Development of a Deep learning-based pipeline to classify Small Round Cells Sarcomas histotypes

Ewing sarcoma (ES), Ewing-like sarcomas (ELS) and undifferentiated synovial sarcoma (SS) represent the main entities belonging to the family of the Small Round Cell Sarcomas (SRCS), a group of rare, heterogenous and highly aggressive mesenchymal tumors. SRCS are classified according to a specific single gene rearrangement. However, despite specific histological features are strongly correlated with the underlying molecular alteration, morphological overlapping may occur, and combined with their rarity, make the diagnosis challenging especially for non-expert pathologists. Within this context, the spreading of digital pathology and the recent developments of deep learning technologies for image processing, offer new opportunities for analysis, interpretation, and classification of histopathological slides. In this study, a deep learning-based framework called DeeRasNET, is specifically developed to classify hematoxylin and eosin-stained slides of ES, SS, BCOR and CIC rearranged sarcomas. Accuracy was the main metrics parameter used to evaluate the model performance. Initially, due to the small size of the datasets implemented for the model training, the classification accuracy for each class of sarcoma resulted low (mean accuracy of 0.6). To increase the performance of the model, we developed a pre-processing semi-automated pipeline comprising an open-source graphical interface unit (called TilerPath) with which we managed the tissue whole slide images, selecting interesting tissue areas and performing a quality control of the images used for classifier implementation. By TilerPath uninformative and misleading images were excluded from the model. After pre-preprocessing by Tilerpath, a total of 18193 tiles, selected from 124 digital slides covering all the four histotypes investigated, was used to train and test DeeRasNET. Finally, the scalability of the system was demonstrated on a validation dataset comprising 2706 tiles randomly selected from cases not included into the training and test set. After quality improvement, the final model showed a strong increase of classification performance, with accuracies ranging from 0.98 to 0.99 among all the sarcoma types. Both the TylerPath and the DeeRASnet source code were released as open-source software.Ewing sarcoma (ES), Ewing-like sarcomas (ELS) and undifferentiated synovial sarcoma (SS) represent the main entities belonging to the family of the Small Round Cell Sarcomas (SRCS), a group of rare, heterogenous and highly aggressive mesenchymal tumors. SRCS are classified according to a specific single gene rearrangement. However, despite specific histological features are strongly correlated with the underlying molecular alteration, morphological overlapping may occur, and combined with their rarity, make the diagnosis challenging especially for non-expert pathologists. Within this context, the spreading of digital pathology and the recent developments of deep learning technologies for image processing, offer new opportunities for analysis, interpretation, and classification of histopathological slides. In this study, a deep learning-based framework called DeeRasNET, is specifically developed to classify hematoxylin and eosin-stained slides of ES, SS, BCOR and CIC rearranged sarcomas. Accuracy was the main metrics parameter used to evaluate the model performance. Initially, due to the small size of the datasets implemented for the model training, the classification accuracy for each class of sarcoma resulted low (mean accuracy of 0.6). To increase the performance of the model, we developed a pre-processing semi-automated pipeline comprising an open-source graphical interface unit (called TilerPath) with which we managed the tissue whole slide images, selecting interesting tissue areas and performing a quality control of the images used for classifier implementation. By TilerPath uninformative and misleading images were excluded from the model. After pre-preprocessing by Tilerpath, a total of 18193 tiles, selected from 124 digital slides covering all the four histotypes investigated, was used to train and test DeeRasNET. Finally, the scalability of the system was demonstrated on a validation dataset comprising 2706 tiles randomly selected from cases not included into the training and test set. After quality improvement, the final model showed a strong increase of classification performance, with accuracies ranging from 0.98 to 0.99 among all the sarcoma types. Both the TylerPath and the DeeRASnet source code were released as open-source software

The Effect of Parent Emotion-related Talk on Infant Behavior and Emotion Regulation

Early parent-infant interactions play a critical role in the social, emotional, and behavioral development of children. While several aspects of parent-infant interactions have been thoroughly examined, parent emotion socialization has not been examined to the same extent. The current work aimed to examine the development of parent emotion-related talk in mothers of infants with and without elevated behavior problems in two studies. The first study examined the developmental trajectory of parent emotion-related talk among mothers of infants with and without elevated behaviors. Furthermore, a secondary goal of the study was to examine the effect of parent emotion-related talk on infant behavior and regulation. The study included 101 mother-infant dyads including 43 infants with and 58 infants without elevated behavior problems. All mothers completed a measure on child behavior, videotaped behavioral observations of mother-infant interactions, and a brief emotion regulation task with their infant at three assessments. Growth analyses demonstrated different developmental changes in parent emotion-related talk in mothers of infants with and without elevated behavior problems. For mothers of infants with elevated behavior problems, the starting point of parent emotion-related talk was very low with a significant linear increase, and no significant variability. However, for mothers of infants without elevated behavior problems, there was significant variability in the starting point of parent emotion-related talk as well as the trajectory over time. Furthermore, for mothers of infants with elevated behavior problems parent emotion-related talk at the first assessment significantly predicted infant emotion regulation at the third assessment. These preliminary results highlight the differences in parent emotion-related talk in mothers of infants. The goal of the second study was to examine the effect of a brief in-home parenting intervention on parent emotion-related talk. The study included 58 mother-infant dyads, with 28 mother-infant dyads assigned to the standard care group and 30 mother-infant dyads assigned to the intervention group. Mothers in the intervention group used more parent emotion-related talk at post-intervention than mothers in the standard care group. Furthermore, maternal depressive symptoms at baseline significantly moderated the effect of the intervention on parent emotion-related talk at post-intervention and follow-up. Specifically, mothers with higher depressive symptoms at baseline who received the intervention, demonstrated higher levels of parent emotion-related talk than mothers with lower scores of depressive symptoms who received the intervention

Jurisdiction Size and Director Compensation in Connecticut Local Health Departments

Objective: To examine if the compensation of local public health directors responds to organizational size in the same manner found for other types of for-profit, not-for-profit, and public managers. Design: Panel data ordinary least squares with fixed effects for the local health department and time period. Control variables include median household income, the unemployment rate, and the part-time versus full-time and independent versus district status of the local public health department. Setting: Sample of Connecticut local health departments over the period from 2001 to 2011. Main Outcome Measures: Annual wage of the local public health director and population in the jurisdiction of the local public health department. Results: The size elasticity of local public health director equals 0.2. Full-time directors are paid more than part-time directors and directors managing district health departments are compensated more than those directing independent health departments. Directors are paid more if they manage health departments in jurisdictions with higher levels of income. Conclusions: The findings for the size elasticity of compensation for local public health directors compares very closely to the size elasticity estimates found for other types of for-profit, not-for-profit, and public managers, perhaps suggesting that local public health directors are similarly motivated

The impact of rare and low-frequency genetic variants in common disease

Despite thousands of genetic loci identified to date, a large proportion of genetic variation predisposing to complex disease and traits remains unaccounted for. Advances in sequencing technology enable focused explorations on the contribution of low-frequency and rare variants to human traits. Here we review experimental approaches and current knowledge on the contribution of these genetic variants in complex disease and discuss challenges and opportunities for personalised medicine

The staging of gastritis with the olga system in the italian setting. histological features and gastric cancer risk

BACKGROUND: Recently OLGA (Operative Link on Gastritis Assessment) classification has been proposed to identify high-risk forms of gastritis that can evolve in gastric cancer (stages III and IV). Helicobacter pylori infection and age older than 40 have been considered as independent risk factor for high-risk OLGA stages

Características clínico-epidemiológicas en pacientes con COVID-19 del centro de salud de Huaura, periodo marzo a diciembre del 2020.

Objetivo: Determinar las características clínico - epidemiológicas de los pacientes con COVID-19 del Centro de Salud de Huaura, periodo marzo a diciembre del 2020. Material y método: estudio observacional, retrospectivo, transversal, descriptivo. Incluyó un total de 231 pacientes con diagnóstico de COVID-19 desde marzo a diciembre del 2020. Resultados: se encontró que según etapas de vida predominaron los adultos con un promedio de 47,2 años, el sexo más frecuente fue el masculino 51,95%; los síntomas más frecuentes desarrollados fueron: tos (64,07%), dolor de garganta (54,55%), cefalea (42,86%), dificultad respiratoria (26,84%), rinorrea (23,81%), mialgia (22,51%), diarrea (11,26%) y dolor precordial (9,96%), menos frecuentes: náuseas/vómitos (7,36%) y dolor abdominal (3,90%), el signo más frecuente que se presentó fue: fiebre 41,99% y las comorbilidades más frecuentes fueron diabetes mellitus (7,79%), seguido de la enfermedad cardiovascular (6,06%), obesidad e hipertensión arterial (4,33%), asma bronquial y artritis reumatoide (3,90%), bronquitis crónica (2,60%), artrosis (2,16%), gastritis (1,73%), enfermedad renal, fibrosis pulmonar e hipotiroidismo (1,30%), y menos prevalente como epilepsia, anemia, gestante, osteoporosis, tuberculosis pulmonar, sobrepeso, cáncer, Alzheimer, ansiedad, hipertiroidismo, enfermedad hepática y entre otras hacen en su totalidad solo un 10,39%. Conclusiones: Las características más importantes de los pacientes con COVID.19, fueron personas que se encontraban en la etapa de vida adulto, las manifestaciones clínicas más frecuentes son fiebre, tos, dolor de garganta, cefalea, dificultad respiratoria, rinorrea, mialgia, diarrea y dolor precordial. Además, las comorbilidades más frecuentes fueron diabetes mellitus, seguido de la enfermedad cardiovascular, obesidad, hipertensión arterial, asma bronquial, artritis reumatoide, bronquitis crónica, artrosis, gastritis, enfermedad renal, fibrosis pulmonar e hipotiroidismo

High throughput data streaming of individual longitudinal electron bunch profiles in a storage ring with single-shot electro-optical sampling

The development of fast detection methods for comprehensive monitoring of electron bunches is a prerequisite to gain comprehensive control over the synchrontron emission in storage rings with their MHz repetition rate. Here, we present a proof-of-principle experiment with at detailed description of our implementation to detect the longitudinal electron bunch profiles via single-shot, near-field electro-optical sampling at the Karlsruhe Research Accelerator (KARA). Our experiment is equipped with an ultra-fast line array camera providing a high-throughput MHz data stream. We characterize statistical properties of the obtained data set and give a detailed description for the data processing as well as for the calculation of the charge density profiles, which where measured in the short-bunch operation mode of KARA. Finally, we discuss properties of the bunch profile dynamics on a coarse-grained level on the example of the well-known synchrotron oscillation.Comment: 8 pages, 5 figure

Article 6 Piloting: State of Play and Stakeholder Experiences

This report is the 3rd edition of a series started in 2019 and provides an updated overview of all aspects related to the piloting and operationalization of Article 6 of the Paris Agreement. Despite the continued uncertainty regarding the finalization of the Article 6 rules, practical Article 6 piloting is continuing apace and the landscape of Article 6 piloting initiatives evolves. Testing how Article 6 cooperation could work in practice in order to inform negotiations as well as getting early access to sources of emissions credits is seen as important to fulfill national mitigation commitments. As a framework for the analysis in our study, we apply a ‘concentric ring’ model that clearly differentiates between piloting activities that aim at generating Internationally Transferred Mitigation Outcomes (ITMOs) or adaptation benefits (ABs), initiatives that will eventually be governed by Article 6 rules and the enabling environment, which is essential to drive piloting efforts forward. In an additional analytical step, we classify piloting activities in the inner circle according to three different phases: the preparatory phase, the pilot phase and the full implementation phase. Moreover, we summarise current stakeholder experiences with Article 6 piloting and provide an overview of our insights from broad and deep stakeholder consultations, including the views of buyer countries, host countries and project developers