Similar Interaction Patterns Research Articles

Phytochemical Identification and Assessment of Amberboa ramosa Mediated Inhibition of Microtubule and EGFR Associated Growth and Metastasis of Breast Cancer Cells: In Vitro and In Silico Perspective

Amberboa ramosa Roxb. (Asteraceae), commonly known as “Badaward”, has been used for the treatment of tumors from ancient times. In this study, we aimed to scientifically authenticate the use of A. ramosa for breast cancer treatment. The whole plant fractions of A. ramosa were evaluated for their cytotoxic properties against MDA-MB-231 breast cancer cell lines. Among these fractions, Pet. ether, ethyl acetate, and ethanolic fractions exhibited considerable levels of cytotoxicity against MDA-MB-231 cells with [Formula: see text] values of 78.55, 90.60, and 92.92 [Formula: see text]g/mL, respectively. The cell migration significantly decreased in ethyl acetate and ethanolic fractions. GC–MS was used to analyze the phytochemical profile of bioactive fractions, which revealed the presence of long-chain hydrocarbons, carboxylic acids, esters, alcohols, and terpenoids. Subsequently, the 96 compounds present in bioactive fractions were efficiently docked with EGFR and microtubule protein to evaluate affinity towards the targets. Eight common compounds were achieved using a docking-based approach against both target proteins, which showed similar interaction patterns as the reference inhibitors (colchicine and erlotinib). The key interactions revealed the potential of screened compounds as promising hits. 2-[1,2-Dihydroxyethyl]-9-[[Formula: see text]-d-ribofuranosyl]hypoxanthine showed the best dock score of −9.337 and −8.548 kcal/mol against EGFR and microtubule, respectively. 5-Oxo-cystofuranoquinol exhibited better dock score as well as binding energy against both targets. Chemical features present in the 08 dual inhibitors were also matched from the present EGFR and microtubule inhibitors for comparing the anticancer potential. Thus, this research revealed many new directions for developing natural lead compounds from Amberboa ramosa as dual inhibitors for treating breast cancer.

If It Concerns Me: An Experimental Investigation of the Influence of Psychological Distance on the Acceptance of Autonomous Shuttle Buses

Autonomous vehicles (AVs) will revolutionize our everyday mobility in the future. However, the prerequisite for this is that the technology is accepted by the population. Currently, AVs are still difficult to grasp for many people, i.e., the topic of autonomous driving is psychologically distant. In other contexts, it has been shown that this psychological distance or proximity can be used to influence product perception. However, the influence of psychological distance has never been investigated in the AV context. To address this research gap, we investigated the impact of psychological distance on the intention to use (ITU) AVs. We manipulated psychological distance in a 2x2x2 scenario-based experiment (N = 2114) on two different dimensions and additionally varied driving modality for comparison purposes: subjects either imagined themselves or an average person (social distance) using either a traditional or autonomous bus (driving modality) either today or in ten years (temporal distance). Our results showed a main effect of driving modality and social distance, with higher ITU for AVs and the average person. Temporal distance interacted with social distance to affect ITU. Interestingly, psychological distance also affected ITU for traditional buses with a similar interaction pattern. Thus, our study suggests that psychological distance affects the ITU of buses in general rather than AV technology. Providers can benefit from framing AVs as temporally close and providing as concrete, detailed information as possible. Future research should examine the underlying mechanisms (e.g., a shift in bus use priorities) that can explain why social distance plays an important role, particularly in future scenarios.

In silico screening of pomegranate (Punica granatum) and celery (Apium graveolens) metabolites for potential anti-type 2 diabetes activity

Background: Alpha (α)-amylase is a popular pharmacological target for controlling postprandial blood glucose levels. The exploration of natural ingredients for drug development is particularly promising. Notably, pomegranate (Punica granatum) and celery (Apium graveolens) are rich in phenols and flavonoids, making them potential candidates for anti-type 2 diabetes treatments. Objective: This study aims to identify the most promising derivatives from pomegranate and celery using a combined metabolomic and in silico approach. Methods: The study began by identifying metabolites from the KnapSack database, selecting based on primary and secondary metabolites also selecting them based on their pharmacokinetic profile. The selected metabolites were then docked with alpha-amylase (PDB ID: 2QV4). Furthermore, the interactions were analyzed using Discovery Studio, and toxicity profiles were assessed in silico using ECOSAR and Toxtree software. Results: The analysis identified punicaflavone and 2-deoxybrassinolide as the compounds with the highest binding affinity, at -10.06 kcal/mol and -10.89 kcal/mol respectively, both surpassing acarbose’s -9.33 kcal/mol. These compounds interacted with 11 common residues in alpha-amylase, mirroring acarbose’s interactions. In silico toxicity analysis revealed that punicaflavone might pose risks to aquatic organisms but does not exhibit potential as a genotoxic or non-genotoxic carcinogen. Conversely, 2-deoxybrassinolide displayed moderate toxicity to aquatic organisms but was also free from genotoxic and non-genotoxic carcinogenic potential. Conclusion: Punicaflavone and 2-deoxybrassinolide emerged as the most promising compounds, demonstrating strong binding affinities and similar interaction patterns with α-amylase as acarbose. Although both compounds may pose risks to aquatic environments, they do not show potential as genotoxic or non-genotoxic carcinogens, supporting their further exploration as anti-diabetic agents.

Using single-sample networks to identify the contrasting patterns of gene interactions and reveal the radiation dose-dependent effects in multiple tissues of spaceflight mice

Transcriptome profiles are sensitive to space stressors and serve as valuable indicators of the biological effects during spaceflight. Herein, we transformed the expression profiles into gene interaction patterns by single-sample networks (SSNs) and performed the integrated analysis on the 301 spaceflight and 290 ground control samples, which were obtained from the GeneLab platform. Specifically, an individual SSN was established for each sample. Based on the topological structures of 591 SSNs, the differentially interacted genes (DIGs) were identified between spaceflights and ground controls. The results showed that spaceflight disrupted the gene interaction patterns in mice and resulted in significant enrichment of biological processes such as protein/amino acid metabolism and nucleic acid (DNA/RNA) metabolism (P-value < 0.05). We observed that the mice exposed to radiation doses within the three intervals (4.66–7.14, 7.592–8.295, 8.49–22.099 mGy) exhibited similar gene interaction patterns. Low and medium doses resulted in changes to the circadian rhythm, while the damaging effects on genetic material became more pronounced in higher doses. The gene interaction patterns in response to space stressors varied among different tissues, with the spleen, lung, and skin being the most responsive to space radiation (P-value < 0.01). The changes observed in gene networks during spaceflight conditions might contribute to the development of various diseases, such as mental disorders, depression, and metabolic disorders, among others. Additionally, organisms activated specific gene networks in response to virus reactivation. We identified several hub genes that were associated with circadian rhythms, suggesting that spaceflight could lead to substantial circadian rhythm dysregulation.

Design and Implementation of Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that uses MRI technology to quantify brain activity by detecting changes in blood flow and applying the Blood-oxygen-level dependent (BOLD) contrast. Current neuroscience research suggests a modular brain structure. To understand the complex patterns of interaction between brain areas. The proposed system makes use of the CNN algorithm, which is an efficient method for dividing segmentation. A brain area is defined as a collection of subjects who exhibit a similar pattern of interaction between their brain areas. A thorough experimental examination utilizing benchmark data demonstrates that our technique is both effective and efficient. Two real-world fMRI studies found that the rifleman technique has the potential to improve comprehension of both normal brain activity and the abnormalities associated with psychiatric diseases. That is, mental diseases are typically defined by How an individual feels, acts, thinks, or perceives events. This is typically linked to specific areas or functions of the brain or the rest of the nervous system. This is unacceptable for a variety of time series sectors. As a result, we provide the statistical region merging approach, which is applied to picture segmentation. The method evaluates the values inside a regional range and combines them using the merging criteria, resulting in a smaller list. . As a result, we gather additional information and compare the findings to those already in the database. As a result, we decide if a person is normal or aberrant, as well as whether they are unwell.

Fat intake modifies association between cigarette smoking and lung cancer in a prospective cohort study: A potential explanation for the lung cancer paradox

Background & AimsIt remains unclear why the association between cigarette smoking and lung cancer was substantially stronger in Western countries than in Asian countries. As experimental studies have revealed that fat intake modulates tobacco carcinogen metabolism and the growth of transplanted or carcinogen-induced lung tumors in mice, the present study sought to investigate whether the association between cigarette smoking and lung cancer was modified by intake of total fat and types of fat (saturated, monounsaturated, and polyunsaturated fats) in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. MethodsDuring a median follow-up of 8.9 years, 1,425 cases of lung cancer were documented from 100,864 participants eligible for the present analysis. Cox proportional hazards regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CI). ResultsAfter adjustment for established or suspected confounders, the strength of the association between cigarette smoking and lung cancer was remarkably larger among individuals with high fat intake. HRs (95% CIs) comparing current with never smokers were 23.0 (13.4, 39.6), 32.7 (20.3, 52.8), and 59.8 (30.2, 118.2) for the tertile 1 (≤ 13.48 g/day), tertile 2 (13.49 - 21.89 g/day), and tertile 3 (≥21.90 g/day) of saturate fat intake, respectively. A similar pattern of the non-significant interaction was observed when the accumulated amount of cigarette smoking (1-19, 20-39, and ≥40 vs. 0 pack-years) was entered into the regression models. ConclusionsThe present study showed that lung cancer risk associated with both the status and accumulated amount of cigarette smoking was remarkably stronger in individuals with high intakes of fat, particularly saturated fat. However, this interaction was not statistically significant and thus warrants further investigations in other studies.

Chronic Ultrasound Prenatal Stress Altered the Brain's Neurochemical Systems in Newborn Rats.

Prenatal stress (PS) affects the development and functioning of the central nervous system, but the exact mechanisms underpinning this effect have not been pinpointed yet. A promising model of PS is one based on chronic exposure of pregnant rodents to variable-frequency ultrasound (US PS), as it mimics the PS with a psychic nature that most adequately captures the human stressors in modern society. The aim of this study was to investigate the effects of US PS on the brain neurotransmitter, neuropeptide, and neurotrophic systems of newborn Wistar rats. We determined the concentration of neurotransmitters and their metabolites (serotonin, HIAA, dopamine, DOPAC, and norepinephrine), neuropeptides (α-MSH, β-endorphin, neurotensin, oxytocin, and substance P), and the neurotrophin brain-derived neurotrophic factor (BDNF) in rat brain tissues by HPLC-ED, ELISA, and multiplex ELISA. Correlation analysis and principal component analysis (PCA) were used to get a sense of the relationship between the biochemical parameters of the brain. The results demonstrated that US PS increases the concentration of serotonin (p=0.004) and DOPAC (p=0.04) in the hippocampus has no effect on the neurotransmitter systems of the frontal cortex, reduces the concentration of BDNF in the entirety of the brain of males (p=0.008), and increases the neuropeptides α-MSH (p=0.02), β-endorphin (p=0.01), oxytocin (p=0.008), and substance P (p < 0.001) in the entire brain. A degree of complexity in the neurotransmitter system network in the frontal cortex and network change in the hippocampus after exposure to US PS have been observed. PCA revealed a similar pattern of neurotransmitter system interactions in the frontal cortex and hippocampus in males and females after exposure to US PS. We suggest that US PS can alter neurodevelopment, which is mediated by changes in the studied neurochemical systems that thus affect the behavioral phenotype in animals.

Dividing Social Networks into Two Communities Using the Maximum Likelihood Method: Application to ESG

This article explores the application of the Maximum Likelihood Estimation method (MLE) for community detection in environmental, social, and governance (ESG) networks. ESG factors are important in assessing the sustainability and ethical impact of investments. By understanding the structure of social networks that discuss and promote ESG practices, we can gain important insights. It proposes a probabilistic framework for identifying community structures by dividing the network into two distinct groups based on connectivity patterns using the MLE method. The network structure is analyzed, and the method identifies groups of united organizations such as companies, investors, and NGOs with similar ESG orientations and interaction patterns. The results reveal important insights into how ESG information flows within and between these communities, highlighting key influencers and central nodes whose connections play a key role in the diffusion of ESG practices. These conclusions can be important in developing targeted communication strategies, identifying potential opportunities for cooperation, and forming informed investment decisions. By providing a solid framework for analyzing ESG networks, this paper is relevant to a broader understanding of ESG dynamics and supports the development of a more sustainable and interconnected global ecosystem.

Dissecting Subtype-Specific Tumor-Time Interactions and Underlying Hidden Drivers in Pediatric Acute Myeloid Leukemia Via Single-Cell Multi-Omics

Introduction: Pediatric acute myeloid leukemia (AML) is a heterogeneous hematological malignancy characterized by various chromosomal abnormalities and somatic mutations. Investigating the origin of tumorigenesis and deciphering the complex interplay between AML cells and their surrounding tumor immune microenvironment (TIME) is crucial to understand the underlying mechanisms driving disease progression and response to therapy. Recent advancements in single-cell multi-omics technologies enabled us to characterize the transcriptional (GEX) and epigenetic (ATAC) landscapes of both tumors and TIMEs. Leveraging our in-house algorithm, scMINER (single-cell Mutual Information-based Network Engineering Ranger, Ding et al., 2023), this study aims to build a comprehensive cell type atlas in diverse pediatric AML bone marrow samples and construct intra- and inter-cellular networks, enabling the identification of hidden drivers that shape subtype-specific AML-TIME interactions. Methods: Using CD45 and CD33 as selective markers by flow cytometry, we have successfully isolated blasts and enriched for T cell and B cell populations for 10X single-cell multiome profiling across AML molecular subtypes, including UBTF-TD (n=1), NPM1 (n=1), and KMT2A rearrangement (n=1). We interrogated the GEX data using scMINER, including two key steps: (i) mutual information-based clustering analysis (MICA) and (ii) mutual information-based network inference engine (MINIE). MICA characterizes the intrinsic nonlinear similarity of gene expression distributions among cells, enabling a high resolution of clusters. MINIE reverse-engineers cluster-specific intracellular gene networks and infers protein activity of transcription factors (TF) or signaling factors (SIG) drivers based on the expression of its predicted regulon targets in the corresponding cluster. The activity profiles overcome the dropout effects of single-cell RNA-seq data and reflect protein activities across all cells. ArchR is used to analyze ATAC data. CellChat is used to construct and evaluate intercellular networks. Results: AML cells from three samples were divided into four major clusters, and we labeled them as NPM1, KMT2A, UBTF_one, and UBTF_two based on sample subtypes (Fig.1A). MICA managed to obtain distinct AML cell sub-clusters. Comparison with single-cell profiles of normal hematopoietic cells and annotation with marker genes revealed their correspondence with different myeloid developmental stages. Notably, the KMT2A cluster shared a high similarity in cell type proportion with the UBTF_two cluster, and the NPM1 cluster is closer to the UBTF_one cluster. This result demonstrated significant tumor heterogeneity within and across AML molecular subtypes. With the help of intracellular gene networks generated by MINIE, we identified multiple subtype-specific hidden drivers by differential activity analysis. For example, KMT2A and UBTF_two clusters exhibited elevated SAMHD1 levels, consistent with previous GSE data (Zhang et al., 2022). Chromatin accessibility analysis of ATAC data showed increased peaks near SAMHD1 in KMT2A and UBTF_two clusters, further supporting this observation. IRF8 showed a similar expression pattern to SAMHD1, suggesting its context-dependent role across AML subtypes. Accurate annotation of myeloid and immune cells greatly enriches the preciseness of the AML and TIME interaction patterns, which vary across AML molecular subtypes (Fig.1B). Interestingly, cells originated from KMT2A cluster exhibited similar interaction patterns to UBTF_two, while NPM1 and UBTF_one were grouped. This result indicated that different AML molecular subtypes may partially utilize the same mechanisms of tumor-TIME interaction, resulting in similar tumor phenotypes. Conclusions: Accurate clustering and well-defined annotation of myeloid and immune cells enabled us to build a comprehensive cell type atlas in AML samples. The subtype-specific network and the following analysis identified candidate hidden drivers that possibly contribute to the heterogeneity of tumor populations. AML subtypes sharing the same hidden drivers have similar cell type proportions and interaction patterns with TIMEs. This observation implies that AML subtypes share cell type-specific dependencies, which can be therapeutic targets to overcome the refractoriness posed by tumor heterogeneity.

Towards Exogenous Coordination of Concurrent Cloud Applications

Cloud computing offers opportunities to increase productivity and reduce costs. Quickly adapting to changing needs is key to maintaining cloud applications. In traditional development, coordination is implemented in computational code. Although change impact analyses are studied, adjusting the implementation is time-consuming and error-prone when the coordination strategy changes. Exogenous coordination separates the implemented coordination and computational code to cope with this problem. This separation improves the reusability of components. Additionally, other applications with similar interaction patterns can reuse the coordination specification. The main contribution of this paper is to propose a methodology to develop and maintain cloud applications following the exogenous approach. To illustrate the idea, we introduce a new framework named OCCIwareBIP, which integrates JavaBIP — a framework for the exogenous coordination of concurrent Java components into OCCIware — a framework for designing cloud applications. We also leverage the coordination model to verify the deadlock-freedom of the cloud application. Finally, we present an application to show the ability of our approach to guarantee the safety and benefits of modularization in developing concurrent cloud applications.

DNA Methylation at ABCG1 and Long-term Changes in Adiposity and Fat Distribution in Response to Dietary Interventions: The POUNDS Lost Trial.

To examine whether participants with different levels of diabetes-related DNA methylation at ABCG1 might respond differently to dietary weight loss interventions with long-term changes in adiposity and body fat distribution. The current study included overweight/obese participants from the POUNDS Lost trial. Blood levels of regional DNA methylation at ABCG1 were profiled by high-resolution methylC-capture sequencing at baseline among 673 participants, of whom 598 were followed up at 6 months and 543 at 2 years. Two-year changes in adiposity and computed tomography-measured body fat distribution were calculated. Regional DNA methylation at ABCG1 showed significantly different associations with long-term changes in body weight and waist circumference at 6 months and 2 years in dietary interventions varying in protein intake (interaction P < 0.05 for all). Among participants assigned to an average-protein (15%) diet, lower baseline regional DNA methylation at ABCG1 was associated with greater reductions in body weight and waist circumference at 6 months and 2 years, whereas opposite associations were found among those assigned to a high-protein (25%) diet. Similar interaction patterns were also observed for body fat distribution, including visceral adipose tissue, subcutaneous adipose tissue, deep subcutaneous adipose tissue, and total adipose tissue at 6 months and 2 years (interaction P < 0.05 for all). Baseline DNA methylation at ABCG1 interacted with dietary protein intake on long-term decreases in adiposity and body fat distribution. Participants with lower methylation at ABCG1 benefitted more in long-term reductions in body weight, waist circumference, and body fat distribution when consuming an average-protein diet.

Prediction of molecular field points using SE(3)-transformer model

Due to their computational efficiency, 2D fingerprints are typically used in similarity-based high-content screening. The interaction of a ligand with its target protein, however, relies on its physicochemical interactions in 3D space. Thus, ligands with different 2D scaffolds can bind to the same protein if these ligands share similar interaction patterns. Molecular fields can represent those interaction profiles. For efficiency, the extrema of those molecular fields, named field points, are used to quantify the ligand similarity in 3D. The calculation of field points involves the evaluation of the interaction energy between the ligand and a small probe shifted on a fine grid representing the molecular surface. These calculations are computationally prohibitive for large datasets of ligands, making field point representations of molecules intractable for high-content screening. Here, we overcome this roadblock by one-shot prediction of field points using generative neural networks based on the molecular structure alone. Field points are predicted by training an SE(3)-Transformer, an equivariant, attention-based graph neural network architecture, on a large set of ligands with field point data. Resulting data demonstrates the feasibility of this approach to precisely generate negative, positive and hydrophobic field points within 0.5 Å of the ground truth for a diverse set of drug-like molecules.

Functional clustering methods for binary longitudinal data with temporal heterogeneity

In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating similar patterns of time-dependent interactions. We present a novel generalized varying-coefficient model that accounts for within-subject variability and simultaneously clusters varying-coefficient functions, without restricting the number of clusters nor overfitting the data. In the analysis of a heterogeneous series of binary data, the model extracts population-level fixed effects, cluster-level varying effects, and subject-level random effects. Various simulation studies show the validity and utility of the proposed method to correctly specify cluster-specific varying-coefficients when the number of clusters is unknown. The proposed method is applied to a heterogeneous series of binary data in the German Socioeconomic Panel (GSOEP) study, where we identify three major clusters demonstrating the different varying effects of socioeconomic predictors as a function of age on the working status.

Association of sleep patterns and cardiovascular disease risk is modified by glucose tolerance status.

To investigate whether the association between sleep patterns and cardiovascular disease (CVD) risk differs according to glucose tolerance status. This prospective study included 358,805 participants initially free of CVD from the UK Biobank. We created a sleep score based on five sleep factors (sleep duration, chronotype, insomnia, snoring, and daytime sleepiness) with one point for each unhealthy factor. Cox proportional hazards models were used to examine the association between sleep and incident CVD, including coronary heart disease (CHD) and stroke, according to normal glucose tolerance (NGT), prediabetes, and diabetes. During a median follow-up of 12.4years, 29,663 incident CVD events were documented. There was a significant interaction between sleep score and glucose tolerance status on CVD (P for interaction=0.002). Each 1 point increment in sleep score was associated with a 7% (95% confidence interval 6%-9%), 11% (8%-14%), and 13% (9%-17%) higher risk of CVD among participants with NGT, prediabetes, and diabetes, respectively. Similar interaction patterns were observed for CHD and stroke. Among the individual sleep factors, sleep duration and insomnia significantly interacted with glucose tolerance status on CVD outcomes (all P for interaction <0.05). All five unhealthy sleep factors accounted for 14.2% (8.7%-19.8%), 19.5% (7.4%-31.0%), and 25.1% (9.7%-39.3%) of incident CVD cases among participants with NGT, prediabetes, and diabetes, respectively. The CVD risk associated with a poor sleep pattern was exacerbated across glucose intolerance status. Our findings emphasise the importance of integrating sleep management into a lifestyle modification programme, particularly in people with prediabetes or diabetes.

Using Markov chains and temporal alignment to identify clinical patterns in Dementia

In the healthcare sector, resorting to big data and advanced analytics is a great advantage when dealing with complex groups of patients in terms of comorbidities, representing a significant step towards personalized targeting. In this work, we focus on understanding key features and clinical pathways of patients with multimorbidity suffering from Dementia. This disease can result from many heterogeneous factors, potentially becoming more prevalent as the population ages. We present a set of methods that allow us to identify medical appointment patterns within a cohort of 1924 patients followed from January 2007 to August 2021 in Hospital da Luz (Lisbon), and to stratify patients into subgroups that exhibit similar patterns of interaction. With Markov Chains, we are able to identify the most prevailing medical appointments attended by Dementia patients, as well as recurring transitions between these. To perform patient stratification, we applied AliClu, a temporal sequence alignment algorithm for clustering longitudinal clinical data, which allowed us to successfully identify patient subgroups with similar medical appointment activity. A feature analysis per cluster obtained allows the identification of distinct patterns and characteristics. This pipeline provides a tool to identify prevailing clinical pathways of medical appointments within the dataset, as well as the most common transitions between medical specialities within Dementia patients. This methodology, alongside demographic and clinical data, has the potential to provide early signalling of the most likely clinical pathways and serve as a support tool for health providers in deciding the best course of treatment, considering a patient as a whole.

Dietary Protein Modifies the Effect of the MC4R Genotype on 2-Year Changes in Appetite and Food Craving: The POUNDS Lost Trial

Background: The melanocortin-4 receptor (MC4R) plays a pivotal role in the regulation of appetite and eating behavior. Variants in the MC4R gene have been related to appetite and obesity.Objective: We aimed to examine whether weight-loss diets modified the effect of the "obesity-predisposing" MC4R genotype on appetite-related measures in a randomized controlled trial.Methods: A total of 811 overweight and obese subjects [25 ≤ body mass index (BMI; kg/m2) ≤ 40] aged 30-70 y were included in the 2-y POUNDS Lost (Preventing Overweight Using Novel Dietary Strategies) trial. We genotyped MC4R rs7227255 in 735 overweight adults and assessed appetite-related characteristics, including craving, fullness, hunger, and prospective consumption, as well as a composite appetite score. We examined the effects of the genotype-by-weight-loss diet intervention interaction on appetite variables by using general linear models in both the whole population and in white participants only.Results: We found that dietary protein intake (low compared with high: 15% of energy compared with 25% of energy, respectively) significantly modified MC4R genetic effects on changes in appetite score and craving (P-interaction = 0.03 and 0.02, respectively) at 2 y, after adjustment for age, sex, ethnicity, baseline BMI, weight change, and baseline perspective phenotype. The obesity-predisposing A allele was associated with a greater increase in overall appetite score (β = 0.10, P = 0.05) and craving (β = 0.13, P = 0.008) compared with the non-A allele among participants who consumed a high-protein diet. MC4R genotype did not modify the effects of fat or carbohydrate intakes on appetite measures. Similar interaction patterns were observed in whites.Conclusion: Our data suggest that individuals with the MC4R rs7227255 A allele rather than the non-A allele might experience greater increases in appetite and food craving when consuming a high-protein weight-loss diet. This trial was registered at clinicaltrials.gov as NCT00072995.

Trait matching and sampling effort shape the structure of the frugivory network in Afrotropical forests.

Frugivory in tropical forests is a major ecological process as most tree species rely on frugivores to disperse their seeds. However, the underlying mechanisms driving frugivore-plant networks remain understudied. Here, we evaluate the data available on the Afrotropical frugivory network to identify structural properties, as well as assess knowledge gaps. We assembled a database of frugivory interactions from the literature with > 10 000 links, between 807 tree and 285 frugivore species. We analysed the network structure using a block model that groups species with similar interaction patterns and estimates interaction probabilities among them. We investigated the species traits related to this grouping structure. This frugivory network was simplified into 14 tree and 14 frugivore blocks. The block structure depended on the sampling effort among species: Large mammals were better-studied, while smaller frugivores were the least studied. Species traits related to frugivory were strong predictors of the species composition of blocks and interactions among them. Fruits from larger trees were consumed by most frugivores, and large frugivores had higher probabilities to consume larger fruits. To conclude, this large-scale frugivory network was mainly structured by species traits involved in frugivory, and as expected by the distribution areas of species, while still being limited by sampling incompleteness.

Analysis of Learning Interaction within Online Kinesiology Courses

ABSTRACT The quality of interaction is an essential factor leading to learner satisfaction, academic achievement, learner engagement, and a positive attitude toward distance education. The purpose of this study is to examine different types of interactions within online kinesiology courses. Adopting a multiple case study design with a mixed-methods approach, the researchers analyzed learning activities and categorized them as: (a) learner-content, or (b) learner-instructor, or (c) learner-learner type of interaction. The interview transcriptions added instructors’ rationale for including specific learning activities as supplemental data. The analysis across the courses revealed that there was a similar pattern of interaction regardless of the course content or course instructor and instructors still see their main role as content providers. The findings suggest the need for further studies to focus on the different types of interactions and their effectiveness on instructional settings in online learning environments. Theoretical and practical implications of the findings are discussed.

Environmental factors and stochasticity affect the fungal community structures in the water and sediments of Hulun Lake, China.

Aquatic fungi form both morphologically and ecologically diverse communities. However, lake ecosystems are frequently overlooked as fungal habitats, despite the potentially important role of fungi in matter cycling and energy flow. Hulun Lake is a typical example of a seasonal glacial lake; however, previous studies have only focused on bacteria in this ecosystem. Therefore, in the current study, internal transcribed spacer ribosomal RNA (ITS rRNA) gene high-throughput sequencing was used to investigate the fungal communities in paired water and sediment samples from the Hulun Lake Basin in China. A significant difference was found between the fungal communities of the two sample types. Across all samples, we identified nine phyla, 30 classes, 78 orders, 177 families, and 307 genera. The dominant phyla in the lake were Ascomycota, Basidiomycota and Chytridiomycota. Our results show that both water and sediments have very high connectivity, are dominated by positive interactions, and have similar interaction patterns. The fungal community structures were found to be significantly affected by environmental factors (temperature, chemical oxygen demand, electrical conductivity, total phosphorus, and pH). In addition, the dispersal limitations of the fungi affected the structure of the fungal communities, and it was revealed that stochasticity is more important than deterministic mechanisms in influencing the structure and function of fungal communities. This study provides unique theoretical support for the study of seasonally frozen lake fungal communities and a scientific basis for the future management and protection of Hulun Lake.

Investigation of cannabidiol’s potential targets in limbic seizures. In-silico approach

Even though the vast armamentarium of FDA-approved antiepileptic drugs is currently available, over one-third of patients do not respond to medication, which arises a need for alternative medicine. In clinical and preclinical studies, various investigations have shown the advantage of specific plant-based cannabidiol (CBD) products in treating certain groups of people with limbic epilepsy who have failed to respond to conventional therapies. This work aims to investigate possible mechanisms by which CBD possesses its anticonvulsant properties. Molecular targets for CBD’s treatment of limbic epilepsy, including hyperpolarization-activated cyclic nucleotide-gated channel 1 (HCN1), gamma-aminobutyric acid aminotransferase (GABA-AT), and gamma-aminobutyric acid type A receptor (GABAA), were used to evaluate its binding affinity. Interactions with the CB1 receptor were initially modeled as a benchmark, which further proved the efficiency of proposed here approach. Considering the successful benchmark, we further used the same concept for in silico investigation, targeting proteins of interest. As a result of molecular docking, molecular mechanics, and molecular dynamics simulations models of CBD-receptor complexes were proposed and evaluated. While CBD possessed decently high affinity and stability within the binding pockets of GABA-AT and some binding sites of GABAA, the most effective binding was observed in the CBD complex with HCN1 receptor. 100 ns molecular dynamics simulation revealed that CBD binds the open pore of HCN1 receptor, forming a similar pattern of interactions as potent Lamotrigine. Therefore, we can propose that HCN1 can serve as a most potent target for cannabinoid antiepileptic treatment. Communicated by Ramaswamy H. Sarma