Miracle-dz/UniFAR-FTUs huggingface dataset:
Moving Object Grasping Method of Mechanical Arm Based on Deep Deterministic Policy Gradient and Hindsight Experience Replay

The mechanical arm is an important component in many types of robots; however, in certain production lines, the conventional grasp strategy cannot satisfy the demands of modern production because of several interference factors such as vibration, noise, and light pollution. This paper proposes a new grasping method for manipulators in stamping automatic production lines. Considering the factors that affect grasping in the production environment, the deep deterministic policy gradient (DDPG) method is selected in this study as the basic reinforcement-learning algorithm, and this algorithm is used to grasp moving objects in stamping automatic production lines. Owing to the low success rate of the conventional DDPG algorithm, the hindsight experience replay (HER) is used to improve the sample utilization efficiency of the agent and learn more effective tracking strategies. Simulation results show an 82% mean success rate of the optimized DDPG-HER algorithm, which is 31% better than that of the conventional DDPG algorithm. This method provides ideas for the research and design of the sorting system used in stamping automation production lines.
Insights into Two-Metal-Ion Catalytic Mechanism of Cap-Snatching Endonuclease of Ebinur Lake Virus in Bunyavirales

The viral endonucleases (ENs) encoded by bunyaviruses and orthomyxoviruses play an essential role in initiating transcription by “snatching” capped primers from the host mRNAs. These ENs are metal-ion-dependent nucleases; however, the details of their catalytic mechanism remain elusive. ABSTRACT The cap-snatching endonuclease (EN) of segmented negative-strand RNA viruses (sNSVs) produces short capped primers for viral transcription by cleaving the host mRNAs. EN requires divalent metals as cofactors for nucleic acid substrates cleavage; however, the detailed mechanism of metal ion-dependent catalysis of ENs remains obscure. In this work, we reported the EN crystal structure of the Ebinur Lake virus (EBIV), an emerging mosquito-borne orthobunyavirus, and investigated its enzymatic properties and metal ion-based catalytic mechanism. In vitro biochemical data showed that EBIV EN is a specific RNA nuclease and prefers to cleave unstructured uridine-rich ssRNA. Structural comparison indicated that the overall structural architecture of EBIV EN is similar to that of other sNSV ENs, while the detailed active site configuration including the binding state of metal ions and the conformation of the LA/LB loop pair is different. Based on sequence conservation analysis, nine active site mutants were constructed, and seven crystal structures of them were determined. Mutations of active site residues associated with the two metal ions (Mn1 and Mn2) coordination abolished EN activity. Crystallographic analyses further revealed that none of these mutants bound two metal ions simultaneously in the active site. Importantly, we found that the perturbation of Mn1-coordination (metal site 1), resulted in the enhancement or elimination of Mn2-coordination (metal site 2). Taken together, our data provide structural evidence to support the two-metal-ion catalytic mechanism of EBIV EN and the correlation of metal binding at the two binding sites, which may be commonly shared by bunyaviruses or other sNSVs. IMPORTANCE The viral endonucleases (ENs) encoded by bunyaviruses and orthomyxoviruses play an essential role in initiating transcription by “snatching” capped primers from the host mRNAs. These ENs are metal-ion-dependent nucleases; however, the details of their catalytic mechanism remain elusive. Here, we reported high-resolution crystal structures of the wild-type and mutant ENs of a novel bunyavirus, the Ebinur Lake virus (EBIV), and revealed the structure and function relationship of EN. The EBIV EN exhibited differences in the details of active site structure compared to its homologues. Our data provided structural evidence to support a two-metal-ion catalytic mechanism of EBIV EN, and found the correlation of metal binding at both binding sites, which might reflect the dynamic structural properties that correlate to EN catalytic function. Taken together, our results revealed the structural characteristics of EBIV EN and made important implications for understanding the catalytic mechanism of cap-snatching ENs.
Understanding changes in behavior during the COVID-19 pandemic: Opportunities to design around new eating experiences

The pandemic has disrupted many of our daily habits and behaviors. Eating behaviors have been modified by lockdowns due to limited food availability. We report on a qualitative study conducted to better understand these changes in eating behaviors as they represent an opportunity to design technologies that both support healthy eating habits and take advantage of the new conditions produced by the COVID-19 pandemic. We interviewed 14 young professionals in Mexico to understand some of the challenges they faced during the pandemic and how they affected their eating behaviors, including changes in socialization while eating, motivation to improve health, increased cooking, and binge-eating. From this analysis, we envision ways in which novel information technologies can be designed for providing new eating experiences, such as improving virtual socialization while eating, and taking advantage of social media to support cooking and interactions at home.
Application of microwave remote sensing data for Indian summer monsoon studies

Microwave remote sensing is increasingly being used for studying the atmospheric and oceanic processes for the last few decades. Advantage of the microwave sensors is that they can see through the clouds and to some extent rain. Monsoon studies received considerable thrust from microwave remote sensing because clouds cover most parts of Asia during the summer monsoon season and the visible and infrared sensors poorly perform over this region. This paper summarizes the significant results obtained towards the understanding and forecasting of Indian summer monsoon especially monsoon onset, intraseasonal oscillation of monsoon, diurnal variability of rainfall and boundary layer fluxes with the help of microwave remote sensing.
Hybrid Multiple Ant Colonies Algorithm for Capacitated Vehicle Routing Problem

A new multiple ant colonies algorithm based on sweep algorithm (SbMACA) was developed for solving the Capacitated Vehicle Routing Problem (CVRP). The SbMACA is different from other ACAs developed before mainly in two aspects. Firstly, once the ants have constructed their solutions, each ant’s solution might be improved by applying Sweep Algorithm which makes improvement to the solutions by exchanging the vertices between subtours. Secondly, a new Multiple Ant Colonies technique is proposed, in which multiple ant colonies are executed separately and simultaneously, and after all colonies are in the state of stagnation, communication among them is carried out in order to do favor to leave the local peaks. Experiment shows that the SbMACA is able to find solutions for CVRP within 0.28% of known optimal solutions and is competitive when compared with the best Tabu Search meta-heuristics.
Surface Modifications of Dental Implant and its Clinical Performance: A Review

Objectives: This review paper aims to provide an overview of various commercially available dental implant surface modifications and treatments, as well as their reported clinical performances. This knowledge would be useful for the practicing clinician in understanding the healing mechanism associated with each type of implant and in selecting the right type of implant for a specific clinical condition. Methods: An electronic search of literatures was performed in PubMed and Scopus database dated from January 1990 until July 2021. The search keywords were dental implant, surface modification, surface treatment, survival rate and/ or clinical performance as MeSH term. Only relevant studies that were published in English, journal article are summarized and discussed in this review. Results: In the last decade, implant surfaces were manufactured in a concerted effort to provide bone in a faster and improved osseointegration process. A variety of surface modifications have been developed and are currently being used to enhance clinical performance, including turned (machined), hydroxyapatite-coated surface, titanium plasma-sprayed, grit-blasted, acid-etched, anodization, laser-microtextured as well as combinations thereof. Conclusion: Dental implant survival rate relies heavily on the successful integration into the jawbone. Geometry and surface topography are critical to the short- and long-term performance of dental implants. Implant surface modifications expedited osseointegration process, which in turn, early and immediate loading of dental implants has emerged as a viable alternative to the conventional loading protocol.
A Comprehensive High-Quality DNA and RNA Extraction Protocol for a Range of Cultivars and Tissue Types of the Woody Crop Avocado

High-quality DNA and RNA forms the basis of genomic and genetic investigations. The extraction of DNA and RNA from woody trees, like avocado (Persea americana Mill.), is challenging due to compounds which interact with nucleic acids and influence separation. Previously reported methods of DNA and RNA extraction from avocado have issues of low yield, quality and applicability across different cultivars and tissue types. In the current study, methods have been optimised for high-quality DNA extraction from 40 avocado cultivars and RNA extraction from multiple tissue types, including roots, stem, leaves, flowers and fruits. The method is based on the modification of the cetyltrimethylammonium bromide buffer, centred around the specific optimisation of chemicals, such as sodium dodecyl sulphate, polyvinylpyrrolidone, sodium sulphite, polyethylene glycol and β-mercaptoethanol. The DNA extraction method yielded high-molecular weight DNA from the leaf tissue of 40 avocado cultivars belonging to Mexican, Guatemalan and West Indian avocado horticultural groups. The method was further optimised for RNA extraction from different avocado plant parts, enabling extraction using amounts as low as ~10 mg of starting material. The DNA and RNA extracted was successfully used for long- and short-read sequencing and gene expression analysis. The methods developed may also be applicable to other recalcitrant plant species.
Physicochemical Properties of Starch Binary Mixtures with Cordia and Ziziphus Gums

The effect of gum Cordia (GC) and gum Ziziphus (GZ) on the physicochemical properties of wheat, potato, and chickpea starches was investigated. Native or acetylated gums were mixed with starch at 2% or 5%. Starches were analyzed using rapid viscoanalyzer (RVA), differential scanning calorimeter (DSC), texture analyzer, and rheometer. In the presence of gums, the data showed clear variations between the starch gels. The effects of gum acetylation on the starch characteristics were significant. According to the starch type, the peak viscosity of the gels increased depending on the gum type or concentration. With the exception of the potato starch, when gums were added, the gelatinization temperature of the starches increased. Gum acetylation significantly increased starch–gel elasticity (high G′), particularly at the 2% concentration. GC-starch gel hardness was ranked as follows: chickpea–5% native gum > wheat–5% native gum > potato–0% gum, whereas GZ followed the order of: chickpea–2% native gum > wheat–2% native gum > potato–2% native gum. Both the gums promoted reduction in syneresis for the wheat and chickpea starches. Although there was no clear trend, the Ea of the native starches was lowered overall as a result of the gums, indicating the limited effect of temperature on the rheological properties of the blends.
Novel VHH-Based Tracers with Variable Plasma Half-Lives for Imaging of CAIX-Expressing Hypoxic Tumor Cells

Hypoxic areas are present in the majority of solid tumors, and hypoxia is associated with resistance to therapies and poor outcomes. A transmembrane protein that is upregulated by tumor cells that have adapted to hypoxic conditions is carbonic anhydrase IX (CAIX). Therefore, noninvasive imaging of CAIX could be of prognostic value, and it could steer treatment strategies. The aim of this study was to compare variants of CAIX-binding VHH B9, with and without a C-terminal albumin-binding domain with varying affinity (ABDlow and ABDhigh), for SPECT imaging of CAIX expression. The binding affinity and internalization of the various B9-variants were analyzed using SK-RC-52 cells. Biodistribution studies were performed in mice with subcutaneous SCCNij153 human head and neck cancer xenografts. Tracer uptake was determined by ex vivo radioactivity counting and visualized by SPECT/CT imaging. Furthermore, autoradiography images of tumor sections were spatially correlated with CAIX immunohistochemistry. B9-variants demonstrated a similar moderate affinity for CAIX in vitro. Maximal tumor uptake and acceptable tumor-to-blood ratios were found in the SCCNij153 model at 4 h post injection for [111In]In-DTPA-B9 (0.51 ± 0.08%ID/g and 8.1 ± 0.85, respectively), 24 h post injection for [111In]In-DTPA-B9-ABDlow (2.39 ± 0.44%ID/g and 3.66 ± 0.81, respectively) and at 72 h post injection for [111In]In-DTPA-B9-ABDhigh (8.7 ± 1.34%ID/g and 2.43 ± 0.15, respectively). An excess of unlabeled monoclonal anti-CAIX antibody efficiently inhibited tumor uptake of [111In]In-DTPA-B9, while only a partial reduction of [111In]In-DTPA-B9-ABDlow and [111In]In-DTPA-B9-ABDhigh uptake was found. Immunohistochemistry and autoradiography images showed colocalization of all B9-variants with CAIX expression; however, [111In]In-DTPA-B9-ABDlow and [111In]In-DTPA-B9-ABDhigh also accumulated in non-CAIX expressing regions. Tumor uptake of [111In]In-DTPA-B9-ABDlow and [111In]In-DTPA-B9-ABDhigh, but not of [111In]In-DTPA-B9, could be visualized with SPECT/CT imaging. In conclusion, [111In]In-DTPA-B9 has a high affinity to CAIX and shows specific targeting to CAIX in head and neck cancer xenografts. The addition of ABD prolonged plasma half-life, increased tumor uptake, and enabled SPECT/CT imaging. This uptake was, however, partly CAIX- independent, precluding the ABD-tracers for use in hypoxia quantification in this tumor type.
CALGB 40603 (Alliance): Long-Term Outcomes and Genomic Correlates of Response and Survival After Neoadjuvant Chemotherapy With or Without Carboplatin and Bevacizumab in Triple-Negative Breast Cancer

PURPOSE CALGB 40603 (NCT00861705), a 2 × 2 randomized phase II trial, demonstrated that adding carboplatin or bevacizumab to weekly paclitaxel (wP) followed by doxorubicin and cyclophosphamide significantly increased the pathologic complete response (pCR) rate in stage II-III triple-negative breast cancer. We now report long-term outcomes (LTOs) and correlative science end points. PATIENTS AND METHODS The Kaplan-Meier method was used to estimate LTOs in 443 patients who initiated study treatment. Log-rank tests and Cox proportional hazards models evaluated the impact of clinical characteristics, pathologic response, calculated residual cancer burden (RCB) in patients with residual disease (RD), treatment assignment, and dose delivery during wP on LTOs, including event-free survival (EFS). Genomic predictors of treatment response and outcomes were assessed on pretreatment tumor samples by mRNA sequencing. RESULTS Among baseline characteristics, only the clinical stage was associated with LTOs. At a median follow-up of 7.9 years, LTOs were not significantly improved with either carboplatin or bevacizumab, overall or in patients with basal-like subtype cancers by genomic analysis. Patients with pCR (n = 205, 46.3%) had significantly higher 5-year EFS (85.5% v 56.6%, log-rank P < .0001) and overall survival (87.9% v 63.4%, P < .0001) rates compared with patients with RD, even those with RCB class I. Among clinical and genomic features, evidence of immune activation, including tumor-infiltrating lymphocytes and low B-cell receptor evenness, was associated with pCR and improved EFS. CONCLUSION Despite higher pCR rates, neither carboplatin nor bevacizumab appeared to improve LTOs although the study was not powered to assess these secondary end points. pCR was associated with superior LTOs even when compared with minimal RD. Markers of immune activation in pretreatment tumor biopsies were independently associated with higher pCR rates and improved survival.
Nanoscale Events on Cyanobiphenyl-Based Self-Assembled Droplets Triggered by Gas Analytes

Liquid crystals (LCs) are prime examples of dynamic supramolecular soft materials. Their autonomous self-assembly at the nanoscale level and the further nanoscale events that give rise to unique stimuli-responsive properties have been exploited for sensing purposes. One of the key features to employ LCs as sensing materials derives from the fine-tuning between stability and dynamics. This challenging task was addressed in this work by studying the effect of the alkyl chain length of cyanobiphenyl LCs on the molecular self-assembled compartments organized in the presence of ionic liquid molecules and gelatin. The resulting multicompartment nematic and smectic gels were further used as volatile organic compound chemical sensors. The LC structures undergo a dynamic sequence of phase transitions, depending on the nature of the LC component, yielding a variety of optical signals, which serve as optical fingerprints. In particular, the materials incorporating smectic compartments resulted in unexpected and rich optical textures that have not been reported previously. Their sensing capability was tested in an in-house-assembled electronic nose and further assessed via signal collection and machine-learning algorithms based on support vector machines, which classified 12 different gas analytes with high accuracy scores. Our work expands the knowledge on controlling LC self-assembly to yield fast and autonomous accurate chemical-sensing systems based on the combination of complex nanoscale sensing events with artificial intelligence tools.
State-of-the-Art Explainability Methods with Focus on Visual Analytics Showcased by Glioma Classification

This study aims to reflect on a list of libraries providing decision support to AI models. The goal is to assist in finding suitable libraries that support visual explainability and interpretability of the output of their AI model. Especially in sensitive application areas, such as medicine, this is crucial for understanding the decision-making process and for a safe application. Therefore, we use a glioma classification model’s reasoning as an underlying case. We present a comparison of 11 identified Python libraries that provide an addition to the better known SHAP and LIME libraries for visualizing explainability. The libraries are selected based on certain attributes, such as being implemented in Python, supporting visual analysis, thorough documentation, and active maintenance. We showcase and compare four libraries for global interpretations (ELI5, Dalex, InterpretML, and SHAP) and three libraries for local interpretations (Lime, Dalex, and InterpretML). As use case, we process a combination of openly available data sets on glioma for the task of studying feature importance when classifying the grade II, III, and IV brain tumor subtypes glioblastoma multiforme (GBM), anaplastic astrocytoma (AASTR), and oligodendroglioma (ODG), out of 1276 samples and 252 attributes. The exemplified model confirms known variations and studying local explainability contributes to revealing less known variations as putative biomarkers. The full comparison spreadsheet and implementation examples can be found in the appendix.
Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients

Background: Multiple sclerosis (MS) is a neurologic disease of the central nervous system which affects almost three million people worldwide. MS is characterized by a demyelination process that leads to brain lesions, allowing these affected areas to be visualized with magnetic resonance imaging (MRI). Deep learning techniques, especially computational algorithms based on convolutional neural networks (CNNs), have become a frequently used algorithm that performs feature self-learning and enables segmentation of structures in the image useful for quantitative analysis of MRIs, including quantitative analysis of MS. To obtain quantitative information about lesion volume, it is important to perform proper image preprocessing and accurate segmentation. Therefore, we propose a method for volumetric quantification of lesions on MRIs of MS patients using automatic segmentation of the brain and lesions by two CNNs. Methods: We used CNNs at two different moments: the first to perform brain extraction, and the second for lesion segmentation. This study includes four independent MRI datasets: one for training the brain segmentation models, two for training the lesion segmentation model, and one for testing. Results: The proposed brain detection architecture using binary cross-entropy as the loss function achieved a 0.9786 Dice coefficient, 0.9969 accuracy, 0.9851 precision, 0.9851 sensitivity, and 0.9985 specificity. In the second proposed framework for brain lesion segmentation, we obtained a 0.8893 Dice coefficient, 0.9996 accuracy, 0.9376 precision, 0.8609 sensitivity, and 0.9999 specificity. After quantifying the lesion volume of all patients from the test group using our proposed method, we obtained a mean value of 17,582 mm3. Conclusions: We concluded that the proposed algorithm achieved accurate lesion detection and segmentation with reproducibility corresponding to state-of-the-art software tools and manual segmentation. We believe that this quantification method can add value to treatment monitoring and routine clinical evaluation of MS patients.