Romantoft5991

Effect sizes for significant differences were medium to large (Cohen's d 0.26-0.67).

Alcohol- and cannabis-involved sexual activity tend to be overreported on retrospective surveys, and preliminary findings suggest that these recall biases may vary by gender. Researchers interested in the co-occurrence of substance use and sexual activity should be aware of this potential random error and consider how to reduce recall biases based on method of data collection.

Alcohol- and cannabis-involved sexual activity tend to be overreported on retrospective surveys, and preliminary findings suggest that these recall biases may vary by gender. Researchers interested in the co-occurrence of substance use and sexual activity should be aware of this potential random error and consider how to reduce recall biases based on method of data collection.Increased nutrient loading has led to eutrophication of coastal shelf waters which has resulted in increased prevalence of persistent hypoxic zones - areas in which the dissolved oxygen content of the water drops below 2 mg/L. The northern Gulf of Mexico, fed primarily by the Mississippi River watershed, undergoes annual establishment of one of the largest hypoxic zones in the world. Exposure to hypoxia can induce physiological impacts in fish cardiac systems that include bradycardia, changes in stroke volume, and altered cardiovascular vessel development. While these impacts have been addressed at the functional level, there is little information regarding the molecular basis for these changes. This study used transcriptomic analysis techniques to interrogate the effects of hypoxia exposure on the developing cardiovascular system in newly hatched larvae of two estuarine species that occupy the same ecological niche - the sheepshead minnow (Cyprinodon variegatus) and the Gulf killifish (Fundulus grandis). Results suggest that while differential gene expression is largely distinct between the two species, downstream impacts on pathways and functional responses such as reduced cardiac hypertrophy, modulation of blood pressure, and increased incidence of apoptosis appear to be conserved. Further, differences in the magnitude of these conserved responses may suggest that the length of embryonic development could impart a level of resiliency to hypoxic perturbation in early life stage fish.Nanostructures generated by self-assembly of peptides yield nanomaterials that have many therapeutic applications, including drug delivery and biomedical engineering, due to their low cytotoxicity and higher uptake by targeted cells owing to their high affinity and specificity towards cell surface receptors. Despite the promising implications of this rapidly expanding field, there is no dedicated resource to study peptide nanostructures. This study endeavours to create a repository of short peptides, which may prove to be the best models to study ordered nanostructures formed by peptide self-assembly. SAPdb has a repertoire of 1049 entries of experimentally validated nanostructures formed by the self-assembly of small peptides. It consists of 328 tripeptides, 701 dipeptides, and 20 single amino acids with some conjugate partners. Each entry encompasses comprehensive information about the peptide, such as chemical modifications, the type of nanostructure formed, experimental conditions like pH, temperature, solvent required for the self-assembly, etc. Our analysis indicates that peptides containing aromatic amino acids favour the formation of self-assembling nanostructures. Additionally, we observed that these peptides form different nanostructures under different experimental conditions. SAPdb provides this comprehensive information in a hassle-free tabulated manner at a glance. User-friendly browsing, searching, and analysis modules have been integrated for easy data retrieval, data comparison, and examination of properties. We anticipate SAPdb to be a valuable repository for researchers engaged in the burgeoning arena of nanobiotechnology. It is freely available at https//webs.iiitd.edu.in/raghava/sapdb.At present, the global pandemic as it relates to novel coronavirus pneumonia is still a very difficult situation. Due to the recent outbreak of novel coronavirus pneumonia, novel chest X-ray (CXR) images that can be used for deep learning analysis are very rare. To solve this problem, we propose a deep learning framework that integrates a convolutional neural network and a capsule network. DenseCapsNet, a new deep learning framework, is formed by the fusion of a dense convolutional network (DenseNet) and the capsule neural network (CapsNet), leveraging their respective advantages and reducing the dependence of convolutional neural networks on a large amount of data. Using 750 CXR images of lungs of healthy patients as well as those of patients with other pneumonia and novel coronavirus pneumonia, the method can obtain an accuracy of 90.7% and an F1 score of 90.9%, and the sensitivity for detecting COVID-19 can reach 96%. These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia CXR radiography detection.Despite that fluorescence spectroscopy coupled with Parallel Factor Analysis (PARAFAC) has been widely used in the investigation of Fluorescent Dissolved Organic Matter (FDOM) in aquatic systems, the proper performance of PARAFAC analysis on datasets originating from various sources is not to be taken for granted. In this study, we examine the impact of the co-analysis of datasets from various natural water systems located in the same geographical region in the Eastern Mediterranean Sea. For this purpose three datasets were formed representative of open sea waters (SW), rivers and streams (RV) and lagoons (LG). The Excitation Emission Matrices (EEMs) derived from fluorescence analysis were subjected to individual PARAFAC analysis per dataset as well as combined analyses i.e. SWRV, SWLG, RVLG, ALL (SW-RV-LG). We evaluated the reliability of the components that were validated in the combined models through the investigation of model's residuals and components correlation. We also assessed the similarity of the olution of extra components in a combined analysis is not always a good fit for the dataset and the model should be assessed in terms of residuals prior acceptance. Finally, our study proposes that the similarity of the common components between combined and individual models is largely dependent on the similarity between the components of the individual models and that the estimation of the Fmax of a component is probably less affected by data diversity compared to the estimation of its spectral position.Trusted methods for identifying different Multiple Myeloma (MM) cells and their biological diversity due to their immunophenotypic variety are often little detailed and difficult to find in literature. In this work, we show that micro-Raman spectroscopy can be used to highlight if there is a certain degree of distinction or correlation between the MM subtype plasmacells in relation to the cluster of differentiation (CD45+/CD38+/CD138-) and (CD45-/CD38+/CD138+). After taking samples from the bone marrow of patients with Multiple Myeloma, the PCs were sorted by flow cytometry, selecting the most common CD of the disease, i.e. ARV-825 nmr CD 45, CD38 and CD138. Some spectral differences are observed comparing the Raman spectra of the two set of samples investigated. link2 To better define in which spectral regions there are greater differences and, therefore, to which biological contributions the changes refers, we also explored the principal component analysis (PCA) of the collected Raman data. The spectral variations between the different sorted cells have been highlighted by plotting loading vectors PC1 and PC2, which shows a net differentiation between the two set of cells. Ultimately, the differences shown by PCA have been associated with the spectral variations observed and explained in terms of changes of proteins and lipid contributions. Thus, the differentiation of Multiple Myeloma subtype plasma cells by confocal micro-Raman spectroscopy can be proposed as a diagnostic tool in the speeding up of cell identification, assessing the intracellular biochemical changes that take place in cancer cells.Geographical origin is an important factor affecting the quality of traditional Chinese medicine. In this paper, the identification of geographical origin of Gastrodia elata was performed by using excitation-emission matrix fluorescence and chemometric methods. Firstly, excitation-emission matrix (EEM) fluorescence spectra of Gastrodia elata samples from different geographical origins were obtained. And then three chemometric methods, including multilinear partial least squares discriminant analysis (N-PLS-DA), unfold partial least squares discriminant analysis (U-PLS-DA), and k-nearest neighbor (kNN) method, were applied to build discriminant models. Finally, 45 Gastrodia elata samples could be differentiated from each other by these classification models according to their geographical origins. The results showed that all models obtained good classification results. Compared with the N-PLS-DA and U-PLS-DA, kNN got more accurate and reliable classification results and could identify Gastrodia elata samples from different geographical origins with 100% accuracy on the training and test set. link3 Therefore, the proposed method was available for easily and quickly distinguishing the geographical origin of Gastrodia elata, which can be considered as a promising alternative method for determining the geographic origin of other traditional Chinese medicines.

Substance use disorder (SUD) and posttraumatic stress disorder (PTSD) are highly comorbid. Self-medication hypothesis (SMH) is a seminal theory aiming to account for the relationship between these disorders. The current study examined hypotheses based on SMH in SUD patients during the very first days of detoxification. Based on SMH we expected a positive association between PTSD and craving concurrently as well as on each following day.

A time series with 108 SUD patients used daily self-report questionnaires assessing PTSD symptom severity (PCL-5) and craving (MaCs) for six consecutive days. Daily associations between PTSD symptom severity and craving on the same and on the following day during the first week of detoxification were estimated using linear mixed models.

There was a significant decrease in PTSD symptom severity during detoxification (ß = -2.06, p < 0.001). Further, PTSD symptom severity predicted craving on the same day (ß = 0.36, p < 0.001) but did not predict craving on the next day (ß = -0.01, p = 0.82).

Results of the current study only partially support assumptions based on SMH, and points towards a more complex and reciprocal relationship between PTSD and SUD.

Results of the current study only partially support assumptions based on SMH, and points towards a more complex and reciprocal relationship between PTSD and SUD.A statistical framework for non-negative matrix factorization based on generalized dual Kullback-Leibler divergence, which includes members of the exponential family of models, is proposed. A family of algorithms is developed using this framework, including under sparsity constraints, and its convergence proven using the Expectation-Maximization algorithm. The framework generalizes some existing methods for different noise structures and contrasts with the recently developed quasi-likelihood approach, thus providing a useful alternative for non-negative matrix factorization. A measure to evaluate the goodness-of-fit of the resulting factorization is described. The performance of the proposed methods is evaluated extensively using real life and simulated data and their utility in unsupervised and semi-supervised learning is illustrated using an application in cancer genomics. This framework can be viewed from the perspective of reinforcement learning, and can be adapted to incorporate discriminant functions and multi-layered neural networks within a deep learning paradigm.