Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel application of anti‐human Fc nanobody for screening high‐producing CHO cells for monoclonal antibody

Animal‐derived anti‐IgG secondary antibodies are currently employed to stain and screen of human monoclonal antibody(mAb)‐producing cells, but using animal‐derived antibodies may raise the concerns of high cost, complicated operations and biological safety issues in biopharmaceutical manufacturing. Nanobodies(VHHs) are attractive forms of antibodies for their straightforward engineering and expression in both eukaryotic and prokaryotic systems. Using phage‐displayed immune llama VHH library, we identified new anti‐Fc VHHs that could bind to human Fc with high affinity. In GFP fusion format, the anti‐Fc VHH‐GFP generated dramatically stronger FACS signals than AF488 conjugated anti‐IgG antibodies when used for staining mAb‐producing CHO cells. Furthermore, preparative sorting of CHO cells based on anti‐Fc VHH‐GFP staining resulted in the enrichment of cell lines capable of synthesizing mAb at high productivity. This safe and cost‐efficient anti‐Fc VHH‐GFP may optimize the process of generating highly productive cell lines for therapeutic mAb production compared to conventional animal‐derived fluorescent antibodies.
Real-Time Robot Pose Correction on Curved Surface Employing 6-Axis Force/Torque Sensor

Research on robot trajectory correction on a curved surface has seen a surge in the last two decades to deal with issues in many machining processes. Due to the intricacies involved in complex machining, the real-time correction of robot end-effector position and orientation is highly challenging. The existing approaches are either computationally expensive or inadequate for high accuracy in desired trajectory tracking. Hence, this research explores and implements a novel sophisticated approach, barring minor complications, to empower industrial robots to adjust poses in real-time automatically. In contrast with other studies, the proposed technique does not require any prior geometric information about the workpiece, such as a CAD model or a 3D scan. Our proposed technique relies solely on force/torque sensor feedback information obtained from a 6-axis force/torque sensor installed at the end-effector. When the developed tool comes in contact with the curved surface, the force sensor transmits the data to the pose correction algorithm. The pose correction algorithm estimates the adjustment required to make the tool normal to the surface. The effectiveness and feasibility of the proposed scheme were validated through numerous experiments carried out with a 6-DOF industrial robot. The final results of the curved surface trajectory correction demonstrate that the contact-based robot pose correction has significantly improved. The estimated average error on depth (Z-axis), and angles (Rx, Ry) are 0.7 $mm$ , 0.7°, and 0.9°, respectively.
Improvement of individual performance in the public sector: Public service motivation and user orientation as levers

Purpose - The purpose of this paper is to explore whether a pro-social motivation called user orientation (UO), which aims at helping specific others, may affect performance of public sector employees in addition to public service motivation (PSM), and whether any personal, and/or contextual factors affect this relationship (age, tenure, role, and context). Design/methodology/approach - Utilizing cross-sectional survey data obtained from 618 Italian public teachers, PLS-structural equation modeling is used to investigate the relationship between PSM, UO, and performance, along with the moderating effects of individual/contextual factors. Findings - Findings show positive relationships between the two levers (PSM/UO) and individual performance (IP), depending on job and organizational tenure, role, and the social environment of the areas the schools are located in. Research limitations/implications - There might be problems related to causal inference and common method variance, due to the use of the cross-sectional self-reported data. Practical implications - Managers should be aware of the crucial role UO and PSM play in order to improve IP in contexts where there is direct contact with the service beneficiaries. Originality/value - The paper contributes to a clearer understanding of which motives are involved in the process that leads public service employees to enhance their performance.
An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance

Face detection for security cameras monitoring large and crowded areas is very important for public safety. However, it is much more difficult than traditional face detection tasks. One reason is, in large areas like squares, stations and stadiums, faces captured by cameras are usually at a low resolution and thus miss many facial details. In this paper, we improve popular cascade algorithms by proposing a novel multi-resolution framework that utilizes parallel convolutional neural network cascades for detecting faces in large scene. This framework utilizes the face and head-with-shoulder information together to deal with the large area surveillance images. Comparing with popular cascade algorithms, our method outperforms them by a large margin.
Speech Enhancement With Robust Beamforming for Spatially Overlapped and Distributed Sources

Most of the existing Beamforming methods are based on the assumptions that the sources are all point sources and the angular separation between the direction of arrival (DOA) of the source and the interference is large enough to assure good performance. In this paper, we consider a tough scenario where the target source and the interference are simultaneously spatially distributed and overlapped. To improve the performance of Beamforming in this scenario, we propose two approaches: the first approach exploits the non-Gaussianity as well as the spectrogram sparsity of the output of the microphone array; the second approach exploits the generalized sparsity with overlapped groups of the Beampattern. The proposed criteria are solved by methods based on linearized preconditioned alternating direction method of multipliers (LPADMM) with high accuracy and high computational efficiency. Numerical simulations and real data experiments show the advantages of the proposed approaches compared to previously proposed Beamforming methods for signal enhancement.
Fusion of hyperspectral and LiDAR data using sparse stacked autoencoder for land cover classification with 3D-2D convolutional neural network

Abstract. Fusion of complementary information from multisensor data is of great importance for identifying the land covers. However, integration of multisource information is a challenging task. A framework is developed to integrate hyperspectral and LiDAR data for land cover classification. In the proposed method, sparse stacked autoencoders are used to represent the spectral and spatial information in a compact form. The spatial information is extracted both from hyperspectral and LiDAR data using morphological operators. The encoded spectral and spatial features are combined with elevation information to form a joint feature vector. The joint features are fed to a convolutional neural network (CNN) classifier to classify the land covers. The CNN classifier is a hybrid three- and two-dimensional (3D)–(2D) model having three 3D convolutional layers and one 2D convolutional layer. The experiments are carried out on two datasets Houston and Samford to evaluate the performance of the proposed method. The results have demonstrated the effectiveness of the method with global κ = 0.9285 and global naive accuracy (OA) of 93.44% for Houston data. For Samford data, it achieves κ = 0.9811 and OA = 98.93 % .
Observations and numerical ­simulations of sea breezes at Hong Kong International Airport

55 W ether – Feruary 223, ol. 8, o. 2 Priest SH. 2010. Coming of age in the academy? The status of our emerging field. J. Sci. Commun. 9(3): C06 Rajput ASD. 2017. Science communication as an academic discipline: an Indian perspective. Curr. Sci. 113(12): 2262–2267. Rajput ASD. 2018. India’s Ph.D. scholar outreach requirement. Science 359(6382): 1343 Rajput ASD. 2019. India aims for national policy on scientific social responsibility. Nature 574: 634 Rose KM, Markowitz EM, Brossard D. 2020. Scientists’ incentives and attitudes toward public communication. Proc. Natl. Acad. Sci. USA. 117(3): 1274–1276. Roten FCV. 2011. Gender differences in scientists’ public outreach and engagement activities. Sci. Commun. 33(1): 52–75. Correspondence to: A. S. D. Rajput
Fusing User Preferences and Spatiotemporal Information for Sequential Recommendation

At present, the research on sequence recommendation mainly focuses on using the historical interaction data between users and items to mine their relationship, so as to predict the next interaction between users and items, then generate the personalized recommendation. Spatiotemporal information is very important to further improve the accuracy and quality of recommendation, but the existing sequence recommendation models are mainly based on recurrent neural network (RNN), and pay less attention to spatiotemporal information. Most of the recommendation models are still in the early stage of merging spatiotemporal context information, and the processing effect of long sequence data is not ideal. a sequential recommendation model integrating user preferences and spatiotemporal information is proposed. The model captures user item long-term preferences through spatiotemporal GRU algorithm and user item short-term preferences through attention mechanism. Finally, the learned long-term and short-term preference features and user portrait features are combined to predict the next recommendation location. The experimental results on two real data sets Foursquare and Brightkite show that the proposed model performs better than state-of-the-arts in three evaluation indicators HR@K, NDCG@K and MAP@K.
The effect of aggressive management of intraoperative body temperature on postoperative APACHE II score and prognosis in high-risk patients undergoing thoracoscopic surgery

Background Intraoperative hypothermia is related with postoperative complication, longer length of stay (LoS) and mortality. Acute Physiology and Chronic Health Evaluation II (APACHE II) it the most commonly used evaluation system for assessing the severity and clinical prognosis of patients. This study sought to examine the effect of intraoperative body temperature on postoperative APACHE II scores and the prognosis of high-risk patients undergoing thoracoscopic surgery. Methods This study used the clinical data of patients from a multicenter randomized controlled trial who had undergone thoracoscopic surgery at our center (NCT03111875). In our center were randomly assigned (1:1) to receive either aggressive warming to a target core temperature of 37 ℃ or routine thermal management to a target of 35.5 ℃ during non-cardiac surgery. Randomisation was computer-generated. Eligible patients (aged ≥45 years) had at least one cardiovascular risk factor, were scheduled for inpatient noncardiac surgery expected to last 2–6 h with general anaesthesia. We retrieved medical information through the electronic medical record system. The primary outcome was the postoperative APACHE II scores, APACHE II score variation. The secondary outcome was Quality of Recovery-15 (QoR-15) scores, LoS in hospital, postoperative complications, infections, and deaths of the patients were recorded, and a logistic regression analysis was conducted to stratify the risk factors for the APACHE II score. Results Group R comprised 121 patients and Group A comprised 84 patients. Group A had lower postoperative APACHE II scores (P=0.046) and a lower probability of a grade increase than Group R (P=0.005). However, no significant differences were found in terms of the QoR-15 scores, LoS, postoperative complications, infections, and deaths between the 2 groups. The logistic regression showed that aggressive warming, age, and the American Society of Anesthesiologists (ASA) grade were risk factors for the deterioration of postoperative APACHE II scores. Conclusions The active adoption of various passive and aggressive warming strategies to keep the core body temperature ≥37 ℃ during thoracoscopic surgery significantly reduced increases in APACHE II scores, which is different from age and ASA grade, and was the only intervention factor.
Effect of Demand Forecast Error on Imbalance Cost in Residential Micro Grid

In this paper, we conduct electric power simulation by forecasting electricity demand in general households with PV power generation based on the current electricity supply and demand system in Japan. Specifically, forecast errors for electricity demand in general households are taken into account, and machine learning is used to forecast the output of residential photovoltaic power generation. The actual electricity demand, which is the difference between the electricity demand in general households and the PV output, is then calculated, and the degree of contribution of the assumed model to the electricity system is evaluated using an index called imbalance cost from the perspective of a retail electricity provider.
HOOFR SLAM System: An Embedded Vision SLAM Algorithm and Its Hardware-Software Mapping-Based Intelligent Vehicles Applications

This paper deals simultaneously with the trajectory estimation and map reconstruction by means of a stereo-calibrated vision system evolving in a large-scale unknown environment. This problem is widely known as Visual SLAM. Our proposal optimizes the execution time of the VSLAM framework while preserving its localization accuracy. The contributions of this paper are structured as follows. First, a novel VSLAM approach based on a “Weighted Mean” of multiple neighbor poses is detailed and is denoted as HOOFR SLAM. This approach provides a localization estimate after computing the camera poses (6-DOF rigid transformation) from the current image frame to previous neighbor frames. Taking advantage of the camera motion, we conjointly incorporate two types of stereo modes: “Static Stereo” mode (SS) through the fixed-baseline of left-right cameras setup along with the “Temporal Multi-view Stereo” mode (TMS). Moreover, instead of computing beforehand the disparity of SS mode for all key-points set, the disparity map in scale estimation step is limited to the inliers of the TMS mode so as to reduce the computational cost. This strategy is suitable to be parallelized on a multiprocessor architecture and exhibits a competitive performance with the other state-of-the-art strategies in many real datasets. Second, we report a hardware-software mapping of the proposed VSLAM approach. To this end, a heterogeneous CPU-GPU architecture-based vision system is considered. Third, a thorough and extensive experimental evaluation of our algorithm implemented on an automotive architecture (the NVIDIA Tegra TX1 system) is studied and analyzed. We report hence the localization and timing results through experiments on five well-known public stereo SLAM datasets: KITTI, Malaga, Oxford, MRT, and St_Lucia datasets.
Science instrumentation progress at the Giant Magellan Telescope

We describe the development status of the first-generation science instruments for the Giant Magellan Telescope (GMT). The first-generation suite includes two infrared and two visible light spectrographs that together will deliver from wide-band imaging to R~200,000 spectroscopy at wavelengths from 0.3 to 5 µm. All four instruments are designed for use with diffraction limited or ground-layer adaptive optics modes. G-CLEF, a visible light echelle designed for broad scientific use and for precision radial velocity measurements, is in fabrication. The other three (GMACS, a wide field multi-object spectrograph; GMTNIRS, a near- to thermal-infrared echelle spectrograph utilizing silicon immersion gratings; and GMTIFS, a near-infrared imager and integral field spectrograph) are in Preliminary Design. The first-generation suite also includes a robotic fiber-feed system called MANIFEST, which enables spectroscopy over the 20 arcmin field of view of the telescope with custom fibers for G-CLEF, GMACS, and GMTNIRS. An additional facility instrument, GMagAO-X, is being developed to provide high-contrast imaging at visible and near-infrared wavelengths and is in Preliminary Design. We also discuss the visible and infrared cameras (called ComCam and AOTC, respectively) that will be used for alignment, verification, and commissioning of the active and adaptive optics modes of the telescope and enable early science activities.
Challenges, Agency and Ecology in the Transition of Youth from State Residential Care to Independent Living in Johannesburg, South Africa

Abstract In South Africa, youth leaving care can be considered a vulnerable group given the limited preparation and support they tend to receive for independent living, exacerbated by the high numbers of young people who are not in employment, education or training. The study explored challenges experienced, coping mechanisms employed, and support provided to care leavers who had transitioned from state residential care to independent living in Johannesburg, South Africa. Qualitative interviews were conducted with 12 care leavers. Key findings included the challenge of housing problems; insufficient food and clothing; and struggles to continue with their education and find employment. Care leavers exercised agency and resilience by drawing on personal motivation; crying; spirituality; and networking. Factors in the social-ecological environment that contributed to care leavers’ challenges included not qualifying for help after reaching 18 years of age; and a lack of formal emotional and material support or preparation before, during and after the transition These findings represents an issue of social justice and have implications for policy, practice and further research.
Depathologising diversity: Trans children and families' experiences of pathologisation in the
 UK

In January 2022 the World Health Organization removed transgender identities from categorisation as a mental illness, marking a significant global shift from the pathologisation of gender diversity. However, a legacy of pathologisation of trans identities continues to impact trans lives, particularly on trans children. Informed by qualitative data from 30 families with trans children, this article examines the continued impacts of pathologisation on trans children and families in the UK, exploring how pathologisation manifests at individual, institutional and societal levels. The article advocates for trans depathologisation as a critical priority for child rights and social justice.
Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

Abstract In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (Ni) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.
An artistic analysis model based on sequence cartoon images for scratch

With the development of visual programming languages, researchers pay attention to the automatic evaluation of visual projects. Previous work focus on the code evaluation but ignored another essential part—the visualization results. Scratch is a widely used programming platform, and projects created on it are displayed in the form of cartoon clips. It is valuable to explore the visual aesthetics embodied in these clips to fill the gap in the assessment system. We propose a model that predicts the human view scores of cartoon clips created on Scratch. Our method is divided into two steps to evaluate the aesthetic of the sequence images that compose cartoon clips. First, we train an image classification network to predict the relative aesthetics of individual images. Then we construct an aesthetic space for the sequence image and improve the rating within a specific range. We put forward ScratchGAN to generate a Scratch‐cartoon‐style aesthetic analysis data set for training the classification network. Experimental results show that our Generative Adversarial Network framework can well transform photos into a Scratch‐cartoon style. The single image assessment network can generate predictions that fit human cartoon aesthetic opinions. Our method achieves satisfactory results in the aesthetic evaluation of sequence cartoon images.
Genetic Insight into Disease Resistance Gene Clusters by Using Sequencing-Based Fine Mapping in Sunflower (Helianthus annuus L.)

Rust and downy mildew (DM) are two important sunflower diseases that lead to significant yield losses globally. The use of resistant hybrids to control rust and DM in sunflower has a long history. The rust resistance genes, R13a and R16, were previously mapped to a 3.4 Mb region at the lower end of sunflower chromosome 13, while the DM resistance gene, Pl33, was previously mapped to a 4.2 Mb region located at the upper end of chromosome 4. High-resolution fine mapping was conducted using whole genome sequencing of HA-R6 (R13a) and TX16R (R16 and Pl33) and large segregated populations. R13a and R16 were fine mapped to a 0.48 cM region in chromosome 13 corresponding to a 790 kb physical interval on the XRQr1.0 genome assembly. Four disease defense-related genes with nucleotide-binding leucine-rich repeat (NLR) motifs were found in this region from XRQr1.0 gene annotation as candidate genes for R13a and R16. Pl33 was fine mapped to a 0.04 cM region in chromosome 4 corresponding to a 63 kb physical interval. One NLR gene, HanXRQChr04g0095641, was predicted as the candidate gene for Pl33. The diagnostic SNP markers developed for each gene in the current study will facilitate marker-assisted selections of resistance genes in sunflower breeding programs.