Placental expression of AChE, α7nAChR and NF-κB in patients with preeclampsia

Objectives: This study aimed to investigate placental expression of AChE, α7nAChR and NF-κB in patients with preeclampsia and discuss about its clinical significance. Material and methods: mRNA expression levels of acetylcholine (AChE), alpha-7 nicotinic acetylcholine receptor (α7nAChR) and nuclear factor-kB (NF-κB) in placenta were detected by qRT-PCR, and protein levels were determined by immunohis­tological analysis and Western Blot in 35 women with preeclampsia (including 20 cases of mild preeclampsia and 15 cases of severe preeclampsia) and 30 cases in control group, respectively. Results: The expression of AChE mRNA and protein in placenta increased significantly in patients with preeclampsia compared with the control group (p < 0.01). It was lower in patients with severe preeclampsia than in patients with mild preeclampsia (p < 0.05). The expression of α7nAChR mRNA and protein in placenta decreased significantly in patients with preeclampsia compared with the control group (p < 0.01). However, the expression of α7nAChR mRNA and protein in patients with severe preeclampsia was higher than that in patients with mild preeclampsia, without significant difference(p > 0.05). The expression of NF-κB protein in placenta decreased significantly in patients with preeclampsia compared with the control group(p < 0.01). It was higher in patients with severe preeclampsia than in patients with mild preeclampsia (p < 0.05), but there was no significant difference between preeclampsia group and control group in the expression of NF-κB mRNA in placenta (p > 0.05). The results of Western blotting assay were consistent with those of immunohistochemistry. Conclusions: Abnormal expression of AChE, α7nAChR and NF-κB in placenta may be associated with preeclampsia. Cho­linergic anti-inflammatory pathway may play an important role in the pathogenesis of preeclampsia

Valley: Video Assistant with Large Language model Enhanced abilitY

Large language models (LLMs), with their remarkable conversational capabilities, have demonstrated impressive performance across various applications and have emerged as formidable AI assistants. In view of this, it raises an intuitive question: Can we harness the power of LLMs to build multimodal AI assistants for visual applications? Recently, several multi-modal models have been developed for this purpose. They typically pre-train an adaptation module to align the semantics of the vision encoder and language model, followed by fine-tuning on instruction-following data. However, despite the success of this pipeline in image and language understanding, its effectiveness in joint video and language understanding has not been widely explored. In this paper, we aim to develop a novel multi-modal foundation model capable of comprehending video, image, and language within a general framework. To achieve this goal, we introduce Valley, a Video Assistant with Large Language model Enhanced abilitY. The Valley consists of a LLM, a temporal modeling module, a visual encoder, and a simple projection module designed to bridge visual and textual modes. To empower Valley with video comprehension and instruction-following capabilities, we construct a video instruction dataset and adopt a two-stage tuning procedure to train it. Specifically, we employ ChatGPT to facilitate the construction of task-oriented conversation data encompassing various tasks, including multi-shot captions, long video descriptions, action recognition, causal relationship inference, etc. Subsequently, we adopt a pre-training-then-instructions-tuned pipeline to align visual and textual modalities and improve the instruction-following capability of Valley. Qualitative experiments demonstrate that Valley has the potential to function as a highly effective video assistant that can make complex video understanding scenarios easy

Effects of dietary copper intake on blood lipids in women of childbearing age and the potential role of gut microbiota

BackgroundCopper (Cu) is a vital trace element involved in numerous physiological processes, including glycolysis and lipid metabolism. Imbalances in Cu homeostasis can contribute to various diseases. However, current research on the impact of Cu on lipid metabolism has yielded inconsistent findings. Moreover, studies investigating the effects of dietary Cu intake on blood lipids among women of childbearing age are rare. Understanding of this relationship could enhance lipid management, given that most women obtain Cu through their diet. Additionally, the gut microbiota may play a role in this process. This study aims to investigate the effects of dietary Cu intake on blood lipids in women of childbearing age and to analyze the role of gut microbiota in this process.MethodsThis study utilized data from the National Health and Nutrition Examination Survey (NHANES) to conduct a preliminary analysis of the correlation between dietary Cu levels and blood lipid indicators in women of childbearing age. Subsequently, an on-site research was conducted to further investigate this relationship, followed by animal experiments to verify the effect of different Cu doses on blood lipid levels. Multiple linear regression models, ANOVA, XGBOOST were employed to analyze the impact of Cu on blood lipids and the role of intestinal microbiota in this process.ResultsIn the population study, the NHANES results were consistent with on-site findings. The TG, and TC levels in women with childbearing were increased with higher dietary Cu intake. Animal experiments have shown that as Cu intake increases, TC levels increase. Furthermore, when the Cu intake reached 8 mg/day (the recommended dietary Cu intake limit of China, RDI), the TG levels in the research animals decrease, alongside a reduction in the abundance of Weissella cibaria (probiotics related to lipid metabolism), and the levels of LPS and IL-6 increase.ConclusionThe blood lipid levels of women of childbearing age increase with higher dietary Cu intake. RDI of 8 mg/day for women of childbearing age in China may need to be appropriately reduced. Regulating the gut microbiota, especially by increasing the abundance of Weissella cibaria may be an effective intervention for blood lipids

Formation of the synaptonemal complex in a gynogenetic allodiploid hybrid fish

Introduction: The correct pairing and separation of homologous chromosomes during meiosis is crucial to ensure both genetic stability and genetic diversity within species. In allodiploid organisms, synapsis often fails, leading to sterility. However, a gynogenetic allodiploid hybrid clone line (GDH), derived by crossing red crucian carp (Carassius auratus ♀) and common carp (Cyprinus carpio ♂), stably produces diploid eggs. Because the GDH line carries 100 chromosomes with 50 chromosomes from the red crucian carp (RCC; ♀, 2n = 2x = 100) and 50 chromosomes from the common carp (CC; C. carpio L., ♂, 2n = 2x = 100), it is interesting to study the mechanisms of homologous chromosome pairing during meiosis in GDH individuals.Methods: By using fluorescence in situ hybridization (FISH) with a probe specific to the red crucian carp to label homologous chromosomes, we identified the synaptonemal complex via immunofluorescence assay of synaptonemal complex protein 3 (SCP3).Results: FISH results indicated that, during early ovarian development, the GDH oogonium had two sets of chromosomes with only one set from Carassius auratus, leading to the failure formation of normal bivalents and the subsequently blocking of meiosis. This inhibition lasted at least 5 months. After this long period of inhibition, pairs of germ cells fused, doubling the chromosomes such that the oocyte contained two sets of chromosomes from each parent. After chromosome doubling at 10 months old, homologous chromosomes and the synaptonemal complex were identified.Discussion: Causally, meiosis proceeded normally and eventually formed diploid germ cells. These results further clarify the mechanisms by which meiosis proceeds in hybrids

Analysis of the Yuntaishan Geology by Laser-induced Breakdown Spectroscopy

peer reviewedThe plasma was generated by focusing a pulsed Nd：YAG laser at 1064nm on the surface of sample and detected by an echelle spectrograph and an intensified charge coupled device(ICCD)．The qualitative analysis of the spring rock and soil were performed using laser induced breakdown spectroscopy(LIBS)．The Ca and Mg elements were found to be in spring comparing the spectra of spring and purified water．Measured the spectra of Zhuyufeng rock and Tanpuxia rock indicated a significant difference between the concentrations of tWO rocks．There were more compositions in Tanpuxia rock than that of Zhuyufeng rock．Furthermore，the experiments of Zhuyufeng soil and the soil near the Southern Central University for Nationalities (SCUEC)were carried on．The result demonstrated that Ni element that is needfull tO plant was included in Zhuymcng soil， and Zn、Cu and Hg were found in the soil near SCUEC

Determination of As in Industrial Wastewater by Laser-Induced Breakdown Spectroscopy

peer reviewedThe wastewater from industrial smelting process contains heavy metals such as arsenic(As)that produce serious environmental pollution and cause actual harm to the health of people． It is necessary to control the pollution at the source and achieve a real-time and online monitoring．The 1aser-induced breakdown spectroscopy(LIBS)iS a new elemental analysis technique，and has the advantage of rapid detection．An LIBs setup has been established．The Nd：YAG Iaser beam is focused onto the sample，then the plasmas are produced．The emission spectra of plasmas are dispersed by an Echelle spectrograph and detected by an intensified charge-coupled device(ICCD)．Experiments have been carried out on the industrial wastewarer collected from the scene．The spectral lines of As element were obtained．The calibration curve of the line intensities versus the concentrations of the As element was acquired by the experiment．The calibration curve can be used for the quantitative analysis of arsenic element with an unknown concentration in the industrial wastewater．The results showed that the LIBS technique can be applied in the rapid detection of As element in industrial wastewater，and has wide range of application

Combining large-scale sensitivity analysis in Computable General Equilibrium models with Machine Learning: An Example Application to policy supporting the bio-economy

Policy design nowadays needs to consider goals related to multiple sustainability dimensions simultaneously. The Tinbergen principle suggests addressing each issue individually with a targeted policy measure. But each single resulting command-and-control, incentive or market-based policy is likely to affect also other policy goals, questioning overall policy coherence (cf. May et al. 2006). CGE modelling can contribute here by quantifying market-mediated impacts of changing policies against the given benchmark, considering key policy indicators such as income and its distribution, Green House Gas Emissions or land cover changes. But each single experiment considers one specific policy mix, from a public choice decision space which is extremely large, once we consider a larger set of policy domains (general economic and social policy, different environmental fields etc.). The growing interaction of global value chains implies that regional policy choices also increasingly affect sustainability outcomes elsewhere, a viewpoint increasingly addressed in policy impact assessments as well. Finally, impacts of policy choice in each region also depend on the policy chosen in others. We combine large-scale sensitivity analysis, changing both policy instruments and key model parameters in different regions, focusing on key sustainability metrics, and fit a neural network to the results. The considered policy instruments are indirect tax rates changes relating to bio-economy sectors, while land supply and different substitution elasticities are subject simultaneously to sensitivity analysis. We find a very good fit to cases where only policy instruments are changed and still quite high once when also parameters are changed. We conclude from there that machine learning techniques are able to provide robust meta-models of CGEs and can be used to predict or even optimize over the response space of the CGE

Enzymatic synthesis of gold nanoflowers with trypsin

Program for New Century Excellent Talents in Fujian Province [X12103]; Natural Science Foundation of China [20701031]; Natural Science Foundation of Fujian Province of China [C0710045]A one-step and eco-friendly approach for the room-temperature synthesis of trypsin-mediated three-dimensional (3D) gold nanoflowers (AuNFs) with high colloidal stability is demonstrated. To prepare AuNFs, ascorbic acid (AA) was quickly added into the premixed solution of HAuCl(4) and trypsin at pH = 5.0. The results show that the molar ratio and feeding order of reactant agents, pH and reaction time play important roles in the formation of NFs. The growth mechanism of AuNFs is suggested as three steps: (1) immobilization of AuCl(4)(-) ions with a positively charged trypsin template, (2) spontaneous reduction of AuCl(4)(-) ions with AA in situ and capping Au(0) by 12 cysteines of trypsin, (3) reduction of more AuCl(4)(-) ions on the Au nuclei formed in the initial stages and anisotropic growth into AuNFs

Combining large-scale sensitivity analysis in Computable General Equilibrium models with Machine Learning: An Example Application to policy supporting the bio-economy

Policy design nowadays needs to consider goals related to multiple sustainability dimensions simultaneously. The Tinbergen principle suggests addressing each issue individually with a targeted policy measure. But each single resulting command-and-control, incentive or market-based policy is likely to affect also other policy goals, questioning overall policy coherence (cf. May et al. 2006). CGE modelling can contribute here by quantifying market-mediated impacts of changing policies against the given benchmark, considering key policy indicators such as income and its distribution, Green House Gas Emissions or land cover changes. But each single experiment considers one specific policy mix, from a public choice decision space which is extremely large, once we consider a larger set of policy domains (general economic and social policy, different environmental fields etc.). The growing interaction of global value chains implies that regional policy choices also increasingly affect sustainability outcomes elsewhere, a viewpoint increasingly addressed in policy impact assessments as well. Finally, impacts of policy choice in each region also depend on the policy chosen in others. We combine large-scale sensitivity analysis, changing both policy instruments and key model parameters in different regions, focusing on key sustainability metrics, and fit a neural network to the results. The considered policy instruments are indirect tax rates changes relating to bio-economy sectors, while land supply and different substitution elasticities are subject simultaneously to sensitivity analysis. We find a very good fit to cases where only policy instruments are changed and still quite high once when also parameters are changed. We conclude from there that machine learning techniques are able to provide robust meta-models of CGEs and can be used to predict or even optimize over the response space of the CGE