The Expression Levels of XLF and Mutant P53 Are Inversely Correlated in Head and Neck Cancer Cells.

XRCC4-like factor (XLF), also known as Cernunnos, is a protein encoded by the human NHEJ1 gene and an important repair factor for DNA double-strand breaks. In this study, we have found that XLF is over-expressed in HPV(+) versus HPV(-) head and neck squamous cell carcinoma (HNSCC) and significantly down-regulated in the HNSCC cell lines expressing high level of mutant p53 protein versus those cell lines harboring wild-type TP53 gene with low p53 protein expression. We have also demonstrated that Werner syndrome protein (WRN), a member of the NHEJ repair pathway, binds to both mutant p53 protein and NHEJ1 gene promoter, and siRNA knockdown of WRN leads to the inhibition of XLF expression in the HNSCC cells. Collectively, these findings suggest that WRN and p53 are involved in the regulation of XLF expression and the activity of WRN might be affected by mutant p53 protein in the HNSCC cells with aberrant TP53 gene mutations, due to the interaction of mutant p53 with WRN. As a result, the expression of XLF in these cancer cells is significantly suppressed. Our study also suggests that XLF is over-expressed in HPV(+) HNSCC with low expression of wild type p53, and might serve as a potential biomarker for HPV(+) HNSCC. Further studies are warranted to investigate the mechanisms underlying the interactive role of WRN and XLF in NHEJ repair pathway

Neural Collapse Inspired Federated Learning with Non-iid Data

One of the challenges in federated learning is the non-independent and identically distributed (non-iid) characteristics between heterogeneous devices, which cause significant differences in local updates and affect the performance of the central server. Although many studies have been proposed to address this challenge, they only focus on local training and aggregation processes to smooth the changes and fail to achieve high performance with deep learning models. Inspired by the phenomenon of neural collapse, we force each client to be optimized toward an optimal global structure for classification. Specifically, we initialize it as a random simplex Equiangular Tight Frame (ETF) and fix it as the unit optimization target of all clients during the local updating. After guaranteeing all clients are learning to converge to the global optimum, we propose to add a global memory vector for each category to remedy the parameter fluctuation caused by the bias of the intra-class condition distribution among clients. Our experimental results show that our method can improve the performance with faster convergence speed on different-size datasets.Comment: 11 pages, 5 figure

HCO3−/Cl− Exchange Inactivation and Reactivation during Mouse Oocyte Meiosis Correlates with MEK/MAPK-Regulated Ae2 Plasma Membrane Localization

Background: Germinal Vesicle (GV) stage mouse oocytes in first meiotic prophase exhibit highly active HCO3−/Cl− exchange—a class of transport nearly ubiquitously involved in regulation of intracellular pH and cell volume. During meiosis, however, oocyte HCO3−/Cl− exchange becomes inactivated during first metaphase (MI), remains inactive in second metaphase (MII), and is reactivated only after egg activation. Previous work using pharmacological manipulations had indicated that activity of the MEK/MAPK signaling pathway was negatively correlated with HCO3−/Cl− exchange activity during meiosis. However, the mechanism by which the exchanger is inactivated during meiotic progression had not been determined, nor had the role of MEK/MAPK been directly established. Methodology/Principal Findings: Expression of a constitutively active form of MEK (MAP kinase kinase), which prevented the normal downregulation of MAPK after egg activation, also prevented reactivation of HCO3−/Cl− exchange. Conversely, suppression of endogenous MAPK activity with dominant negative MEK activated the normally quiescent HCO3−/Cl− exchange in mature MII eggs. A GFP-tagged form of the HCO3−/Cl− exchanger isoform Ae2 (Slc4a2) was strongly expressed at the GV oocyte plasma membrane, but membrane localization decreased markedly during meiotic progression. A similar pattern for endogenous Ae2 was confirmed by immunocytochemistry. The loss of membrane-localized Ae2 appeared selective, since membrane localization of a GFP-tagged human dopamine D1 receptor did not change during meiotic maturation. Conclusions: Direct manipulation of MAPK activity indicated that GFP-tagged Ae2 localization depended upon MAPK activity. Inactivation of HCO3−/Cl− exchange during the meiotic cell cycle may therefore reflect the loss of Ae2 from the oocyte plasma membrane, downstream of MEK/MAPK signaling. This identifies a novel role for MEK/MAPK-mediated cytostatic factor (CSF) activity during meiosis in membrane protein trafficking in mouse oocytes, and shows for the first time that selective retrieval of membrane proteins is a feature of meiosis in mammalian oocytes

Air travel demand forecasting based on big data: A struggle against public anxiety

It is of great significance to accurately grasp the demand for air travel to promote the revival of long-distance travel and alleviate public anxiety. The main purpose of this study is to build a high-precision air travel demand forecasting framework by introducing effective Internet data. In the age of big data, passengers before traveling often look for reference groups in search engines and make travel decisions under their informational influence. The big data generated based on these behaviors can reflect the overall passenger psychology and travel demand. Therefore, based on big data mining technology, this study designed a strict dual data preprocessing method and an ensemble forecasting framework, introduced search engine data into the air travel demand forecasting process, and conducted empirical research based on the dataset composed of air travel volume of Shanghai Pudong International Airport. The results show that effective search engine data is helpful to air travel demand forecasting. This research provides a theoretical basis for the application of big data mining technology and data spatial information in air travel demand forecasting and tourism management, and provides a new idea for alleviating public anxiety

Prevalence Rates of Personality Disorder and Its Association With Methamphetamine Dependence in Compulsory Treatment Facilities in China

Methamphetamine use is popular and rapidly increasing in China, and the co-occurrence of personality disorders has an impact on treatment outcomes and may increase vulnerability of developing dependence. The aim of the present study was to investigate the prevalence rates of personality disorders in methamphetamine users and further explore the association between personality disorders and methamphetamine use status. Five hundred and seventy-seven male methamphetamine users were recruited. The self-developed questionnaire was used for demographics, and a Structural Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) (SCID-I/II) was performed covering psychiatric diagnosis. Our study found the prevalence of antisocial personality disorder in male methamphetamine users was 71.4%, followed by borderline (20.2%) and obsessive-compulsive (17.9%) personality disorder. Borderline and antisocial personality disorders were found to be risk factors of methamphetamine dependence (adjusted odds ratio = 2.891, p = 0.007 and adjusted odds ratio = 1.680, p = 0.042). These findings suggested personality disorders were highly prevalent in male methamphetamine users, and the comorbidity of antisocial and borderline personality disorders are especially associated with methamphetamine dependence

The Role of Tryptophan in π Interactions in Proteins:An Experimental Approach

In proteins, the amino acids Phe, Tyr, and especially Trp are frequently involved in π interactions such as π-π, cation-π, and CH-π bonds. These interactions are often crucial for protein structure and protein-ligand binding. A powerful means to study these interactions is progressive fluorination of these aromatic residues to modulate the electrostatic component of the interaction. However, to date no protein expression platform is available to produce milligram amounts of proteins labeled with such fluorinated amino acids. Here, we present a Lactococcus lactis Trp auxotroph-based expression system for efficient incorporation (≥95%) of mono-, di-, tri-, and tetrafluorinated, as well as a methylated Trp analog. As a model protein we have chosen LmrR, a dimeric multidrug transcriptional repressor protein from L. lactis. LmrR binds aromatic drugs, like daunomycin and riboflavin, between Trp96 and Trp96' in the dimer interface. Progressive fluorination of Trp96 decreased the affinity for the drugs 6- to 70-fold, clearly establishing the importance of electrostatic π-π interactions for drug binding. Presteady state kinetic data of the LmrR-drug interaction support the enthalpic nature of the interaction, while high resolution crystal structures of the labeled protein-drug complexes provide for the first time a structural view of the progressive fluorination approach. The L. lactis expression system was also used to study the role of Trp68 in the binding of riboflavin by the membrane-bound riboflavin transport protein RibU from L. lactis. Progressive fluorination of Trp68 revealed a strong electrostatic component that contributed 15-20% to the total riboflavin-RibU binding energy

Adaptive probabilistic load forecasting for individual buildings

Building-level load forecasting has become essential with the support of fine-grained data collected by widely deployed smart meters. It acts as a basis for arranging distributed energy resources, implementing demand response, etc. Compared to aggregated-level load, the electric load of an individual building is more stochastic and thus spawns many probabilistic forecasting methods. Many of them resort to artificial neural networks (ANN) to build forecasting models. However, a well-designed forecasting model for one building may not be suitable for others, and manually designing and tuning optimal forecasting models for various buildings are tedious and time-consuming. This paper proposes an adaptive probabilistic load forecasting model to automatically generate high-performance NN structures for different buildings and produce quantile forecasts for future loads. Specifically, we cascade the long short term memory (LSTM) layer with the adjusted Differential ArchiTecture Search (DARTS) cell and use the pinball loss function to guide the model during the improved model fitting process. A case study on an open dataset shows that our proposed model has superior performance and adaptivity over the state-of-the-art static neural network model. Besides, the improved fitting process of DARTS is proved to be more time-efficient than the original one

Psychiatric Co-morbidity in Ketamine and Methamphetamine Dependence:a Retrospective Chart Review

Both ketamine and methamphetamine (MA) have become very popular and have been abused worldwide over the past two decades. However, the relationship between dependence on ketamine or MA and psychiatric comorbidities is still unclear. This study aimed to examine the frequency of co-morbid psychiatric disorders in patients dependent on ketamine or methamphetamine who were receiving treatment at three substance abuse treatment clinics (SACs) in Hong Kong. This was a retrospective chart review. The medical records of 183 patients (103 with ketamine and 80 with MA dependence) treated between January 2008 and August 2012 were retrieved. Patients’ demographic data, patterns of substance abuse and comorbid psychiatric diagnoses were recorded. The mean age of onset and duration of substance abuse were 18.1 ± 4.7 and 9.2 ± 6.2 years for ketamine and 19.9 ± 8.8 and 10.5 ± 9.8 years for MA users, respectively. Psychotic disorders were more common in MA dependent users (76.2 % vs. 28.2 %, p &lt; 0.001), whereas mood disorders were more common in ketamine dependent users (27.2 % vs. 11.2 %, p = 0.008). Ketamine and MA dependence have a notably different profile of psychiatric co-morbidity. Compared with MA dependence, ketamine dependence is more likely to be associated with mood disorders and less likely with psychotic disorders.</p