Functional Clusters Research Articles

Understanding the roles of the WFDC cluster in sperm development and function.

Understanding the roles of the WFDC cluster in sperm development and function.

A functional data analysis approach for modelling frequency‐modulated tonal sounds in animal communication

Abstract Frequency modulation (FM) is believed to play a major role in encoding information in tonal vocal communication. However, most studies aimed at investigating acoustic variability rely on the manual measurement of acoustic parameters, and the implementation of multivariate techniques, which represent only partially sound FM. Thus, innovative modelling approaches able to capture local FM dynamics are needed for tonal sounds modelling. These kinds of methodological developments in bioacoustics might be key for further understanding animal behaviour and ecology. We propose the application of a functional data analysis (FDA) approach to model extracted FM patterns, which entails the transformation of acoustic signals into continuous functions. We describe a Raven‐to‐R FDA workflow for modelling tonal sounds and we highlight two of its potential applications for classification aims and FM analysis in relation to behavioural, social, and environmental factors. For illustrative purposes, the approach performance was tested on FM patterns (contours) of signature whistles emitted by bottlenose dolphins (Tursiops truncatus), to investigate their acoustic variability and potential information content. Our results show that tonal sounds are inherently of functional nature and can be treated as such for FM analysis. We found that building the FDA sound curves using 25 B‐spline basis functions was the most appropriate setting for the functionalisation of dolphin whistles. Our approach was able to accurately reconstruct the FM patterns of the modelled tonal sounds and even to capture their fine‐scale modulations, with a precision of 80%. Functional clustering showed an accuracy up to 89% for short whistles. Estimated functional coefficients of the regression model suggested that different contextual factors have a significant effect on the FM patterns of SWs. The adoption of an FDA approach appears to be quite promising for the study of frequency‐modulated tonal sounds, both for classification aims and in‐depth FM analysis. The limitations and advantages of the approach were discussed. We developed and tested a new methodology able to fill knowledge gaps in animal communication, and we anticipate it will lead to advances in the ethological and ecological understanding of wild animal populations.

SOM neural network-based port function analysis: a case study in 21st-century Maritime Silk Road

The 21st-century Maritime Silk Road initiative by the Chinese government has garnered growing global attention. As pivotal facilitators of international trade, the maritime routes and ports along this route are attracting the interest of various stakeholders. There is a pressing need for extensive research to augment the existing theoretical frameworks. This paper introduces a Self-Organizing Map (SOM) neural network-based methodology for port function clustering, applied to 24 major ports spanning from the South China Sea to the ASEAN region in 2023. The clustering outcomes are cross-validated against port rankings derived from Principal Component Analysis. The study reveals several key insights: (1) Singapore Port, Hong Kong Port, Shenzhen Port, and Guangzhou Port emerge as the principal shipping hubs within the region; (2) The relationship between China and Singapore is identified as a linchpin for the sustainable development of the 21st-century Maritime Silk Road; (3) Guangdong Province is highlighted as a central economic and logistical node. Finally, the recommendations for the accelerated development of the Hainan Free Trade Port and Fujian Coastal Port is concluded.

From keywords to key embeddings – contrasting French and Swedish web registers using multilingual deep learning

Abstract The pervasiveness of the internet has given web language use a central role in society. However, the lack of multilingual corpora and scalable methods has led to the focus on English in web language research. To address this gap, the present paper sets itself in the register research tradition and explores French and Swedish web registers from a cross-linguistic angle. Methodologically we combine keyword analysis with multilingual deep learning, suggesting an approach that enables computational comparisons across languages. Specifically, we extract keywords for French and Swedish web registers, then associate the keywords with fastText word embeddings, and finally, cluster these key embeddings. The findings indicate that there are topical and functional clusters, and they are linguistically motivated and multilingual. The same clusters occur within the same registers in both languages pointing to shared topical and functional similarities – the registers are strikingly similar. The dissimilarities, in contrast, indicate that certain registers like Narrative blogs are to some extent different in French and Swedish. Moreover, grammatical specificities such as the location of adjectives explain some dissimilarities.

Zhenwu Decoction Modulates PDE3A to Alleviate Inflammation in Diabetic Nephropathy

Background: Diabetic nephropathy, a complex microvascular complication of diabetes, poses substantial health and economic challenges globally. ZWD, known for its multi-component composition, has demonstrated potential therapeutic effects in DN, yet its molecular mechanisms require further elucidation to support its clinical application. Aim: This study aimed to comprehensively investigate the active components, target molecules, and molecular mechanisms of Zhenwu Decoction (ZWD), a Traditional Chinese Medicine (TCM) formula, in the treatment of diabetic nephropathy (DN), with a focus on its anti-inflammatory effects through the modulation of the PDE3A-mediated cAMP-PKA-NFκB axis. Objective: The primary objective was to identify the active components of ZWD, their targets, and the underlying molecular pathways through which ZWD exerts its therapeutic effects on DN, specifically focusing on the modulation of inflammation in renal cells under high glucose conditions. Method: A combined approach of network pharmacology, single-cell transcriptomics, and molecular docking analyses was employed to investigate the active components, target molecules, and underlying mechanisms of ZWD in treating DN. A high-glucose-induced HK-2 cell model was established to simulate in vitro DN conditions. Cell viability was assessed using the CCK-8 assay, while quantitative real-time PCR (qPCR) and Western blotting were employed to quantify gene expression and protein levels, respectively. Results: Our analysis uncovered 141 active components in ZWD, which targeted 171 proteins involved in pathways related to hypoxia response, neuroactive ligand-receptor interaction, urotensin II signaling, and other pathways implicated in diabetes and vascular diseases. Among these, 75 overlapping genes were pinpointed as potential therapeutic targets of ZWD for DN. Protein-protein interaction networks revealed three functional clusters of targets potentially associated with oxidative stress responses, lipid metabolism, and hormonal regulation. Cell-type specific analyses showed differential expression of these targets in DN, particularly in proximal tubular epithelial cells. Notably, through molecular docking, we discovered a strong binding affinity between PDE3A and betasitosterol. In vitro experiments confirmed that ZWD extract modulated PDE3A expression, leading to the suppression of the PKA/AMPK/NF-κB axis and attenuation of high glucose-induced inflammation in HK-2 cells. Conclusion: This study identified that Zhenwu Decoction (ZWD) suppressed high glucose-induced inflammation in renal cells by inhibiting the PDE3A-mediated cAMP-PKA-NFκB axis, highlighting the potential of ZWD as an effective adjunct therapy for diabetic nephropathy and paving the way for innovative anti-inflammatory treatments.

Item Classification by Difficulty Using Functional Principal Component Clustering and Neural Networks.

Maintaining consistent item difficulty across test forms is crucial for accurately and fairly classifying examinees into pass or fail categories. This article presents a practical procedure for classifying items based on difficulty levels using functional data analysis (FDA). Methodologically, we clustered item characteristic curves (ICCs) into difficulty groups by analyzing their functional principal components (FPCs) and then employed a neural network to predict difficulty for ICCs. Given the degree of similarity between many ICCs, categorizing items by difficulty can be challenging. The strength of this method lies in its ability to provide an empirical and consistent process for item classification, as opposed to relying solely on visual inspection. The findings reveal that most discrepancies between visual classification and FDA results differed by only one adjacent difficulty level. Approximately 67% of these discrepancies involved items in the medium to hard range being categorized into higher difficulty levels by FDA, while the remaining third involved very easy to easy items being classified into lower levels. The neural network, trained on these data, achieved an accuracy of 79.6%, with misclassifications also differing by only one adjacent difficulty level compared to FDA clustering. The method demonstrates an efficient and practical procedure for classifying test items, especially beneficial in testing programs where smaller volumes of examinees tested at various times throughout the year.

Enhancing the stability and performance of Ni-rich cathode materials through Ta doping: a combined theoretical and experimental study.

As the demand for high-energy batteries to power electric vehicles continues to grow, Ni-rich cathode materials have emerged as promising candidates due to their high capacity. However, these materials are prone to rapid degradation under increased voltages, posing significant challenges for their long-term stability and safety. In this study, we investigate the effects of tantalum (Ta) doping on the performance and stability of LiNi0.80Mn0.1Co0.1O2 (NMC811) cathode materials. Using a combined theoretical and experimental approach, we employ density functional theory (DFT) and cluster expansion models to analyze the electronic structure and oxygen vacancy formation enthalpy in Ta-doped NMC811. Experimental validation is conducted using cycling and gas measurements via on-line electrochemical mass spectrometry (OEMS) on in-house synthesized cathode active materials. Both theoretical and experimental approaches show an improvement in oxygen binding due to tantalum doping, with the DFT results highlighting the impact of Ni4+ concentration on the proximity of the vacancy. Our results suggest that Ta doping inhibits the formation of oxygen vacancy-induced side phases, reducing cracking and enhancing the longevity and safety of Ni-rich cathodes.

Functions of consciousness in emotional processing.

Contrary to the leading theories of consciousness on offer, it is a fruitful working hypothesis that conscious experiences facilitate a variety of functional capacities that are distinct to particular psychological tasks, individuals, and species (i.e., functional pluralism). In this paper, I illustrate this novel methodological point by identifying some of the functional contributions that consciousness makes to (human) emotional processing. I first consolidate empirical evidence of the capacities and limitations of unconscious emotional processing, drawing on a) experimental paradigms that employ the tools of vision science (masking and suppression of emotionally relevant stimuli), and b) theoretical and clinical research on emotional disorder (Generalized Anxiety Disorder). After comparing the functional characteristics of unconscious and conscious emotional processes, I argue that conscious experiences facilitate a cluster of functions that are specific to emotion, including increased capacities for representing fine-grained evaluative information, inhibition, and flexible response.

Exploitation of Key Regulatory Modules and Genes for High-Salt Adaptation in Schizothoracine by Weighted Gene Co-Expression Network Analysis

Schizothoracine fishes in saltwater lakes of the Tibetan Plateau are important models for studying the evolution and uplift of the Tibetan Plateau. Examining their adaptation to the high-salt environment is interesting. In this study, we first assembled the RNA-Seq data of each tissue of G. przewalskii, G. selincuoensis, and G. namensis from Qinghai Lake, Selincuo Lake, and Namtso Lake, respectively, obtained by the group previously. After obtaining reliable results, the adaptation of the gills, kidneys, and livers of the three species to the high-salinity environment was assessed by weighted gene co-expression network analysis (WGCNA). Using module eigengenes (ME), 21, 22, and 22 gene modules were identified for G. przewalskii, G. selincuoensis, and G. nemesis, respectively. Functional clustering analysis of genes in the significant association module identified several genes associated with osmolarity-regulated potential KEGG pathways in the gills of three species of Schizothoracine fish. Th17 cell differentiation pathway was up-regulated in the gills of all three species; histocompatibility class 2 II antigen and E alpha (h2-ea) were up-regulated genes in this pathway. Functional clustering analysis of genes in apparently related modules in the kidney unveiled several differential KEGG pathways. The pentose phosphate pathway was up-regulated in the three Schizothoracine fishes, and glucose-6-phosphate dehydrogenase (g6pd) was an up-regulated gene in this pathway. In the livers of the three Schizothorax species, the propanoate metabolism pathway was up-regulated, and succinate-CoA ligase GDP-forming subunit beta (suclg2) was an up-regulated gene in this pathway. The above analyses provide reference data for the adaptation of Schizothorax to high-salt environments and lay the foundation for future studies on the adaptive mechanism of Schizothorax in the plateau. These results partly fill the void in the knowledge gap in the survival adaptations of Schizothoracine fishes to highland saline lakes.

Topological functional network analysis of cortical blood flow in hyperacute ischemic rats

Acute cerebral ischemia alters brain network connectivity, leading to notable increases in both anatomical and functional connectivity while observing a reduction in metabolic connectivity. However, alterations of the cerebral blood flow (CBF) based functional connectivity remain unclear. We collected continuous CBF images using laser speckle contrast imaging (LSCI) technology to monitor ischemic occlusion-reperfusion progression through occlusion of the left carotid artery. We also used a dense cortical grid atlas to construct CBF-based functional connectivity networks for hyperacute ischemic rodents. Graph theoretical analysis was used to measure network topological characteristics and construct topological connection graphs. Coactivation pattern (CAP) analysis was utilized to examine the spatiotemporal characteristics of the global network. Additionally, we measured evoked functional hyperemia and correlated it with network topologies. Network analysis indicated a significant increase in functional connectivity, global efficiency, local efficiency, small-worldness, clustering coefficient, and regional degree centrality primarily within the left ischemic intra-hemisphere, accompanied by weaker changes in the right intra-hemisphere. Inter-hemisphere networks exhibited reduced homologous connections, global efficiency, and small-worldness. CAP analysis revealed increased strength of the left negative activation brain network's state fraction of time and transition probability from equilibrium-to-imbalance states. Left network metrics declined following blood flow reperfusion. Furthermore, positive/negative correlations between barrel-evoked intensity and regional network topologies were reversed as negative/positive correlations after cerebral ischemia. These findings suggest a damaged CBF functional network mechanism following acute cerebral ischemia and a disrupted association between resting state and evoked hyperemia.

Functional data analysis to investigate controls on and changes in the seasonality of UK baseflow

ABSTRACT Continuous streamflow is critical for sustaining ecological systems and ensuring water resource security. Understanding controls on and changes in flows, including the seasonality of baseflow, is therefore an important task. Baseflow seasons have typically been investigated separately, potentially missing hydroecologically important timing changes. Instead, we apply a functional data analysis clustering approach to seasonal patterns of baseflow hydrographs for 671 catchments across Great Britain (GB). The baseflow clusters are characterized as early-, mid-, and late-season peaks. The spatial distribution of the baseflow seasonality clusters is closely connected to the baseflow index and a partition tree shows the influence of catchment topological, hydrogeological and soil factors. Changes in timing of baseflow seasonality are compared to climate seasonality. In GB there appears to be a small but systematic influence of a warming climate on baseflow seasonality via effective rainfall with a tendency for earlier seasonal baseflow peaks, with greater timing changes in snow-influenced catchments.

Neural Modulation Alteration to Positive and Negative Emotions in Depressed Patients: Insights from fMRI Using Positive/Negative Emotion Atlas.

Although it has been noticed that depressed patients show differences in processing emotions, the precise neural modulation mechanisms of positive and negative emotions remain elusive. FMRI is a cutting-edge medical imaging technology renowned for its high spatial resolution and dynamic temporal information, making it particularly suitable for the neural dynamics of depression research. To address this gap, our study firstly leveraged fMRI to delineate activated regions associated with positive and negative emotions in healthy individuals, resulting in the creation of the positive emotion atlas (PEA) and the negative emotion atlas (NEA). Subsequently, we examined neuroimaging changes in depression patients using these atlases and evaluated their diagnostic performance based on machine learning. Our findings demonstrate that the classification accuracy of depressed patients based on PEA and NEA exceeded 0.70, a notable improvement compared to the whole-brain atlases. Furthermore, ALFF analysis unveiled significant differences between depressed patients and healthy controls in eight functional clusters during the NEA, focusing on the left cuneus, cingulate gyrus, and superior parietal lobule. In contrast, the PEA revealed more pronounced differences across fifteen clusters, involving the right fusiform gyrus, parahippocampal gyrus, and inferior parietal lobule. These findings emphasize the complex interplay between emotion modulation and depression, showcasing significant alterations in both PEA and NEA among depression patients. This research enhances our understanding of emotion modulation in depression, with implications for diagnosis and treatment evaluation.

Bioinformatics analysis of colorectal cancer transcriptomic data reveals novel prognostic signature and potential biomarker genes

Objective Colorectal cancer (CRC) is a type of digestive system cancer. At the molecular level, some factors, including genetic and epigenetic factors, as well as various signaling pathways such as oxidative stress and inflammation, play an active role in the onset of CRC. Genetic and epigenetic mutations, particularly in oncogenes and tumor suppressor genes, occur during colorectal adenocarcinoma development as a result of a change in gastrointestinal epithelial cell proliferation and self-renewal rates. This study aimed to determine the genes and molecular mechanisms that play a role in the emergence of this disease by analyzing the CRC data. Material and methods Microarray data selected for bioinformatics analysis is Gene Expression data stored with the code GSE110224 in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Gene expression analysis, functional clustering analysis, enrichment analysis, and pathway analysis were performed using this data set. Results Analysis of raw transcriptomic data revealed 1770 common DEGs in CRC. While the expression level of 769 of these genes increased, the expression level of 1001 genes decreased. A Protein–protein interaction (PPI) network was created from the first 25 genes with increased expression levels and 11 signature genes were identified. Increased expression of REG1A, MMP3, FOXQ1 and CEMIP genes and decreased expression of AQP8, CA1, CLDN8, PYY, CA4, CEACAM7 and SLC30A10 genes were observed. Conclusions This approach revealed a CRC-specific molecular profile and may provide some guidance for further investigation of potential biomarkers for diagnosis and prognosis prediction of CRC patients.

Elevated Expression of Cell Adhesion, Metabolic, and Mucus Secretion Gene Clusters Associated with Tumorigenesis, Metastasis, and Poor Survival in Pancreatic Ductal Adenocarcinoma.

Technological advances in identifying gene expression profiles are being applied to study an array of cancers. The goal of this study was to identify differentially expressed genes in pancreatic ductal adenocarcinoma (PDAC) and examine their potential role in tumorigenesis and metastasis. The transcriptomic profiles of PDAC and non-tumorous tissue samples were derived from the gene expression omnibus (GEO), which is a public repository. The GEO2R tool was used to further derive differentially expressed genes from those profiles. In this study, a total of 68 genes were derived from upregulated PDAC genes in three or more transcriptomic profiles and were considered PDAC gene sets. The identified PDAC gene sets were examined in the molecular signatures database (MSigDB) for ontological investigation, which revealed that these genes were involved in the extracellular matrix and associated with the cell adhesion process in PDAC tumorigenesis. The gene set enrichment analysis showed greater enrichment scores for the gene sets. Moreover, the identified gene sets were examined for protein-protein interaction using the STRING database. Based on functional k-means clustering, the following three functional cluster groups were identified in this study: extracellular matrix/cell adhesion, metabolic, and mucus secretion-related protein groups. The receiver operating characteristic (ROC) curve revealed greater specificity and sensitivity for these cluster genes in predicting PDAC tumorigenesis and metastases. In addition, the expression of the cluster genes affects the overall survival rate of PDAC patients. Using the cancer genome atlas (TCGA) database, the associations between expression levels and clinicopathological features were validated. Overall, the genes identified in this study appear to be critical in PDAC development and can serve as potential diagnostic and prognostic targets for pancreatic cancer treatment.

The relationship between brain structure and function during novel grammar learning across development

Abstract In this study, we explored the relationship between developmental differences in gray matter structure and grammar learning ability in 159 Dutch-speaking individuals (8 to 25 yr). The data were collected as part of a recent large-scale functional MRI study (Menks WM, Ekerdt C, Lemhöfer K, Kidd E, Fernández G, McQueen JM, Janzen G. Developmental changes in brain activation during novel grammar learning in 8–25-year-olds. Dev Cogn Neurosci. 2024;66:101347. https://doi.org/10.1016/j.dcn.2024.101347) in which participants implicitly learned Icelandic morphosyntactic rules and performed a grammaticality judgment task in the scanner. Behaviorally, Menks et al. (2024) showed that grammaticality judgment task performance increased steadily from 8 to 15.4 yr, after which age had no further effect. We show in the current study that this age-related grammaticality judgment task performance was negatively related to cortical gray matter volume and cortical thickness in many clusters throughout the brain. Hippocampal volume was positively related to age-related grammaticality judgment task performance and L2 (English) vocabulary knowledge. Furthermore, we found that grammaticality judgment task performance, L2 grammar proficiency, and L2 vocabulary knowledge were positively related to gray matter maturation within parietal regions, overlapping with the functional MRI clusters that were reported previously in Menks et al. (2024) and which showed increased brain activation in relation to grammar learning. We propose that this overlap in functional and structural results indicates that brain maturation in parietal regions plays an important role in second language learning.

Patterns of Social Connection Among Older Adults in England

Issues related to social connection are increasingly recognized as a global public health priority. However, there is a lack of a holistic understanding of social connection and its health impacts given that most empirical research focuses on a single or few individual concepts of social connection. To explore patterns of social connection and their associations with health and well-being outcomes. This cohort study included participants aged 50 years and older from the fourth wave of the English Longitudinal Study of Aging (2008-2009). Machine learning cluster analysis and regression analysis were used. The analyses were performed from January to July 2024. Social connection clusters informed by the cluster analysis. This study considered outcomes related to mental health (depression), hedonic (life satisfaction, pleasure) and eudaimonic (self-realization) well-being, general health (self-reported health), and health behavior (moderate or vigorous physical activity). Key confounders, identified using directed acyclic graphs, including age, sex, ethnicity, education, social class, and wealth, were controlled for. Among 7706 participants aged 50 years and older (mean [SD] age, 64.7 [9.6] years; 4248 [55.1%] female; 7536 [97.8%] White), 5 clusters were identified, including disconnected (974 [12.6%]), gapped structure/poor function (1109 [14.4%]), gapped structure/high function (1582 [20.5%]), poor function/mixed quality (1501 [19.5%]), and highly connected (2540 [33.0%]). All clusters had poorer outcomes compared with the highly connected cluster (eg, depression among individuals in disconnected vs highly connected clusters: odds ratio [OR], 2.73; 95% CI, 2.24 to 3.33), many of which persisted after controlling for baseline outcome (eg, depression among individuals in disconnected vs highly connected clusters: OR, 1.95; 95% CI, 1.57 to 2.43). The difference was smallest between the highly connected and gapped structure/high function clusters across most outcomes (eg, depression among individuals in gapped structure/high function vs highly connected: OR, 1.34; 95% CI, 1.10-1.64; after controlling for baseline outcome: OR, 1.28; 95% CI, 1.03-1.59). This cohort study highlights the importance of considering multidimensional measures of social connection and understanding the nuance of its heterogenous patterns. Understanding the typologies of social connection has substantial implications for exploring modifiable risk factors for social disconnection and for understanding the mechanisms linking social connection to health-related outcomes.

Quantitative Proteomic Analysis of APP/PS1 Transgenic Mice.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the central nervous system (CNS), with its etiology still shrouded in uncertainty. The interplay of extracellular amyloid-β (Aβ) deposition, intracellular neurofibrillary tangles (NFTs) composed of tau protein, cholinergic neuronal impairment, and other pathogenic factors is implicated in the progression of AD. The current study endeavors to delineate the proteomic landscape alterations in the hippocampus of an AD murine model, utilizing proteomic analysis to identify key physiological and pathological shifts induced by the disease. This endeavor aims to shed light on the underlying pathogenic mechanisms, which could facilitate early diagnosis and pave the way for novel therapeutic interventions for AD. To dissect the proteomic perturbations induced by Aβ and Presenilin-1 (PS1) in the AD pathogenesis, we undertook a label-free quantitative (LFQ) proteomic analysis focusing on the hippocampal proteome of the APP/PS1 transgenic mouse model. Employing a multi-faceted approach that included differential protein functional enrichment, cluster analysis, and protein-protein interaction (PPI) network analysis, we conducted a comprehensive comparative proteomic study between APP/PS1 transgenic mice and their wild-type C57BL/6 counterparts. Mass spectrometry identified a total of 4817 proteins in the samples, with 2762 proteins being quantifiable. Comparative analysis revealed 396 proteins with differential expression between the APP/PS1 and control groups. Notably, 35 proteins exhibited consistent temporal regulation trends in the hippocampus, with concomitant alterations in biological pathways and PPI networks. This study presents a comparative proteomic profile of transgenic (APP/PS1) and wild-type mice, highlighting the proteomic divergences. Furthermore, it charts the trajectory of proteomic changes in the AD mouse model across the developmental stages from 2 to 12 months, providing insights into the physiological and pathological implications of the disease-associated genetic mutations.

Crucial role and conservation of the three [2Fe-2S] clusters in the human mitochondrial ribosome.

Mitochondria synthesize only a small set of their proteins on endogenous mitoribosomes. These particles differ in structure and composition from both their bacterial 70S ancestors and cytosolic 80S ribosomes. Recently published high resolution structures of the human mitoribosome revealed the presence of three [2Fe-2S] clusters in the small and large subunits. Each of these clusters is coordinated in a bridging fashion by cysteine residues from two different mitoribosomal proteins. Here, we investigated the cell biological and biochemical roles of all three Fe/S clusters in mitochondrial function and assembly. First, we found a requirement of both early and late factors of the mitochondrial iron-sulfur cluster assembly machinery for protein translation indicating that not only the mitoribosome [2Fe-2S] clusters but also the [4Fe-4S] cluster of the mitoribosome assembly factor METTL17 are required for mitochondrial translation. Second, siRNA-mediated depletion of the cluster-coordinating ribosomal proteins bS18m, mS25 or mL66 and complementation with either the respective wild-type or cysteine-exchange proteins unveiled the importance of the clusters for assembly, stability, and function of the human mitoribosome. As a consequence, the lack of cluster binding to mitoribosomes impaired the activity of the mitochondrial respiratory chain complexes and led to altered mitochondrial morphology with a loss of cristae membranes. Finally, in silico investigation of the phylogenetic distribution of the cluster-coordinating cysteine motifs indicated their presence in most metazoan mitoribosomes, with exception of ray-finned fish. Collectively, our study highlights the functional need of mitochondrial Fe/S protein biogenesis for both protein translation and respiratory energy supply in most metazoan mitochondria.

Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors.

Functional concurrent, or varying-coefficient, regression models are a form of functional data analysis methods in which functional covariates and outcomes are collected concurrently. Two active areas of research for this class of models are identifying influential functional covariates and clustering their relations across observations. In various applications, researchers have applied and developed methods to address these objectives separately. However, no approach currently performs both tasks simultaneously. In this paper, we propose a fully Bayesian functional concurrent regression mixture model that simultaneously performs functional variable selection and clustering for subject-specific trajectories. Our approach introduces a novel spiked Ewens-Pitman attraction prior that identifies and clusters subjects' trajectories marginally for each functional covariate while using similarities in subjects' auxiliary covariate patterns to inform clustering allocation. Using simulated data, we evaluate the clustering, variable selection, and parameter estimation performance of our approach and compare its performance with alternative spiked processes. We then apply our method to functional data collected in a novel, smartphone-based smoking cessation intervention study to investigate individual-level dynamic relations between smoking behaviors and potential risk factors.

Molecular docking and transcriptomic analysis reveal the mechanism of myosin-derived peptides activating bitter receptor of hT2R1

To better understand the bitterness effect and molecule mechanism of myosin-derived peptides activating bitter receptors, the interaction between myosin-derived peptides of dry-cured ham and bitter receptors was investigated by molecular docking and molecular dynamics simulation; the signal transduction mechanism of myosin-derived peptides was explored by HEK-293T cells using calcium imaging and transcriptomics analysis. Lower CDOCKER energy was observed during the interaction between myosin-derived peptides and hT2R1 by molecular docking compared with hT2R4, hT2R5, hT2R8, hT2R14 and hT2R16. Hydrogen bonds and hydrophobic interaction were the most important interaction forces which stabilized the interaction of hT2R1 and myosin-derived peptides. Compared with LEKEKSELK and TEELEEAKK, the RMSF values and EC50 values of HVLATLGEK were lower, indicating that hT2R1 was more sensitive to HVLATLGEK stimulation. Transcriptomics and KEGG analyses showed that 767 differentially expressed genes were found and mainly involved in cAMP signaling pathway, neuroactive ligand-receptor interaction, calcium signaling pathway and MAPK signaling pathway after stimulating of HVLATLGEK. Protein-protein interaction network further demonstrated that DDIT3, FOS, FOSB, MYC, EGR1 and CCN2 were the key genes to connect the six functional clusters including ligand-receptor interaction and signal transduction.