Miracle-dz/UniFAR-FTUs huggingface dataset:
Zebrafish knock-ins swim into the mainstream

ABSTRACT The zebrafish is an increasingly popular model organism for human genetic disease research. CRISPR/Cas9-based approaches are currently used for multiple gene-editing purposes in zebrafish, but few studies have developed reliable ways to introduce precise mutations. Point mutation knock-in using CRISPR/Cas9 and single-stranded oligodeoxynucleotides (ssODNs) is currently the most promising technology for this purpose. Despite some progress in applying this technique to zebrafish, there is still a great need for improvements in terms of its efficiency, optimal design of sgRNA and ssODNs and broader applicability. The papers discussed in this Editorial provide excellent case studies on identifying problems inherent in the mutation knock-in technique, quantifying these issues and proposing strategies to overcome them. These reports also illustrate how the procedures for introducing specific mutations can be straightforward, such that ssODNs with only the target mutation are sufficient for generating the intended knock-in animals. Two of the studies also develop interesting point mutant knock-in models for cardiac diseases, validating the translational relevance of generating knock-in mutations and opening the door to many possibilities for their further study. Summary: With the co-publication of three research articles describing CRISPR/Cas9-based approaches for knocking in mutations in zebrafish (dmm035352, dmm035469 and dmm035972), this Editorial reflects on the current state of genome editing for disease modeling in zebrafish.
Group Consensus for Discrete-Time Heterogeneous Multiagent Systems with Input and Communication Delays

Group consensus seeking is investigated for a class of discrete-time heterogeneous multiagent systems composed of first-order and second-order agents with both communication and input time delays. Considering two types of system topologies, novel protocols based on the competitive and cooperative relationships among the agents are presented, respectively. By matrix theory and frequency domain analysis method, the sufficient conditions solving consensus problem are obtained. The results show that the achievement of group consensus is bound up with the input time delays, coupling weights between the agents and the system’s control parameters, but it is irrelevant to the communication delays. Finally, numerical simulations are presented to illustrate the correctness of the theoretical results.
Stimulation Effect of Inter-subject Variability in tDCS–Multi-scale Modeling Study

Transcranial direct current stimulation (tDCS) is an emerging non-invasive neuromodulation method that is convenient and popular in clinical use. However, there is a practical issue in applying tDCS; it is difficult to optimize the montage for each individual because of inherent inter-subject variability. Thus, the stimulation effect of such individual anatomical head variation has been investigated using anatomically realistic models. In this work, we developed a multi-scale computational model, which combined head models based on magnetic resonance imaging (MRI) and multi-compartmental neuronal models of pyramidal neurons (PNs), to investigate both the macroscopic and microscopic effects oftDCS. We constructed three different head models and compared the stimulation effects of tDCS in the primary cortex area (Brodmann area 4) with respect to the electric fields induced and steady-state membrane polarizations. We observed that the electric field behavior and induced somatic polarizations varied across subjects in accordance with the thicknesses of cerebrospinal fluid (CSF) and skull measured in each model. Thus, we concluded that variations in the CSF and skull might be correlated with the effects of tDCS.
Approximate Multipliers Based on New Approximate Compressors

Approximate computing is an emerging trend in digital design that trades off the requirement of exact computation for improved speed and power performance. This paper proposes novel approximate compressors and an algorithm to exploit them for the design of efficient approximate multipliers. By using the proposed approach, we have synthesized approximate multipliers for several operand lengths using a 40-nm library. Comparison with previously presented approximated multipliers shows that the proposed circuits provide better power or speed for a target precision. Applications to image filtering and to adaptive least mean squares filtering are also presented in the paper.
Hemorheological mechanisms for increased thrombosis in subjects using gestodene

There are several possible mechanisms by which combined oral contraceptives (COC) use increase venous thromboembolism (VTE) risk. Melodene® is a monophasic COC containing the third‐generation progestin Gestodene (GSD), which is associated with increased risk of VTE. Therefore, the aim of this study was to investigate the possible alterations in viscoelastic parameters of whole blood and plasma clots along with the biophysical characteristics of erythrocytes and specifically fibrin fibers in females using a COC containing GSD. GSD appeared to have a significant impact on the biophysical characteristics of fibrin fiber networks. When GSD is combined with ethinylestradiol the viscoelastic properties of whole blood clots tend to become more prothrombotic. The alterations to and aggregation of erythrocytes accompanied with spontaneous formation of a fibrin “blanket” provides a possible mechanism for the increased occurrence of “red” clots, which can lead to occlusions in the vascular system. Thus, the increased risk of VTE associated with these COCs can be attributed to these erythrocyte‐and‐fibrin‐rich‐clots occluding venous vessels. However, our findings also propose that these changes to the biophysical properties of both erythrocytes and fibrin, specifically spontaneous expansion of deformed fibrin networks, can also occlude vessels in the microcirculation, which could have lasting, subclinical complications for female users. We recommend that a thorough risk assessment, with specific focus on coagulation and other factors affecting fibrin formation, be done for each female before prescribing a GSD‐containing COC. Females that “qualify” then need to be monitored on a regular basis to lower the risk of thrombotic events.
Automated Classification Using End-to-End Deep Learning

According to a study [1] by the Ministry of Health in Singapore, since 2009, cancer, ischaemic heart disease and pneumonia together accounted for approximately 60% of the total causes of death. It has been 9 years, and Pneumonia and other Acute Upper Respiratory Infections still is one of the top 10 conditions of hospitalization. In cases of respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD), it has been found that close to 55% of cases are misdiagnosed. This is shocking as, an early diagnosis of respiratory diseases can lead to an earlier treatment intervention, ultimately lessening symptoms, slowing the progression, and improving overall quality of life. With the advent of Deep Neural Network architectures which have shown phenomenal results in the field of Computer Assisted Diagnosis (CAD), we hope to implement a Lung Classification Model using End-to-End Deep learning to classify Chest X-Ray images into one of 14 primary classes of lung diseases. Using our implementation of the Densely Connected Convolutional Neural Network model architecture, we aim to increase existing model accuracy in Lung Disease classification by iteratively reducing the search space and region of interest for different. We shall experiment on a 14-class classification model and compare the results with a binary classifier as well, to understand the performance of DenseNets on Chest X-Ray (CXR) Data with a reduced search space.
Leaky Lens Antenna as Optically Pumped Pulsed THz Emitter

Optically pumped pulsed THz emitters exploit the transient motion of photo-generated charge carriers in semiconductors, to produce, coupled to micro-antenna, radiated power over a wide bandwidth up to the THz frequencies. The radiation performance of the antenna greatly affects dispersion of the energy spectrum generated by the photoconductive source and if not properly designed it causes low radiated power. This work presents the design, the fabrication process, the electromagnetic and the thermal analyses of a pulsed photoconductive micro-antenna based on the leaky lens antenna concept. This device shows high radiation efficiency over a band ranging from 0.1 to 1.5 THz, thus being a suitable emitter for THz time-domain sensing system.
CXCL8 gene silencing promotes neuroglial cells activation while inhibiting neuroinflammation through the PI3K/Akt/NF‐κB‐signaling pathway in mice with ischemic stroke

Ischemic stroke is known as a neurodegenerative disorder, which induces long‐period tissue damage. Chemokine (C–X–C motif) ligand 8 (CXCL8) is involved in acute inflammation and tumor progression through the phosphoinositide‐3‐kinase/protein kinase B/nuclear factor‐κB (PI3K/Akt/NF‐κB)‐signaling pathway. In this study, we aimed to explore the mechanism of CXCL8 in ischemic stroke in relation to the PI3K/Akt/NF‐κB‐signaling pathway.
Performance Bounds of a Single LEO Satellite Providing Geolocation of an RF Emitter

The recursive Posterior Cramer Rao Bound (rPCRB) is computed for a Low Earth Orbit (LEO) satellite providing geolocation estimates of an RF emitter. This bound takes no assumptions on the geometry of the situation, allows geolocation parameters to vary over time, and can be meaning-fully compared with nonlinear estimation algorithm performance. A stationary emitter is assumed, it’s only velocity coming from the earth’s rotation, which is modeled and estimated. The measurements are instantaneous frequencies corrupted with AWGN noise. Previous relevant literature uses simplified geometric assumptions and the traditional Cramer Rao Bound (CRB), producing reasonable results only in benign and select scenarios. Here, more realistic performance bounds are given and the potential of geolocating emitters in ’search and rescue’ scenarios is demonstrated. The performance bounds are computed for various scenarios and compared with an Unscented Kalman Filter using estimated instantaneous frequencies from synthesized data.
Combining probiotics and an angiotensin‐II type 1 receptor blocker has beneficial effects on hepatic fibrogenesis in a rat model of non‐alcoholic steatohepatitis

Intestinal endotoxin is important for the progression of non‐alcoholic steatohepatitis (NASH). Circulating endotoxin levels are elevated in most animal models of diet‐induced non‐alcoholic fatty liver disease (NAFLD) and NASH. Furthermore, plasma endotoxin levels are significantly higher in NAFLD patients, which is associated with small intestinal bacterial overgrowth and increased intestinal permeability. By improving the gut microbiota environment and restoring gut‐barrier functions, probiotics are effective for NASH treatment in animal models. It is also widely known that hepatic fibrosis and suppression of activated hepatic stellate cells (Ac‐HSCs) can be attenuated using an angiotensin‐II type 1 receptor blocker (ARB). We thus evaluated the effect of combination probiotics and ARB treatment on liver fibrosis using a rat model of NASH.
Development of Terahertz Radiation Source with Slit-Array Structure

We have proposed a slit-array configuration demonstrating higher efficiency in terahertz radiation. Based on the radiation mechanism, we planned to develop terahertz emitter and coherent radiation source. Research on the geometrical structure, the output methods, and the radiation characteristics are being performed, and the experimental setup is under construction. In this paper, we report on the radiation physics and the consideration on effectively outputting the terahertz radiation, and introduce the recent progress in experiments.
Phase-Plane Models in Korelia Software

In this work, the possibilities of the Korelia program for analysis of dynamic processes by the phase plane method are explored. The dynamics model is described using a specialized programming language. An algorithm for constructing nullclines of the process and for calculating equilibrium points is presented. The type of points is determined by the state matrix eigenvalues. All derived dependencies and parameters are displayed in graphical form. The approach is illustrated by a model of plasma renin activity after treatment of experimental animals with nicardipine. The special point is initial condition and it is of the stable focus type.
Phase Harmonics and Correlation Invariants in Convolutional Neural Networks

We prove that linear rectifiers act as phase transformations on complex analytic extensions of convolutional network coefficients. These phase transformations are linearized over a set of phase harmonics, computed with a Fourier transform. The correlation matrix of one-layer convolutional network coefficients is a translation invariant representation, which is used to build statistical models of stationary processes. We prove that it is Lipschitz continuous and that it has a sparse representation over phase harmonics. When network filters are wavelets, phase harmonic correlations provide important information about phase alignments across scales. We demonstrate numerically that large classes of one-dimensional signals and images are precisely reconstructed with a small fraction of phase harmonic correlations.
Slow‐twitch skeletal muscle defects accompany cardiac dysfunction in transgenic mice with a mutation in the myosin regulatory light chain

Myosin light chain 2 (MYL2) gene encodes the myosin regulatory light chain (RLC) simultaneously in heart ventricles and in slow‐twitch skeletal muscle. Using transgenic mice with cardiac‐specific expression of the human R58Q‐RLC mutant, we sought to determine whether the hypertrophic cardiomyopathy phenotype observed in papillary muscles (PMs) of R58Q mice is also manifested in slow‐twitch soleus (SOL) muscles. Skinned SOL muscles and ventricular PMs of R58Q animals exhibited lower contractile force that was not observed in the fast‐twitch extensor digitorum longus muscles of R58Q vs. wild‐type‐RLC mice, but mutant animals did not display gross muscle weakness in vivo. Consistent with SOL muscle abnormalities in R58Q vs. wild‐type mice, myosin ATPase staining revealed a decreased proportion of fiber type I/type II only in SOL muscles but not in the extensor digitorum longus muscles. The similarities between SOL muscles and PMs of R58Q mice were further supported by quantitative proteomics. Differential regulation of proteins involved in energy metabolism, cell‐cell interactions, and protein‐protein signaling was concurrently observed in the hearts and SOL muscles of R58Q mice. In summary, even though R58Q expression was restricted to the heart of mice, functional similarities were clearly observed between the hearts and slow‐twitch skeletal muscle, suggesting that MYL2 mutated models of hypertrophic cardiomyopathy may be useful research tools to study the molecular, structural, and energetic mechanisms of cardioskeletal myopathy associated with myosin RLC. —Kazmierczak, K. , Liang, J., Yuan, C. ‐C, Yadav, S. , Sitbon, Y. H. , Walz, K. , Ma, W. , Irving, T. C. , Cheah, J. X. , Gomes, A. V. , Szczesna‐Cordary, D. Slow‐twitch skeletal muscle defects accompany cardiac dysfunction in transgenic mice with a mutation in the myosin regulatory light chain. FASEB J. 33, 3152–3166 (2019). www.fasebj. org
Automated Sleep Period Estimation in Wearable Multi-sensor Systems

Sleep period determination is essential to accurate sleep quality analysis. In this paper, we propose an automated algorithm for wearable multi-sensor systems to precisely estimate the sleep period. It leverages the information of accelerometer, and vital signs such as heart rate and breathing rate. Compared to the sleep periods determined by the clinical diagnosing-grade equipment, our algorithm achieves average time differences of 9.0 and 10.4 minutes for healthy subjects and clinical patients, respectively.
Stroke and Universal Design

Universal Design (UD) is usually stated to be "for all ages and abilities". Given that stroke is a major source of disability, it is important that UD recommendations take stroke-specific problems into account. Within the framework of EU project STARR, we have investigated user requirements of stroke survivors. In this project we have used a mix of interviews, focus groups, design workshops and technology tests to come up with a set of design recommendations, which we present as a first step towards universal design recommendations which are inclusive for stroke survivors. Our general recommendations are: make it fun, do not make people fail, empower and encourage. The technology needs to be highly adaptable to different sets of abilities. Safety, but also aesthetics and simplicity is important, but it is pointed out that designs should not be "childish" - this can be felt to be degrading. It is important to be able to see and follow your progress and win small victories often. Consider social applications and activities - being able to connect to others in the same situation can enable discussions and provide peer support. More stroke consequence specific recommendations are to design to allow one-sided use (hemiplegia), avoid sensory and activity overload (fatigue), complement speech with images (aphasia), limit demand on memory, support learning and avoid errors (memory problems), and include multiple modalities in your design (reduced vision or hearing).
The Virtual Kitchen Challenge: preliminary data from a novel virtual reality test of mild difficulties in everyday functioning

ABSTRACT Background: Efficient, objective measures of mild functional difficulties are lacking. Preliminary data from a novel, non-immersive virtual reality, performance-based task (Virtual Kitchen Challenge; VKC) were obtained to address this gap. Methods: 14 older and 21 younger adults completed cognitive tests and two everyday tasks (breakfast, lunch) in the VKC with virtual objects and a touch-screen and in the Real Kitchen with real objects (order counterbalanced). Automated performance measures were obtained from the VKC program and human coders scored VKC and Real Kitchen videos for errors. Results: Older adults made more errors than younger adults on the VKC and Real Kitchen, with similar error patterns across measures. VKC automated measures were significantly related to measures from human coders, performance on the Real Kitchen, and cognitive test scores. Conclusion: The VKC is a valid and highly efficient performance-based measure of subtle functional difficulties with great potential for future clinical and research applications.
Real-time simulation of the Prony filter for identifying low frequency oscillations in short-time

This paper specializes on the real-time simulation of the Prony filter for capturing low frequency dynamics in power systems. The development is accomplished integrating two platforms: OPAL-RT and Matlab & Simulink. Where a computational efficient Prony calculation is implemented in order to yield damping and frequency estimates in short-time. The analysis is performed through a sliding analisys window, which it is used to guarantee instantaneous estimates. The low frequency oscillations’ identification process is applied on two systems. Experimental results confirm the propositions performance, precision, and reliability.
Mixed Kinematics and Camera Based Vehicle Dynamic Sideslip Estimation for an RC Scaled Model

Vehicle side slip angle is at the basis of many vehicle dynamics control systems. Many methods are available to estimate side-slip angle using on board sensors (usually accelerometers and gyros). The technical advances pertaining autonomous vehicles made an additional kind of sensor available: cameras. This study develops a mixed kinematic vision-based side slip angle estimation. The proposed algorithm merges the information of a commercial grade inertial measurement system, wheel encoders and information from a camera. The camera measurement are integrated in a Kalman filter observer. The paper implements and tests the approach on an instrumented RC scale vehicle, comparing the proposed approach against a kinematic based estimation. Experimental results show a decrease of a factor between 2 and 10 (depending on the type of maneuver) of the estimation mean squared error.
Fusion of Deep Convolutional Neural Networks for Microaneurysm Detection in Color Fundus Images

Microaneurysms (MAs) are common signsof several diseases, appearing as small circular darkish spots in color fundus images. The presence of even a single MA may suggest diseases (e.g. diabetic retinopathy), thus, their reliable recognition is a critical issue in both human clinical practice and computer-aided systems. As for their automatic recognition, deep learning techniques became very popular in the recent years. In this paper, we also apply such deep convolutional neural network (DCNN) based techniques; however, we organize them into a supernetwork with a fusionbased approach. The combination of the member DCNNs is achieved with interconnecting them in a joint fully-connected layer. The advantage of the method is that this large architecture can be trained as a single neural network, and thus, the member DCNNs are also trained with taking the predictions of the other members into consideration. The competitiveness of our approach is also validated with experimental studies, where the ensemble-based system outperformed each member DCNN. As a primary application domain with strong clinical motivation, the methodology was tested for image-level classification. More specifically, a retinal image is divided into subimages to provide the required inputs for the DCNN-based architecture, and the whole image is labeled as a positive case, if the presence of MA is predicted in any of the subimages. Additionally, we also demonstrate how our architecture can be trained to accurately localize MAs with training only the local neighborhoods of the lesions; empirical tests showing solid performance are also enclosed.
Time-Incremental Convolutional Neural Network for Arrhythmia Detection in Varied-Length Electrocardiogram

Automatic arrhythmia detection plays an important role in early prevention and diagnosis of cardiovascular diseases. Convolutional neural network (CNN) introduced a simple, end-to-end solution to multi-class arrhythmia classification, but the restriction that it could only accept fixed-length input resulted in noises or key information losses in training. Meanwhile, CNN's high memory consumption and computation cost also limited its application. To address these issues, we proposed a time-incremental convolutional neural network (TI-CNN), which utilized recurrent cells to introduce flexibility in input length for CNN models, and featured halved parameter amount as well as more than 90% computation reduction in real-time processing. The experiment results showed that, TI-CNN reached an overall classification accuracy of 77.3%. In comparison with a classical 16-layer CNN named VGGNet, TI-CNN achieved accuracy increases of more than 6% in average and up to 22% in detecting paroxysmal arrhythmias. Combining all these excellent features, TI-CNN offered an exemplification for all kinds of varied-length signal processing problems.