Propagation-dependent fair rate allocation in heterogeneous cellular system

In this day and age, the cellular communication network is advancing at an amazing speed. Heterogeneous wireless access technologies would play an increasingly critical role in next generation wireless communication systems. The ultimate objective of this thesis is to implement fair and priority-based rate allocation in heterogeneous cellular system using Max-Min fairness criterion. Two coeffcients are introduced successfully in the proposed algorithm. Consequently, it accomplish the goal of resource allocation in terms of spectral effciency and prioritization in heterogeneous cellular networks. However, the implementation is simplified to prove the correctness of the algorithm. More realistic scenario should be taken into consideration. Besides, interference would affect the final optimal solution

Lee-Yang Zeros of a Bosonic system associated with a single trapped ion

Zeros of partition functions, in particular Lee-Yang zeros, in a complex plane provide important information for understanding phase transitions. A recent discovery on the equivalence between the coherence of a central quantum system and the partition function of the environment in the complex plane enabled the experimental study of Lee-Yang zeros, with several pioneering experiments on spin systems. Lee-Yang zeros have not been observed in Bosonic systems. Here we propose an experimental scheme to demonstrate Lee-Yang zeros in Bosonic systems associated with a single trapped ion by introducing strong coupling between the spin and motion degrees of freedom, i.e. beyond the weak coupling Lamb-Dicke regime. Our scheme provides new possibilities for quantum simulation of the thermodynamics of Bosonic systems in the complex plane.Comment: 6 pages,6 figure

Four lignans from Portulaca oleracea L. and its antioxidant activities

International audienceA new lignan, named oleralignan (1) and three known compounds (+)-syringaresinol (2), (+)-lirioresinol A (3) and monomethyl 3,30,4,40-tetrahydroxy-δ-truxinate (4) were isolated from the P. oleracea for the first time. The compound (1) were identified by 1D, 2D NMR spectroscopic methods and high resolution electrospray ionization time-of-flight mass spectrometry. In addition, it was found that the four lignans presented the scavenging activities in 1,1-diphenyl-2-picryl-hydrazyl (DPPH) radical quenching assay more than that of hydroxyl anisole (BHA)

Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter

Background: Traditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines. Objective: The aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency. Methods: We collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA®). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC). Results: Of the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 72 % recall and 86 % precision. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC. Conclusion: Patients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation

A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems

As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a crucial role in augmenting their effectiveness in providing recommendations. However, existing approaches for LLM4Rec often assess performance using restricted sets of candidates, which may not accurately reflect the models' overall ranking capabilities. In this paper, our objective is to investigate the comprehensive ranking capacity of LLMs and propose a two-step grounding framework known as BIGRec (Bi-step Grounding Paradigm for Recommendation). It initially grounds LLMs to the recommendation space by fine-tuning them to generate meaningful tokens for items and subsequently identifies appropriate actual items that correspond to the generated tokens. By conducting extensive experiments on two datasets, we substantiate the superior performance, capacity for handling few-shot scenarios, and versatility across multiple domains exhibited by BIGRec. Furthermore, we observe that the marginal benefits derived from increasing the quantity of training samples are modest for BIGRec, implying that LLMs possess the limited capability to assimilate statistical information, such as popularity and collaborative filtering, due to their robust semantic priors. These findings also underline the efficacy of integrating diverse statistical information into the LLM4Rec framework, thereby pointing towards a potential avenue for future research. Our code and data are available at https://github.com/SAI990323/Grounding4Rec.Comment: 17 page

Loss of nuclear PTEN in HCV-infected human hepatocytes

Background Hepatitis C virus (HCV) infection is a major risk factor for chronic hepatitis and hepatocellular carcinoma (HCC); however, the mechanism of HCV-mediated hepatocarcinogenesis is not well understood. Insufficiency of PTEN tumor suppressor is associated with more aggressive cancers, including HCC. We asked whether viral non-coding RNA could initiate oncogenesis in HCV infected human hepatocytes. The results presented herein suggest that loss of nuclear PTEN in HCV-infected human hepatocytes results from depletion of Transportin-2, which is a direct target of viral non-coding RNA, vmr11. Methods The intracellular distribution of PTEN in HCV-infected cells was monitored by immunostaining and Western blots of nuclear and cytoplasmic proteins. Effects of PTEN depletion were examined by comparing expression arrays of uninfected cells with either HCV-infected or vmr11-transfected cells. Target genes suggested by array analyses were validated by Western blot. The influence of nuclear PTEN deficiency on virus production was determined by quantitative analysis of HCV genomic RNA in culture media of infected hepatocytes. Results Import of PTEN to the nucleus relies on the interaction of Transportin-2 and PTEN proteins; we show that depletion of Transportin-2 by HCV infection or by the introduction of vmr11 in uninfected cells results in reduced nuclear PTEN. In turn, nuclear PTEN insufficiency correlates with increased virus production and the induction of ?-H2AX, a marker of DNA double-strand breaks and genomic instability. Conclusion An HCV-derived small non-coding RNA inhibits Transportin-2 and PTEN translocation to the nucleus, suggesting a direct viral role in hepatic oncogenesis

Use of an Immobilized Monoclonal Antibody to Examine Integrin α5β1 Signaling Independent of Cell Spreading

Cell attachment to the extracellular matrix (ECM) engages integrin signaling into the cell, but part of the signaling response also stem from cell spreading (3). To analyze specific integrin signaling-mediated responses independent of cell spreading, we developed a method engaging integrin signaling by use of an immobilized anti-integrin monoclonal antibody (mab) directed against the fibronectin (FN) receptor integrin α5β1. ECV 304 cells were plated onto FN or immobilized mab JBS5 (anti-integrin α5β1) or onto poly-L-lysin (P-L-L), which mediates integrin-independent attachment. Cells attached and spread on FN, while cells on JBS5 or P-L-L attached but did not spread. Importantly, plating onto FN or mab JBS5 gave rise to identical integrin-induced responses, including a down-regulation of the cyclin-dependent kinase (Cdk2) inhibitors p21(CIP1) and p27(KIP1), while attachment to P-L-L did not. We conclude that engagement of the FN-receptor integrin α5β1 induces integrin signaling regulating the Cdk2-inhibitors independent of cell spreading and present a method for how integrin signaling can be analyzed separate from the effects of cell spreading

Category-Specific CNN for Visual-aware CTR Prediction at JD.com

As one of the largest B2C e-commerce platforms in China, JD com also powers a leading advertising system, serving millions of advertisers with fingertip connection to hundreds of millions of customers. In our system, as well as most e-commerce scenarios, ads are displayed with images.This makes visual-aware Click Through Rate (CTR) prediction of crucial importance to both business effectiveness and user experience. Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR. Despite being extensively studied, this field still face two key challenges. First, although encouraging progress has been made in offline studies, applying CNNs in real systems remains non-trivial, due to the strict requirements for efficient end-to-end training and low-latency online serving. Second, the off-the-shelf CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf CNNs were designed for classification thus never take categories as input features. While in e-commerce, categories are precisely labeled and contain abundant visual priors that will help the visual modeling. Unaware of the ad category, these CNNs may extract some unnecessary category-unrelated features, wasting CNN's limited expression ability. To overcome the two challenges, we propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early incorporates the category knowledge with a light-weighted attention-module on each convolutional layer. This enables CSCNN to extract expressive category-specific visual patterns that benefit the CTR prediction. Offline experiments on benchmark and a 10 billion scale real production dataset from JD, together with an Online A/B test show that CSCNN outperforms all compared state-of-the-art algorithms