Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Drug-Target Interaction (DTI) based on Machine Learning with Lasso Dimensionality Reduction and SMOTE from Protein Sequence and Drug Fingerprint

The identification of Drug-Target Interaction (DTI) is an important process in pharmaceutical scientific research to develop new therapeutic agents for diseases. However, experimental methods involving identification of DTIs are time-consuming, expensive, and challenging. Computational methods that can accurately predict DTI pairs are of great interest because they can significantly reduce time and resources in drug discovery and research. This study presents a machine-learning-based model named, kNN-DTIPred, for DTI prediction that addresses two common problems of datasets: high-dimensionality and class imbalance. First, target protein feature vectors are extracted using Pseudo-Position Specific Scoring Matrix (PsePSSM). Using OpenBabel software, drug compounds are represented using FP2 Molecular Fingerprint. Lasso Dimensionality Reduction is then used to obtain only the most discriminating features while SMOTE is applied for class balancing. Five machine learning models were compared on 4 datasets. The best model was obtained by k-Nearest Neighbors classifier with overall prediction accuracy 98.23%, 94.77%, 95.07%, and 93.09% for enzymes, ion channel, G protein-coupled receptors and nuclear receptor datasets respectively. The area under the curve reached 97.05%, 95.95%, 94.89%, and 94.29%, respectively for the datasets mentioned. Additionally, our results showed that Lasso Dimensionality Reduction and SMOTE have significantly improved the predictive performance. This study has demonstrated that the proposed kNN-DTIPred model is highly accurate and effective in predicting drug-target pairs which can accelerate the DTI identification process by limiting the search space to be investigated in laboratory experiments.
A Review on Layered Architecture and Application domains of Blockchain Technology

Blockchain technology is one of the revolutionary technology in this digital era due to its vital features like immutability, anonymity and enhanced security. It’s scope is just not limited to cryptocurrency(money) domain, now it pledges to improve the present models of almost all kind of industries including government, media, medicine, law and Supply Chain Management (SCM). Blockchain is also referred as Distributed Ledger Technology (DLT), which makes the data immutable and transparent through the use of cryptographic hashing. This paper surveyed on the layered architecture of blockchain technology and various application domains. Literature survey of around 20 papers has been done to understand the basic concepts of blockchain technology. This paper helps to explore the fundamentals of blockchain technology and its application domains.
Timepix3-based Single Layer X-ray Fluorescence Compton Camera

XFCT (X-ray fluorescence CT) is a new type of medical imaging system, which can present molecular and functional information in organisms. XF (X-ray fluorescence) imaging has the advantages of low cost, easy storage and transportation of contrast agents and low internal radiation. However, the mechanical collimation limits the detection efficiency of the system. Compton camera is an imaging system composed of double-layer detectors, which can obtain the direction of incident rays without a collimator. So combining the advantages of Compton camera system and XF photon imaging is a solution worth exploring. Timepix3 is one of the most advanced high resolution photon counting detectors, which has well time resolution and can obtain the depth position where particle deposit energy. In this work, we use Si-Timepix3 single-layer detector to build up a Compton camera system, in order to detect the 43keV XF photons from Gd solution excited by X-ray. We establish the first real experimental platform to verify the feasible of XFCC (X-ray Fluorescence Compton Camera) system and obtain the reconstruction images.
A Review of Real Time Digital Simulations: Theory and Applications for the Energy Transition

The arrival of the energy transition to the global electricity scene has led to the implementation of new technologies in power systems and modified the way that the energy is generated, distributed and consumed. The analysis of the operation and the consequences of the inclusion of new technologies become a very important topic in the studies carried out by the utilities. Real-Time Digital Simulators (RTDS) have become a powerful modeling tool that allow to analyze power system models with a high level of fidelity, permitting to anticipate problems and the assessment of solution strategies. Due to the increase in the use of this technology and considering that there are no review articles on RTDS applications written in Spanish, this paper presents an explanation of the characteristics of RTDS along with an updated state of the art related to applications in Energy Transition as a theoretical framework with a large amount of information collected for future researches in Latin America.
Deep Learning-based Fully Automated Scan Range Detection in Chest CT Imaging

This study aimed to develop an automated scan range selection for minimal patient irradiation in CT examinations within the chest region. A total number of 20,820 chest CT images acquired for various indications were collected, the 3D lung masks were generated using a Deep Neural Network (DNN) developed by our group. Consequently, 2D projected localizer images and masks were computed in lateral (lat) and anterior-posterior (AP) directions. We developed a deep learning algorithm to predict the lung mask from 2D localizer images. Thereby, the scan range is automatically determined without the technologist’s intervention. Lastly, the impact of over-ranging on patients’ effective dose was investigated through personalized dosimetry of the given cohort. A significant over-scanning range (31±24 mm) was observed in the clinical setting for more than 95% of cases. The average Dice coefficient for 2D lung segmentation was 0.96 and 0.97 for AP and lateral projections, respectively. The proposed approach resulted in errors of 0.08±1.46 and -1.5±4.1 mm in the superior and inferior directions, respectively. The effective dose (ED) was reduced by 21% in the unseen external dataset when using the proposed automated scan range selection.
Estimating infant upper extremities motion with an RGB-D camera and markerless deep neural network tracking: A validation study

Quantitative biomarkers of infant motion may be predictive of the development of movement disorders. This study presents and validates a low cost, markerless motion tracking method for the estimation of upper body kinematics of infants from which proper biomarkers may be extracted. The method requires a single RGB-D camera, a 2D motion tracking software publicly available (DeepLabCut) and an algorithm generating 3D point coordinates from the 2D tracked points, dealing with missing data, originating from various sources, for estimating joint kinematics. The proposed method is validated using known point kinematics obtained from a doll, with size and shape of an infant, lying on a turntable rotating at 33⅓ rpm. Two camera image plane orientations are tested: parallel to the turntable motion plane and forming a 45° angle with respect to the motion plane. The latter enhances the occurrence of body parts occlusions during motion as expected in live infant motion recordings. The length of upper body segments, elbow and shoulder joint angles and the linear point velocity determined with the proposed method are evaluated against reference values obtained from the known motion of the turntable. The relevant Mean Absolute Errors (MAE) found indicate the margin of error to expect when processing live infant motion. The proposed method may be improved if enhanced hardware and tracking software are employed, therefore reducing the above-mentioned margin of error. Clinical Relevance — The validation of the proposed method carried out in this study allows clinicians to select proper quantitative biomarkers obtained from infants upper body motion that may be useful for predicting movement disorders.
An investigation of the ankle contact forces in a foot with hammer toe deformity. A comparison of patient-specific approaches using finite element modeling and musculoskeletal simulation

The internal forces and stresses in the tissue are important as they are linked to the risk of mechanical trauma and injuries. Despite their value, the internal stresses and forces cannot be directly measured in-vivo. A previously validated 3D finite element model (FEM) was constructed using Magnetic Resonance Imaging (MRI) of a person with diabetes and hammer toe deformity. The foot model simulated at five different instances during the stance phase of gait. The internal stress distribution on the talus that was obtained using the FEM simulation, was used to calculate the joint reaction force at the ankle joint. In addition, the musculoskeletal model (MSM) of the participant with hammer toe foot was developed based on the gait analysis and was used to determine the muscle forces and joint reactions. The result showed that the vertical reaction forces obtained from the FEM and MSM follow a similar trend through the stance phase of gait cycle and are significantly correlated ($\mathrm{R}=0.99$). The joint reaction forces obtained through the two methods do not differ for the first 25% of the gait cycle, while the maximum difference was ∼0.7 Body weight that was observed at 50% of the stance phase. Clinical Relevance: Finite element modeling and musculoskeletal simulation can shed light on the internal forces at the ankle in pathological conditions such as hammer toe. The similarities and differences observed in the joint reaction forces calculated from the two methods can have implications in assessing the effect of clinical interventions.
Human Performance of Three Hands in Unimanual, Bimanual and Trimanual Tasks

Trimanual operation using a robotic supernumerary limb is a new and challenging mechanism for human operators that could enable a single user to perform tasks requiring more than two hands. Foot-controlled interfaces have previously proven able to be intuitively controlled, enabling simple tasks to be performed. However, the effect of going from unimanual to bimanual and then to trimanual tasks on subjects performance and coordination is not well understood. In this paper, unimanual, bimanual and trimanual teleoperation tasks were performed in a virtual reality scene to evaluate the impact of extending to trimanual actions. 15 participants were required to move their limbs together in a coordinated reaching activity. The results show that the addition of another hand resulted in an increase in operating time, where the time increased in going from unimanual to bimanual operation and then increased further when going from bimanual to trimanual. Moreover, the success rate for performing bimanual and trimanual tasks was strongly influenced by the subject's performance in ipsilateral hand-foot activities, where the ipsilateral combination had a lower success rate than contralateral limbs. The addition of a hand did not affect any two-hand coordination rate and even in some cases reduced coordination deviations. Clinical relevance - This work can contribute to build efficient training and learning framework on human multiple limbs motion control and coordination for both rehabilitation and augmentation.
AI for Social Good in Healthcare: Moving Towards a Clear Framework and Evaluating Applications

AI for social good (AI4SG) is an area of research that is centered around the notion that machine learning should be used for the benefit of society. Since the definition of AI4SG may be vague or varied, we define AI4SG and focus on the most studied application of AI4SG - healthcare. Our research applies principles of AI4SG to healthcare data that is either user generated or sourced from electronic health records (EHR) and produces meaningful and actionable insights for clinicians and the public in applications such as vaccine misinformation, suicide prevention, and detection of prostate cancer.
Seismic source functions from free‐field ground motions recorded on SPE: Implications for source models of small, shallow explosions

Reduced displacement potentials (RDPs) for chemical explosions of the Source Physics Experiments (SPE) in granite at the Nevada Nuclear Security Site are estimated from free‐field ground motion recordings. Far‐field P wave source functions are proportional to the time derivative of RDPs. Frequency domain comparisons between measured source functions and model predictions show that high‐frequency amplitudes roll off as ω− 2, but models fail to predict the observed seismic moment, corner frequency, and spectral overshoot. All three features are fit satisfactorily for the SPE‐2 test after cavity radius Rc is reduced by 12%, elastic radius is reduced by 58%, and peak‐to‐static pressure ratio on the elastic radius is increased by 100%, all with respect to the Mueller‐Murphy model modified with the Denny‐Johnson Rc scaling law. A large discrepancy is found between the cavity volume inferred from RDPs and the volume estimated from laser scans of the emplacement hole. The measurements imply a scaled Rc of ~5 m/kt1/3, more than a factor of 2 smaller than nuclear explosions. Less than 25% of the seismic moment can be attributed to cavity formation. A breakdown of the incompressibility assumption due to shear dilatancy of the source medium around the cavity is the likely explanation. New formulas are developed for volume changes due to medium bulking (or compaction). A 0.04% decrease of average density inside the elastic radius accounts for the missing volumetric moment. Assuming incompressibility, established Rc scaling laws predicted the moment reasonable well, but it was only fortuitous because dilation of the source medium compensated for the small cavity volume.
Spatial dependency of the PPG morphology at right carotid common artery

PhotoPlethysmoGraphy (PPG) is ubiquitously employed in wearable devices for health monitoring. Photodiode signal inversion is observed in rare occasions, most of the time when the sensor is pressed against the skin. We report in this article such observations made at the right common carotid artery site. Indeed we have systematically observed a photodiode signal inversion when the PPG sensor is placed where the pulse is the best felt at the carotid. In addition to be inverted, the pulse is steeper during the systolic phase. Such inversion has implications in terms of pulse arrival time (PAT) measurements In our experiments, this causes a difference of 20 ms in the carotid PAT when measured at the absolute maximum slope. The mechanical and optical properties of tissues must be better accounted to explain the PPG signal morphology. Clinical Relevance- Understanding the role of mechanical tissue properties seems relevant in order to obtain more reproducible results in PPG signal analysis.
True location of deep brain stimulation electrodes differs from what is seen on postoperative magnetic resonance images: An anthropomorphic phantom study

Deep brain stimulation (DBS) is an established yet growing treatment for a range of neurological and psychiatric disorders. Over the last decade, numerous studies have underscored the effect of electrode placement on the clinical outcome of DBS. As a result, imaging is now extensively used for DBS electrode localization, even though the accuracy of different modalities in determining the true coordinates of DBS electrodes is less explored. Postoperative magnetic resonance imaging (MRI) is a gold standard method for DBS electrode localization, however, the geometrical distortion induced by the lead's artifact could limit the accuracy. In this work, we investigated to what degree the difference between the true location of the lead's tip and the location of the tip estimated from the MRI artifact varies depending on the MRI sequence parameters, acquisition plane, phase encoding direction, and the implant”s extracranial trajectory. Clinical Relevance— Results will help researchers and clinicians to estimate the true location of DBS leads and contacts from postoperative MRI scans
An Innovative Neutron Spectrometer for the Characterization of Complex Neutron Fields

This work presents the first measurements performed with an innovative neutron spectrometer in the context of a medical cyclotron, namely the IBA 18/18 HC in operation at the Bern University Hospital (Inselspital). The aim was to test the capabilities and performance of this new spectrometer in view of the realization of a neutron beam facility exploiting the external beam line of the Bern cyclotron. The spectrometer, named DIAMON (Direction-aware Isotropic and Active neutron MONitor with spectrometric capabilities), relies on a series of silicon sensors distributed in a Polyethylene body, and allows for real time spectroscopy of neutron fields from thermal to fast energies. The spectral response, the directionality of the neutron field as well as the dosimetric information are provided in real-time by means of a high-level graphical user interface. Neutron beams were produced by the interaction of 18 MeV protons with several selected materials. The measured neutron fluence rate was found to be linear with respect to the beam current up to 105 neutrons/(cm2·s). Furthermore, the DIAMON’s response was found to be in agreement, within the experimental uncertainties, with the LB 6411 Neutron Dose Rate Probe, a well-established neutron detector. DIAMON was also successfully employed for the measurement of the neutron energy spectrum generated during routine production of 18F for Positron Emission Tomography applications. The results show the potential of DIAMON for the characterization of complex neutron fields such as those produced in a medical cyclotron for radioisotope production.
Comparison of Well-to-Wheel energy efficiency between combustion vehicles and electric vehicles.

The amount of energy required by the transport sector is largely based on fossil fuels, but it is a finite source of energy. The use of these fuels results in the emission of pollutants such as greenhouse gas. As an alternative to reducing pollutants in the transport sector, there is the alternative of replacing the fleet with electric vehicles. However, if the electrical matrix depends on non-renewable energy sources, the objective of reducing pollutants may not be achieved, in addition to interfering with the efficiency of energy use. As a way of evaluating the energy efficiency of different vehicular propulsion, a well-to-wheel (WTW) efficiency analysis was performed considering vehicles with internal combustion engine (ICE) based on gasoline, compressed natural gas, diesel and ethanol. For electric vehicles, hybrid electric vehicles (HEV) operating with gasoline and diesel, and battery electric vehicles (BEV) were considered. As an evaluation tool, calculations with WTW efficiency values were performed. The values referring to the weel-to-tank (WTT) and tank-to-wheels (TTW) stages were obtained from the literature. For the production of electricity, non-renewable sources of energy (natural gas, diesel and nuclear generators) and renewable sources (hydroelectric, solar and wind) were considered. The results obtained indicate that among the ICE category, diesel-powered ICE is the category that has the highest WTW efficiency. In addition, the lowest efficiency of ICE is represented by vehicles powered by ethanol. For electric vehicles, it was observed that the best WTW general efficiency values are obtained when renewable energy sources are used as electrical energy supply to the BEV.
Collaborative Deep Learning for Privacy Preserving Diabetic Retinopathy Detection

Convolutional Neural Networks (CNNs) are an emerging research area for detection of Diabetic Retinopathy (DR) development in fundus images with highly reliable results. However, its accuracy depends on the availability of big datasets to train such a deep network. Due to the privacy concerns, the strict rules on medical data limit accessibility of images in publicly available datasets. In this paper, we propose a collaborative learning approach to train CNN models with multiple datasets while preserving the privacy of datasets in a distributed learning environment without sharing them. First, CNN networks are trained with private datasets, and tested with the same publicly available images. Based on their initial accuracies, the CNN model with the lowest performance among datasets is forwarded to second lowest performed dataset to retrain it using the transfer learning approach. Then, the retrained network is forwarded to next dataset. This procedure is repeated for each dataset from the lowest performed dataset to the highest. With this ascending chain order fashion, the network is retrained again and again using different datasets and its performance is improved over the time. Based on our experimental results on five different retina image datasets, DR detection accuracy is increased to 93.5% compared with the accuracies of merged datasets (84%) and individual datasets (73%, 78%, 83%, 85%).
Conceptual Design of a Sensorless Smart Parking Payment Solution

In a world where Industry 4.0 is the order of the day, businesses endeavour to find ways to harness technology through innovation or duplication of solutions that have been successfully implemented elsewhere. For some resource-constrained entities, innovation is critical to mitigating costs while at the same time digitally transforming their business operations. In this paper, we present a solution that makes smart decisions and alerts stakeholders without having car parking sensors or the need of smartphones for the users. We then compared our solution to alternative designs of smart parking payment solutions with respect to costs, implementation, and viability to users. We are also confident that many local authorities in different countries will be able to implement the proposed solution with much ease or little guidance under the same context.
ARSeg: An Attention RegSeg Architecture for CXR Lung Segmentation

Lung disease is one of the most prevalent causes of illness and death in adults and children worldwide. Researchers focused in developing computer-aided diagnosis/detection systems to provide a visual proof and to detect subtle signs of lung diseases. Various systems were proposed to separate the lung regions from organs and other tissues on Chest X-Ray images. However, there is still challenging cases such as the presence of the rib cage edges and lungs affected with abnormalities. For such, we present a new developed segmentation method called Attention RegSeg to detect the precise lung regions and improve the performance of the lung segmentation task. Attention RegSeg employs attention mechanism to suppress irrelevant features transmitted by skip connections. Experimental results showed an interesting performance with an accuracy of 95.69% and an F1-score of 95.63% using varying loss functions and three datasets (Montgomery, Shenzhen, and JSRT) as learning data. The proposed model outperformed recent state-of-the-art works and proved its ability to segment lungs overcoming challenging cases such as lungs affected with tuberculosis.