Spinal cord stimulation for cancer-related pain in adults

Background: This is an update of a review first published in The Cochrane Library in Issue 3, 2013. Cancer-related pain places a heavy burden on public health with related high expenditure. Severe pain is associated with a decreased quality of life in patients with cancer. A significant proportion of patients with cancer-related pain are under-treated. There is a need for more effective control of cancer-related pain. Spinal cord stimulation (SCS)may have a role in pain management. The effectiveness and safety of SCS for patients with cancer-related pain is currently unknown. Objectives: This systematic review evaluated the effectiveness of SCS for cancer-related pain compared with standard care using conventional analgesic medication. We also appraised risk and potential adverse events associated with the use of SCS. Search methods: This is an update of a review first published in The Cochrane Library in Issue 3, 2013. The search strategy for the update was the same as in the original review. We searched the following bibliographic databases in order to identify relevant studies: the Cochrane Central Register of Controlled Trials (CENTRAL) in The Cochrane Library;MEDLINE; EMBASE; and CBM(Chinese Biomedical Database) in October 2014. We also handsearched relevant journals. There were no language restrictions. Selection criteria: We planned to include randomised controlled trials (RCTs) that directly compared SCS with other interventions with regards to the effectiveness of pain management.We also planned to include cross-over trials that compared SCS with another treatment.We planned to identify non-randomised controlled trials but these would only be included if no RCTs could be found. Data collection and analysis: The literature search for the update of this review found 121 potentially eligible articles. The initial search strategy yielded 430 articles. By scrutinising titles and abstracts, we found 412 articles irrelevant to the analytical purpose of this systematic review due to different scopes of diseases or different methods of intervention (intrathecal infusion system; oral medication) or aims other than pain control (spinal cord function monitoring, bladder function restoration or amelioration of organ metabolism). The remaining 18 trials were reviewed as fullmanuscripts. No RCTs were identified. Fourteen sporadic case reports and review articles were excluded and four beforeand- after case series studies (92 participants) were included. Two review authors independently selected the studies to be included in the review according to the prespecified eligibility criteria. A checklist for methodological quality of non-randomised controlled trials was used (STROBE checklist) and all review authors discussed and agreed on the inclusion of trials and the results of the quality assessment. Main results: No new studies were identified for inclusion in this update of the review. Four before-and-after case series studies (a total of 92 participants) met our criteria for inclusion in the previous version of the review. All included trials adopted a visual analogue scale (VAS) to evaluate pain relief. Heterogeneity existed in terms of baseline characteristics, electrode and stimulator parameters, level of implantation and route of implantation; each trial reported data differently. In two trials, pain relief was achieved in 76% (48/63) of participants at the end of the follow-up period. In the third trial, pre-procedure VAS was 6 to 9 (mean 7.43 ); the one-month postimplant VAS was 2 to 4 (mean 3.07); the 12-month post-implant VAS was 1 to 3 (mean 2.67). In the fourth trial, the pre-procedure VAS was 6 to 9 (mean 7.07); 1 to 4 (mean 2.67) at one-month; 1 to 4 (mean 1.87) at 12 months. Analgesic use was largely reduced. The main adverse events were infection of sites of implantation, cerebrospinal fluid (CSF) leakage, pain at the sites of electrodes, dislodgement of the electrodes, and system failure; however, the incidence in participants with cancer could not be calculated. Since all trials were small, non-randomised controlled trials, they carried high or unclear risk of all types of bias. Authors’ conclusions: Since the first publication of this review, no new studies were identified. Current evidence is insufficient to establish the role of SCS in treating refractory cancer-related pain. Future randomised studies should focus on the implantation of SCS in participants with cancer related pain

An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-Level Structural Information

In this paper, we focus on the problem of unsupervised image-sentence matching. Existing research explores to utilize document-level structural information to sample positive and negative instances for model training. Although the approach achieves positive results, it introduces a sampling bias and fails to distinguish instances with high semantic similarity. To alleviate the bias, we propose a new sampling strategy to select additional intra-document image-sentence pairs as positive or negative samples. Furthermore, to recognize the complex pattern in intra-document samples, we propose a Transformer based model to capture fine-grained features and implicitly construct a graph for each document, where concepts in a document are introduced to bridge the representation learning of images and sentences in the context of a document. Experimental results show the effectiveness of our approach to alleviate the bias and learn well-aligned multimodal representations.Comment: To be published in AAAI202

The distribution variation of pathogens and virulence factors in different geographical populations of giant pandas

Intestinal diseases caused by opportunistic pathogens seriously threaten the health and survival of giant pandas. However, our understanding of gut pathogens in different populations of giant pandas, especially in the wild populations, is still limited. Here, we conducted a study based on 52 giant panda metagenomes to investigate the composition and distribution of gut pathogens and virulence factors (VFs) in five geographic populations (captive: GPCD and GPYA; wild: GPQIN, GPQIO, and GPXXL). The results of the beta-diversity analyzes revealed a close relationship and high similarity in pathogen and VF compositions within the two captive groups. Among all groups, Proteobacteria, Firmicutes, and Bacteroidetes emerged as the top three abundant phyla. By using the linear discriminant analysis effect size method, we identified pathogenic bacteria unique to different populations, such as Klebsiella in GPCD, Salmonella in GPYA, Hafnia in GPQIO, Pedobacter in GPXXL, and Lactococcus in GPQIN. In addition, we identified 12 VFs that play a role in the intestinal diseases of giant pandas, including flagella, CsrA, enterobactin, type IV pili, alginate, AcrAB, capsule, T6SS, urease, type 1 fimbriae, polar flagella, allantoin utilization, and ClpP. These VFs influence pathogen motility, adhesion, iron uptake, acid resistance, and protein regulation, thereby contributing to pathogen infection and pathogenicity. Notably, we also found a difference in virulence of Pseudomonas aeruginosa between GPQIN and non-GPQIN wild populations, in which the relative abundance of VFs (0.42%) of P. aeruginosa was the lowest in GPQIN and the highest in non-GPQIN wild populations (GPXXL: 23.55% and GPQIO: 10.47%). In addition to enhancing our understanding of gut pathogens and VFs in different geographic populations of giant pandas, the results of this study provide a specific theoretical basis and data support for the development of effective conservation measures for giant pandas

Leveraging phone-level linguistic-acoustic similarity for utterance-level pronunciation scoring

Recent studies on pronunciation scoring have explored the effect of introducing phone embeddings as reference pronunciation, but mostly in an implicit manner, i.e., addition or concatenation of reference phone embedding and actual pronunciation of the target phone as the phone-level pronunciation quality representation. In this paper, we propose to use linguistic-acoustic similarity to explicitly measure the deviation of non-native production from its native reference for pronunciation assessment. Specifically, the deviation is first estimated by the cosine similarity between reference phone embedding and corresponding acoustic embedding. Next, a phone-level Goodness of pronunciation (GOP) pre-training stage is introduced to guide this similarity-based learning for better initialization of the aforementioned two embeddings. Finally, a transformer-based hierarchical pronunciation scorer is used to map a sequence of phone embeddings, acoustic embeddings along with their similarity measures to predict the final utterance-level score. Experimental results on the non-native databases suggest that the proposed system significantly outperforms the baselines, where the acoustic and phone embeddings are simply added or concatenated. A further examination shows that the phone embeddings learned in the proposed approach are able to capture linguistic-acoustic attributes of native pronunciation as reference.Comment: Accepted by ICASSP 202

An ASR-free Fluency Scoring Approach with Self-Supervised Learning

A typical fluency scoring system generally relies on an automatic speech recognition (ASR) system to obtain time stamps in input speech for either the subsequent calculation of fluency-related features or directly modeling speech fluency with an end-to-end approach. This paper describes a novel ASR-free approach for automatic fluency assessment using self-supervised learning (SSL). Specifically, wav2vec2.0 is used to extract frame-level speech features, followed by K-means clustering to assign a pseudo label (cluster index) to each frame. A BLSTM-based model is trained to predict an utterance-level fluency score from frame-level SSL features and the corresponding cluster indexes. Neither speech transcription nor time stamp information is required in the proposed system. It is ASR-free and can potentially avoid the ASR errors effect in practice. Experimental results carried out on non-native English databases show that the proposed approach significantly improves the performance in the "open response" scenario as compared to previous methods and matches the recently reported performance in the "read aloud" scenario.Comment: Accepted by ICASSP 202

Improving associative memory in younger and older adults with unitization: evidence from meta-analysis and behavioral studies

IntroductionThe finding that familiarity can support associative memory by unitizing the to -be-learned items into a novel representation has been widely accepted, but its effects on overall performance of associative memory and recollection are still controversial.MethodsThe current study aims to elucidate these discrepancies by identifying potential moderating factors through a combined approach of meta-analysis and behavioral experiment.ResultsResults consistently showed that changes in the level of unitization and age groups were two important moderators. Specifically, unitization enhanced younger and older adults’ associative memory and its supporting processes (i.e., familiarity and recollection) when the level of unitization between studied and rearranged pairs was changed. However, when this level remained constant, unitization exhibited no impact on associative memory and familiarity in younger adults, but showed an enhanced effect in older adults. Furthermore, results revealed a marked group difference between younger and older adults in associative memory when the unitization level of noncompound words remained unaltered. Upon breaking this condition, the group difference was reduced by enhancing familiarity or recollection.DiscussionThese findings not only clarify some of the inconsistencies in the literature concerning the impact of unitization on associative memory, but also suggest that unitization is a beneficial strategy for reducing group difference in associative memory, with its effectiveness varying according to the level of unitization changes

Impact of Sensing Errors on Headway Design: From α-Fair Group Safety to Traffic Throughput

Headway, namely the distance between vehicles, is a key design factor for ensuring the safe operation of autonomous driving systems. There have been studies on headway optimization based on the speeds of leading and trailing vehicles, assuming perfect sensing capabilities. In practical scenarios, however, sensing errors are inevitable, calling for a more robust headway design to mitigate the risk of collision. Undoubtedly, augmenting the safety distance would reduce traffic throughput, highlighting the need for headway design to incorporate both sensing errors and risk tolerance models. In addition, prioritizing group safety over individual safety is often deemed unacceptable because no driver should sacrifice their safety for the safety of others. In this study, we propose a multi-objective optimization framework that examines the impact of sensing errors on both traffic throughput and the fairness of safety among vehicles. The proposed framework provides a solution to determine the Pareto frontier for traffic throughput and vehicle safety. ComDrive, a communication-based autonomous driving simulation platform, is developed to validate the proposed approach. Extensive experiments demonstrate that the proposed approach outperforms existing baselines