Miracle-dz/UniFAR-FTUs huggingface dataset:
Low CDK Activity and Enhanced Degradation by APC/CCDH1 Abolishes CtIP Activity and Alt-EJ in Quiescent Cells

Alt-EJ is an error-prone DNA double-strand break (DSBs) repair pathway coming to the fore when first-line repair pathways, c-NHEJ and HR, are defective or fail. It is thought to benefit from DNA end-resection—a process whereby 3′ single-stranded DNA-tails are generated—initiated by the CtIP/MRE11-RAD50-NBS1 (MRN) complex and extended by EXO1 or the BLM/DNA2 complex. The connection between alt-EJ and resection remains incompletely characterized. Alt-EJ depends on the cell cycle phase, is at maximum in G2-phase, substantially reduced in G1-phase and almost undetectable in quiescent, G0-phase cells. The mechanism underpinning this regulation remains uncharacterized. Here, we compare alt-EJ in G1- and G0-phase cells exposed to ionizing radiation (IR) and identify CtIP-dependent resection as the key regulator. Low levels of CtIP in G1-phase cells allow modest resection and alt-EJ, as compared to G2-phase cells. Strikingly, CtIP is undetectable in G0-phase cells owing to APC/C-mediated degradation. The suppression of CtIP degradation with bortezomib or CDH1-depletion rescues CtIP and alt-EJ in G0-phase cells. CtIP activation in G0-phase cells also requires CDK-dependent phosphorylation by any available CDK but is restricted to CDK4/6 at the early stages of the normal cell cycle. We suggest that suppression of mutagenic alt-EJ in G0-phase is a mechanism by which cells of higher eukaryotes maintain genomic stability in a large fraction of non-cycling cells in their organisms.
The Combination of a Human Biomimetic Liver Microphysiology System with BIOLOGXsym, a Quantitative Systems Toxicology (QST) Modeling Platform for Macromolecules, Provides Mechanistic Understanding of Tocilizumab- and GGF2-Induced Liver Injury

Biologics address a range of unmet clinical needs, but the occurrence of biologics-induced liver injury remains a major challenge. Development of cimaglermin alfa (GGF2) was terminated due to transient elevations in serum aminotransferases and total bilirubin. Tocilizumab has been reported to induce transient aminotransferase elevations, requiring frequent monitoring. To evaluate the clinical risk of biologics-induced liver injury, a novel quantitative systems toxicology modeling platform, BIOLOGXsym™, representing relevant liver biochemistry and the mechanistic effects of biologics on liver pathophysiology, was developed in conjunction with clinically relevant data from a human biomimetic liver microphysiology system. Phenotypic and mechanistic toxicity data and metabolomics analysis from the Liver Acinus Microphysiology System showed that tocilizumab and GGF2 increased high mobility group box 1, indicating hepatic injury and stress. Tocilizumab exposure was associated with increased oxidative stress and extracellular/tissue remodeling, and GGF2 decreased bile acid secretion. BIOLOGXsym simulations, leveraging the in vivo exposure predicted by physiologically-based pharmacokinetic modeling and mechanistic toxicity data from the Liver Acinus Microphysiology System, reproduced the clinically observed liver signals of tocilizumab and GGF2, demonstrating that mechanistic toxicity data from microphysiology systems can be successfully integrated into a quantitative systems toxicology model to identify liabilities of biologics-induced liver injury and provide mechanistic insights into observed liver safety signals.
Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods.
Algebraic Number Theory And Code Design For Rayleigh Fading Channels

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Noniterative Generalized Camera Model for Near-Central Camera System

This paper proposes a near-central camera model and its solution approach. ’Near-central’ refers to cases in which the rays do not converge to a point and do not have severely arbitrary directions (non-central cases). Conventional calibration methods are difficult to apply in such cases. Although the generalized camera model can be applied, dense observation points are required for accurate calibration. Moreover, this approach is computationally expensive in the iterative projection framework. We developed a noniterative ray correction method based on sparse observation points to address this problem. First, we established a smoothed three-dimensional (3D) residual framework using a backbone to avoid using the iterative framework. Second, we interpolated the residual by applying local inverse distance weighting on the nearest neighbor of a given point. Specifically, we prevented excessive computation and the deterioration in accuracy that may occur in inverse projection through the 3D smoothed residual vectors. Moreover, the 3D vectors can represent the ray directions more accurately than the 2D entities. Synthetic experiments show that the proposed method can achieve prompt and accurate calibration. The depth error is reduced by approximately 63% in the bumpy shield dataset, and the proposed approach is noted to be two digits faster than the iterative methods.
Probiotic Lactic Acid Bacteria for Vaginal Application. Optimization of Biomass Production and Freeze-Drying Conditions

Probiotic formula for the vaginal tract must contain high numbers of viable beneficial bacteria that maintain their characteristics during the production and further storage, in order to exert the claimed probiotic effect. Four probiotic strains—Lactobacillus gasseri CRL1320, Limosilactobacillus reuteri CRL1324, Ligilactobacillus salivarius CRL1328 and Lacticaseibacillus rhamnosus CRL1332—originally isolated from vagina of healthy women exhibiting beneficial properties were evaluated. Therefore, the optimization of growth parameters and dry biomass production with high viability while preserving probiotic features of strains is a great challenge. Different growth conditions in MRS medium were set up in a laboratory bioreactor; two initial pH and agitation were recorded speeds during static or controlled fermentations. Production conditions of 37 °C with controlled pH 6.5 and 5.5-MRS with 150 and 75 rpm stirring speeds were used for CRL1329 and CRL1332 strains, respectively, while static and free pH MRS for CRL1324 and CRL1328 probiotics allowed us to obtain maximal cell viability counts. However, during 12 h of fermentation, biomass yields of 19.3, 16.2 and 15.2 g/L were achieved when CRL1329, CRL1328 and CRL1324 probiotic strains were grown in static and free pH MRS. The highest biomass yield for CRL1332 strain was produced under controlled MRS-initial pH 6.5 and 75 rpm fermentation conditions. To preserve probiotic high biomass viability, freeze-drying was carried out in the presence of different cryoprotective agents. Thus, the highest viable numbers (10.9–11.8 log CFU/g) with survival rates between 91.3 and 95.6% were attained in the presence of 10% trehalose (L. reuteri and L. salivarius), lactose (L. rhammosus) and lactose + trehalose + sucrose mix (L. gasseri). When stability during post-freeze-drying storage was evaluated, probiotic strains showed a remarkably higher viability recovery when stored at 4 °C than at 25 °C for 12 and 3 months, respectively. In addition, surface characteristics of vaginal probiotics were affected to different extents during storage depending on the strain, protective agent and storage time/temperature. Critical factors for growth conditions, drying process and storage stability of probiotic lactobacilli strains were optimized in view to preserve cell high viability and surface features for the design of vaginal probiotic formula.
Development and validation of postmenopausal attitude scale

Background: Menopause is not only a biological event but also psychosocial. Attitudes, perceptions, and expectations are some of the psychosocial phenomena surrounding menopause. Many women perceive it as a welcome relief from menses and the risk of pregnancy while those who desire children consider it an unwelcome event. Both women in midlife and health professionals agree that attitudes toward menopause play a potential role in the experience of menopause, including its perceived severity. However, there is currently no comprehensive tool to assess menopausal women's attitudes. Methodology: The aim of this study is to develop post-menopausal women attitude scale. To develop the tool authors followed the various steps like review of literature, obtaining content validity and establishing content validity ratio, factor analysis and estimating the reliability of the tool. Results: The content validity ratio of the tool is 1 and reliability of the estimated by Cronbach alpha and it was found to be 0.765. The version of the scale is a five point Likert scale with 25 items. There were 10 items under the heading Attitude towards Physical Factor, 5 items under the heading Attitude towards psychological Factor, 5 items under the heading Attitude towards Sexual Factors, and 5 items under the heading Attitude towards Management of Menopause. Conclusion: Proper validation of tool is essential, and it is crucial process in the new construction of the tool. The prepared tool will comprehensively measure all the domains of attitude related to menopause.
GIRRT Motion Planning Algorithm for Humanoid Robot

Aiming at such problems as unknown goal configuration, grasp space limitation, a large number of barriers in configuration space and high-dimension space redundancy during planning motion of humanoid robot arms grasping target, this paper proposes a generalized inverse rapid-exploring random tree (GIRRT) collision avoidance method. Firstly, it establishes kinematics and inverse kinematics models for upper arm of humanoid robot based on hierarchical sub-dimension space motion planning concept model. Secondly, it proposes a weighted minimal norm GIRRT planning algorithm. Finally, the effectiveness of the algorithm proposed in this paper has been verified via computer 3D simulation.
A Lightweight Model for 3D Point Cloud Object Detection

With the rapid development of deep learning, more and more complex models are applied to 3D point cloud object detection to improve accuracy. In general, the more complex the model, the better the performance and the greater the computational resource consumption it has. However, complex models are incompatible for deployment on edge devices with restricted memory, so accurate and efficient 3D point cloud object detection processing is necessary. Recently, a lightweight model design has been proposed as one type of effective model compression that aims to design more efficient network computing methods. In this paper, a lightweight 3D point cloud object detection network architecture is proposed. The core innovation of the proposal consists of a lightweight 3D sparse convolution layer module (LW-Sconv module) and knowledge distillation loss. Firstly, in the LW-Sconv module, factorized convolution and group convolution are applied to the standard 3D sparse convolution layer. As the basic component of the lightweight 3D point cloud object detection network proposed in this paper, the LW-Sconv module greatly reduces the complexity of the network. Then, the knowledge distillation loss is used to guide the training of the lightweight network proposed in this paper to further improve the detection accuracy. Finally, extensive experiments are performed to verify the algorithm proposed in this paper. Compared with the baseline model, the proposed model can reduce the FLOPs and parameters by 3.7 times and 7.9 times, respectively. The lightweight model trained with knowledge distillation loss achieves comparable accuracy to the baseline. Experiments show that the proposed method greatly reduces the model complexity while ensuring detection accuracy.
Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images

Change detection in very-high-spatial-resolution (VHR) remote sensing images is a very challenging area with applicability in many problems ranging from damage assessment to land management and environmental monitoring. In this study, we investigated the change detection problem associated with analysing the vegetation corresponding to crops and natural ecosystems over VHR multispectral and hyperspectral images obtained by sensors onboard drones or satellites. The challenge of applying change detection methods to these images is the similar spectral signatures of the vegetation elements in the image. To solve this issue, a consensus multi-scale binary change detection technique based on the extraction of object-based features was developed. With the objective of capturing changes at different granularity levels taking advantage of the high spatial resolution of the VHR images and, as the segmentation operation is not well defined, we propose to use several detectors based on different segmentation algorithms, each applied at different scales. As the changes in vegetation also present high variability depending on capture conditions such as illumination, the use of the CVA-SAM applied at the segment level instead of at the pixel level is also proposed. The results revealed the effectiveness of the proposed approach for identifying changes over land cover vegetation images with different types of changes and different spatial and spectral resolutions.
Viability and In Vitro Gastrointestinal Transit Tolerance of Multispecies Probiotic Combinations Incorporated into Orange Juice and Drinking Water

Little is known about how combining probiotics affects the storage survival and functional performance of individual probiotics when incorporated into non-dairy drinks. Viability of Lacticaseibacillus rhamnosus GG (LG), Limosilactobacillus reuteri ATCC 55730 (LR), Bifidobacterium animalis subsp. lactis BB-12 (Bb), and Propionibacterium jensenii 702 (PJ), either alone or in multi-species combinations included in orange juice (OJ), were assessed during storage in refrigerated conditions and compared with bottled water (BW). The tolerance of probiotics included in refrigerated OJ to simulated gastrointestinal conditions was also examined. LG and LR viabilities were significantly higher in OJ than in BW (p ≤ 0.001), while the reverse was evident for PJ. Bb maintained high viability in both drinks. LG-PJ in both drinks and Bb-PJ in BW resulted in greater viabilities among the paired combinations compared to their respective monocultures when incorporated separately (p ≤ 0.001). The viability of LG in the LG-Bb-PJ combination improved significantly in BW compared with LG alone (p ≤ 0.001). OJ did not alter bacterial tolerance to simulated gastric juice but diminished tolerance to simulated intestinal juice (SIJ). In all combinations, tolerance of LG and LR to SIJ was improved, whereas tolerance of PJ declined significantly compared with respective monocultures (p ≤ 0.001). In conclusion, probiotic storage stability and gastrointestinal transit tolerance were species-dependent and affected by carrier type and combinations. These effects should be considered when formulating probiotic products.
Ultra‐low loss waveguide platform and its integration with silicon photonics

Planar waveguides with ultra‐low optical propagation loss enable a plethora of passive photonic integrated circuits, such as splitters and combiners, filters, delay lines, and components for advanced modulation formats. An overview is presented of the status of the field of ultra‐low loss waveguides and circuits, including the design, the trade‐off between bend radius and loss, and fabrication rationale. The characterization methods to accurately measure such waveguides are discussed. Some typical examples of device and circuit applications are presented. An even wider range of applications becomes possible with the integration of active devices, such as lasers, amplifiers, modulators and photodetectors, on such an ultra‐low loss waveguide platform. A summary of efforts to integrate silicon nitride and silica‐based low‐loss waveguides with silicon and III/V based photonics, either hybridly or heterogeneously, will be presented. The approach to combine these integration technologies heterogeneously on a single silicon substrate is discussed and an application example of a high‐bandwidth receiver is shown.
Efficient and scalable DBSCAN framework for clustering continuous trajectories in road networks

Abstract Clustering the trajectories of vehicles moving on road networks is a key data mining technique for understanding human mobility patterns, as well as their interactions with urban environments. The development of efficient and scalable trajectory clustering algorithms, however, still faces challenges because of the computational costs when measuring similarities among a large number of network-constrained trajectories. To address this problem, a novel trajectory clustering framework based on the well-developed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach is proposed. This proposed framework accurately quantifies similarities using a trajectory representation of continuous polylines in the space and time dimensions, and does not require trajectory discretization. Further, the proposed framework utilizes the space-time buffering concept to formulate -neighborhood queries that directly retrieve the -neighbors of trajectories and thus avoids computing a trajectory similarity matrix. State-of-the-art trajectory databases and index structures are incorporated to further improve trajectory clustering performance. A comprehensive case study was carried out using an open dataset of 20,161 trajectories. Results show that the proposed framework efficiently executed trajectory clustering on the large test dataset within 3 min. This was approximately 2,700 times faster than existing DBSCAN algorithms.
Robotic Inserting a Moving Object Using Visual-Based Control With Time-Delay Compensator

Tracking-and-inserting a moving peg using a robot manipulator is a challenging task in manufacturing. In the past decades, various visual-based methods have been proposed for robotic manipulating static targets, which usually ignore the time delay in robot command transmission and image processing. However, for tracking and inserting a moving peg, time delays cannot be overlooked because they can reduce the tracking performance and even cause manipulations to fail. In this article, a robot visual-based control with a time-delay compensator is presented to solve the problem of inserting a moving peg. The time-delay compensator was designed using radial basis function neural network, a feedback compensator aimed at eliminating the tracking errors caused by the time delays. Thus, we could manipulate a moving object using a commercial industry robot, even with the time-variant delays in the control loop. Furthermore, the visual-based controller with the pseudoinverse image Jacobian matrix was designed using a linearization model. Thus, the matrix could be efficiently updated using the model. In the experiment, we inserted a peg into a moving hole using an eye-in-hand robot with precision.
Investigation of Electromagnetic Shielding for Wire Mesh Composite for Aircraft against Lightning Strike

Nowadays, fly-wire is only used for flying-related things. All plane controls depend on electronics, but they also must deal with high-intensity radiated fields. This equipment might need an electromagnetic shield to protect it from outside electromagnetic pollution. The current work aims to develop a mesh around the operating equipment to protect and make it work better. AL6061 was used to create a shield with a metal matrix composite. Here three combinations of Metal Matrix Composite (MMCs) were considered to protect from the high-intensity radiated fields. These MMCs are AL6061+10%Al_2 O_3+5% FA (MMC-1), AL6061+15% Al_2 O_3+5% FA (MMC-2), and AL6061+20% Al_2 O_3+5% FA (MMC-3) made the process of shielding more effective at different percentages. Explements are Measures that take the electrical characteristics, and they are by Shielding Effectiveness (SE) of different concentrations of fly ash and Al_2 O_3. SE of 50 dB is achieved; hence these MMCs can be used in aircraft and other applications as a shield.