrepant interval data, can be combined with induced ordered weighted averaging operator. The appropriate fuzzy interval can be determined. The transmembrane regions can be predicted by choosing the threshold. The real application in transmembrane prediction is used to show the efficiency of the proposed method.
Systems of Weakly Coupled Hamilton-Jacobi Equations with Implicit Obstacles

Abstract In this paper we study systems of weakly coupled Hamilton-Jacobi equations with implicit obstacles that arise in optimal switching problems. Inspired by methods from the theory of viscosity solutions and weak $\text{KAM}$ theory, we extend the notion of Aubry set for these systems. This enables us to prove a new result on existence and uniqueness of solutions for the Dirichlet problem, answering a question of F. Camilli, P. Loreti, and N. Yamada.
Research Addressing the Ongoing Changes in the Practice of Family Medicine

The articles in this issue are divided into 3 categories: 1) increasing our understanding of people’s (patients’) behaviors; 2) changing how we practice Family Medicine; and 3) rethinking common clinical problems. These categories include multiple topics: nonprescription antibiotic use, recording electronic smoking/vaping, virtual wellness visits, an electronic pharmacist consult service, documenting social determinants of health, medical-legal partnerships, local professionalism, implications of peripheral neuropathy, harm-reduction informed care, decreasing cardiovascular risk, persistent symptoms, and colonoscopy harm.
Short-term jet lag exposure under varying day lengths does not affect anxiety-like or depression-like behavior in mice

Abstract Jet lag is a disruption to daily biological circadian rhythms that affect millions of people worldwide each year. Jet lag negatively impacts health, including behavioral changes in mood. Seasonal changes in day length have also been associated with mood disorders. In this study, we aim to understand how jet lag, under different day lengths, affects anxiety and depression. Using the mouse as a model organism, we find that neither jet lag nor day length negatively impacted anxiety or depression. Compared to studies that demonstrate the negative impacts of chronic jet lag on mood, this study reveals a less severe effect on mood within the first two days after jet lag.
Using molecular dynamics simulations to show differences of movement over time in the CCR5 variants of genetically edited children

Abstract In 2018 He Jiankui presented a study where he claimed he successfully used CRISPR to genetically modify a set of twins, intending to provide them with resistance against HIV. Dr. He modified CCR5, C-C chemokine receptor type 5, in two patients which have been given the names “Lulu” and “Nana”. It has not been clinically confirmed that the variants introduced provide a protective effect. Because of the barriers to collecting human clinical data, here we use homology modeling to predict the effects of these proteins. Molecular dynamics simulations are used to compare these new variants to known variants using 3D protein models produced from predicted translations. This allows us to follow and compare the individual amino acid movement of the variants within a simulated aqueous environment over time. The simulations of the novel variants in twins Lulu and Nana showed differences in the amino acid residue movement of the protein over a 20-nanosecond interval when compared with known variants. This allows us to determine the stability of the protein by following the way the amino acids within the protein move in the simulated aqueous environment. Stability and amino acid movement affects the regulation and expression of the protein and can be altered when specific amino acids are targeted. The differences in the model simulations presented here suggest potential changes in functionality or stability.
Instantaneous frequency and Wigner-Ville Distribution using the Maximum Entropy Method: Application for gas and hydrates identification.

SUMMARY Time-frequency (TF) analysis can reveal important details of seismic data and provide valuable information for reservoir characterization. Resolution in the TF plane is clearly critical for interpretation. Many methods applied to time-frequency representation introduce spurious or cross-terms, essentially when using bilinear functions such as the Wigner Ville Distribution (WVD). Most techniques proposed to overcome this shortcoming use a smoothed kernel which in turn can adversely affect the component’s concentration in the TF plane. We propose to apply the Maximum Entropy Method (MEM) to WVD to obtain a robust and high resolution timefrequency representation of seismic traces, and we also introduce a formula to estimate instantaneous frequency (IF) in time domain. We apply our approach to the spectral decomposition and IF analysis of a seismic dataset from the Gulf of Mexico. The analysis allows us to identify hydrates and a gas pocket.
Explaining Image Aesthetics Assessment: An Interactive Approach

Assessing visual aesthetics is important for organizing and retrieving photos. That is one reason why several works aim to automate such an assessment using deep neural networks. The underlying models, however, lack explainability. Due to the subjective nature of aesthetics, it is challenging to find objective ground truths and explanations for aesthetics based on them. Hence, such models are prone to socio-cultural biases that come with the data, which raises questions on a wide range of ethical and technical issues. This paper presents an explainable artificial intelligence framework that adapts and combines three types of explanations for the concept of aesthetic assessment: 1) model constraints for built-in interpretability, 2) analysis of perturbation impacts on decisions, and 3) generation of artificial images that represent maxima or minima of values in the latent feature space. The objective is to improve human understanding through the explanations by creating an intuition for the model’s decision making. We identify issues that arise when humans interact with the explanations and derive requirements from human feedback to address the needs of different user groups. We evaluate our novel interactive explainable artificial intelligence technology in a study with end users (N=20). Our participants have different levels of experience in deep learning, allowing us to include experts, intermediate users, and laypersons. Our results show the benefits of the interactivity of our approach. All users found our system helpful in understanding how the aesthetic assessment was executed, reporting varying needs for explanatory details.
FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning

Unidentified devices in a network can result in devastating consequences. It is, therefore, necessary to fingerprint and identify IoT devices connected to private or critical networks. With the proliferation of massive but heterogeneous IoT devices, it is getting challenging to detect vulnerable devices connected to networks. Current machine learning-based techniques for fingerprinting and identifying devices necessitate a significant amount of data gathered from IoT networks that must be transmitted to a central cloud. Nevertheless, private IoT data cannot be shared with the central cloud in numerous sensitive scenarios. Federated learning (FL) has been regarded as a promising paradigm for decentralized learning and has been applied in many different use cases. It enables machine learning models to be trained in a privacy-preserving way. In this article, we propose a privacy-preserved IoT device fingerprinting and identification mechanisms using FL; we call it FL4IoT. FL4IoT is a two-phased system combining unsupervised-learning-based device fingerprinting and supervised-learning-based device identification. FL4IoT shows its practicality in different performance metrics in a federated and centralized setup. For instance, in the best cases, empirical results show that FL4IoT achieves ∼99% accuracy and F1-Score in identifying IoT devices using a federated setup without exposing any private data to a centralized cloud entity. In addition, FL4IoT can detect spoofed devices with over 99% accuracy.
Efficiently Making Cross-Engine Transactions Consistent

Database systems are becoming increasingly multi-engine. In particular, a main-memory engine may coexist with a traditional storage-centric engine in a system to support various applications. It is desirable to allow applications to access data in both engines using cross-engine transactions. But existing systems are either only designed for singleengine accesses, or impose many restrictions by limiting crossengine transactions to certain isolation levels and operations. The result is inadequate cross-engine support in terms of correctness, performance and programmability.
CalorieCam360: Simultaneous Eating Action Recognition of Multiple People Using an Omnidirectional Camera

In recent years, as people become more health-conscious, dietary management has become increasingly important. Existing methods record only one person’s meals or eating movements, but cannot record the meals of multiple people at the same time. Therefore, we aim to record the meals of all people around a dining table using an omnidirectional camera simultaneously. In this study, we propose CalorieCam360, a system that records the entire dining table using only an omnidirectional camera and a smartphone. Note that all the processing is done inside the smartphone application without using any external servers. Since the images from the omnidirectional camera are distorted and cannot be used for detection as they are, the distortion is corrected using plane projection. The corrected images are used to detect rectangular objects that serve as references for object size, and the area is calculated by combining object detection and region segmentation to estimate the amount of calories from the area. The system then uses person detection and region segmentation to track the person and the food and records the amount of food consumed and its calorie content for each person. We demonstrate that CalorieCam360 can record an entire meal at once for multiple people around the table.
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation

This paper investigates the problem of scene graph generation in videos with the aim of capturing semantic relations between subjects and objects in the form of ⟨ subject, predicate, object⟩ triplets. Recognizing the predicate between subject and object pairs is imbalanced and multi-label in nature, ranging from ubiquitous interactions such as spatial relationships (e.g. in front of) to rare interactions such as twisting. In widely-used benchmarks such as Action Genome and VidOR, the imbalance ratio between the most and least frequent predicates reaches 3,218 and 3,408, respectively, surpassing even benchmarks specifically designed for long-tailed recognition. Due to the long-tailed distributions and label co-occurrences, recent state-of-the-art methods predominantly focus on the most frequently occurring predicate classes, ignoring those in the long tail. In this paper, we analyze the limitations of current approaches for scene graph generation in videos and identify a one-to-one correspondence between predicate frequency and recall performance. To make the step towards unbiased scene graph generation in videos, we introduce a multi-label meta-learning framework to deal with the biased predicate distribution. Our meta-learning framework learns a meta-weight network for each training sample over all possible label losses. We evaluate our approach on the Action Genome and VidOR benchmarks by building upon two current state-of-the-art methods for each benchmark. The experiments demonstrate that the multi-label meta-weight network improves the performance for predicates in the long tail without compromising performance for head classes, resulting in better overall performance and favorable generalizability. Code: https://github.com/shanshuo/ML-MWN.
A Primer on Seq2Seq Models for Generative Chatbots

The recent spread of Deep Learning-based solutions for Artificial Intelligence and the development of Large Language Models has pushed forwards significantly the Natural Language Processing area. The approach has quickly evolved in the last ten years, deeply affecting NLP, from low-level text pre-processing tasks –such as tokenisation or POS tagging– to high-level, complex NLP applications like machine translation and chatbots. This article examines recent trends in the development of open-domain data-driven generative chatbots, focusing on the Seq2Seq architectures. Such architectures are compatible with multiple learning approaches, ranging from supervised to reinforcement and, in the last years, allowed to realise very engaging open-domain chatbots. Not only do these architectures allow to directly output the next turn in a conversation but, to some extent, they also allow to control the style or content of the response. To offer a complete view on the subject, we examine possible architecture implementations as well as training and evaluation approaches. Additionally, we provide information about the openly available corpora to train and evaluate such models and about the current and past chatbot competitions. Finally, we present some insights on possible future directions, given the current research status.
On graphs that do not contain a theta nor a wheel Part I: two subclasses

Truemper configurations are four types of graphs (namely thetas, wheels, prisms and pyramids) that play an important role in the proof of several decomposition theorems for hereditary graph classes. In this paper, we prove two structure theorems: one for graphs with no thetas, wheels and prisms as induced subgraphs, and one for graphs with no thetas, wheels and pyramids as induced subgraphs. A consequence is a polynomial time recognition algorithms for these two classes.
Designing to Drive Practice Change: Automated Extraction of Resident Clinical Experiences

Exposure to patients and clinical diagnoses drives learning in graduate medical education (GME). However, variation exists in the breadth of experiences. Measuring such variation would provide practice data to inform residents’ understanding of the breadth of their patient experiences. We have developed an automated system to identify resident provider-patient interactions (rPPIs) and demonstrated accurate attribution at a single institution. The objective of this study was to understand the landscape of trainee planned learning, and iteratively design a tool to be used for this goal. To achieve these objectives at two institutions new to the AMA “Advancing Change” initiative, we used a mixed-methods approach to develop and evaluate a “mid-point report” of patients encounters. Qualitative outcomes include a guided exploration of usefulness, usability, and intent to use, as well as understanding the resources trainees would use for learning and how our system may deliver these resources. Quantitative outcomes from a summative usability test of the midpoint report will include time on task, task completion rate, and proportion of trainees who perceive the report to be useful to identify gaps in clinical experiences and guide learning.