Miracle-dz/UniFAR-FTUs huggingface dataset:
2019 ARIA Care pathways for allergen immunotherapy

Allergen immunotherapy (AIT) is a proven therapeutic option for the treatment of allergic rhinitis and/or asthma. Many guidelines or national practice guidelines have been produced but the evidence‐based method varies, many are complex and none propose care pathways. This paper reviews care pathways for AIT using strict criteria and provides simple recommendations that can be used by all stakeholders including healthcare professionals. The decision to prescribe AIT for the patient should be individualized and based on the relevance of the allergens, the persistence of symptoms despite appropriate medications according to guidelines as well as the availability of good‐quality and efficacious extracts. Allergen extracts cannot be regarded as generics. Immunotherapy is selected by specialists for stratified patients. There are no currently available validated biomarkers that can predict AIT success. In adolescents and adults, AIT should be reserved for patients with moderate/severe rhinitis or for those with moderate asthma who, despite appropriate pharmacotherapy and adherence, continue to exhibit exacerbations that appear to be related to allergen exposure, except in some specific cases. Immunotherapy may be even more advantageous in patients with multimorbidity. In children, AIT may prevent asthma onset in patients with rhinitis. mHealth tools are promising for the stratification and follow‐up of patients.
Interface-aware signal temporal logic

Safety and security are major concerns in the development of Cyber-Physical Systems (CPS). Signal temporal logic (STL) was proposed as a language to specify and monitor the correctness of CPS relative to formalized requirements. Incorporating STL into a development process enables designers to automatically monitor and diagnose traces, compute robustness estimates based on requirements, and perform requirement falsification, leading to productivity gains in verification and validation activities; however, in its current form STL is agnostic to the input/output classification of signals, and this negatively impacts the relevance of the analysis results. In this paper we propose to make the interface explicit in the STL language by introducing input/output signal declarations. We then define new measures of input vacuity and output robustness that better reflect the nature of the system and the specification intent. The resulting framework, which we call interface-aware signal temporal logic (IA-STL), aids verification and validation activities. We demonstrate the benefits of IA-STL on several CPS analysis activities: (1) robustness-driven sensitivity analysis, (2) falsification and (3) fault localization. We describe an implementation of our enhancement to STL and associated notions of robustness and vacuity in a prototype extension of Breach, a MATLAB®/Simulink® toolbox for CPS verification and validation. We explore these methodological improvements and evaluate our results on two examples from the automotive domain: a benchmark powertrain control system and a hydrogen fuel cell system.
3d printing, muscles and skeleton: mechanical functions of living wood.

Wood is well defined as an engineering material. But living wood in the tree is often regarded only as a passive skeleton consisting of a sophisticated pipe system for the ascent of sap and a tree like structure made of a complex material to resist external forces. There are two other active key roles of living wood in the field of biomechanics: i) additive manufacturing of the whole structure by cell division and expansion and ii) muscle function of living fibres or tracheids generating forces at the sapwood periphery. The living skeleton representing most of the sapwood is a mere accumulation of dead tracheids and libriform fibres after their programmed cell death. It keeps the record of two active roles of living wood in its structure, chemical composition and state of residual stresses. Models and field experiments define four biomechanical traits based on stem geometry and wood properties parameters resulting from additive manufacturing and force generation. Geometrical parameters resulting from primary and secondary growth play the bigger role. Passive wood properties are only secondary parameters while dissymmetric force generation is a key for movement, posture control and tree reshaping after accidents.
ContextAiDe

Mobile crowd-sensing (MCS) enables development of context-aware applications by mining relevant information from a large set of devices selected in an ad hoc manner. For example, MCS has been used for real-time monitoring such as Vehicle ad hoc Networks-based traffic updates as well as offline data mining and tagging for future use in applications with location-based services. However, MCS could be potentially used for much more demanding applications such as real-time perpetrator tracking by online mining of images from nearby mobile users. A recent example is tracking the miscreant responsible for the Boston bombing. We present a new design approach for tracking using MCS for such complex processing in real time. Since MCS applications assume an unreliable underlying computational platform, most typically sample size for recruited devices is guided by concerns such as fault tolerance and reliability of information. As the real-time requirements get stricter coupled with increasing complexity of data-mining approaches, the communication and computation overheads can impose a very tight constraint on the sample size of devices needed for realizing real-time operation. This results in trade-off in acquiring context-relevant data and resource usage incurred while the real-time operation requirements get updated dynamically. Such effects have not been properly studied and optimized to enable real-time MCS applications such as perpetrator tracking. In this article, we propose ContextAiDe architecture, a combination of API, middleware, and optimization engine. The key innovation in ContextAiDe is context-optimized recruitment for execution of computation- and communication-heavy MCS applications in edge environment. ContextAiDe uses a notion of two types of contexts, exact (hard constraints), which have to be satisfied, and preferred (soft constraints), which may be satisfied to a certain degree. By adjusting the preferred contexts, ContextAiDe can optimize the operational overheads to enable real-time operation. ContextAiDe provides an API to specify contexts requirements and the code of MCS app, offload execution environment, a middleware that enables context-optimized and a fault-tolerant distributed execution. ContextAiDe evaluation using a real-time perpetrator tracking application shows reduced energy consumption of 37.8%, decrease in data transfer of 24.8%, and 43% less time compared to existing strategy. In spite of a small increase in the minimum distance from the perpetrator, iterations of optimization tracks the perpetrator successfully. Pro-actively learning the context and using stochastic optimization strategy minimizes the performance degradation caused due to uncertainty (<20%) in usage-dependent contexts.
Dietary B-Vitamin Intake and Risk of Breast, Endometrial, Ovarian and Colorectal Cancer among Canadians

Abstract Few studies have explored the associations of thiamin, niacin and riboflavin with risk of cancer despite their role in potentially cancer-associated one-carbon metabolism. Using multivariable Cox proportional hazards regression models modified for the case-cohort design, we examined the associations of dietary intake of the above-mentioned B vitamins, as well as folate, and vitamins B6 and B12, with risk of the breast (n = 922), endometrial (n = 180), ovarian (n = 104) and colorectal (n = 266) cancers among age-stratified subcohorts of 3,185 women who were randomly selected from a cohort of 73,909 participants. None of the B-vitamins were associated with risk of breast or colorectal cancers. However, relatively high dietary intake of folate intake was inversely associated with risk of endometrial (HRq4 vs q1: 0.52; 95% CI: 0.29–0.93) and ovarian (HRq3 vs q1: 0.39; 95% CI: 0.19–0.80) cancers while relatively high dietary intake of vitamin B6 was inversely associated with ovarian cancer risk (HRq3 vs q1: 0.49; 95% CI: 0.24–0.98). These findings suggest that dietary intake of folate may reduce risk of endometrial and ovarian cancers and dietary intake of vitamin B6 may reduce risk of ovarian cancer.
The Impact of COVID-19 on Highway Traffic and Management: The Case Study of an Operator Perspective

The COVID-19 pandemic created an enormous disruption to the everyday life of the modern society. Among the various urban systems, transportation services were among those that suffered the most significant impacts, particularly severe in the case of highways. This paper addresses the challenges and responses to the pandemic from a private highway operator’s perspective and from a multidisciplinary perspective. Highway operators faced two main challenges: on one hand, the need to cope with the potential disruption caused by the pandemic and a national lockdown for almost three months, the provision of road services, and the requirement to ensure the proper operation and maintenance, and on the other hand, the strong negative impact of the pandemic on levels of traffic. Our case study shows that the operator’s management response in question is essentially characterised by being a first response to short term impacts while balancing for workers health and safety, engineering and management, internal business management, and overall economic impact. Highway operators were hardly prepared for such an event and became more focused on prioritising their employees and clients’ safety to avoid service disruption. Regarding levels of traffic, the pandemic has had severe effects, although to a varying degree, depending on the different types of vehicles (heavy, light, passenger, freight, among other types of vehicles) and the location of highways (coastal vs. interior). The lessons learnt can be valuable in future disruptive events and for other highway concession operators.
Fewest switches surface hopping with Baeck-An couplings

In the Baeck-An (BA) approximation, first-order nonadiabatic coupling vectors are given in terms of adiabatic energy gaps and the second derivative of the gaps with respect to the coupling coordinate. In this paper, a time-dependent (TD) BA approximation is derived, where the couplings are computed from the energy gaps and their second time-derivatives. TD-BA couplings can be directly used in fewest switches surface hopping, enabling nonadiabatic dynamics with any electronic structure methods able to provide excitation energies and energy gradients. Test results of surface hopping with TD-BA couplings for ethylene and fulvene show that the TD-BA approximation delivers a qualitatively correct picture of the dynamics and a semiquantitative agreement with reference data computed with exact couplings. Nevertheless, TD-BA does not perform well in situations conjugating strong couplings and small velocities. Considered the uncertainties in the method, TD-BA couplings could be a competitive approach for inexpensive, exploratory dynamics with a small trajectories ensemble. We also assessed the potential use of TD-BA couplings for surface hopping dynamics with time-dependent density functional theory (TDDFT), but the results are not encouraging due to singlet instabilities near the crossing seam with the ground state.
Milk Price Comparison Between Six Years from Romania and European Union

Milk price tendency is essential for the national economy and protection of milk producers. The constant need to align with European standards motivate this study to assess the interaction of milk prices in our country compared to the European Union. The objective of the study was to statistically compare the prices from Romania and UE. For a deep overview we took into consideration the quantity from Romania and most important qualitative milk parameters namely protein and fat content. The data was from 6 years from 2013 until 2018 and from all four seasons. Analysis of variance was performed together with mean comparisons between different years and seasons. Milk medium prices were higher with at least 20% in UE compared to Romania. Prices were higher in autumn and winter rather than in spring and summer proportionally with the quantity of milk. Protein and fat content was significantly higher in autumn and winter season.
Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

Static functional connectivity (FC) analyses based on functional magnetic resonance imaging (fMRI) data have been extensively explored for studying various psychiatric conditions in the brain, including cocaine addiction. A recently emerging, more powerful technique, dynamic functional connectivity (DFC), studies how the FC dynamics change during the course of the fMRI experiments. The aim in this paper was to develop a computational approach, using a machine learning framework, to determine if DFC features were more successful than FC features in the classification of cocaine‐dependent patients and healthy controls. fMRI data were obtained from of 25 healthy and 58 cocaine‐dependent participants while performing a motor response inhibition task, stop signal task. Group independent component analysis was carried out on all participant data to compute spatially independent components (ICs). Eight ICs were selected manually as relevant brain networks, which were used to classify healthy versus cocaine‐dependent participants. FC and DFC measures of the chosen IC pairs were used as features for the classification algorithm. Support Vector Machines were used for both feature selection/reduction and participant classification. Based on DFC with only seven IC pairs, participants were successfully classified with 95% accuracy (and with 90% accuracy with three IC pairs), whereas static FC yielded only 81% accuracy. Visual, sensorimotor, default mode, and executive control networks, amygdala, and insula played the most significant role in the DFC‐based classification. These findings support the use of DFC‐based classification of fMRI data as a potential biomarker for the identification of cocaine dependence.
Prevalence of Unexplained Left Ventricular Hypertrophy by Cardiac Magnetic Resonance Imaging in MESA

Background Hypertrophic cardiomyopathy is defined as unexplained left ventricular (LV) hypertrophy (wall thickness ≥15 mm) and is prevalent in 0.2% of adults (1:500) in population‐based studies using echocardiography. Cardiac magnetic resonance imaging (MRI) allows for more accurate wall thickness measurement across the entire ventricle than echocardiography. The prevalence of unexplained LV hypertrophy by cardiac MRI is unknown. MESA (Multi‐Ethnic Study of Atherosclerosis) recruited individuals without overt cardiovascular disease 45 to 84 years of age. Methods and Results We studied 4972 individuals who underwent measurement of regional LV wall thickness by cardiac MRI as part of the MESA baseline exam. American Heart Association criteria were used to define LV segments. We excluded participants with hypertension, LV dilation (≥95% predicted end‐diastolic volume) or dysfunction (ejection fraction ≤50%), moderate‐to‐severe left‐sided valve lesions by cardiac MRI, severe aortic valve calcification by cardiac computed tomography (aortic valve Agatston calcium score >1200 in women or >2000 in men), obesity (body mass index >35 kg/m2), diabetes mellitus, and current smoking. Sixty‐seven participants (aged 64±10 years, 9% female) had unexplained LV hypertrophy (wall thickness ≥15 mm in at least 2 adjacent LV segments), representing 1.4% (1 in 74) participants, 2.6% of men and 0.2% of women. Prevalence was similar across categories of race/ethnicity. Hypertrophy was focal in 17 (25.4%), intermediate in 44 (65.7%), and diffuse in 5 (7.5%) participants. Conclusions The prevalence of unexplained LV hypertrophy in a population‐based cohort using cardiac MRI was 1.4%. This may have implications for the diagnosis of patients with hypertrophic cardiomyopathy and will require further study.
Spatial-Temporal Analysis of Cancer Risk in Epidemiologic Studies with Residential Histories

Exploring spatial-temporal patterns of disease incidence identifies areas of significantly elevated risk and can lead to discoveries of disease risk factors. One popular way to investigate patterns in risk over space and time is spatial-temporal cluster detection analysis. The identification of significant clusters could lead to etiological hypotheses to explain the pattern of elevated risk and to additional epidemiologic studies to explore these hypotheses. Several methodological issues and data challenges that arise in space–time cluster analysis of chronic diseases, such as cancer, include poor spatial precision of residence locations, long disease latencies, and adjustment for known risk factors. This article reviews the key challenges faced when performing cluster analyses of chronic diseases and presents a spatial-temporal analysis of non-Hodgkin lymphoma (NHL) risk addressing these challenges. Residential histories, collected as part of a population-based case-control study of NHL (the National Cancer Institute [NCI] Surveillance, Epidemiology, and End Results [SEER] NHL study) in four SEER centers (Detroit metropolitan area, Los Angeles, Seattle metropolitan area, and Iowa) were geocoded. In this analysis, we explored previously detected spatial-temporal clusters and adjusted for exposure to polychlorinated biphenyls (PCBs) and genetic polymorphisms in four genes, previously found to be associated with NHL, using a generalized additive model framework. We found that the genetic factors and PCB exposure did not fully explain previously detected areas of elevated risk.
Inhibitory Actions of Tropeines on the α3 Glycine Receptor Function

Glycine receptors (GlyRs) are chloride-permeable pentameric ligand-gated ion channels. The inhibitory activity of GlyRs is essential for many physiological processes, such as motor control and respiration. In addition, several pathological states, such as hyperekplexia, epilepsy, and chronic pain, are associated with abnormal glycinergic inhibition. Recent studies have pointed out that positive allosteric modulators targeting the GlyR α3 subunit (α3GlyR) displayed beneficial effects in chronic pain models. Interestingly, previous electrophysiological studies have shown that tropeines, which are a family of synthetic antagonists of the serotonin type 3 receptors (5-HT3Rs), potentiate the activity of GlyRs conformed by α1 subunits. However, despite its importance as a pharmacological target in chronic pain, it is currently unknown whether the α3GlyR function is modulated by tropeines. Using electrophysiological techniques and molecular docking simulations, here we show that tropeines are inhibitors of the α3GlyR function. Tropisetron, a prototypical tropeine, exerted concentration-dependent inhibitory effects on α3GlyRs at the low micromolar range. In addition, three other tropeines showed similar effects. Single-channel recordings show that tropisetron inhibition is associated with a decrease in the open probability of the ion channel. Molecular docking assays suggest that tropeines preferentially bind to an agonist-free, closed state of the ion channel. The tropeine binding occurs in a discrete pocket around the vicinity of the orthosteric site within the extracellular domain of α3GlyR. Thus, our results describe the pharmacological modulation of tropeines on α3GlyRs. These findings may contribute to the development of GlyR-selective tropeine derivatives for basic and/or clinical applications.
Synthesis and molecular structure of biologically significant bis(1,3-dimesityl-4,5-naphthoquinoimidazol-2-ylidene)gold(I) complexes with chloride and dichloridoaurate counter-ions.

Diffraction-quality single crystals of two gold(I) complexes, namely bis(1,3-dimesityl-4,5-naphthoquinoimidazol-2-ylidene)gold(I) chloride benzene monosolvate, [Au(C29H26N2O2)2]Cl·C6H6 or [(NQMes)2Au]Cl·C6H6, 2, and bis(1,3-dimesityl-4,5-naphthoquinoimidazol-2-ylidene)gold(I) dichloridoaurate(I) dichloromethane disolvate, [Au(C29H26N2O2)2][AuCl2]·2CH2Cl2 or [(NQMes)2Au][AuCl2]·2CH2Cl2, 4, were isolated and studied with the aid of single-crystal X-ray diffraction analysis. Compound 2 crystallizes in a monoclinic space group C2/c with eight molecules in the unit cell, while compound 4 crystallizes in the triclinic space group P-1 with two molecules in the unit cell. The crystal lattice of compound 2 reveals C-H.Cl- interactions that are present throughout the entire structure representing head-to-tail contacts between the aromatic (C-H) hydrogens of naphthoquinone and Cl- counter-ions. Compound 4 stacks with the aid of short interactions between a naphthoquinone O atom of one molecule and the mesityl methyl group of another molecule along the a axis, leading to a one-dimensional strand that is held together by strong π-η2 interactions between the imidazolium backbone and the [AuCl2]- counter-ion. The bond angles defined by the AuI atom and two carbene C atoms [C(carbene)-Au-C(carbene)] in compounds 2 and 4 are nearly rectilinear, with an average value of ∼174.1 [2]°. Though 2 and 4 share the same cation, they differ in their counter-anion, which alters the crystal lattice of the two compounds. The knowledge gleaned from these studies is expected to be useful in understanding the molecular interactions of 2 and 4 under physiological conditions.
Automated Tracking Systems for the Assessment of Farmed Poultry

Simple Summary With the advent of artificial intelligence, the poultry sector is gearing up to adopt and embrace sensor technologies to enhance the production and the welfare of birds. Automated tracking and tracing of poultry birds has several advantages in poultry farms: overcoming the subjectivity of human measurements, enhancing the ability to provide quality care for the birds during their life on the farm, providing the ability to predict events and thereby enabling timely interventions, and many more. However, the technologies behind automated tracking systems are not ripe due to the lags in algorithms and practical implementation issues. This mini review provides a brief critical assessment of the current and recent advancements of automated tracking systems in the poultry industry and offers an outlook on future directions. Abstract The world’s growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animals on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of artificial intelligence (AI)-assisted technology individualised, and per-herd assessments of livestock became possible and accurate. Various studies have shown that cameras linked with specialised systems of AI can properly analyse flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on recent advancements, this review explores the aspects of AI in the detection, counting, and tracking of poultry in commercial and research-based applications.