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Abstract Background It is not clear if conventional liver fat cutoff of 5.56% weight which has been used for identifying fatty liver in western populations is also applicable for Asians. In Asian women of reproductive age, we evaluate the optimum metabolic syndrome (MetS)-linked liver fat cutoff, the specific metabolomic alterations apparent at this cutoff, as well as prospective associations of preconception liver fat levels with gestational dysglycemia. Methods Liver fat (measured by magnetic resonance spectroscopy), MetS, and nuclear magnetic resonance (NMR)-based plasma metabolomic profiles were assessed in 382 Asian women, who were planning to conceive. Ninety-eight women went on to become pregnant and received an oral glucose tolerance test at week 26 of gestation. Results The optimum liver fat cutoff for diagnosing MetS was 2.07%weight. Preconception liver fat was categorized into Low (liver fat

Abstract Background Phenotypic age (PhenoAge), a widely used marker of biological aging, has been shown to be a robust predictor of all-cause mortality and morbidity in different populations. Existing studies on biological aging have primarily focused on individual domains, resulting in a lack of a comprehensive understanding of the multi-systemic dysregulation that occurs in aging. Methods PhenoAge was evaluated based on a linear combination of chronological age (CA) and 9 clinical biomarkers in 952 multi-ethnic Asian women of reproductive age. Phenotypic age acceleration (PhenoAgeAccel), an aging biomarker, represents PhenoAge after adjusting for CA. This study conducts an in-depth association analysis of PhenoAgeAccel with clinical, nutritional, lipidomic, gut microbiome, and genetic factors. Results Higher adiposity, glycaemia, plasma saturated fatty acids, kynurenine pathway metabolites, GlycA, riboflavin, nicotinamide, and insulin-like growth factor binding proteins were positively associated with PhenoAgeAccel. Conversely, a healthier diet and higher levels of pyridoxal phosphate, all-trans retinol, betaine, tryptophan, glutamine, histidine, apolipoprotein B, and insulin-like growth factors were inversely associated with PhenoAgeAccel. Lipidomic analysis found 132 lipid species linked to PhenoAgeAccel, with PC(O-36:0) showing the strongest positive association and CE(24:5) demonstrating the strongest inverse association. A genome-wide association study identified rs9864994 as the top genetic variant (P = 5.69E-07) from the ZDHHC19 gene. Gut microbiome analysis revealed that Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were inversely associated with PhenoAgeAccel. Integrative network analysis of aging-related factors underscored the intricate links among clinical, nutritional and lipidomic variables, such as positive associations between kynurenine pathway metabolites, amino acids, adiposity, and insulin resistance. Furthermore, potential mediation effects of blood biomarkers related to inflammation, immune response, and nutritional and energy metabolism were observed in the associations of diet, adiposity, genetic variants, and gut microbial species with PhenoAgeAccel. Conclusions Our findings provide a comprehensive analysis of aging-related factors across multiple platforms, delineating their complex interconnections. This study is the first to report novel signatures in lipidomics, gut microbiome and blood biomarkers specifically associated with PhenoAgeAccel. These insights are invaluable in understanding the molecular and metabolic mechanisms underlying biological aging and shed light on potential interventions to mitigate accelerated biological aging by targeting modifiable factors.

Background/Objectives: This study evaluated metabolites and lipid composition in the calf muscles of Type 2 diabetes mellitus (T2DM) patients and age-matched healthy controls using multi-dimensional MR spectroscopic imaging. We also explored the association between muscle metabolites, lipids, and intra-abdominal fat in T2DM. Methods: Participants included 12 T2DM patients (60.3 ± 8.6 years), 9 age-matched healthy controls (AMHC) (60.9 ± 7.8 years), and 10 young healthy controls (YHC) (28.3 ± 1.8 years). We acquired the 2D MR spectra of calf muscles using an enhanced accelerated 5D echo-planar correlated spectroscopic imaging (EP-COSI) technique and abdominal MRI with breath-hold 6-point Dixon sequence. Results: In YHC, choline levels were lower in the gastrocnemius (GAS) and soleus (SOL) muscles but higher in the tibialis anterior (TA) compared to AMHC. YHC also showed a higher unsaturation index (U.I.) of extramyocellular lipids (EMCL) in TA, intramyocellular lipids (IMCL) in GAS, carnosine in SOL, and taurine and creatine in TA. T2DM patients exhibited higher choline in TA and myo-inositol in SOL than AMHC, while triglyceride fat (TGFR2) levels in TA were lower. Correlation analyses indicated associations between IMCL U.I. and various metabolites in muscles with liver, pancreas, and abdominal fat estimates in T2DM. Conclusions: This study highlights distinct muscle metabolite and lipid composition patterns across YHC, AMHC, and T2DM subjects. Associations between IMCL U.I. and abdominal fat depots underscore the interplay between muscle metabolism and adiposity in T2DM. These findings provide new insights into metabolic changes in T2DM and emphasize the utility of advanced MR spectroscopic imaging in characterizing muscle-lipid interactions.

Abstract Background Familial partial lipodystrophy (FPLD) is an inherited disorder of white adipose tissue that causes premature cardiometabolic disease. There is no clear diagnostic criteria for FPLD, and this may explain the under-detection of this condition. Aim This pilot study aimed to describe the clinical features of women with FPLD and to explore the value of adipose tissue measurements that could be useful in diagnosis. Methods In 8 women with FPLD and 4 controls, skinfold measurements, DXA and whole-body MRI were undertaken. Results Whole genome sequencing was negative for monogenic metabolic causes, but polygenic scores for partial lipodystrophy were elevated in keeping with FPLD type 1. The mean age of diagnosis of DM was 31 years in the FPLD group. Compared with controls, the FPLD group had increased HOMA-IR (10.3 vs 2.9, p = 0.028) and lower mean thigh skinfold thickness (19.5 mm vs 48.2 mm, p = 0.008). The FPLD group had lower percentage of leg fat and an increased ratio of trunk to leg fat percentage on DXA. By MRI, the FPLD group had decreased subcutaneous adipose tissue (SAT) volume in the femoral and calf regions (p

Background: The influence of prenatal exposure to per- and poly- fluoroalkyl substances (PFAS) on birth size and offspring adiposity is unclear, especially for the newer, shorter-chained replacement PFAS. Methods: In the GUSTO multi-ethnic Singaporean mother-offspring cohort, 12 PFAS were measured in 783 cord plasma samples using ultra-performance-liquid chromatography-tandem-mass-spectrometer (UPLC-MS/MS). Outcomes included offspring anthropometry, other indicators of body composition/metabolic health, and MRI-derived abdominal adiposity (subset) at birth and 6 years of age. PFAS were modeled individually, in categories of long-chain and short-chain PFAS, and as scores of three principal components (PC) derived using PC analysis (PC1, PC2, and PC3 reflect predominant exposure patterns to “very-long-PFAS”, “long-PFAS”, and “short-PFAS”, respectively). Associations with outcomes were assessed using multivariable linear regressions, adjusted for important covariates such as maternal sociodemographic and lifestyle factors. Results: Overall, cord PFAS levels showed either no or positive associations (mostly for long-chain PFAS) with birth weight, length and head circumference. In general, PFAS were associated with higher neonatal abdominal adiposity, driven by shorter-chain PFAS. Perfluoroheptanoic acid (PFHpA) was associated with higher volumes of superficial subcutaneous adipose tissue (sSAT) (3.75 [1.13, 6.37] mL per SD increase in PFAS) and internal adipose tissue (IAT) (1.39 [0.41, 2.38] mL). Higher levels of perfluorobutanesulfonic acid (PFBS), short-chain PFAS, and PC3 were associated with higher IAT volume (β range 1.22–1.41 mL/SD, all P

Abstract Both hyperthyroidism and elevated plasma branched chain amino acids (BCAA) are associated with insulin resistance. BCAA utilization and clearance relative to thyroid status changes remains unclear. We investigate amino acids changes, specifically BCAA, during the transition from hyperthyroidism to euthyroidism, and the impact of active brown adipose tissue (BAT) on the metabolic effects of BCAA. Newly diagnosed Graves’ disease participants were recruited. Hyperthyroidism was treated via a titration dosing regimen of thionamide anti-thyroid drug to establish euthyroidism over 12–24 weeks. All underwent energy expenditure (EE) measurement within a chamber calorimeter, 18F-fluorodeoxyglucose (18F-FDG) positron-emission tomography/magnetic resonance (PET/MR) imaging and plasma amino acids measurement during hyperthyroidism and euthyroidism. PET BAT maximum standardized uptake value (SUVmax), SUVmean and MR supraclavicular fat fraction (FF) quantified BAT activity. Twenty-two patients completed the study. Plasma BCAA level was significantly reduced in BAT-positive but not in BAT-negative patients during the transition from hyperthyroidism to euthyroidism. Plasma valine but not leucine and isoleucine correlated positively with insulin and HOMA-IR in hyperthyroidism. Plasma valine, leucine and isoleucine correlated with insulin and HOMA-IR in euthyroidism. Plasma valine correlated with insulin and HOMA-IR in BAT-negative but not in BAT-positive participants in both hyperthyroid and euthyroid state. However, the change (i.e. decrease) in plasma valine concentration from hyperthyroid to euthyroid state was affected by BAT-status. BAT utilizes and promotes BCAA plasma clearance from hyperthyroid to euthyroid state. Active BAT can potentially reduce circulating BCAA and may help to ameliorate insulin resistance and improve metabolic health. Clinical trial registration: The trial was registered at clinicaltrials.gov as NCT03064542.

Abstract Background Nausea and vomiting of pregnancy (NVP) is common and underlying mechanisms are poorly understood. Longer-term offspring outcomes are also not well documented. This study aimed to determine if NVP, even in milder forms, is associated with adverse pregnancy and childhood growth outcomes. Methods In the GUSTO prospective mother-offspring cohort, women with singleton pregnancies (n = 1172) recruited in first trimester responded to interviewer-administered questions at 26–28 weeks’ gestation about earlier episodes of NVP since becoming pregnant. Pregnancy outcomes were obtained from medical records. Offspring height and weight measured at 15 time-points between birth to 72 months (m) were standardised for age and sex. Results 58.5% (n = 686) reported mild-moderate vomiting (mNVP), 10.5% (n = 123) severe vomiting (sNVP) and 5.7% (n = 67) severe vomiting with hospitalisation (shNVP). There was no difference in odds of gestational diabetes, hypertensive disorders of pregnancy, labour induction or caesarean section after adjustment for covariates. sNVP was associated with late preterm delivery [34+ 0–36+ 6 weeks’, adjusted OR = 3.04 (95% CI 1.39,6.68)], without increased odds of neonatal unit admission. Compared with no NVP, boys born to mothers with sNVP were longer at birth [adjusted β = 0.38 standard deviations (SDs) (95% CI 0.02,0.73)], remained taller [0.64 SDs (0.23,1.04) at 72 m] and heavier [0.57 SDs (0.05,1.08) at 60 m] without differences in BMI. Conversely, girls born to mothers with shNVP were lighter from 48 m [− 0.52 SDs (− 1.00, − 0.03)] onwards with lower BMI [− 0.61 SDs (− 1.12,-0.09)]. Conditional growth modelling revealed significant sex-divergence in weight-gain at birth-3 m, 6-9 m and 4–5 years. Conclusions Severe NVP was associated with late preterm delivery, and both mild-moderate and severe NVP associated with sex-dependent differences in early childhood growth. Boys whose mothers had NVP were taller and heavier from birth with faster growth in the first year, whereas, girls had poorer weight gain and were lighter by 48 m. As even milder severities of NVP could have long-term impact on offspring growth, further research is needed to determine mechanisms involved and implications on future health. Trial registration Clinicaltrials.gov identifier NCT01174875 .

Sensory adaptation is the process whereby brain circuits adjust neuronal activity in response to redundant sensory stimuli. Although sensory adaptation has been extensively studied for individual neurons on timescales of tens of milliseconds to a few seconds, little is known about it over longer timescales or at the population level. We investigated population-level adaptation in the barrel field of the mouse somatosensory cortex (S1BF) using in vivo two-photon calcium imaging and Neuropixels recordings in awake mice. Among stimulus-responsive neurons, we found both adapting and facilitating neurons, which decreased or increased their firing, respectively, with repetitive whisker stimulation. The former outnumbered the latter by 2:1 in layers 2/3 and 4; hence, the overall population response of mouse S1BF was slightly adapting. We also discovered that population adaptation to one stimulus frequency (5 Hz) does not necessarily generalize to a different frequency (12.5 Hz). Moreover, responses of individual neurons to repeated rounds of stimulation over tens of minutes were strikingly heterogeneous and stochastic, such that their adapting or facilitating response profiles were not stable across time. Such representational drift was particularly striking when recording longitudinally across 8-9 days, as adaptation profiles of most whisker-responsive neurons changed drastically from one day to the next. Remarkably, repeated exposure to a familiar stimulus paradoxically shifted the population away from strong adaptation and toward facilitation. Thus, the adapting vs. facilitating response profile of S1BF neurons is not a fixed property of neurons but rather a highly dynamic feature that is shaped by sensory experience across days.

A communication tool in the form of sign language is required for deaf and dump persons as there is no oral communication possible between them. They perform the conversion of sign languages into voice/text. Recently, many algorithms are developed for this purpose. An Indian Sign Language Recognition (ISLR) system is presented in this paper. It uses curvelet transform based entropy features for the recognition, and the transform is applied only to the segmented hand region. Then, the features of each sign of English alphabets are modelled by a classier network called Hidden Markov Models (HMM). The system gives an average accuracy of 82.95% using 3rd level features which can help to reduce the communication gap between deaf-dumb and normal people in the world.

A consanguineous family from Pakistan was ascertained to have a novel deafness-dystonia syndrome with motor regression, ichthyosis-like features and signs of sensory neuropathy. By applying a combined strategy of linkage analysis and whole-exome sequencing in the presented family, a homozygous nonsense mutation, c.4G>T (p.Glu2*), in FITM2 was identified. FITM2 and its paralog FITM1 constitute an evolutionary conserved protein family involved in partitioning of triglycerides into cellular lipid droplets. Despite the role of FITM2 in neutral lipid storage and metabolism, no indications for lipodystrophy were observed in the affected individuals. In order to obtain independent evidence for the involvement of FITM2 in the human pathology, downregulation of the single Fitm ortholog, CG10671, in Drosophila melanogaster was pursued using RNA interference. Characteristics of the syndrome, including progressive locomotor impairment, hearing loss and disturbed sensory functions, were recapitulated in Drosophila, which supports the causative nature of the FITM2 mutation. Mutation-based genetic counseling can now be provided to the family and insight is obtained into the potential impact of genetic variation in FITM2.

There are two types of fat tissues, white adipose tissue (WAT) and brown adipose tissue (BAT), which essentially perform opposite functions in whole body energy metabolism. There is a large interest in identifying novel biophysical properties of WAT and BAT by a quantitative and easy-to-run technique. In this work, we used high-resolution pulsed field gradient diffusion NMR spectroscopy to study the apparent diffusion coefficient (ADC) of fat molecules in rat BAT and WAT samples. The ADC of fat in BAT and WAT from rats fed with a chow diet was compared with that of rats fed with a high-fat diet to monitor how the diffusion properties change due to obesity-associated parameters such as lipid droplet size, fatty acid chain length, and saturation. Feeding a high-fat diet resulted in increased saturation, increased chain lengths, and reduced ADC of fat in WAT. The ADC of fat was lower in BAT relative to WAT in rats fed both chow and high-fat diets. Diffusion of fat was restricted in BAT due to the presence of small multilocular lipid droplets. Our findings indicate that in vivo diffusion might be a potential way for better delineation of BAT and WAT in both lean and obese states.

Changes in the function of inhibitory interneurons (INs) during cortical development could contribute to the pathophysiology of neurodevelopmental disorders. Using all-optical in vivo approaches, we find that parvalbumin (PV) INs and their immature precursors are hypoactive and transiently decoupled from excitatory neurons in postnatal mouse somatosensory cortex (S1) of Fmr1 KO mice, a model of fragile X syndrome (FXS). This leads to a loss of parvalbumin INs (PV-INs) in both mice and humans with FXS. Increasing the activity of future PV-INs in neonatal Fmr1 KO mice restores PV-IN density and ameliorates transcriptional dysregulation in S1, but not circuit dysfunction. Critically, administering an allosteric modulator of Kv3.1 channels after the S1 critical period does rescue circuit dynamics and tactile defensiveness. Symptoms in FXS and related disorders could be mitigated by targeting PV-INs.

Motor skill learning induces changes in synaptic structure and function in the primary motor cortex (M1). In the fragile X syndrome (FXS) mouse model an impairment in motor skill learning and associated formation of new dendritic spines was previously reported. However, whether modulation of synaptic strength through trafficking of AMPA receptors (AMPARs) with motor skill training is impaired in FXS is not known. Here, we performed in vivo imaging of a tagged AMPA receptor subunit, GluA2, in layer (L)2/3 neurons in the primary motor cortex of wild-type (WT) and Fmr1 knock-out (KO) male mice at different stages of learning a single forelimb-reaching task. Surprisingly, in the Fmr1 KO mice, despite impairments in learning there was no deficit in motor skill training-induced spine formation. However, the gradual accumulation of GluA2 in WT stable spines, which persists after training is completed and past the phase of spine number normalization, is absent in the Fmr1 KO mouse. These results demonstrate that motor skill learning not only reorganizes circuits through formation of new synapses, but also strengthens existing synapses through accumulation of AMPA receptors and GluA2 changes are better associated with learning than new spine formation.

Two-Photon Microscopy has become an invaluable tool for biological and medical research, providing high sensitivity, molecular specificity, inherent three-dimensional sub-cellular resolution and deep tissue penetration. In terms of imaging speeds, however, mechanical scanners still limit the acquisition rates to typically 10-100 frames per second. Here we present a high-speed non-linear microscope achieving kilohertz frame rates by employing pulse-modulated, rapidly wavelength-swept lasers and inertia-free beam steering through angular dispersion. In combination with a high bandwidth, single-photon sensitive detector, this enables recording of fluorescent lifetimes at speeds of 88 million pixels per second. We show high resolution, multi-modal - two-photon fluorescence and fluorescence lifetime (FLIM) - microscopy and imaging flow cytometry with a digitally reconfigurable laser, imaging system and data acquisition system. These high speeds should enable high-speed and high-throughput image-assisted cell sorting.

Fluorescence calcium imaging using a range of microscopy approaches, such as two-photon excitation or head-mounted miniscopes, is one of the preferred methods to record neuronal activity and glial signals in various experimental settings, including acute brain slices, brain organoids, and behaving animals. Because changes in the fluorescence intensity of genetically encoded or chemical calcium indicators correlate with action potential firing in neurons, data analysis is based on inferring such spiking from changes in pixel intensity values across time within different regions of interest. However, the algorithms necessary to extract biologically relevant information from these fluorescent signals are complex and require significant expertise in programming to develop robust analysis pipelines. For decades, the only way to perform these analyses was for individual laboratories to write their custom code. These routines were typically not well annotated and lacked intuitive graphical user interfaces (GUIs), which made it difficult for scientists in other laboratories to adopt them. Although the panorama is changing with recent tools like CaImAn, Suite2P, and others, there is still a barrier for many laboratories to adopt these packages, especially for potential users without sophisticated programming skills. As two-photon microscopes are becoming increasingly affordable, the bottleneck is no longer the hardware, but the software used to analyze the calcium data optimally and consistently across different groups. We addressed this unmet need by incorporating recent software solutions, namely NoRMCorre and CaImAn, for motion correction, segmentation, signal extraction, and deconvolution of calcium imaging data into an open-source, easy to use, GUI-based, intuitive and automated data analysis software package, which we named EZcalcium.

Fluorescence calcium imaging using a range of microscopy approaches, such as two-photon excitation or head-mounted "miniscopes," is one of the preferred methods to record neuronal activity and glial signals in various experimental settings, including acute brain slices, brain organoids, and behaving animals. Because changes in the fluorescence intensity of genetically encoded or chemical calcium indicators correlate with action potential firing in neurons, data analysis is based on inferring such spiking from changes in pixel intensity values across time within different regions of interest. However, the algorithms necessary to extract biologically relevant information from these fluorescent signals are complex and require significant expertise in programming to develop robust analysis pipelines. For decades, the only way to perform these analyses was for individual laboratories to write their custom code. These routines were typically not well annotated and lacked intuitive graphical user interfaces (GUIs), which made it difficult for scientists in other laboratories to adopt them. Although the panorama is changing with recent tools like CaImAn, Suite2P, and others, there is still a barrier for many laboratories to adopt these packages, especially for potential users without sophisticated programming skills. As two-photon microscopes are becoming increasingly affordable, the bottleneck is no longer the hardware, but the software used to analyze the calcium data optimally and consistently across different groups. We addressed this unmet need by incorporating recent software solutions, namely NoRMCorre and CaImAn, for motion correction, segmentation, signal extraction, and deconvolution of calcium imaging data into an open-source, easy to use, GUI-based, intuitive and automated data analysis software package, which we named EZcalcium.

Background/Objectives: This study evaluated metabolites and lipid composition in the calf muscles of Type 2 diabetes mellitus (T2DM) patients and age-matched healthy controls using multi-dimensional MR spectroscopic imaging. We also explored the association between muscle metabolites, lipids, and intra-abdominal fat in T2DM. Methods: Participants included 12 T2DM patients (60.3 ± 8.6 years), 9 age-matched healthy controls (AMHC) (60.9 ± 7.8 years), and 10 young healthy controls (YHC) (28.3 ± 1.8 years). We acquired the 2D MR spectra of calf muscles using an enhanced accelerated 5D echo-planar correlated spectroscopic imaging (EP-COSI) technique and abdominal MRI with breath-hold 6-point Dixon sequence. Results: In YHC, choline levels were lower in the gastrocnemius (GAS) and soleus (SOL) muscles but higher in the tibialis anterior (TA) compared to AMHC. YHC also showed a higher unsaturation index (U.I.) of extramyocellular lipids (EMCL) in TA, intramyocellular lipids (IMCL) in GAS, carnosine in SOL, and taurine and creatine in TA. T2DM patients exhibited higher choline in TA and myo-inositol in SOL than AMHC, while triglyceride fat (TGFR2) levels in TA were lower. Correlation analyses indicated associations between IMCL U.I. and various metabolites in muscles with liver, pancreas, and abdominal fat estimates in T2DM. Conclusions: This study highlights distinct muscle metabolite and lipid composition patterns across YHC, AMHC, and T2DM subjects. Associations between IMCL U.I. and abdominal fat depots underscore the interplay between muscle metabolism and adiposity in T2DM. These findings provide new insights into metabolic changes in T2DM and emphasize the utility of advanced MR spectroscopic imaging in characterizing muscle-lipid interactions. [ABSTRACT FROM AUTHOR]

Background: It is not clear if conventional liver fat cutoff of 5.56% weight which has been used for identifying fatty liver in western populations is also applicable for Asians. In Asian women of reproductive age, we evaluate the optimum metabolic syndrome (MetS)-linked liver fat cutoff, the specific metabolomic alterations apparent at this cutoff, as well as prospective associations of preconception liver fat levels with gestational dysglycemia. Methods: Liver fat (measured by magnetic resonance spectroscopy), MetS, and nuclear magnetic resonance (NMR)-based plasma metabolomic profiles were assessed in 382 Asian women, who were planning to conceive. Ninety-eight women went on to become pregnant and received an oral glucose tolerance test at week 26 of gestation. Results: The optimum liver fat cutoff for diagnosing MetS was 2.07%weight. Preconception liver fat was categorized into Low (liver fat < 2.07%), Moderate (2.07% ≤ liver fat < 5.56%), and High (liver fat ≥ 5.56%) groups. Individual MetS traits showed worsening trends, going from Low to Moderate to High groups. Multiple plasma metabolomic alterations, previously linked to incident type 2 diabetes (T2D), were already evident in the Moderate group (adjusted for ethnicity, age, parity, educational attainment, and BMI). Both a cross-sectional multi-metabolite score for incident T2D and mid-gestational glucose area under the curve showed increasing trends, going from Low to Moderate to High groups (p < 0.001 for both). Gestational diabetes incidence was 2-fold (p = 0.23) and 7-fold (p < 0.001) higher in the Moderate and High groups relative to the Low group. Conclusions: In Asian women of reproductive age, moderate liver fat accumulation below the conventional fatty liver cutoff was not metabolically benign and was linked to gestational dysglycemia. The newly derived cutoff can aid in screening individuals before adverse metabolic phenotypes have consolidated, which provides a longer window for preventive strategies. [ABSTRACT FROM AUTHOR]

Background: Phenotypic age (PhenoAge), a widely used marker of biological aging, has been shown to be a robust predictor of all-cause mortality and morbidity in different populations. Existing studies on biological aging have primarily focused on individual domains, resulting in a lack of a comprehensive understanding of the multi-systemic dysregulation that occurs in aging. Methods: PhenoAge was evaluated based on a linear combination of chronological age (CA) and 9 clinical biomarkers in 952 multi-ethnic Asian women of reproductive age. Phenotypic age acceleration (PhenoAgeAccel), an aging biomarker, represents PhenoAge after adjusting for CA. This study conducts an in-depth association analysis of PhenoAgeAccel with clinical, nutritional, lipidomic, gut microbiome, and genetic factors. Results: Higher adiposity, glycaemia, plasma saturated fatty acids, kynurenine pathway metabolites, GlycA, riboflavin, nicotinamide, and insulin-like growth factor binding proteins were positively associated with PhenoAgeAccel. Conversely, a healthier diet and higher levels of pyridoxal phosphate, all-trans retinol, betaine, tryptophan, glutamine, histidine, apolipoprotein B, and insulin-like growth factors were inversely associated with PhenoAgeAccel. Lipidomic analysis found 132 lipid species linked to PhenoAgeAccel, with PC(O-36:0) showing the strongest positive association and CE(24:5) demonstrating the strongest inverse association. A genome-wide association study identified rs9864994 as the top genetic variant (P = 5.69E-07) from the ZDHHC19 gene. Gut microbiome analysis revealed that Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were inversely associated with PhenoAgeAccel. Integrative network analysis of aging-related factors underscored the intricate links among clinical, nutritional and lipidomic variables, such as positive associations between kynurenine pathway metabolites, amino acids, adiposity, and insulin resistance. Furthermore, potential mediation effects of blood biomarkers related to inflammation, immune response, and nutritional and energy metabolism were observed in the associations of diet, adiposity, genetic variants, and gut microbial species with PhenoAgeAccel. Conclusions: Our findings provide a comprehensive analysis of aging-related factors across multiple platforms, delineating their complex interconnections. This study is the first to report novel signatures in lipidomics, gut microbiome and blood biomarkers specifically associated with PhenoAgeAccel. These insights are invaluable in understanding the molecular and metabolic mechanisms underlying biological aging and shed light on potential interventions to mitigate accelerated biological aging by targeting modifiable factors. [ABSTRACT FROM AUTHOR]

The rapid development in computer technology plays an essential role in the research works for performing fast and accurate identification of flower species through the processing of flower images with the support of mobile devices that seeks more attention in the research areas. It is highly significant for maintaining the sensitivity of ecological balance, and therefore, the image-processing approaches have provided improved outcomes in recent days. To rectify these existing problems and improve the accuracy attains the flower classification, the CNN variants of ensemble techniques can be used for the dynamic ensemble selection of the CNN networks. This paper develops an enhanced ensemble deep learning-based flower classification model to get efficient classification results with optimal models. The pre-processing is done through the Contrast Limited Adaptive Histogram Equalization (CLAHE) and filtering techniques. The pre-processed images are considered for the optimal pattern extraction phase, where optimal hybrid patterns are extracted from the Local Binary Pattern (LBP) and Local Vector Pattern (LVP). Here, the optimal hybrid pattern extraction phase contains the optimization in it using the enhanced heuristic algorithm named Improved Rat Swarm Optimizer (IRSO). The flower classification is performed with an Adaptive Dynamic Ensemble Transfer learning-based Convolutional Neural Network (ADET-CNN). Here, the optimal models are selected with the support of the same RSO algorithm. The experimental results show a better efficacy of the developed flower classification framework. [ABSTRACT FROM AUTHOR]