Nemonoxacin (Taigexyn^®): A New Non-Fluorinated Quinolone

Nemonoxacin (Taigexyn®), a novel C-8-methoxy non-fluorinated quinolone, has been approved for use in community-acquired pneumonia (CAP) in Taiwan (2014) and mainland China (2016). The FDA granted nemonoxacin ‘qualified infectious disease product’ and ‘fast-track’ designations for CAP and acute bacterial skin and skin structure infection in December 2013. It possesses a broad spectrum of bactericidal activity against typical and atypical respiratory pathogens. In particular, nemonoxacin has activity against resistant Gram-positive cocci, including penicillin-resistant Streptococcus pneumoniae and methicillin-resistant Staphylococcus aureus. Oral nemonoxacin was compared with oral levofloxacin for efficacy and safety in three randomized, double-blinded, controlled Phase II–III clinical trials for the treatment of CAP. This article will review the microbiological profile of nemonoxacin against respiratory pathogens including S. pneumoniae and S. aureus, and microbiological outcome data from the three Phase II–III studies

Antecedents of Coordination Effectiveness of Software Developer Dyads from Interacting Teams: An Empirical Investigation

Among numerous reasons for software project failure, coordination failure is considered as especially salient. Prior studies on coordination in software development are confined to team internal coordination and do not explicitly differentiate team internal and external coordination processes. This study proposes a research model to explain the antecedents of coordination effectiveness of software developer dyads from interacting teams. We explore the antecedents by integrating inter-personal coordination and technology-based coordination. Data were collected from 59 software developer dyads from different interacting teams as well as software developers’ managers. The results reveal that implicit knowledge sharing has a significant positive impact on coordination effectiveness. Social capital (mutual trust and project commitment) has a significant impact on knowledge sharing with mutual trust directly affecting both implicit and explicit knowledge sharing. Project commitment also has a direct impact on explicit knowledge sharing and mutual trust, but it does not directly affect implicit knowledge sharing

Audio Deepfake Detection: A Survey

Audio deepfake detection is an emerging active topic. A growing number of literatures have aimed to study deepfake detection algorithms and achieved effective performance, the problem of which is far from being solved. Although there are some review literatures, there has been no comprehensive survey that provides researchers with a systematic overview of these developments with a unified evaluation. Accordingly, in this survey paper, we first highlight the key differences across various types of deepfake audio, then outline and analyse competitions, datasets, features, classifications, and evaluation of state-of-the-art approaches. For each aspect, the basic techniques, advanced developments and major challenges are discussed. In addition, we perform a unified comparison of representative features and classifiers on ASVspoof 2021, ADD 2023 and In-the-Wild datasets for audio deepfake detection, respectively. The survey shows that future research should address the lack of large scale datasets in the wild, poor generalization of existing detection methods to unknown fake attacks, as well as interpretability of detection results

Distinguishing Neural Speech Synthesis Models Through Fingerprints in Speech Waveforms

Recent strides in neural speech synthesis technologies, while enjoying widespread applications, have nonetheless introduced a series of challenges, spurring interest in the defence against the threat of misuse and abuse. Notably, source attribution of synthesized speech has value in forensics and intellectual property protection, but prior work in this area has certain limitations in scope. To address the gaps, we present our findings concerning the identification of the sources of synthesized speech in this paper. We investigate the existence of speech synthesis model fingerprints in the generated speech waveforms, with a focus on the acoustic model and the vocoder, and study the influence of each component on the fingerprint in the overall speech waveforms. Our research, conducted using the multi-speaker LibriTTS dataset, demonstrates two key insights: (1) vocoders and acoustic models impart distinct, model-specific fingerprints on the waveforms they generate, and (2) vocoder fingerprints are the more dominant of the two, and may mask the fingerprints from the acoustic model. These findings strongly suggest the existence of model-specific fingerprints for both the acoustic model and the vocoder, highlighting their potential utility in source identification applications.Comment: Submitted to ICASSP 202

The Impact of Excluding Trials from Network Meta-Analyses - An Empirical Study

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Network meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, which has an inherent appeal for clinicians, patients, and policy decision makers. Two recent reports have shown that the impact of excluding a treatment on NMAs can be substantial. However, no one has assessed the impact of excluding a trial from NMAs, which is important because many NMAs selectively include trials in the analysis. This article empirically examines the impact of trial exclusion using both the arm-based (AB) and contrast-based (CB) approaches, by reanalyzing 20 published NMAs involving 725 randomized controlled trials and 449,325 patients. For the population-averaged absolute risk estimates using the AB approach, the average fold changes across all networks ranged from 1.004 (with standard deviation 0.004) to 1.072 (with standard deviation 0.184); while the maximal fold changes ranged from 1.032 to 2.349. In 12 out of 20 NMAs, a 1.20-fold or larger change is observed in at least one of the population- averaged absolute risk estimates. In addition, while excluding a trial can substantially change the estimated relative effects (e.g., log odds ratios), there is no systematic difference in terms of changes between the two approaches. Changes in treatment rankings are observed in 7 networks and changes in inconsistency are observed in 3 networks. We do not observe correlations between changes in treatment effects, treatment rankings and inconsistency. Finally, we recommend rigorous inclusion and exclusion criteria, logical study selection process, and reasonable network geometry to ensure robustness and generalizability of the results of NMAs

On the performance of an integrated communication and localization system: an analytical framework

Quantifying the performance bound of an integrated localization and communication (ILAC) system and the trade-off between communication and localization performance is critical. In this letter, we consider an ILAC system that can perform communication and localization via time-domain or frequency-domain resource allocation. We develop an analytical framework to derive the closed-form expression of the capacity loss versus localization Cramer-Rao lower bound (CRB) loss via time-domain and frequency-domain resource allocation. Simulation results validate the analytical model and demonstrate that frequency-domain resource allocation is preferable in scenarios with a smaller number of antennas at the next generation nodeB (gNB) and a larger distance between user equipment (UE) and gNB, while time-domain resource allocation is preferable in scenarios with a larger number of antennas and smaller distance between UE and the gNB.Comment: 5 pages, 3 figure