Miracle-dz/UniFAR-FTUs huggingface dataset:
Tyrosinase suppresses vasculogenic mimicry in human melanoma cells

Melanoma is a type of skin cancer that derives from melanocytes; this tumor is highly metastatic and causes poor clinical outcomes in patients. Vasculogenic mimicry (VM), a vascular-like network that is formed by tumor cells instead of endothelial cells, promotes the growth and metastasis of tumors by providing tumors with oxygen- and nutrient-containing blood. VM correlates with a poor prognosis in patients with melanoma, but the melanoma-specific mechanisms of VM are unknown. The present study revealed that treatment with the melanogenesis stimulators 3-isobutyl 1-methylxanthine (IBMX) and α-melanocyte-stimulating hormone (α-MSH) significantly inhibited VM in MNT-1 human pigmented melanoma cells. Tyrosinase (TYR), an essential enzyme in melanin production, was upregulated on treatment with α-MSH and IBMX, prompting an examination of the association between TYR and VM. A TYR inhibitor, arbutin, promoted VM in melanoma cells. Furthermore, CRISPR/Cas9-mediated knockout (KO) of TYR increased VM by melanoma cells. Notably, even in non-pigmented melanoma cells, TYR attenuated VM. Although re-expression of wild-type TYR suppressed VM in TYR-KO cells, T373K TYR, a frequently detected mutation in individuals with albinism, failed to inhibit VM. Overall, these results demonstrated that TYR negatively regulates VM, providing novel insights into the antioncogenic function of TYR in melanomas.
The effectiveness of automated digital health solutions at successfully managing obesity and obesity-associated disorders: A PICO-structured investigation

Most adults in the UK and USA are classified as overweight or obese. Recent studies suggest that the prevalence of obesity has further increased during the SARS-CoV-2 pandemic and associated lockdowns. Digital technologies may be effective at managing obesity and related comorbidities, a potential further justified by social isolation and distancing circumstances. This review of published literature employed a Patient-Intervention-Comparison-Outcome structured approach on the use of digital solutions to determine the effectiveness of their use in the management and treatment of obesity, hypertension, and type 2 diabetes and included commercially available, automated devices and applications that did not require intervention from a clinician. Our search covered studies published between January 2004 and February 2019, and 18 papers were included in the final analysis. The digital solutions reviewed were smartphone applications, wearable activity trackers, and ‘digital medicine offerings’ (DMO), including ingestible sensors and wearable patches. This study found that not all interventions were effective at encouraging the lifestyle changes required for the management of obesity. Smartphone applications requiring interaction from the patient appeared to be more effective at encouraging engagement with treatment interventions than more passive wearable activity trackers. Automated feedback from smartphone applications was effective at managing type 2 diabetes, while DMO were effective at reducing blood pressure. With the advancement of new technologies alongside a rapid increase in the prevalence of obesity and associated disorders, further studies comparing the various technologies available in larger sample populations for longer periods would help determine the most cost-effective preventive and therapeutic strategies.
DeFLoc: Deep Learning Assisted Indoor Vehicle Localization Atop FM Fingerprint Map

Indoor vehicle localization is an underlying technology for realizing Autonomous Valet Parking (AVP), which demands high accuracy and reliability. However, existing localization technologies, such as GPS, WiFi, Bluetooth, suffer from either low availability or high cost, which are not practical in the real world. In order to put AVP into practice, We desperately need an efficient and reliable indoor vehicle localization technology. In this paper, we propose a Deep learning and FM fingerprint map based indoor vehicle Localization method, namely DeFLoc, which leverages FM signals to achieve accurate and practical indoor localization. In order to reduce the workload of the FM fingerprints collecting process, DeFLoc uses partially uniform sampling to decrease sample data volume and reconstructs the FM fingerprint map from collected incomplete fingerprints precisely using a dedicated deep Convolutional Neural Network (CNN). To alleviate the influence of signal distortions in some FM frequencies, we further design smooth layers in the neural network for improving the accuracy of map reconstruction. Moreover, we devise a continuous vehicle localization algorithm by considering the preferences of vehicle movements to assist us to calibrate localization. We implemented a prototype of DeFLoc and conducted extensive experiments both in simulation and practice. Evaluation results show that our proposed reconstruction model improves accuracy by 40% over conventional matrix completion methods even under the 60% data missing rate. With the precisely reconstructed fingerprint map, DeFLoc achieves over 90% localization accuracy, which indicates DeFLoc can realize accurate and practical indoor vehicle localization.
Intelligent Multi-AUG Ocean Data Collection Scheme in Maritime Wireless Communication Network

The new generation of wireless networks is strengthening ocean development capabilities. The deep-sea detection ability of the offshore platform is being improved by Ocean Internet of Everything (OIoE) system formed by various equipment. This paper studies the application of a new generation of Artificial Intelligence (AI) wireless communication technology in marine monitoring platforms. The purpose is to solve the problem of high data redundancy and high energy consumption in multi-Automatic Underwater Glider (AUG) monitoring. In response to these problems, this paper proposes a matrix completion-based Sampling Points Selection joint Multi-AUG Path Planning (SPS-MAPP) scheme, which includes AUGs path planning and collection information matrix completion. In parts of AUGs path planning, the SPS-MAPP designed a multi-AUG trajectory optimization method using intermittent satellite communication. It proposes a reinforcement learning reward rule based on the idea of artificial potential field, which can effectively increase the coverage of AUGs trajectory. In parts of information processing, the SPS-MAPP proposes a 3-D matrix completion method, which searches for key nodes and edge nodes by designing regular sample points and operators to recover the entire space information. The experimental results show that the proposed SPS-MAPP scheme can effectively improve the subsea monitoring capability of multi-AUG.
Image-Based Visual Impedance Force Control for Contact Aerial Manipulation

In this paper, an image-based impedance control strategy for force tracking of an unmanned aerial manipulator (UAM) is presented. Firstly, image features with nice decoupling characteristics are designed and the relationship between the camera motion and the image features is derived. Then, a two-stage strategy is proposed to achieve force tracking of the UAM on a planar object in an arbitrary pose. The first stage drives the end-effector perpendicular to the object’s planer surface by pure visual servoing. To achieve force tracking under the visual guidance, an adaptive visual impedance control method which adjusts the target stiffness according to the force tracking error and the visual feature error is proposed in the second stage. The closed-loop system is proved asymptotically stable by means of Lyapunov analysis. Further, the stability in free flight phase of the stage two is also analyzed and ensured. Finally, experiments were carried out including a whiteboard cleaning task in different poses. The experimental results illustrate the validity and effectiveness of the proposed approach. Note to Practitioners—This work is motivated by the contact force tracking problem of an unmanned aerial manipulator (UAM) without the position measurement. In recent years, impedance control is widely used in force tracking problems. However, position measurement is needed both for the robot and the object in most studies. To generate desired force under visual guidance, image-based visual servoing is combined with a novel impedance controller with variable stiffness. The idea is intuitive. For a human in contact with a wall, the contact force is controlled by adjusting his arm stiffness, which means the stiffness is adapted to the difference between the desired and actual contact force. Furthermore, the stiffness is also adapted to the visual tracking error, removing the assumption that no tracking error is in the inner loop of the impedance controller in the previous works. The proposed strategy is a promising solution for real applications and is validated by a board cleaning experiment in this paper.
Quasi one-dimensional waveguide H-junction

A novel quasi one-dimensional waveguide H-junction is presented in this paper,which significantly limits the structure of waveguide feeding networks within a quasi one-dimensional direction,and satisfies the demanding compactness requirement of some applications.Based on balanced excitation,the equivalent circuit parameters for coupling slot with finite wall thickness are extracted using variational method,and the complete equivalent circuit model for H-Junction is established.Combining equivalent circuit and full wave analysis,a hybrid design method for H-Junction with high accuracy and efficiency is proposed.The simulation and experimental results indicate that the hybrid design method is feasible,and the H-Junction together with the waveguide feeding network based on it demonstrate good performance.
Comparison between Nanoparticle Encapsulation and Surface Loading for Lysosomal Enzyme Replacement Therapy

Poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) enhance the delivery of therapeutic enzymes for replacement therapy of lysosomal storage disorders. Previous studies examined NPs encapsulating or coated with enzymes, but these formulations have never been compared. We examined this using hyaluronidase (HAse), deficient in mucopolysaccharidosis IX, and acid sphingomyelinase (ASM), deficient in types A–B Niemann–Pick disease. Initial screening of size, PDI, ζ potential, and loading resulted in the selection of the Lactel II co-polymer vs. Lactel I or Resomer, and Pluronic F68 surfactant vs. PVA or DMAB. Enzyme input and addition of carrier protein were evaluated, rendering NPs having, e.g., 181 nm diameter, 0.15 PDI, −36 mV ζ potential, and 538 HAse molecules encapsulated per NP. Similar NPs were coated with enzyme, which reduced loading (e.g., 292 HAse molecules/NP). NPs were coated with targeting antibodies (> 122 molecules/NP), lyophilized for storage without alterations, and acceptably stable at physiological conditions. NPs were internalized, trafficked to lysosomes, released active enzyme at lysosomal conditions, and targeted both peripheral organs and the brain after i.v. administration in mice. While both formulations enhanced enzyme delivery compared to free enzyme, encapsulating NPs surpassed coated counterparts (18.4- vs. 4.3-fold enhancement in cells and 6.2- vs. 3-fold enhancement in brains), providing guidance for future applications.
A Machine Learning-Based Framework for Fast Prediction of Wide-Area Remedial Control Actions in Interconnected Power Systems

This paper presents a novel real-time machinelearning-based framework for remedial control action (RCA) prediction to prevent transient instability in interconnected power systems. Due to the fast dynamics of rotor angle oscillations and considering communication latencies, there is limited time to compute RCA, making RCA calculation impractical in events quickly evolving into transient instability. The proposed algorithm predicts RCA based on pre-fault and post-fault voltage values of generator buses. To cover credible scenarios, reduce prediction complexities, and increase accuracy, a micro model strategy is employed in which independent models are built for each transmission line of the system. The proposed framework consists of three main modules: stability prediction, coherency prediction, and RCA prediction. In the coherency prediction module, a time-varying algorithm is developed that determines the optimal number of generator groups and prevents unnecessarily overestimated RCAs. For each of the considered scenarios and obtained coherency patterns, a mixed-integer linear programming (MILP) model is utilized to extract the islanding and load shedding patterns considering transient stability constraints. The effectiveness of the proposed approach is demonstrated on the IEEE 39-bus system and the 74-bus Nordic test system, followed by a discussion of results.
Study on the transmission characteristics of magnetic resonance wireless power transfer system

Magnetic coupling resonance wireless power transfer technology has attracted worldwide attention in recent years due to its mid-range, non-radiative, and high-efficiency power transfer. However, in regard to its practical applications, there are still some issues that need to be considered and studied with respect to coil design, such as coil structure, and parasitic parameter extraction. This paper investigated the characteristics of magnetic coupling resonance wireless power transfer systems with different coil structures, including circular coils and rectangular coils arranged in parallel. We calculated the magnetic field distributions and mutual inductances by subdividing the receiving coils and computing the magnetic flux density of each subdivision. The proposed analysis was validated by means of the finite element analysis and the experimental results. We investigated the effects of the coil's structure, and topological structures, on the power transfer efficiency. The results demonstrate that using circular coils in parallel is more advantageous than using rectangular coils.
Robust Multiple Car Tracking With Occlusion Reasoning - eScholarship

In this paper, the authors address the problem of traffic surveillance in an Advanced Transportation Management System. They propose a new approach for detecting and tracking vehicles in road traffic scenes that attains a level of accuracy and reliability which lies beyond currently available systems. High accuracy and reliability are obtained by using an explicit occlusion reasoning step, employing a contour tracker based on intensity and motion boundaries.
Multi-Elemental Analysis of Human Optic Chiasm—A New Perspective to Reveal the Pathomechanism of Nerve Fibers’ Degeneration

The effect of metals on the functioning of the human eye is multifactorial and includes enzyme activity modulation, trace metal metabolic pathways changes, and cytotoxic activity. Functional dysfunctions appear mostly as a result of the accumulation of toxic xenobiotic metals or disturbances of micronutrients’ homeostasis. So far, the affinity of selected metals to eye tissues, i.e., the cornea, choroid, lens, and anterior chamber fluid, has been most studied. However, it is known that many eye symptoms are related to damage to the optic nerve. In order to fill this gap, the aim of the study is to perform a multi-element analysis of tissue collected postmortem from optic chiasm and optic nerves. A total of 178 samples from 107 subjects were tested. The concentrations of 51 elements were quantified by inductively coupled plasma mass spectrometry (ICP-MS) after the wet-mineralization step. In terms of elemental composition, the optic chiasm is dominated by two trace elements, i.e., iron (Fe) and zinc (Zn), besides macro-elements Ca, K, Na, P, and Mg. The subjects formed a homogeneous cluster (over 70% subjects) with the highest accumulation of aluminum (Al). The remaining two departing clusters were characterized by an increased content of most of the elements, including toxic elements such as bismuth (Bi), uranium (U), lead (Pb), chromium (Cr), and cadmium (Cd). Changes in elemental composition with age were analyzed statistically for the selected groups, i.e., females, males, and subjects with alcohol use disorder (AUD) and without AUD. A tendency of women to lose Se, Cu, Zn, Fe with age was observed, and a disturbed Ca/Mg, Na/K ratio in subjects with AUD. Although the observed trends were not statistically significant, they shed new light on the risks and possible pathologies associated with metal neurotoxicity in the visual tract.
Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images

Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.
A combined use of Chondrocytes Micro Grafts (CMG) Mixed with Platelet Rich Plasma (PRP) in Patients Affected by Pinch Nose Deformity

Background: The combined use of autologous chondrocytes micro-grafts (CMG) mixed with Platelet Rich Plasma (PRP) is an alternative that opens a new era in this field. Materials and Methods: At the Department of Plastic and Reconstructive Surgery, University of Rome “Tor Vergata”, Italy, 15 patients, affected by pinch nose deformity underwent nasal alar reconstruction with chondrocytes micro-grafts gentle poured onto PRP in solid form. A CT scan control was performed after 12 months. Pearson’s Chi-square test was used to investigate difference in cartilage density (CD) between native and newly formed cartilage. Results: The constructs of CMG-PRP subcutaneously injected resulted in cartilage tissue with adequate central nutritional perfusion. Postoperative follow-up evaluation has shown optimal aesthetic results and improvement of nasal obstruction. These composite grafts provide functional support to the alar cartilages, usually collapsed in pinch nose deformity because of excessive resection during previous surgery. Conclusion: This report demonstrated that chondrocytes micrografts derived from nasal septum poured onto PRP in solid form are a useful method for cartilage regeneration in patients affected by external nasal valve collapse.
How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules

Simple Summary Pulmonary nodules are considered a sign of bronchogenic carcinoma, detecting them early will reduce their progression and can save lives. Lung cancer is the second most common type of cancer in both men and women. This manuscript discusses the current applications of artificial intelligence (AI) in lung segmentation as well as pulmonary nodule segmentation and classification using computed tomography (CT) scans, published in the last two decades, in addition to the limitations and future prospects in the field of AI. Abstract Pulmonary nodules are the precursors of bronchogenic carcinoma, its early detection facilitates early treatment which save a lot of lives. Unfortunately, pulmonary nodule detection and classification are liable to subjective variations with high rate of missing small cancerous lesions which opens the way for implementation of artificial intelligence (AI) and computer aided diagnosis (CAD) systems. The field of deep learning and neural networks is expanding every day with new models designed to overcome diagnostic problems and provide more applicable and simply used models. We aim in this review to briefly discuss the current applications of AI in lung segmentation, pulmonary nodule detection and classification.
Argon Laser Synechiolysis for Irido-Corneal Adhesion

Purpose To introduce a new method for treating irido-corneal adhesion (ICA) by using argon laser. Case Presentation. A 60-year-old woman with advanced glaucoma who is undergoing trabeculectomy. In follow-up visits, choroidal effusion and iridocorneal adhesion were seen in the examination. The massive choroidal effusion was treated with medical therapy. In further follow-up examination, ICA was repaired with argon laser. Result Early laser treatment prior to surgical management is suggested in postoperative synechia. Conclusion Early argon laser synechiolysis is a less-invasive procedure which prevents probable surgical complications such as local detachment of Descemet's membrane or bleeding in surgical synechiolysis.
A Novel Shape Based Plant Growth Prediction Algorithm Using Deep Learning and Spatial Transformation

Plant growth prediction is challenging, as the growth rate varies depending on environmental factors. It is an essential task for efficient cultivation in controlled environments, such as in plant factories. In this paper, we propose a novel deep learning network for predicting future plant images from a number of past and current images. In particular, our focus is on the estimation of leaf shape in a plant, because the amount of plant growth is commonly quantified based on the leaf area. A spatial transform is applied to a sequence of plant images within the network, and the growth behavior is measured using a set of affine transform parameters. Instead of conventional sequential image fusion, the affine transform parameters for all pairs of successive images are fused together to predict the shape of the future plant image. Then, an RGB reconstruction subnet divides the plants into multiple patches to make global and local growth predictions based on hierarchical auto-encoders. A variety of experimental results show that the proposed network is robust to dynamic plant movements and can accurately predict the shapes of future plant images.
Managing the Transition to Widespread Metagenomic Monitoring: Policy Considerations for Future Biosurveillance

The technological possibilities and future public health importance of metagenomic sequencing have received extensive attention, but there has been little discussion about the policy and regulatory issues that need to be addressed if metagenomic sequencing is adopted as a key technology for biosurveillance. In this article, we introduce metagenomic monitoring as a possible path to eventually replacing current infectious disease monitoring models. Many key enablers are technological, whereas others are not. We therefore highlight key policy challenges and implementation questions that need to be addressed for “widespread metagenomic monitoring” to be possible. Policymakers must address pitfalls like fragmentation of the technological base, private capture of benefits, privacy concerns, the usefulness of the system during nonpandemic times, and how the future systems will enable better response. If these challenges are addressed, the technological and public health promise of metagenomic sequencing can be realized.