Miracle-dz/UniFAR-FTUs huggingface dataset:
Introduction of a Polyethylene Glycol Linker Improves Uptake of 67Cu-NOTA-Conjugated Lactam-Cyclized Alpha-Melanocyte-Stimulating Hormone Peptide in Melanoma

Simple Summary There is a need to develop new theranostic approaches for malignant melanoma. Only 35% of patients with metastatic melanoma reach the milestone of 5-year survival, despite the success of new immunotherapy. We have developed a new class of peptides to target melanocortin-1 receptors (MC1Rs) that display elevated levels in human melanoma. In this study, we examined the melanoma targeting and biodistribution properties of two 67Cu-tagged peptides in tumor-bearing mice. We found that one of the peptides, namely 67Cu-NOTA-PEG2Nle-CycMSHhex, exhibited favorable melanoma targeting and biodistribution properties that underscored its potential as an MC1R-targeted therapeutic peptide for melanoma treatment in the future. Abstract The aim of this study was to evaluate the effect of linker on tumor targeting and biodistribution of 67Cu-NOTA-PEG2Nle-CycMSHhex {67Cu-1,4,7-triazacyclononane-1,4,7-triyl-triacetic acid-polyethylene glycol-Nle-c[Asp-His-DPhe-Arg-Trp-Lys]-CONH2} and 67Cu-NOTA-GGNle-CycMSHhex {67Cu-NOTA-GlyGlyNle-CycMSHhex} on melanoma-bearing mice. NOTA-PEG2Nle-CycMSHhex and NOTA-GGNle-CycMSHhex were synthesized and purified by HPLC. The biodistribution of 67Cu-NOTA-PEG2Nle-CycMSHhex and 67Cu-NOTA-GGNle-CycMSHhex was determined in B16/F10 melanoma-bearing C57 mice. The melanoma imaging property of 67Cu-NOTA-PEG2Nle-CycMSHhex was further examined in B16/F10 melanoma-bearing C57 mice. 67Cu-NOTA-PEG2Nle-CycMSHhex exhibited higher tumor uptake than 67Cu-NOTA-GGNle-CycMSHhex at 2, 4, and 24 h post-injection. The tumor uptake of 67Cu-NOTA-PEG2Nle-CycMSHhex was 27.97 ± 1.98, 24.10 ± 1.83, and 9.13 ± 1.66% ID/g at 2, 4, and 24 h post-injection, respectively. Normal organ uptake of 67Cu-NOTA-PEG2Nle-CycMSHhex was lower than 2.6% ID/g at 4 h post-injection, except for kidney uptake. The renal uptake of 67Cu-NOTA-PEG2Nle-CycMSHhex was 6.43 ± 1.31, 2.60 ± 0.79, and 0.90 ± 0.18% ID/g at 2, 4, and 24 h post-injection, respectively. 67Cu-NOTA-PEG2Nle-CycMSHhex showed high tumor to normal organ uptake ratios after 2 h post-injection. The B16/F10 melanoma lesions could be clearly visualized by single photon emission computed tomography (SPECT) using 67Cu-NOTA-PEG2Nle-CycMSHhex as an imaging probe at 4 h post-injection. The favorable tumor targeting and biodistribution properties of 67Cu-NOTA-PEG2Nle-CycMSHhex underscored its potential as an MC1R-targeted therapeutic peptide for melanoma treatment.
iRHOM2 regulates inflammation and endothelial barrier permeability via CX3CL1

Acute lung injury (ALI) is associated with increased lung inflammation and lung permeability. The present study aimed to determine the role of inactive rhomboid-like protein 2 (iRHOM2) in ALI in lipopolysaccharide (LPS)-induced pulmonary microvascular endothelial cell model. Human pulmonary microvascular endothelial cells (HPMVECs) were transfected with small interfering RNA targeting iRHOM2 and C-X3-C motif chemokine ligand 1 (CX3CL1) overexpression plasmids and treated with LPS. Cell viability was detected using a Cell Counting Kit-8 assay, while levels of TNFα, IL-1β, IL-6 and p65 were measured by reverse transcription-quantitative PCR and western blotting. Apoptosis levels were measured using a TUNEL assay. Endothelial barrier permeability was detected, followed by analysis of zonula occludens-1, vascular endothelial-cadherin and occludin by immunofluorescence staining or western blotting. The interaction of iRHOM2 and CX3CL1 was analyzed using an immune-coprecipitation assay. Through bioinformatics analysis, it was found that CX3CL1 was upregulated in the LPS group compared with the control. Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that the TNF signaling pathway affected by iRHOM2 and cytokine-cytokine receptor interaction, including CX3CL1, served a key role in ALI. HPMVECs treated with LPS exhibited a decrease in cell viability and an increase in inflammation, apoptosis and endothelial barrier permeability, while these effects were reversed by iRHOM2 silencing. However, CX3CL1 overexpression inhibited the effects of iRHOM2 silencing on LPS-treated HPMVECs. The present study demonstrated a novel role of iRHOM2 as a regulator that affects inflammation, apoptosis and endothelial barrier permeability; this was associated with CX3CL1.
A novel approach to mine infrequent weighted itemset using coherent rule mining algorithm

Objectives: The main objective of this work is to find out the infrequent weighted item set without using a minimum support threshold value. Methods: Coherent rule mining method is used for discover the rare item set by utilize the properties of propositional logic. In such type of rule mining there is no need to preset minimum support to find out association rules. To produce a coherent rule between two item sets, logic based conditions are tested with the contingency table. Findings: The proposed method achieves high performance in terms of Run time and accuracy. Application/Improvements: The proposed infrequent weighted item set mining is done by Coherent rule. It reduces the search space and improves the infrequent weighted item set mining performance.
Characterization of various tissue mimicking materials for medical ultrasound imaging

Tissue mimicking materials are physical constructs exhibiting certain desired properties, which are used in machine calibration, medical imaging research, surgical planning, training, and simulation. For medical ultrasound, those specific properties include acoustic propagation speed and attenuation coefficient over the diagnostic frequency range. We investigated the acoustic characteristics of polyvinyl chloride (PVC) plastisol, polydimethylsiloxane (PDMS), and isopropanol using a time-of-light technique, where a pulse was passed through a sample of known thickness contained in a water bath. The propagation speed in PVC is approximately 1400ms-1 depending on the exact chemical composition, with the attenuation coefficient ranging from 0:35 dB cm-1 at 1MHz to 10:57 dB cm-1 at 9 MHz. The propagation speed in PDMS is in the range of 1100ms-1, with an attenuation coefficient of 1:28 dB cm-1 at 1MHz to 21:22 dB cm-1 at 9 MHz. At room temperature (22 °C), a mixture of water-isopropanol (7:25% isopropanol by volume) exhibits a propagation speed of 1540ms-1, making it an excellent and inexpensive tissue-mimicking liquid for medical ultrasound imaging.
Deep Hybrid Neural Network With Attention Mechanism for Video Hash Retrieval Method

With the advent of 5G technology, a large amount of video data will be generated and uploaded to the Internet every day, which makes it an urgent problem to effectively retrieve similar video content from massive video databases. However, most of the existing video retrieval methods usually ignore the time information contained in the video, which causes the problem that the temporal information feature is not obvious when extracting video features. Therefore, this paper proposes a deep hybrid neural network with attention mechanism for video hash retrieval method. The method first feeds the extracted video key frames into the residual network embedded with the attention mechanism and the bidirectional gated recurrent unit network to extract the spatio-temporal information features of the video, so as to improve the representation ability of the features, and then integrates the hash learning technology into the network framework to generate the binary hash code of the video to shorten the video retrieval time. By designing objective function to capture the information of the global data structure using the similarity relationship between codes, the class label information can be effectively used to improve the retrieval performance. Compared with the existing methods, the experimental results show that the method has a certain improvement in the retrieval accuracy on the commonly used video datasets UCF-101 and JHMDB, which verififies the feasibility of the method.
Mesoscopic Effects in Bose-Einstein Condensate Fluctuations of an Ideal Gas in a Box

The mesoscopic effects in the quantum trapped gases of the Bose atoms constitute the main subject of the present thesis. These effects are the most difficult for the theoretical analysis in the quantum statistical physics since they can? t be seen by neither a standard quantum mechanics of the simple microscopic systems of one or very few atoms nor a standard statistical physics of the macroscopic systems that are infinite in the bulk (thermodynamic) limit. Most of the experiments on the cold quantum gases performed in the last decade, starting from the first demonstration of BEC in 1995, involve the mesoscopic systems of a finite number of atoms. The mesoscopic effects should manifest themselves most clearly and easily near a critical temperature of BEC; however, they could be observed also above and below the critical temperature. Here I study the quantum and thermal fluctuations of the Bose-Einstein condensate (BEC) in a box with the periodic boundary conditions under a particle-number constraint. The above constraint is the only reason for the BEC and is crucial for the mesoscopic effects in the BEC fluctuations, especially in the vicinity of the critical temperature in the Bose gas. I employ the particle-number conserving operator formalism of Girardeau and Arnowitt introduced in 1959 to analyze the canonical ensemble fluctuations. I present analytical formulas and numerical calculations for the central moments of the ground state occupation fluctuations in an ideal Bose gas in a box with a mesoscopic number of particles. I present the analysis of the BEC statistics both on a temperature at a fixed number of particles and on a number of particles at a fixed temperature. Both analyses are valid for the purpose of understanding the important mesoscopic effects near the critical temperature. I emphasize the non-Gaussian nature of the fluctuations. The presented formalism can be generalized to the case of a weakly interacting Bose gas in a box in the framework of the Bogoliubov approximation. The work in this direction is in progress but is not included in the present thesis.
Poisson–Nernst–Planck model with Chang-Jaffe, diffusion, and ohmic boundary conditions

Using the linear Poisson–Nernst–Planck impedance-response continuum model, we investigate the possible equivalences of three different types of boundary conditions previously proposed to model the electrode behavior of an electrolytic cell in the shape of a slab. We show analytically that the boundary conditions proposed long ago by Chang-Jaffe are fully equivalent to the ohmic boundary conditions only if the positive and negative ions have the same mobility, or when only ions of a single polarity are mobile. In the case where the ions have different and non-zero mobilities, we fit exact impedance spectra created for ohmic boundary conditions by using the Chang-Jaffe Poisson–Nernst–Planck response model, one that is dominated by diffusion effects. These fits yield conditions for essentially exact or approximate numerical correspondence for the complex impedance between the two models even in the unequal mobility case. Finally, diffusion type boundary conditions are shown to be fully equivalent to the ohmic one. Some limiting cases of the model parameters are investigated.
Stroke Prediction using Optimization and Exploratory Data Analysis

A stroke is a medical condition that causes brain damage by rupturing blood vessels. It can also happen if the brain's blood supply and other nutrients are cut off. It is the leading cause of death and disability worldwide. , according to the World Health Organization (WHO). It is a potentially fatal illness that primarily affects adults over the age of 65. Doctors devote a significant amount of time and effort to predicting strokes. As a result. , the primary goal of the study is to use various Machine Learning approaches to predict the likelihood of stroke occurring using hyper parameter tuning to achieve greater accuracy and optimize the outcomes. After going through the dataset, we discovered that the standard algorithms we used. , such as Support Vector Machine (SVC), Decision Tree Classifier, Random Forest Classifier, XGBoost, and KNeighbors, as well as some feature selection methods, could only predict 80 to 85 percent of the time, so we came up with the idea of optimization in machine learning, where we use the technology or concept of hyper parameter tuning, which helped us to gain a prediction of about 95 percent. With this, we also used an Exploratory Data Analysis (EDA) concept for visualization, which helped us to study the attribute. The above-mentioned prognosis was achieved using Hyper Parameter Tuning, which involves checking and analyzing the parameters of each algorithm in such a way that after setting to some predefined parameters, it produces the expected accuracy. To evaluate the data, we employed the EDA approach, in which we compared many associated health behaviors in different combinations with respect to stroke, and each EDA diagram concluded the relationship of these attributes to the cause of stroke. As a result, this study evaluates the performance of various machine learning algorithms that use Hyper parameters tuning with EDA.
Algorithms for wavelength assignment and call control in optical networks

Routing and channel assignment is a fundamental problem in computer/communication networks. In wavelength division multiplexing (WDM) optical networks, the problem is called routing and wavelength assignment or routing and path coloring (RPC) problem: given a set of connection requests, find a routing path to connect each request and assign each path a wavelength channel (often called a color) subject to certain constraints. One constraint is the distinct channel assignment: the colors (channels) of the paths in the same optical fiber must be distinct. Another common constraint is the channel continuity: a path is assigned a single color. When a path may be assigned different colors on different fibers, the RPC problem is known as the routing and call control (RCC) problem. When the routing paths are given as part of the problem input, the RPC and RCC problems are called the path coloring and call control problems, respectively. Major optimization goals for the above problems include to minimize the number of colors for realizing a given set of requests and to maximize the number of accommodated requests using a given number of colors. Those optimization problems are NP-hard for most network topologies, even for simple networks like rings and trees of depth one. In this thesis, we make the following contributions. (1) We give better approximation algorithms which use at most 3L (L is the maximum number of paths in a fiber) colors for the minimum path coloring problem in trees of rings. The 3L upper bound is tight since there are instances requiring 3L colors. We also give better approximation algorithms for the maximum RPC problem in rings. (2) We develop better algorithms for the minimum and maximum RPC problems on multi-fiber networks. (3) We develop better algorithms for the call control problem on simple topologies. (4) We develop carving-decomposition based exact algorithms for the maximum edge-disjoint paths problem in general topologies. We develop and implement tools for computing optimal branch/carving decompositions of planar graphs to provide a base for the branch/carving-decomposition based algorithms. These tools are of independent interests. Keywords: communication networks; path coloring; call control; approximation algorithms; trees of rings; planar branch-decomposition; computational study
Design and Implementation of a Soft-Switching Converter With High Step-Up Ratio

The main purpose of this paper is the development of a DC/DC boost converter with high conversion efficiency. The proposed converter has high step-up ratio and is equipped with soft-switching technology. The main inductor used in the developed converter is the coupled inductor rather than the general inductor, therefore, the voltage conversion ratio of the converter can be improved, so that the high voltage conversion ratio characteristics of the converter power switch can be obtained within the same duty cycle. The developed converter is connected to a resonance branch circuit in the traditional high step-up converter, and is able to achieve zero-voltage switching (ZVS) with the main switch using a simple power switch signal control to reduce the switching loss caused by the traditional hard switching method in the high step-up converter, thereby improving converter efficiency. To verify the performance of the proposed converter, a circuit analysis on the high step-up soft-switching converter is first performed, and proceed to designing each component in the circuit. Then, the feasibility of the proposed converter is verified using the PSIM simulation software. Finally, actualize the proposed high step-up soft-switching boost converter with the TMS320F2809 digital signal processor (DSP), and verify that the main switch can indeed achieve zero-voltage switch, and that the conversion efficiency can reach as high as 91% when the developed converter is under full load of 300W, proof that the developed converter truly has excellent conversion performance.
Computational simulation-based comparative analysis of standard 3D printing and conical nozzles for pneumatic and piston-driven bioprinting

Bioprinting is an application of additive manufacturing that can deliver promising results in regenerative medicine. Hydrogels, as the most used materials in bioprinting, are experimentally analyzed to assure printability and suitability for cell culture. Besides hydrogel features, the inner geometry of the microextrusion head might have an equal impact not only on printability but also on cellular viability. In this regard, standard 3D printing nozzles have been widely studied to reduce inner pressure and get faster printings using highly viscous melted polymers. Computational fluid dynamics is a useful tool capable of simulating and predicting the hydrogel behavior when the extruder inner geometry is modified. Hence, the objective of this work is to comparatively study the performance of a standard 3D printing and conical nozzles in a microextrusion bioprinting process through computational simulation. Three bioprinting parameters, namely pressure, velocity, and shear stress, were calculated using the level-set method, considering a 22G conical tip and a 0.4 mm nozzle. Additionally, two microextrusion models, pneumatic and piston-driven, were simulated using dispensing pressure (15 kPa) and volumetric flow (10 mm3/s) as input, respectively. The results showed that the standard nozzle is suitable for bioprinting procedures. Specifically, the inner geometry of the nozzle increases the flow rate, while reducing the dispensing pressure and maintaining similar shear stress compared to the conical tip commonly used in bioprinting.
Facial Expression Recognition in Classrooms: Ethical Considerations and Proposed Guidelines for Affect Detection in Educational Settings

Recent technological and educational shifts have made it possible to capture students’ facial expressions during learning with the goal of detecting learners’ emotional states. Those interested in affect detection argue these tools will support automated emotions-based learning interventions, providing educational professionals with the opportunity to develop individualized, emotionally responsive instructional offerings at scale. Despite these proposed use-cases, few have considered the inherent ethical concerns related to detecting and reporting on learners’ emotions specifically within applied educational contexts. As such, this article utilizes a Reflexive Principlism approach to establish a typology of proactive reflexive ethical implications in tracking students’ emotions through changes in their facial expressions. Through this approach the authors differentiate between use in research and applied education contexts, arguing that the latter should be curtailed until the ethics of affective computing in educational settings is better established.
Creating Inclusive Interface and Online Learning Environments

This study seeks to examine educational learning environments with a focus on new platforms of learning, course design, equity, inclusion, and user interface (ex: seeing impaired, cultural differences, gender) that includes the perspectives of students with disabilities. The evolution of learning from singular multi-age classrooms through institutional schools and into dynamic online platforms has radically reshaped the educational ecosystem (Sarkar, 2020), and there have been calls for a deeper understanding of inclusive learning practices to meet the needs of diverse students (Philips & Salem, 2021). Adams (2017) also outlines how the aesthetic interface of each learning environment impacts the quality of learning. Currently, however, there is a lack of existing integrative research that looks at user design through the lens of inclusion, students with disabilities and cultural differences. This qualitative research project included three elementary students in grades two and three, as well as interviews with three adult educators with whom those students work. The unitbased research project took place through the school’s resource center, and those students who participated, created digital posters based on a unit done around inclusion. The research participants identified equity gaps within online educational platforms, and that the cost of entry creates significant.The participants additionally distinguished that the way in which an online educational platform is designed matters to students with disabilities and the educators who teach them, with color, language adaptations and text to speech and speech to text functionalities highlighted. Lastly, students want to feel a sense of inclusion, both in person and on digital platforms. They noted how these connections can be created through their interests, senses of feeling in relationship to their peers and seeing reflections of their identity in color and design. Through this understanding educational online platforms can be effectively designed in
Decision-Making during the Pandemic: Compassion, Trust, and the Altruistic Paradox

The occurrence of breakthrough infections with SARS-CoV-2 in vaccinated individuals argues against abandoning mitigation efforts such as social distancing. Some public health messages, however, promote vaccination by increasing psychological distress, which interferes with social distancing. Prosocial messages present an alternative approach that may avoid this problem. Accordingly, the present study examined the relation of pandemic mitigation with scores on prosocial personality traits (i.e., altruism, sympathy, and trust) and vaccination intentions. Regression analyses indicated that while vaccination intentions increased significantly with an increase in trust, distancing increased significantly with increases in altruism and sympathy. Because older adults are much more vulnerable to COVID-19 than younger adults, these findings reveal an altruistic paradox, in which older adults, perhaps the most altruistic portion of the population, may be dependent on the altruistic behavior of younger adults, who may be the least altruistic portion. The challenge for public health messaging will be to motivate younger adults to take the consequences of their mitigation decisions for others into account.