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Background Increased physical activity is a common feature of anorexia nervosa (AN). Although high activity levels are associated with greater risk of developing AN, particularly when combined with dieting, most individuals who diet and exercise maintain a healthy body weight. It is unclear why some individuals develop AN while most do not. A rodent model of resilience and vulnerability to AN would be valuable to research. Dopamine, which is believed to play a crucial role in AN, regulates both reward and activity and may modulate vulnerability. Methods Adolescent and young adult female C57BL/6N mice were tested in the activity-based anorexia (ABA) model, with an extended period of food restriction in adult mice. ABA was also tested in dopamine transporter knockdown mice and wild-type littermates. Mice that adapted to conditions and maintained a stable body weight were characterized as resilient. Results In adults, vulnerable and resilient phenotypes emerged in both the ABA and food-restricted mice without wheels. Vulnerable mice exhibited a pronounced increase in running throughout the light cycle, which dramatically peaked prior to requiring removal from the experiment. Resilient mice exhibited an adaptive decrease in total running, appropriate food anticipatory activity, and increased consumption, thereby achieving stable body weight. Hyperdopaminergia accelerated progression of the vulnerable phenotype. Conclusions Our demonstration of distinct resilient and vulnerable phenotypes in mouse ABA significantly advances the utility of the model for identifying genes and neural substrates mediating AN risk and resilience. Modulation of dopamine may play a central role in the underlying circuit.

Purpose To provide a guideline curriculum related to Artificial Intelligence (AI), for the education and training of European Medical Physicists (MPs). Materials and methods The proposed curriculum consists of two levels: Basic (introducing MPs to the pillars of knowledge, development and applications of AI, in the context of medical imaging and radiation therapy) and Advanced. Both are common to the subspecialties (diagnostic and interventional radiology, nuclear medicine, and radiation oncology). The learning outcomes of the training are presented as knowledge, skills and competences (KSC approach). Results For the Basic section, KSCs were stratified in four subsections: (1) Medical imaging analysis and AI Basics; (2) Implementation of AI applications in clinical practice; (3) Big data and enterprise imaging, and (4) Quality, Regulatory and Ethical Issues of AI processes. For the Advanced section instead, a common block was proposed to be further elaborated by each subspecialty core curriculum. The learning outcomes were also translated into a syllabus of a more traditional format, including practical applications. Conclusions This AI curriculum is the first attempt to create a guideline expanding the current educational framework for Medical Physicists in Europe. It should be considered as a document to top the sub-specialties’ curriculums and adapted by national training and regulatory bodies. The proposed educational program can be implemented via the European School of Medical Physics Expert (ESMPE) course modules and – to some extent – also by the national competent EFOMP organizations, to reach widely the medical physicist community in Europe.

In the present research, storage conditions of extra virgin olive oil (EVOO) have been monitored using cost-effective fluorescence sensors integrated with intelligent algorithms. Three different Spanish pure EVOOs (Arbequina, Cornicabra, and Picual varieties), as well as samples adulterated with expired EVOO, were kept under different environmental conditions: presence or absence of light, bottle material (plastic or glass), and clearness of glass (uncolored or brown glass). To evaluate the effect of these conditions, 54 samples were prepared, and their emission spectra were measured 10 times during a 58-day period. The most statistically relevant information from these spectra was located by the relief-F feature selection method, which led to the design of machine learning-based models. With this aim, up to 158,250 artificial neural network-based models were designed and tested. It was possible to distinguish different storage conditions in terms of light exposure, bottle material, and clearness, as well as locate adulterations. All of the trained and optimized classifiers for all three EVOO varietals reached accuracies ranging between 91 and 100% after meticulous validations. The used technique is fast, portable, user-friendly, inexpensive, and non-destructive, and, therefore, it could be of great use for the olive oil sector as it enables real-time quality control and fraud detection.

The applicability of an aerial drone as a carrier for new passive and active miniaturized air sampling systems, including solid phase microextration Arrow (SPME Arrow) and in-tube extraction (ITEX), was studied in this research. Thermal desorption, gas chromatography and mass spectrometry were used for the determination of volatile organic compounds (VOCs) collected by the sampling systems. The direct comparison of the profiles of VOCs, simultaneously sampled in air by SPME Arrow system including four different coatings, allowed the elucidation of their adsorption selectivity. A more complex experimental design, involving 20 samples (10 flights) and non-supervised pattern recognition techniques, was needed for the clarification of the same sampling parameters in the case of five ITEX sorbent materials. In addition, ITEX sampling accessories, such as particle, water and ozone traps, were evaluated by comparing the results obtained for air samples simultaneously collected by two ITEX systems, packed with the same sorbent and furnished or not with sampling accessories. The effect of the aerial drone horizontal displacement (HD) on the sampling efficiency was clear in the case of SPME Arrow. The number of detected compounds and their relative peak area values (RPA) revealed a clear increase (4 and 43%, respectively) in comparison with samples collected without drone HD. However, just minor differences were observed in the case of ITEX (2 compounds and 9% of the Sigma RPA). In addition, the system was able to provide almost simultaneous passive (SPME Arrow) and active (ITEX) samplings at different altitudes (5 and 50 m), being a good tool for low cost vertical profiling studies (Sigma RPA decreased over 35% for the samples collected at 50 m). Finally, the successful simultaneous air sampling by SPME Arrow and ITEX systems in two difficult access places, such as boreal forest and wetlands, was demonstrated, resulting in 21 and 31 detected compounds in forest and wetlands by SPME Arrow, and 27 and 39 compounds by ITEX. (C) 2019 The Authors. Published by Elsevier B.V.

Objectives To determine between-days reliability and the minimal detectable change for shoulder and elbow joint position sense assessment using a validated mobile app, in subjects with and without shoulder pain. Design Reliability study. Setting Clinical measurement. Participants Subjects with (n = 25) and without shoulder pain (n = 29). Main outcome measures Subjects were assessed by the same examiner in two sessions, with one-week interval. Active joint repositioning tests of shoulder flexion and scaption and elbow flexion were assessed at the target-angles of 50°, 70°, 90° and 110°. Intra-class correlation coefficient, standard error of measurement and minimal detectable change were calculated for constant, absolute, total and variable errors. Results Good to excellent reliability was found for constant, absolute and total errors at the target-angle of 50° of scaption for healthy subjects; at 110° of shoulder flexion and all target-angles for elbow for both groups. Conclusions The mobile app is a reliable tool and may be useful for assessing shoulder joint position sense mainly at 110° of flexion and for elbow between 50° and 110° of flexion in subjects with and without shoulder pain. Minimal detectable changes were demonstrated and may help clinicians to follow-up rehabilitation and researchers to interpret findings of studies.

Extra virgin olive oil (EVOO), the star of the Mediterranean diet, is a valuable product within the food sector. In certain cases, due to specific and special olive varietals and blends, as well as organoleptic properties, EVOOs are even authenticated with protected designation of origin (PDO) labels. Because of this, false labeling and even adulterations have been reported in this sector. During this research, a laser diode has been used to gather 254 fluorescence spectra from that many artificially prepared samples of fresh PDO EVOO mixed with small amounts of old olive oils (adulterants). The database was then treated and employed to train and optimize an intelligent model based on a supervised artificial neural network known as multilayer perceptron (MLP). The goal of this non-linear model was dual: to identify the PDO of every sample and to quantify the amount of fresh EVOO. After meticulous validation using blinded samples to assess the performance of the MLP, the outcome was successful. The algorithm could perfectly distinguish the PDOs and determine the amount of fresh EVOO with a mean absolute error of around 1.5% (w/w). Therefore, the combination of a laser diode and cognitive modeling leads to a fast and cost-effective tool able to authenticate PDO labels of EVOOs and estimate the amount of potential adulterating agents such as olive oils from old harvests.

Rehabilitation clinicians strive to provide cost-effective, patient-centered care that optimizes outcomes. A barrier to this ideal is the lack of a universal system for describing, or specifying, rehabilitation interventions. Current methods of description vary across disciplines and settings, creating barriers to collaboration, and tend to focus mostly on functional deficits and anticipated outcomes, obscuring connections between clinician behaviors and changes in functioning. The Rehabilitation Treatment Specification System (RTSS) is the result of more than a decade of effort by a multidisciplinary group of rehabilitation clinicians and researchers to develop a theory-based framework to specify rehabilitation interventions. The RTSS describes interventions for treatment components, which consist of a target (functional change brought about as a direct result of treatment), ingredients (actions taken by clinicians to change the target), and a hypothesized mechanism of action, as stated in a treatment theory. The RTSS makes explicit the connections between functional change and clinician behavior, and recognizes the role of patient effort in treatment implementation. In so doing, the RTSS supports clinicians' efforts to work with their patients to set achievable goals, select appropriate treatments, adjust treatment plans as needed, encourage patient participation in the treatment process, communicate with team members, and translate research findings to clinical care. The RTSS may help both expert and novice clinicians articulate their clinical reasoning processes in ways that benefit treatment planning and clinical education, and may improve the design of clinical documentation systems, leading to more effective justification and reimbursement for services. Interested clinicians are invited to apply the RTSS in their local settings.

The field of rehabilitation remains captive to the black-box problem: our inability to characterize treatments in a systematic fashion across diagnoses, settings, and disciplines, so as to identify and disseminate the active ingredients of those treatments. In this article, we describe the Rehabilitation Treatment Specification System (RTSS), by which any treatment employed in rehabilitation may be characterized, and ultimately classified according to shared properties, via the 3 elements of treatment theory: targets, ingredients, and (hypothesized) mechanisms of action. We discuss important concepts in the RTSS such as the distinction between treatments and treatment components, which consist of 1 target and its associated ingredients; and the distinction between targets, which are the direct effects of treatment, and aims, which are downstream or distal effects. The RTSS includes 3 groups of mutually exclusive treatment components: Organ Functions, Skills and Habits, and Representations. The last of these comprises not only thoughts and feelings, but also internal representations underlying volitional action; the RTSS addresses the concept of volition (effort) as a critical element for many rehabilitation treatments. We have developed an algorithm for treatment specification which is illustrated and described in brief. The RTSS stands to benefit the field in numerous ways by supplying a coherent, theory-based framework encompassing all rehabilitation treatments. Using a common framework, researchers will be able to test systematically the effects of specific ingredients on specific targets; and their work will be more readily replicated and translated into clinical practice.

Social interaction deficits seen in psychiatric disorders emerge in early-life and are most closely linked to aberrant neural circuit organization and function. Due to technical limitations, we have had little understanding of the typical ontogeny of social behavior neural circuits or how early caregiving adversity impacts infant neurobiology to perturb lifelong social interaction. Using a suite of invasive procedures in awake, behaving young infant rats, including optogenetics, microdialysis and microinfusions, we charted the gradual increase in social behavior deficits following adversity-rearing and dissected circuits controlling this process. Persistently elevated dopamine in basolateral amygdala (BLA) was necessary and sufficient in initiating social behavior pathology, as demonstrated by manipulation of amygdala dopamine during adversity and during expression of social behavior deficits with the mother and peers. Taken together, these data highlight mesolimbic dopamine circuit organization and function as a potential therapeutic target in understanding behavioral deficits associated with psychiatric disorders.

A set of 10 honeys comprising a diverse range of botanical origins have been successfully characterized through fluorescence spectroscopy using inexpensive light-emitting diodes (LEDs) as light sources. It has been proven that each LED-honey combination tested originates a unique emission spectrum, which enables the authentication of every honey, being able to correctly label it with its botanical origin. Furthermore, the analysis was backed up by a mathematical analysis based on partial least square models which led to a correct classification rate of each type of honey of over 95%. Finally, the same approach was followed to analyze rice syrup, which is a common honey adulterant that is challenging to identify when mixed with honey. A LED-dependent and unique fluorescence spectrum was found for the syrup, which presumably qualifies this approach for the design of uncomplicated, fast, and cost-effective quality control and adulteration assessing tools for different types of honey.

Objective To evaluate within- and between-days reliability of two normalization methods of surface electromyography (sEMG) recordings of the trapezius muscle. Methods Nineteen women were allocated into 2 groups (healthy and with neck-shoulder pain). The sEMG was recorded in two sessions with 7 days in between sessions. The four portions of the trapezius muscle (the clavicular and acromial fibers of the upper trapezius, the middle and the lower trapezius) were evaluated during maximal and submaximal isometric voluntary contractions. The within- and between-days reliability of both maximal and submaximal contractions were assessed through Intraclass Correlation Coefficient (ICC(2,1) was used for within-day analyses of both maximal and submaximal contractions, and for between-days analyses of maximal contractions while ICC(2,3) was used for between-days analyses of submaximal contractions), Coefficient of Variation, Standard Error of Measurement, and Bland–Altman analysis. Results In general, submaximal contractions presented higher within-day reliability, with higher ICC values (e.g., middle trapezius – mean of 0.97), smaller Coefficient of Variation and Standard Error of Measurement ranges compared to maximal contractions (ICC values, e.g. for middle trapezius – mean of 0.94) in both groups. The same pattern was observed for between-days analyses, with submaximal contractions presenting higher ICC values (e.g., middle trapezius – mean of 0.84), smaller Coefficient of Variation and Standard Error of Measurement ranges than maximal contractions (ICC values, e.g. for middle trapezius – mean of 0.73) in both groups. Conclusion Submaximal contractions are recommended for normalization procedures of trapezius sEMG, in both subjects with neck-shoulder pain and healthy individuals.

In this work, the galvanic deposition method is used to deposit coatings of brushite/hydroxyapatite/polyvinyl acetate on 304 stainless steel. Coatings are obtained at different temperatures and with different sacrificial anodes, consisting of a mixture of brushite and hydroxyapatite. Samples are aged in a simulated body fluid (SBF), where a complete conversion of brushite into hydroxyapatite with a simultaneous change in morphology and wettability occurred. The corrosion tests show that, compared with bare 304, the coating shifts Ecorr to anodic values and reduces icorr Ecorr, and icorr has different values at different aging times due to chemical interactions at the solid/liquid interface. The best performing deposits are those obtained by using Al as the sacrificial anode. The metal ion release, measured after 21 days of aging, is very low and is attributable to the presence of a coating that slows the steel corrosion. Coating cytotoxicity is investigated through cell viability assays with MC3T3-E1 osteoblastic cells. The results reveal a high cytocompatibility comparable to that of a pure cell culture medium.

Objectives To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction. Methods Anthropomorphic phantoms (mimicking non-overweight and overweight patient), containing lesions of 6 mm in diameter with 20HU contrast, were scanned at five different dose levels (2,6,10,15,20 mGy) on a CT system, using clinical routine protocols for liver lesion detection. Images were reconstructed using ASiR-V 0% (surrogate for FBP), 60 % and TF at low, medium and high strength. Noise texture was characterized by computing a normalized Noise Power Spectrum filtered by an eye filter. The similarity against FBP texture was evaluated using peak frequency difference (PFD) and root mean square deviation (RMSD). Low contrast detectability was assessed using a channelized Hotelling observer and the area under the ROC curve (AUC) was used as figure of merit. Potential dose reduction was calculated to obtain the same AUC for TF and ASiR-V. Results FBP-like noise texture was more preserved with TF (PFD from -0.043mm-1 to -0.09mm-1, RMSD from 0.12mm-1 to 0.21mm-1) than with ASiR-V (PFD equal to 0.12 mm-1, RMSD equal to 0.53mm-1), resulting in a sharper image. AUC was always higher with TF than ASIR-V. In average, TF compared to ASiR-V, enabled a radiation dose reduction potential of 7%, 25 % and 33 % for low, medium and high strength respectively. Conclusion Compared to ASIR-V, TF at high strength does not impact noise texture and maintains low contrast liver lesions detectability at significant lower dose.