GhostVec: A New Threat to Speaker Privacy of End-to-End Speech Recognition System

Speaker adaptation systems face privacy concerns, for such systems are trained on private datasets and often overfitting. This paper demonstrates that an attacker can extract speaker information by querying speaker-adapted speech recognition (ASR) systems. We focus on the speaker information of a transformer-based ASR and propose GhostVec, a simple and efficient attack method to extract the speaker information from an encoder-decoder-based ASR system without any external speaker verification system or natural human voice as a reference. To make our results quantitative, we pre-process GhostVec using singular value decomposition (SVD) and synthesize it into waveform. Experiment results show that the synthesized audio of GhostVec reaches 10.83\% EER and 0.47 minDCF with target speakers, which suggests the effectiveness of the proposed method. We hope the preliminary discovery in this study to catalyze future speech recognition research on privacy-preserving topics.Comment: accepted in ACM Multimedia Asia 202

Reprogramming Self-supervised Learning-based Speech Representations for Speaker Anonymization

Current speaker anonymization methods, especially with self-supervised learning (SSL) models, require massive computational resources when hiding speaker identity. This paper proposes an effective and parameter-efficient speaker anonymization method based on recent End-to-End model reprogramming technology. To improve the anonymization performance, we first extract speaker representation from large SSL models as the speaker identifies. To hide the speaker's identity, we reprogram the speaker representation by adapting the speaker to a pseudo domain. Extensive experiments are carried out on the VoicePrivacy Challenge (VPC) 2022 datasets to demonstrate the effectiveness of our proposed parameter-efficient learning anonymization methods. Additionally, while achieving comparable performance with the VPC 2022 strong baseline 1.b, our approach consumes less computational resources during anonymization.Comment: accepted in ACM Multimedia Asia202

After-effects of repetitive transcranial magnetic stimulation with parameter dependence on long-term potentiation-like plasticity and object recognition memory in rats

ObjectiveTo investigate the after-effects of 25-Hz repetitive transcranial magnetic stimulation (rTMS) at 60, 100, and 120% resting motor threshold (rMT) on long-term potentiation (LTP) in the rat hippocampus, to clarify the intensity dependence of rTMS, and to determine whether it simultaneously affects learning and memory ability.MethodsFive rats were randomly selected from 70 male Wistar rats, and evoked rMT potentials were recorded in response to magnetic stimulation. The remaining 65 rats were randomly assigned to five groups (n = 13), including sham rTMS, 1 Hz 100% rMT, and 25 Hz rTMS groups with 3 subgroups of 60% rMT, 100% rMT, and 120% rMT. Five rats in each group were anesthetized and induced by a priming TMS-test design for population spike (PS) response of the perforant path-dentate gyrus in the hippocampus; the remaining eight rats in each group were evaluated for object recognition memory in the novel object recognition (NOR) task after the different rTMS protocols.ResultsForty-five percent (approximately 1.03 T) of the magnetic stimulator output was confirmed as rMT in the biceps femoris muscle. The PS ratio was ranked as follows: 25 Hz 100% rMT (267.78 ± 25.71%) > sham rTMS (182 ± 9.4%) >1 Hz 100% rMT (102.69 ± 6.64%) > 25 Hz 120% rMT (98 ± 11.3%) > 25 Hz 60% rMT (36 ± 8.5%). Significant differences were observed between the groups, except for the difference between the 25 Hz 120% rMT and the 1 Hz 100% rMT groups (p = 0.446). LTP was successfully induced over the 60-min recording period only in the sham rTMS and 25 Hz 100% rMT groups. Moreover, these two groups spent more time exploring a novel object than a familiar object during the NOR task (p < 0.001), suggesting long-term recognition memory retention. In the between-group analysis of the discrimination index, the following ranking was observed: 25 Hz 100% rMT (0.812 ± 0.158) > sham rTMS (0.653 ± 0.111) > 25 Hz 120% rMT (0.583 ± 0.216) >1 Hz 100% rMT (0.581 ± 0.145) > 25 Hz 60% rMT (0.532 ± 0.220).ConclusionThe after-effect of 25-Hz rTMS was dependent on stimulus intensity and provided an inverted (V-shaped) bidirectional modulation on hippocampal plasticity that involved two forms of metaplasticity. Furthermore, the effects on the recognition memory ability were positively correlated with those on LTP induction in the hippocampus in vivo

REMEDIAL APPLICATIONS OF SILENCING RIBONUCLEIC ACIDS AND MODALITIES FOR ITS DELIVERY TO THE KIDNEYS - A REVIEW

Background: The Kidney has been the target organ for the delivery of silencing ribonucleic acids (silencing RNA) administered systemically in comparison to other body tissues. Materials and method: In this review, we discussed different approaches made to delivering proteins to the kidneys in different conditions like normal and pathological defects. Data from clinical experiments have been used to discuss and support the administration of silencing RNA for the treatment of kidney diseases. Results: Results were achieved using the available genome wide RNA libraries. Conclusion: The research results are helpful in application to 3D and conventional models to find the involvement of signal pathways in kidney diseases

Capsaicin Protects Cardiomyocytes against Anoxia/Reoxygenation Injury via Preventing Mitochondrial Dysfunction Mediated by SIRT1

Capsaicin (Cap) has been reported to have beneficial effects on cardiovascular system, but the mechanisms underlying these effects are still poorly understood. Apoptosis has been shown to be involved in mitochondrial dysfunction, and upregulating expression of SIRT1 can inhibit the apoptosis of cardiomyocytes induced by anoxia/reoxygenation (A/R). Therefore, the aim of this study was to test whether the protective effects of Cap against the injury to the cardiomyocytes are mediated by SIRT1. The effects of Cap with or without coadministration of sirtinol, a SIRT1 inhibitor, on changes induced by A/R in the cell viability, activities of lactate dehydrogenase (LDH), creatine phosphokinase (CPK), levels of intracellular reactive oxygen species (ROS), and mitochondrial membrane potential (MMP), related protein expression, mitochondrial permeability transition pore (mPTP) opening, and apoptosis rate in the primary neonatal rat cardiomyocytes were tested. Cap significantly increased the cell viability, upregulated expression of SIRT1 and Bcl-2, and decreased the LDH and CPK release, generation of ROS, loss of MMP, mPTP openness, activities of caspase-3, release of the cytochrome c, and apoptosis of the cardiomyocytes. Sirtinol significantly blocked the cardioprotective effects of Cap. The results suggest that the protective effects of Cap against A/R-induced injury to the cardiomyocytes are involved with SIRT1

Case report: A novel 5'-UTR-exon1-intron1 deletion in MLYCD in an IVF child with malonyl coenzyme A decarboxylase deficiency and literature review

The subject of the study is an 11-month old IVF baby girl with the typical clinical manifestation of malonyl coenzyme A decarboxylase deficiency, including developmental delay, limb weakness, cardiomyopathy, and excessive excretion of malonic acid and methylmalonic acid. Whole genome sequencing (WGS) revealed a novel heterozygous nonsense mutation (c.672delG, p.Trp224Ter) in the MLYCD gene of the proband and her father and a novel heterozygous deletion in 5'-UTR-exon1-intron1 of the MLYCD gene of the proband and her mother. The patient's cardiac function and limb weakness improved considerably after 3 months of a low-fat diet supplemented with L-carnitine. Furthermore, mapping of gene mutations and clinical manifestations was done by case collection

A Model Predictive Control for Heat Supply at Building Thermal Inlet Based on Data-Driven Model

At present, the traditional control strategy of heating systems is still unable to achieve building heating on demand, which enhances the energy consumption of heating and affects the thermal comfort of buildings. Therefore, this study puts forward a novel data-driven MPC for building thermal inlet, which allows the optimal operation of the district heating system and has been verified by simulation with three public buildings. In this method, the indoor temperature at the next moment reaches the temperature set value by changing the current flow rate. First, based on the energy consumption monitoring platform and the measured data of the buildings, the building indoor temperature prediction model at the next moment is established by using long short-term memory (LSTM). Compared with subspace model identification (SMI), LSTM has higher prediction accuracy, and the R2 was about 0.9 in three buildings. Second, the particle generated by particle swarm optimization, which represents the flow variation, is input to the trained LSTM to predict the indoor temperature. By minimizing the objective function, the optimal flow change at the current time can be calculated. The results showed that the MPC based on a data-driven model can adjust the flow rate in time to maintain a stable indoor temperature with ±0.5 °C error. In addition, when the temperature setting needs to be changed, the indoor temperature can reach the new set value in 3 h, which outperforms the PID control. The method proposed in this paper can greatly reduce the influence of regulation lag by adjusting the flow in advance

A Model Predictive Control for Heat Supply at Building Thermal Inlet Based on Data-Driven Model

At present, the traditional control strategy of heating systems is still unable to achieve building heating on demand, which enhances the energy consumption of heating and affects the thermal comfort of buildings. Therefore, this study puts forward a novel data-driven MPC for building thermal inlet, which allows the optimal operation of the district heating system and has been verified by simulation with three public buildings. In this method, the indoor temperature at the next moment reaches the temperature set value by changing the current flow rate. First, based on the energy consumption monitoring platform and the measured data of the buildings, the building indoor temperature prediction model at the next moment is established by using long short-term memory (LSTM). Compared with subspace model identification (SMI), LSTM has higher prediction accuracy, and the R2 was about 0.9 in three buildings. Second, the particle generated by particle swarm optimization, which represents the flow variation, is input to the trained LSTM to predict the indoor temperature. By minimizing the objective function, the optimal flow change at the current time can be calculated. The results showed that the MPC based on a data-driven model can adjust the flow rate in time to maintain a stable indoor temperature with ±0.5 °C error. In addition, when the temperature setting needs to be changed, the indoor temperature can reach the new set value in 3 h, which outperforms the PID control. The method proposed in this paper can greatly reduce the influence of regulation lag by adjusting the flow in advance

Inflammatory response-based prognostication and personalized therapy decisions in clear cell renal cell cancer to aid precision oncology

Abstract Objective The impact of inflammatory response on tumor development and therapeutic response is of significant importance in clear cell renal cell carcinoma (ccRCC). The customization of specialized prognostication approaches and the exploration of supplementary treatment options hold critical clinical implications in relation to the inflammatory response. Methods In the present study, unsupervised clustering was implemented on TCGA-KIRC tumors using transcriptome profiles of inflammatory response genes, which was then validated in two ccRCC datasets (E-MATB-1980 and ICGC) and two immunotherapy datasets (IMvigor210 and Liu et al.) via SubMap and NTP algorithms. Combining co-expression and LASSO analyses, inflammatory response-based scoring system was defined, which was evaluated in pan-cancer. Results Three reproducible inflammatory response subtypes (named IR1, IR2 and IR3) were determined and independently verified, each exhibiting distinct molecular, clinical, and immunological characteristics. Among these subtypes, IR2 had the best OS outcomes, followed by IR3 and IR1. In terms of anti-angiogenic agents, sunitinib may be appropriate for IR1 patients, while axitinib and pazopanib may be suitable for IR2 patients, and sorafenib for IR3 patients. Additionally, IR1 patients might benefit from anti-CTLA4 therapy. A scoring system called IRscore was defined for individual ccRCC patients. Patients with high IRscore presented a lower response rate to anti-PD-L1 therapy and worse prognostic outcomes. Pan-cancer analysis demonstrated the immunological features and prognostic relevance of the IRscore. Conclusion Altogether, characterization of inflammatory response subtypes and IRscore provides a roadmap for patient risk stratification and personalized treatment decisions, not only in ccRCC, but also in pan-cancer