Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural killer cell-derived exosome-entrapped paclitaxel can enhance its anti-tumor effect.

OBJECTIVE To study the effectiveness of natural killer cell-derived exosome (NK-Exos)-entrapped paclitaxel (PTX-NK-Exos) in enhancing its anti-tumor effect. MATERIALS AND METHODS The NK-Exos were isolated through ultra-high-speed centrifugation, and the PTX-NK-Exos system was constructed via electroporation. The morphology, particle size, Zeta potential and entrapment rate of PTX-NK-Exos were evaluated using transmission electron microscope (TEM), dynamic light scattering (DLS), Western blotting and high-performance liquid chromatography (HPLC), respectively. The uptake of Exos in human breast cancer MCF-7 cells was observed under a laser confocal microscope. Moreover, the effect of PTX-NK-Exos on MCF-7 cell viability was determined through methyl thiazolyl tetrazolium (MTT) assay, flow cytometry and 4',6-diamidino-2-phenylindole (DAPI) staining. The effects of PTX-NK-Exos on messenger ribonucleic acid (mRNA) and protein expressions of B-cell lymphoma-2 (Bcl-2), Bcl-2 associated X protein (Bax) and Caspase-3 in MCF-7 cells were detected using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blotting, respectively. RESULTS The NK-Exos were successfully isolated via ultra-high-speed centrifugation, and they had uniform particle size and high expression of markers for Exos. MCF-7 cells could take up Exos. The PTX-NK-Exos drug delivery system was successfully prepared using electroporation. In PTX group and NK-Exos group, the proliferation of MCF-7 cells declined, the nuclear apoptosis was evident and the apoptosis rate of MCF-7 cells rose compared with those in Control group. In PTX group and PTX-NK-Exos group, the migration of MCF-7 cells declined compared with that in Control group. According to the results of qRT-PCR and Western blotting, PTX-NK-Exos exerted an anti-tumor effect through inducing the up-regulation of Bax and Caspase-3 in the apoptotic signaling pathway in tumor cells. CONCLUSIONS Exos isolated through ultra-high-speed centrifugation can be used to prepare the PTX-NK-Exos drug delivery system through electroporation. Drug-loaded Exos can effectively inhibit proliferation and induce apoptosis of tumor cells, thereby exerting an anti-tumor effect.
Blood-Brain Barrier, Blood-Brain Tumor Barrier, and Fluorescence-Guided Neurosurgical Oncology: Delivering Optical Labels to Brain Tumors

Recent advances in maximum safe glioma resection have included the introduction of a host of visualization techniques to complement intraoperative white-light imaging of tumors. However, barriers to the effective use of these techniques within the central nervous system remain. In the healthy brain, the blood-brain barrier ensures the stability of the sensitive internal environment of the brain by protecting the active functions of the central nervous system and preventing the invasion of microorganisms and toxins. Brain tumors, however, often cause degradation and dysfunction of this barrier, resulting in a heterogeneous increase in vascular permeability throughout the tumor mass and outside it. Thus, the characteristics of both the blood-brain and blood-brain tumor barriers hinder the vascular delivery of a variety of therapeutic substances to brain tumors. Recent developments in fluorescent visualization of brain tumors offer improvements in the extent of maximal safe resection, but many of these fluorescent agents must reach the tumor via the vasculature. As a result, these fluorescence-guided resection techniques are often limited by the extent of vascular permeability in tumor regions and by the failure to stain the full volume of tumor tissue. In this review, we describe the structure and function of both the blood-brain and blood-brain tumor barriers in the context of the current state of fluorescence-guided imaging of brain tumors. We discuss features of currently used techniques for fluorescence-guided brain tumor resection, with an emphasis on their interactions with the blood-brain and blood-tumor barriers. Finally, we discuss a selection of novel preclinical techniques that have the potential to enhance the delivery of therapeutics to brain tumors in spite of the barrier properties of the brain.
A Narrow Groove Structure Based Plasmonic Refractive Index Sensor

A refractive index (RI) sensor based on plastic optical fiber (POF) with narrow groove structures coated with a gold layer (POF-NGS (Au)) is proposed and demonstrated. The proposed sensor was characterized using intensity interrogation, where the change in transmission power is induced by light filed interaction and Surface Plasmon Resonance (SPR). Narrow groove structures with lengths of 5 mm were fabricated using an ultraviolet laser, and a gold layer was sputtered to the surface of the whole fiber. The effect of the machining pitch on the RI response was studied. In this paper, dB and <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> were used to represent the change of transmission power. Experiments have shown that the POF-NGS (Au) RI sensor has liner response between 1.340 and 1.356. The curvature of the output is largest when the machining pitch is <inline-formula> <tex-math notation="LaTeX">$400~\mu \text{m}$ </tex-math></inline-formula> with sensitivity 12.5 dB/RIU (<inline-formula> <tex-math notation="LaTeX">$126~\mu \text{W}$ </tex-math></inline-formula>/RIU). The repeatability and temperature sensitivity characteristics were also characterized. Furthermore, the proposed sensor offers improved stability and signal to noise ratio.
Cxnet-M3: A Deep Quintuplet Network for Multi-Lesion Classification in Chest X-Ray Images Via Multi-Label Supervision

Medical image analysis is motivated by the success of deep learning, where annotations are usually expensive and not easy to obtain. In this paper, we propose a deep quintuplet network CXNet-m3, where the classification of lesion type of chest x-ray images (CXRs) could benefit from easily accessible annotations like patient age, gender, identity and view position. To improve classification performance, a novel loss function combining both deep metric learning and deep learning is first designed based on multiple labels. Then, a deep model based on transfer learning is built to optimize the loss function. To solve the problem of slow convergence, a quintuplet mining algorithm is presented to provide valuable training samples for the proposed classification model. The experimental results on Chest X-ray14 database show that our classification method outperforms some state-of-art models under Area Under Curve (AUC) score, reaching 0.824 on an average. Besides, our proposal achieves more than 0.9 AUC values in the case of Infiltration, Atelectasis, Cardiomegaly and Nodule.
Snap & Hear: Comic Book Analyst for Children Having Literacy and Visual Barriers

Comic books are very popular across the world due to the unique experience they provide for all of us in the society without any age limitation. Because of this attraction, which comic books have received, it has proved that comic literature will be able to survive in the twenty first century, even with the existence of multidimensional movie theatres as its competitors. While the biggest global filmmakers are busy with making movies from comic books, many researchers have been investigating their time on digitizing the comic stories as it is, expecting to create a new era in the comic world. But most of them have focused only on one or few components of the story. This paper is based on a research which aims to give the full experience of enjoying the comic books for everyone in the world despite of visual and literacy barriers people are having. Proposed solution comes as a web application that translates input image of a comic story into a text format and delivers it as an audio story to the user. The story will be created using extracted components such as characters, objects, speech text and balloons and considering the association among them with the use of image processing and deep learning technologies.
Incremental Attribute Reduction Method Based on Chi-Square Statistics and Information Entropy

Attributes in datasets are usually not equally significant. Some attributes are unnecessary or redundant. Attribute reduction is an important research issue of rough set theory, which can find minimum subsets of attributes with the same classification effect as the whole dataset by removing unnecessary or redundant attributes. We use Chi-square statistics to evaluate the significance of condition attributes. It can reduce the search space of attribute reduction and improve the speed of attribute reduction. Conditional entropy of relative attributes is adopted as a heuristic function. Two decision table reduction algorithms, forward selection and backward deletion, are proposed to approach the optimal solution. Based on this, an efficient incremental attribute reduction method for dynamically changing datasets is proposed by preserving intermediate variables. The intermediate variable is the observation frequency matrix of joint events of each condition attribute and decision attribute. Experimental results show that the proposed algorithms can improve performance in terms of processing time.
Design of a Cosimulation Platform With Hardware-in-the-Loop for Cyber-Attacks on Cyber-Physical Power Systems

This paper presents a real-time cosimulation platform with hardware-in-the-loop (HIL) for performing cyber-physical power system (CPPS) analyses based on RT-LAB and OPNET software. The platform is capable of simulating a power grid and communication network while also reflecting the impact of actual devices and cyber-attacks on the power system. Furthermore, modelling and implementation methods focused on distributed denial of service (DDOS) and man-in-the-middle (MITM) attacks in a communication network are elaborated. A case study of DDOS and MITM attacks in a typical CPPS is demonstrated based on this platform. The simulation results validate the capability of the platform and also show the importance of considering the separate impacts of the communication system, actual devices and cyber-attack in power system simulations.
Influence of Changes in the Level of Volatile Compounds Emitted during Rapeseed Quality Degradation on the Reaction of MOS Type Sensor-Array

This study presents the applicability of a three-parameters method for digital description of spoiled rapeseed odor based on the use of an electronic nose. The method consists of the use of three parameters to describe the sensor response, i.e., the maximum resistance value, the response time and the cleaning time of the active surface of the sensor. Reference chemical methods, i.e., determination of the ergosterol content and analysis of volatile compounds by gas chromatography-mass spectrometry, were used to monitor qualitative changes occurring in the stored material. A 31-day profile of volatile compounds and changes in the ergosterol content was determined in the study. A total of 18 chemical groups of volatile organic compounds was identified. There was a strong positive correlation between the cleaning time and the percentage content of alcohols and alkenes, as well as ergosterol, as a marker of qualitative changes. The maximum response was another parameter that effectively described the changes occurring in the seeds. This parameter was strongly negatively correlated with esters and amides in the case of six sensors, and with ergosterol, alkenes and to a lesser degree with alcohols in the case of the other two sensors. The study results clearly demonstrated a relationship between the sensor responses and the percentage content of alcohols and alkenes, which provided novel practical information for the oilseed branch.
Autistic Symptoms and Social Cognition Predict Real-World Outcomes in Patients With Schizophrenia

Objective Real-world functioning is a complex construct influenced by different factors. The impact of social cognition and autism spectrum disorder (ASD) symptoms on different aspects of the life of people with schizophrenia has been demonstrated independently, but it is unclear how these factors are related to functioning when considered concurrently. We hypothesized that ASD symptoms could play a major role in predicting real-world functioning in schizophrenia. Methods Existent databases from two studies (SCOPE Phase 3 and SCOPE Phase 5), in which a total of 361 patients (mean age 41.7 years; 117 females) were assessed with measures of symptom severity, neuro- and socio-cognitive abilities, functional capacity, social skills, and informant-reported real-world functioning outcomes, were analyzed. Results Active social avoidance, social skills, ASD symptoms, and emotion processing emerged as predictors of real-world interpersonal relationships. Cognitive performance, positive symptoms, and functional capacity emerged as predictors of real-world participation in daily activities. Cognitive performance, emotion processing, positive symptoms severity, and social skills emerged as predictors of real-world work outcomes. Conclusion Among other demographic, clinical, and functional capacity variables, increased ASD symptoms emerged as a significant predictor of poorer social relationships and may therefore represent a key factor in predicting real-world social functioning in schizophrenia.
Does low nutritional quality act as a plant defence? An experimental test of the slow‐growth, high‐mortality hypothesis

Abstract. 1. The slow‐growth, high‐mortality hypothesis was experimentally tested in this study by investigating the effects of plant quality and natural enemies on leaf‐miner growth, performance, and survivorship. Two leaf miners (Acrocercops albinatella and Brachys tesselatus) occurring on the turkey oak Quercus laevis were studied using a factorial design that manipulated predation/parasitism pressure and plant nutritional quality.
A Novel Mobile Target Localization Approach for Complicate Underground Environment in Mixed LOS/NLOS Scenarios

Accurate positioning of the shearer remains a challenge for automation of the longwall coal mining process. In this paper, the popular Ultra-wideband (UWB) positioning system that has attracted considerable attention is adopted to obtain the target node location. Unfortunately, localization accuracy is still unsatisfactory and unreliable in mixed line of sight (LOS) and non-line of sight (NLOS) scenarios. To ameliorate localization accuracy of UWB for complicate underground environment where the positioning scenarios suffered from frequently switching among LOS, NLOS, and mixed LOS-NLOS condition, the novel positioning algorithm GMM-IMM-EKF was proposed. Gaussian mixed model (GMM) was employed to re-estimate the measurement distance, and two parallel variational Bayesian adaptive Kalman filters (VBAKFs) under the structure of interacting multiple model (IMM) was utilized to smoothen the result of GMM to eliminate the LOS and NLOS errors, respectively. Then, the position of the target node was determined by exploiting extended Kalman filter (EKF) based on the outcome of IMM-VBAKF. The proposed approach was assessed by exploiting UWB P440 modules. Comparative experimental verification demonstrated that GMM-IMM-EKF strategy outperformed other positioning approaches, which can effectively reduce the adverse effect of NLOS errors and achieve higher positioning accuracy in underground environment with LOS/NLOS/LOS-NLOS transition conditions.
Non-Invasive Multi-Camera Gait Analysis System and its Application to Gender Classification

Objective: Most studies have conducted human gait analysis using expensive and invasive photogrammetric systems. The objective of this study was to demonstrate that non-invasive and cost-effective systems based on depth cameras may be able to retrieve relevant features of human gait patterns. We aimed to prove this by solving the problem of gait classification by gender. Methods: 81 participants (40 female and 41 male) walked at a self-selected speed across a 4.8-meter walkway. Gait data was recorded using multiple depth sensors. Analysis in time domain included joint excursions by gait phases, range of movement (ROM), central tendency and dispersion measures, spatial variables, and center of mass (COM) position. The spectral analysis included principal frequency, magnitude, and phase shift during walking. Only features with significant differences by gender were used to train a support vector machine (SVM) classifier. Results: A total of 108 features presented significant differences by gender (p<0.05). On this basis, the accuracy of the chosen model was 96.7%. Trunk rotation, trunk sway, knee abduction/adduction, and pelvic obliquity were the most differentiated between the groups. The COM position shown a significant difference by gender (p=0.0065) with 51.7% and 51.0% for men and women respectively. Women proved to have significantly shorter normalized step width than men (p=0.0472). Conclusion: The proposed method was able to retrieve most of human gait features correctly, including differences in gait pattern by gender. Significance: Depth cameras represent a cost-effective system that could be used for a deeper biomechanical human gait analysis.
An Improved C4.5 Data Mining Driven Algorithm for the Diagnosis of Coronary Artery Disease

Coronary artery disease (CAD) is one of the deadly diseases in the world, especially in developed countries. This disease is not epidemic but it re-mains the single most common cause of death. This research used an im-proved C4.5 data mining algorithm for the diagnosis of CAD. A performance evaluation of the improved algorithm was carried out against the traditional C4.5 Algorithm. Consequently, the improved C4.5 data mining algorithm has shown better performance with an overall accuracy of 97.23 %, 97.03 % specificity, and 96.39% of sensitivity. The improved algorithm built a tree with twenty-seven leaves and forty-seven sizes, which can be converted into the production rules for knowledge base of expert system to diagnose CAD. This helps in addressing the problematic bottleneck of knowledge acquisition process in expert system for diagnosis of CAD.
Modelling stock recruitment relationships to examine stock management policies

Modelling stock–recruitment relationships to examine stock policies. Simulation studies are used widely for ﬁsh stock management. In such studies, forecasting future recruitment, which can vary greatly between years, has become an essential part of evaluating management strategies. We propose a new forecasting algorithm to predict recruitment for short- or medium-term stochastic projections, using a stock–recruitment relationship. We address cases in which the spawning stock has dropped below previously observed levels, or in which predicted recruitment is situated close to the maximum observed level. The relative prediction error of seven existing algorithms was compared with that of the new model using leave-one-out cross-validation for 61 data sets from ICES, the Japanese Fisheries Agency, and PICES. The new algorithm had the smallest prediction error for 49 of the data sets, but was slightly biased by the precautionary treatment of predictions of high recruitment.
Observations on Euchloe olympia (W. H. Edwards) (Pieridae) in a Central Illinois Sand Prairie.

ABSTRACT. Euchloe olympia was studied in a sand prairie in central Illinois each year for a period of five years to monitor behavior. Adults were followed by groups of observers; groups tracked the type of flowers utilized for nectar, the flight time between visits to plants or the substrate, the distance flown between each plant or substrate, and any additional behaviors (such as oviposition and courtships). We made over 1300 observations of 53 butterflies. Individuals were found to differ significantly in various aspects of their behavior; including nectar source, average time nectaring and flight activity. The main larval food plant was found to be Lepidium virginicum, while the main adult nectar source was found to be Nuttallanthus canadensis. Individuals consistently showed considerable constancy in the flowers they visited.
Mode of attachment of six epilithic crustose Corallinaceae (Rhodophyta)

Abstract Spores from six British species of epilithic crustose Corallinaceae (Rhodophyta), belonging to the genera Phymatolithon, Lithothamnion and Lithophyllum, have been grown on glass slides in the laboratory. Scanning and transmission electron microscopy have been used to study methods of attachment both in these cultured specimens, and in crusts collected from the shore. Mucilage forms the adhesive for spore settlement and early development. As the young crust becomes calcified, attachment involves two processes; the moulding of the hypothallial filaments to surface irregularities, and the deposition of a thickening layer of crystals which fills in microscopic gaps between the cells and the substratum, ensuring a precise fit. The crystalline deposit has an associated organic component. If a space later forms beneath a crust, then a third process occurs; clusters of aragonite crystals bound by organic material are precipitated in the gap so helping to resecure the alga.
Adversarial Attentive Multi-Modal Embedding Learning for Image-Text Matching

Matching the image and text with deep models has been extensively studied in recent years. Mining the correlation between image and text to learn effective multi-modal features is crucial for image-text matching. However, most existing approaches model the different types of correlation independently. In this work, we propose a novel model named Adversarial Attentive Multi-modal Embedding Learning (AAMEL) for image-text matching. It combines adversarial networks and attention mechanism to learn effective and robust multi-modal embeddings for better matching between the image and text. Adversarial learning is implemented as an interplay between two processes. First, two attention models are proposed to exploit two types of correlation between the image and text for multi-modal embedding learning and to confuse the other process. Then the discriminator tries to distinguish the two types of multi-modal embeddings learned by the two attention models, in which the two attention models are reinforced mutually. Through adversarial learning, it is expected that both the two embeddings from the attention models can well exploit the two types of correlation, and thus they can deceive the discriminator that they are generated from the other attention-based model. By integrating the attention mechanism and adversarial learning, the learned multi-modal embeddings are more effective for image and text matching. Extensive experiments have been conducted on the benchmark datasets of Flickr30K and MSCOCO to demonstrate the superiority of the proposed approaches over the state-of-the-art methods on image-text retrieval.