Role of Circulating Biomarkers in Platinum-Resistant Ovarian Cancer

Ovarian cancer (OC) is the second most common cause of death in women with gynecological cancer. Considering the poor prognosis, particularly in the case of platinum-resistant (PtR) disease, a huge effort was made to define new biomarkers able to help physicians in approaching and treating these challenging patients. Currently, most data can be obtained from tumor biopsy samples, but this is not always available and implies a surgical procedure. On the other hand, circulating biomarkers are detected with non-invasive methods, although this might require expensive techniques. Given the fervent hope in their value, here we focused on the most studied circulating biomarkers that could play a role in PtR OC.     

Detect,Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework

Machine Learning - In this paper we introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision...     

Aligning Single‐Walled Carbon Nanotubes By Means Of Langmuir–Blodgett Film Deposition: Optical,Morphological, and Photo‐electrochemical Studies

An alkoxy‐substituted poly(phenylene thiophene) is used in order to suspend single‐walled carbon nanotubes in an organic solvent. The suspension is spread on the air–water interface of a Langmuir trough and the floating film is characterized by means of Brewster angle microscopy and UV‐visible reflection spectroscopy and the compression isotherm is recorded. The polymer/carbon‐nanotube blend is transferred onto different substrates using the Langmuir–Blodgett technique. AFM measurements indicate the formation of globular structures for the samples transferred at low surface‐pressure values and a tubular morphology for high‐pressure‐deposited samples. AFM analysis is repeated on a sample exposed to soft X‐rays for about 5 h and a highly organized structure of bundles of carbon nanotubes rises up. Samples with different numbers of layers are transferred onto ITO substrates by means of the Langmuir–Blodgett method and are tested as photocathodes in a photo‐electrochemical cell. A V_oc of 0.18 V, an I_sc of 85.8 mA, FF of 40.0%, and η of (6.23 × 10^?3)% are obtained.     

An artificial neural network approach for the control of the laser milling process

Laser milling (LM) can be classified as a layer manufacturing process in which the material is removed by a laser beam by means of the ablation mechanism. It is a laser machining process which uses a laser beam to produce 3D shapes into a wide variety of materials. It is also known as laser ablation. It shows clear advantages versus the traditional milling such as the unlimited choice of materials, the direct use of computer-aided design structure data, the high geometric flexibility, and the touchless tool. LM requires the selection of optimal machining parameters for the job. Unlike the mechanical milling and the mechanical incision, the depth of the single removed layer is chosen at the beginning as input parameter of the process. In LM, the ablated depth depends from the process parameters such as laser power, scan speed, pulse duration, and pulse frequency. This work aims to develop an algorithm that can predict the parameters necessary to execute the material removal with a preset ablation depth. Using the results of an experimental campaign, the laser milling process was modeled by means of a back-propagation artificial neural network. Then, an iterative algorithm, based on the previous trained neural network, permitted to calculate the scanning velocity and pulse frequency that approached for the best the preset ablation depth. The developed approach represents a mean for the rational selection of laser ablation process parameters. It can be performed in an intuitive manner since it uses simple artificial intelligence like the artificial neural network.     

Effect of diffusion-sensitizing gradient timings on the exponential,biexponential and diffusional kurtosis model parameters: in-vivo measurements in the rat thalamus

Object  

To investigate whether spacing (Δ) and duration (δ) of the diffusion-sensitizing gradient pulses differentially affect exponential (D′), biexponential (D _slow, D _fast and f _slow) and diffusional kurtosis (D and K) model parameters.     

Using shape for self-assembly

A 1980 poem by Alan Mackay outlines his aspiration 'to see what all have seen but think what none have thought': a daunting task, which he accomplished not once, but several times. A 'truly myriadminded, manysided man-a veritable triacontahedron' in the words of his colleagues and friends, Alan Mackay pursued a lifelong interest in the problems of morphogenesis and form, a comprehension of which necessitated him crisscrossing the borders of the inanimate and animate world of soft and hard materials, through the integration of concepts and methods of chemistry, physics, mathematics and biology. In other words, he realized in his time a genuinely interdisciplinary approach to complex problems that still to this day remains beyond much of the academic community. Being invited to contribute a paper on the theme 'beyond crystals', we naturally wondered how Alan Mackay would think about the world of nanoscale self-assembly where so much depends on shape and form.     

Machine learning-based tools to model and to remove the off-target effect

A RNA interference, also called a gene knockdown, is a biological technique which consists of inhibiting a targeted gene in a cell. By doing so, one can identify statistical dependencies between a gene and a cell phenotype. However, during such a gene inhibition process, additional genes may also be modified. This is called the “off-target effect”. The consequence is that there are some additional phenotype perturbations which are “off-target”. In this paper, we study new machine learning tools that both model the cell phenotypes and remove the “off-target effect”. We propose two new automatic methods to remove the “off-target” components from a data sample. The first method is based on vector quantization (VQ). The second method we propose relies on a classification forest. Both methods rely on analyzing the homogeneity of several repetitions of a gene knockdown. The baseline we consider is a Gaussian mixture model whose parameters are learned under constraints with a standard Expectation–Maximization algorithm. We evaluate these methods on a real data set, a semi-synthetic data set, and a synthetic toy data set. The real data set and the semi-synthetic data set are composed of cell growth dynamic quantities measured in time laps movies. The main result is that we obtain the best recognition performance with the probabilistic version of the VQ-based method.