idmTPreg: Regression Model for Progressive Illness Death Data

The progressive illness-death model is frequently used in medical applications. For example, the model may be used to describe the disease process in cancer studies. We have developed a new R package called idmTPreg to estimate regression coefficients in datasets that can be described by the progressive illness-death model. The motivation for the development of the package is a recent contribution that enables the estimation of possibly time-varying covariate effects on the transition probabilities for a progressive illness-death data. The main feature of the package is that it befits both non-Markov and Markov progressive illness-death data. The package implements the introduced estimators obtained using a direct binomial regression approach. Also, variance estimates and confidence bands are implemented in the package. This article presents guidelines for the use of the package.BERC 2014-2017 SEV-2013-0323 MTM2016-76969-P FP7/2011: Marie Curie Initial Training Network MEDIASRE

An open-source framework for synthetic post-dive Doppler ultrasound audio generation

Doppler ultrasound (DU) measurements are used to detect and evaluate venous gas emboli (VGE) formed after decompression. Automated methodologies for assessing VGE presence using signal processing have been developed on varying real-world datasets of limited size and without ground truth values preventing objective evaluation. We develop and report a method to generate synthetic post-dive data using DU signals collected in both precordium and subclavian vein with varying degrees of bubbling matching field-standard grading metrics. This method is adaptable, modifiable, and reproducible, allowing for researchers to tune the produced dataset for their desired purpose. We provide the baseline Doppler recordings and code required to generate synthetic data for researchers to reproduce our work and improve upon it. We also provide a set of pre-made synthetic post-dive DU data spanning six scenarios representing the Spencer and Kisman-Masurel (KM) grading scales as well as precordial and subclavian DU recordings. By providing a method for synthetic post-dive DU data generation, we aim to improve and accelerate the development of signal processing techniques for VGE analysis in Doppler ultrasound

Taxanes trigger cancer cell killing in vivo by inducing non-canonical T cell cytotoxicity

Although treatment with taxanes does not always lead to clinical benefit, all patients are at risk of their detrimental side effects such as peripheral neuropathy. Understanding the in vivo mode of action of taxanes can help design improved treatment regimens. Here, we demonstrate that in vivo, taxanes directly trigger T cells to selectively kill cancer cells in a non-canonical, T cell receptor-independent manner. Mechanistically, taxanes induce T cells to release cytotoxic extracellular vesicles, which lead to apoptosis specifically in tumor cells while leaving healthy epithelial cells intact. We exploit these findings to develop an effective therapeutic approach, based on transfer of T cells pre-treated with taxanes ex vivo, thereby avoiding toxicity of systemic treatment. Our study reveals a different in vivo mode of action of one of the most commonly used chemotherapies, and opens avenues to harness T cell-dependent anti-tumor effects of taxanes while avoiding systemic toxicity

Taxanes trigger cancer cell killing in vivo by inducing non-canonical T cell cytotoxicity

Although treatment with taxanes does not always lead to clinical benefit, all patients are at risk of their detrimental side effects such as peripheral neuropathy. Understanding the in vivo mode of action of taxanes can help design improved treatment regimens. Here, we demonstrate that in vivo, taxanes directly trigger T cells to selectively kill cancer cells in a non-canonical, T cell receptor-independent manner. Mechanistically, taxanes induce T cells to release cytotoxic extracellular vesicles, which lead to apoptosis specifically in tumor cells while leaving healthy epithelial cells intact. We exploit these findings to develop an effective therapeutic approach, based on transfer of T cells pre-treated with taxanes ex vivo, thereby avoiding toxicity of systemic treatment. Our study reveals a different in vivo mode of action of one of the most commonly used chemotherapies, and opens avenues to harness T cell-dependent anti-tumor effects of taxanes while avoiding systemic toxicity

Spectral unmixing of hyperspectral images based on block sparse structure

Spectral unmixing (SU) of hyperspectral images (HSIs) is one of the important areas in remote sensing (RS) that needs to be carefully addressed in different RS applications. Despite the high spectral resolution of the hyperspectral data, the relatively low spatial resolution of the sensors may lead to mixture of different pure materials within the image pixels. In this case, the spectrum of a given pixel recorded by the sensor can be a combination of multiple spectra each belonging to a unique material in that pixel. SU is then used as a technique to extract the spectral characteristics of the different materials within the mixed pixels and to recover the spectrum of each pure spectral signature, called endmember. Block-sparsity exists in hyperspectral images as a result of spectral similarity between neighboring pixels. In block-sparse signals, the nonzero samples occur in clusters and the pattern of the clusters is often supposed to be unavailable as prior information. This paper presents an innovative SU approach for HSIs based on block-sparse structure. Hyperspectral unmixing problem is solved using pattern coupled sparse Bayesian learning strategy. To evaluate the performance of the proposed SU algorithm, it is tested on both synthetic and real hyperspectral data and the quantitative results are compared to those of other state-of-the-art methods in terms of abundance angle distance and mean squared error. The achieved results show the superiority of the proposed algorithm over the other competing methods by a significant margin. (c) 2023 Society of Photo-Optical Instrumentation Engineers (SPIE

Enhanced visible-light photocatalytic activity of strontium-doped zinc oxide nanoparticles

Strontium-doped zinc oxide nanoparticles (Zn1-xSrxO NPs; x=0, 0.02, 0.04, and 0.06) were synthesized by a sol-gel method. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) images showed NPs with nearly spherical shapes, with sizes from 27 to 41 nm for high Sr concentration and undoped ZnO NPs, respectively. X-ray diffraction (XRD) patterns, selected area electron diffraction (SAED) patterns, and Raman spectra indicated that the undoped and Sr-doped ZnO NPs were crystallized in a hexagonal wurtzite structure. However, the Raman results revealed a decrease in the crystalline quality with an increase in the Sr concentration in the ZnO structure. Evidence of dopant incorporation is demonstrated by X-ray photoelectron spectroscopy (XPS) of the Sr-doped ZnO NPs. From the results of optical characterizations, the band-gap values of the Zn0.98Sr0.02O and Zn0.96Sr0.04O NPs decreased, while the band-gap value of the Zn0.94Sr0.06O NPs increased in comparison to the band-gap value of the undoped ZnO NPs. Finally, the obtained NPs were used as a photocatalyst to remove methylene blue (MB). Observations showed that the efficiency of the photocatalyst activity of the ZnO NPs was significantly increased by increasing the Sr, but until an optimum concentration