Miracle-dz/UniFAR-FTUs huggingface dataset:
Depression, diabetes, their comorbidity and all-cause and cause-specific mortality: a prospective cohort study

Aims/hypothesis: To investigate the risk of all-cause and cause-specific mortality among participants with neither, one, or both of diabetes and depression in a large prospective cohort study in the United Kingdom. Methods: Our study population included 499,830 UK Biobank participants without schizophrenia and bipolar disorder at baseline. Type 1 or type 2 diabetes and depression were identified using self-reported diagnoses, prescribed medication and hospital records. Mortality was identified from death records using the primary cause of death to define cause-specific mortality. We performed Cox proportional hazards models to estimate the risk of all-cause mortality and mortality due to cancer, circulatory disease and causes of death other than circulatory disease or cancer among participants with either depression (n=41,791) or diabetes alone (n=22,677) and with comorbid diabetes and depression (n=3,597), compared to the group with neither condition (n=431,765) adjusting for sociodemographic and lifestyle factors, comorbidities, and history of CVD or cancer. We investigated for interaction between diabetes and depression. Results: During a median of 6.8 (IQR: 6.1 - 7.5) years of follow-up, there were 13,724 deaths (cancer (n=7,976), circulatory disease (n=2,827), and other causes (n=2,921)). Adjusted hazard ratios of all-cause mortality and mortality due to cancer, circulatory disease and other causes were highest among people with comorbid depression and diabetes (HRs 2.16, 95% CI 1.94 - 2.42; 1.62, 95% CI 1.35 - 1.93; 2.22 95% CI 1.80 - 2.73 and 3.60, 95% CI 2.93 - 4.42, respectively). Among those with comorbid diabetes and depression, the risks of all-cause, cancer and other mortality exceeded the sum of the risks due to diabetes and depression alone. Conclusions/interpretation: We confirmed the negative impact of depression and diabetes on mortality outcomes, and also identified that comorbid depression and diabetes had synergistic effects on all-cause mortality which was largely driven by deaths due to cancer and causes other than circulatory disease and cancer.
Urinary Biomarkers and Attainment of Cefepime Therapeutic Targets in Critically Ill Children

INTRODUCTION: The recommended therapeutic target for cefepime (FEP) is the time above MIC (fT>MIC). The frequency of target attainment and risk factor for sub-therapeutic concentrations in children have not been extensively studied. METHODS: We performed a prospective observational pilot study in children in our PICU receiving standard dosing of FEP for suspected sepsis ([≥]2 SIRS criteria). Three FEP concentrations were measured per subject and a urine sample was collected prior to PK sampling for measurement of urinary biomarkers. We used log linear regression to calculate the fT>MIC for each subject across a range of MIC values (1-16 {micro}g/mL). We compared clinical factors/biomarkers between patients who did and did not achieve 100% fT>MIC for 8 {micro}g/mL (cut-point for Pseudomonas) and tested the correlation between covariates and FEP troughs. RESULTS: 21 subjects were enrolled (median SIRS criteria: 3). PK sampling occurred after a median of 5 doses (range: 3-9). 43% of subjects achieved 100% fT>MIC for an MIC of 8 {micro}g/mL. Younger age (p=.005), higher estimated GFR (p=.03), and lower urinary NGAL (p=.006) and KIM-1 (.03) were associated with failure to attain 100% fT>8 {micro}g/mL. Age (r = 0.53), eGFR (r = -0.58), urinary NGAL (r = 0.42) and KIM-1 (r = 0.50) were significantly correlated with FEP troughs. CONCLUSIONS: A significant proportion of critically ill children failed to attain target concentrations for treatment of Pseudomonas aeruginosa with FEP. Younger patients and those with good kidney function (high GFR, low urinary biomarkers) may be at highest risk for subtherapeutic FEP concentrations.
Emergency-department accesses in home care paediatric patients: Occurrence and risks of use in a six-year retrospective investigation in Northern Italy

Objective To assess the determinants of ED use in paediatric patients enrolled in an Integrated Paediatric Home Care (IPHC) program. Methods A retrospective study was conducted using administrative databases on a cohort of patients enrolled in an IPHC program between January 1st, 2012, and December 31st, 2017, in Northern Italy. ED visits that occurred during the IPHC program were considered. Data were collected considering sociodemographic, clinical and organizational variables. A multivariable stepwise logistic regression analysis was performed. The dependent variable to identify possible associations was ED visit. Results A total of 463 ED visits occurred in 465 children, with an incidence rate of 1. The risk of ED visits significantly increased among children involved in the IPHC program after hospital discharge (OR 1.94). Additionally, the risk of ED visits increased significantly as the duration of IPHC increased (OR 5.80 between 101 and 200 days, to OR 7.84 between 201 and 300 days, OR 12.54 between 301 and 400 days and OR 18.67 to more than 400 days). Conclusion The overall results represent a practical perspective to contribute improving both the service quality of IPHC and reducing low acuity and improper ED use.
Transcriptomic profiling of adjuvant colorectal cancer identifies three key prognostic biological processes and a disease specific role for granzyme B

Background Colorectal cancer (CRC) is a leading cause of cancer-related deaths, with a 5% 5-year survival rate for metastatic disease, yet with limited therapeutic advancements due to insufficient understanding of and inability to accurately capture high-risk CRC patients who are most likely to recur. We aimed to improve high-risk classification by identifying biological pathways associated with outcome in adjuvant stage II/III CRC. Methods and findings We included 1062 patients with stage III or high-risk stage II colon carcinoma from the prospective three-arm randomized phase 3 AVANT trial, and performed expression profiling to identify a prognostic signature. Data from validation cohort GSE39582, The Cancer Genome Atlas, and cell lines were used to further validate the prognostic biology. Our retrospective analysis of the adjuvant AVANT trial uncovered a prognostic signature capturing three biological functions—stromal, proliferative and immune—that outperformed the Consensus Molecular Subtypes (CMS) and recurrence prediction signatures like Oncotype Dx in an independent cohort. Importantly, within the immune component, high granzyme B (GZMB) expression had a significant prognostic impact while other individual T-effector genes were less or not prognostic. In addition, we found GZMB to be endogenously expressed in CMS2 tumor cells and to be prognostic in a T cell independent fashion. A limitation of our study is that these results, although robust and derived from a large dataset, still need to be clinically validated in a prospective study. Conclusions This work furthers our understanding of the underlying biology that propagates stage II/III CRC disease progression and provides scientific rationale for future high-risk stratification and targeted treatment evaluation in biomarker defined subpopulations of resectable high-risk CRC. Our results also shed light on an alternative GZMB source with context-specific implications on the disease’s unique biology.
Great egret (Ardea alba) habitat selection and foraging behavior in a temperate estuary: Comparing natural wetlands to areas with shellfish aquaculture

Movement by animals to obtain resources and avoid predation often depends on natural cycles, and human alteration of the landscape may disrupt or enhance the utility of different habitats or resources to animals through the phases of these cycles. We studied habitat selection by GPS/accelerometer-tagged great egrets (Ardea alba) foraging in areas with shellfish aquaculture infrastructure and adjacent natural wetlands, while accounting for tide-based changes in water depth. We used integrated step selection analysis to test the prediction that egrets would express stronger selection for natural wetlands (eelgrass, tidal marsh, and other tidal wetlands) than for shellfish aquaculture areas. We also evaluated differences in foraging behavior among shellfish aquaculture areas and natural wetlands by comparing speed travelled (estimated from distance between GPS locations) and energy expended (Overall Dynamic Body Acceleration) while foraging. We found evidence for stronger overall habitat selection for eelgrass than for shellfish aquaculture areas, with results conditional on water depth: egrets used shellfish aquaculture areas, but only within a much narrower range of water depths than they used eelgrass and other natural wetlands. We found only slight differences in our metrics of foraging behavior among shellfish aquaculture areas and natural wetlands. Our results suggest that although great egrets appear to perceive or experience shellfish aquaculture areas as suitable foraging habitat during some conditions, those areas provide less foraging opportunity throughout tidal cycles than natural wetlands. Thus, expanding the footprint of shellfish aquaculture into additional intertidal areas may reduce foraging opportunities for great egrets across the range of tidal cycles. Over longer time scales, the ways in which natural wetlands and shellfish aquaculture areas adapt to rising sea levels (either through passive processes or active management) may change the ratios of these wetland types and consequently change the overall value of Tomales Bay to foraging great egrets.
Response to self-care practice messages among patients with diabetes mellitus visiting Jimma University medical center facility based cross sectional design application of extended parallel process model

Objective To determine response to self-care practice message among patients with diabetes in Jimma University Medical center based on the Extended Parallel Process Model. Design A facility-based cross-sectional study was conducted. Setting Jimma University Medical Center is found in Jimma town. Participants A total of 343 patients with diabetes participated in the study; making a response rate of 93.9%. All patients with diabetes who were 18 years and above and who were on follow up and registered were included in the study and those with Gestational DM were excluded. Data analysis Multivariable logistic regression analysis. Result Responsive respondents scored high in self-care practice score as compared to other respondents. educational status, information sources, knowledge, and preferred message appeals were independent predictors of controlling the danger of diabetes. Conclusion There is a significant gap in controlling the danger of diabetes. Variables like the level of education, knowledge of diabetes mellitus, information sources, and message appeals were independent predictors of controlling the danger of diabetes. Designing message having higher efficacy while maintaining the level of threat is the best that fits the existing audience’s message processing to bring about desired diabetic self-care Practice.
Turning Gait Planning of a Quadruped Walking Robot with an Articulated Spine

We suggest a turning gait planning of a quadruped walking robot with an articulated spine. Robot developer has tried to implement a gait more similar to that of natural animals with high stability margin. Therefore, so many types of walking robot with reasonable gait have been developed. But there is a big difference with a natural animal walking motion. A key point is the fact that natural animals use their waist–joint(articulated spine) to walk. For example, a crocodile which has short legs relative to a long body uses their waist to walk more quickly and to turn more effectively. The other animals such as tiger, dog and so forth, also use their waist. Therefore, this paper proposes discontinuous turning gait planning for a newly modeled quadruped walking robot with an articulated spine which connects the front and rear parts of the body. Turning gait is very important as same as straight gait. All animals need a turning gait to avoid obstacle or to change walking direction. Turning gait has mainly two types of gaits; circular gait and spinning gait. We apply articulated spine to above two gaits, which shows the majority of an articulated spine more effectively. Firstly, we describe a kinematic relation of a waist-joint, the hip, and the center of gravity of body, and then apply a spinning gait. Next, we apply a waist-joint to a circular gait. We compare a gait stability margin with that of a conventional single rigid body walking robot. Finally, we show the validity of a proposed gait with simulation.
Localising the smart grid based on a South African context: A user centred approach to the micro-grid

Implementing a smarter grid for improved management and use of power is extremely important for the future and development of South Africa. Even more important is finding the best way to implement these grids in South Africa. A number of evolutions are expected, but ultimately the eventual solution will need to satisfy the requirements of the grid, while adhering to the relevant standards to result in a suitable solution for South Africa. It can therefore not be assumed that a solution which works successfully in another country will have the same results in South Africa. The economy, financial strength and technological maturity of South Africa are among the factors which are unique and will require an implementation tailored to fit South Africa and these factors. The architecture of the grid gives it structure. This report considers the architecture for implementing the grid in South Africa, employing a view which places more importance on the consumers. An existing architecture, the OPEN meter architecture, is identified and localised based on a South African context, and then validated using existing international frameworks which define characteristics that every grid architecture must have.
An Interacting Multiple Model for Trajectory Prediction of Intelligent Vehicles in Typical Road Traffic Scenario

This article presents an interacting multiple model (IMM) for short-term prediction and long-term trajectory prediction of an intelligent vehicle. This model is based on vehicle’s physics model and maneuver recognition model. The long-term trajectory prediction is challenging due to the dynamical nature of the system and large uncertainties. The vehicle physics model is composed of kinematics and dynamics models, which could guarantee the accuracy of short-term prediction. The maneuver recognition model is realized by means of hidden Markov model, which could guarantee the accuracy of long-term prediction, and an IMM is adopted to guarantee the accuracy of both short-term prediction and long-term prediction. The experiment results of a real vehicle are presented to show the effectiveness of the prediction method.
Design of Robotized Workplace for Verification of Selected Types of Algorithms and Methods for Randomly Oriented Objects

The article deals with designing of research robotized workplace, in which will be verified chosen algorithms for identification and also methods for reliably gripping of randomly oriented objects. By using CAD system is created 3D construction documentation. Design of research robotized workplace mainly consists from industrial robot SCARA, control PC, vibratory tray and vibratory conveyor, grippers, linear and rotary modules, drive motor and other equipment for workplace.
Prot2GO: Predicting GO Annotations From Protein Sequences and Interactions

Protein is the main material basis of living organisms and plays crucial role in life activities. Understanding the function of protein is of great significance for new drug discovery, disease treatment and vaccine development. In recent years, with the widespread application of deep learning in bioinformatics, researchers have proposed many deep learning models to predict protein functions. However, the existing deep learning methods usually only consider protein sequences, and thus cannot effectively integrate multi-source data to annotate protein functions. In this article, we propose the Prot2GO model, which can integrate protein sequence and PPI network data to predict protein functions. We utilize an improved biased random walk algorithm to extract the features of PPI network. For sequence data, we use a convolutional neural network to obtain the local features of the sequence and a recurrent neural network to capture the long-range associations between amino acid residues in protein sequence. Moreover, Prot2GO adopts the attention mechanism to identify protein motifs and structural domains. Experiments show that Prot2GO model achieves the state-of-the-art performance on multiple metrics.
Clinically relevant mitochondrial-targeted therapy improves chronic outcomes after traumatic brain injury

Abstract Pioglitazone, an FDA-approved compound, has been shown to target the novel mitochondrial protein mitoNEET and produce short-term neuroprotection and functional benefits following traumatic brain injury. To expand on these findings, we now investigate the dose- and time-dependent effects of pioglitazone administration on mitochondrial function after experimental traumatic brain injury. We then hypothesize that optimal pioglitazone dosing will lead to ongoing neuroprotection and cognitive benefits that are dependent on pioglitazone-mitoNEET signalling pathways. We show that delayed intervention is significantly more effective than early intervention at improving acute mitochondrial bioenergetics in the brain after traumatic brain injury. In corroboration, we demonstrate that mitoNEET is more heavily expressed, especially near the cortical contusion, in the 18 h following traumatic brain injury. To explore whether these findings relate to ongoing pathological and behavioural outcomes, mice received controlled cortical impact followed by initiation of pioglitazone treatment at either 3 or 18 h post-injury. Mice with treatment initiation at 18 h post-injury exhibited significantly improved behaviour and tissue sparing compared to mice with pioglitazone initiated at 3 h post-injury. Further using mitoNEET knockout mice, we show that this therapeutic effect is dependent on mitoNEET. Finally, we demonstrate that delayed pioglitazone treatment improves serial motor and cognitive performance in conjunction with attenuated brain atrophy after traumatic brain injury. This study illustrates that mitoNEET is the critical target for delayed pioglitazone intervention after traumatic brain injury, mitochondrial-targeting is highly time-dependent after injury and there is an extended therapeutic window to effectively treat mitochondrial dysfunction after brain injury.
Human Versus Machine: How Do We Know Who Is Winning? ROC Analysis for Comparing Human and Machine Performance under Varying Cost-Prevalence Assumptions

Abstract Background Receiver operating characteristic (ROC) analysis is commonly used for comparing models and humans; however, the exact analytical techniques vary and some are flawed. Objectives The aim of the study is to identify common flaws in ROC analysis for human versus model performance, and address them. Methods We review current use and identify common errors. We also review the ROC analysis literature for more appropriate techniques. Results We identify concerns in three techniques: (1) using mean human sensitivity and specificity; (2) assuming humans can be approximated by ROCs; and (3) matching sensitivity and specificity. We identify a technique from Provost et al using dominance tables and cost-prevalence gradients that can be adapted to address these concerns. Conclusion Dominance tables and cost-prevalence gradients provide far greater detail when comparing performances of models and humans, and address common failings in other approaches. This should be the standard method for such analyses moving forward.
Depressive symptoms among recent Latinx immigrants in South Florida: The role of premigration trauma and stress, postimmigration stress, and gender.

In the past decade, recent Latinx immigrants (RLIs) from South and Central America have arrived in the United States seeking asylum from countries affected by war, political upheaval, and high crime and poverty rates. The premigration stress and trauma they experience are further compounded by postimmigration stress due to discrimination, lack of access to health care, and financial instability. Evidence suggests RLIs who experience such stress and trauma have an increased risk of developing depressive symptoms. We examined the combined effect of premigration stress and trauma and postimmigration stress on postimmigration depressive symptoms; we also explored the moderating effect of gender. Hierarchical multiple regression and moderation analyses were conducted on a cross-sectional sample of 540 young adult RLIs (age range: 18-34 years, 50.2% men) in South Florida. Higher levels of postimmigration stress, β = . 37, p < . 001, were associated with increased postimmigration depressive symptoms. No significant associations emerged between premigration stress and trauma and postimmigration depressive symptoms. Moderation analyses revealed no significant interaction effect of gender. Post hoc analyses indicated that country/region of origin moderated the relation between postimmigration stress and depressive symptoms such that the association was stronger among Venezuelan, β = 1.51, p < . 001; other South American, β = 1.06, p < . 001; and Central American/Mexican RLIs, β = 1.38, p < . 001, compared with Caribbean RLIs, β = . 45, p = . 122. These findings suggest that interventions focused on addressing postimmigration stress early in the immigration process can potentially lower subsequent depressive symptoms among RLIs.
Breast-Cancer Biomarker (C-erbB-2) Detection in Saliva/Serum Based on InGaAs/Si Heterojunction Dopingless TFET Biosensor

This research article reports the electrical detection of breast-cancer biomarker (C-erbB-2) in saliva/serum based on In1-xGaxAs/Si heterojunction dopingless tunnel FET (HJ-DL-TFET) biosensor for highly sensitive and real-time detection. The work takes into account the interface charge modulation effect in dopingless extended gate heterostructure TFET with embedded nanocavity biosensors for the precise, reliable, and fast detection of antigens present in the body fluids such as saliva in place of blood serum. The reported biosensor is numerically simulated in 2D using the SILVACO ATLAS exhaustive calibrated simulation framework. For the biomolecule immobilization, the proposed biosensor has a dual cavity engraved beneath the dual gate structure. This improves the control of biomolecules over the source-to-channel tunneling rate, as well as the control over the electrical performance parameters of the proposed biosensor. Here, a numerical model for the C-erbB-2 interface charge equivalent is also developed. The analysis of device sensitivity in both saliva and serum environments for various C-erbB-2 concentrations has been carried out. Our study reveals that III-V In1-xGaxAs/Si heterojunction with <inline-formula> <tex-math notation="LaTeX">${x}$ </tex-math></inline-formula> composition of 0.2 and extended gate geometry provides an increased tunneling probability, improves the gate control to get a higher <inline-formula> <tex-math notation="LaTeX">${I}_{\textit {ON}}/{I}_{\textit {OFF}}$ </tex-math></inline-formula> ratio and higher sensitivity. In addition to this, the impact of interface charges corresponding to the different amounts of C-erbB-2 biomarkers on the biosensor sensitivity (in terms of <inline-formula> <tex-math notation="LaTeX">${I}_{\textit {ON}}/{I}_{\textit {OFF}}$ </tex-math></inline-formula> ratio) yields higher sensitivity of the order of <inline-formula> <tex-math notation="LaTeX">${10}^{{6}}$ </tex-math></inline-formula>.