Miracle-dz/UniFAR-FTUs huggingface dataset:
Hovering Is Not Helping: Relationships among Helicopter Parenting, Attachment, Academic Outcomes, and Mental Health in College Students

Helicopter parenting (or overparenting) refers to developmentally inappropriate or intrusive tactics to control a child’s behavior. Helicopter parents are usually trying to help their children, but their behavior has been associated with adverse academic, adjustment, and mental health outcomes in older children (i.e., adolescents, young adults), who should be developing more autonomy. The current study examined potential associations between helicopter parenting, attachment security, and academic and mental health outcomes in college students, hypothesizing that higher rates of helicopter parenting would be associated with more insecure attachment with parental figures and closest friends, poorer mental health (i.e., higher rates of depression and/or anxiety), and reduced academic motivation, performance, and self-efficacy. Our sample of 135 college students completed measures of anxiety, depression, and somatization, academic self-efficacy and motivation, perceptions of parental involvement, and dimensions of attachment in multiple relationships (i.e., mother, father, and closest friend). As expected, the bivariate and regression analyses revealed that higher levels of parental involvement (i.e., helicopter parenting) predicted significantly more insecure parental and peer attachment, greater internalizing, and lower effort regulation among college students.
Feature Representations for Human Activity Recognition in Color and Depth Sequences

Nowadays tremendous amounts of visual data including images and videos are explosively increasing with the rapid development of digital cameras and the Internet. As one of the most active and promising research areas in computer vision, recognition of human activity has demonstrated great capacity and potential for automatic and intelligent analysis of visual information. Motivated by this trend and demand, this dissertation is dedicated to developing effective and efficient feature representation techniques to recognize human activities in both color and depth modalities. We first present a general surveillance event detection system which explores motion feature extraction, video clip representation, and detection model learning to handle the large-scale noisy and imbalanced data learning problem. We then propose a novel framework of super sparse coding vector to jointly model and aggregate low-level motion features and spatio-temporal cues into a discriminative and compact representation. Along with the advance of imaging techniques, the recent emergence of costeffectiveness and easy-operation depth sensors greatly facilitates the task of human activity recognition. Our first approach using depth cameras is to employ the skeleton joints recovered from depth maps to capture the action information of static posture, motion property, and overall dynamics. Based on the projected depth maps, we also introduce the approach of depth motion maps to characterize the accumulated motion intensity and distribution in a global manner. In order to explicitly capture the 3-dimensional shapes and motion cues, we extend the surface normal to polynormal which is used to jointly encode the local geometric and temporal information. We then propose a novel scheme of super normal vector to aggregate the low-level polynormal into a discriminative representation. Extensive experiments demonstrate that the feature representation techniques developed in this dissertation achieve the state-of-the-art performances on various activity recognition tasks.
Novel sum-of-sinusoids simulation channel modeling for 6G multiple-input multiple-output vehicle-to-everything communications

In this paper, a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output (MIMO) communications in vehicle-to-everything (V2X) environments. In the proposed sum-of-sinusoids (SoS) channel model, the waves that emerge from the transmitter undergo line-of-sight (LoS) and non-line-of-sight (NLoS) propagation to the receiver, which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation (6G). We derive expressions for the real and imaginary parts of the complex channel impulse response (CIR), which characterize the physical propagation characteristics of V2X wireless channels. The statistical properties of the real and imaginary parts of the complex CIRs, i.e., autocorrelation functions (ACFs), Doppler power spectral densities (PSDs), cross-correlation functions (CCFs), and variances of ACFs and CCFs, are derived and discussed. Simulation results are generated and match those predicted by the underlying theory, demonstrating the accuracy of our derivation and analysis. The proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.
STEWIE: eSTimating grapE berries number and radius from images using a Weakly supervIsed nEural network

Counting tasks with overlapping and occluded tar-gets are often tackled by means of neural networks outputting density maps. While this approach has been proven to be highly effective for crowd-counting tasks, it has not been exploited extensively in other fields (like fruit counting). Furthermore, this approach has never been used to infer the shape or the size of the recognized objects. In this paper, we present a novel deep learning-based methodology to automatically estimate the number of grape berries present in an image and evaluate their average radius as a double output of the network. For the model training, we employ a public dataset consisting of 300 vines images, where each berry center has been dot-annotated. Since the dataset does not directly provide information about the berry radii, we first develop a numerical optimization methodology to calculate the radius of the berries, by exploiting the dot annotations, some prior knowledge (berry maximum size), and a current state-of-the-art segmentation model. Then, we employ the combined information (berry center and radius) to train a custom neural network that outputs two density maps, from which we infer the number of berries in the image and their average size.
Millimeter-Wave Beam-Scanning Phased Array With Switchable Broadside and Endfire Radiation Pattern Using Phase-Controlled Elements

To improve the millimeter-wave signal coverage, this article presents a beam-scanning millimeter-wave phased array with switchable broadside and endfire radiation patterns. The proposed array is made up of four phase-controlled antenna elements, which are linearly arranged. Each antenna element comprises two symmetrical rectangular stacked patches and a linearly tapered slot, which are fed by an “n”-shaped microstrip feeding line with two ports. When the port-to-port phase difference is 0, the antenna works in common mode. The stacked patches are excited, realizing broadside radiation pattern. The antenna is switched to differential mode with a phase difference of 180°. The tapered slot is excited, achieving endfire radiation pattern. In this way, the millimeter-wave phased array has switchable radiation pattern and a beam-scanning angle of ±55° and ±65° in the broadside and endfire directions, respectively. Compared with the previous works, the proposed array has a wider operating bandwidth of 24–28 GHz with competitively large scanning angle and high gain. The proposed array does not require any active component, so has low loss and structural complexity.
Defining the role of digital public health in the evolving digital health landscape: policy and practice implications in Canada.

In this article, we argue that current digital health strategies across Canada do not appropriately consider the implications of digital technologies (DTs) for public health functions because they adopt a primarily clinical focus. We highlight differences between clinical medicine and public health, suggesting that conceptualizing digital public health (DPH) as a field distinct from, but related to, digital health is essential for the development of DTs in public health. Focussing on DPH may allow for DTs that deeply consider fundamental public health principles of health equity, social justice and action on the social and ecological determinants of health. Moreover, the digital transformation of health services catalyzed by the COVID-19 pandemic and changing public expectations about the speed and convenience of public health services necessitate a specific DPH focus. This imperative is reinforced by the need to address the growing role of DTs as determinants of health that influence health behaviours and outcomes. Making the distinction between DPH and digital health will require more specific DPH strategies that are aligned with emergent digital strategies across Canada, development of intersectoral transdisciplinary partnerships and updated competencies of the public health workforce to ensure that DTs in public health can improve health outcomes for all Canadians.
Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI

Purpose This study aims to evaluate deep learning (DL) denoising reconstructions for image quality improvement of Doppler ultrasound (DUS)-gated fetal cardiac MRI in congenital heart disease (CHD). Methods Twenty-five fetuses with CHD (mean gestational age: 35 ± 1 weeks) underwent fetal cardiac MRI at 3T. Cine imaging was acquired using a balanced steady-state free precession (bSSFP) sequence with Doppler ultrasound gating. Images were reconstructed using both compressed sensing (bSSFP CS) and a pre-trained convolutional neural network trained for DL denoising (bSSFP DL). Images were compared qualitatively based on a 5-point Likert scale (from 1 = non-diagnostic to 5 = excellent) and quantitatively by calculating the apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (aCNR). Diagnostic confidence was assessed for the atria, ventricles, foramen ovale, valves, great vessels, aortic arch, and pulmonary veins. Results Fetal cardiac cine MRI was successful in 23 fetuses (92%), with two studies excluded due to extensive fetal motion. The image quality of bSSFP DL cine reconstructions was rated superior to standard bSSFP CS cine images in terms of contrast [3 (interquartile range: 2–4) vs. 5 (4–5), P < 0.001] and endocardial edge definition [3 (2–4) vs. 4 (4–5), P < 0.001], while the extent of artifacts was found to be comparable [4 (3–4.75) vs. 4 (3–4), P = 0.40]. bSSFP DL images had higher aSNR and aCNR compared with the bSSFP CS images (aSNR: 13.4 ± 6.9 vs. 8.3 ± 3.6, P < 0.001; aCNR: 26.6 ± 15.8 vs. 14.4 ± 6.8, P < 0.001). Diagnostic confidence of the bSSFP DL images was superior for the evaluation of cardiovascular structures (e.g., atria and ventricles: P = 0.003). Conclusion DL image denoising provides superior quality for DUS-gated fetal cardiac cine imaging of CHD compared to standard CS image reconstruction.
Renal Expression and Localization of the Receptor for (Pro)renin and Its Ligands in Rodent Models of Diabetes, Metabolic Syndrome, and Age-Dependent Focal and Segmental Glomerulosclerosis

The (pro)renin receptor ((P)RR), a versatile protein found in various organs, including the kidney, is implicated in cardiometabolic conditions like diabetes, hypertension, and dyslipidemia, potentially contributing to organ damage. Importantly, changes in (pro)renin/(P)RR system localization during renal injury, a critical information base, remain unexplored. This study investigates the expression and topographic localization of the full length (FL)-(P)RR, its ligands (renin and prorenin), and its target cyclooxygenase-2 and found that they are upregulated in three distinct animal models of renal injury. The protein expression of these targets, initially confined to specific tubular renal cell types in control animals, increases in renal injury models, extending to glomerular cells. (P)RR gene expression correlates with protein changes in a genetic model of focal and segmental glomerulosclerosis. However, in diabetic and high-fat-fed mice, (P)RR mRNA levels contradict FL-(P)RR immunoreactivity. Research on diabetic mice kidneys and human podocytes exposed to diabetic glucose levels suggests that this inconsistency may result from disrupted intracellular (P)RR processing, likely due to increased Munc18-1 interacting protein 3. It follows that changes in FL-(P)RR cellular content mechanisms are specific to renal disease etiology, emphasizing the need for consideration in future studies exploring this receptor’s involvement in renal damage of different origins.
Evaluating Machine Learning Algorithms for Predicting Financial Aid Eligibility: A Comparative Study of Random Forest, Gradient Boosting and Neural Network

Financial aid ensures equitable access to higher education, irrespective of students' social or economic backgrounds. However, as the financial aid source are limited, the administrators are burdened with the task of determining the student eligibility for financial aid in a fair and unbias manner. Additionally, the process of determining eligibility by human evaluators can benefit from machine learning assisted decision support tools. This study investigates the feasibility of using machine learning algorithms to achieve this goal. Three algorithms were selected for this comparative study with Decision Tree acts as a baseline. The algorithms are trained against a highly imbalanced dataset provided by ZAWAF. The training process incorporates k-fold cross-validation and employs stratified sampling techniques. It was found that all machine learning models outperformed the baseline, with the MLP-ANN model exhibiting the highest accuracy and precision scores. This demonstrates the potential for machine learning models to be integrated as decision support for the distribution of financial aid.
Prognosis Prediction of Alzheimer's Disease Based on Multi-Modal Diffusion Model

Alzheimer's Disease is a complex and currently one of the most prevalent illnesses. Due to these factors there is a growing emphasis on the early diagnosis of Alzheimer's Disease and our approach involves leveraging deep learning techniques to overcome this challenge. In this paper, we introduce a deep learning model designed to predict the progression of Alzheimer's Disease. Our model is based on the diffusion model and utilizes a multi-modal dataset that includes Magnetic Resonance Imaging data (image) and biospecimen data (clinical non-image) associated with Alzheimer's Disease. The proposed model operates through image-to-image translation based on a conditional diffusion process. Our findings validate that our model can generate images that faithfully capture the structural changes in the brains of Alzheimer's patients. Moreover, it outperforms other models according to various evaluation metrics such as PSNR, SSIM, and FID. Additionally, we demonstrate the superiority of a multi-modal dataset over a single modality dataset. We anticipate that the adoption of our proposed model will facilitate the early diagnosis of Alzheimer's Disease, thereby making a significant contribution to the medical field.
Evaluation of Suitable Reference Genes for Quantitative Real-Time PCR in Various Tissues of Apocynum venetum

Apocynum venetum L. is an economically valuable plant with tolerance to drought and salinity. Its leaves are utilized in tea production and pharmaceuticals, while the stem bark serves as a high-quality fiber material. To gain insights into the gene expression patterns of A. venetum using quantitative real-time PCR (qRT-PCR), it is crucial to identify appropriate reference genes. This study selected nine candidate genes, including α-tubulin (TUA), β-tubulin (TUB), actin (ACT), cyclophilin (CYP), elongation factor-1α (EF-1α), the B family of regulatory subunits of protein phosphatase (PPP2R2, PPP2R3, and PPP2R5), and phosphoglycerate kinase (PGK), to determine the most appropriate reference genes in the leaf, stem, and root tissues of A. venetum. A comprehensive ranking by geNorm, NormFinder, BestKeeper, and RefFinder software and Venn diagrams was used to screen more stable reference genes in different tissues. The two most stable reference genes were CYP and TUA in leaves, PGK and PPP2R3 in stems, and TUA and EF-1α in roots, respectively. The relative expression values of the four genes involved in proline metabolism under polyethylene glycol treatment were used to validate the screened reference genes, and they exhibited highly stable expression levels. These findings represent the first set of stable reference genes for future gene expression studies in A. venetum. They significantly contribute to enhancing the accuracy and reliability of gene expression analyses in this economically important plant species.
Advancing the path to in-vivo imaging in freely moving mice via multimode-multicore fiber based holographic endoscopy

Abstract. Significance Hair-thin multimode optical fiber-based holographic endoscopes have gained considerable interest in modern neuroscience for their ability to achieve cellular and even subcellular resolution during in-vivo deep brain imaging. However, the application of multimode fibers in freely moving animals presents a persistent challenge as it is difficult to maintain optimal imaging performance while the fiber undergoes deformations. Aim We propose a fiber solution for challenging in-vivo applications with the capability of deep brain high spatial resolution imaging and neuronal activity monitoring in anesthetized as well as awake behaving mice. Approach We used our previously developed M3CF multimode-multicore fiber to record fluorescently labeled neurons in anesthetized mice. Our M3CF exhibits a cascaded refractive index structure, enabling two distinct regimes of light transport that imitate either a multimode or a multicore fiber. The M3CF has been specifically designed for use in the initial phase of an in-vivo experiment, allowing for the navigation of the endoscope’s distal end toward the targeted brain structure. The multicore regime enables the transfer of light to and from each individual neuron within the field of view. For chronic experiments in awake behaving mice, it is crucial to allow for disconnecting the fiber and the animal between experiments. Therefore, we provide here an effective solution and establish a protocol for reconnection of two segments of M3CF with hexagonally arranged corelets. Results We successfully utilized the M3CF to image neurons in anaesthetized transgenic mice expressing enhanced green fluorescent protein. Additionally, we compared imaging results obtained with the M3CF with larger numerical aperture (NA) fibers in fixed whole-brain tissue. Conclusions This study focuses on addressing challenges and providing insights into the use of multimode-multicore fibers as imaging solutions for in-vivo applications. We suggest that the upcoming version of the M3CF increases the overall NA between the two cladding layers to allow for access to high resolution spatial imaging. As the NA increases in the multimode regime, the fiber diameter and ring structure must be reduced to minimize the computational burden and invasiveness.
Spurious prospective effects between general and domain-specific self-esteem: A reanalysis of a meta-analysis of longitudinal studies

A recent meta-analysis, of 38 studies with data from 43 independent samples (total N = 24,668), claimed evidence for positive reciprocal prospective effects, and hence for both top-down and bottom-up processes, between general and domain-specific self-esteem. However, the meta-analytic cross-lagged effects were estimated while adjusting for a prior measurement of the outcome variable and it is known that such adjusted cross-lagged effects may be spurious due to correlations with residuals and regression to the mean. In the present reanalyses, we found all of the prospective effects to be spurious. Consequently, claims about increasing prospective effects and top-down and bottom-up processes between general and domain-specific self-esteem can be questioned. It is important for researchers to be aware of the limitations of cross-lagged panel analyses, and of analyses of correlational data in general, in order not to overinterpret findings.
miR-1202 acts as anti-oncomiR in myeloid leukaemia by down-modulating GATA-1S expression

Transient abnormal myelopoiesis (TAM) is a Down syndrome-related pre-leukaemic condition characterized by somatic mutations in the haematopoietic transcription factor GATA-1 that result in exclusive production of its shorter isoform (GATA-1S). Given the common hallmark of altered miRNA expression profiles in haematological malignancies and the pro-leukaemic role of GATA-1S, we aimed to search for miRNAs potentially able to modulate the expression of GATA-1 isoforms. Starting from an in silico prediction of miRNA binding sites in the GATA-1 transcript, miR-1202 came into our sight as potential regulator of GATA-1 expression. Expression studies in K562 cells revealed that miR-1202 directly targets GATA-1, negatively regulates its expression, impairs GATA-1S production, reduces cell proliferation, and increases apoptosis sensitivity. Furthermore, data from TAM and myeloid leukaemia patients provided substantial support to our study by showing that miR-1202 down-modulation is accompanied by increased GATA-1 levels, with more marked effects on GATA-1S. These findings indicate that miR-1202 acts as an anti-oncomiR in myeloid cells and may impact leukaemogenesis at least in part by down-modulating GATA-1S levels.
Physical activity and cognitive function in adults born very preterm or with very low birth weight–an individual participant data meta-analysis

Objective Individuals born very preterm (<32 weeks of gestation) or with very low birthweight (<1500g) have lower cognitive function compared with term-born peers. Furthermore, some studies suggest that they are less physically active as young adults than controls, but the relationship between physical activity and cognitive function remains unclear. We performed an individual participant data meta-analysis to examine whether being born preterm/with very low birth weight is associated with physical activity in adulthood and examined if cognitive function mediates this association. Study design Cohorts with data on physical activity and cognitive function in adults born very preterm/very low birth weight and term-born controls were recruited from the Research on European Children and Adults Born Preterm, and the Adults Born Preterm International Collaboration Consortia. A systematic literature search was performed in PubMed and Embase. Results Five cohorts with 1644 participants aged 22–28 years (595 very preterm/very low birth weight and 1049 controls) were included. Adults born very preterm/very low birth weight reported 1.11 (95% CI: 0.68 to 1.54) hours less moderate to vigorous physical activity per week than controls, adjusted for cohort, age and sex. The difference between individuals born very preterm/very low birth weight and controls was larger among women than among men. Neither intelligence quotient nor self-reported executive function mediated the association between very preterm/very low birth weight and moderate to vigorous physical activity. Results were essentially the same when we excluded individuals with neurosensory impairments. Conclusion Adults born very preterm/very low birth weight, especially women, reported less moderate to vigorous physical activity than their term-born peers. Cognitive function did not mediate this association. Considering the risk of adverse health outcomes among individuals born preterm, physical activity could be a target for intervention.