Miracle-dz/UniFAR-FTUs huggingface dataset:
PMEPA1 interference activates PTEN/PI3K/AKT, thereby inhibiting the proliferation, invasion and migration of pancreatic cancer cells and enhancing the sensitivity to gemcitabine and cisplatin

To explore the biological activity of transmembrane prostateandrogen induced RNA (PMEPA1) in human pancreatic cancer (hPAC) cells and its drug sensitivity to gemcitabine (GEM) and cisplatin (DDP). Gene Expression Profiling Interactive Analysis (GEPIA) and Cancer Cell Line Encyclopedia (CCLE) were consulted to indicate the expression of PMEPA1 in hPAC tissues and cells. Quantitative real‐time PCR (RT‐qPCR) and western blot were performed to verify the indication. RT‐qPCR and western blot also detected the expressions of PTEN/PI3K/AKT before and after transfection of PMEPA1 siRNA plasmids. Cell counting Kit‐8 (CCK‐8) and EdU staining were performed to examine cell proliferation before and after transfection of phosphatase and tensin homologue delet2ed on chromosome ten (PTEN) siRNA plasmids. Transwell and wound healing detected the invasion and migration of hPAC cells. The expressions of MMP‐2 and MMP‐9 were detected by western blot. After GEM or DDP treatment, cell viability was observed by commercial kits and cell apoptosis by flow cytometry. GEPIA and CCLE predicted increased expression of PMEPA1 in hPAC tissues and cells, which was confirmed by quantitative reverse transcription polymerase chain reaction (RT‐qPCR) and western blot. PMEPA1 was also shown to be associated with disease‐free survival. Transfection of PMEPA1 siRNA plasmids affected the expressions of PTEN/PI3K/AKT. PMEPA1 interference inhibited the proliferation, invasion and migration of hPAC cells. Furthermore, PMEPA1 interference also enhanced the sensitivity of hPAC cells to GEM and DDP via PTEN interference. PMEPA1 interference inhibits the proliferation, invasion and migration of pancreatic cancer cells and enhances the sensitivity to GEM and cisplatin by activating PTEN/PI3K/AKT signaling.
The relationship between falling and fear of falling among community-dwelling elderly

Abstract Researchers have repeatedly examined the relationship between a previous experience of a fall and subsequent fear of falling (FOF); however, few studies have investigated the effects of falling along various timelines among older adults. The objective of this study was to determine whether experiencing a fall in the previous month or the previous year led to FOF among the elderly. The National Health and Aging Trends Study (NHATS) in the U.S. collected information indicative of basic trends in the behavior of individuals aged 65 years and older. In the current study, we applied multiple logistic regression analysis of results from round 7 of the NHATS with the aim of identifying the risk factors associated with FOF among 5559 participants aged 65 years or older. FOF was reported by 48.8% of those who experienced a fall in the previous year and 46.8% experienced a fall in the previous month. The results of regression analysis revealed that after adjusting for sex, age, related chronic disease, activities of daily living, and instrumental activities of daily living, FOF was significantly associated with experiencing a fall during the previous month (OR = 2.29, 95% CI: 1.78–2.95) or during the previous year (OR = 2.60, 95% CI: 2.16–3.14). Our results indicate that experiences of falling during the previous month or the previous year were both significantly associated with a fear of falling, and caregivers should keep this in mind when dealing with community-living elderly individuals.
Property of lipolase modified with stearic acid

Lipolase was chemically modified with fatty acids.The hydrolysis activity,interfacial hydrolysis activity and esterification activity in hexane of the modified lipolase were assayed.The results showed that the interfacial hydrolysis activity and esterification activity of the modified lipolase increased apparently,although the hydrolysis activity decreased a little compared with the unmodified lipolase.The surface tension and the interfacial tension of the modified and unmodified lipolase were also investigated.The surface tension and interfacial tension of the solution of modified lipolase were much lower than those of unmodified lipolase.The Langmuir-Blodgtt monolayer film of modified lipolase was obtained by the use of LB technique and the surface pressure was higher than that of unmodified one,showing that it was much easier to congregate on the interface.All results showed that lipolase modified with fatty acids had an enhanced activity on the interface,making it advantageous to use modified lipolase in organic solvent and two phase system.
MICROX-2 cross section library based on ENDF/B-VII

New cross section libraries of a neutron transport code MICROX-2 have been generated for advanced reactor design and fuel cycle analyses. A total of 386 nuclides were processed, including 10 thermal scattering nuclides, which are available in ENDF/B-VII release 0 nuclear data. The NJOY system and MICROR code were used to process nuclear data and convert them into MICROX-2 format. The energy group structure of the new library was optimized for both the thermal and fast neutron spectrum reactors based on Contributon and Point-wise Cross Section Driven (CPXSD) method, resulting in a total of 1173 energy groups. A series of lattice cell level benchmark calculations have been performed against both experimental measurements and Monte Carlo calculations for the effective/infinite multiplication factor and reaction rate ratios. The results of MICROX-2 calculation with the new library were consistent with those of 15 reference cases. The average errors of the infinite multiplication factor and reaction rate ratio were 0.31% {delta}k and 1.9%, respectively. The maximum error of reaction rate ratio was 8% for {sup 238}U-to-{sup 235}U fission of ZEBRA lattice against the reference calculation done by MCNP5. (authors)
Investigating the Impact of Generation Citizen’s Action Civics Education Program on Student Academic Engagement

Abstract Student-centered, project-based, guided experiential civics education approaches, like action civics, may have benefits for general academic outcomes. Action civics programs provide applied settings for learning civics: students work together in groups to take action on a local issue. To the best of our knowledge, we conducted the first quasi-experimental evaluation of the impact of a school-based action civics intervention, Generation Citizen (GC), on academic engagement. Exposure to GC was associated with increased participation in non-GC classes and there was no statistically significant effect on unexcused absences. Oftentimes, schools deprioritize civics education due to limited resources and/or focusing on other subjects; our findings suggest that action civics education may have useful, generalized spillover benefits for student academic engagement.
Innovative technologies for infrastructure construction and maintenance through collaborative robots based on an open design approach

In this paper, the socio-economic problems facing the construction industry in Japan are presented, including the COVID19 pandemic, an aging population, aging infrastructure, and severe natural disasters, and a project to tackle the problem is introduced. This project, called ‘Collaborative robots for adaptation of diverse environments and innovation of infrastructure construction,’ is a sub-project of the Moonshot project organized by the Cabinet Office in Japan. The key approach to proceeding with this project is an ‘open design’ for robotics. Based on this approach, three aspects of the research plans are introduced: (1) robots that flexibly adapt to the environment, (2) Many-modal AI for environment evaluation, and (3) Physical AI and dynamic collaboration of multi-robots. GRAPHICAL ABSTRACT
Multi-spectral cloud detection based on a multi-dimensional and multi-grained dense cascade forest

Abstract. Cloud detection in satellite images is a vital step for cloud/land recognition, cloud/snow discrimination, and cloud shadow removal. Accurate cloud detection plays an important role in land resource management, environmental pollution monitoring, and land target recognition. Deep learning (DL) algorithms have shown great progress in cloud detection. However, as the complexity of the DL-based model increases, cloud detection efficiency decreases. DL-based cloud detection models are unable to successfully balance the performance-efficiency tradeoff. In our study, a multi-dimensional and multi-grained dense cascade forest (MDForest) is proposed for multi-spectral cloud detection. MDForest is a deep forest structure that automatically extracts low-level and high-level features from satellite cloud images end-to-end; a multi-dimensional and multi-grained scanning mechanism is introduced to capture the spectral information of multi-spectral satellite images while enhancing the representation learning ability of cascade forest. The experimental results on the HJ-1A/1B dataset show that MDForest improves the performance of cloud detection and possesses a good inference efficiency compared with DL-based cloud detection methods, which makes the proposed MDForest satisfy the application where good performance and high efficiency are both required.
Pointwise classification of mobile laser scanning point clouds of urban scenes using raw data

Abstract. Mobile laser scanning (MLS), which can quickly collect a high-resolution and high-precision point cloud of the surroundings of a vehicle, is an appealing technology for three-dimensional (3D) urban scene analysis. In this regard, the classification of MLS point clouds is a common and core task. We focus on pointwise classification, in which each individual point is categorized into a specific class by applying a binary classifier involving a set of local features derived from the neighborhoods of the point. To speed up the neighbor search and enhance feature distinctiveness for pointwise classification, we exploit the topological and semantic information in the raw data acquired by light detection and ranging (LiDAR) and recorded in scan order. First, a two-dimensional (2D) scan grid for data indexing is recovered, and the relative 3D coordinates with respect to the LiDAR position are calculated. Subsequently, a set of local features is extracted using an efficient neighbor search method with a low computational complexity independent of the number of points in a point cloud. These features are further merged to produce a variety of binary classifiers for specific classes via a GentleBoost supervised learning algorithm combining decision trees. The experimental results on the Paris-rue-Cassette database demonstrate that the proposed approach outperforms the state-of-the-art methods with a 10% improvement in the F1 score, whereas it uses simpler geometric features derived from a spherical neighborhood with a radius of 0.5 m.
Addition Reactions of Diketene. IV. Reaction of Diketene with Thioureas, Thioamide, and Aminothiol

The reaction of benzimidazoline-2-thione (2) with diketene (1) gave the oxazine derivative 3 and the thiazine derivative 4. However, the reaction of 2-indolinethione (6) with diketene afforded only the thiazine derivative 7. On the other hand, no cyclized compound was obtained from 2-indolinone (8) and diketene. The reactions of N, N'-diphenylthiourea (11) and 2-aminobenzothiol (14) with diketene were also studied ; 11 and 14 gave the thiouracil 12 and the benzothiazine 16, respectively.
Iris-ocular-periocular: toward more accurate biometrics for off-angle images

Abstract. Iris is one of the most well-known biometrics; it is a nonintrusive and contactless authentication technique with high accuracy, enhanced security, and unique distinctiveness. However, its dependence on image quality and its frontal image acquisition requirement limit its recognition performance and hinder its potential use in standoff applications. Standoff biometric systems require a less controlled environment than traditional systems, so their captured images will likely be nonideal, including off-angle. We present convolutional neural network (CNN)-based deep learning frameworks to improve the recognition performance of iris, ocular, and periocular biometric modalities for off-angle images. Our contribution is fourfold: first, the performances of popular AlexNet, GoogLeNet, and ResNet50 architectures are presented for off-angle biometrics. Second, we study the effect of the gaze angle difference between training and testing images on iris, ocular, and periocular recognitions. Third, we investigate the network behavior for untrained gaze angles and the information fusion capability of CNN networks at multiple off-angle images. Finally, deep learning-based results are compared with a traditional iris recognition algorithm using the gallery approach. Our results with off-angle images ranging from −50 deg to 50 deg in gaze angle show that the proposed methods improve the recognition performance of iris, ocular, and periocular recognition.
Hyperspectral image classification using multi-branch-multi-scale residual fusion network

Abstract. Hyperspectral images (HSIs) contain rich spectral information and spatial information. How to apply these two information types and fully combine the correlation between them remains a challenge worthy of further research and discussion. In this study, a multi-branch-multi-scale residual fusion network (MB-MS-RFN) for HSI classification is proposed. First, a 3D multi-branch-multi-scale convolution residual network, which can acquire image features of different scale in the training process and consider the correlation between spectral information and spatial information, is developed. Instead of deepening the network, the multi-branch structure widens the network horizontally to obtain more accurate classification. Finally, the different levels of HSI features are fused to obtain better classification results. Several experiments have been carried out to verify the proposed framework, and the results have demonstrated that the proposed MB-MS-RFN framework can improve the classification performance of HSIs. The performance of the MB-MS-RFN was evaluated using the Indian Pines, Pavia University, and Kennedy Space Center datasets; the performances’ overall accuracies were 99.66%, 99.92%, and 99.97%, respectively. The results from a series of experiments confirm that the proposed method offers several advantages in classification accuracy compared with five other methods.
Bootstrap internal validation command for predictive logistic regression models

Overfitting is a common problem in the development of predictive models. It leads to an optimistic estimation of apparent model performance. Internal validation using bootstrapping techniques allows one to quantify the optimism of a predictive model and provide a more realistic estimate of its performance measures. Our objective is to build an easy-to-use command, bsvalidation, aimed to perform a bootstrap internal validation of a logistic regression model.
Automatic cloud and snow detection for GF-1 and PRSS-1 remote sensing images

Abstract. In the remote sensing image processing field, cloud and snow detection for high-resolution sensors and cloud and snow morphology in different latitudes of the world is challenging. A deep learning training model (Softmax) was developed to improve the accuracy of cloud and snow identification from Gaofen-1 and Pakistan Remote Sensing Satellite-1 images. First, more than 1800 scenes remote sensing images in various regions over the world are collected. Next, the texture details and spectral information of the objects are extracted. Finally, the Softmax model is applied to process the features to obtain the final cloud and snow masks. The cloud and snow detection results are evaluated by performing statistical analysis. The overall accuracy for cloud detection reaches 92.64% (kappa coefficient = 0.83) and for snow detection reaches 93.94% (kappa coefficient = 0.8). The algorithm is not only accurate but also computationally efficient. It is of great importance for image processing in ground segment and corresponding applications.
Interactive two-stream graph neural network for skeleton-based action recognition

Abstract. Action recognition has wide applications in fields such as human–computer interaction, virtual reality, and robotics. Since human actions can be represented as a sequence of skeleton graphs, approaches based on graph neural networks (GNNs) have attracted considerable attention in the research action recognition. Recent studies have demonstrated the effectiveness of two-stream GNNs in which discriminative features for action recognition are extracted from both the joint stream and the bone stream. Each stream is generated by GNNs that support message passing along fixed connections between vertices. However, existing two-stream approaches have two limitations: no interaction is allowed between the two streams and temporary contacts between joints or bones cannot be modeled. To address these issues, we propose the interactive two-stream graph neural network, which employs a joint–bone communication block to accelerate the interaction between the joint stream and the bone stream. Furthermore, an adaptive strategy is introduced to enable dynamic connections between vertices. Extensive experiments on three large-scale datasets have demonstrated the effectiveness of our proposed method.
Deep reinforced bitrate ladders for adaptive video streaming

In the typical transcoding pipeline for adaptive video streaming, raw videos are pre-chunked and pre-encoded according to a set of resolution-bitrate or resolution-quality pairs on the server-side, where the pair is often named as bitrate ladder. Different from existing heuristics, we argue that a good bitrate ladder should be optimized by considering video content features, network capacity, and storage costs on the cloud. We propose DeepLadder, a per-chunk optimization scheme which adopts state-of-the-art deep reinforcement learning (DRL) method to optimize the bitrate ladder w.r. t the above concerns. Technically, DeepLadder selects the proper setting for each video resolution autoregressively. We use over 8,000 video chunks, measure over 1,000,000 perceptual video qualities, collect real-world network traces for more than 50 hours, and invent faithful virtual environments to help train DeepLadder efficiently. Across a series of comprehensive experiments on both Constant Bitrate (CBR) and Variable Bitrate (VBR)-encoded videos, we demonstrate significant improvements in average video quality bandwidth utilization, and storage overhead in comparison to prior work as well as the ability to be deployed in the real-world transcoding framework.
A Simulation Software for the Evaluation of Vulnerabilities in Reputation Management Systems

Multi-agent distributed systems are characterized by autonomous entities that interact with each other to provide, and/or request, different kinds of services. In several contexts, especially when a reward is offered according to the quality of service, individual agents (or coordinated groups) may act in a selfish way. To prevent such behaviours, distributed Reputation Management Systems (RMSs) provide every agent with the capability of computing the reputation of the others according to direct past interactions, as well as indirect opinions reported by their neighbourhood. This last point introduces a weakness on gossiped information that makes RMSs vulnerable to malicious agents’ intent on disseminating false reputation values. Given the variety of application scenarios in which RMSs can be adopted, as well as the multitude of behaviours that agents can implement, designers need RMS evaluation tools that allow them to predict the robustness of the system to security attacks, before its actual deployment. To this aim, we present a simulation software for the vulnerability evaluation of RMSs and illustrate three case studies in which this tool was effectively used to model and assess state-of-the-art RMSs.
The Processing-in-Memory Model

As computational resources become more efficient and data sizes grow, data movement is fast becoming the dominant cost in computing. Processing-in-Memory is emerging as a key technique for reducing costly data movement, by enabling computation to be executed on compute resources embedded in the memory modules themselves. This paper presents the Processing-in-Memory (PIM) model, for the design and analysis of parallel algorithms on systems providing processing-in-memory modules. The PIM model focuses on keys aspects of such systems, while abstracting the rest. Namely, the model combines (i) a CPU-side consisting of parallel cores with fast access to a small shared memory of size M words (as in traditional parallel computing), (ii) a PIM-side consisting of P PIM modules, each with a core and a local memory of size Θ(n/P) words for an input of size n (as in traditional distributed computing), and (iii) a network between the two sides. The model combines standard parallel complexity metrics for both shared memory (work and depth) and distributed memory (local work, communication time) computing. A key algorithmic challenge is to achieve load balance among the PIM modules in both their communication and their local work, while minimizing the communication time. We demonstrate how to overcome this challenge for an ordered search structure, presenting a parallel PIM-skiplist data structure that efficiently supports a wide range of batch-parallel queries and updates.
Supervised Average Consensus in Anonymous Dynamic Networks

How to reach consensus on an average value in a dynamic crowd without revealing identity? In this work, we study the problem of Average Network Consensus in Anonymous Dynamic Networks (ADN). Network dynamicity is specified by the sequence of topology-graph isoperimetric numbers occurring over time, which we call the isoperimetric dynamicity of the network. The consensus variable is the average of values initially held by nodes, which is customary in the Network-consensus literature. Given that having an algorithm to compute the average one can compute the network size (i.e. the Counting problem) and viceversa, we further focus on the latter. We propose a deterministic distributed Average Network Consensus algorithm for ADNs that we call isoperimetric Scalable Coordinated Anonymous Local Aggregation (iSCALA), and we analyze its performance for different scenarios, including worst-case (adversarial) and stochastic dynamic topologies. Our solution utilizes supervisor nodes, which have been shown to be necessary for computations in ADNs. The algorithm uses the isoperimetric dynamicity of the network as an input, meaning that only the isoperimetric number parameters (or their lower bound) must be given, but topologies may occur arbitrarily or stochastically as long as they comply with those parameters. Previous work for adversarial ADNs overestimates the running time to deal with worst-case scenarios. For ADNs with given isoperimetric dynamicity, our analysis shows improved performance for some practical dynamic topologies, with cubic time or better for stochastic ADNs, and our experimental evaluation confirms that such performance is close to what could be achieved if the algorithm had centralized control on stopping conditions; thus, there is no substantial benefit of having centralized stopping control in the ADN system.