Estimating the short-term effect of PM2.5 on the mortality of cardiovascular diseases based on instrumental variables.

BACKGROUND: PM2.5 can induce and aggravate the occurrence and development of cardiovascular diseases (CVDs). The objective of our study is to estimate the causal effect of PM2.5 on mortality rates associated with CVDs using the instrumental variables (IVs) method. METHODS: We extracted daily meteorological, PM2.5 and CVDs death data from 2016 to 2020 in Binzhou. Subsequently, we employed the general additive model (GAM), two-stage predictor substitution (2SPS), and control function (CFN) to analyze the association between PM2.5 and daily CVDs mortality. RESULTS: The 2SPS estimated the association between PM2.5 and daily CVDs mortality as 1.14% (95% CI: 1.04%, 1.14%) for every 10 µg/m3 increase in PM2.5. Meanwhile, the CFN estimated this association to be 1.05% (95% CI: 1.02%, 1.10%). The GAM estimated it as 0.85% (95% CI: 0.77%, 1.05%). PM2.5 also exhibited a statistically significant effect on the mortality rate of patients with ischaemic heart disease, myocardial infarction, or cerebrovascular accidents (P < 0.05). However, no significant association was observed between PM2.5 and hypertension. CONCLUSION: PM2.5 was significantly associated with daily CVDs deaths (excluding hypertension). The estimates from the IVs method were slightly higher than those from the GAM. Previous studies based on GAM may have underestimated the impact of PM2.5 on CVDs.

Study on ring-road incident duration based on latent class accelerated hazard model.

Accurately estimating the duration of freeway incidents can enhance emergency management practices and reduce the likelihood of secondary incidents. To investigate the mechanisms through which key factors influence incident duration, this study sorted out the characteristics and variables of the incident duration on a special freeway in Zhejiang Province, that is, the ring road, and developed a latent class accelerated hazard model. Heterogeneity was incorporated into the model. Three distributions (Weibull, Log-normal, and Log-logistic) were compared, and the Log-logistic distribution exhibited superior performance. The analysis revealed two distinct latent classes: Latent Class 1 and Class 2, had class membership probability of 0.53 and 0.47, respectively, with a total of 11 variables being statistically significant at the 0.05 significance level. It is worth noting that, some neglected explanatory variables are discussed in depth in this study. For example, the mechanism of which specific lane is closed has an impact on the incident duration, rather than a general discussion of the number of lane closures. Furthermore, the way in which the driver involved in the incident reports to the police has a significant impact on the duration of incidents. Notably, potential heterogeneity and its influencing mechanism are captured in the model. Additionally, by predicting class membership using posterior probabilities, it was determined that most data points were more likely to belong to Class 1, and the incident duration primarily ranged between 0 and 60 minutes. These findings are helpful to reduce the duration of incidents on ring-roads and freeways in China, and provide theoretical support for the formulation of freeway incident management and treatment policies.

Antidepressant Properties of a Four-compound Cocktail Identified from Si-Ni-San by HIF-1 Pathway Modulation.

BACKGROUND: Si-Ni-San (SNS) is the formula prescription of Traditional Chinese Medicine (TCM) with anti-depression properties, but its underlying mechanisms remain unclear. OBJECTIVE: This study provides novel approaches for the study of Traditional Chinese Medicine (TCM) and offers new opportunities for exploring the pharmacological properties of SNS. METHODS: The ingredients in SNS implicated in the treatment of depression were identified and studied using network pharmacology. SwissTargetPrediction and molecular docking were used to study the interaction of SNS ingredients and their targets. The protective effect of these ingredients and their cocktail in rat pheochromocytoma cells (PC12) exposed to corticosterone (Cor) were evaluated using the CCK-8 assay, Hoechst 33342 staining, 2',7'-dichlorodihydro fluorescein diacetate (H2DCFDA) staining, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay, and in-cell Western analysis. RESULTS: The network pharmacology study showed that the HIF-1 signaling pathway was the most crucial pathway implicated in the anti-depressive property of SNS. MAPK1 (ERK2), MAPK3 (ERK1), AKT1, VEGFA, STAT3, and EGF were identified as hub target proteins in the HIF-1 signaling pathway. Quercetin, naringenin, licochalcone A, and kaempferol from SNS, which targeted the six proteins mentioned above, were used to create a cocktail. This cocktail exerted protective properties, decreased the oxidative stress in PC12 exposed to Cor, and successfully regulated the expressions of AKT1, p-AKT1, ERK1, ERK2, p-ERK1/2, STAT3, p- STAT3, and VEGFA induced by Cor exposure. The SwissTargetPrediction and molecular docking study showed that the cocktail may regulate the HIF-1 signaling pathway by directly binding with AKT1 and MAPK1. CONCLUSION: The cocktail from SNS comprised of quercetin, naringenin, licochalcone A, and kaempferol exerts anti-depression potentiality by modulating the HIF-1 signaling pathway via direct interactions with AKT1 and MAPK1.

Research Progress of Pyroptosis in Diabetic Kidney Disease.

Pyroptosis, known as one typical mode of programmed cell death, is generally characterized by the cleaved gasdermin family (GSDMs) forming pores in the cell membrane and inducing cell rupture, and the activation of aspartate-specific proteases (caspases) has also been found during this process. Diabetic Kidney Disease (DKD) is caused by the complication of diabetes in the kidney, and the most important kidney's function, Glomerular Filtration Rate (GFR), happens to drop to less than 90% of its usual and even lead to kidney failure in severe cases. The persistent inflammatory state induced by high blood glucose implies the key pathology of DKD, and growing evidence shows that pyroptosis serves as a significant contributor to this chronic immune-mediated inflammatory disorder. Currently, the expanded discovery of GSDMs, pyroptosis, and its association with innate immunity has been more attractive, and overwhelming research is needed to sort out the implication of pyroptosis in DKD pathology. In this review, we comb both classical studies and newly founds on pyroptosis, prick off the novel awakening of pyroptosis in DKD, and center on the significance of pyroptosis in DKD treatment, aiming to provide new research targets and treatment strategies on DKD.

Realization of skyrmion shift register.

The big data explosion demands novel data storage technology. Among many different approaches, solitonic racetrack memory devices hold great promise for accommodating nonvolatile and low-power functionalities. As representative topological solitons, magnetic skyrmions are envisioned as potential information carriers for efficient information processing. While their advantages as memory and logic elements have been vastly exploited from theoretical perspectives, the corresponding experimental efforts are rather limited. These challenges, which are key to versatile skyrmionic devices, will be studied in this work. Through patterning concaved surface topography with designed arrays of indentations on standard Si/SiO2 substrates, we demonstrate that the resultant non-flat energy landscape could lead to the formation of hexagonal and square skyrmion lattices in Ta/CoFeB/MgO multilayers. Based on these films, one-dimensional racetrack devices are subsequently fabricated, in which a long-distance deterministic shifting of skyrmions between neighboring indentations is achieved at room temperature. Through separating the word line and the bit line, a prototype shift register device, which can sequentially generate and precisely shift complex skyrmionic data strings, is presented. The deterministic writing and long-distance shifting of skyrmionic bits can find potential applications in transformative skyrmionic memory, logic as well as the in-memory computing devices.

A chromosome-level genome assembly and annotation of the medicinal plant Lepidium apetalum.

OBJECTIVES: As a traditional Chinese medicine, Lepidium apetalum is commonly used for purging the lung, relieving dyspnea, alleviating edema, and has the significant pharmacological effects on cardiovascular disease, hyperlipidemia, etc. In addition, the seeds of L. apetalum are rich in unsaturated fatty acids, sterols, glucosinolates and have a variety of biological activity compounds. To facilitate genomics, phylogenetic and secondary metabolite biosynthesis studies of L. apetalum, we assembled the high-resolution genome of L. apetalum. DATA DESCRIPTION: We completed chromosome-level genome assembly of the L. apetalum genome (2n = 32), using Illumina HiSeq and PacBio Sequel sequencing platform as well as high-throughput chromosome conformation capture (Hi-C) technique. The assembled genome was 296.80 Mb in size, 34.41% in GC content, and 23.89% in repeated sequence content, including 316 contigs with a contig N50 of 16.31 Mb. Hi-C scaffolding resulted in 16 chromosomes occupying 99.79% of the assembled genome sequences. A total of 46 584 genes and 105 pseudogenes were predicted, 98.37% of which can be annotated to Nr, GO, KEGG, TrEMBL, SwissPort, Pfam and KOG databases. The high-quality reference genome generated by this study will provide accurate genetic information for the molecular biology research of L. apetalum.

A Bayesian network for estimating hypertension risk due to occupational aluminum exposure.

Background: The correlation between metals and hypertension, such as sodium, zinc, potassium, and magnesium, has been confirmed, while the relationship between aluminum and hypertension is not very clear. This study aimed to evaluate the correlation between plasma aluminum and hypertension in electrolytic aluminum workers by the Bayesian networks (BN). Methods: In 2019, 476 male workers in an aluminum factory were investigated. The plasma aluminum concentration of workers was measured by inductively coupled plasma mass spectrometry. The influencing factors on the prevalence of hypertension were analyzed by the BN. Results: The prevalence of hypertension was 23.9% in 476 male workers. The risk of hypertension from plasma aluminum in the Q2, Q3, and Q4 groups was 5.20 (1.90-14.25), 6.92 (2.51-19.08), and 7.33 (2.69-20.01), respectively, compared with that in the Q1 group. The risk of hypertension from the duration of exposure to aluminum of >10 years was 2.23 (1.09-4.57), compared without aluminum exposure. Area under the curve was 0.80 of plasma aluminum and the duration of exposure to aluminum was based on covariates, indicating that aluminum exposure had important predictive value in the prevalence of hypertension in the occupational population. The results of the study using the BN model showed that if the plasma aluminum of all participants was higher than Q4 (≥47.86 µg/L) and the participants were drinking, smoking, diabetes, central obesity, dyslipidemia, and aged >50 years, the proportion of hypertension was 71.2%. Conclusions: The prevalence of hypertension increased significantly with the increase of plasma aluminum level.

Revealing the potential of quantum dot nanomaterials in photocatalytic applications.

The practical fabrication of quantum dot materials, including their size, shape, form, crystallinity, and chemical composition, is a crucial research area in the field of photocatalysis. Quantum dots can effectively enhance the separation and transfer of carriers and expand the utilization of visible light when used in heterogeneous junctions with wide bandgap semiconductors. Additionally, they exhibit excellent photosensitivity properties that significantly improve the material's capacity for absorbing visible light. This paper systematically presents an overview of the outstanding optical properties exhibited by quantum dots based on both domestic and international research on photocatalytic materials. Furthermore, it summarizes the research content, characteristics, and current challenges associated with common types of quantum dots and photocatalytic materials while highlighting their applications in environmental remediation and energy production. Finally, this paper anticipates future trends in the development of photocatalysis by providing valuable insights into more efficient semiconductor materials that are cost-effective yet environmentally friendly.

A role for miRNAs in the regulation of brown adipose tissue whitening in goats (Capra Hircus).

A study of the mechanism of and metabolic regulation of brown adipose tissue (BAT) production is important for improving the survival rate of young animals. In the present study, we observed that perirenal adipose tissue in goats undergoes a rapid BAT whitening after birth. However, the underlying regulatory mechanism remains unknown. To address this further, we investigated the role of miRNAs in regulating the whitening process of BAT in goats. First, we identified the dynamic expression profiles of miRNAs during the whitening of BAT in Dazu black goat using RNA-seq. We identified a total of 1374 miRNAs, including 408 existing miRNAs, 693 known miRNAs, and 273 novel miRNAs. By analysis of the differentially expressed miRNAs (DE miRNAs), we found that 102 highly expressed miRNAs, including chi-miR-144-3p, chi-miR-144-5p, chi-miR-378-5p, chi-miR-136-3p, chi-miR-381, chi-miR-323b, chi-miR-1197-3p, chi-miR-411b-3p, and chi-miR-487a-3p, were enriched in BAT. In addition, 60 highly expressed miRNAs, including chi-miR-184, chi-miR-193a, chi-miR-193b-3p, chi-let-7c-5p, and chi-let-7e-5p, were enriched in white fat-like tissue. An analysis of miRNAs that were linearly downregulated (profile 0) or linearly upregulated (profile 19) over the D0-D28 period found that these DE miRNAs were mainly enriched in the Hippo signaling pathway, Cytokine-cytokine receptor interactions, and the TGF-beta signaling pathway. Furthermore, we confirmed that chi-let-7e-5p promotes the proliferation and differentiation of brown adipocytes. These results should facilitate a better understanding of the molecular regulation of miRNAs involved in BAT whitening in goats.

Goat kids born during the cold season are prone to perishing due to harsh temperatures. However, implementing artificial warming and increasing heat production in goat kids can enhance their survival chances. Newborn Goat kids possess significant amounts of brown adipose tissue (BAT) in the perirenal region, and BAT is known to play a vital role in regulating body temperature via non-shivering thermogenesis. A preliminary investigation revealed that the perirenal adipose tissue in goat kids undergoes BAT whitening during the first month of life. However, the mechanism underlying BAT whitening remains unknown. Previous research suggests that miRNAs serve as critical regulators of metabolic homeostasis in adipose tissue. Thus, a comprehensive screening of the expression profile of miRNAs during BAT whitening in Dazu black goats and subsequent identification of miRNAs that regulate BAT thermogenesis should provide a foundation for future research on BAT development and regulation in goats.

Vehicle Detection Algorithms for Autonomous Driving: A Review.

Autonomous driving, as a pivotal technology in modern transportation, is progressively transforming the modalities of human mobility. In this domain, vehicle detection is a significant research direction that involves the intersection of multiple disciplines, including sensor technology and computer vision. In recent years, many excellent vehicle detection methods have been reported, but few studies have focused on summarizing and analyzing these algorithms. This work provides a comprehensive review of existing vehicle detection algorithms and discusses their practical applications in the field of autonomous driving. First, we provide a brief description of the tasks, evaluation metrics, and datasets for vehicle detection. Second, more than 200 classical and latest vehicle detection algorithms are summarized in detail, including those based on machine vision, LiDAR, millimeter-wave radar, and sensor fusion. Finally, this article discusses the strengths and limitations of different algorithms and sensors, and proposes future trends.

Bayesian network analysis of factors influencing type 2 diabetes, coronary heart disease, and their comorbidities.

OBJECTIVE: Bayesian network (BN) models were developed to explore the specific relationships between influencing factors and type 2 diabetes mellitus (T2DM), coronary heart disease (CAD), and their comorbidities. The aim was to predict disease occurrence and diagnose etiology using these models, thereby informing the development of effective prevention and control strategies for T2DM, CAD, and their comorbidities. METHOD: Employing a case-control design, the study compared individuals with T2DM, CAD, and their comorbidities (case group) with healthy counterparts (control group). Univariate and multivariate Logistic regression analyses were conducted to identify disease-influencing factors. The BN structure was learned using the Tabu search algorithm, with parameter estimation achieved through maximum likelihood estimation. The predictive performance of the BN model was assessed using the confusion matrix, and Netica software was utilized for visual prediction and diagnosis. RESULT: The study involved 3,824 participants, including 1,175 controls, 1,163 T2DM cases, 982 CAD cases, and 504 comorbidity cases. The BN model unveiled factors directly and indirectly impacting T2DM, such as age, region, education level, and family history (FH). Variables like exercise, LDL-C, TC, fruit, and sweet food intake exhibited direct effects, while smoking, alcohol consumption, occupation, heart rate, HDL-C, meat, and staple food intake had indirect effects. Similarly, for CAD, factors with direct and indirect effects included age, smoking, SBP, exercise, meat, and fruit intake, while sleeping time and heart rate showed direct effects. Regarding T2DM and CAD comorbidities, age, FBG, SBP, fruit, and sweet intake demonstrated both direct and indirect effects, whereas exercise and HDL-C exhibited direct effects, and region, education level, DBP, and TC showed indirect effects. CONCLUSION: The BN model constructed using the Tabu search algorithm showcased robust predictive performance, reliability, and applicability in forecasting disease probabilities for T2DM, CAD, and their comorbidities. These findings offer valuable insights for enhancing prevention and control strategies and exploring the application of BN in predicting and diagnosing chronic diseases.

Effect of dietary patterns on mild cognitive impairment and dementia: a machine learning bibliometric and visualization analysis.

Objective: As a spectrum of neurodegenerative conditions, dementia presents a significant challenge to worldwide health. Mild cognitive impairment (MCI) is recognized as the intermediate stage between normal cognitive functioning and dementia. Studies highlight the significant impact of dietary patterns on the management of MCI and dementia. Currently, comprehensive research on dietary patterns specific to MCI and dementia is limited, but bibliometric analysis offers a method to pinpoint essential research directions. Methods: On November 18, 2023, a search was conducted in the Web of Science Core Collection (WoSCC) for publications on diet and MCI/dementia. Tools such as Rstudio, CiteSpace, and VOSviewer were employed to create a knowledge atlas. This atlas analyzed collaborations, reference co-citations, keyword patterns, and emerging trends. Results: The search yielded 1,493 publications on diet and MCI/dementia, indicating a growing interest despite fluctuations. Contributions came from 70 countries/regions and 410 organizations across 456 journals. The USA and China led in publication numbers, with significant contributions from Columbia University and Harvard Medical School. Top authors include Scarmeas Nikolaos, Morris Martha Clare, and Samieri Cecilia. The Ketogenic, Mediterranean, and MIND diets emerged as key dietary patterns for cognitive decline prevention, highlighting the role of genetic factors, especially ApoE polymorphisms, in cognitive deterioration. Conclusion: This study provides core countries, institutions, and authors in the field, and points out the development directions in the field. Future research directions in dietary for MCI and dementia will focus on: (1) the potential effects of the KD in alleviating oxidative stress and modulating gut microbiota in neurodegenerative diseases; (2) how diet influences cognitive health through patterns of ApoE and protein expression; (3) investigating the interactions between gut microbiota and brain function, known as the "gut-brain axis."

Combined contamination of microplastic and antibiotic alters the composition of microbial community and metabolism in wheat and maize rhizosphere soil.

The widespread application of antibiotics and plastic films in agriculture has led to new characteristics of soil pollution. The impacts of combined contamination of microplastics and antibiotics on plant growth and rhizosphere soil bacterial community and metabolisms are still unclear. We conducted a pot experiment to investigate the effects of polyethylene (0.2%) and norfloxacin/doxycycline (5 mg kg-1), as well as the combination of polyethylene and antibiotics, on the growth, rhizosphere soil bacterial community and metabolisms of wheat and maize seedlings. The results showed that combined contamination caused more serious damage to plant growth than individual contamination, and aggravated root oxidative stress responses. The diversity and structure of soil bacterial community were not markedly altered, but the composition of the bacterial community, soil metabolisms and metabolic pathways were altered. The co-occurrence network analysis indicated that combined contamination may inhibit the growth of wheat and maize seedings by simplifying the interrelationships between soil bacteria and metabolites, and altering the relative abundance of specific bacteria genera (e.g. Kosakonia and Sphingomonas) and soil metabolites (including sugars, organic acids and amino acids). The results help to elucidate the potential mechanisms of phytotoxicity of the combination of microplastic and antibiotics.

Baicalein triggers ferroptosis in colorectal cancer cells via blocking the JAK2/STAT3/GPX4 axis.

Colorectal cancer (CRC) is a prevalent form of gastrointestinal malignancy with challenges in chemotherapy resistance and side effects. Effective and low toxic drugs for CRC treatment are urgently needed. Ferroptosis is a novel mode of cell death, which has garnered attention for its therapeutic potential against cancer. Baicalein (5, 6, 7-trihydroxyflavone) is the primary flavone extracted from the dried roots of Scutellaria baicalensis that exhibits anticancer effects against several malignancies including CRC. In this study, we investigated whether baicalein induced ferroptosis in CRC cells. We showed that baicalein (1-64 µM) dose-dependently inhibited the viability of human CRC lines HCT116 and DLD1. Co-treatment with the ferroptosis inhibitor liproxstatin-1 (1 µM) significantly mitigated baicalein-induced CRC cell death, whereas autophagy inhibitor chloroquine (25 µM), necroptosis inhibitor necrostatin-1 (10 µM), or pan-caspase inhibitor Z-VAD-FMK (10 µM) did not rescue baicalein-induced CRC cell death. RNA-seq analysis confirmed that the inhibitory effect of baicalein on CRC cells is associated with ferroptosis induction. We revealed that baicalein (7.5-30 µM) dose-dependently decreased the expression levels of GPX4, key regulator of ferroptosis, in HCT116 and DLD1 cells by blocking janus kinase 2 (JAK2)/STAT3 signaling pathway via direct interaction with JAK2, ultimately leading to ferroptosis in CRC cells. In a CRC xenograft mouse model, administration of baicalein (10, 20 mg/kg, i.g., every two days for two weeks) dose-dependently inhibited the tumor growth with significant ferroptosis induced by inhibiting the JAK2/STAT3/GPX4 axis in tumor tissue. This study demonstrates that ferroptosis contributes to baicalein-induced anti-CRC activity through blockade of the JAK2/STAT3/GPX4 signaling pathway, which provides evidence for the therapeutic application of baicalein against CRC.

Evolution of olfactory receptor superfamily in bats based on high throughput molecular modelling.

The origin of flight and laryngeal echolocation in bats is likely to have been accompanied by evolutionary changes in other aspects of their sensory biology. Of all sensory modalities in bats, olfaction is perhaps the least well understood. Olfactory receptors (ORs) function in recognizing odour molecules, with crucial roles in evaluating food, as well as in processing social information. Here we compare OR repertoire sizes across taxa and apply a new pipeline that integrates comparative genome data with protein structure modelling and then we employ molecular docking techniques with small molecules to analyse OR functionality based on binding energies. Our results suggest a sharp contraction in odorant recognition of the functional OR repertoire during the origin of bats, consistent with a reduced dependence on olfaction. We also compared bat lineages with contrasting different ecological characteristics and found evidence of differences in OR gene expansion and contraction, and in the composition of ORs with different tuning breadths. The strongest binding energies of ORs in non-echolocating fruit-eating bats were seen to correspond to ester odorants, although we did not detect a quantitative advantage of functional OR repertoires in these bats compared with echolocating insectivorous species. Overall, our findings based on molecular modelling and computational docking suggest that bats have undergone olfactory evolution linked to dietary adaptation. Our results from extant and ancestral bats help to lay the groundwork for targeted experimental functional tests in the future.

Penicillium polonicum-mediated green synthesis of silver nanoparticles: Unveiling antimicrobial and seed germination advancements.

Silver nanoparticles (AgNPs), widely recognized for their nanoscale geometric size and unique properties, such as large specific surface area, high permeability, and high safety, were synthesized using the endophytic fungus Penicillium polonicum PG21 through a green approach. Four key synthesis factors-48 h, 45 °C, pH 9.0, and 80 mM AgNPs concentration-were optimized. Characterization via ultraviolet-visible spectroscopy, transmission electron microscopy, Fourier-transform infrared spectroscopy, and X-ray diffraction revealed the AgNPs as approximately 3-25 nm spherical particles with numerous functional groups ensuring stability. AgNPs were tested against various fungal and bacterial plant pathogens, including Botrytis cinerea (EB-1), Alternaria alternata (EB-2, EB-3), Fusarium solani (RG-1), Williamsia serinedens (SL-1), Sphingopyxis macrogoltabida (SL-2), Bacillus velezensis (SL-3), and Pseudomonas mediterranea (SL-4), causing agricultural challenges. PG21-synthesized AgNPs exhibited inhibition rates against all tested fungi, with 60 µg/mL AgNPs demonstrating optimal inhibition rates. Notably, EB-1 experienced a significant growth inhibition, reaching an inhibition rate reached of 74.22 ± 1.54%. Conversely, RG-1 exhibited the smallest inhibitory effect at 48.13 ± 0.92%. The effect of AgNPs on safflower seed germination and growth revealed notable increases in shoot length, fresh weight, stem length, and number of lateral roots-1.4, 1.4, 1.33, and 10.67 times higher than the control, respectively, at an AgNPs concentration of 80 µg/mL. In conclusion, green-synthesized AgNPs demonstrate pathogen toxicity, showcasing potential applications in disease management for industrial crops and promoting plant growth.

Association between vitamin C in serum and trouble sleeping based on NHANES 2017-2018.

Vitamin C is an important micronutrient for human. Association between vitamin C and trouble sleeping was less studied. Therefore, the purpose of this study was to investigate the possible link between vitamin C in serum and trouble sleeping. The cross-sectional data was derived from the National Health and Nutrition Examination Survey (NHANES, 2017-2018). Trouble sleeping was measured by asking participants: "Have you ever told doctor had trouble sleeping". Responses to this question was "yes" or "no". vitamin C in serum was obtained by measuring the serum samples. We used multivariable binary logistic regressions to examine the possible link between vitamin C in serum and trouble sleeping, and then a subgroup analysis was performed. Moreover, the non-linear relationship between vitamin C in serum and trouble sleeping was further detected using a restricted cubic spline (RCS) model. A total of 3227 participants were included in the study. After adjusting all potential confounders, the results of multivariable logistic regression showed the significant negative association between vitamin C in serum and trouble sleeping(OR = 0.816; 95% CI:0.669 ~ 0.995). The significant inverse association was also found in female(OR = 0.713; 95% CI:0.546 ~ 0.931), age ≤ 65 years(OR = 0.773; 95% CI:0.600 ~ 0.996), and in participants with high cholesterol level(OR = 0.738; 95% CI:0.548 ~ 0.994). In addition, the RCS model demonstrated the significant non-linear relationship between vitamin C in serum and trouble sleeping (P value of nonlinear = 0.010). Our study demonstrates the significant negative association between vitamin C in serum and trouble sleeping.

Global gene expression profiling of perirenal brown adipose tissue whitening in goat kids reveals novel genes linked to adipose remodeling.

BACKGROUND: Brown adipose tissue (BAT) is known to be capable of non-shivering thermogenesis under cold stimulation, which is related to the mortality of animals. In the previous study, we observed that goat BAT is mainly located around the kidney at birth, and changes to white adipose tissue (WAT) in the perirenal adipose tissue of goats within one month after birth. However, the regulatory factors underlying this change is remain unclear. In this study, we systematically studied the perirenal adipose tissue of goat kids in histological, cytological, and accompanying molecular level changes from 0 to 28 d after birth. RESULTS: Our study found a higher mortality rate in winter-born goat kids, with goat birthing data statistics. Then we used thermal imaging revealing high temperature in goat hips at postnatal 0 d and gradually decrease during 28 d. This is consistent with the region of perirenal BAT deposition and highlights its critical role in energy expenditure and body temperature regulation in goat kids. Additionally, we found a series of changes of BAT during the first 28 d after birth, such as whitening, larger lipid droplets, decreased mitochondrial numbers, and down-regulation of key thermogenesis-related genes (UCP1, DIO2, UCP2, CIDEA, PPARGC1a, C/EBPb, and C/EBPa). Then, we used RNA-seq found specific marker genes for goat adipose tissue and identified 12 new marker genes for BAT and 10 new marker genes for WAT of goats. Furthermore, 12 candidate genes were found to potentially regulate goat BAT thermogenesis. The mechanism of the change of this biological phenomenon does not involve a large-scale death of brown adipocytes and subsequent proliferation of white adipocytes. While apoptosis may play a limited role, it is largely not critical in this transition process. CONCLUSIONS: We concluded that perirenal BAT plays a crucial role in thermoregulation in newborn goat kids, with notable species differences in the expression of adipose tissue marker genes, and we highlighted some potential marker genes for goat BAT and WAT. Additionally, the change from BAT to WAT does not involve a large-scale death of brown adipocytes and subsequent proliferation of white adipocytes.

Interpolation linearization predistortion technology for FMCW LiDAR.

Frequency-modulated continuous-wave (FMCW) laser ranging technology is an important development direction of light detection and ranging (LiDAR) for the future. It has the advantages of high ranging accuracy, high resolution, wide range, and no ranging blind zone. A distributed feedback laser can be used as a high-quality light source in FMCW laser ranging systems because of its wide frequency modulation range, simple frequency modulation mode, and small package. Aiming at the nonlinear problem of the laser in the frequency modulation process, we present a novel, to our knowledge, predistortion algorithm based on interpolation linear fitting to enhance the linearity of the FMCW laser for LiDAR systems. The sweeping frequency curve of the laser is obtained using the Hilbert transform, and then the sweeping frequency curve is segmented and linearly fitted to calculate the interpolated driving current signals corresponding to linear frequency changes. Using this method, we achieved a nonlinearity error lower than 1e-7 for the swept-frequency signal and demonstrated that the ranging error is less than ±5c m at a distance of 100 m in the FMCW system. In addition, we also demonstrated a 3D static object point cloud with high imaging quality. Compared with the iterative predistortion algorithm based on the function fitting, this method avoids fitting errors at the inflection points of the triangular swept-frequency signal and the complexity of multiple iterative calculations. It enables rapid generation of pre-distorted swept-frequency signals, making it particularly suitable for real-time applications of automotive LiDAR systems.

Exploring brain effective connectivity of early MCI with GRU_GC model on resting-state fMRI.

BACKGROUND: Investigating the functional interactions between different brain regions and revealing the transmission of information by computing brain connectivity have great potential and significance in the diagnosis of early Mild Cognitive Impairment (EMCI). METHODS: The Granger causality with Gate Recurrent Unit (GRU_GC) model is a suitable method that allows the detection of a nonlinear causal relationship and solves the limitation of fixed time lag, which cannot be detected by the classical Granger method. The model can transmit time series signals with any transmission delay length, and the time series can be screened and learned through the gate model. RESULTS: The classification experiment of 89 EMCI and 73 neurologically healthy controls (HC) shows that the accuracy reached 87.88%. Compared with multivariate variables GC (MVGC) and Long Short-Term Memory-based GC (LSTM_GC), the GRU_GC significantly improved the estimation of brain connectivity communication. Constructing a difference network to explore the brain effective connectivity between EMCI and HC. CONCLUSIONS: The GRU_GC can discover the abnormal brain regions, including the parahippocampal gyrus, the posterior cingulate gyrus. The method can be used in clinical applications as an effective brain connectivity analysis tool and provides auxiliary means for the medical diagnosis of EMCI.