Achievable efficiencies for probabilistically cloning the states

We present an example of quantum computational tasks whose performance is enhanced if we distribute quantum information using quantum cloning. Furthermore we give achievable efficiencies for probabilistic cloning the quantum states used in implemented tasks for which cloning provides some enhancement in performance.Comment: 9 pages, 8 figure

Negative CT Contrast Agents for the Diagnosis of Malignant Osteosarcoma

© 2019 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim The current positive computed tomography (CT) contrast agents (PCTCAs) including clinical iodides, present high CT density value (CT-DV). However, they are incapable for the accurate diagnosis of some diseases with high CT-DV, such as osteosarcoma. Because bones and PCTCAs around osteosarcoma generate similar X-ray attenuations. Here, an innovative strategy of negative CT contrast agents (NCTCAs) to reduce the CT-DV of osteosarcoma is proposed, contributing to accurate detection of osteosarcoma. Hollow mesoporous silica nanoparticles, loading ammonia borane molecules and further modified by polyethylene glycol, are synthesized as NCTCAs for the diagnosis of osteosarcoma. The nanocomposites can produce H2 in situ at osteosarcoma areas by responding to the acidic microenvironment of osteosarcoma, resulting in nearly 20 times reduction of CT density in osteosarcoma. This helps form large CT density contrast between bones and osteosarcoma, and successfully achieves accurate diagnosis of osteosarcoma. Meanwhile, The NCTCAs strategy greatly expands the scope of CT application, and provides profound implications for the precise clinical diagnosis, treatment, and prognosis of diseases

Adding Chinese herbal medicine to probiotics for irritable bowel syndrome-diarrhea: A systematic review and meta-analysis of randomized controlled trials

© 2020 Beijing University of Chinese Medicine Objective: This study assessed whether Chinese herbal medicine (CHM) combined with probiotics/synbiotics for irritable bowel syndrome - diarrhea (IBS-D) was more effective and safer than probiotics/synbiotics alone. Methods: Ten databases were searched for randomized control trials (RCTs) of IBS-D as diagnosed by Manning or Rome criteria. Trials comparing probiotics and probiotics with CHM were included. The Cochrane risk of bias (ROB) was evaluated for each trial. RevMan 5.3 was used to conduct a meta-analysis. Results: Twenty-six RCTs were included (25 Chinese, 1 English), involving 2045 participants. Meta-analysis was conducted on two outcomes: overall symptom improvement and relapse. CHM combined with live Bifidobacterium and Lactobacillus preparations reduced relapse rate (RR 0.28, 95%CI 0.15–0.52, 3 trials, n = 205) compared with probiotics alone. The subgroup analysis showed the benefit of CHM prescriptions based on soothing liver and invigorating spleen (1.28, 1.14–1.44, 3, 244), invigorating spleen and resolving dampness (1.20, 1.03–1.41, 2, 128), or warming and invigorating spleen and kidney formulae (1.27, 1.09–1.46, 2, 210) combined with triple Bifidobacterium preparations than the same probiotics alone which improved overall symptoms for IBS-D. There was unclear bias in almost domains of ROB. Most studies had a high risk of bias due to lack of blinding of investigator and participants, and selective reporting. Conclusions: This study showed that CHM combined with probiotics may reduce relapse rate by 72%, and improve overall symptoms of IBS-D (as diagnosed by Rome II and III) compared to probiotics alone. From the limited subgroup analysis, only soothing liver and invigorating spleen formulae, represented by Tongxie Yaofang, added to triple Bifidobacterium preparations may be superior to the single preparations in terms of overall symptoms. However, due to the poor methodological quality and small sample size of the trials, these findings must be interpreted with caution

Continuous-action reinforcement learning for memory allocation in virtualized servers

In a virtualized computing server (node) with multiple Virtual Machines (VMs), it is necessary to dynamically allocate memory among the VMs. In many cases, this is done only considering the memory demand of each VM without having a node-wide view. There are many solutions for the dynamic memory allocation problem, some of which use machine learning in some form. This paper introduces CAVMem (Continuous-Action Algorithm for Virtualized Memory Management), a proof-of-concept mechanism for a decentralized dynamic memory allocation solution in virtualized nodes that applies a continuous-action reinforcement learning (RL) algorithm called Deep Deterministic Policy Gradient (DDPG). CAVMem with DDPG is compared with other RL algorithms such as Q-Learning (QL) and Deep Q-Learning (DQL) in an environment that models a virtualized node. In order to obtain linear scaling and be able to dynamically add and remove VMs, CAVMem has one agent per VM connected via a lightweight coordination mechanism. The agents learn how much memory to bid for or return, in a given state, so that each VM obtains a fair level of performance subject to the available memory resources. Our results show that CAVMem with DDPG performs better than QL and a static allocation case, but it is competitive with DQL. However, CAVMem incurs significant less training overheads than DQL, making the continuous-action approach a more cost-effective solution.This research is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 754337 (EuroEXA) and the European Union’s 7th Framework Programme under grant agreement number 610456 (Euroserver). It also received funding from the Spanish Ministry of Science and Technology (project TIN2015-65316-P), Generalitat de Catalunya (contract 2014-SGR-1272), and the Severo Ochoa Programme (SEV-2015-0493) of the Spanish Government.Peer ReviewedPostprint (author's final draft

Accurate and Real-Time Temperature Monitoring during MR Imaging Guided PTT.

Photothermal therapy (PTT) is an efficient approach for cancer treatment. However, accurately monitoring the spatial distribution of photothermal transducing agents (PTAs) and mapping the real-time temperature change in tumor and peritumoral normal tissue remain a huge challenge. Here, we propose an innovative strategy to integrate T1-MRI for precisely tracking PTAs with magnetic resonance temperature imaging (MRTI) for real-time monitoring temperature change in vivo during PTT. NaBiF4: Gd@PDA@PEG nanomaterials were synthesized with favorable T1-weighted performance to target tumor and localize PTAs. The extremely weak susceptibility (1.04 × 10-6 emu g-1 Oe1-) of NaBiF4: Gd@PDA@PEG interferes with the local phase marginally, which maintains the capability of MRTI to dynamically record real-time temperature change in tumor and peritumoral normal tissue. The time resolution is 19 s per frame, and the detection precision of temperature change is approximately 0.1 K. The approach achieving PTT guided by multimode MRI holds significant potential for the clinical application

The effect of e-cigarettes on smoking cessation and cigarette smoking initiation: An evidence-based rapid review and meta-analysis.

The contribution made by e-cigarettes to smoking cessation continues to be controversial. Reports suggest that teenagers are becoming increasingly addicted to e-cigarettes and that e-cigarette use in adolescents is associated with subsequent cigarette smoking. Systematic searches of eleven databases were conducted (January 2015 to June 2020). Systematic reviews, randomized controlled trials (RCTs) and cohort studies comparing e-cigarettes with placebo e-cigarettes, nicotine replacement therapy (NRT) or no e-cigarette use were included. The two primary outcomes were smoking cessation among smokers and smoking initiation among non-smoking teenagers. The secondary outcome was adverse events. Data were synthesized using risk ratio (RR) or adjusted odds ratio (AOR) with 95% confidence interval (CI). Six systematic reviews, 5 RCTs and 24 cohort studies were identified. For smoking cessation, findings from 4 systematic reviews indicated that e-cigarettes contributed to cessation while one found the opposite. Meta-analysis of 5 RCTs suggested that e-cigarettes were superior to NRT or placebo for smoking cessation (RR=1.55; 95% CI: 1.00-2.40; I =57.6%; low certainty; 5 trials, n=4025). Evidence from 9 cohort studies showed that e-cigarette use was not associated with cessation (AOR=1.16; 95% CI: 0.88-1.54; I =69.0%; n=22220). Subgroup analysis suggested that intensive e-cigarette use may be associated with cessation. In terms of smoking initiation, adolescents who ever used e-cigarettes had a greater risk for smoking initiation than non-users (AOR=2.91; 95% CI: 2.61-3.23; I =61.0%; 15 trials, n=68943), the findings were consistent with one included systematic review. No serious adverse events were reported in the included studies. Low certainty evidence suggests that e-cigarettes appear to be potentially effective for smoking cessation. The use of e-cigarettes in adolescents may be associated with smoking initiation. No serious adverse events were reported. [Abstract copyright: © 2021 Zhang Y.Y. et al.

Whistle detection and classification for whales based on convolutional neural networks

Passive acoustic observation of whales is an increasingly important tool for whale research. Accurately detecting whale sounds and correctly classifying them into corresponding whale species are essential tasks, especially in the case when two species of whales vocalize in the same observed area. Whistles are vital vocalizations of toothed whales, such as killer whales and long-finned pilot whales. In this paper, based on deep convolutional neural networks (CNNs), a novel method is proposed to detect and classify whistles of both killer whales and long-finned pilot whales. Compared with traditional methods, the proposed one can automatically learn the sound characteristics from the training data, without specifying the sound features for classification and detection, and thus shows better adaptability to complex sound signals. First, the denoised sound to be analyzed is sent to the trained detection model to estimate the number and positions of the target whistles. The detected whistles are then sent to the trained classification model, which determines the corresponding whale species. A GUI interface is developed to assist with the detection and classification process. Experimental results show that the proposed method can achieve 97% correct detection rate and 95% correct classification rate on the testing set. In the future, the presented method can be further applied to passive acoustic observation applications for some other whale or dolphin species