Compensating the Degradation of Near-Infrared Absorption of Black Silicon Caused by Thermal Annealing

We propose the use of thin Ag film deposition to remedy the degradation of near-infrared (NIR) absorption of black Si caused by high-temperature thermal annealing. A large amount of random and irregular Ag nanoparticles are formed on the microstructural surface of black Si after Ag film deposition, which compensates the degradation of NIR absorption of black Si caused by thermal annealing. The formation of Ag nanoparticles and their contributions to NIR absorption of black Si are discussed in detail

Dose-related liver injury of Geniposide associated with the alteration in bile acid synthesis and transportation.

Fructus Gardenia (FG), containing the major active constituent Geniposide, is widely used in China for medicinal purposes. Currently, clinical reports of FG toxicity have not been published, however, animal studies have shown FG or Geniposide can cause hepatotoxicity in rats. We investigated Geniposide-induced hepatic injury in male Sprague-Dawley rats after 3-day intragastric administration of 100 mg/kg or 300 mg/kg Geniposide. Changes in hepatic histomorphology, serum liver enzyme, serum and hepatic bile acid profiles, and hepatic bile acid synthesis and transportation gene expression were measured. The 300 mg/kg Geniposide caused liver injury evidenced by pathological changes and increases in serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and γ-glutamytransferase (γ-GT). While liver, but not sera, total bile acids (TBAs) were increased 75% by this dose, dominated by increases in taurine-conjugated bile acids (t-CBAs). The 300 mg/kg Geniposide also down-regulated expression of Farnesoid X receptor (FXR), small heterodimer partner (SHP) and bile salt export pump (BSEP). In conclusion, 300 mg/kg Geniposide can induce liver injury with associated changes in bile acid regulating genes, leading to an accumulation of taurine conjugates in the rat liver. Taurocholic acid (TCA), taurochenodeoxycholic acid (TCDCA) as well as tauro-α-muricholic acid (T-α-MCA) are potential markers for Geniposide-induced hepatic damage

Identification and characterization of PhoP regulon members in Yersinia pestis biovar Microtus

Abstract Background The transcription regulator PhoP has been shown to be important for Y. pestis survival in macrophages and under various in vitro stresses. However, the mechanism by which PhoP promotes bacterial intracellular survival is not fully understood. Our previous microarray analysis suggested that PhoP governed a wide set of cellular pathways in Y. pestis. A series of biochemical experiments were done herein to study members of the PhoP regulon of Y. pestis biovar Microtus. Results By using gel mobility shift assay and quantitative RT-PCR, a total of 30 putative transcription units were characterized as direct PhoP targets. The primer extension assay was further used to determine the transcription start sites of 18 PhoP-dependent promoters and to localize the -10 and -35 elements. The DNase I footprinting was used to identify the PhoP-binding sites within 17 PhoP-dependent promoters, enabling the identification of PhoP box and matrix that both represented the conserved signals for PhoP recognition in Y. pestis. Data presented here providing a good basis for modeling PhoP-promoter DNA interactions that is crucial to the PhoP-mediated transcriptional regulation. Conclusion The proven direct PhoP targets include nine genes encoding regulators and 21 genes or operons with functions of detoxification, protection against DNA damages, resistance to antimicrobial peptides, and adaptation to magnesium limitation. We can presume that PhoP is a global regulator that controls a complex regulatory cascade by a mechanism of not only directly controlling the expression of specific genes, but also indirectly regulating various cellular pathways by acting on a set of dedicated regulators. These results help us gain insights into the PhoP-dependent mechanisms by which Y. pestis survives the antibacterial strategies employed by host macrophages.</p

10 Years of GWAS in intraocular pressure

Intraocular pressure (IOP) is the only modifiable risk factor for glaucoma, the leading cause of irreversible blindness worldwide. In this review, we summarize the findings of genome-wide association studies (GWASs) of IOP published in the past 10 years and prior to December 2022. Over 190 genetic loci and candidate genes associated with IOP have been uncovered through GWASs, although most of these studies were conducted in subjects of European and Asian ancestries. We also discuss how these common variants have been used to derive polygenic risk scores for predicting IOP and glaucoma, and to infer causal relationship with other traits and conditions through Mendelian randomization. Additionally, we summarize the findings from a recent large-scale exome-wide association study (ExWAS) that identified rare variants associated with IOP in 40 novel genes, six of which are drug targets for clinical treatment or are being evaluated in clinical trials. Finally, we discuss the need for future genetic studies of IOP to include individuals from understudied populations, including Latinos and Africans, in order to fully characterize the genetic architecture of IOP

Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions

AbstractRecently, the recognition task of spontaneous facial micro-expressions has attracted much attention with its various real-world applications. Plenty of handcrafted or learned features have been employed for a variety of classifiers and achieved promising performances for recognizing micro-expressions. However, the micro-expression recognition is still challenging due to the subtle spatiotemporal changes of micro-expressions. To exploit the merits of deep learning, we propose a novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatiotemporal deformations of micro-expression sequence. Specifically, the proposed deep model is constituted of several recurrent convolutional layers for extracting visual features and a classificatory layer for recognition. It is optimized by an end-to-end manner and obviates manual feature design. To handle sequential data, we exploit two ways to extend the connectivity of convolutional networks across temporal domain, in which the spatiotemporal deformations are modeled in views of facial appearance and geometry separately. Besides, to overcome the shortcomings of limited and imbalanced training samples, two temporal data augmentation strategies as well as a balanced loss are jointly used for our deep network. By performing the experiments on three spontaneous micro-expression datasets, we verify the effectiveness of our proposed micro-expression recognition approach compared to the state-of-the-art methods.Abstract Recently, the recognition task of spontaneous facial micro-expressions has attracted much attention with its various real-world applications. Plenty of handcrafted or learned features have been employed for a variety of classifiers and achieved promising performances for recognizing micro-expressions. However, the micro-expression recognition is still challenging due to the subtle spatiotemporal changes of micro-expressions. To exploit the merits of deep learning, we propose a novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatiotemporal deformations of micro-expression sequence. Specifically, the proposed deep model is constituted of several recurrent convolutional layers for extracting visual features and a classificatory layer for recognition. It is optimized by an end-to-end manner and obviates manual feature design. To handle sequential data, we exploit two ways to extend the connectivity of convolutional networks across temporal domain, in which the spatiotemporal deformations are modeled in views of facial appearance and geometry separately. Besides, to overcome the shortcomings of limited and imbalanced training samples, two temporal data augmentation strategies as well as a balanced loss are jointly used for our deep network. By performing the experiments on three spontaneous micro-expression datasets, we verify the effectiveness of our proposed micro-expression recognition approach compared to the state-of-the-art methods