Miracle-dz/UniFAR-FTUs huggingface dataset:
CD47 is Required for Mesenchymal Progenitor Proliferation and Fracture Repair

CD47 is a ubiquitous and pleiotropic cell-surface receptor. Disrupting CD47 enhances injury repair in various tissues but the role of CD47 has not been studied in bone injuries. In a murine closed-fracture model, CD47-null mice showed decreased callus bone volume, bone mineral content, and tissue mineral content as assessed by microcomputed tomography 10 days post-fracture, and increased fibrous volume as determined by histology. To understand the cellular basis for this phenotype, mesenchymal progenitors (MSC) were harvested from bone marrow. CD47-null MSC showed decreased large fibroblast colony formation (CFU-F), significantly less proliferation, and fewer cells in S-phase, although osteoblast differentiation was unaffected. However, consistent with prior research, CD47-null endothelial cells showed increased proliferation relative to WT cells. Similarly, in a murine ischemic fracture model, CD47-null mice showed reduced fracture callus bone volume and bone mineral content relative to WT. Consistent with our in vitro results, in vivo EdU labeling showed decreased cell proliferation in the callus of CD47-null mice, while staining for CD31 and endomucin demonstrated increased endothelial cell mass. Finally, WT mice administered a CD47 morpholino, which blocks CD47 protein production, showed a callus phenotype similar to that of non-ischemic and ischemic fractures in CD47-null mice, suggesting the phenotype was not due to developmental changes in the knockout mice. Thus, inhibition of CD47 during bone healing reduces both non-ischemic and ischemic fracture healing, in part, by decreasing MSC proliferation. Furthermore, the increase in endothelial cell proliferation and early blood vessel density caused by CD47 disruption is not sufficient to overcome MSC dysfunction.
Endocannabinoid and neuroplasticity-related changes as susceptibility factors in a rat model of posttraumatic stress disorder

Traumatic experiences result in the development of posttraumatic stress disorder (PTSD) in 10-25% of exposed individuals. While human clinical studies suggest that susceptibility is potentially linked to endocannabinoid (eCB) signaling, neurobiological PTSD susceptibility factors are poorly understood. Employing a rat model of PTSD, we characterized distinct resilient and susceptible subpopulations based on generalized fear, a core symptom of PTSD. In these groups, we assessed i. ) eCB levels by mass spectrometry and expression variations of eCB system- and iii. ) neuroplasticity-related genes by real-time quantitative PCR in the circuitry relevant in trauma-induced changes. Furthermore, employing supervised and semi-supervised machine learning based statistical analytical models, we assessed iv. ) gene expression patterns with the most robust predictive power regarding PTSD susceptibility. According to our findings, in our model, generalized fear responses occurred with sufficient variability to characterize distinct resilient and susceptible subpopulations. Susceptible subjects showed lower prelimbic and higher ventral hippocampal levels of eCB 2-arachidonoyl-glycerol (2-AG) compared to resilient subjects. Ventral hippocampal 2-AG content positively correlated with the strength of fear generalization. Furthermore, susceptibility was associated with i. ) neuronal prefrontal, hippocampal and amygdalar hypoactivity, ii. ) marked decrease in the expression of genes of transcription factors modulating neuroplasticity and iii. ) an altered expression pattern of eCB-related genes, including enzymes involved in eCB metabolism. Unsupervised and semi-supervised statistical approaches highlighted that hippocampal gene expression patterns possess strong predictive power regarding susceptibility. Taken together, the marked eCB and neuroplasticity changes in susceptible individuals associated with abnormal activity patterns in the fear circuitry possibly contribute to context coding deficits, resulting in generalized fear. Highlights We assessed endocannabinoid and neuroplasticity correlates of PTSD susceptibility Posttrauma generalized fear strength separates susceptible and resilient individuals Susceptible individuals show altered endocannabinoid levels in the fear circuitry Neuroplasticity and endocannabinoid gene expression patterns predict susceptibility
Robust tensor-completion algorithm for 5D seismic-data reconstruction

Multidimensional seismic data reconstruction has emerged as a primary topic of research in the field of seismic data processing. Although there exists a large number of algorithms for multidimensional seismic data reconstruction, they often adopt the [Formula: see text] norm to measure the discrepancy between observed and reconstructed data. Strictly speaking, these algorithms assume well-behaved noise that ideally follows a Gaussian distribution. When erratic noise contaminates the seismic traces, a 5D reconstruction must adopt a robust criterion to measure the difference between observed and reconstructed data. We develop a new formulation to the parallel matrix factorization tensor completion method and adapt it for coping with erratic noise. We use synthetic and field-data examples to examine our robust reconstruction technique.
Making 802.11 DCF near-optimal: Design, implementation, and evaluation

This paper proposes a new wireless MAC protocol called Optimal DCF (O-DCF). O-DCF modifies the rule of adapting CSMA parameters, such as backoff time and transmission length, based on a function of the supply-demand differential captured by the local queue length. O-DCF is fully compatible with 802.11 hardware, so that it can be easily implemented only with a simple device driver update. O-DCF is inspired by the recent theoretical studies on queue-based CSMA for high throughput and fairness. O-DCF effectively bridges the gap between theory and practice, implemented and tested in an off-the-shelf 802.11 chipset. Through extensive simulations and real experiments with a 16-node wireless network testbed, we evaluate the performance of O-DCF and show that it outperforms other competitive ones, such as 802.11 DCF, optimal CSMA, and DiffQ for various scenarios.
A machine learning approach for predicting atmospheric aerosol size distributions

An accurate model and parameterization of aerosol concentration is needed to predict the performance of electro-optical imaging systems. Current models have been shown to vary widely in their ability to accurately predict aerosol size distributions and subsequent scattering properties of the atmosphere. One of the more prevalent methods for modeling particle size spectra consists of fitting a modified gamma function to measurement data, however this limits the distribution to a single mode. Machine learning models have been shown to predict complex multimodal aerosol particle size spectra. Here we establish an empirical model for predicting aerosol size spectra using machine learning techniques. This is accomplished through measurements of aerosols size distributions over the course of eight months. The machine learning models are shown to extend the functionality of Advanced Navy Aerosol Model (ANAM), developed to model the size distribution of aerosols in the maritime environment.
Changes in the future summer Mediterranean climate: contribution of teleconnections and local factors

Abstract. This study analyzes future climate for the Mediterranean region projected with the high-resolution coupled CM2.5 model, which incorporates a new and improved land model (LM3). The simulated climate changes suggest pronounced warming and drying over most of the region. However, the changes are distinctly smaller than those of the CMIP5 multi-model ensemble. In addition, the changes over much of southeast and central Europe indicate very modest warming compared to the CMIP5 projections and also a tendency toward wetter conditions. These differences indicate a possible role of factors such as land surface–atmospheric interactions in these regions. Our analysis also highlights the importance of correctly projecting the magnitude of changes in the summer North Atlantic Oscillation, which has the capacity to partly offset anthropogenic warming and drying over the western and central Mediterranean. Nevertheless, the projections suggest a decreasing influence of local atmospheric dynamics and teleconnections in maintaining the regional temperature and precipitation balance, in particular over arid regions like the eastern and southern Mediterranean, which show a local maximum of warming and drying. The intensification of the heat low in these regions rather suggests an increasing influence of warming land surface on the local surface atmospheric circulation and progressing desertification.
Utilizing Priors to Guide Exploration

—Exploration is a important component of any reinforcement learning algorithm. Consequently, recent works have developed reward-free methods of exploration that seek to obtain maximum coverage of an environment which is then utilized for a downstream task. However, we show that reward-free exploration is hard in complex environments. In addition, when there is knowledge of the downstream tasks, there is no straightforward method to prioritize exploration to the desired region while still being able to eventually reach regions outside of the desire one(s). We propose to modify existing entropy maximizing exploration algorithms by adding a varying distance metric before estimating entropy. Specifically, by dynamically stretching and compressing the representation space we are able to direct the exploration of entropy maximizing agents. We show that, in a fewer number of steps, our method yields better coverage of desired regions compared to reward-free method while being able to achieve full coverage given enough time.
Renewable Minigrid Electriﬁcation in Off-Grid Rural Ghana: Exploring Households Willingness to Pay

: Renewable energy minigrids hold signiﬁcant prospects for Africa’s energy sector and its economic development in general. The government of Ghana has established pilot renewable minigrids in ﬁve off-grid communities as a testing ground for the electriﬁcation of over 600 existing rural communities that cannot be electriﬁed via the national grid. Although there is evidence on willingness to pay (WTP) values for renewable-generated electricity in some developing countries, little is known about households’ WTP for renewable-based electricity in Ghana and, in particular, about renewable minigrids for rural electriﬁcation. This paper provides one of the ﬁrst WTP estimates for renewable-based electricity for rural electriﬁcation in a developing economy context such as Ghana. Using data from a contingent valuation survey undertaken in all ﬁve pilot renewable minigrid project communities, we found that rural households are willing to pay an average of 30 GHC/month ( ≈ 5 USD/month) for high-quality renewable-powered electricity services, which is twice the amount they are currently paying based on the Uniform National Tariffs. The hypothetical bias is addressed by conducting a survey among active users of the minigrids. The starting point bias is reduced by employing random starting bids. The respondents are willing to pay between 9 and 11% of their discretionary incomes to cover the cost of accessing reliable renewable-powered electricity in the rural, off-grid communities in Ghana. The paper concludes by discussing the policy implications of these ﬁndings regarding the development of tariff regulations and business models for renewable minigrids in the rural, off-grid sector.
Regulation of species densities of seagrass-associated macrobenthos: Evidence from field experiments in the Indian River estuary, Florida

In order to provide insight into the regulation of species densities of seagrass-associated macrobenthos, field experiments in the Indian River estuary, Florida were initiated in the sea-grass Halodu/e wrightii to test effects of (1) excluding predators by caging, (2) enclosing pre-dators inside cages, (3) adding dense populations of suspension feeders, (4) providing organic enrichment, (5) removing seagrass blades, and (6) erecting cages at different times. The 11 most abundant species were selected for statistical testing of responses to the experimental treatments. Analyses showed that macrobenthic species differed markedly in their responses to the various treatments over a period of one year. Several species had increased densities with organic enrichment and one increased depending on the time when a cage was erected. Some species had increased densities when seagrass blades were clipped while others showed decreased densities when blades were removed. Certain species occurred in high densities only inside predator exclusion cages. These variations in response did not consistently correspond to taxonomic groupings or feeding types. These field experiments may encourage caution by marine ecologists wishing to generalize from one species or group of species to the community level of organization.
A PCB-Incorporated Inductor Based Filter Design Solution for Differential Mode Noise of 3-Phase SiC-MOSFET Based VSI

Electromagnetic emission is a challenging problem for the Wide Band Gap (WBG) device based power electronic converters. The Differential Mode (DM) noise contains switching frequency components and high-frequency noise peaks. High-frequency DM noise peak depends upon the loop inductance of the converter and output capacitance of the switch. Optimization of switching frequency or variable switching frequency could attenuate switching frequency noise and its harmonics only, but not the switching transient noise. Using a passive filter is common for IGBT based VSI, where the high-frequency DM noise peak occurs in the <inline-formula> <tex-math notation="LaTeX">$1MHz$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$5MHz$ </tex-math></inline-formula> range. However, the high-frequency DM noise peak appears beyond <inline-formula> <tex-math notation="LaTeX">$10MHz $ </tex-math></inline-formula> for SiC-MOSFET based VSI. It requires a high corner frequency, lower inductance, and capacitance of the DM filter. However, getting an inductor with a high Self-Resonating Frequency (SRF) is challenging. This paper proposes a PCB-incorporated inductor based segmented LCLC filter with high SRF value for high-frequency DM noise of SiC-MOSFET based VSI. The filter design is based on mathematical modeling of the DM noise of SiC-MOSFET based VSI. The proposed filter is implemented and verified on a SiC-MOSFET based VSI, driving a <inline-formula> <tex-math notation="LaTeX">$5 kVA$ </tex-math></inline-formula> induction motor at <inline-formula> <tex-math notation="LaTeX">$40 kHz$ </tex-math></inline-formula> switching frequency.
An Effective Checkerboard Calibration Technique for a Stereo Camera System

One of the most important abilities of the human vision system is the capacity to perceive the environment in three dimensions. Due to the spatial separation between the two eyes in the horizontal direction, the two images produced in the two eyes are different from one another. These results served as the basis for in-depth investigation into the extraction of depth from two or more images taken from the same scene from varied angles, giving the impression of three-dimensional shapes. The three-dimensional space is approximated by calculating the pixel difference between the two stereo pictures taken from reality. This research describes a framework for creating a disparity map and depth map in order to minimize or reduce the issues that are now present. Multiple cameras are becoming more common in mobile devices, and these stereo pictures may be utilised for depth sensing. However, because they frequently contain a variety of attributes, such as resolutions, deviations, and fields of view, using the raw images from the cameras is difficult. As part of this study, we propose and evaluate techniques for quickly and easily acquiring stereo images from several cameras in a matter of 0.346 seconds.
A Comparative Study for Autism spectrum disorder Using EfficientNet, ResNet50

ASD, or autism spectrum disorder, encompasses individuals with varying abilities and can greatly influence their daily lives. Those with ASD often face challenges in comprehending and utilizing both verbal and nonverbal forms of communication, forming and maintaining relationships, and adapting to changes in routines or environments. The severity of symptoms can vary greatly, ranging from mild to severe. The early identification and intervention for affected children are crucial to enhance their cognitive abilities. To tackle this issue, the researchers employed a ResNet50 CNN model to analyze facial characteristics and identify distinctions between children with ASD and typically developing children. The model was trained on a comprehensive dataset of facial images and evaluated based on its accuracy in distinguishing between the two groups. The potential application of this model lies in aiding early detection and intervention, leading to improved outcomes for children with ASD. In the study, two deep learning models were considered: EfficientNet and ResNet50. These models were trained on the dataset prepared for the experiment. By comparing their performances and results, it was found that both models were effective in detecting differences; however, the EfficientNet model exhibited higher accuracy and parameter efficiency compared to the ResNet50 model. For future research, potential directions include exploring video-based prediction of autism and evaluating the different phases or stages of autism. This research contributes valuable insights into the field of Autism Spectrum Disorder detection and underscores the potential of EfficientNet and ResNet50 models for facial image analysis using convolutional neural networks.
Combining Deep Features with the Invariant Local Binary Pattern features for Skin Cancer Classification

Melanoma, a potentially fatal form of skin cancer, represents a global health concern, necessitating efficient and precise detection for improved patient outcomes. However, the subjective and daunting nature of manual examination and diagnosing skin lesions by dermatologists often leads to delayed identification or misdiagnosis. This research paper introduces a novel technique for automated melanoma classification to address this pressing problem. The proposed research methodology combines deep features with rotation-invariant handcrafted local-binary pattern (RI-LBP) features for skin lesion classification. RI-LBP extracts microstructures present at the local level whereas deep features capture the high-level intuitive features present in a skin lesion image. After combining RI-LBP with the deep features, the combined feature vector is used to train and test the XGBoost machine learning classifier for classification purposes. Moreover, the investigation undertakes a comparative analysis of numerous machine learning algorithms to evaluate their efficacy in the classification of melanoma using the suggested combined feature set. The XGBoost classifier has obtained an Area Under the Curve value of 0.91 in differentiating pernicious melanomas and benign skin lesion dermoscopic images. Thus, the proposed method is suitable for aiding dermatologists, enhancing the efficacy and precision of melanoma identification and treatment, and ultimately yielding superior patient care and outcomes.
NISQ Quantum Computing: A Security-Centric Tutorial and Survey [Feature]

Quantum computing (QC) demonstrates substantial theoretical promise in addressing classically intractable problems. Recent investments and advancements across QC system stacks, including hardware, software, and algorithms, underscore a pivotal shift from theoretical exploration to the practical realization of applications. Despite this progress, the prevailing emphasis has predominantly centered on performance enhancement, often overlooking security considerations. In response to this gap, our article presents a comprehensive tutorial and survey aimed at identifying and categorizing vulnerabilities inherent in quantum computing systems. Beginning with an overview encompassing essential principles, ecosystem components, and unique attributes in the quantum computing system stack, we also provide a summary of development resources to facilitate efficient initiation in this domain. Building on this foundational knowledge, we introduce a taxonomy of QC security organized by victim layer and security attack objectives. Utilizing this taxonomy as a guiding framework, the article delivers an extensive survey of the latest advancements in QC security, with the overarching goal of equipping the reader with a comprehensive understanding of quantum computing system principles and an informed awareness of diverse and dynamic QC security threats. The intention is to benefit both industry stakeholders and research communities, ultimately aiming to proactively identify and mitigate security concerns within QC systems, thereby establishing a robust foundation for secure quantum computing environments.
Opto-Electronic Oscillator Design for Photonic Radar System

Research in photonic generation of microwave signals, particularly through opto-electronic oscillator (OEO)-based techniques, has thrived over the last three decades. These methods employ varied OEO designs, incorporating optical components like optical fiber, polarization modulators, chirped fiber Bragg gratings and ring resonators. Various OEO schemes have been developed to enhance spectral purity, aiming for ultra-low phase noise in the generation of single-frequency RF signals. In this paper two different schemes of OEOs are simulated in OptiSystem and we achieved 60 dBm gain for single loop OEO and 83 dBm gain for dual loop OEO at 10 GHz. Conducting signal-to-noise ratio analysis, histogram analysis, and assessing phase noise stability serves as a comprehensive approach to substantiate and validate the efficacy of these OEOs and finally a 10 GHz OEO is developed and experimentally demonstrated in our laboratory for photonic radar system.
Medical Assistance for Detecting Cataract, Jaundice and Strabismus using Deep Learning

Eye diseases are prevailing either due to poor diet, unhealthy lifestyle, hereditary factors or pre-term birth. Many people are unable to get adequate access to trained medical professionals and proper healthcare infrastructure. Often, the eye ailments remain undetected in the early stages, when taking proper prescriptions and precautions can prevent any further serious health issues. This paper proposes a medical diagnosis system for the automatic machine analysis of eye conditions including cataract, jaundice, and strabismus, along with a potent chatbot developed using Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques for and effective medical assistance. In cataract patients, the eye lens gets cloudy and hinders the proper vision. Jaundice is a liver disease, symptoms of which include yellowish eye. Strabismus is another eye condition when both eyes are not aligned symmetrically when focusing on any visual. The Convolutional Neural Network (CNN) models are developed and designed to attained an accuracy of 99.31%, 99.77% and 97.82% for detecting cataract, jaundice and strabismus respectively.