Subcellular location and function of a putative juvenile hormone esterase binding protein in Drosophila melanogaster

Insect development, metamorphosis and reproduction are regulated in part by the action of juvenile hormone (JH). The titer of JH is regulated in turn by the action of the enzymes juvenile hormone epoxide hydrolase and juvenile hormone esterase (JHE). Because of the potential for disruption of regulation of insect development through perturbation of the action of JH, the biology of JHE has been well studied. A putative juvenile hormone esterase binding protein, P29 was identified in the tobacco hornworm, Manduca sexta. Following sequencing of the Drosophila melanogaster genome, we identified a homolog of P29 in D. melanogaster, and used this insect for analysis of the biology and function of P29 in relation to JHE.;The gene encoding D. melanogaster P29 (DmP29), CG3776 was cloned, recombinant DmP29 expressed in E. coli and two anti-DmP29 antisera raised. In vitro binding of the P29 homolog to Drosophila JHE was confirmed. P29 mRNA and an immunoreactive protein of 25 kDa were detected in Drosophila larvae, pupae and adults. The predicted size of the protein is 30kD. Drosophila P29 is predicted to localize to mitochondria (MitoProt; 93% probability) and has a 6kD N-terminal targeting sequence. Subcellular organelle fractionation and confocal microscopy of Drosophila S2 cells confirmed that the immunoreactive 25kD protein is present in mitochondria but not in the cytosol. Expression of P29 without the predicted N-terminal targeting sequence in High Five(TM) cells showed that the N-terminal targeting sequence is shorter than predicted, and that a second, internal mitochondrial targeting signal is also present. An immunoreactive protein of 50 kDa in the hemolymph does not result from alternative splicing of CG3776 but may result from dimerization of P29.;We investigated the potential ligands of DmP29 by testing three hypotheses: (i) DmP29 binds to D. melanogaster JHE: We produced a stably transformed insect cell line that expresses DmJHE and confirmed that DmP29 binds to D. melanogaster P29. DmJHE binds to both the 25 kD and 50 kD immunoreactive proteins. (ii) DmP29 binds other, non-specific esterases including two esterases predicted to be targeted to the mitochondria: We did not detect any interaction between DmP29 and non-specific esterases. (iii) DmP29 binds to other proteins in D. melanogaster: Ligand blot analysis, immunoprecipitation experiments and affinity binding experiments showed that larval serum protein 1 binds the 25 kD P29. The possible biological relevance of the in vitro DmP29-JHE interaction is provided by detection of JHE activity in D. melanogaster mitochondrial fractions; 0.48 nmol JH hydrolysed/min/mg mitochondrial protein, 97% of which was inhibited by the JHE-specific inhibitor OTFP. However, the DmP29-LSP interactions may not be biologically relevant, given the high abundance, and sticky nature of these proteins. Interaction of DmP29 with LSP may result from non-specific associations. We used P29 hypo- and hyper-expression mutants to elucidate the function of P29 and the potential interaction of P29 with JHE. The hypomorphic mutant EP835 of P29 had reduced JHE activity when compared to wild type flies. Hyperexpression of P29 in EP/Gal4 during the early larval stages was lethal, while hyperexpression during the third instar resulted in reduced size of adult flies. This phenotype showed that overexpression of P29 interfered with insect development. Hyperexpression in newly eclosed but not in older females resulted in reduced fecundity, indicating that overexpression of P29 affected ovarian development. Fecundity was not affected by P29 hyperexpression in the male. Hypermorphic adults exhibited male-male courtship behavior. Hyperexpressed females showed reduced receptivity to males. Hyperexpressed females had decreased production of courtship pheromone, cis, cis-7, 11-hepta cosadiene, which resulted in male flies being unable to locate female flies. Hyperexpression of P29 in males resulted in decreased production of the aggregation pheromone, cis-vaccenyl acetate. For EP835/Gal4, the hypermorphic mutant, all hyperexpression phenotypes were consistent with a reduced JH titer in Drosophila. Flies that hypo- or hyper-expressed P29 had a significantly shorter lifespan: Reduced lifespan correlated with increased egg production (hypomorphic flies) and hyperactivity (hypermorphic flies), respectively. Hence, the titer of P29 appeared to be positively correlated with the titer of JHE and negatively correlated with the titer of JH. Based on the collective phenotypes and detection of JHE activity in mitochondria, we hypothesize that JHE is stored in mitochondria and that P29 functions in transport of JHE to the cytosol

In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks

The sixth-generation (6G) mobile networks are expected to feature the ubiquitous deployment of machine learning and AI algorithms at the network edge. With rapid advancements in edge AI, the time has come to realize intelligence downloading onto edge devices (e.g., smartphones and sensors). To materialize this version, we propose a novel technology in this article, called in-situ model downloading, that aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network. Its distinctive feature is the adaptation of downloading to time-varying situations (e.g., application, location, and time), devices' heterogeneous storage-and-computing capacities, and channel states. A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level to support adaptive model downloading. We further propose a virtualized 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library. Furthermore, experiments are conducted to quantify 6G connectivity requirements and research opportunities pertaining to the proposed technology are discussed.Comment: The paper has been submitted to IEEE for possible publicatio

Water Balance Analysis of Hulun Lake, a Semi-Arid UNESCO Wetland, Using Multi-Source Data

Hulun Lake is the largest lake in northeastern China, and its basin is located in China and Mongolia. This research aims to analyze the dynamic changes in the water volume of Hulun Lake and to estimate the groundwater recharge of the lake during the past 60 years. Multi-source data were used, and water-level-data-interpolation extrapolation, water-balance equations, and other methods were applied. The proportion of the contribution of each component to the quantity of water in Hulun Lake during the last 60 years was accurately calculated. Evaporation loss was the main component in the water loss in Hulun Lake. In the last 60 years, the average annual runoff into the lake was about 1.202 billion m3, and it was the factor with the largest variation range and the leading factor affecting the changes in the quantity of water in Hulun Lake. There was groundwater recharge in Hulun Lake for a long period, and the average annual groundwater recharge was about 776 million m3 (excluding leakage). The contribution ratio of the river water, groundwater, and precipitation to the recharging of Hulun Lake was about 5:3:2. The changes in the quantity of water in Hulun Lake are affected by climate change and human activities in China and Mongolia, especially those in Mongolia

ABCG2 is associated with HER-2 Expression, lymph node metastasis and clinical stage in breast invasive ductal carcinoma

<p>Abstract</p> <p>Background</p> <p>ABCG2 is an ABC transporter. It has been demonstrated that endogenous ABCG2 expression in certain cancers is a possible reflection of the differentiated phenotype of the cell of origin and likely contributes to intrinsic drug resistance. But little is known about the contribution of ABCG2 to the drug resistance and the clinicopathological characteristics in breast cancer. In the present study, we investigated the expression of ABCG2 and the correlations between ABCG2 expression and patients' clinicopathological and biological characteristics.</p> <p>Methods</p> <p>Immunohistochemistry was employed on the tissue microarray paraffin sections of surgically removed samples from 196 breast cancer patients with clinicopathological data.</p> <p>Results</p> <p>The results showed that ABCG2 was expressed in different intensities and distributions in the tumor cells of the breast invasive ductal carcinoma. A positive stain for ABCG2 was defined as a brown stain observed in the cytoplasm and cytomembrane. A statistically significant correlation was demonstrated between ABCG2 expression and HER-2 expression (p = 0.001), lymph node metastasis (p = 0.049), and clinical stage (p = 0.015) respectively.</p> <p>Conclusion</p> <p>ABCG2 correlated with Her-2 expression, lymph node metastasis and clinical stage in breast invasive ductal carcinoma. It could be a novel potential bio-marker which can predict biological behavior, clinical progression, prognosis and chemotherapy effectiveness.</p

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion alongside the number of attacks types detected. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.Comment: ACM Computing Surveys, 2022, 35 pages, 10 Figures, 8 Table

Association of Toll-Like Receptor 4 Gene Polymorphism and Expression with Urinary Tract Infection Types in Adults

Background: Innate immunity of which Toll-like receptor (TLR) 4 and CXCR1 are key elements plays a central role in the development of urinary tract infection (UTI). Although the relation between the genetics of TLR4 and CXCR1 and UTI is investigated partly, the polymorphisms and expression of TLR4 and CXCR1 in different types of UTI in adults are not extremely clear. Methodology/Principal Findings: This study investigates the presence of TLR4 A (896) G and CXCR1 G (2608) C polymorphisms in 129 UTI patients using RFLP-PCR. Gene and allelic prevalence were compared with 248 healthy controls. Flow cytometry was used to detect TLR4 and CXCR1 expression in the monocytes of UTI patients and healthy controls. TLR4 (896) AG genotype and TLR4 (896) G allele had higher prevalence in UTI (especially in acute cystitis and urethritis) patients, whereas CXCR1 (2608) GC genotype and CXCR1 (2608) C allele had lower prevalence in UTI patients than controls. TLR4 expression was significantly lower in chronic UTI patients than in acute pyelonephritis or healthy controls. CXCR1 expression was similar in both controls and patients. TLR4 expression in chronic UTI patients after astragalus treatment was higher than pre-treatment. Conclusions: The results indicate the relationship between the carrier status of TLR4 (896) G alleles and the development of UTI, especially acute cystitis and urethritis, in adults. TLR4 expression levels are correlated with chronic UTI

Endocrine disrupting and carcinogenic effects of decabromodiphenyl ether

BackgroundDecabromodiphenyl ether (BDE209), an essential industrial flame retardant that is widely used, has recently been reported to be increasing in human serum. Due to the structural similarity between BDE209 and thyroid hormones, its toxic effects on the thyroid are of particular concern.MethodsOriginal articles in the PubMed database were collected using the terms “BDE209”, “decabromodiphenyl ether”, “endocrine disrupting”, “thyroid”, “carcinogenesis”, “polybrominated diphenyl ethers”, “PBDEs,” and their synonyms from inception up to October of 2022.ResultsOf the 748 studies initially identified, 45 were selected, which emphasized the adverse effects of BDE209 on endocrine system. BDE209 may have a toxic effect not only on thyroid function but also on thyroid cancer tumorigenesis at multiple levels, such as by directly interfering with the TR, hypothalamic-pituitary-thyroid (HPT) axis, enzyme activity, and methylation. However, it is impossible to draw a definitive conclusion on the exact pathway of thyroid toxicity from BDE209.ConclusionsAlthough the toxic effects of BDE209 on the thyroid have been well investigated, its tumorigenic effects remain unclear and further research is necessary

Context De-confounded Emotion Recognition

Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight.Comment: Accepted by CVPR 2023. CCIM is available at https://github.com/ydk122024/CCI