Notes

Multimodal opioid sparing onco-anesthesia: Any general opinion apply standard via Community regarding Onco-Anesthesia along with Perioperative Attention (SOAPC).
Background There is substantial comorbidity between trauma-related disorders (TRDs), dissociative disorders (DDs) and personality disorders (PDs), especially in patients who report childhood trauma and emotional neglect. However, little is known about the course of these comorbid disorders, despite the fact that this could be of great clinical importance in guiding treatment. Objective This study describes the two-year course of a cohort of patients with (comorbid) TRDs, DDs and PDs and aims to identify possible predictors of course. Possible gender differences will be described, as well as features of non-respondents. Method Patients (N = 150) referred to either a trauma treatment program or a PD treatment program were assessed using five structured clinical interviews for diagnosing TRDs, DDs, PDs and trauma histories. Three self-report questionnaires were used to assess general psychopathology, dissociative symptoms and personality pathology in a more dimensional way. Data on demographics and received treatment were obtained using psychiatric records. We described the cohort after a two-year follow-up and used t-tests or chi-square to test possible differences between respondents and non-respondents and between women and men. We used regression analysis to identify possible course predictors. Results A total of 85 (56.7%) of the original 150 patients participated in the follow-up measurement. Female respondents reported more sexual abuse than female non-respondents. Six patients (4.0%; all women) died because of suicide. Levels of psychopathology significantly declined during the follow-up period, but only among women. Gender was the only significant predictor of change. Conclusions Comorbidity between TRDs, DDs and PDs was more the rule than the exception, pleading for a more dimensional and integrative view on pathology following childhood trauma and emotional neglect. Courses significantly differed between men and women, advocating more attention to gender in treatment and future research.While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Three formulations of MTL are compared i) MTL deep neural networks, which share several hidden layers but have final layers unique to each task; ii) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types; and iii) a Hierarchical Bayesian model in which tasks share a common Dirichlet Process prior. We offer the code for this work in open source. These techniques are investigated in the context of predicting future mood, stress, and health using data collected from surveys, wearable sensors, smartphone logs, and the weather. Empirical results demonstrate that using MTL to account for individual differences provides large performance improvements over traditional machine learning methods and provides personalized, actionable insights.Regulatory science comprises the tools, standards, and approaches that regulators use to assess safety, efficacy, quality, and performance of drugs and medical devices. A major focus of regulatory science is the design and analysis of clinical trials. Clinical trials are an essential part of clinical research programs that aim to improve therapies and reduce the burden of disease. These clinical experiments help us learn about what works clinically and what does not work. The results of clinical trials support therapeutic and policy decisions. When designing clinical trials, investigators make many decisions regarding various aspects of how they will carry out the study, such as the primary objective of the study, primary and secondary endpoints, methods of analysis, sample size, etc. This paper provides a brief review of the clinical development of new treatments and argues for the use of Bayesian methods and decision theory in clinical research.Recent advances in deep learning have achieved promising performance for medical image analysis, while in most cases ground-truth annotations from human experts are necessary to train the deep model. In practice, such annotations are expensive to collect and can be scarce for medical imaging applications. Therefore, there is significant interest in learning representations from unlabelled raw data. In this paper, we propose a self-supervised learning approach to learn meaningful and transferable representations from medical imaging video without any type of human annotation. We assume that in order to learn such a representation, the model should identify anatomical structures from the unlabelled data. Therefore we force the model to address anatomy-aware tasks with free supervision from the data itself. Specifically, the model is designed to correct the order of a reshuffled video clip and at the same time predict the geometric transformation applied to the video clip. Experiments on fetal ultrasound video show that the proposed approach can effectively learn meaningful and strong representations, which transfer well to downstream tasks like standard plane detection and saliency prediction.Anatomical landmarks are a crucial prerequisite for many medical imaging tasks. Usually, the set of landmarks for a given task is predefined by experts. The landmark locations for a given image are then annotated manually or via machine learning methods trained on manual annotations. In this paper, in contrast, we present a method to automatically discover and localize anatomical landmarks in medical images. Specifically, we consider landmarks that attract the visual attention of humans, which we term visually salient landmarks. We illustrate the method for fetal neurosonographic images. First, full-length clinical fetal ultrasound scans are recorded with live sonographer gaze-tracking. Next, a convolutional neural network (CNN) is trained to predict the gaze point distribution (saliency map) of the sonographers on scan video frames. The CNN is then used to predict saliency maps of unseen fetal neurosonographic images, and the landmarks are extracted as the local maxima of these saliency maps. find more Finally, the landmarks are matched across images by clustering the landmark CNN features. We show that the discovered landmarks can be used within affine image registration, with average landmark alignment errors between 4.1% and 10.9% of the fetal head long axis length.A relevant number of reports have examined the role of airborne signals in plant-plant communication, indicating that volatile organic compounds (VOCs) can prime neighboring plants against pathogen and/or herbivore attacks. Conversely, there is very limited information available on the possibility of the emission of VOCs by emitter plants under abiotic stress conditions, which may alert neighboring unstressed plants and prime these individuals (receivers) against the same stressors. The present opinion paper briefly reviews a few reports examining the effect of infochemicals produced by emitters on receiver plants subjected to abiotic stresses typical of global climate change. The ecological implications of these dynamics, as well as some concerns related to the potential roles of inter-plant communication in environmentally controlled experiments, have arisen. Some possible inter-plant communications applications (biomonitoring and biostimulation), mediated by airborne signals, and some directions for future studies on this topic, are also provided.12-Oxo-phytodienoic acid (OPDA), an intermediate in the jasmonic acid (JA) biosynthesis pathway, regulates diverse signaling functions in plants, including enhanced resistance to insect pests. We previously demonstrated that OPDA promoted enhanced callose accumulation and heightened resistance to corn leaf aphid (CLA; Rhopalosiphum maidis), a phloem sap-sucking insect pest of maize (Zea mays). In this study, we used the electrical penetration graph (EPG) technique to monitor and quantify the different CLA feeding patterns on the maize JA-deficient 12-oxo-phytodienoic acid reductase (opr7opr8) plants. CLA feeding behavior was unaffected on B73, opr7opr8 control plants (- OPDA), and opr7opr8 plants that were pretreated with OPDA (+ OPDA). link2 However, exogenous application of OPDA on opr7opr8 plants prolonged aphid salivation, a hallmark of aphids' ability to suppress the plant defense responses. Collectively, our results indicate that CLA utilizes its salivary secretions to suppress or unplug the OPDA-mediated sieve element occlusions in maize.We study the bias of the isotonic regression estimator. While there is extensive work characterizing the mean squared error of the isotonic regression estimator, relatively little is known about the bias. In this paper, we provide a sharp characterization, proving that the bias scales as O(n -β/3) up to log factors, where 1 ≤ β ≤ 2 is the exponent corresponding to Hölder smoothness of the underlying mean. Importantly, this result only requires a strictly monotone mean and that the noise distribution has subexponential tails, without relying on symmetric noise or other restrictive assumptions.Porous silicon photoluminescence is characterized by a broad emission band that displays unusually long (tens to hundreds of micro-seconds), wavelength-dependent emissive lifetimes. The photoluminescence is associated with quantum confinement of excitons in silicon nanocrystallites contained within the porous matrix, and the broad emission spectrum derives from the wide distribution of nanocrystallite sizes in the material. The longer emissive lifetimes in the ensemble of quantum-confined emitters correspond to the larger nanocrystallites, with their longer wavelengths of emission. link3 The quenching of this photoluminescence by aromatic, redox-active molecules aminochrome (AMC), dopamine, adrenochrome, sodium anthraquinone-2-sulfonate, benzyl viologen dichloride, methyl viologen dichloride hydrate, and ethyl viologen dibromide is studied, and dynamic and static quenching mechanisms are distinguished by the emission lifetime analysis. Because of the dependence of the emission lifetime on emission wavelength from the silicon nanocrystallite ensemble, a pronounced blue shift is observed in the steady-state emission spectrum upon exposure to dynamic-type quenchers. Conversely, static-type quenching systems show uniform quenching across all emission wavelengths. Thus, the difference between static and dynamic quenching mechanisms is readily distinguished by ratiometric photoluminescence spectroscopy. The application of this concept to imaging of AMC, the oxidized form of the neurotransmitter dopamine that is of interest for its role in neurodegenerative diseases, is demonstrated. It is found that static electron acceptors result in no ratiometric contrast, while AMC shows a strong contrast, allowing ready visualization in a 2-D imaging experiment.
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