Abstract Premature Ovarian Insufficiency (POI) is a highly heterogeneous condition characterized by ovarian dysfunction in women occurring before the age of 40, representing a significant cause of ...female infertility. It manifests through primary or secondary amenorrhea. While more than half of POI cases are idiopathic, genetic factors play a pivotal role in all instances with known causes, contributing to approximately 20–25% of cases. This article comprehensively reviews the genetic factors associated with POI, delineating the primary candidate genes. The discussion delves into the intricate relationship between these genes and ovarian development, elucidating the functional consequences of diverse mutations to underscore the fundamental impact of genetic effects on POI. The identified genetic factors, encompassing gene mutations and chromosomal abnormalities, are systematically classified based on whether the resulting POI is syndromic or non-syndromic. Furthermore, this paper explores the genetic interplay between mitochondrial genes, such as Required for Meiotic Nuclear Division 1 homolog Gene ( RMND1) , Mitochondrial Ribosomal Protein S22 Gene ( MRPS22 ), Leucine-rich Pentapeptide Repeat Gene ( LRPPRC ), and non-coding RNAs, including both microRNAs and Long non-coding RNAs, with POI. The insights provided serve to consolidate and enhance our understanding of the etiology of POI, contributing to establishing a theoretical foundation for diagnosing and treating POI patients, as well as for exploring the mechanisms underlying the disease.
Biochars generally result in short-term positive priming of native soil organic carbon (SOC), but longer-term carbon (C) stabilization, and these effects can be altered by global warming. However, ...uncertainty remains about the mechanisms associated with these priming effects, temperature sensitivity of native SOC, and microbial responses to biochars of differing properties. To address these knowledge gaps, rice straw biochars (produced at 300 and 800 °C at 2% w/w application rate), and their labile (water extracted) fraction and recalcitrant (chemically oxidized) fraction (obtained from the equivalent weight of biochar) were incubated in a C4 dominated soil at 15, 25, and 35 °C. Our results showed that 300 °C biochar and its recalcitrant fraction resulted in an increased SOC mineralization due to positive priming across the incubation thermosequence. This was likely linked to an observed increase in the abundance of K-strategists (fungi and Actinobacteria). The biochar produced at 800 °C and its recalcitrant fraction resulted in the stabilization of native SOC (i.e., negative priming) at all temperatures, likely due to the adsorptive protection of native SOC by the large surface area. The water extractable C from both biochars generally induced SOC stabilization across the thermosequence, which could be attributed to microbial shifts to r-strategists preferentially utilizing labile C components in biochar. Both biochars increased SOC stabilization with warming from 15 to 25 °C, supporting the role of biochar application in soil C sequestration in cooler regions. The lower SOC stabilization by biochars with temperature increases from 25 to 35 °C was correlated with the biochar-induced increases in fungal growth (K-strategist) under warming. The low-temperature biochar increased the abundance of aromatic C decomposers and concomitantly lowered the Q10 and activation energy (Ea) of native SOC. The findings from this study highlight that the low- and high-temperature biochars can result in various changes in native SOC mineralization, as well as temperature sensitivity, mainly by microbial population alterations and physicochemical interactions.
•300 °C biochar and its recalcitrant fraction caused SOC loss at 15, 25 and 35 °C.•The SOC loss by 300 °C biochar was likely due to increased fungi and Actinobacteria.•800 °C biochar and its recalcitrant fraction stabilized SOC at 15, 25 and 35 °C.•Water extracts of biochar stabilized SOC, likely due to C substrate switching.•Biochars increased SOC stabilization with warming from 15 to 25 °C, but not to 35 °C.
Studies have highlighted the importance of histone deacetylase (HDAC)-mediated epigenetic processes in the development of diabetic complications. Inhibitors of HDAC are a novel class of therapeutic ...agents in diabetic nephropathy, but currently available inhibitors are mostly nonselective inhibit multiple HDACs, and different HDACs serve very distinct functions. Therefore, it is essential to determine the role of individual HDACs in diabetic nephropathy and develop HDAC inhibitors with improved specificity. First, we identified the expression patterns of HDACs and found that, among zinc-dependent HDACs, HDAC2/4/5 were upregulated in the kidney from streptozotocin-induced diabetic rats, diabetic db/db mice, and in kidney biopsies from diabetic patients. Podocytes treated with high glucose, advanced glycation end products, or transforming growth factor-β (common detrimental factors in diabetic nephropathy) selectively increased HDAC4 expression. The role of HDAC4 was evaluated by in vivo gene silencing by intrarenal lentiviral gene delivery and found to reduce renal injury in diabetic rats. Podocyte injury was associated with suppressing autophagy and exacerbating inflammation by HDAC4-STAT1 signaling in vitro. Thus, HDAC4 contributes to podocyte injury and is one of critical components of a signal transduction pathway that links renal injury to autophagy in diabetic nephropathy.
The combined health impact of physical activity (PA) and air pollution on chronic obstructive pulmonary disease (COPD) remains unclear. We investigated the joint effects of habitual PA and long-term ...fine particulate matter (PM
) exposure on COPD incidence in a prospective population-based cohort.
A prospective cohort study was conducted using data from the UK Biobank. Incidence of COPD was ascertained through linkage to the UK National Health Services register. Annual mean PM
concentration was obtained using land use regression model. PA was measured by questionnaire and wrist-worn accelerometer. Cox proportional hazard models were applied to examine the associations between PM
, PA, and COPD. Additive and multiplicative interactions were examined.
A total of 266,280 participants free of COPD at baseline were included in data analysis with an average follow-up of 10.64 years, contributing to around 2.8 million person-years. Compared with participants with low level of PA, those with higher PA levels had lower risks of COPD incidence hazard ratio (HR): 0.769, 95% CI: 0.720, 0.820 for moderate level; HR: 0.726, 95% CI: 0.679, 0.776 for high level. By contrast, PM
was associated with increased risk of COPD (HR per interquartile range increment: 1.065, 95% CI: 1.032, 1.099). Limited evidence of interaction between habitual PA and PM
exposure was found. Similar results were found for accelerometer-measured PA.
Our study suggests that habitual PA could reduce risk of COPD incidence, and such protective effects were not affected by ambient PM
pollution exposure.
Behavior of thick magnetorheological elastomers Gordaninejad, Faramarz; Wang, Xiaojie; Mysore, Praveen
Journal of intelligent material systems and structures,
06/2012, Letnik:
23, Številka:
9
Journal Article
Recenzirano
In this study, the behavior of thick magnetorheological elastomers is experimentally investigated. Two types of magnetorheological elastomer specimens of varying concentrations, with circular and ...rectangular shapes having thicknesses from 6.35 mm to a maximum of 25.4 mm, are prepared. The magnetorheological elastomer samples are studied under quasi-static compression and double lap-shear tests. The shear and the Young’s moduli of the magnetorheological elastomers are obtained under different applied magnetic fields. It is observed that the field-induced change in the modulus is independent of the thickness of the magnetorheological elastomer and is only dependent on the iron particle concentration and the magnetic field strength. With the increase in the applied magnetic field, it is observed that the change in modulus varies from a linear behavior at lower applied magnetic fields to a nonlinear one at higher magnetic fields. It is found that compressive and shear moduli only depend on the applied magnetic fields and are independent of the sample thickness. In addition, the maximum induced change in material modulus under compression is shown to be 99%, whereas in shear it is found to be 68% when compared to its off-state.
Water–rock interaction (WRI) is a topic of interest in geology and geotechnical engineering. Many geological hazards and engineering safety problems are severe under the WRI. This study focuses on ...the water weakening of rock strength and its influencing factors (water content, immersion time, and wetting–drying cycles). The strength of the rock mass decreases to varying degrees with water content, immersion time, and wetting–drying cycles depending on the rock mass type and mineral composition. The corresponding acoustic emission count and intensity and infrared radiation intensity also weaken accordingly. WRI enhances the plasticity of rock mass and reduces its brittleness. Various microscopic methods for studying the pore characterization and weakening mechanism of the WRI were compared and analyzed. Various methods should be adopted to study the pore evolution of WRI comprehensively. Microscopic methods are used to study the weakening mechanism of WRI. In future work, the mechanical parameters of rocks weakened under long-term water immersion (over years) should be considered, and more attention should be paid to how the laboratory scale is applied to the engineering scale.
Highlights
This paper reviews the water weakening on rock strength and its influencing factors.
Various microscopic methods for studying the pore characterization and weakening mechanism of WRI are compared and analyzed.
We study the weakening mechanism of WRIs using microscopic methods.
Future works on WRI laboratory tests were suggested.
The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for ...solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices, it involves a large amount of users' privacy. This also makes edge networks face more and more security issues, such as Denial-of-Service (DoS) attacks, unauthorized access, packet sniffing, and man-in-the-middle attacks. To combat these issues and enhance the security of edge networks, we propose a deep learning-based intrusion detection algorithm. Based on the generative adversarial network (GAN), we designed a powerful intrusion detection method. Our intrusion detection method includes three phases. First, we use the feature selection module to process the collaborative edge network traffic. Second, a deep learning architecture based on GAN is designed for intrusion detection aiming at a single attack. Finally, we propose a new intrusion detection model by combining several intrusion detection models that aim at a single attack. Intrusion detection aiming at multiple attacks is realized through the designed GAN-based deep learning architecture. Besides, we provide a comprehensive evaluation to verify the effectiveness of the proposed method.
Fractured rocks are a type of complex media that widely exist in various projects including energy, hydraulic, and underground space engineering, whose permeability properties are a hotspot in ...current rock mechanics domain. Aiming at investigating the seepage characteristics of the fracture surfaces in different rock strata, uniaxial compressive test and permeability test were performed on single-fracture homogenous and heterogeneous rocks. Specifically, rock’s physical and mechanical parameters were measured in uniaxial tests while the initial width of the single fracture was determined through CT scanning. In combination with test results and the calculation model of the displacement of single-fracture heterogeneous rock under triaxial stress condition, the calculation formula of the permeability coefficient of single-fracture heterogeneous rock was derived. Results show that hydraulic pressure in the fracture can affect the permeability coefficient of the fractured rock. Hydraulic fracturing effect occurred with the increase of hydraulic pressure in the fracture, which then generates slight normal deformations of the rock masses on both two sides of the fracture surface, decreases the contact area in the fracture, and leads to the increases of both fracture width and permeability coefficient. For single-fracture rock, the lithological properties of the rock masses on both two sides of the fracture surface impose significant effects on the permeability coefficient. Under same hydraulic pressure and confining pressure, the permeability coefficient of single-fracture coarse sandstone is greatest, followed by that of single-fracture heterogeneous rock, and finally by single-fracture fine sandstone. Theoretical calculation results agree well with the test results, suggesting that the derived theoretical formula can adequately describe the variation tendencies of permeability coefficient with confining pressure and hydraulic pressure in the fracture.
Stock market prediction is of great importance for financial analysis. Traditionally, many studies only use the news or numerical data for the stock market prediction. In the recent years, in order ...to explore their complementary, some studies have been conducted to equally treat dual sources of information. However, numerical data often play a much more important role compared with the news. In addition, the existing simple combination cannot exploit their complementarity. In this paper, we propose a numerical-based attention (NBA) method for dual sources stock market prediction. Our major contributions are summarized as follows. First, we propose an attention-based method to effectively exploit the complementarity between news and numerical data in predicting the stock prices. The stock trend information hidden in the news is transformed into the importance distribution of numerical data. Consequently, the news is encoded to guide the selection of numerical data. Our method can effectively filter the noise and make full use of the trend information in news. Then, in order to evaluate our NBA model, we collect news corpus and numerical data to build three datasets from two sources: the China Security Index 300 (CSI300) and the Standard & Poor's 500 (S&P500). Extensive experiments are conducted, showing that our NBA is superior to previous models in dual sources stock price prediction.
Globally, diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. As the most common microvascular complication of diabetes, DKD is a thorny, clinical problem in terms of its ...diagnosis and management. Intensive glucose control in DKD could slow down but not significantly halt disease progression. Revisiting the tremendous advances that have occurred in the field would enhance recognition of DKD pathogenesis as well as improve our understanding of translational science in DKD in this new era.
In this review, we summarize advances in the understanding of the local microenvironmental changes in diabetic kidneys and discuss the involvement of genetic and epigenetic factors in the pathogenesis of DKD. We also review DKD prevalence changes and analyze the challenges in optimizing the diagnostic approaches and management strategies for DKD in the clinic. As we enter the era of ‘big data’, we also explore the possibility of linking systems biology with translational medicine in DKD in the current healthcare system.
Newer understanding of the structural changes of diabetic kidneys and mechanisms of DKD pathogenesis, as well as emergent research technologies will shed light on new methods of dealing with the existing clinical challenges of DKD.
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