Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Insights into the Binding of Red Fluorescent Protein mCherry-Specific Nanobodies

Red fluorescent proteins (RFPs) have broad applications in life science research, and the manipulation of RFPs using nanobodies can expand their potential uses. However, the structural information available for nanobodies that bind with RFPs is still insufficient. In this study, we cloned, expressed, purified, and crystallized complexes formed by mCherry with LaM1, LaM3, and LaM8. Then, we analyzed the biochemical properties of the complexes using mass spectrometry (MS), fluorescence-detected size exclusion chromatography (FSEC), isothermal titration calorimetry (ITC), and bio-layer interferometry (BLI) technology. We determined the crystal structure of mCherry-LaM1, mCherry-LaM3, and mCherry-LaM8, with resolutions of 2.05 Å, 3.29 Å, and 1.31 Å, respectively. In this study, we systematically compared various parameters of several LaM series nanobodies, including LaM1, LaM3, and LaM8, with previously reported data on LaM2, LaM4, and LaM6, specifically examining their structural information. After designing multivalent tandem LaM1-LaM8 and LaM8-LaM4 nanobodies based on structural information, we characterized their properties, revealing their higher affinity and specificity to mCherry. Our research provides novel structural insights that could aid in understanding nanobodies targeting a specific target protein. This could provide a starting point for developing enhanced mCherry manipulation tools.
How can the safety of antibody therapy for retinopathy of prematurity be improved?

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness and is increasing as premature infant survival increases worldwide [1]. The appearance of ROP has changed since its initial description as retrolental fibroplasia in 1942 [2]. In addition, there are different manifestations of ROP throughout the world [3]. Experimentation in animal models has been essential to improve our understanding of the pathophysiology of ROP and develop treatments. From early models, high oxygen at birth was discovered as a cause of blindness [4,5]. The twophase hypothesis of ROP pathophysiology was developed. In phase I, high inspired oxygen induced damage to newly developed retinal blood vessels and was followed by retinal hypoxia in phase II when infants were removed from high supplemental oxygen that induced blood vessel growth into the vitreous, initiating the formation of complex fibrovascular tractional retinal detachments [4]. The first treatment for ROP was lowering inspired oxygen [5]. As advances led to survival of extremely premature infants and ability to regulate oxygen supplementation, it was recognized that phase I pathophysiology evolved to not only include vascular death, but also delayed retinal vascular development seen in extremely premature and less developed infants [6]. Treatments also evolved to include regulation of oxygen and application of cryotherapy or laser to the peripheral avascular retina in treatment-warranted ROP to reduce the hypoxic stimulus from avascular retina [7] [review]. Further advances in understanding the pathophysiology of ROP were made. An early theory posited that angiogenic factors were released from spindle cells, believed to be precursors of endothelial cells, and that ablative treatments were effective because they damaged the spindle cells [8]. Later studies found other cell types were involved in releasing angiogenic factors, notably vascular endothelial growth factor (VEGF), and that physiologic angiogenesis was important in development. Although many factors have been associated with the pathophysiology of ROP, the most well studied and translatable to date has been VEGF, which was originally recognized as important in adult retinal and choroidal vascular diseases. Experiments using the most representative model of ROP involving fluctuations of oxygenation in the newborn rat discovered that overactivation of VEGF receptor 2 in endothelial cells was responsible not only for the abnormal growth of blood vessels into the vitreous but also for slowing normal peripheral developmental retinal vascularization [9,10].
Circulating Biomarkers to Predict Diabetic Retinopathy in Patients with Diabetic Kidney Disease

The purpose of this review is to outline the currently available circulating biomarkers to predict diabetic retinopathy (DR) in patients with diabetic kidney disease (DKD). Studies have extensively reported the association between DR and DKD, suggesting the presence of common pathways of microangiopathy. The presence of other ocular complications including diabetic cataracts may hinder the detection of retinopathy, which may affect the visual outcome after surgery. Unlike DKD screening, the detection of DR requires complex, costly machines and trained technicians. Recognizing potential biological markers related to glycation and oxidative stress, inflammation and endothelial dysfunction, basement membrane thickening, angiogenesis, and thrombosis as well as novel molecular markers involved in the microangiopathy process may be useful as predictors of retinopathy and identify those at risk of DR progression, especially in cases where retinal visualization becomes a clinical challenge. Further investigations could assist in deciding which biomarkers possess the highest predictive power to predict retinopathy in clinical settings.
Adversarial Examples in Self-Driving: A Review of Available Datasets and Attacks

Autonomous vehicles rely on computer vision models for perception, which have been shown to be vulnerable to adversarial attacks. These attacks pose various risks from reducing user confidence in the technology to directly influencing the technology to make a particular action [1]. Research in adversarial machine learning (ML) has led to increased awareness of ML’s vulnerabilities to such attacks and strategies to mitigate their effects and enhance ML generalizability [1]. Although there exists a dataset for assessing a computer vision model’s efficacy against adversarial attacks in the real-world [2], there is no such dataset for benchmarking computer vision algorithms for autonomous vehicles. This effort is a preliminary study on publicly available datasets for self-driving applications through the lens of adversarial ML. We include a discussion of attacks that have been performed in this space. From black-box to white-box attacks, the more knowledge is available to the adversary, the more they can leverage to employ the attack [1]. Adversaries are fully capable of utilizing the publicly available datasets to test their own black-box attacks [3], [4]. Adversarial examples can be useful in both enhancing algorithm resilience to these attacks through inclusion in training [5] as well as for use in benchmarking current approaches against the attacks [2]. Building resilient autonomy in transportation will require mitigation of any vulnerabilities, especially those from potential adversaries. This survey of datasets and attacks on self-driving vehicles is a first step in developing a dataset of adversarial attacks in this domain. The dataset can assist current and future computer vision research in self-driving to benchmark algorithm effectiveness against such attacks in the real-world.
Self-consistent Photon Number Resolving Detector System to Optimize Photon Statistics Distribution

We establish the photon number resolution (PNR) model and calculate the conversion coefficient which can be used to optimize the voltage grouping of pulse heights corresponding to different photon number states. Then we build a self-consistent PNR detection system based on photon-counting photomultiplier, and directly obtain the photon statistics distribution of 100-MHz pulsed laser at different counting rates. We demonstrate experimentally that our model can be used to acquire more accuracy average photon number, and optimize photon statistics distribution without any calibration of the detector. Our results are valuable to precision measurements such as quantum metrology and quantum radar.
miR-205 Regulates the Fusion of Porcine Myoblast by Targeting the Myomaker Gene

Skeletal muscle formation is an extremely important step in animal growth and development. Recent studies have found that TMEM8c (also known as Myomaker, MYMK), a muscle-specific transmembrane protein, can promote myoblast fusion and plays a key role in the normal development of skeletal muscle. However, the effect of Myomaker on porcine (Sus scrofa) myoblast fusion and the underlying regulatory mechanisms remain largely unknown. Therefore, in this study, we focused on the role and corresponding regulatory mechanism of the Myomaker gene during skeletal muscle development, cell differentiation, and muscle injury repair in pigs. We obtained the entire 3′ UTR sequence of porcine Myomaker using the 3′ RACE approach and found that miR-205 inhibited porcine myoblast fusion by targeting the 3′ UTR of Myomaker. In addition, based on a constructed porcine acute muscle injury model, we discovered that both the mRNA and protein expression of Myomaker were activated in the injured muscle, while miR-205 expression was significantly inhibited during skeletal muscle regeneration. The negative regulatory relationship between miR-205 and Myomaker was further confirmed in vivo. Taken together, the present study reveals that Myomaker plays a role during porcine myoblast fusion and skeletal muscle regeneration and demonstrates that miR-205 inhibits myoblast fusion through targeted regulation of the expression of Myomaker.
Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.
Impact of training data on LMMSE demosaicing for Colour-Polarization Filter Array

Linear minimum mean square error can be used to demosaic images from a colour-polarization filter array sensor. However, the role of training data on its performance is yet an open question. We study the model selection using crossvalidation techniques. The results show that the training model converges quickly, and that there is no significant difference in training the model with more than 12 images of approximately 1.5 megapixels. We also found that the selected trained model performs better compared to a dedicated Colour-Polarization Filter Array demosaicing algorithm in terms of Peak Signal-to-Noise Ratio.
A Ferritin Nanoparticle-Based Zika Virus Vaccine Candidate Induces Robust Humoral and Cellular Immune Responses and Protects Mice from Lethal Virus Challenge

The severe consequences of the Zika virus (ZIKV) infections resulting in congenital Zika syndrome in infants and the autoimmune Guillain–Barre syndrome in adults warrant the development of safe and efficacious vaccines and therapeutics. Currently, there are no approved treatment options for ZIKV infection. Herein, we describe the development of a bacterial ferritin-based nanoparticle vaccine candidate for ZIKV. The viral envelope (E) protein domain III (DIII) was fused in-frame at the amino-terminus of ferritin. The resulting nanoparticle displaying the DIII was examined for its ability to induce immune responses and protect vaccinated animals upon lethal virus challenge. Our results show that immunization of mice with a single dose of the nanoparticle vaccine candidate (zDIII-F) resulted in the robust induction of neutralizing antibody responses that protected the animals from the lethal ZIKV challenge. The antibodies neutralized infectivity of other ZIKV lineages indicating that the zDIII-F can confer heterologous protection. The vaccine candidate also induced a significantly higher frequency of interferon (IFN)-γ positive CD4 T cells and CD8 T cells suggesting that both humoral and cell-mediated immune responses were induced by the vaccine candidate. Although our studies showed that a soluble DIII vaccine candidate could also induce humoral and cell-mediated immunity and protect from lethal ZIKV challenge, the immune responses and protection conferred by the nanoparticle vaccine candidate were superior. Further, passive transfer of neutralizing antibodies from the vaccinated animals to naïve animals protected against lethal ZIKV challenge. Since previous studies have shown that antibodies directed at the DIII region of the E protein do not to induce antibody-dependent enhancement (ADE) of ZIKV or other related flavivirus infections, our studies support the use of the zDIII-F nanoparticle vaccine candidate for safe and enhanced immunological responses against ZIKV.
Some Aspects of Unitary Addition Cayley Graph of Gaussian Integers Modulo n

In this paper, the Unitary addition Cayley graph defined by Sinha et al. is extended to the ring of Gaussian integers modulo n. Some graph theoretic properties of unitary addition Cayley graphs of gaussian integers modulo n are discussed here. Moreover, the condition for which the above mentioned graphs are Eulerian and Hamiltonian are also discussed.
Hardware Trojan Mitigation Technique in Network-on-Chip (NoC)

Due to globalization in the semiconductor industry, malevolent modifications made in the hardware circuitry, known as hardware Trojans (HTs), have rendered the security of the chip very critical. Over the years, many methods have been proposed to detect and mitigate these HTs in general integrated circuits. However, insufficient effort has been made for hardware Trojans (HTs) in the network-on-chip. In this study, we implement a countermeasure to congeal the network-on-chip hardware design in order to prevent changes from being made to the network-on-chip design. We propose a collaborative method which uses flit integrity and dynamic flit permutation to eliminate the hardware Trojan inserted into the router of the NoC by a disloyal employee or a third-party vendor corporation. The proposed method increases the number of received packets by up to 10% more compared to existing techniques, which contain HTs in the destination address of the flit. Compared to the runtime HT mitigation method, the proposed scheme also decreases the average latency for the hardware Trojan inserted in the flit’s header, tail, and destination field up to 14.7%, 8%, and 3%, respectively.
Arts-Based Classroom Evaluation: Impact Assessment Through Student Drawings

Abstract Classroom evaluations are implemented with the best of intentions – to measure student satisfaction in the classroom. However, survey questions do not always apply to the learning environment, especially in nontraditional classrooms. We want to improve our craft as teachers and as facilitators, but we lose the chance to critically reflect on our practice when our participants do not feel that they can give constructive feedback because of the risk that a less-than-stellar review could jeopardize whether the class is even continued in future years. Is there another way to gather informal feedback from students or participants on a regular basis to build a collaborative and artistic community in our learning spaces? This article introduces student-generated drawings as a tool for teachers in all subject areas, for casual check-ins, general information gathering, and in-depth explorations of student experiences and opinions. Over 1,000 drawings were collected and analyzed to investigate the advantages of this approach in arts-based learning environments.
Super pixels transmission map-based object detection using deep neural network in UAV video

ABSTRACT Object detection has become a very prominent subject for research in recent times. This study's main goal is to suggest a technique for video saliency object detection. It seems to sense that using the depth information in photos to detect salient things. Since depth offers abundant information about scene structure, object forms, and other 3D cues. This information is very compatible to distinguish between objects in the foreground and background. As a result of the high object density, small object size, and cluttered background, aerial photos and movies provide results with low precision. In this paper, the proposed SPTM (Super Pixel Transmission Map)-YOLO model, the input RGB image has applied Dark Channel Prior (DCP) method for estimating the transmission map. From the transmission map only, the background probability is estimated with the help of SLIC (simple linear iterative clustering algorithm) superpixel segmentation. That foreground extracted image is further learned with YOLO architecture to detect the objects effectively. For object detection in aerial images, this proposed SPTM-YOLO approach outperforms classic YOLO by up to 6% accuracy. Accurate detection of things that are small in size, partially occluded, and out of view is possible.
Unsupervised multi-class co-segmentation via joint object detection and segmentation with energy minimization

Multi-class co-segmentation is a challenging task because of the variety and complexity of the objects and images. To get more accurate object proposals is the key step for the existing co-segmentation methods to obtain better performance. In this paper, we propose a novel method to co-segment multiple regions from a group of images in an unsupervised way. The key idea is to discover unknown object proposals for each image via joint object detection and object-level segmentation. First, object proposals of each image are generated by object-like windows (or boxes) and object-level segmentation using graph cuts, and two Gaussian mixture models (GMMs) are employed to characterize the object proposals for all images and single image, respectively. Then, a weighted graph for each image is constructed on super-pixel level, and multi-label graph cuts with global and local energy is employed to obtain the final co-segmentation results. In contrast to previous methods, our method could obtain the object proposals with high objectness by object-level segmentation. Experimental results demonstrate the good performance of the proposed method on the multi-class co-segmentation.
Estimation of Cloud Physical Parameters from Airborne Solar Spectral Reflectance Measurements for Stratocumulus Clouds

Abstract A new method is proposed to retrieve various cloud physical parameters of water clouds from the solar-flux reflectances at four wavelengths measured by using the airborne Multi-channel Cloud Pyranometer (MCP) system. The MCP system was designed to measure the spectral reflectances at nine wavelengths in the visible and near-infrared region. The estimation procedure assumes a locally plane-parallel and vertically homogeneous water-cloud layer with monomodal particle size distributions of a fixed width. The cloud optical thickness τ500 and the effective particle radius re, of the water-cloud layer can be simultaneously retrieved from the MCP reflectances measured at the visible channel of λ=500 nm and at the near-infrared channel of λ=1650 nm. Under the assumption that cloud pressure height is known, the cloud liquid water content (LWC) can then be retrieved from the MCP reflectance at the oxygen absorption-band channel cantered at λ=760 nm. Finally, the in-cloud water vapor amount uH2O can be esti.
The electrostatic calculation of molecular energies - IV. Optimum paired-electron orbitals and the electrostatic method

Equations which determine the optimum form of paired-electron orbitals are derived. It is shown that for large nuclear separations these equations become the Hartree-Fock equations for appropriate valence states of the separated atoms. An electrostatic interpretation of chemical bonding is developed using optimum paired-electron orbital functions. For these wave functions this simple procedure yields results identical with those obtained by the conventional method of calculation based on the Hamiltonian integral. Numerical computations by the electrostatic method are also discussed.
PISA architecture chip resource scheduling algorithm design and implementation

In order to speed up the efficiency in designing PISA, a series of resource constraints are imposed. The resource scheduling situation under data dependency, control dependency, and various resource dependency constraints is to be explored. In this regard, a mixed integer programming model is developed and the optimization objective is determined to keep the number of occupied pipeline stages as short as possible. On the one hand, the data pre-processing work can be carried out, and the data can be transformed into a matrix of 0-1 variables to facilitate the control of the basic information in each module, and the processed data are judged to determine whether the decision variables can satisfy the constraints; on the other hand, the constraints limiting the objective function already in the problem are determined, and the corresponding equations are listed for the three different data dependencies of read-after-write, write-after-read, and write-after-write. At the same time to find the constraints implied by the problem itself, according to the linking relationship of the basic blocks in each board to adjust the order of the boards, due to the directed basic block linking information determines between adjacent boards, any basic block of the latter board must be linked with a certain basic block of the previous board, that is, the remaining hidden constraints can be found by the Big M method. After determining the complete decision variables, constraints and objective functions, a mixed integer programming model can be established to optimize the minimum number of occupied pipeline levels as the objective, and the results are calculated by using the lingo solver, and finally the results are optimized and verified by the heuristic algorithm - particle swarm algorithm to obtain the pipeline of problem one The number of basic blocks is the most when the number of levels is 4, and the corresponding number is 57, and the total number of pipeline levels is 53 at this time.
A Shared Pose Regression Network for Pose Estimation of Objects from RGB Images

In this paper we propose a shared regression network to jointly estimate the pose of multiple objects, replacing multiple object-specific solutions. We demonstrate that this shared network can outperform other similar approaches that rely on multiple object-specific models by evaluating it on the TLESS dataset using the VSD (Visible Surface Discrepancy). Our approach offers a less complex solution, with fewer parameters, lower memory consumption and less training required. Furthermore, it inherently handles symmetric objects by using a depth-based loss during training and can predict in real-time. Finally, we show how our proposed pipeline can be used for fine-tuning a feature extractor jointly on all objects while training the shared pose regression network. This fine-tuning process improves the pose estimation performance.