A clinical evaluation of an ex vivo organ culture system to predict patient response to cancer therapy

IntroductionEx vivo organ cultures (EVOC) were recently optimized to sustain cancer tissue for 5 days with its complete microenvironment. We examined the ability of an EVOC platform to predict patient response to cancer therapy.MethodsA multicenter, prospective, single-arm observational trial. Samples were obtained from patients with newly diagnosed bladder cancer who underwent transurethral resection of bladder tumor and from core needle biopsies of patients with metastatic cancer. The tumors were cut into 250 μM slices and cultured within 24 h, then incubated for 96 h with vehicle or intended to treat drug. The cultures were then fixed and stained to analyze their morphology and cell viability. Each EVOC was given a score based on cell viability, level of damage, and Ki67 proliferation, and the scores were correlated with the patients’ clinical response assessed by pathology or Response Evaluation Criteria in Solid Tumors (RECIST).ResultsThe cancer tissue and microenvironment, including endothelial and immune cells, were preserved at high viability with continued cell division for 5 days, demonstrating active cell signaling dynamics. A total of 34 cancer samples were tested by the platform and were correlated with clinical results. A higher EVOC score was correlated with better clinical response. The EVOC system showed a predictive specificity of 77.7% (7/9, 95% CI 0.4–0.97) and a sensitivity of 96% (24/25, 95% CI 0.80–0.99).ConclusionEVOC cultured for 5 days showed high sensitivity and specificity for predicting clinical response to therapy among patients with muscle-invasive bladder cancer and other solid tumors

The Edinburgh Social Cognition Test (ESCoT):Examining the effects of age on a new measure of theory of mind and social norm understanding

<div><p>Current measures of social cognition have shown inconsistent findings regarding the effects of healthy aging. Moreover, no tests are currently available that allow clinicians and researchers to examine cognitive and affective theory of mind (ToM) and understanding of social norms within the same test. To address these limitations, we present the Edinburgh Social Cognition Test (ESCoT) which assesses cognitive and affective ToM and inter- and intrapersonal understanding of social norms. We examined the effects of age, measures of intelligence and the Broader Autism Phenotype (BAP) on the ESCoT and established tests of social cognition. Additionally, we investigated the convergent validity of the ESCoT based on traditional social cognition measures. The ESCoT was administered alongside Reading the Mind in Films (RMF), Reading the Mind in Eyes (RME), Judgement of Preference and Social Norm Questionnaire to 91 participants (30 aged 18–35 years, 30 aged 45–60 years and 31 aged 65–85 years). Poorer performance on the cognitive and affective ToM ESCoT subtests were predicted by increasing age. The affective ToM ESCoT subtest and RMF were predicted by gender, where being female predicted better performance. Unlike the ESCoT, better performance on the RMF was predicted by higher verbal comprehension and perceptual reasoning abilities, while better performance on the RME was predicted by higher verbal comprehension scores. Lower scores on inter-and intrapersonal understanding of social norms were both predicted by the presence of more autism-like traits while poorer interpersonal understanding of social norms performance was predicted by increasing age. These findings show that the ESCoT is a useful measure of social cognition and, unlike established tests of social cognition, performance is not predicted by measures of verbal comprehension and perceptual reasoning. This is particularly valuable to obtain an accurate assessment of the influence of age on our social cognitive abilities.</p></div

Correcting BLAST e-values for low-complexity segments

The statistical estimates of BLAST and PSI-BLAST are of extreme importance to determine the biological relevance of sequence matches. While being very effective in evaluating most matches, these estimates usually overestimate the significance of matches in the presence of low complexity segments. In this paper we present a model, based on divergence measures and statistics of the alignment structure, that corrects BLAST e-values for low complexity sequences without filtering or excluding them. We evaluate our method and compare it to other known methods using the Gene Ontology (GO)knowledge resource as a benchmark. Various performance measures, including ROC analysis, indicate that the new model improves over the state of the art. The program is available at biozon.org/ftp/ and www.cs.technion.ac.il/~itaish/lowcomp

Distributed GSC beamforming using the relative transfer function

ABSTRACT A speech enhancement algorithm in a noisy and reverberant enclosure for a wireless acoustic sensor network (WASN) is derived. The proposed algorithm is structured as a two stage beamformers (BFs) scheme, where the outputs of the first stage are transmitted in the network. Designing the second stage BF requires estimating the desired signal components at the transmitted signals. The contribution here is twofold. First, in spatially static scenarios, the first stage BFs are designed to maintain a fixed response towards the desired signal. As opposed to competing algorithms, where the response changes and repeated estimation thereof is required. Second, the proposed algorithm is implemented in a generalized sidelobe canceler (GSC) form, separating the treatment of the desired speech and the interferences and enabling a simple timerecursive implementation of the algorithm. A comprehensive experimental study demonstrates the equivalent performance of the centralized GSC and of the proposed algorithm for both narrowband and speech signals

A consolidated perspective on multi-microphone speech enhancement and source separation

Added equation (108)International audienceSpeech enhancement and separation are core problems in audio signal processing, with commercial applications in devices as diverse as mobile phones, conference call systems, hands-free systems, or hearing aids. In addition, they are crucial pre-processing steps for noise-robust automatic speech and speaker recognition. Many devices now have two to eight microphones. The enhancement and separation capabilities offered by these multichannel interfaces are usually greater than those of single-channel interfaces. Research in speech enhancement and separation has followed two convergent paths, starting with microphone array processing and blind source separation, respectively. These communities are now strongly interrelated and routinely borrow ideas from each other. Yet, a comprehensive overview of the common foundations and the differences between these approaches is lacking at present. In this article, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: a) the acoustic impulse response model, b) the spatial filter design criterion, c) the parameter estimation algorithm, and d) optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field

Near-field source extraction using speech presence probabilities for ad hoc microphone arrays

Ad hoc wireless acoustic sensor networks (WASNs) hold great potential for improved performance in speech processing applications, thanks to better coverage and higher diversity of the received signals. We consider a multiple speaker scenario where each of the WASN nodes, an autonomous system comprising of sensing, processing and communicating capabilities, is positioned in the near-field of one of the speakers. Each node aims at extracting its nearest speaker while suppressing other speakers and noise. The ad hoc network is characterized by an arbitrary number of speakers/nodes with uncontrolled microphone constellation. In this paper we propose a distributed algorithm which shares information between nodes. The algorithm requires each node to transmit a single audio channel in addition to a soft time-frequency (TF) activity mask for its nearest speaker. The TF activity masks are computed as a combination of estimates of a model-based speech presence probability (SPP), direct to reverberant ratio (DRR) and direction of arrival (DOA) per TF bin. The proposed algorithm, although sub-optimal compared to the centralized solution, is superior to the single-node solution

Study of the Absorption of Electromagnetic Radiation by 3D, Vacuum-Packaged, Nano-Machined CMOS Transistors for Uncooled IR Sensing

There is an ongoing effort to fabricate miniature, low-cost, and sensitive thermal sensors for domestic and industrial uses. This paper presents a miniature thermal sensor (dubbed TMOS) that is fabricated in advanced CMOS FABs, where the micromachined CMOS-SOI transistor, implemented with a 130-nm technology node, acts as a sensing element. This study puts emphasis on the study of electromagnetic absorption via the vacuum-packaged TMOS and how to optimize it. The regular CMOS transistor is transformed to a high-performance sensor by the micro- or nano-machining process that releases it from the silicon substrate by wafer-level processing and vacuum packaging. Since the TMOS is processed in a CMOS-SOI FAB and is comprised of multiple thin layers that follow strict FAB design rules, the absorbed electromagnetic radiation cannot be modeled accurately and a simulation tool is required. This paper presents modeling and simulations based on the LUMERICAL software package of the vacuum-packaged TMOS. A very high absorption coefficient may be achieved by understanding the physics, as well as the role of each layer