Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilization of online systems to promote youth participation in research: A methodological study

BACKGROUND Online surveys can align with youth’s increased use of the internet and can be a mechanism for expanding youth participation in research. This is particularly important during the coronavirus disease 2019 (COVID-19) pandemic, when in-person interactions are limited. However, the advantages and drawbacks of online systems used for research need to be carefully considered before utilizing such methodologies. AIM To describe and discuss the strengths and limitations of an online system developed to recruit adolescent girls for a sexual health research study and conduct a three-month follow up survey. METHODS This methodology paper examines the use of an online system to recruit and follow participants three months after their medical visit to evaluate a mobile sexual and reproductive health application, Health-E You/Salud iTuTM, for adolescent girls attending school-based health centers (SBHCs) across the United States. SBHC staff gave adolescent girls a web link to an online eligibility and consent survey. Participants were then asked to complete two online surveys (baseline and 3-month follow-up). Surveys, reminders, and incentives to complete them were distributed through short message service (SMS) text messages. Upon completing each survey, participants were also sent an email with a link to an electronic gift card as a thank-you for their participation. Barriers to implementing this system were discussed with clinicians and staff at each participating SBHC. RESULTS This online recruitment and retention system enabled participant recruitment at 26 different SBHCs in seven states across the United States. Between September 2021 and June 2022, 415 adolescent girls were screened using the Qualtrics online survey platform, and 182 were eligible to participate. Of those eligible, 78.0% (n = 142) completed the baseline survey. Participants were racially, geographically, and linguistically diverse. Most of the participants (89.4%) were non-White, and 40.8% spoke Spanish. A total of 62.0% (n = 88) completed the 3-month follow-up survey. Limitations of this system included reliance on internet access (via Wi-Fi or cell service), which was not universally available or reliable. In addition, an individual unrelated to the study obtained the survey link, filled out multiple surveys, and received multiple gift cards before the research team discovered and stopped this activity. As a result, additional security protocols were instituted. CONCLUSION Online systems for health research can increase the reach and diversity of study participants, reduce costs for research personnel time and travel, allow for continued study operation when in-person visits are limited (such as during the COVID-19 pandemic), and connect youth with research using technology. However, there are challenges and limitations to online systems, which include limited internet access, intermittent internet connection, data security concerns, and the potential for fraudulent users. These challenges should be considered prior to using online systems for research.
Do Women Benefit from Women Education Aid? Evidence from Panel Data

Generally, the literature on aid focuses on the potential growth effects of aggregate aid. Due to the fact that donors have consistently asserted the multidimensionality of their purposes, it is necessary to conduct a much more disaggregated analysis of aid effectiveness. In this study, the effect of women education aid on 72 developing countries is examined empirically over the period 1990–2016. Using cross-country regression, this study examines the effectiveness of aid targeted at women’s education. Based on the fact that donors provide a large amount of women’s education aid to countries whose voting positions in the UN General Assembly are similar, this analysis exploits an instrumental variable. This study shows that women’s education aid has a significantly positive effect on women’s education. The results of this study are robust when different sensitivity checks are performed. The findings have significant policy implications for donor countries and international aid organizations, as they assist in identifying the most effective types of foreign aid flow to the various sectors of the recipient country’s economy.
Does Narrow Row Spacing Suppress Weeds and Increase Yields in Corn and Soybean? A Meta-Analysis

Abstract Narrow row spacing (<76 cm) could improve crop competitiveness, suppress weeds and might provide yield advantage. Many studies have been conducted to evaluate the impact of narrow row spacing; however, no quantitative synthesis of these studies exists. The objectives of this meta-analysis were to (1) quantify the overall effect of narrow row spacing (<76 cm) on weed density, biomass, control, weed seed production, and yield in corn (Zea mays L.) and soybean [Glycine max (L.) Merr. ] compared with 76-cm row spacing, and (2) assess the influence of agronomic management decisions (tillage type, weed management, herbicide application frequency and time) on effect of narrow row spacing on weed suppression and corn and soybean yield. We compiled 1,904 pair-wise observations from 35 studies conducted in 12 states in the United States during 1961 to 2018. Averaged across individual observations, narrow row spacing suppressed weed density by 34%, weed biomass by 55%, and weed seed production by 45%, while it improved weed control by 32% and crop yield by 11% compared with 76-cm row spacing. Narrow row spacing in soybean suppressed weed density by 42%, weed biomass by 71%, and increased crop yield by 12% compared with 76-cm row spacing. Although narrow row spacing had a nonsignificant effect on response variables in corn, the number of studies (n = 1 to 6) and observations (n = 1 to 59) addressing each response variable were limited. Tillage type (conventional and reduced) did not influence the response of weed density, control, and seed production in narrow row spacing; however, weed biomass and weed seed production were more greatly reduced with the sequential application of herbicides compared with a single application. Thus, narrow row spacing in soybean can be integrated with other options for management of herbicide-resistant weeds.
Fake News for All: How Citizens Discern Disinformation in Autocracies

ABSTRACT Research on autocracies often posits that propaganda can manipulate citizens’ beliefs, but existing work does not systematically investigate how well individuals recognize misinformation in authoritarian environments and whether susceptibility to propaganda is related to vulnerability to false news. I present the results of four surveys in Russia, in which more than 60,000 participants evaluated 74 true and false news headlines. I find that Russians’ capacity to discern falsehoods is comparable to discernment found in other political contexts, and they could often detect false news stories. However, consumers of state media gave less accurate evaluations than consumers of independent media, and government supporters were substantially more susceptible to pro-regime misinformation than opposition-minded citizens. Supporters also strongly rejected true messages inconsistent with their political dispositions. These results help understand why in environments dominated by propaganda individuals can be quite vulnerable to information manipulation. At the same time, regime critics in my study often fell for propaganda-inconsistent falsehoods. These results highlight the broader challenge of fighting misinformation and propaganda in a situation when many citizens exhibit political biases.
Multi-omics investigation of the resistance mechanisms of pomalidomide in multiple myeloma

Background Despite significant therapeutic advances over the last decade, multiple myeloma remains an incurable disease. Pomalidomide is the third Immunomodulatory drug that is commonly used to treat patients with relapsed/refractory multiple myeloma. However, approximately half of the patients exhibit resistance to pomalidomide treatment. While previous studies have identified Cereblon as a primary target of Immunomodulatory drugs’ anti-myeloma activity, it is crucial to explore additional mechanisms that are currently less understood. Methods To comprehensively investigate the mechanisms of drug resistance, we conducted integrated proteomic and metabonomic analyses of 12 plasma samples from multiple myeloma patients who had varying responses to pomalidomide. Differentially expressed proteins and metabolites were screened, and were further analyzed using pathway analysis and functional correlation analysis. Also, we estimated the cellular proportions based on ssGSEA algorithm. To investigate the potential role of glycine in modulating the response of MM cells to pomalidomide, cell viability and apoptosis were analyzed. Results Our findings revealed a consistent decrease in the levels of complement components in the pomalidomide-resistant group. Additionally, there were significant differences in the proportion of T follicular helper cell and B cells in the resistant group. Furthermore, glycine levels were significantly decreased in pomalidomide-resistant patients, and exogenous glycine administration increased the sensitivity of MM cell lines to pomalidomide. Conclusion These results demonstrate distinct molecular changes in the plasma of resistant patients that could be used as potential biomarkers for identifying resistance mechanisms for pomalidomide in multiple myeloma and developing immune-related therapeutic strategies.
Novel Classification Algorithm for Ballistic Target Based on HRRP Frame

Nowadays, the identification of ballistic missile warheads in a cloud of decoys and debris is essential for defense systems in order to optimize the use of ammunition resources, avoiding to run out of all the available interceptors in vain. This paper introduces a novel solution for the classification of ballistic targets based on the computation of the inverse Radon transform of the target signatures, represented by a high-resolution range profile frame acquired within an entire period of the main rotation of the target. Namely, the precession for warheads and the tumbling for decoys are taken into account. The pseudo-Zernike moments of the resulting transformation are evaluated as the final feature vector for the classifier. The extracted features guarantee robustness against target's dimensions and rotation velocity, and the initial phase of the target's motion. The classification results on simulated data are shown for different polarizations of the electromagnetic radar waveform and for various operational conditions, confirming the validity of the algorithm.
Real Time Phase Unwrapping Implementation Based on Unscented Kalman Filter for Interferometric Fiber Sensor Applications

Phase unwrapping is a vital process in optical interferometric measurement. Phase unwrapping algorithms usually need to face performance and computation complexity compromise. Unscented Kalman filter based phase unwrapping has been proved to be a robust method with high signal-to-noise performance. However, it calls for high computational cost, which increases the operation time and system power consumption. In this paper, we propose an field programmable gate array (FPGA) based solution to use the unscented Kalman filter to fulfill very fast phase unwrapping task. Hardware-friendly algorithm using the unscented Kalman filter is optimized to facilitate the pipeline design. The phase unwrapping engine is fully implemented on an Intel Stratix III FPGA. Both simulation data and actual data collected from phase-sensitive optical time-domain reflectometer (Φ-OTDR) were used to verify the proposed phase unwrapping engine. The realized SNR of the sensor signal is 42.85 dB, and the average phase error is below 0.01 rad compared with its software-version counterpart. The computation speed can be increased by 470 times, which demonstrates its superiority in the high implementation throughput.
A Simple Strategy to Reduce Contrast Media Use and Risk of Contrast-Induced Renal Injury during PCI: Introduction of an “Optimal Contrast Volume Protocol” to Daily Clinical Practice

Contrast-induced acute kidney injury is the leading cause of iatrogenic acute nephropathy. Development of contrast-induced nephropathy (CIN) increases the risk of adverse long- and short-term patients outcomes, the hospital costs, and length of hospitalization. There are a couple of methods described for CIN prevention (statin prescription, prehydration, contrast media (CM) clearance from the blood system, and decrease amounts of contrast volume). The CM volume to patient’s creatinine clearance ratio is the main factor to predict the risk of CIN development. The safe CM to creatinine clearance ratio limits have been established. The usage of CM amount depends on personal operators habits and inside center regulations. There is no standardized contrast usage protocol worldwide. The aim of this study was to establish an easy to use, cheap, and efficient protocol to estimate a personalized safe CM dose limit for every patient based on their kidney function. These limits are announced during the “Time Out” before the procedure. Our study included 519 patients undergoing interventional coronary procedures: 207 patients into the “Optimal Contrast Volume” arm and 312 into the control group. Applying the protocol into a daily clinical practice leads to a significant reduction in CM volume used for all type of procedures and the development of CIN in comparison with a control group.
Fire Return Interval Within the Northern Boundary of the Larch Forest

Larch (Larix spp.) dominant forests compose a large proportion of the forests of Russia (i.e., about 40% of forested areas). These forests range from the Yenisei ridge on the west to the Pacific Ocean on the east, and from Lake Baikal on the south to the 73rd parallel in the north. Larch stands comprise the world s northern most forest at Ary-Mas (72 deg 28' N, 102 deg 15' E). Larch dominated forests occupy about 70% of the permafrost areas in Siberia. Larch forms high closure stands as well as open forests, and is found mainly over permafrost, where other tree species barely survive. Wildfires are typical for this territory with the majority occurring as ground fires due to low crown closure. Due to the thin active layer in permafrost soils and a dense lichen-moss cover, ground fires may cause stand mortality. The vast areas of larch-dominant forests is generally considered as a "carbon sink"; however, positive long-term temperature trends at higher latitudes are expected to result in an increase of fire frequency, and thus may convert this area to a source for greenhouse gases. There are recent observations regarding the increase of fire frequency within non-protected territories. Surprisingly, there are few publications on fire chronoseqences for the huge forested territory between the Ural Mountains and the Pacific Ocean. Also there is a general understanding that bimodal (late spring -- early summer and late summer-beginning of fall) fire seasonal distribution in the south becomes uni-modal (late spring -- early summer) in the north. The purpose of this study is to investigate the wildfire history at the northern edge of the zone of larch dominance.
Unsupervised Deep Basis Pursuit Based Resolution Enhancement for Forward Looking MIMO SAR Imaging

Nowadays, radar-based image reconstruction is becoming important in higher level automated driving, especially for all weather conditions. In this article, we present an unsupervised deep learning method for forward looking multiple-input multiple-output synthetic aperture radar (FL-MIMO SAR) to enhance the angular resolution. We present mathematical analysis for the composite antenna pattern generated by FL-MIMO SAR as well as image reconstruction with deep learning for FL-MIMO SAR. We present a computationally efficient deep basis pursuit (DBP) method to solve for convolutional neural network (CNN) with unsupervised learning (i.e., without ground truth) and present modified backprojection algorithm to reconstruct SAR image with enhanced angular resolution. We present experimental results to verify our proposed methodology and compare the performance with compressed sensing-based backprojection algorithm on both simulation and real data.
Modified Depthwise Parallel Attention UNet for Retinal Vessel Segmentation

Retinal fundus images contain highly informative geometrical features for detecting diabetic retinopathy (DR), including vessels, especially thin and low-contrast vessels, which are predominant features for accurately diagnosing diabetic retinopathy. Automatic segmentation methods have been developed based on deep convolutional neural networks to replace manual labeling. These methods have shown acceptable performance in fundus vessel segmentation. The UNet model is a well-known architecture of deep neural networks often used for vessel segmentation tasks and has achieved significant performance. However, segmentation tasks remain challenging due to multiple convolutions, down-sampling operations, and inadequate feature fusion in the encoder-decoder architecture. Also, traditional convolution increases the number of multiplications while performing convolution operations. These challenges lead to the loss of information related to thin and low-contrast vessels, eventually affecting the segmentation performance. To tackle this issue, we propose incorporating depthwise parallel attention in the existing UNet framework (DPA-UNet) to achieve accurate vessel segmentation. This approach entails the integration of a depthwise convolution block in the downsampling path and a parallel attention mechanism in the upsampling path of UNet. The primary benefit of depthwise convolution and global information embedding (GIE) is the ability to capture intricate information characteristics across channels. This helps to minimize the information degradation caused by conventional convolution and downsampling techniques. A parallel attention network is proposed in the upsampling path of the existing UNet to optimize the channel and spatial information acquired from the encoder-decoder. Extensive experiments are conducted on three publicly available datasets, namely DRIVE, STARE, and CHASE_DB1, to validate the performance of the proposed model. The findings indicate that the UNET model with depthwise parallel attention achieved a competitive performance with fewer network parameters in segmenting retinal vessels.
Machine learning-based early diagnosis of autism according to eye movements of real and artificial faces scanning

Background Studies on eye movements found that children with autism spectrum disorder (ASD) had abnormal gaze behavior to social stimuli. The current study aimed to investigate whether their eye movement patterns in relation to cartoon characters or real people could be useful in identifying ASD children. Methods Eye-tracking tests based on videos of cartoon characters and real people were performed for ASD and typically developing (TD) children aged between 12 and 60 months. A three-level hierarchical structure including participants, events, and areas of interest was used to arrange the data obtained from eye-tracking tests. Random forest was adopted as the feature selection tool and classifier, and the flattened vectors and diagnostic information were used as features and labels. A logistic regression was used to evaluate the impact of the most important features. Results A total of 161 children (117 ASD and 44 TD) with a mean age of 39.70 ± 12.27 months were recruited. The overall accuracy, precision, and recall of the model were 0.73, 0.73, and 0.75, respectively. Attention to human-related elements was positively related to the diagnosis of ASD, while fixation time for cartoons was negatively related to the diagnosis. Conclusion Using eye-tracking techniques with machine learning algorithms might be promising for identifying ASD. The value of artificial faces, such as cartoon characters, in the field of ASD diagnosis and intervention is worth further exploring.
Efficient Extraction of Deep Image Features Using a Convolutional Neural Network (CNN) for Detecting Ventricular Fibrillation and Tachycardia

To safely select the proper therapy for ventricular fibrillation (VF), it is essential to distinguish it correctly from ventricular tachycardia (VT) and other rhythms. Provided that the required therapy is not the same, an erroneous detection might lead to serious injuries to the patient or even cause ventricular fibrillation (VF). The primary innovation of this study lies in employing a CNN to create new features. These features exhibit the capacity and precision to detect and classify cardiac arrhythmias, including VF and VT. The electrocardiographic (ECG) signals utilized for this assessment were sourced from the established MIT-BIH and AHA databases. The input data to be classified are time–frequency (tf) representation images, specifically, Pseudo Wigner–Ville (PWV). Previous to Pseudo Wigner–Ville (PWV) calculation, preprocessing for denoising, signal alignment, and segmentation is necessary. In order to check the validity of the method independently of the classifier, four different CNNs are used: InceptionV3, MobilNet, VGGNet and AlexNet. The classification results reveal the following values: for VF detection, there is a sensitivity (Sens) of 98.16%, a specificity (Spe) of 99.07%, and an accuracy (Acc) of 98.91%; for ventricular tachycardia (VT), the sensitivity is 90.45%, the specificity is 99.73%, and the accuracy is 99.09%; for normal sinus rhythms, sensitivity stands at 99.34%, specificity is 98.35%, and accuracy is 98.89%; finally, for other rhythms, the sensitivity is 96.98%, the specificity is 99.68%, and the accuracy is 99.11%. Furthermore, distinguishing between shockable (VF/VT) and non-shockable rhythms yielded a sensitivity of 99.23%, a specificity of 99.74%, and an accuracy of 99.61%. The results show that using tf representations as a form of image, combined in this case with a CNN classifier, raises the classification performance above the results in previous works. Considering that these results were achieved without the preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapies, also opening the door to their use in other ECG rhythm detection applications.
A randomized clinical trial to evaluate transoral head and neck surgery using a single-port flexible robotic system

This randomized IDEAL stage 2 clinical trials examined the safety and efficacy of a novel single-port flexible robot for transoral robotic surgery in patients with benign and malignant head and neck tumors. Rates of conversion and perioperative problems within a month of surgery were the main objectives. The Fisher's exact test and the Mann-Whitney U test were used in the statistical analysis, which used a p-value of 0.05 or less to determine significance. With the use of the da Vinci SP technology, 25 participants got TORS that allowed them to swiftly enter their nasopharynx, oropharynx, and hypopharynx. Notably, there were no serious complications or unfavorable robot-related occurrences throughout the 30-day follow-up period or any modifications to the robotic surgical equipment. The novel single-port flexible automated device demonstrates both security and practicality for carrying out transoral endoscopic procedures for head and neck conditions; based on this possible IDEAL stage 2 clinical trials, allowing accessibility to several anatomical regions, such as the larynx, oropharynx, nasopharynx, and hypopharynx.