Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Channel Impedance Plethysmography Neck Patch Device for Unobtrusive Wearable Cardiovascular Monitoring

Background: Wearable and unobtrusive sensing devices are rapidly evolving for long-term cardiovascular monitoring. However, most of the cardiovascular device requires multi-channel physiological signals acquisition, especially in continuous blood pressure (BP) measurement using pulse transition time (PTT) based methods. The multi-devices implementation could impede wearable applications. Objective: This study developed a wearable neck patch device using single-channel impedance plethysmography (IPG) sensing for cardiovascular monitoring, including continuous BP and heart rate (HR) measurement. Methods: IPG-based BP model was derived based on the Bramwell-Hill equation. A patch IPG device was designed and installed above the carotid artery of the subject neck. To validate the BP and HR functions of our device, the Bland-Altman plots were performed to evaluate the estimation error between the reference and the proposed devices within 20 healthy subjects. Results: The BP performance indicates that systolic BP (SBP) estimation error was −0.16 ± 2.97 mmHg and 2.43 ± 1.71 mmHg in terms of mean error (ME) and mean absolute error (MAE), and 0.09 ± 3.30 mmHg and 2.83 ± 1.68 mmHg for diastolic BP (DBP) estimation. Moreover, the HR accuracy has the ME and MAE of 0.02 ± 0.17 bpm and 0.14 ± 0.08 bpm; mean percentage error (MPE) and mean absolute percentage error (MAPE) obtained 0.04 ± 0.23 % and 0.19 ± 0.12 %. Based on statistical results, the BP and HR function of our device satisfied with AAMI/ANSI criteria below 5 ± 8 mmHg and ± 5 bpm or ± 10%. Conclusion: This study implemented a wearable neck patch device with single-channel IPG acquisition that provided two significant cardiovascular parameters of continuous BP and HR, and its performance agreed with standard criteria based on validation with reference sensors. Significance: The proposed proof-of-concept IPG neck patch device has a high potential for wearable applications and low-cost manufacturing in cardiovascular monitoring.
Analytical Modeling of Exposure Process in Pinned Photodiode CMOS Image Sensors

The output features of pixels in CMOS image sensors (CISs) are influenced by different exposure conditions. This article presents an analytical model to describe the output characteristics of the exposure process in pinned photodiode (PPD) CMOS image sensors with the accumulation of three charge sources: photogenerated charge, p-n junction-generated charge, and emission charge. In the proposed model, the difference between the time-based and light intensity-based photo response process is illustrated. The model also reveals the relationship between photodiode potential and light intensity at different exposure values. At the low exposure values, the PD potential increases with light intensity, and a contrary trend is observed at the high exposure values. This concludes that a larger linear output range can be obtained in high light intensity conditions. Furthermore, the improved model provides development analysis in terms of light intensity influence for long exposure time noise. The models were verified with technology computer-aided design simulation and the test devices were fabricated using a 0.18- ${\mu }\text{m}$ CIS process. The model demonstrates good consistency with simulation and measured results.
Deep learning for internet of things in fog computing: Survey and Open Issues

In recent years, the internet of things is getting very popular where it arose in several areas such as education, and healthcare to enhance our live. This popularity has led to an increase number of IoT devices and thus generates massive volume of data. However, this data requires efficient methods of analysis to provide intelligent services. Recently, the deep learning can meet the requirements of IoT data analysis by providing techniques for large scale data analysis and meaningful feature extraction. The deep learning implementation is traditionally delivered to cloud computing due to its high compute resources provisioning. However, given the sheer volume of IoT data, the cloud computing fall to meet the IoT requirements, it presents many issues in term of time response, large data transmission, energy consumption, etc. To address this challenges the fog computing, new layer between cloud computing and internet of things devices, appears. So, moving the implementation of deep learning to fog computing can achieve the requirements of internet of things systems and enhance their performances. In this paper, we introduce deep learning for internet of things, next the application of deep learning in internet of things. We address fog computing for the internet of things. Finally, we present the deep learning in fog computing.
Development of E-Learning Content Based on Kelase-Tat Twam Asi in Supporting Learning During the Covid-19 Pandemic

The main purpose of this paper was to provide information about the existence of e-learning content design based on Kelase-Tat Twam Asi. That design showed three main facilities on the Kelase platform needed to support the online learning process for 2nd grade elementary school students in community service activities at Timpag Village during the Covid-19 pandemic. The model was used to develop the design was the 4D model with a focus on the define stage, design stage, develop stage, and disseminate stage. Six subjects were involved in testing the design quality using trial tools in the form of questionnaires. Data analysis of the design test results was carried out by comparing the percentage average of design quality with the range of percentages found in the design quality standards. The test result showed that the design quality was included in the category of very high quality with a percentage was 92.50%.
Rapoport's rule and determinants of species range size in snakes

Understanding determinants of species' range size is paramount to explaining global ecological patterns and estimating extinction risk of species. Here, we examined whether a sample of 536 snake species exhibits a latitudinal gradient of range size in support of Rapoport's rule, and determined predictors of range size from a set of environmental and biological factors.
Background Filtering and Object Detection With a Stationary LiDAR Using a Layer-Based Method

The connected vehicle environment is significant for the future road network. For constructing the connected vehicle environment, real-time data acquirement is always the prerequisite. Recently, using Light Detection and Ranging (LiDAR)-based roadside infrastructures are becoming a prevalent method of obtaining real-time traffic data. However, the collected raw data from LiDAR cannot usually be used directly. The steps of data processing, like background filtering and object detection, are necessary. The processed data can then be employed in different applications. This paper proposed a novel layer-based searching method that is established with the help of the point distribution features to distinguish moving objects from the point cloud. It aimed to address the unexpected influence of factors such as congested situations and package loss. The new approach was also evaluated compared with the state-of-the-art methods by applying field data. The results showed that the proposed method is more effective than other methods. This method may be applicable to other types of rotating LiDAR for improving the background filtering performance.
Patch-Based Three-Stage Aggregation Network for Object Detection in High Resolution Remote Sensing Images

High-resolution remote sensing image object detection plays an increasingly important role in image processing and interpretation. The application of region-based convolutional neural network (R-CNN) greatly enhances the performance of object detection. However, the attributes of remote sensing images such as overlarge image size, similar background, disequilibrium distribution of categories make this task more challenging. The previous works have focused on extracting multi-scale features of region proposals, often ignoring the quality of region of interest (ROI). In this work, we proposed a patch-based three-stage aggregation network (PTAN) for object detection in high-resolution remote sensing images. It consists of a three-stage cascade structure that sequentially improves the quality of candidate regions by increasing the IoU threshold stage by stage, and adopts a resampling strategy to obtain sufficient region proposals. At the same time, we also proposed patch-based strategy and applied it to the framework during training and inference. Ablation experiments and comprehensive evaluations on a communal remote sensing image object detection dataset DOTA demonstrate the effectiveness and robustness of the proposed framework, which obtained a mean average precision (mAP) value of 0.7958 on validation dataset and a front-rank mAP of 0.7858 on testing dataset. On another remote sensing image object detection dataset NWPU VHR-10, the proposed PTAN obtained a mAP value of 0.9187, outperforming other five object detectors.
Text-to-Clipart using AttnGAN

The Attentional Generative Adversarial Network (AttnGAN) [1] is a state-of-the-art text-to-image generation model. One of the key factors of AttnGAN’s success is the ability to evaluate the similarity between an input sentence and the generated image in the same feature space (Deep Attentional Multimodal Similarity Model, DAMSM). However, the network architecture of AttnGAN is complicated and vast, which necessitates considerable computational costs in the training process. When AttnGAN is applied to a text-to-image generation task in different image domains such as clipart, the output images are simpler than the high-resolution natural images that AttnGAN originally assumes. Therefore, we propose a lightweight AttnGAN aiming at reducing the training computational cost without compromising the quality of the generated images. In particular, we focus on the image encoder; replacing it from Inception-v3 to VGG-16 reduces the DAMSM training time by approximately half of the original implementation.
Stability Analysis of $\ell _{0,\infty }$-Norm Based Convolutional Sparse Coding Using Stripe Coherence

Theoretical guarantees for the <inline-formula><tex-math notation="LaTeX">$\ell _{0,\infty }$</tex-math></inline-formula>-pseudo-norm based convolutional sparse coding have been established in a recent work. However, the stability analysis in the noisy case via the stripe coherence is absent. This coherence is a stronger characterization of the convolutional dictionary, and a considerably more informative measure than the standard global mutual coherence. The present paper supplements this missing part. Formally, three main results together with their proofs are given. The first one is for the stability of the solution to the <inline-formula><tex-math notation="LaTeX">$P_{0,\infty }^{\epsilon }$</tex-math></inline-formula> problem, the second one and the third one are for the stable recovery of orthogonal matching pursuit (OMP) algorithm in the presence of noise. Under a reasonable assumption, the first two results are compared with the corresponding two that use the global mutual coherence in the previous work, respectively, showing the advantages of our results: 1) the stability guarantee conditions are at least as strong as the existing ones; 2) the upper-bound of the distance between the true sparse vector and the solution to the <inline-formula><tex-math notation="LaTeX">$P_{0,\infty }^{\epsilon }$</tex-math></inline-formula> problem is tighter than the counterpart, so is the one between the true sparse vector and the solution obtained by OMP. Moreover, the second result is compared with the third one, stressing the difference in the restriction on noise energy. Also, some experiments are presented to intuitively compare the aforesaid upper-bounds.
Diabetes Status is Associated With Plantar Soft Tissue Stiffness Measured Using Ultrasound Reverberant Shear Wave Elastography Approach

Introduction: The purpose of this study was to investigate the association between the mechanical properties of plantar soft tissue and diabetes status. Method: 51 (M/F: 21/30) participants with prediabetes onset (fasting blood sugar [FBS] level > 100 mg/dL), age >18 years, and no lower limb amputation were recruited after ethical approval was granted from Pontificia Universidad Catolica del Peru ethical review board. Ultrasound reverberant shear wave elastography was used to assess the soft tissue stiffness at the 1st metatarsal head (MTH), 3rd MTH, and the heel at both feet. Results: Spearman’s rank-order correlation (rho) test indicated a significant (P < .05) positive correlations between FBS level and the plantar soft tissue shear wave speed at the 1st MTH: rho = 0.402 (@400 Hz), rho = 0.373 (@450 Hz), rho = 0.474 (@500 Hz), rho= 0.395 (@550 Hz), and rho = 0.326 (@600 Hz) in the left foot and rho = 0.364 (@450 Hz) in the right foot. Mann-Whitney U test indicated a significantly (P < .05) higher shear wave speed in the plantar soft tissue with the following effect sizes (r) at the 1st MTH of the left foot at all tested frequencies: r = 0.297 (@450 Hz), r = 0.345 (@500 Hz), r = 0.322 (@550 Hz), and r = 0.275 (@600 Hz), and at the 1st MTH of right foot r = 0.286 (@400 Hz) in diabetes as compared with the age and body mass index matched prediabetes group. Conclusion: An association between fasting blood sugar level and the stiffness of the plantar soft tissue with higher values of shear wave speed in diabetes versus prediabetes group was observed. This indicated that the proposed approach can improve the assessment of the severity of diabetic foot complications with potential implications in patient stratification.
The Role of Ga-68 PSMA PET/CT Scan on Differentiating of Oligometastatic and High Risk Prostate Cancer

Objectives: In this study, we aimed to investigate whether Ga-68 prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) scanning is adequate to predict intermediate risk, high risk, or oligometastatic prostate cancer (PCa) as an initial staging modality. Methods: The Ga-68 PSMA PET/CT scan images of 50 PCa patients pathologically proven by transrectal ultrasound guided biopsy were evaluated retrospectively. The association of standard uptake value maximum (SUVmax) value of the area with the highest PSMA expression within the primary tumor with the risk groups and metastatic burden is investigated. Results: The SUVmax value was 6.18 in oligometastatic patients where it was measured as 10.93 in patients with higher metastatic burden (p=0.037). The cut-off SUVmax value for multiple metastases was 7.96 (p=0.047). According to the regression model, SUVmax value has a positive influence [odds ratio (OR)=1.42], which was statistically significant (p=0.038). SUVmax values for intermediate and high risk patients were 6.91 and 11.44, respectively (p=0.014). The cut-off SUVmax value for the high risk group was 10.55 (p=0.006). In the regression model, SUVmax value has a positive influence (OR=1.198), which was statistically significant (p=0.021). Conclusion: In this paper, we demonstrated the association between SUVmax value of primary tumor and Gleason score. Our results also allowed us to suggest that primary tumor SUVmax is a sufficiently accurate predictor of D’Amico risk groups in newly diagnosed PCa cases. Additionally, Ga-68 PSMA PET/CT turns out to be a useful tool in determining oligometastatic PCa, which requires a different treatment approach.
The orphan nuclear receptor NR0B2 could be a novel susceptibility locus associated with microsatellite‐stable, APC mutation‐negative early‐onset colorectal carcinomas with metabolic manifestation

Colorectal cancer (CRC) is a high incidence cancer and major cause of cancer mortality. Though disease‐causing tumor suppressors for major syndromes are well characterized, about 10% of CRC is familial but without mutations in known tumor suppressors. We exhaustively screened 100 polyposis families for APC germline mutations and identified 13, which are APC mutation‐negative, microsatellite‐stable (MSS), and with undetectable mutation in known tumor suppressors. Whole exome sequencing in three probands uncovered two with germline frameshift NR0B2 mutations, c. 293_301delTTGGGTTGGinsAC and c.227delT. Sanger Sequencing identified a third proband with NR0B2 c. 157_166delCATCGCACCT frameshift mutation. All three mutations deleted the C‐terminus activation/repression domain of NR0B2, thus are loss‐of‐function mutations. Real‐time RT‐PCR performed on tumor and matched mucosa of one patient revealed that NR0B2 downstream targets, SMAD3 was derepressed while GLI1 was downregulated in the colonic mucosa compared to healthy controls. Truncated NR0B2 molecule was predicted to have weakened binding with interacting partners SMAD3, GLI1, BCL2, and RXRα, implying perturbation of TGF‐β, Hedgehog, anti‐apoptotic and nuclear hormone receptor signaling pathways. Immunostaining also revealed nuclear retention of the most severely truncated NR0B2 molecule compared to the wildtype. Microsatellite and sequencing analysis did not detect loss of wildtype allele in probands' tumors. The patient who acquired somatic KRAS mutation progressed rapidly whist the other two patients manifested with late‐onset obesity and diabetes. We propose that haploinsufficiency of NR0B2 is associated with a novel CRC syndrome with metabolic phenotypes.
Predicting the Risk of Preeclampsia using Soft Voting-based Ensemble and Its Recommendation

Preeclampsia is one of the leading causes of maternal deaths and pregnancy-related complications in many low-income countries. Factors related to preeclampsia include lack of prenatal care and access to medical services, lack of facilities, and inadequate diagnosis and treatment of preeclampsia patients. Early detection and prompt treatment of preeclampsia are essential for the prevention of eclampsia and other life-threatening complications. Several studies have developed the risk prediction models for preeclampsia using mobile applications and machine learning techniques. The use of a soft voting-based ensemble method and recommendation system for women at high risk of preeclampsia remains unexplored. We developed a mobile application with two functionalities of preeclampsia prediction and recommendation system for women at high risk of preeclampsia. We employed the soft voting ensemble learning for the preeclampsia prediction. We obtained a high accuracy value of 98.51% ± 0.0186% compared to the six individual classifiers (k-Nearest Neighbors, Linear SVM, RBF SVM, Gaussian Process, Multi-Layer Perceptron, and Ada Boost). Our proposed recommender system also yielded in a high accuracy value of 96.66% ± 0.0229%. Both our proposed ensemble based on soft-voting and recommendation system had small variance values, which indicated high stability.
Clinical application of a phenotype‐based NGS panel for differential diagnosis of inherited kidney disease and beyond

Understanding the genetic causes of kidney disease is essential for accurate diagnosis and could lead to improved therapeutic strategies and prognosis. To accurately and promptly identify the genetic background of kidney diseases, we applied a targeted next‐generation sequencing gene panel including 203 genes associated with kidney disease, as well as diseases originating in other organs with mimicking symptoms of kidney disease, to analyze 51 patients with nonspecific nephrogenic symptoms, followed by validation of its efficacy as a diagnostic tool. We simultaneously screened for copy number variants (CNVs) in each patient to obtain a higher diagnostic yield (molecular diagnostic rate: 39.2%). Notably, one patient suspected of having Bartter syndrome presented with chloride‐secreting diarrhea attributable to homozygous SLC26A3 variants. Additionally, in eight patients, NGS confirmed the genetic causes of undefined kidney diseases (8/20, 40%), and initial clinical impression and molecular diagnosis were matched in 11 patients (11/20, 55%). Moreover, we found seven novel pathogenic/likely pathogenic variants in PKD1, PKHD1, COL4A3, and SLC12A1 genes, with a possible pathogenic variant in COL4A3 (c.1229G>A) identified in two unrelated patients. These results suggest that targeted NGS‐panel testing performed with CNV analysis might be advantageous for noninvasive and comprehensive diagnosis of suspected genetic kidney diseases.
Ivacaftor in People with Cystic Fibrosis and a 3849+10kb C→T or D1152H Residual Function Mutation

Rationale: Ivacaftor’s clinical effects in the residual function mutations 3849 + 10kb C→T and D1152H warrant further characterization. Objectives: To evaluate ivacaftor’s effect in people with cystic fibrosis aged ≥6 years with 3849 + 10kb C→T or D1152H residual function mutations and to explore the correlation between ivacaftor-induced organoid-based cystic fibrosis transmembrane conductance regulator function measurements and clinical response to ivacaftor. Methods: Participants were randomized (1:1) in this placebo-controlled crossover study; each treatment sequence included two 8-week treatments with an 8-week washout period. The primary endpoint was absolute change in lung clearance index2.5 from baseline through Week 8. Additional endpoints included lung function, patient-reported outcomes, and in vitro intestinal organoid–based measurements of ivacaftor-induced cystic fibrosis transmembrane conductance regulator function. Results: Of 38 participants, 37 completed the study. The primary endpoint was met; the Bayesian posterior probability of improvement in lung clearance index2.5 with ivacaftor versus placebo was >99%. Additional endpoints improved with ivacaftor. Safety findings were consistent with ivacaftor’s known safety profile. Dose-dependent swelling was observed in 23 of 25 viable organoid cultures with ivacaftor treatment. Correlations between ivacaftor-induced organoid swelling and clinical endpoints were negligible to low. Conclusions: In people with cystic fibrosis aged ≥6 years with a 3849 + 10kb C→T or D1152H mutation, ivacaftor treatment improved clinical endpoints compared with placebo; however, there was no correlation between organoid swelling and change in clinical endpoints. The organoid assay may assist in identification of ivacaftor-responsive mutations but in this study did not predict magnitude of clinical benefit for individual people with cystic fibrosis with these two mutations. Clinical trial registered with ClinicalTrials.gov (NCT03068312).
On the Performance of Full-Duplex Spatial Modulation MIMO System With and Without Transmit Antenna Selection Under Imperfect Hardware Conditions

Hardware impairments (HI) due to imperfect manufactured electronic parts degrade the performance of wireless systems. In this paper, we consider a bidirectional full-duplex (FD) spatial modulation (SM) multiple-input multiple-output (MIMO) system with non-ideal hardware and residual self-interference (RSI). We propose to use the transmission antenna selection (TAS) as a solution to mitigate this degradation. The performance of the considered system is analyzed using mathematical analysis. Specifically, we first derive the exact closed-form expressions of the outage probability (OP), symbol error probability (SEP), and achievable data rate (ADR) for two cases: with and without TAS. Given these expressions, we conduct a detailed evaluation of the system performance under various scenarios to gain insight into its behavior. Essential conclusions are then made on HI and RSI’s impacts, especially for the case with TAS and high transmission rates.