Miracle-dz/UniFAR-FTUs huggingface dataset:
Using transfer learning for automated microbleed segmentation

Introduction Cerebral microbleeds are small perivascular haemorrhages that can occur in both grey and white matter brain regions. Microbleeds are a marker of cerebrovascular pathology, and are associated with an increased risk of cognitive decline and dementia. Microbleeds can be identified and manually segmented by expert radiologists and neurologists, usually from susceptibility-contrast MRI. The latter is hard to harmonize across scanners, while manual segmentation is laborious, time-consuming, and subject to inter- and intra-rater variabiltiy. Automated techniques so far have shown high accuracy at a neighborhood (“patch”) level at the expense of a high number of false positives voxel-wise lesions. We aimed to develop an automated, more precise microbleeds segmentation tool able to use standardizable MRI contrasts. Methods We first trained a ResNet50 network on another MRI segmentations task (cerberospinal fluid versus background segmentation) using T1-weighted, T2-weighted, and T2* MRI. We then used transfer learning to train the network for the detection of microbleeds with the same contrasts. As a final step, we employed a combination of morphological operators and rules at the local lesion level to remove false positives. Manual segmentations of microbleeds from 78 participants were used to train and validate the system. We assessed the impact of patch size, freezing weights of the initial layers, mini-batch size, learning rate, as well as data augmentation on the performance of the Microbleed ResNet50 network. Results The proposed method achieved a high performance, with a patch-level sensitivity, specificity, and accuracy of 99.57%, 99.16%, and 99.93%, respectively. At a per lesion level, sensitivity, precision, and Dice similarity index values were 89.1%, 20.1%, and 0.28 for cortical GM; 100%, 100%, and 1.0 for deep GM; and 91.1%, 44.3%, and 0.58 for WM, respectively. Discussion The proposed microbleed segmentation method is more suitable for the automated detection of microbleeds with high sensitivity.
Right inferior frontal cortex damage impairs the initiation of inhibitory control, but not its implementation

Inhibitory control is one of the most important control functions in the human brain. Much of our understanding of its neural basis comes from seminal work showing that lesions to the right inferior frontal cortex (rIFC) increase stop-signal reaction time (SSRT), a latent variable that expresses the speed of inhibitory control. However, recent work has identified substantial limitations of the SSRT method. Notably, SSRT is confounded by trigger failures: stop-signal trials in which inhibitory control was never initiated. Such trials inflate SSRT, but are typically indicative of attentional, rather than inhibitory deficits. Here, we used hierarchical Bayesian modeling to identify stop-signal trigger failures in human rIFC lesion patients, non-rIFC lesion patients, and healthy comparisons. Furthermore, we measured scalp-EEG to detect β-bursts, a neurophysiological index of inhibitory control. rIFC lesion patients showed a more than five-fold increase in trigger failure trials and did not exhibit the typical increase of stop-related frontal β- bursts. However, on trials in which such β-bursts did occur, rIFC patients showed the typical subsequent upregulation of β over sensorimotor areas, indicating that their ability to implement inhibitory control, once triggered, is intact. These findings suggest that the role of rIFC in inhibitory control has to be fundamentally reinterpreted.
Combination therapy targeting inflammasome and fibrogenesis alleviates inflammation and fibrosis in a zebrafish model of silicosis

Silicosis is a long-term lung disease caused by the inhalation of large amounts of crystalline silica dust. As there is no effective treatment available, patients are provided with supportive care, and some may be considered for lung transplantation. There is therefore an evident need for a better understanding of the disease’s biology and for identifying new therapeutic targets and therapies. In this context, our group has developed a larval zebrafish model of silicosis by injecting silica crystals into the hindbrain ventricle, a cavity into which immune cells can be recruited and that mimics the alveolar environment of the human lung. The injection of silica crystals into this cavity led to the initiation of local and systemic immune responses driven through both TLR- and inflammasome-dependent signaling pathways, followed by fibrosis, as happens in human patients. The combination of the inflammasome inhibitor VX-765 and the antifibrotic agent pirfenidone was found to be the best therapy to alleviate both inflammation and fibrosis. The zebrafish model of silicosis developed here is a unique tool that will shed light onto the molecular mechanisms involved in the progression of this devastating disease and for identifying novel drugs that improve the quality of life of silicosis patients.
A Pairwise Imputation Strategy for Retaining Predictive Features When Combining Multiple Datasets

In the training of predictive models using high-dimensional genomic data, multiple studies’ worth of data are often combined to increase sample size and improve generalizability. A drawback of this approach is that there may be different sets of features measured in each study due to variations in expression measurement platform or technology. It is often common practice to work only with the intersection of features measured in common across all studies, which results in the blind discarding of potentially useful feature information that is measured only in individual or subsets of all studies. We characterize the loss in predictive performance incurred by using only the intersection of feature information available across all studies when training predictors using gene expression data from microarray and sequencing datasets. We study the properties of linear and polynomial regression for imputing discarded features and demonstrate improvements in the external performance of predictors through simulation and in gene expression data collected on breast cancer patients. We propose and evaluate a pairwise imputation strategy that imputes cross-study missing features in each pair of studies and averages imputed features across pairs. Finally, we provide insights on which subsets of intersected and study-specific features should be used so that missing-feature imputation best promotes cross-study replicability. All code with directions to reproduce results in this paper is available at https://github.com/YujieWuu/Pairwise_imputation
Temporal PHATE: A multi-view manifold learning method for brain state trajectories

Brain activity as measured with functional magnetic resonance imaging (fMRI) gives the illusion of intractably high dimensionality, rife with collection and biological noise. Non-linear dimensionality reductions like PCA, UMAP, tSNE, and PHATE have proven useful for high-throughput biomedical data, but have not been extensively used in fMRI, which is known to reﬂect the redundancy and co-modulation of neural population activity. Here we take the manifold-geometry preserving method PHATE and extend it for use in brain activity timeseries data in a method we call temporal PHATE (T-PHATE). We observe that in addition to the intrinsically lower dimensionality of fMRI data, it also has signiﬁcant autocorrelative structure that we can exploit to faithfully denoise the signal and learn brain activation manifolds. We empirically validate T-PHATE on three fMRI tasks and show that T-PHATE manifolds improve visualization ﬁdelity, stimulus feature classiﬁcation, and neural event segmentation. T-PHATE demonstrates impressive improvements over previous cutting-edge approaches to understanding the nature of cognition from fMRI and bodes potential applications broadly for high-dimensional datasets of temporally-diffuse processes.
Hippocampal subfields and their neocortical interactions during autobiographical memory

Advances in ultra-high field 7 Tesla functional magnetic resonance imaging (7T fMRI) have provided unprecedented opportunities to gain insights into the neural underpinnings supporting human memory. The hippocampus, a heterogeneous brain structure comprising several subfields plays a central role during vivid re-experiencing of autobiographical memories (AM). However, due to technical limitations, how hippocampal subfields differentially support AM, whether this contribution is specific to one portion along the hippocampal long-axis, and how subfields are functionally connected with other brain regions typically associated with AM retrieval remains elusive. Here, we leveraged technical advances of parallel imaging and employed a submillimeter Echo Planar Imaging sequence over the whole brain while participants re-experienced vivid, detail-rich AM. We found that all hippocampal subfields along the long-axis were engaged during AM retrieval. Nonetheless, only the pre/parasubiculum within the anterior body of the hippocampus, contributed over and above to AM retrieval. Moreover, whole-brain functional connectivity analyses of the same data revealed that this part of the hippocampus was the only one that was strongly connected to other brain regions typically associated with AM, such as the ventromedial prefrontal cortex (vmPFC) and medial/lateral parietal regions. In the context of the broader literature, our results support recent proposals that the anterior body of the pre/parasubiculum may play an essential role in scene-based cognition, such as the re-experience of personal past events. Highlights All hippocampal subfields differentiate AM retrieval from mental arithmetic problem solving The anterior body of the pre/parasubiculum engages in AM more than other subfields The anterior body of the pre/parasubiculum is strongly connected to the AM network The pre/parasubiculum may be preferentially involved in scene-based cognition
Genome mining as a biotechnological tool for the discovery of novel biosynthetic genes in lichens

The ever-increasing demand for novel drugs highlights the need for bioprospecting unexplored taxa for their biosynthetic potential. Lichen-forming fungi (LFF) are a rich source of natural products but their implementation in pharmaceutical industry is limited, mostly because the genes corresponding to a majority of their natural products is unknown. Furthermore, it is not known to what extent these genes encode structurally novel molecules. Advance in next-generation sequencing technologies has expanded the range of organisms that could be exploited for their biosynthetic potential. In this study, we mine the genomes of nine lichen-forming fungal species of the genus Umbilicaria for biosynthetic genes, and categorize the BGCs as “associated product structurally known”, and “associated product putatively novel”. We found that about 25-30% of the biosynthetic genes are divergent when compared to the global database of BGCs comprising of 1,200,000 characterized biosynthetic genes from planta, bacteria and fungi. Out of 217 total BGCs, 43 were only distantly related to known BGCs, suggesting they encode structurally and functionally unknown natural products. Clusters encoding the putatively novel metabolic diversity comprise PKSs (30), NRPSs (12) and terpenes (1). Our study emphasizes the utility of genomic data in bioprospecting microorganisms for their biosynthetic potential and in advancing the industrial application of unexplored taxa. We highlight the untapped structural metabolic diversity encoded in the lichenized fungal genomes. To the best of our knowledge, this is the first investigation identifying genes coding for NPs with potentially novel therapeutic properties in LFF.
An Exploratory Analysis of Interactive VR-Based Framework for Multi-Componential Analysis of Emotion

In this work, we present a preliminary analysis on emotion formation using a full Component Process Model (CPM), which considers emotional experience as multi-processes with synchronized changes across five main components: Appraisal, Motivation, Physiology, Expression and Feeling. We propose an empirical data-driven setup for generating an affective dataset using interactive Virtual Reality (VR) games with objective and subjective measures. We conducted a pilot study with several participants who played 28 VR games. The result confirms the suitability of the proposed experimental setup for investigating emotional experience. VR games were able to trigger diverse emotions, as evidenced by the collected self-reports. Using a CPM-based survey, we found that VR games could activate emotional experience based on cognitive appraisal, motivation, physiology, and facial expressions. We showed that more than two dimensions (expressions, physiology, novelty, valence, motivation, agency, arousal) are required to define the emotional experience. We also analyzed the relationship between discrete emotions and CPM components. These preliminary results suggest that CPM components may have different levels of involvement depending on the experienced emotion.
Performance Enhancement of the IPMSM for HEV Applications Using Grain-Oriented Electrical Steel and Design Optimization

This paper proposes a method for enhancing the performance of an interior permanent magnet synchronous motor (IPMSM) using grain-oriented electrical steel (GO) and design optimization. As the GO has superior magnetic characteristics in the rolling direction, the GO is applied to the stator teeth to increase the torque and reduce the iron loss of the IPMSM. However, such an approach leads to higher saturation on the core, resulting in worse pulsation characteristics. To handle with pulsation problem on the IPMSM, design optimization is conducted. In this paper, the interpolation multi-objective robust optimization algorithm (IMROA) was proposed. The IMROA can reduce the calculation time by interpolating the objective region and utilizing a stepwise sampling strategy. Moreover, the IMROA considers the robustness of the found solution and can derive a robust global solution that has robustness on the manufacturing tolerance and deformation during the operation and prevents unexpected results on the IPMSM.
Digital Twins Federation for Remote Medical Care of De-Hospitalized Patients

Governments are promoting digitalization also in the medical world to create a platform that virtualizes the doctor/patient relationship in order to minimize patients hospitalization and related costs without loosing information which usually characterizes the real interaction. A tool that is being consolidated to respond to these scenarios is that of Digital Twins. Although some pioneering research works have already proposed the DT of an human organ, the challenging target of designing the DT of a human patient as a whole, which allows to address successfully the de-hospitalization and remote treatment of the patient, is far from reality. To contribute to this goal in this paper we introduce the federation of DTs as a platform to virtualize the disease of a patient as a whole so that each of the human organs involved in the patient pathology cam be monitored/treated in relationship to the others.
BiLight: A Smart Bicycle Monitoring and Finding System for Rider Safety Based on IoT Technologies

This paper proposes a smart bicycle monitoring and finding system, called BiLight, for rider safety and bicycle security based on Internet of Things (IoT) technologies. On the bicycle side, a developing board, an acceleration sensor, and a Bluetooth/iBeacon duel-mode module are integrated in the IoT device of BiLight to recognize the riding user, sense the bicycle status, and communicate with the owner smartphone/nearby smartphones, respectively. On the biker side, a smartphone is used to collect the acceleration data of riding from the IoT device, forward the collected riding data to the BiLight server, and send the alerting messages to the owner smartphone/emergency contact. In addition, the current locations of the stolen/missing bicycle (equipped with the IoT device) are sent by nearby smartphones receiving the iBeacon signals with an abnormal bit (broadcasted from the IoT device). In particular, we explore the densely connected convolutional network in BiLight to extract diverse features of riding for improving the rider recognition accuracy. An Android-based prototype with the Jetson Nano developing board is implemented to verify the feasibility and superiority of our BiLight system. Experimental results show that our developed model outperforms existing approaches and can accurately determine the rider identity.
Augmented/Mixed Reality Audio for Hearables: Sensing, control, and rendering

Augmented or mixed reality (AR/MR) is emerging as one of the key technologies in the future of computing. Audio cues are critical for maintaining a high degree of realism, social connection, and spatial awareness for various AR/MR applications, such as education and training, gaming, remote work, and virtual social gatherings to transport the user to an alternate world called the metaverse. Motivated by a wide variety of AR/MR listening experiences delivered over hearables, this article systematically reviews the integration of fundamental and advanced signal processing techniques for AR/MR audio to equip researchers and engineers in the signal processing community for the next wave of AR/MR.
Performance and Reliability Improvement of Partially Shaded PV Arrays by One-time Electrical Reconfiguration

Partial shading is the most unexpected scenario encountered by the arrays that degrade the performance causing power reduction, non-convex characteristics curves, losses, hotspot, module damage, and system failure. The adoption of various reconfiguration techniques has recently provided relief to the PV array to reduce power losses during partial shading. However, these techniques exhibit vulnerabilities such as reliable operation, ease of implementation and, higher cost and complexity due to the requirement of abundant manpower, labour, complex algorithms, dynamic operation, switches and sensors that cause additional power losses. Hence, this paper presents a low cost and less complex reconfiguration technique for PV arrays to effectively increase the power generation during partial shading scenarios. The proposed reconfiguration do not require any manpower, labour, algorithms or additional devices to reduce the losses in arrays during shading. The efficacy of the technique is tested using two array sizes under various shading scenarios using MATLAB modelling and real-time field experiments. Also, for better analysis, the performance of the proposed technique is compared with conventional configurations and Sudoku reconfiguration. The investigation proclaimed that the proposed reconfiguration technique has an average power enhancement of 20% higher than any other conventional configurations.
A Novel Diagnostic Method for Single and Dual Power Switch Open-Circuit Faults of Six-Phase FTPMSM System Even in Fault Tolerant Operation

This article proposes a new diagnostic method based on average current Park vector (ACPV) for single and dual power switch open-circuit faults of six-phase fault-tolerant permanent magnet synchronous motor (FTPMSM) system, which can be applied for the system both in nonfault and fault tolerant operation conditions. The fault diagnosis structure is composed of two parts: the ACPV moduli-based fault detection and the ACPV polarity-based fault location. By analyzing the ACPV before and after the fault, the normalized ACPV moduli-based fault detection is proposed by using the ACPV components in three orthogonal subspaces, which has good robustness to the speed and load changes. The ACPV polarity-based fault location is then proposed to locate the faulted power switches of the FTPMSM system. Finally, the effectiveness of the proposed fault diagnosis method is validated by a six-phase FTPMSM experimental platform. The fault diagnosis approach has the advantageous features of simple implementation, good robustness to various disturbances, no requirement of the extra sensors, and low computational burden, which can achieve single and dual power switch open-circuit fault diagnosis for the FTPMSM system both in normal and fault tolerant operation conditions.
OPTIMASI VALUE AT RISK RETURN ASET TUNGGAL DAN PORTOFOLIO MENGGUNAKAN SIMULASI MONTE CARLO DILENGKAPI GUI MATLAB

Value at Risk (VaR) is a scale that can measure the maximum loss that may happen for a specified period of time in the normal market conditions at a certain level of confidence. The most important thing in the VaR is to determine the type of methodology and assuming appropriate with the distribution of the return. One of the methods in calculating the VaR is Monte Carlo simulation. VaR with Monte Carlo simulation method assumes that the return value is normal distribution simulated using the appropriate parameters and portfolio return is linier towards its single asset return. From the results and analysis research conducted use GUI Matlab, VaR single asset of value risk on the stock of United Tractors Tbk (UNTR) is greater than Bank Rakyat Indonesia (Persero) Tbk (BBRI), Astra International Tbk (ASII), and Bank Negara Indonesia Tbk ( BBNI), VaR value of portfolio consisting of two assets, the three assets, and four assets have lower value than the sum of its single asset of the value of VaR. Matlab (Matrix Laboratory) is an interactive programming system with the basic elements of array database which dimensions do not need to be stated in particular, while the GUI is the submenu of Matlab. In this research, determining the level of trust and specified time period is very important to count of VaR value because it can describe how much investors bear the risk. Keywords : Value at Risk, time period, confidence level, Monte Carlo simulation
NSGA-II based high gain observer improved optimization method

This paper purpose is to optimize a high gain state observer for the estimation nonlinear chaotic systems.The upgrading method, based on the use of a non dominated sorting genetic algorithm (NSGA-II), relies on a quadratic optimization fitness function is presented to generate the most suitable value of the observer influential parameter θ that define the observation gain. NSGA-II algorithm is considered as a competent multiobjective exploration approach. In fact, the proposed criteria grants an adjustment of the observation error taking into consideration the correction factor of the observer. Furthermore, a remarkable specification of the proposed optimization approach is its independence to initial conditions allowing to override the problem of suboptimal conditions, which are widely used in optimization methods. Experimental simulation is proposed to illustrate the efficiency and prominent results of the designed observation approach, applied to state reconstruction of the well-known unified nonlinear perturbed chaotic systems.
Lessons Learned from an Eye Tracking Study for Targeted Advertising in the Wild

With the ambitious objective in mind to assess the validity of gaze-based interest prediction and test its application for targeted advertising on a self-order terminal, we set out to conduct a user study at a fast food restaurant. We recorded the customers’ eye movements while they were placing their order, and used the collected data to infer which information about the meals they were interested in. We then wanted to test whether the predictions can be used to display targeted advertisements, and influence the customers’ purchase intention for a complementary product. The results did not corroborate the hypothesized relationship between visual attention and interest. However, due to the multiplicity of underlying assumptions, we were unable to identify whether a link does indeed not exist, or whether the result was caused by the experimental setup.In this paper, we describe the design and results of our user study, and discuss our lessons learned. Our observations shall guide other researchers to avoid these common pitfalls.