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This article reports the construction of an articulated manipulator’s hybrid dynamic model and trajectory planning and optimization of the manipulator using deep reinforcement learning (RL) on the dynamic model. The hybrid model was composed of a physical-based reduced-order dynamic model, linear friction and damping terms, and a deep neural network model to compensate for the nonlinear characteristics of the manipulator. The hybrid model then served as the digital twin of the manipulator for trajectory planning to optimize energy efficiency and operation speed by using RL while taking obstacle avoidance into consideration. The proposed strategy was simulated and experimentally validated. The energy consumption along paths was reduced and the speed was increased so the manipulator could achieve more efficient motion.

Overexpression of anti-apoptotic proteins MCL1 and Bcl-xL are frequently observed in many cancers. Inhibitors targeting MCL1 are in clinical development, however numerous cancer models are intrinsically resistant to this approach. To discover mechanisms underlying resistance to MCL1 inhibition, we performed multiple flow-cytometry based genome-wide CRISPR screens interrogating two drugs that directly (MCL1i) or indirectly (CDK9i) target MCL1. Remarkably, both screens identified three components (CUL5, RNF7 and UBE2F) of a cullin-RING ubiquitin ligase complex (CRL5) that resensitized cells to MCL1 inhibition. We find that levels of the BH3-only pro-apoptotic proteins Bim and Noxa are proteasomally regulated by the CRL5 complex. Accumulation of Noxa caused by depletion of CRL5 components was responsible for re-sensitization to CDK9 inhibitor, but not MCL1 inhibitor. Discovery of a novel role of CRL5 in apoptosis and resistance to multiple types of anticancer agents suggests the potential to improve combination treatments.

The recent development of CRISPR-Cas systems as easily accessible and programmable tools for genome editing and regulation is spurring a revolution in biology. Paired with the rapid expansion of reference and personalized genomic sequence information, technologies based on CRISPR-Cas are enabling nearly unlimited genetic manipulation, even in previously difficult contexts, including human cells. Although much attention has focused on the potential of CRISPR-Cas to cure Mendelian diseases, the technology also holds promise to transform the development of therapies to treat complex heritable and somatic disorders. In this Review, we discuss how CRISPR-Cas can affect the next generation of drugs by accelerating the identification and validation of high-value targets, uncovering high-confidence biomarkers and developing differentiated breakthrough therapies. We focus on the promises, pitfalls and hurdles of this revolutionary gene-editing technology, discuss key aspects of different CRISPR-Cas screening platforms and offer our perspectives on the best practices in genome engineering.

In this article, an innovative offline deep imitation learning algorithm for optimal trajectory planning is proposed. While many state‐of‐the‐art works achieved optimal trajectory planning, their systems were stable or quasistable, and their approaches rarely optimized the system's initial conditions (ICs). Here, a new unstable dynamic system task called "internal sliding object stabilization control" is proposed, modeled, and solved by deep imitation learning. Given the system's ICs, the neural networks (NNs) can imitate the iterative linear quadratic regulator (iLQR), generate optimal trajectories, and compute faster. A proportional–integral–derivative (PID) controller is used to track the unstable trajectories. Leveraging on the gradients of NNs, it can optimize the system's ICs, avoid obstacles stepwise, and ensure the worst bounds of NNs for safety. Subsequently, thorough simulations are conducted, including comparing the iLQR and PID controllers in the task, optimizing the system's different ICs by gradient descent, and finding the worst bound of the performance by gradient ascent. Results show that the proposed algorithm achieves considerably improved performance. Finally, experiments are conducted with a real manipulator to compare the proposed structure with the original iLQR. Results indicate that the proposed algorithm resembles the iLQR well. Program code and experiment results are in https://github.com/DanielYamChen/ISOSC.git. [ABSTRACT FROM AUTHOR]

Japanese adverbs are difficult to classify, with little progress made since the 1930s. Now in the age of large language models, linguists need a framework for lexical grouping that incorporates quantitative, evidence-based relationships rather than purely theoretical categorization. We herein address this need for the case of Japanese adverbs by developing a semantic positioning approach that incorporates large language model embeddings with fuzzy set theory to achieve empirical Japanese adverb groupings. To perform semantic positioning, we (i) obtained multi-dimensional embeddings for a list of Japanese adverbs using a BERT or RoBERTa model pre-trained on Japanese text, (ii) reduced the dimensionality of each embedding by principle component analysis (PCA), (iii) mapped the relative position of each adverb in a 3D plot using K-means clustering with an initial cluster count of n = 3 , (iv) performed silhouette analysis to determine the optimal cluster count, (v) performed PCA and K-means clustering on the adverb embeddings again to generate 2D semantic position plots, then finally (vi) generated a centroid distance matrix. Fuzzy set theory informs our workflow at the embedding step, where the meanings of words are treated as quantifiable vague data. Our results suggest that Japanese adverbs optimally cluster into n = 4 rather than n = 3 groups following silhouette analysis. We also observe a lack of consistency between adverb semantic positions and conventional classification. Ultimately, 3D/2D semantic position plots and centroid distance matrices were simple to generate and did not require special hardware. Our novel approach offers advantages over conventional adverb classification, including an intuitive visualization of semantic relationships in the form of semantic position plots, as well as a quantitative clustering "fingerprint" for Japanese adverbs that express vague language data as a centroid distance matrix. [ABSTRACT FROM AUTHOR]

Objectives RA is an autoimmune disease characterized by chronic inflammation and joint destruction. Biologics are crucial to achieving treat-to-target goals in patients with RA. The global spread and continuous variation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitate the monitoring of variant-specific humoral responses post-vaccination. The aim of this study was to investigate how different biologic treatments for vaccinated RA patients might affect their neutralizing antibodies against multiple SARS-CoV-2 variants. Methods We recruited RA patients who had received three doses of conventional SARS-CoV-2 vaccines and were treated with various biologics, e.g. TNF inhibitor (etanercept), IL-6 inhibitor (tocilizumab), CTLA4-Ig (abatacept) or anti-CD20 (rituximab). Serum samples were used to profile the binding and neutralizing antibodies using our own SARS-CoV-2 variant (CoVariant) protein array, developed previously. Results Compared with healthy controls, only RA therapy with rituximab showed a reduction in neutralizing antibodies capable of targeting spike proteins in SARS-CoV-2 wild-type and most variants. This reduction was not observed in binding antibodies against SARS-CoV-2 wild-type or its variants. Conclusion After receiving three doses of SARS-CoV-2 vaccination, RA patients who underwent rituximab treatment generated sufficient antibodies but exhibited lower neutralizing activities against wild-type and multiple variants, including current Omicron. Other biological DMARDs, e.g. TNF inhibitor, IL-6 inhibitor and CTLA4-Ig, did not show obvious inhibition. [ABSTRACT FROM AUTHOR]

Human noroviruses (HuNoV) are major causes of acute gastroenteritis around the world. The high mutation rate and recombination potential of noroviruses are significant challenges in studying the genetic diversity and evolution pattern of novel strains. In this review, we describe recent advances in the development of technologies for not only the detection but also the analysis of complete genome sequences of noroviruses and the future prospects of detection methods for tracing the evolution and genetic diversity of human noroviruses. The mechanisms of HuNoV infection and the development of antiviral drugs have been hampered by failure to develop the infectious virus in a cell model. However, recent studies have demonstrated the potential of reverse genetics for the recovery and generation of infectious viral particles, suggesting the utility of this genetics-based system as an alternative for studying the mechanisms of viral infection, such as cell entry and replication. [ABSTRACT FROM AUTHOR]

Intron removal requires assembly of the spliceosome on precursor mRNA (pre-mRNA) and extensive remodelling to form the spliceosomes catalytic centre. Here we report the cryo-electron microscopy structure of the yeast Saccharomyces cerevisiae pre-catalytic B complex spliceosome at near-atomic resolution. The mobile U2 small nuclear ribonucleoprotein particle (snRNP) associates with U4/U6.U5 tri-snRNP through the U2/U6 helix II and an interface between U4/U6 di-snRNP and the U2 snRNP SF3b-containing domain, which also transiently contacts the helicase Brr2. The 3 region of the U2 snRNP is flexibly attached to the SF3b-containing domain and protrudes over the concave surface of tri-snRNP, where the U1 snRNP may reside before its release from the pre-mRNA 5 splice site. The U6 ACAGAGA sequence forms a hairpin that weakly tethers the 5 splice site. The B complex proteins Prp38, Snu23 and Spp381 bind the Prp8 N-terminal domain and stabilize U6 ACAGAGA stempre-mRNA and Brr2U4 small nuclear RNA interactions. These results provide important insights into the events leading to active site formation., Author(s): Clemens Plaschka (corresponding author) [1]; Pei-Chun Lin (corresponding author) [1]; Kiyoshi Nagai (corresponding author) [1] Splicing of pre-mRNA is an important step in eukaryotic gene expression, in which non-coding [...]

In this study, we formulated a mathematical model for electorate demarcation in line with Taiwan’s electoral reforms, minimizing legislative seats for the main opposition party in Taipei City. The model is compared with historical election data to establish characteristics that are most contested and germane to the reform. Examination of the constituency demarcation for the Seventh Legislator Election ascertains that electorate demarcation can/can not be manipulated to create electoral advantage. Demonstrable evidence of a bias toward disproportionate representation in the single-member district plurality, two-ballot voting system ultimately attests the importance of scenario analysis before adopting new electorate systems.

This study investigates differences in the partial scoring performance of examinees in elimination testing and conventional dichotomous scoring of multiple-choice tests implemented on a computer-based system. Elimination testing that uses the same set of multiple-choice items rewards examinees with partial knowledge over those who are simply guessing. This study provides a computer-based test and item analysis system to reduce the difficulty of grading and item analysis following elimination tests. The Rasch model, based on item response theory for dichotomous scoring, and the partial credit model, based on graded item response for elimination testing, are the kernel of the test-diagnosis subsystem to estimate examinee ability and item-difficulty parameters. This study draws the following conclusions: (1) examinees taking computer-based tests (CBTs) have the same performance as those taking paper-and-pencil tests (PPTs); (2) conventional scoring does not measure the same knowledge as partial scoring; (3) the partial scoring of multiple choice lowers the number of unexpected responses from examinees; and (4) the different question topics and types do not influence the performance of examinees in either PPTs or CBTs. (Contains 2 figures and 9 tables.)

This paper presents a decision model that uses empirical Bayesian estimation to construct a server-dependent M/M/2/L queuing system. A Markovian queue with a number of servers depending upon queue length with finite capacity is discussed. This study uses the number of customers for initiating and turning off the second server as decision variables to formulate the expected cost minimization model. In order to conform to the reality, we first collect data of interarrival time and service time by observing a queuing system, then apply the empirical Bayesian method to estimate its traffic intensity. In this research, traffic intensity is used to represent the demand for service facilities. The system initiates another server whenever the number of customers in the system reaches a certain length N and removes the second server as soon as the number of customers in system reduces to Q. Associating the costs with the opening of the second server and the waiting cost of customers, a relationship is developed to obtain the optimal value of N and Q to minimize cost. The mean number of customers in the system and the queue length of customers are derived as the characteristic values of the system. Model development and the implications of the data are discussed in detail.

Nowadays, the grinding process is mostly automatic, yet post-grinding quality inspection is mostly carried out manually. Although the conventional inspection technique may have cumbersome setup and tuning processes, the data-driven model, with its vision-based dataset, provides an opportunity to automate the inspection process. In this study, a convolutional neural network technique with transfer learning is proposed for three kinds of inspections based on 750–1000 surface raw images of the ground workpieces in each task: classifying the grit number of the abrasive belt that grinds the workpiece, estimating the surface roughness of the ground workpiece, and classifying the degree of wear of the abrasive belts. The results show that a deep convolutional neural network can recognize the texture on the abrasive surface images and that the classification model can achieve an accuracy of 0.9 or higher. In addition, the external coaxial white light was the most suitable light source among the three tested light sources: the external coaxial white light, the high-angle ring light, and the external coaxial red light. Finally, the model that classifies the degree of wear of the abrasive belts can also be utilized as the abrasive belt life estimator. [ABSTRACT FROM AUTHOR]

This study explored the construction development process of an individual case to identify and summarize the problems small-to medium-sized developers could be having with such process in Taiwan. To identify the problems, the writers used a case-based approach and interviewed the chairman of one small-to medium-sized enterprise (SME). Findings derived from the interview were validated by further interviewing four members of the construction union in Taiwan, three government officials, and four managers of mortgage banks. The theory of constraints approach was used to examine the construction development process of the SME and indicate the undesirable effects that hindered it and needed immediate attention. The study results were systemized into a conceptual model for developers' future reference to improve management of the development process and identify the root causes of problems. DOI: 10.1061/(ASCE)CO. 1943-7862.0000057 CE Database subject headings: Construction management; Taiwan; Problem solving.

Background: Norovirus (NoV) infection is common in pediatric patients with immunodeficiency and is more likely to cause severe disease. Objective Our study aims to figure out the clinical differences and distribution of intestinal microbiota in immunocompromised children with NoV gastroenteritis. Methods: Pediatric patients admitted to Shang-Ho Hospital with diagnosis of acute gastroenteritis including different immune status were enrolled and their medical records were reviewed. NoV gastroenteritis was validated using RT-PCR molecular methods. Viral shedding period was determined by real-time RT-PCR assays. Intestinal microbiota enrichment analysis was carried out by next generation sequencing after fecal DNA extraction and subsequent Linear Discriminant Analysis (LDA) Effect Size (LEfSe) method. Results: Significantly higher frequency of diarrhea [mean, (IQR), 3.8 (3–5) /day] and longer viral shedding time [mean, IQR, 8.5 (5–13) days] was found in immunocompromised NoV infections than in immunocompetent patients without NoV infections (p = 0.013*) and immunocompetent patients with NoV infections (p = 0.030**). The fever prevalence was significantly lower in immunocompromised NoV infections than in different immune or infection status. Intestinal microbiota metagenomics analysis showed no significant community richness difference while the LEfSe analysis showed a significant difference in commensal richness at the phylum level, the family level, and the genus level in patients under different immune status. Conclusion: We evaluated the clinical significances and microbiota composition in immunocompromised children with norovirus gastroenteritis. This will further facilitate studies of the interaction between the intestinal microbiota in such patients with precise determination of their bacterial infection control and probiotic supplements strategy. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCRs) play pivotal roles in cell proliferation, differentiation, and survival. Although many studies indicate that the stimulation of GPCRs leads to NF-[kappa]B activation, the molecular mechanism by which GPCRs induced NF-[kappa]B activation remains largely unknown. Bcl10 is an essential adaptor molecule connecting antigen receptor signaling cascades to NF-[kappa]B activation in lymphocytes. However, the function of Bcl10 in nonlymphoid cells remains to be determined. In this study, we demonstrated that the deficiency of Bcl10 resulted in the defect in NF-[kappa]B activation induced by either expressing the constitutively active mutant of G protein or stimulation of cells with lysophosphatidic acid or endothelin-1, which activate their GPCR. In contrast, TNF-[alpha]-, LPS-, and integrin-induced NF-[kappa]B activation was not affected in Bcl10-deficient cells. Together, our results provide genetic evidence showing that Bcl10 is a key signaling component mediating NF-[kappa]B activation induced by GPCRs in nonlymphoid cells. lysophosphatidic acid | signal transduction | endothelin-1

We report on a hybrid 12-dimensional full body state estimator for a hexapod robot executing a jogging gait in steady state on level terrain with regularly alternating ground contact and aerial phases of motion. We use a repeating sequence of continuous time dynamical models that are switched in and out of an extended Kalman filter to fuse measurements from a novel leg pose sensor and inertial sensors. Our inertial measurement unit supplements the traditionally paired three-axis rate gyro and three-axis accelerometer with a set of three additional three-axis accelerometer suites, thereby providing additional angular acceleration measurement, avoiding the need for localization of the accelerometer at the center of mass on the robot's body, and simplifying installation and calibration. We implement this estimation procedure offline, using data extracted from numerous repeated runs of the hexapod robot RHex (bearing the appropriate sensor suite) and evaluate its performance with reference to a visual ground-truth measurement system, comparing as well the relative performance of different fusion approaches implemented via different model sequences. Index Terms--Extended Kalman filter (EKF), hybrid estimation model, inertial measurement unit (IMU), legged robot, leg pose sensor (LPS), sensor fusion.

We report on a continuous-time rigid-body pose estimator for a walking hexapod robot. Assuming at least three legs remain in ground contact at all times, our algorithm uses the outputs of six leg-configuration sensor models together with a priori knowledge of the ground and robot kinematics to compute instantaneous estimates of the 6-degrees-of-freedom (6-DOF) body pose. We implement this estimation procedure on the robot RHex by means of a novel sensory system incorporating a model relating compliant leg member strain to leg configuration delivered to the onboard CPU over a customized cheap high-performance local wireless network. We evaluate the performance of this algorithm at widely varying body speeds and over dramatically different ground conditions by means of a 6-DOF vision-based ground-truth measurement system (GTMS). We also compare the odometry performance to that of sensorless schemes--both legged as well as on a wheeled version of the robot--using GTMS measurements of elapsed distance. Index Terms--Body pose estimation, hexapod robot, leg configuration sensor, legged locomotion sensing, legged odometry, robot proprioception, strain-based sensor.

We report on the methodology of developing a hybrid model and utilizing it as a template to initiate running behavior in a legged robot. The hybrid model is comprised of a physics-based, rolling, spring-loaded, inverted pendulum (R-SLIP) model, and a data-driven model that compensates for unmodeled dynamics using Gaussian process (GP) regression. The hybrid R-SLIP-GP model retains the R-SLIP's intrinsic running dynamics, as well as improves the template's accuracy without intensive data training and iteration efforts. The proposed hybrid model was evaluated via simulation and in empirical robot running experiments. The results confirm that the added GP-based model greatly improved the model's accuracy, especially discrepancies resulting from the originally difficult-to-model complex leg-ground interactions. In addition, the R-SLIP-GP model was utilized as a complete motion template to design and control the robot's running motion. The experimental results demonstrate that the hybrid template can initiate more stable and power-efficient running motion in the robot than using the R-SLIP template. [ABSTRACT FROM AUTHOR]

Featured Application: Inventory Management. The ABC classification problem is approached as a ranking problem by the most current classification models; that is, a group of inventory items is expressed according to its overall weighted score of criteria in descending order. In this paper, we present an extended version of the Hadi-Vencheh model for multiple-criteria ABC inventory classification. The proposed model is one based on the nonlinear weighted product method (WPM), which determines a common set of weights for all items. Our proposed nonlinear WPM incorporates multiple criteria with different measured units without converting the performance of each inventory item, in terms of converting each criterion into a normalized attribute value, thereby providing an improvement over the model proposed by Hadi-Vencheh. Our study mainly includes various criteria for ABC classification and demonstrates an efficient algorithm for solving nonlinear programming problems, in which the feasible solution set does not have to be convex. The algorithm presented in this study substantially improves the solution efficiency of the canonical coordinates method (CCM) algorithm when applied to large-scale, nonlinear programming problems. The modified algorithm was tested to compare our proposed model results to the results derived using the Hadi-Vencheh model and demonstrate the algorithm's efficacy. The practical objectives of the study were to develop an efficient nonlinear optimization solver by optimizing the quality of existing solutions, thus improving time and space efficiency. [ABSTRACT FROM AUTHOR]