Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular imaging in the investigation of hypoglycaemic syndromes and their management.

There has been recent progress in molecular imaging using a variety of cellular targets for the investigation of adult non-diabetic hypoglycaemic syndromes and its integration into patient management. These targets include peptide receptors (somatostatin receptors (SSTRs) and glucagon-like peptide-1 receptor (GLP-1R)) the amine precursor uptake and decarboxylation system utilising the diphydroxyphenylaline (DOPA) analogue 6-[18F]-l-fluoro-l-3,4-dihydroxyphenylalanine (18F-FDOPA), and glycolytic metabolism with 2-[18F]fluoro-2-deoxy-d-glucose (FDG). Accurate preoperative localisation and staging is critical to enable directed surgical excision or enucleation with minimal morbidity and preservation of residual pancreatic function. Benign insulinoma has near ubiquitous dense GLP-1R expression enabling accurate localisation with radiolabelled-exendin-4 compounds (e.g. 68Ga-NOTA-exendin-4 PET/CT), whilst the rarer and more difficult to manage metastatic insulinoma typically express SSTR and is preferably imaged with radiolabelled-SSTR analogues such as 68Ga-DOTA-octreotate (DOTATATE) PET/CT for staging and assessment of suitability for peptide receptor radionuclide therapy (PRRT). Similar to other metastatic neuroendocrine tumours, FDG PET/CT is used in the setting of higher-grade metastatic insulinoma to provide important prognostic information that can guide treatment and determine suitability for PRRT. Interestingly, these three tracers appear to represent a spectrum of differentiation, which we conceptually describe as the 'triple-flop' phenomenon, with GLP-1R > SSTR > FDG in benign insulinoma and the opposite in higher-grade disease. This paper will review the clinical syndromes of adult hypoglycaemia (including a practical overview of the differential diagnoses to be considered), comparison of techniques for insulinoma localisation with emphasis on molecular imaging before discussing its implications for management of metastatic insulinoma.
A novel calibration approach using satellite and visibility observations to estimate fine particulate matter exposures in Southwest Asia and Afghanistan

ABSTRACT In order to study effects of ambient particulate matter (PM) it was previously necessary to have access to a comprehensive air monitoring network. However, there are locations in the world where PM levels are above generally accepted exposure standards but lack a monitoring infrastructure. This is true in Iraq and other locations in Southwest Asia and Afghanistan where U.S. and other coalition troops were deployed beginning in 2001. Since aerosol optical depth (AOD), determined by satellite, and visibility are both highly related to atmospheric PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) concentrations, we employed a novel approach that took advantage of historic airport visibility measurements to calibrate the AOD–visibility relationship and determine visibility spatially and temporally (2006–2007) over an approximately 17,000 km2 region of Iraq. We obtained daily visibility predictions that were highly associated with satellite-based 1x1 km AOD daily observations (R2=0.87). Based on a previously derived calibration between PM2.5 and visibility, we were able to predict spatially and temporally resolved PM2.5 concentrations. Variability of PM2.5 among sites was high, with daily concentrations differing by as much as ~30 μg/m3. This study demonstrates the feasibility of characterizing historic PM2.5 exposures in Iraq and other locations in Southwest Asia and Afghanistan with similar climate characteristics. This is of utility for epidemiologists seeking to assess the potential health effects related to PM2.5 exposures among previously deployed military personnel and of the population of the region. Implications: This study demonstrates the ability to utilize aerosol optical depth to successfully estimate visibility spatially and temporally in Southwest Asia and Afghanistan. This enables for the estimation of spatially resolved PM2.5 concentrations in the region. The ability to caracterize PM2.5 concentrations in Southwest Asia and Afghanistan is highly important for epidemiologists investigating the relationship between chronic exposure to PM2.5 and respiratory diseases among military personnel deployed to the region. This information will better position policy makers to draft meaningful legislation relating to military health.
Effects of chronic anthropogenic disturbance and rainfall on the specialization of ant–plant mutualistic networks in the Caatinga, a Brazilian dry forest

Anthropogenic disturbance and climate change might negatively affect the ecosystem services provided by mutualistic networks. However, the effects of such forces remain poorly characterized. They may be especially important in dry forests, which (1) experience chronic anthropogenic disturbances (CADs) as human populations exploit forest resources, and (2) are predicted to face a 22% decline in rainfall under climate change. In this study, we investigated the separate and combined effects of CADs and rainfall levels on the specialization of mutualistic networks in the Caatinga, a seasonally dry tropical forest typical of north-eastern Brazil. More specifically, we examined interactions between plants bearing extrafloral nectaries (EFNs) and ants. We analysed whether differences in network specialization could arise from environmentally mediated variation in the species composition, namely via the replacement of specialist by generalist species. We characterized these ant-plant networks in 15 plots (20 × 20 m) that varied in CAD intensity and mean annual rainfall. We quantified CAD intensity by calculating three indices related to the main sources of disturbance in the Caatinga: livestock grazing (LG), wood extraction (WE) and miscellaneous resource use (MU). We determined the degree of ant-plant network specialization using four metrics: generality, vulnerability, interaction evenness and H2 '. Our results indicate that CADs differentially influenced network specialization: we observed positive, negative, and neutral responses along LG, MU and WE gradients, respectively. The pattern was most pronounced with LG. Rainfall also shaped network specialization, markedly increasing it. While LG and rainfall were associated with changes in network species composition, this trend was not related to the degree of species specialization. This result suggests that shifts in network specialization might be related to changes in species behaviour, not species composition. Our study highlights the vulnerability of such dry forest ant-plant networks to climate change. Moreover, dry forests experience highly heterogeneous anthropogenic disturbances, creating a geographic mosaic of selective forces that may shape the co-evolution of interactions between ants and EFN-bearing plants.
Efficiency and Performance Analysis of a Sparse and Powerful Second Order SVM Based on LP and QP

Productivity analysis is done on the new algorithm “Second Order Support Vector Machine (SOSVM)”, which could be thought as an offshoot of the popular SVM and based on its conventional QP version as well as the LP one. Our main goal is to produce a machine which is: 1) sparse & efficient; 2) powerful (kernel based) but not overfitted; 3) easily realizable. Experiments on benchmark data shows that to classify a new pattern, the proposed machine, SOSVM requires samples up to as little as 2.7% of original data set or 4.8% of conventional QP SVM or 48.3% of Vapnik’s LP SVM, which is already sparse. Despite this heavy test cost reduction, its classification accuracy is very similar to the most powerful QP SVM while being very simple to be produced. Moreover, two new terms called “Generalization Failure Rate (GFR)” and “Machine-Accuracy-Cost (MAC)” are defined to measure generalization-deficiency and accuracy-cost of a detector, respectively and used to compare such among different machines. Results show that our machine possesses GFR up to as little as 1.4% of the QP SVM or 1.5% of Vapnik’s LP SVM and MAC up to as little as 2.6% of the QP SVM or 35.9% of the Vapnik’s sparse LP SVM. Finally, having only two types of parameters to tune, this machine is straight forward and cheaper to be produced compared to the most popular & state-of-the-art machines in this direction. These collectively fulfill the three key goals that the machine is built for.
Impact of enzalutamide on quality of life in men with metastatic castration-resistant prostate cancer after chemotherapy : additional analyses from the AFFIRM randomized clinical trial

Impact of enzalutamide on quality of life in men with metastatic castration-resistant prostate cancer after chemotherapy: additional analyses from the AFFIRM randomized clinical trial D. Cella1*, C. Ivanescu2, S. Holmstrom3, C. N. Bui4, J. Spalding4 & K. Fizazi5 Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, USA; Consulting, Quintiles, Hoofddorp; Global Data Science, Health Economics & Outcomes Research, Astellas Pharma Global Development, Leiden, The Netherlands; Health Economics & Clinical Outcomes Research, Astellas Pharma US, Inc., Northbrook, USA; Institut Gustave Roussy, University of Paris Sud, Villejuif, France
Improving QED-Tutrix by Automating the Generation of Proofs

The idea of assisting teachers with technological tools is not new. Mathematics in general, and geometry in particular, provide interesting challenges when developing educative softwares, both in the education and computer science aspects. QED-Tutrix is an intelligent tutor for geometry offering an interface to help high school students in the resolution of demonstration problems. It focuses on specific goals: 1) to allow the student to freely explore the problem and its figure, 2) to accept proofs elements in any order, 3) to handle a variety of proofs, which can be customized by the teacher, and 4) to be able to help the student at any step of the resolution of the problem, if the need arises. The software is also independent from the intervention of the teacher. QED-Tutrix offers an interesting approach to geometry education, but is currently crippled by the lengthiness of the process of implementing new problems, a task that must still be done manually. Therefore, one of the main focuses of the QED-Tutrix' research team is to ease the implementation of new problems, by automating the tedious step of finding all possible proofs for a given problem. This automation must follow fundamental constraints in order to create problems compatible with QED-Tutrix: 1) readability of the proofs, 2) accessibility at a high school level, and 3) possibility for the teacher to modify the parameters defining the "acceptability" of a proof. We present in this paper the result of our preliminary exploration of possible avenues for this task. Automated theorem proving in geometry is a widely studied subject, and various provers exist. However, our constraints are quite specific and some adaptation would be required to use an existing prover. We have therefore implemented a prototype of automated prover to suit our needs. The future goal is to compare performances and usability in our specific use-case between the existing provers and our implementation.
Finite-Length Analysis of Irregular Repetition Slotted ALOHA in the Waterfall Region

A finite-length analysis is introduced for irregular repetition slotted ALOHA (IRSA) that enables to accurately estimate its performance in the moderate-to-high packet loss probability regime, i.e., in the so-called waterfall region. The analysis is tailored to the collision channel model, which enables mapping the description of the successive interference cancellation process onto the iterative erasure decoding of low-density parity-check codes. The analysis provides accurate estimates of the packet loss probability of IRSA in the waterfall region as demonstrated by Monte Carlo simulations.
Injury risk factors to farm and ranch operators in the Central United States

BACKGROUND This study focused on risk factors for serious injuries in farm and ranch operators in the central United States. METHODS The Central States Center for Agricultural Safety and Health, in collaboration with the National Agricultural Statistics Service, sent mail surveys to 6953, 6912, and 6912 farms/ranches in 2011-2013, respectively, covering seven Midwestern states. RESULTS The average survey response rate was 35%. The average annual incidence rate (injuries/100 workers) was 6.91 for all injuries and 2.40 for serious injuries. Univariate analyses determined several demographic and farm production-related risk factors for serious injury. Adjusted analysis showed a greater risk of serious injury for operators of age 45-54 years (vs. 65 and higher), those who worked 75-99% of their time (vs. less time), and those who operated larger land areas (vs. smaller). CONCLUSION The identified risk factors should be considered when targeting injury prevention programs.
Forsythoside A Modulates Zymosan-Induced Peritonitis in Mice

Acute inflammation is a protective response of the host to physical injury and invading infection. Timely treatment of acute inflammatory reactions is essential to prevent damage to organisms that can eventually lead to chronic inflammation. Forsythoside A (FTA), an active constituent of Forsythia suspensa, has been reported to have anti-inflammatory, antioxidant, and antibacterial properties. Despite increasing knowledge of its anti-inflammatory effects, the mechanism and the effects on acute inflammation are poorly understood. This study is aimed at exploring the pro-resolving effects of FTA on zymosan-induced acute peritonitis. FTA significantly alleviated peritonitis as evidenced by the decreased number of neutrophils and levels of tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6), and monocyte chemoattractant protein-1 (MCP-1) in the peritoneal cavity, without interfering with interleukin-10 (IL-10). FTA showed marked regulation of inflammatory cytokines and chemokine levels in zymosan-stimulated RAW 264.7 macrophages. Moreover, FTA could suppress the activation of NF-κB. In conclusion, FTA alleviated zymosan-induced acute peritonitis through inhibition of NF-κB activation.
Blood Pressure and Risk of Vascular Dementia: Evidence From a Primary Care Registry and a Cohort Study of Transient Ischemic Attack and Stroke

Background and Purpose— Vascular dementia is the second most common form of dementia but reliable evidence on age-specific associations between blood pressure (BP) and risk of vascular dementia is limited and some studies have reported negative associations at older ages. Methods— In a cohort of 4.28 million individuals, free of known vascular disease and dementia and identified from linked electronic primary care health records in the United Kingdom (Clinical Practice Research Datalink), we related BP to time to physician-diagnosed vascular dementia. We further determined associations between BP and dementia in a prospective population-based cohort of incident transient ischemic attack and stroke (Oxford Vascular Study). Results— For a median follow-up of 7.0 years, 11 114 initial presentations of vascular dementia were observed in the primary care cohort after exclusion of the first 4 years of follow-up. The association between usual systolic BP and risk of vascular dementia decreased with age (hazard ratio per 20 mm Hg higher systolic BP, 1.62; 95% confidence interval, 1.13–2.35 at 30–50 years; 1.26, 1.18–1.35 at 51–70 years; 0.97, 0.92–1.03 at 71–90 years; P trend=0.006). Usual systolic BP remained predictive of vascular dementia after accounting for effect mediation by stroke and transient ischemic attack. In the population-based cohort, prior systolic BP was predictive of 5-year risk of dementia with no evidence of negative association at older ages. Conclusions— BP is positively associated with risk of vascular dementia, irrespective of preceding transient ischemic attack or stroke. Previous reports of inverse associations in old age could not be confirmed.
Industrial renewal in the 21st century: evidence from US cities

ABSTRACT Industrial renewal in the 21st century: evidence from US cities. Regional Studies. Where and why do new industries emerge? Using revisions of official industrial classifications, this paper documents the appearance of new industries in the US economy between 2000 and 2010 stemming directly from technological advances. Examining differences in new industry creation across cities, this paper shows that new industries mainly emerge in human capital abundant places and cities that specialize in industries that demand similar skills. Instrumental variables estimates that exploit the location of 19th-century land-grant colleges as an instrument for contemporary differences in human capital assigns a causal interpretation to these results.
The Effects of Combinations of Cognitive Impairment and Pre-frailty on Adverse Outcomes from a Prospective Community-Based Cohort Study of Older Chinese People

Objectives To examine how various combinations of cognitive impairment (overall performance and specific domains) and pre-frailty predict risks of adverse outcomes; and to determine whether cognitive frailty may be defined as the combination of cognitive impairment and the presence of pre-frailty. Design Community-based cohort study. Participants Chinese men and women (n = 3,491) aged 65+ without dementia, Parkinson’s disease and/or frailty at baseline. Measurements Frailty was characterized using the Cardiovascular Health Study criteria. Overall cognitive impairment was defined by a Cantonese Mini-Mental Status Examination (CMMSE) total score (<21/24/27, depending on participants’ educational levels); delayed recall impairment by a CMMSE delayed recall score (<3); and language and praxis impairment by a CMMSE language and praxis score (<9). Adverse outcomes included poor quality of life, physical limitation, increased cumulative hospital stay, and mortality. Results Compared to those who were robust and cognitively intact at baseline, those who were robust but cognitively impaired were more likely to develop pre-frailty/frailty after 4 years (P < 0.01). Compared to participants who were robust and cognitively intact at baseline, those who were pre-frail and with overall cognitive impairment had lower grip strength (P < 0.05), lower gait speed (P < 0.01), poorer lower limb strength (P < 0.01), and poorer delayed recall at year 4 [OR, 1.6; 95% confidence interval (CI), 1.2–2.3]. They were also associated with increased risks of poor quality of life (OR, 1.5; 95% CI, 1.1–2.2) and incident physical limitation at year 4 (OR, 1.8; 95% CI, 1.3–2.5), increased cumulative hospital stay at year 7 (OR, 1.5; 95% CI, 1.1–2.1), and mortality over an average of 12 years (OR, 1.5; 95% CI, 1.0–2.1) after adjustment for covariates. There was no significant difference in risks of adverse outcomes between participants who were pre-frail, with/without cognitive impairment at baseline. Similar results were obtained with delayed recall and language and praxis impairments. Conclusion Robust and cognitively impaired participants had higher risks of becoming pre-frail/frail over 4 years compared with those with normal cognition. Cognitive impairment characterized by the CMMSE overall score or its individual domain score improved the predictive power of pre-frailty for poor quality of life, incident physical limitation, increased cumulative hospital stay, and mortality. Our findings support to the concept that cognitive frailty may be defined as the occurrence of both cognitive impairment and pre-frailty, not necessarily progressing to dementia.
Resource efficiency in container-instance clusters

Cloud computing providers have recently begun offering container instances, which provide an efficient route to application deployment within a lightweight, isolated and well-defined execution environment. Cloud providers currently offer Container Service Platforms (CSPs), which support the flexible orchestration of containerised applications. Existing CSP frameworks do not offer any form of intelligent resource scheduling: applications are usually scheduled individually, rather than taking a holistic view of all registered applications and available resources in the cloud. This can result in increased execution times for applications, resource wastage through underutilized container-instances, and a reduction in the number of applications that can be deployed, given the available resources. This paper presents a cloud-based Container Management Service (CMS) framework, which offers increased deployment density, scalability and resource efficiency for containerised applications. CMS extends the state-of-the-art by providing additional functionalities for orchestrating containerised applications by joint optimisation of sets of containerised applications and resource pool on the cloud. We evaluate CMS on a cloud-based CSP i.e., Amazon EC2 Container Management Service (ECS) and conducted extensive experiments using sets of CPU and Memory intensive containerised applications against the direct deployment strategy of Amazon ECS. The results show that CMS achieves up to 25% higher cluster utilisation and up to 2.5 times faster execution times.
Epistemic network analysis of students' longer written assignments as formative/summative evaluation

This paper reports on an exploratory trial of developing pedagogical visualizations of 16 students' written assignments on literary analysis using two sets of keywords and Epistemic Network Analysis (ENA). The visualizations are aimed at summative evaluation as a tool for the professor to support assessment and understanding of subject learning. Results show that ENA can visually distinguish low, middle and high performing students, but not statistically significantly. Thus, our trial provides a tool for the professor that supports understanding of subject learning and formative assessment.
An Accumulation-of-Evidence Task Using Visual Pulses for Mice Navigating in Virtual Reality

The gradual accumulation of sensory evidence is a crucial component of perceptual decision making, but its neural mechanisms are still poorly understood. Given the wide availability of genetic and optical tools for mice, they can be useful model organisms for the study of these phenomena; however, behavioral tools are largely lacking. Here, we describe a new evidence-accumulation task for head-fixed mice navigating in a virtual reality environment. As they navigate down the stem of a virtual T-maze, they see brief pulses of visual evidence on either side, and retrieve a reward on the arm with the highest number of pulses. The pulses occur randomly with Poisson statistics, yielding a diverse yet well-controlled stimulus set, making the data conducive to a variety of computational approaches. A large number of mice of different genotypes were able to learn and consistently perform the task, at levels similar to rats in analogous tasks. They are sensitive to side differences of a single pulse, and their memory of the cues is stable over time. Moreover, using model-free and model-based analyses, we show that the mice indeed accumulate evidence: they use multiple pulses of evidence from throughout the cue region of the maze to make their decision, albeit with a small overweighting of earlier cues, and their performance is affected by the magnitude but not the duration of evidence. Additionally, analysis of the mice’s running patterns revealed that trajectories are fairly stereotyped yet modulated by the amount of sensory evidence, suggesting that the navigational component of this task may provide a continuous readout correlated to the underlying cognitive variables. Our task, which can be readily integrated with state-of-the-art techniques, is thus a valuable tool to study the circuit mechanisms and dynamics underlying perceptual decision making, particularly under more complex behavioral contexts.