Miracle-dz/UniFAR-FTUs huggingface dataset:
Atezolizumab in non-squamous non-small cell lung cancer.

Immune therapy opened a new era in treatment of advanced non-small cell lung cancer (NSCLC). Immune checkpoint inhibitors (ICIs) targeting the programmed death-ligand 1 (PD-L1)/programmed death-1 (PD-1) pathway have achieved great success over conventional cytotoxic chemotherapy.
High resolution volumetric imaging of primary and secondary tumor spheroids using multi-angle Light Sheet Fluorescence Microscopy (LSFM)

Breast cancer and Glioblastoma brain cancer are severe malignancies with poor prognosis. In this study, primary Glioblastoma and secondary breast cancer spheroids are formed and treated with the well-known Temozolomide and Doxorubicin chemotherapeutics, respectively. High resolution imaging of both primary and secondary cancer cell spheroids is possible using a custom multi-angle Light Sheet Fluorescence Microscope. Such a technique is successful in realizing preclinical drug screening, while enables the discrimination among physiologic tumor parameters.
A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin

Segmenting objects of interest from 3D data sets is a common problem encountered in biological data. Small field of view and intrinsic biological variability combined with optically subtle changes of intensity, resolution, and low contrast in images make the task of segmentation difficult, especially for microscopy of unstained living or freshly excised thick tissues. Incorporating shape information in addition to the appearance of the object of interest can often help improve segmentation performance. However, the shapes of objects in tissue can be highly variable and design of a flexible shape model that encompasses these variations is challenging. To address such complex segmentation problems, we propose a unified probabilistic framework that can incorporate the uncertainty associated with complex shapes, variable appearance, and unknown locations. The driving application that inspired the development of this framework is a biologically important segmentation problem: the task of automatically detecting and segmenting the dermal-epidermal junction (DEJ) in 3D reflectance confocal microscopy (RCM) images of human skin. RCM imaging allows noninvasive observation of cellular, nuclear, and morphological detail. The DEJ is an important morphological feature as it is where disorder, disease, and cancer usually start. Detecting the DEJ is challenging, because it is a 2D surface in a 3D volume which has strong but highly variable number of irregularly spaced and variably shaped “peaks and valleys. ” In addition, RCM imaging resolution, contrast, and intensity vary with depth. Thus, a prior model needs to incorporate the intrinsic structure while allowing variability in essentially all its parameters. We propose a model which can incorporate objects of interest with complex shapes and variable appearance in an unsupervised setting by utilizing domain knowledge to build appropriate priors of the model. Our novel strategy to model this structure combines a spatial Poisson process with shape priors and performs inference using Gibbs sampling. Experimental results show that the proposed unsupervised model is able to automatically detect the DEJ with physiologically relevant accuracy in the range 10-20 μm.
Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Proteins are an important class of biological macromolecules that play many key roles in cellular functions including gene expression, catalyzing metabolic reactions, DNA repair and replication. Therefore, a detailed understanding of these processes provides critical information on how cells function. Integrative structural MS methods offer structural and dynamical information on protein complex assembly, complex connectivity, subunit stoichiometry, protein oligomerization and ligand binding. Recent advances in integrative structural MS have allowed for the characterization of challenging biological systems including large DNA binding proteins and membrane proteins. This protocol describes how to integrate diverse MS data such as native MS and ion mobility-mass spectrometry (IM-MS) with molecular dynamics simulations to gain insights into a helicase-nuclease DNA repair protein complex. The resulting approach provides a framework for detailed studies of ligand binding to other protein complexes involved in important biological processes.
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding

Distant supervision is an effective method to generate large scale labeled data for relation extraction, which assumes that if a pair of entities appears in some relation of a Knowledge Graph (KG), all sentences containing those entities in a large unlabeled corpus are then labeled with that relation to train a relation classifier. However, when the pair of entities has multiple relationships in the KG, this assumption may produce noisy relation labels. This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly. Specifically, we make use of the type information and the translation law derived from typical KG embedding model to learn embeddings for certain sentence patterns. As the supervision signal is only determined by the two aligned entities, neither hard relation labels nor extra noise-reduction model for the bag of sentences is needed in this way. The experiments show that the approach performs well in current distant supervision dataset.
Loss of HACE1 promotes colorectal cancer cell migration via upregulation of 
YAP1

Colorectal cancer (CRC) is the third‐leading cause of cancer mortality worldwide. HACE1 function as a tumor‐suppressor gene and is downregulated in several kinds of cancers. However, the distribution and clinical significance of HACE1 in CRC is still not clarified. In this study, we found that the HACE1 expression is greatly downregulated in CRC tissues and cell lines. Moreover, the HACE1 expression was significantly associated with inhibition of CRC cell proliferation, metastasis, and invasion. HACE1 inhibited epithelial–mesenchymal transition in CRC cells. Furthermore, we found that HACE1 altered the protein expression of the Hippo pathway by downregulation of YAP1. HACE1 suppresses the invasive ability of CRC cells by negatively regulating the YAP1 pathway. Our data indicates that HACE1 directly targets YAP1 and induces downregulation of YAP1, thereby increasing the activity of the Hippo pathway. In summary, these findings demonstrated that HACE1–YAP1 axis had an important part in the CRC development and progression.
Assessing the influence of treatment nonadherence on noninferiority trials using the tipping point approach

In noninferiority (NI) trials, an ongoing methodological challenge is how to handle in the analysis the subjects who are nonadherent to their assigned treatment. Some investigators perform the intent‐to‐treat (ITT) as the primary analysis and the per‐protocol (PP) analysis as sensitivity analysis, whereas others do the reverse since ITT results may be anticonservative in the NI setting. But even when there is agreement between the ITT and PP approaches, NI of the experimental therapy to the comparator is not guaranteed. We propose that a tipping point method be used to further assess the impact of nonadherence on the results of a NI trial. In this approach, data from the nonadherers obtained after treatment discontinuation is not used, and their outcomes under the counterfactual situation of complete adherence are considered missing. The tipping point analysis indicates how sensitive the NI trial results are to the values of these missing counterfactual outcomes. The advantages of this approach are that a model or mechanism for the missing outcomes does not have to be assumed, and all subjects who were randomized are included in the analysis. We consider both binary and continuous outcomes and propose extensions to accommodate different types of nonadherence. The methods are illustrated with examples from two NI trials, one to evaluate different doses of radiation therapy to treat painful bone metastases and the other to compare treatments for reducing depression in adolescents.
Assessment of the antiproliferative and apoptotic roles of sulfonamide carbonic anhydrase IX inhibitors in HeLa cancer cell line

Abstract Carbonic anhydrase IX (CA IX) has recently been validated as an antitumor/antimetastatic drug target. In this study, we examined the underlying molecular mechanisms and the anticancer activity of sulfonamide CA IX inhibitors against cervical cancer cell lines. The effects of several sulfonamides on HeLa, MDA-MB-231, HT-29 cancer cell lines, and normal cell lines (HEK-293, PNT-1A) viability were determined. The compounds showed high cytotoxic and apoptotic activities, mainly against HeLa cells overexpressing CA IX. We were also examined for intracellular reactive oxygen species (ROS) production; intra-/extracellular pH changes, for inhibition of cell proliferation, cellular mitochondrial membrane potential change and for the detection of caspase 3, 8, 9, and CA IX protein levels. Of the investigated sulfonamides, one compound was found to possess high cytotoxic and anti-proliferative effects in HeLa cells. The cytotoxic effect occurred via apoptosis, being accompanied by a return of pHe/pHi towards normal values as for other CA IX inhibitors investigated earlier.
Self-Supervised GAN to Counter Forgetting

GANs involve training two networks in an adversarial game, where each network's task depends on its adversary. Recently, several works have framed GAN training as an online or continual learning problem. We focus on the discriminator, which must perform classification under an (adversarially) shifting data distribution. When trained on sequential tasks, neural networks exhibit \emph{forgetting}. For GANs, discriminator forgetting leads to training instability. To counter forgetting, we encourage the discriminator to maintain useful representations by adding a self-supervision. Conditional GANs have a similar effect using labels. However, our self-supervised GAN does not require labels, and closes the performance gap between conditional and unconditional models. We show that, in doing so, the self-supervised discriminator learns better representations than regular GANs.
Why scream about sound in space? The functions of audience discourse about unrealistic science in narrative fiction

Sound in space. Featherless dinosaurs. Physics-defying stunts. Unrealistic science in narrative fiction is often a subject of commentary and critique. However, there is limited research investigating the significance, risks and benefits of this discourse for audiences. This article analyses interviews and focus group discussions to develop a typology of functions that are served by audience discourse about the perceived realism of science in fiction. This typology illustrates how discourse about the realism of science in fiction can serve diverse functions for diverse audiences. Practitioners who use fictional examples in science communication may benefit from an awareness of the multifaceted nature of the discourse in which they are participating.
BRCA1 and BARD1 mediate apoptotic resistance but not longevity upon mitochondrial stress in Caenorhabditis elegans

Interventions that promote healthy aging are typically associated with increased stress resistance. Paradoxically, reducing the activity of core biological processes such as mitochondrial or insulin metabolism promotes the expression of adaptive responses, which in turn increase animal longevity and resistance to stress. In this study, we investigated the relation between the extended Caenorhabditis elegans lifespan elicited by reduction in mitochondrial functionality and resistance to genotoxic stress. We find that reducing mitochondrial activity during development confers germline resistance to DNA damage‐induced cell cycle arrest and apoptosis in a cell‐non‐autonomous manner. We identified the C. elegans homologs of the BRCA1/BARD1 tumor suppressor genes, brc‐1/brd‐1, as mediators of the anti‐apoptotic effect but dispensable for lifespan extension upon mitochondrial stress. Unexpectedly, while reduced mitochondrial activity only in the soma was not sufficient to promote longevity, its reduction only in the germline or in germline‐less strains still prolonged lifespan. Thus, in animals with partial reduction in mitochondrial functionality, the mechanisms activated during development to safeguard the germline against genotoxic stress are uncoupled from those required for somatic robustness and animal longevity.
Offset and Misalignment Estimation for the Online Calibration of an MEMS-IMU Using FIR-Filter Modulating Functions

The paper addresses the problem of the reliable estimation of the vehicle motion using low cost, automotive grade IMUs. These sensors are prone to systematic errors that may reduce the quality of the motion estimation. The proposed method yields a real-time estimate of the intrinsic longitudinal and lateral acceleration offset as well as the extrinsic yaw misalignment due to inaccurate mounting. The estimation scheme is based on a kinematic model of the vehicle dynamics. From this viewpoint two algebraic regressor equations are derived and analyzed for their conditioning and estimation capabilities. By using an FIR-Filter based on the modulating functions approach we filter the algebraic expressions and obtain accurate time derivatives of the signals. The estimation procedures are evaluated and discussed with real measurement data of a low cost IMU taken in different driving situations.
Convolutional neural networks and hash learning for feature extraction and of fast retrieval of pulmonary nodules

With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the “semantic gap” that exists between the low level visual information captured by imaging devices and high level semantic information perceived by the human. Using deep convolution neural network (CNN) to construct the CBMIR system can fully characterize the high level semantic features information for medical image retrieval. The existing network mostly used for the natural images can’t produce a good result directly applied to medical image. This paper used UNet method to preprocessing under the guidance of medical knowledge. Then, multi-scale receiving field convolution module is used to extract features of the segmented images with different sizes. Finally, encoded the features and used a coarse to fine search strategy with an average search accuracy of 0.73.
ASHP Guidelines on Handling Hazardous Drugs

ASHP published its first guidance on hazardous drugs (HDs) in 1983 as part of the 1983–84 ASHP Practice Spotlight: Safe Handling of Cytotoxic Drugs. [1][1],[2][2] This was followed by technical assistance bulletins in 1985 and 1990 and the ASHP Guidelines on Handling Hazardous Drugs in 2006. [3][3
Blockchain-Based Distributed Software-Defined Vehicular Networks via Deep Q-Learning

Nowadays, in order to support flexibility, agility, and ubiquitous accessibility among vehicles, software defined networking has been proposed to integrate with vehicular networks, known as software defined vehicular network (SDVN). Due to a variety of data, flows, and vehicles in SDVN, a distributed SDVN is necessary. However, how to reach consensus in distributed SDVN efficiently and safely is an intractable problem. In this paper, we use a permissioned blockchain approach to reach consensus in distributed SDVN. The existing permissioned blockchain has a number of drawbacks, such as low throughput. We virtualize the underlying resources (e.g., computing resources and networking resources), jointly considering the trust features of blockchain nodes to improve the throughput. Accordingly, we formulate view change, computing resources allocation, and networking resources allocation as a joint optimization problem. In order to solve this joint problem, we use a novel deep Q-learning approach. Simulation results show the effectiveness of our proposed scheme.
Long-term functionality of a soft electrode array for epidural spinal cord stimulation in a minipig model

Long-term biointegration of man-made neural interfaces is influenced by the mechanical properties of the implant materials. Substantial experimental work currently aims at replacing conventional hard implant materials with soft alternatives that can favour a lower immune response. Here we assess the performance of a soft electrode array implanted in the spinal epidural space of a minipig model for a period of 6 months. The electrode array includes platinum-silicone electrode contacts and elastic thin-film gold interconnects embedded in silicone. textbfIn-vivo electrode impedance and voltage transients were monitored over time. Following implantation, epidural stimulation produced muscle-specific evoked potentials and visible muscle contractions. Over time, postoperative and stimulation induced changes in electrode impedance were observed. Such trends provide a basis for future technological improvements aiming at ensuring the stability of soft implantable electrodes for neural interfacing.
Elbow training device using the Mechanically Adjustable Stiffness Actuator(MASA)

This paper presents an elbow training device using the Mechanically Adjustable Stiffness Actuator(MASA) for stroke survivors with hemiplegia. The MASA is a series elastic actuator whose mechanical stiffness is variable. Stiffness and the neutral position of the spring is mechanically changed using two identical actuators. Since assisting torque of the actuators is transmitted through the springs, changing stiffness of the MASA results in different level of assistance by the actuators. Then, according to the performance of the patients during given tracking tasks, the level of assistance is controlled via changing stiffness. A prototype of the rehabilitation device using the MASA is introduced and a preliminary experiments with 10 healthy subjects show the level of assistance changes.
Microbiome Analysis of Biofilms of Silver Nanoparticle-Dispersed Silane-Based Coated Carbon Steel Using a Next-Generation Sequencing Technique

Previously, we demonstrated that silver nanoparticle-dispersed silane-based coating could inhibit biofilm formation in conditions where seawater was used as a bacterial source and circulated in a closed laboratory biofilm reactor. However, it is still unclear whether the microbiome of a biofilm of silver nanoparticle-dispersed silane-based coating samples (Ag) differs from that of a biofilm of non-dispersed silane-based coating samples (Non-Ag). This study aimed to perform a microbiome analysis of the biofilms grown on the aforementioned coatings using a next-generation sequencing (NGS) technique. For this, a biofilm formation test was conducted by allowing seawater to flow through a closed laboratory biofilm reactor; subsequently, DNAs extracted from the biofilms of Ag and Non-Ag were used to prepare 16S rRNA amplicon libraries to analyze the microbiomes by NGS. Results of the operational taxonomy unit indicated that the biofilms of Non-Ag and Ag comprised one and no phyla of archaea, respectively, whereas Proteobacteria was the dominant phylum for both biofilms. Additionally, in both biofilms, Non-Ag and Ag, Marinomonas was the primary bacterial group involved in early stage biofilm formation, whereas Anaerospora was primarily involved in late-stage biofilm formation. These results indicate that silver nanoparticles will be unrelated to the bacterial composition of biofilms on the surface of silane-based coatings, while they control biofilm formation there.
A multimodal virtual keyboard using eye-tracking and hand gesture detection

A large number of people with disabilities rely on assistive technologies to communicate with their families, to use social media, and have a social life. Despite a significant increase of novel assitive technologies, robust, non-invasive, and inexpensive solutions should be proposed and optimized in relation to the physical abilities of the users. A reliable and robust identification of intentional visual commands is an important issue in the development of eye-movements based user interfaces. The detection of a command with an eyetracking system can be achieved with a dwell time. Yet, a large number of people can use simple hand gestures as a switch to select a command. We propose a new virtual keyboard based on the detection of ten commands. The keyboard includes all the letters of the Latin script (upper and lower case), punctuation marks, digits, and a delete button. To select a command in the keyboard, the user points the desired item with the gaze, and select it with hand gesture. The system has been evaluated across eight healthy subjects with five predefined hand gestures, and a button for the selection. The results support the conclusion that the performance of a subject, in terms of speed and information transfer rate (ITR), depends on the choice of the hand gesture. The best gesture for each subject provides a mean performance of $8 . 77 \pm 2 . 90$ letters per minute, which corresponds to an ITR of $57 . 04 \pm 14 . 55$ bits per minute. The results highlight that the hand gesture assigned for the selection of an item is inter-subject dependent.
A Point Process Characterization Of Electrodermal Activity

Electrodermal activity (EDA) is a measure of sympathetic activity using skin conductance that has applications in research and in clinical medicine. However, current EDA analysis does not have physiologically-based statistical models that use stochastic structure to provide nuanced insight into autonomic dynamics. Therefore, in this study, we analyzed the data of two healthy volunteers under controlled propofol sedation. We identified a novel statistical model for EDA and used a point process framework to track instantaneous dynamics. Our results demonstrate for the first time that point process models rooted in physiology and built upon inherent statistical structure of EDA pulses have the potential to accurately track instantaneous dynamics in sympathetic tone.
Sufficient Conditions for Reconstructing 2-D Rainfall Maps

The ground level rainfall at a given time is modeled as a 2-D spatial random process <inline-formula> <tex-math notation="LaTeX">${r}(\textit {x, y})$ </tex-math></inline-formula>, the rain field. Existing measurement equipment, such as rain gauges, weather stations, or recently proposed microwave links, samples <inline-formula> <tex-math notation="LaTeX">${r}(\textit {x, y})$ </tex-math></inline-formula> spatially in specific points or along lines. Given these samples, our purpose is to reconstruct <inline-formula> <tex-math notation="LaTeX">${r}(\textit {x, y})$ </tex-math></inline-formula>. In this paper, we study the question: “under what conditions can a given topology of ground measurements guarantee reconstructability of the rain field? ” Based on the assumption that rain fields are sparse, we present a statistical approach to this problem by first characterizing the statistics of the measurements, and then answering the question by applying methods from compressed sensing theory, and in particular Donoho and Tanner’s phase transition diagram for sparse recovery. We conclude by suggesting a solution in a form of a simple diagram, allowing one to evaluate the potential reconstruction of <inline-formula> <tex-math notation="LaTeX">${r}(\textit {x, y})$ </tex-math></inline-formula> in different resolutions without the need for computations.