Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of patients in the governance of a sustainable healthcare system: A scoping review

Patients, healthcare providers and insurers need a governance framework to establish the ‘rules of use’ to deliver more responsible use of services. The objective of this review was to provide an overview of frameworks and analyze the definitions of patient accountability to identify themes and potential gaps in the literature. Fifteen bibliographic databases were searched until July 2021. This included: MEDLINE, EMBASE, CINAHL, PsycINFO, SPORTDiscus, Allied and Complementary Medicine Database, Web of Science, HealthSTAR, Scopus, ABI/INFORM Global, Cochrane Library, ERIC, International Bibliography of the Social Sciences, Sociological Abstracts, Worldwide Political Science Abstracts and International Political Science Abstracts. Searches were also completed in Google Scholar. Inclusion criteria included articles focused on accountability of patients, and exclusions included articles that were not available, not written in English, with missing information, and commentaries or editorials. In total, 85530 unique abstracts were identified, and 27 articles were included based on the inclusion criteria. The results showed that patient accountability is rarely used and poorly defined. Most studies focused on what patients should be held to account for and agreed that patients should be responsible for behaviours that may contribute to adverse health outcomes. Some studies promoted a punitive approach as a mechanism of enforcement. Most studies argued for positive incentives or written agreements and contracts. While many studies recognized the value of patient accountability frameworks, there was a concern that these frameworks could further exacerbate existing socioeconomic disparities and contribute to poor health-related behaviours and outcomes (e.g., stigmatizing marginalized groups). Shared models of accountability between patients and healthcare providers or patients and communities were preferred. Before committing to a patient accountability framework for improving patient health and sustaining a healthcare system, the concept must be acceptable and reasonable to patients, providers, and society as a whole.
Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study

Background Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient’s outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician’s interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders. Objective We present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish patients with schizophrenia from those with other psychiatric disorders and control subjects. Methods We plan to recruit three outpatient groups: (1) 50 patients with schizophrenia, (2) 50 patients with unipolar major depressive disorder, and (3) 50 individuals with no psychiatric history. Using an internally developed semistructured interview, psychometrically validated clinical outcome measures, and a multimodal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system’s capacity in classifying subjects (schizophrenia, depression, or control), to evaluate the system’s sensitivity to within-group symptom severity, and to determine if such a system can further classify variations in disorder subtypes. Results Data collection began in July 2020 and is expected to continue through December 2022. Conclusions If successful, this study will help advance current progress in developing state-of-the-art technology to aid clinical psychiatric assessment and treatment. If our findings suggest that these technologies are capable of resolving diagnoses and symptoms to the level of current psychometric testing and clinician judgment, we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care; to aid clinicians in objectively evaluating diagnoses, severity of symptoms, and treatment efficacy through time; and to reduce treatment-related morbidity. International Registered Report Identifier (IRRID) DERR1-10.2196/36417
Organic Priors in Non-Rigid Structure from Motion

This paper advocates the use of organic priors in classical non-rigid structure from motion (NRSfM). By organic priors, we mean invaluable intermediate prior information intrinsic to the NRSfM matrix factorization theory. It is shown that such priors reside in the factorized matrices, and quite surprisingly, existing methods generally disregard them. The paper's main contribution is to put forward a simple, methodical, and practical method that can effectively exploit such organic priors to solve NRSfM. The proposed method does not make assumptions other than the popular one on the low-rank shape and offers a reliable solution to NRSfM under orthographic projection. Our work reveals that the accessibility of organic priors is independent of the camera motion and shape deformation type. Besides that, the paper provides insights into the NRSfM factorization -- both in terms of shape and motion -- and is the first approach to show the benefit of single rotation averaging for NRSfM. Furthermore, we outline how to effectively recover motion and non-rigid 3D shape using the proposed organic prior based approach and demonstrate results that outperform prior-free NRSfM performance by a significant margin. Finally, we present the benefits of our method via extensive experiments and evaluations on several benchmark datasets.
Increasing Precipitation Efficiency Amplifies Climate Sensitivity by Enhancing Tropical Circulation Slowdown and Eastern Pacific Warming Pattern

The role of precipitation efficiency (PE)—the fraction of column‐integrated condensate that reaches the surface as rain—in the global temperature response to CO2 rise is yet to be quantified. Here we employ 36 limited‐domain cloud resolving models (CRMs) from the Radiative‐Convective Equilibrium Model Intercomparison Project and find that they strongly imply higher PE at warmer temperatures. We then analyze 35 general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 and find that increasing PE is associated with tropical circulation slowdown and greater eastern equatorial Pacific warming. These changes trigger pan‐tropical positive cloud feedback through stratiform anvil cloud reduction and stratocumulus suppression, resulting in higher Effective Climate Sensitivity (ECS). We find that in 24 of 35 GCMs matching the CRMs in simulating increasing PE with greenhouse warming, mean ECS is 1 K higher than in PE‐decreasing GCMs. Thus, further constraining PE sensitivity to temperature could reduce uncertainty over future climate projections.
Mental Health Problems and Bullying Victimization Among Sexual Minority Adolescents in Greek Secondary Schools

Abstract This study provides the first empirical account of mental health issues among sexual minority adolescents in Greece and the effects on mental health of both bullying and victimization in relation to adolescents’ sexual orientation. A sample of 757 adolescents (M age = 15.98, SD = 0.84) completed self-reported scales measuring school bullying victimization experiences, levels of depression, feelings of loneliness, hopelessness, feeling of belonging in school, self-esteem, and sense of wellbeing. Statistically significant differences were observed between heterosexuals and gay adolescents in depression, loneliness, bullying behavior and school belongingness. Gay adolescents are more likely to present higher levels of depression. Furthermore, sexual orientation was also found to be significant moderator of the effect of bullying victimization on loneliness. Bullying was associated with low sense of school belonging and victimization with depressive symptomatology, loneliness and low sense of school belonging and self-esteem. Students’ self-esteem and school belongingness were found to have a protective role against loneliness, depression and hopelessness. The findings of the current study provide valuable information to school psychologists, teachers, policy makers, and other professionals whose goals are to enhance adolescent functioning and adaptation. It is suggested that intervention strategies designed to promote resilience should incorporate sexual orientation issues.
Cerebral cortical activity after withdrawal of life‐sustaining measures in critically ill patients

Establishing when cerebral cortical activity stops relative to circulatory arrest during the dying process will enhance trust in donation after circulatory determination of death. We used continuous electroencephalography and arterial blood pressure monitoring prior to withdrawal of life sustaining measures and for 30 min following circulatory arrest to explore the temporal relationship between cessation of cerebral cortical activity and circulatory arrest. Qualitative and quantitative EEG analyses were completed. Among 140 screened patients, 52 were eligible, 15 were enrolled, 11 completed the full study, and 8 (3 female, median age 68 years) were included in the analysis. Across participants, EEG activity stopped at a median of 78 (Q1 = −387, Q3 = 111) seconds before circulatory arrest. Following withdrawal of life sustaining measures there was a progressive reduction in electroencephalographic amplitude (p = .002), spectral power (p = .008), and coherence (p = .003). Prospective recording of cerebral cortical activity in imminently dying patients is feasible. Our results from this small cohort suggest that cerebral cortical activity does not persist after circulatory arrest. Confirmation of these findings in a larger multicenter study are needed to help promote stakeholder trust in donation after circulatory determination of death.
Characterization of Transgenic NSG-SGM3 Mouse Model of Precision Radiation-Induced Chronic Hyposalivation

Regenerative medicine holds promise to cure radiation-induced salivary hypofunction, a chronic side effect in patients with head and neck cancers, therefore reliable preclinical models for salivary regenerative outcome will promote progress towards therapies. In this study, our objective was to develop a cone beam computed tomography-guided precision ionizing radiation-induced preclinical model of chronic hyposalivation using immunodeficient NSGSGM3 mice. Using a Schirmer's test based sialagogue-stimulated saliva flow kinetic measurement method, we demonstrated significant differences in hyposalivation specific to age, sex, precision-radiation dose over a chronic (6 months) timeline. NSG-SMG3 mice tolerated doses from 2.5 Gy up to 7.5 Gy. Interestingly, 5–7.5 Gy had similar effects on stimulated-saliva flow (∼50% reduction in young female at 6 months after precision irradiation over sham-treated controls), however, >5 Gy led to chronic alopecia. Different groups demonstrated characteristic saliva fluctuations early on, but after 5 months all groups nearly stabilized stimulated-saliva flow with low-inter-mouse variation within each group. Further characterization revealed precision-radiation-induced glandular shrinkage, hypocellularization, gland-specific loss of functional acinar and glandular cells in all major salivary glands replicating features of human salivary hypofunction. This model will aid investigation of human cell-based salivary regenerative therapies.
Comparison of clinical results between high grade patterns in stage I lung adenocarcinoma

The histological subtype has been introduced in invasive lung adenocarcinoma. The predominant micropapillary and solid subtypes are categorized as high‐grade patterns and provide a worse prognosis. However, the prognostic analysis of high‐grade patterns has not previously been fully investigated. Thus, this study aimed to investigate the prognostic role of high‐grade patterns in pathological stage I lung adenocarcinoma.
Determine the vertical ground reaction forces and knee mechanics with different gait inclinations in the sheep model

Our current understanding of knee mechanics and anterior cruciate ligament (ACL) function is predominately based on data recorded during simulations of clinical examinations or the application of nonphysiologic loads and motions. These methodologies provide little information on knee and ACL mechanics during activities of daily living (ADLs). Additionally, researchers have not directly measured knee kinetics, knee contact pressures, and ACL forces, and it is unknown how these parameters change with different activities. This study quantified the effects of activity level on vertical ground reaction forces, knee kinematics, and joint and ligament forces during in vivo motions. Five female Suffolk sheep were walked twice weekly on a treadmill during level (0°), inclined (+6°), and declined (−6°) gait for 12 weeks. Electromagnetic (EM) trackers were surgically implanted onto the left distal femur and the left proximal tibia, and in vivo motions were recorded for all activities. Following sacrifice, the in vivo motions were applied to their respective knees using a serial robot with a multi‐axis load cell. In vitro simulations were repeated to measure (a) total knee forces, (b) contact pressure maps, and (c) ACL‐only forces. Declining the gait surface led to increased posterior translation during the swing phase and decreased flexion at hoof‐strike, decreased medial contact pressure at push‐off, decreased ACL force at hoof‐strike and increased ACL force at push‐off. This study established a system that can be used to examine knee mechanics and ACL forces during ADLs for different knee states to define design requirements for ACL reconstruction techniques.
SPP1 as a key gene in the lymph node metastasis and a potential predictor of poor prognosis in head and neck carcinoma.

BACKGROUND Lymph node metastasis (LNM) is an important cause leading to recurrence and development of head and neck carcinoma (HNC), with the precise mechanisms unclear. Thus, we aimed to identify the key genes involved in LNM and further evaluate their expressions and roles. METHODS A cohort of HNC in the TCGA was analyzed. The study involved three phases (one screening and two validation phases). First, the differentially expressed genes regarding LNM were screened, from which a key gene was identified by a series of data mining approaches. Then, the expressions and roles of the key gene were validated in HNC through bioinformatics. Afterward, the expression of the key gene was detected by qPCR, western blot, and Immunohistochemistry based on a cell model and a tissue microarray. Further, colony formation and transwell migration and invasion assays were used to evaluate the roles of the key gene. RESULTS SPP1 was overexpressed in HNC tissues and was identified as the key gene. Overexpression of SPP1 in HNC was correlated with advanced pathological stages and T-stage, as well as the presence of LNM, which predicted poor prognosis. The expression of SPP1 was closely associated with the infiltration of immune cells in HNC, especially M2 macrophages. Lab experiments confirmed that SPP1 silence in HNC cells resulted in weakened invasive and metastatic abilities. CONCLUSION This study reveals that SPP1 may be a key gene associated with LNM in HNC, raising the possibility of SPP1 as a target for HNC prevention and treatment. This article is protected by copyright. All rights reserved.
Robust and efficient computation of retinal fractal dimension through deep approximation

A retinal trait, or phenotype, summarises a specific aspect of a retinal image in a single number. This can then be used for further analyses, e.g. with statistical methods. However, reducing an aspect of a complex image to a single, meaningful number is challenging. Thus, methods for calculating retinal traits tend to be complex, multi-step pipelines that can only be applied to high quality images. This means that researchers often have to discard substantial portions of the available data. We hypothesise that such pipelines can be approximated with a single, simpler step that can be made robust to common quality issues. We propose Deep Approximation of Retinal Traits (DART) where a deep neural network is used predict the output of an existing pipeline on high quality images from synthetically degraded versions of these images. We demonstrate DART on retinal Fractal Dimension (FD) calculated by VAMPIRE, using retinal images from UK Biobank that previous work identified as high quality. Our method shows very high agreement with FD VAMPIRE on unseen test images (Pearson r=0.9572). Even when those images are severely degraded, DART can still recover an FD estimate that shows good agreement with FD VAMPIRE obtained from the original images (Pearson r=0.8817). This suggests that our method could enable researchers to discard fewer images in the future. Our method can compute FD for over 1,000img/s using a single GPU. We consider these to be very encouraging initial results and hope to develop this approach into a useful tool for retinal analysis.
Nonlinear Optics in Chip-based Microresonators and their Applications

Optical micro-resonators have been studied for decades as a platform to investigate optical physics, and to miniaturize bulky optical systems. In the last decade, optical frequency combs, which have revolutionized the precision measurement of time and frequency, have been demonstrated in optical micro-resonators via the combined effect of parametric oscillation and cascaded four-wave mixing. More recently, soliton mode-locking has made possible low-noise/reproducible generation of these miniature combs (microcombs). In this thesis, we demonstrated the generation of soliton microcombs from silica wedge disk micro-resonators and the characteristics of the soliton microcombs are described. We also applied soliton microcombs to dual-comb spectroscopy and distance measurement (LIDAR) for the first time. Also, ways to improve spectral resolution, signal-to-noise ratio, and spectral coverage are discussed. In addition to soliton microcombs, a novel spiral resonator is studied as a stable optical frequency reference. Combined with a frequency comb, this new type of chip-based reference cavity is also applied to generate stable microwaves via optical frequency division. Lastly, we generated a stimulated Brillouin laser (SBL) from the optical micro-resonator and its phonon-limited linewidth is studied. Application of the SBL for rotation measurement is also demonstrated. This thesis is organized into six chapters. Throughout the thesis, the implication and potential of my PhD work toward chip-based advanced optics system are discussed.
JIP3 interacts with dynein and kinesin-1 to regulate bidirectional organelle transport

Celestino, Gama, Castro-Rodrigues et al. characterize how the retrograde motor dynein interacts with cargo adaptors of the RILP/JIP3 superfamily and selectively uncouple dynein and the opposing motor kinesin-1 from JIP3/UNC-16 in Caenorhabditis elegans, which reveals that JIP3 drives bidirectional organelle transport in neurons.
GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response

High energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New approaches to speed up detector simulation are therefore needed. The generation of calorimeter responses is often the most expensive component of the simulation chain for HEP experiments. It was shown that deep learning techniques, especially Generative Adversarial Networks, may be used to reproduce the calorimeter response. However, those applications are challenging, as the generated responses need evaluation not only in terms of image consistency: different physics-based quality metrics should be also taken into consideration. In our work, we develop a multitask GAN-based framework with the goal to speed up the response generation of the Electromagnetic Calorimeter (ECAL) of the LHCb detector at LHC. We introduce the Auxiliary Regressor as a second task to evaluate a proxy metric of the given input that is used by the Discriminator of the GAN. We show that this approach improves the stability of GAN and the model produces samples with better physics distributions.
Unsupervised Learning for Combinatorial Optimization with Principled Objective Design

Using machine learning to solve combinatorial optimization (CO) problems is challenging, especially when the data is unlabeled. This work proposes an unsupervised learning framework for CO problems. Our framework follows a standard relaxation-plus-rounding approach and adopts neural networks to parameterize the relaxed solutions so that simple back-propagation can train the model end-to-end. Our key contribution is the observation that if the relaxed objective satisﬁes entry-wise concavity, a low optimization loss guarantees the quality of the ﬁnal integral solutions. This observation signiﬁcantly broadens the applicability of a previous framework inspired by Erdos’ probabilistic method [1]. In particular, this observation can guide the design of objective models in the applications where the objectives are not given explicitly while requiring being modeled in prior. We evaluate our framework by solving a synthetic graph optimization problem, and two real-world applications including resource allocation in circuit design and approximate computing. Our framework 2 largely outperforms the baselines based on naïve relaxation, reinforcement learning and Gumbel-softmax tricks.
Study on SiPM performance at low temperatures

: Radon is the main background source of dark matter and neutrino experiments. We designed a liquid scintillation detector to measure the radon concentration (mBq / m 3 ) at low temperatures using silicon photomultipliers (SiPMs) arrays. The SiPM performance characteristics are closely related to the lower detection limit of the detector. In this study, we built an automatic and accurate low-temperature measurement system to study the single photoelectron spectrum, dark noise, energy resolution, optical crosstalk, and after-pulse of the SiPM at diﬀerent temperatures. As a result, we obtained the variation trend of the SiPM parameters at diﬀerent temperatures, and the SiPM optimal working conditions were obtained, which can improve the detector’s sensitivity.