Miracle-dz/UniFAR-FTUs huggingface dataset:
Subtyping of Small Cell Lung Cancer using plasma cell-free nucleosomes

Small cell lung cancer (SCLC) is an aggressive malignancy with exceptionally poor prognosis and limited therapeutic advances in the past few decades. Although SCLCs are treated as a single disease entity in clinic, emerging data support subtypes of SCLC driven by expression of distinct transcription regulators, which engender unique therapeutic vulnerabilities. However, the translational potential of these observations is limited by access to tumor biopsies. Here, we apply chromatin immunoprecipitation of cell-free nucleosomes carrying active chromatin modifications followed by sequencing (cfChIP-seq) to 286 plasma samples from patients with advanced SCLC, non-SCLC cancers, and healthy adults. In addition to providing reliable estimates of SCLC circulating free DNA (cfDNA) tumor fraction, cfChIP-seq recovers the unique epigenetic states of SCLC tissue and cells of origin, and importantly tumor gene expression. Comparison of cfChIP-seq signals to matched tumor transcriptomes shows genome-wide concordance presenting a direct link between gene expression in the tumor and plasma cell-free nucleosomes. We devise a classifier that discriminates between SCLC lineage-defining transcription factor subtypes based on cfChIP-seq assay. This work sets the stage to non-invasively profile SCLC transcriptomes using plasma cfDNA histone modifications.
A Semi-Automated Harvesting Prototype for Shaking Fruit Tree Limbs

Mechanizing the fruit removal operation during fresh-market apple harvesting is essential for labor and cost savings for fruit growers. This study introduces a semi-automated mechanical fruit harvesting system that uses a fruit removal technique based a unique limb shaking mechanism called a dual motor actuator (DMA). The harvesting system was tested on â€˜Gala‘ apples grown in a formally trained fruiting wall orchard. A manipulator arm, positioned by a human, held a fruit catchframe and an end-effector. The system was mounted on a mobile orchard platform. The DMA was developed as an infinitely variable pattern end-effector that applies rhythmic motions to a fruiting limb to remove fruit. The novelty of the DMA design is the use of two eccentrics mounted to electric motors that are pinned together to form a triangle with an adjustable base. Pattern, rhythm, and actuation time were varied to characterize the removal efficiency and fruit damage percentages. Removal efficiency averaged only 35% across the fruiting limb but increased to 88% within the actuation zone of the fruiting section, showing the potential of a semi-selective harvesting technique for localized fruit removal on branches trained to trellis wires. A circular pattern using a 200 cycles min -1 rhythm achieved the lowest overall damage (10%) of all combinations of the patterns and rhythms tested. Bruise percentage was also lowest (4%) using the same pattern and rhythm. Rhythm and actuation time were significant factors that influenced the fruit removal condition. No fruit was removed outside of the targeted actuation zone when using a single limb grasper. This localized limb actuation method shows great potential for removing apples from a limb while maintaining fresh-market quality fruit.
Using a substitute species to inform translocation of an endangered territorial mammal

Substitute species can inform management strategies without exposing endangered species unnecessarily. Further, experimental approaches may help to identify the causes of translocation failures, leading to improve the chances of success. We used a surrogate subspecies, Tamiasciurus fremonti fremonti to test different translocation techniques to inform potential management actions on the endangered Mt. Graham red squirrel (Tamiasciurus fremonti grahamensis). We fitted VHF radio collars to 54 animals, and we monitored their survival and movements until individuals settled on a new territory. We considered the effect of season, translocation technique (soft or hard release), and body mass on survival, distance moved after release, and time to settlement of translocated animals. Survival probability averaged 0.48 after 60 days from the translocation event and was not affected by season or translocation technique. 54% of the mortality was caused by predation. Distance moved and number of days to settlement varied with season, where winter was characterized by shorter distances (average of 364 m in winter versus 1752 m in fall) and a smaller number of days (6 in winter versus 23 in fall). These data emphasize the potential of substitute species to provide valuable information on possible outcomes of management strategies for closely related endangered species.
Coupling Cell Communication and Optogenetics: Implementation of a Light-Inducible Intercellular System in Yeast

Cell communication is a widespread mechanism in biology, allowing the transmission of information about environmental conditions. In order to understand how cell communication modulates relevant biological processes such as survival, division, differentiation or apoptosis, different synthetic systems based on chemical induction have been successfully developed. In this work, we coupled cell communication and optogenetics in the budding yeast Saccharomyces cerevisiae. Our approach is based on two strains connected by the light-dependent production of α-factor pheromone in one cell type, which induces gene expression in the other type. After the individual characterization of the different variants of both strains, the optogenetic intercellular system was evaluated by combining the cells under contrasting illumination conditions. Using luciferase as a reporter gene, specific co-cultures at 1:1 ratio displayed activation of the response upon constant blue-light, which was not observed for the same cell mixtures grown in darkness. Then, the system was assessed at several dark/blue-light transitions, where the response level varies depending on the moment in which illumination was delivered. Furthermore, we observed that the amplitude of response can be tuned by modifying the initial ratio between both strains. Finally, the two-population system showed higher fold-inductions in comparison with autonomous strains. Altogether, these results demonstrated that external light information is propagated through a diffusible signaling molecule to modulate gene expression in a synthetic system, which will pave the road for studies allowing optogenetic control of population-level dynamics. Graphical Abstract
Newcastle disease burden in Nepal and efficacy of Tablet I2 vaccine in commercial and backyard poultry production

Poultry (Gallus domesticus) farming plays an important role as an income generating enterprise in a developing country like Nepal, contributing more than 4% to the national GDP. It is also one of the major sources of protein for growing population. Newcastle Disease (ND) is a major poultry disease affecting both commercial and backyard poultry production worldwide. There were more than 90 reported cases of ND outbreaks in Nepal in 2018, with over 74,986 birds being affected. ND might be responsible for over 7% of total poultry mortality in the country. Recent outbreak of ND in 2021 affected many farms throughout Nepal, and caused massive poultry production loss. ND is caused by a single stranded RNA virus which presents very similar clinical symptoms as Influenza A (commonly known as Bird flu), adding much complexity to clinical disease identification and intervention. We conducted a nationwide ND and Influenza A prevalence study, collecting samples from commercial and backyard poultry farms from across the major poultry production hubs of Nepal, and conducted both serological and molecular assessments-giving us disease exposure history and identification of floating strains of ND Virus (NDV). Of 600 commercial chickens tested from various farms, both NDV (n=381, 64%) and IA (n=125, 21%) antibodies were detected in the majority of the samples. In backyard chicken (n=108, 39 farms), sero-prevalence was also relatively high for both NDV (n=38, 35%) and IA (n=17, 16%). Out of the 40 commercial farms, majority had detectable NDV (n=31, 78%) and IA (n=15, 38%) virus present. In backyard farms (n=36), we also detected NDV (n=6, 16%) and IA (n=1, 3%) virus. We Genotyped (strain) detected NDV, and found Genotype II to be present in most of the commercial farms (which might be coming from live vaccine usage) and Genotype I in some backyard poultry samples. The identified Genotype I strain is reported for the first time, and hence could be an endemic NDV strain found in Nepal. Our 2021 ND outbreak investigation identified Genotype VII c as the causative strain. Additionally, we have developed a thermostable I-2 NDV vaccine (Ranigoldunga™) in tablet formulation and tested on various (mixed) breeds of chicken (G. domesticus). This vaccine seems to be highly effective against NDV, including a virulent 2021 outbreak strain (Genotype VII c). The I-2 Tablet ND vaccine showed more than 85% efficacy when administered either ocularly or in water, and has a stability of 30 days in room temperature.
Responses of alternating current field measurement (ACFM) to rolling contact fatigue (RCF) cracks in railway rails

Rolling contact fatigue (RCF) cracks are a widespread problem that impairs the service life of railway rails and wheels, with an associated high cost of labour and capital expenditure for remediation. Severe RCF cracks cause serious safety issues as they can turn down at a larger propagation angle into the rail potentially leading to a rail break. Rail grinding can effectively eliminate RCF cracks if they are detected when they are small enough to be removed. Alternating current field measurement (ACFM) is one of the electromagnetic (EM) techniques that can be used for defect detection and sizing in the rail industry. ACFM has been reported to be more accurate in providing length and depth information than conventional UT for small RCF cracks and is less sensitive to lift-off than eddy current methods. The aim of the present research is to analyse the response of ACFM signals to single and multiple RCF cracks in railway rails using experimental measurements and FE based modelling tools, focusing on the influences of crack vertical angle and multiple cracks (number, spacing, size, uniformity) on the ACFM signal to improve the accuracy of dimension predictions. A novel method (using the Bz signal) is proposed to determine the vertical angle of the RCF cracks, which then allows the crack vertical depth to be determined from the pocket length (standard output from ACFM measurements) and therefore the appropriate amount of rail grinding to remove the RCF cracks. It was found that the vertical angle influences the pocket length determined from the measured ΔBxmax/Bx value when the cracks are shallow (vertical angles < 30°), therefore greater accuracy can be obtained when compensating the ΔBxmax/Bx value using the determined vertical angle. It is shown that the variations of crack surface length, crack inner spacing and crack number for multiple cracks also influence the ΔBxmax/Bx values determined for multiple cracks. The influences of asymmetrical crack shapes on crack sizing are discussed, in general it has been found that for accurate sizing of RCF cracks using a single ACFM scan the cracks should be regular, where the assumption of semi ellipse shapes is appropriate. The methods developed in the project were assessed using calibration samples (machined cracks with different sizes and vertical angles) and rails removed from service containing single and multiple RCF cracks. It was found that the new approach proposed in this work allowed the vertical angle to be predicted well for single and multiple RCF cracks (difference to measurements < 14.3 %). In addition the error in pocket length prediction is greatly decreased when using the sizing method including compensation determined from the crack vertical angle.
Analytical Modeling and Experimental Validation of a Gelatin-based Shape Sensor for Soft Robots

Shape sensing of soft robots has been a challenge due to the large deformation of the soft robots and their low stiffness. In this study, a simple yet accurate soft sensor for soft robotic application with small force ranges was proposed, modeled, prototyped, and experimentally validated. The proposed soft sensor is based on a gelatin-graphite composite that exhibited piezoresistive properties. The sensing element was molded to a cylindrical shape and was embedded in a soft flexural structure as a common type of soft flexural robots. Afterward, a mechano-electrical model for predicting the changes in the resistance of the sensing element was proposed and its predictions were validated through an experimental study. To demonstrate usability for force sensing, the sensor was calibrated with a nonlinear model and exhibited a force measurement range of 0.035-0.82N with an average absolute error of 3.7% and a resolution of 4%. Also, the mechano-electrical model was fairly accurate in predicting the piezoresistivity phenomenon of the sensing element under large bending deformations.
Memristor-based Deep Spiking Neural Network with a Computing-In-Memory Architecture

Spiking Neural Networks (SNNs) are artificial neural network models that show significant advantages in terms of power and energy when realizing deep learning applications. However, the data-intensive nature of machine learning applications imposes a challenging problem to neural network implementations in terms of latency, energy efficiency and memory bottleneck. Therefore, we introduce a scalable deep SNN to address the problem of latency and energy efficiency. We integrate a Computing-In-Memory (CIM) architecture built with a fabricated memristor crossbar array to reduce the memory bandwidth in vector-matrix multiplication, a key operation in deep learning. By applying an inter-spike interval (ISI) encoding scheme to the input signals, we demonstrate the spatiotemporal information processing capability of our designed architecture. The memristor crossbar array has an enhanced heat dissipation layer that reduces the resistance variation of the memristors by ~30%. We further develop a time-to-first-spike (TTFS) method to classify the outputs. The designed circuits and architecture can achieve very high accuracies with both digit recognition and the MNIST dataset. Our architecture can classify handwritten digits while consuming merely 2.9mW of power with an inference speed of 2μs/image. Only 2.51pJ of energy per synaptic connection makes it suitable to apply in deep learning accelerators.
Step Change Detection for Improved ROCOF Evaluation of Power System Waveforms

In the analysis of power grid waveforms, the presence of amplitude or phase steps can disrupt the estimation of frequency and rate-of-change-of-frequency (ROCOF). Standard methods based on phasor-models fail in the extraction of signal parameters during these signal dynamics, often yielding large frequency and ROCOF deviations. To address this challenge, we propose a technique that approximates components of the signal (e.g., amplitude and frequency variations) using dictionaries based on parameterized models of common signal dynamics. Distinct from a previous iteration of this method developed by the authors, the proposed technique allows for the identification of multiple steps in a window, as well as the presence of interfering tones. The method is shown to improve signal reconstruction when applied to real-world waveforms, as compared to standard static and dynamic phasor-based algorithms.
Self-Cueing Real-Time Attention Scheduling in Criticality-Aware Visual Machine Perception

This paper presents a self-cueing real-time frame-work for attention prioritization in AI-enabled visual perception systems that minimizes a notion of state uncertainty. By attention prioritization we refer to inspecting some parts of the scene before others in a criticality-aware fashion. By self-cueing, we refer to not needing external cueing sensors for prioritizing attention, thereby simplifying design. We show that attention prioritization saves resources, thus enabling more efficient and responsive real-time object tracking on resource-limited embedded platforms. The system consists of two components: First, an optical flow-based module decides on the regions to be viewed on a subframe level, as well as their criticality. Second, a novel batched proportional balancing (BPB) scheduling policy decides how to schedule these regions for inspection by a deep neural network (DNN), and how to parallelize execution on the GPU. We implement the system on an NVIDIA Jetson Xavier platform, and empirically demonstrate the superiority of the proposed architecture through an extensive evaluation using a real-word driving dataset.
Towards intelligent compiler optimization

The future of computation is massively parallel and heterogeneous with specialized accelerator devices and instruction sets in both edge- and cluster-computing. However, software development is bound to become the bottleneck. To extract the potential of hardware wonders, the software would have to solve the following problems: heterogeneous device mapping, capability discovery, parallelization, adaptation to new ISAs, and many others. This systematic complexity will be impossible to manually tame for human developers. These problems need to be offloaded to intelligent compilers. In this paper, we present the current research that utilizes deep learning, polyhedral optimization, reinforcement learning, etc. We envision the future of compilers as consisting of empirical testing, automatic statistics collection, continual learning, device capability discovery, multiphase compiling – precompiling and JIT tuning, and classification of workloads. We devise a simple classification experiment to demonstrate the power of simple graph neural networks (GNNs) paired with program graphs. The test performance demonstrates the effectiveness and representational appropriateness of GNNs for compiler optimizations in heterogeneous systems. The benefits of intelligent compilers are time savings for the economy, energy savings for the environment, and greater democratization of software development.
Direction of Arrival Positioning Requirements for Location-Aware Beamforming in 5G mmWave UDN

The rapid growth in the number of simultaneously operating transceivers in deployed radio access networks of the fifth and subsequent generations leads to the problem of an unacceptably high level of intra-system interference, provided they are densified to one device per square meter. Adaptive Location-Aware Beamforming (LAB) can potentially compensate for high interference levels by maximizing the antenna pattern to the source / receiver of the desired signal and minimizing the antenna pattern to the source / receiver of the interfering signal. The prerequisite for LAB is precise Direction of Arrival (DOA) positioning of neighbor user equipment (UE). This study analyzes the algorithm of multiple classification of UE signals from the gNodeB (gNB) point of view in the F2 millimeter wave (mmWave) frequency range. The content of this investigation can be divided into two parts. In the first part mathematical model of the DOA algorithm is given. In the second part simulation results for DOA scenarios in 5G mmWave Ultra-Dense Networks (UDN) are described. The contribution of this investigation is the 2D-MUSIC resolution estimation in 5G UDN scenarios, which reveals the dependence of the resolution threshold for 2D-MUSIC algorithm on the number of elements in the antenna array and Signal-to-Noise-and-Interference Ratio (SINR). Simulation results reveal, that the resolution of the 2D-MUSIC algorithm is achieved with two degrees angular separation of devices, the number of antenna array elements more than 64 and SINR more than 30 dB.
Low-IR-Drop Test Pattern Regeneration Using A Fast Predictor

IR-drop becomes an important issue for testing in advanced technology nodes. In this paper, we propose a low-IR-drop test pattern regeneration to produce IR-drop-safe patterns. To speed up IR-drop analysis, we apply an existing machine learning model to predict IR-drop of test patterns. Because we already know the IR-drop of test patterns, we learn from test patterns to determine low-IR-drop preferred values and extract important bit assignments. By applying our techniques, we regenerate test patterns without predicted IR-drop violations. Experimental results show that our test length overhead is only 2.37% on average, and there is no fault coverage loss. Finally, we perform accurate IR-drop simulation on 10 IR-drop-safe patterns and no IR-drop violations are found.
Calibration and Evaluation of a Motion Measurement System for PET Imaging Studies

Positron Emission Tomography (PET) enables functional imaging of deep brain structures, but the bulk and weight of current systems preclude their use during many natural human activities, such as locomotion. The proposed long-term solution is to construct a robotic system that can support an imaging system surrounding the subject's head, and then move the system to accommodate natural motion. This requires a system to measure the motion of the head with respect to the imaging ring, for use by both the robotic system and the image reconstruction software. We report here the design, calibration, and experimental evaluation of a parallel string encoder mechanism for sensing this motion. Our results indicate that with kinematic calibration, the measurement system can achieve accuracy within 0.5mm, especially for small motions.
outcome varying from non-pathogenic to a severe spectrumand duplication syndromes : a clinical Further delineation of the 15 q 13 microdeletion

http://jmg.bmj. com/content/46/8/511.full. html Updated information and services can be found at: These include: Material Supplemental http://jmg.bmj. com/content/suppl/2009/07/27/jmg.2008. 063412.DC1. html References http://jmg.bmj. com/content/46/8/511.full. html#related-urls Article cited in: http://jmg.bmj. com/content/46/8/511.full. html#ref-list-1 This article cites 64 articles, 16 of which can be accessed free at:
Sound event detection using class activation maps

In this paper, we present the system for sound event detection in domestic environments as defined in the DCASE 2021 challenge Task 4. The task aims to provide audio event localization timestamps in addition to event class probabilities. We aim to explore the usage of class activation maps, known from image processing, in such sound event detection systems. We propose two systems. The first system is a convolutional neural network trained for sound event classification using only weakly labeled and unlabeled data. The strong labels are obtained using class activation mapping, which is a popular technique, especially in image processing. In the second proposed system, we modified the baseline system, provided by the DCASE organizers, in which we added the class activation mapping as a part of the attention mechanism. The experimental results show that the class activation maps enable improvement of the system performance in comparison with the baseline.
Microstructure and optical properties of etched silicon layers for photovoltaic applications

Improving performance of photovoltaic devices requires the suppression of light reflectance. For these purposes we produced porous silicon layers by anodic etching method. Properties of formed structures are determined by applied technological operations and strongly influence its optical properties. The surface microstructure was studied by the atomic force microscopy. We analyzed microstructural properties by statistical and multifractal method. Obtained results provide reliable information about the development of microstructure properties and their relation to the optical properties.