Does hyperspectral always matter? A critical assessment of near infrared versus hyperspectral near infrared in the study of heterogeneous samples

Near Infrared spectroscopy (NIR), in combination with Chemometrics, has been used for many years in diverse scenarios, mostly focused on the classification and quantitation of properties in food, pharmaceutical prepara- tions, artwork material, etc. This success has been possible due to their desirable properties: fast, reliable (under certain conditions), non-destructive, easy to implement from a hardware perspective, and able to create robust and transferable multivariate models. For some years now, another modality has been gaining the attention of NIR users, especially in the Food sector. That is the plausibility of using NIR in the hyperspectral (HSI) domain. This adds to the previously mentioned abilities, the benefit of scanning the whole surface of samples, acquiring much richer spatial infor- mation and, therefore, assuring the quality of the final product more accurately by including parameters that depend on the surface distribution of certain components. This is especially relevant in heterogeneous samples. While this statement is generally true, there are certain situations where this oversampling feature is not strictly needed, and the problem can be easily solved with a classical NIR spectrophotometer. Besides, NIR-hyperspectral imaging (NIR-HSI), despite the abovementioned advantages, has several drawbacks that must be highlighted as well, like their measuring speed, instability, or price. This manuscript will demonstrate that for certain situations, tuning the focal distance of a NIR spectropho- tometer is a more feasible, reliable, and inexpensive strategy to collect all the needed information of samples with a certain degree of heterogeneity

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics

The food industry needs tools to improve the efficiency of their production processes by minimizing waste, detecting timely potential process issues, as well as reducing the efforts and workforce devoted to laboratory analysis while, at the same time, maintaining high-quality standards of products. This can be achieved by developing on-line monitoring systems and models. The present work presents a feasibility study toward establishing the on-line monitoring of a pesto sauce production process by means of NIR spectroscopy and chemometric tools. The spectra of an intermediate product were acquired on-line and continuously by a NIR probe installed directly on the process line. Principal Component Analysis (PCA) was used both to perform an exploratory data analysis and to build Multivariate Statistical Process Control (MSPC) charts. Moreover, Partial Least Squares (PLS) regression was employed to compute real time prediction models for two different pesto quality parameters, namely, consistency and total lipids content. PCA highlighted some differences related to the origin of basil plants, the main pesto ingredient, such as plant age and supplier. MSPC charts were able to detect production stops/restarts. Finally, it was possible to obtain a rough estimation of the quality of some properties in the early production stage through PLS

Therapist reactions to patient personality: A pilot study of clinicians’ emotional and neural responses using three clinical vignettes from "In Treatment" series

Introduction: Therapists’ responses to patients play a crucial role in psychotherapy and are considered a key component of the patient–clinician relationship, which promotes successful treatment outcomes. To date, no empirical research has ever investigated therapist response patterns to patients with different personality disorders from a neuroscience perspective. Methods: In the present study, psychodynamic therapists (N = 14) were asked to complete a battery of instruments (including the Therapist Response Questionnaire) after watching three videos showing clinical interactions between a therapist and three patients with narcissistic, histrionic/borderline, and depressive personality disorders, respectively. Subsequently, participants’ high-density electroencephalography (hdEEG) was recorded as they passively viewed pictures of the patients’ faces, which were selected from the still images of the previously shown videos. Supervised machine learning (ML) was used to evaluate whether: (1) therapists’ responses predicted which patient they observed during the EEG task and whether specific clinician reactions were involved in distinguishing between patients with different personality disorders (using pairwise comparisons); and (2) therapists’ event-related potentials (ERPs) predicted which patient they observed during the laboratory experiment and whether distinct ERP components allowed this forecast. Results: The results indicated that therapists showed distinct patterns of criticized/devalued and sexualized reactions to visual depictions of patients with different personality disorders, at statistically systematic and clinically meaningful levels. Moreover, therapists’ late positive potentials (LPPs) in the hippocampus were able to determine which patient they observed during the EEG task, with high accuracy. Discussion: These results, albeit preliminary, shed light on the role played by therapists’ memory processes in psychotherapy. Clinical and neuroscience implications of the empirical investigation of therapist responses are discussed

Imaging Modalities for the Diagnosis of Vascular Graft Infections:A Consensus Paper amongst Different Specialists

Vascular graft infection (VGI) is a rare but severe complication of vascular surgery that is associated with a bad prognosis and high mortality rate. An accurate and prompt identification of the infection and its extent is crucial for the correct management of the patient. However, standardized diagnostic algorithms and a univocal consensus on the best strategy to reach a diagnosis still do not exist. This review aims to summarize different radiological and Nuclear Medicine (NM) modalities commonly adopted for the imaging of VGI. Moreover, we attempt to provide evidence-based answers to several practical questions raised by clinicians and surgeons when they approach imaging in order to plan the most appropriate radiological or NM examination for their patients

Bragg-Scattering conversion at telecom wavelengths towards the photon counting regime

9openopenKatarzyna Krupa; Alessandro Tonello; Victor Kozlov; Vincent Couderc; Philippe Di Bin; Stefan Wabnitz; Alain Barthelemy; Laurent Labonte; Sebastien TanzilliKatarzyna, Krupa; Alessandro, Tonello; Kozlov, Victor; Vincent, Couderc; Philippe Di, Bin; Wabnitz, Stefan; Alain, Barthelemy; Laurent, Labonte; Sebastien, Tanzill

Imaging Modalities for the Diagnosis of Vascular Graft Infections: A Consensus Paper amongst Different Specialists

Vascular graft infection (VGI) is a rare but severe complication of vascular surgery that is associated with a bad prognosis and high mortality rate. An accurate and prompt identification of the infection and its extent is crucial for the correct management of the patient. However, standardized diagnostic algorithms and a univocal consensus on the best strategy to reach a diagnosis still do not exist. This review aims to summarize different radiological and Nuclear Medicine (NM) modalities commonly adopted for the imaging of VGI. Moreover, we attempt to provide evidence-based answers to several practical questions raised by clinicians and surgeons when they approach imaging in order to plan the most appropriate radiological or NM examination for their patients

Direct generation of photon triplets using cascaded photon-pair sources

Non-classical states of light, such as entangled photon pairs and number states, are essential for fundamental tests of quantum mechanics and optical quantum technologies. The most widespread technique for creating these quantum resources is the spontaneous parametric down-conversion (SPDC) of laser light into photon pairs. Conservation of energy and momentum in this process, known as phase-matching, gives rise to strong correlations which are used to produce two-photon entanglement in various degrees of freedom. It has been a longstanding goal of the quantum optics community to realise a source that can produce analogous correlations in photon triplets, but of the many approaches considered, none have been technically feasible. In this paper we report the observation of photon triplets generated by cascaded down-conversion. Here each triplet originates from a single pump photon, and therefore quantum correlations will extend over all three photons in a way not achievable with independently created photon pairs. We expect our photon-triplet source to open up new avenues of quantum optics and become an important tool in quantum technologies. Our source will allow experimental interrogation of novel quantum correlations, the post-selection free generation of tripartite entanglement without post- selection and the generation of heralded entangled-photon pairs suitable for linear optical quantum computing. Two of the triplet photons have a wavelength matched for optimal transmission in optical fibres, ideally suited for three-party quantum communication. Furthermore, our results open interesting regimes of non-linear optics, as we observe spontaneous down-conversion pumped by single photons, an interaction also highly relevant to optical quantum computing.Comment: 7 pages, 3 figures, 1 table; accepted by Natur

Interplay between COVID-19, pollution, and weather features on changes in the incidence of acute coronary syndromes in early 2020

Coronavirus disease 2019 (COVID-19) has caused an unprecedented change in the apparent epidemiology of acute coronary syndromes (ACS). However, the interplay between this disease, changes in pollution, climate, and aversion to activation of emergency medical services represents a challenging conundrum. We aimed at appraising the impact of COVID-19, weather, and environment features on the occurrence of ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) in a large Italian region and metropolitan area

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982