Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Utility of Word Embeddings for Enriching OpenWordNet-PT

The maintenance of wordnets and lexical knwoledge bases typically relies on time-consuming manual effort. In order to minimise this issue, we propose the exploitation of models of distributional semantics, namely word embeddings learned from corpora, in the automatic identification of relation instances missing in a wordnet. Analogy-solving methods are first used for learning a set of relations from analogy tests focused on each relation. Despite their low accuracy, we noted that a portion of the top-given answers are good suggestions of relation instances that could be included in the wordnet. This procedure is applied to the enrichment of OpenWordNet-PT, a public Portuguese wordnet. Relations are learned from data acquired from this resource, and illustrative examples are provided. Results are promising for accelerating the identification of missing relation instances, as we estimate that about 17% of the potential suggestions are good, a proportion that almost doubles if some are automatically invalidated.
Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation

Monitoring and management of plant water status over the critical period between flowering and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.
Vine Identification and Characterization in Goblet-Trained Vineyards Using Remotely Sensed Images

This paper proposes a novel approach for living and missing vine identification and vine characterization in goblet-trained vine plots using aerial images. Given the periodic structure of goblet vineyards, the RGB color coded parcel image is analyzed using proper processing techniques in order to determine the locations of living and missing vines. Vine characterization is achieved by implementing the marker-controlled watershed transform where the centers of the living vines serve as object markers. As a result, a precise mortality rate is calculated for each parcel. Moreover, all vines, even the overlapping ones, are fully recognized providing information about their size, shape, and green color intensity. The presented approach is fully automated and yields accuracy values exceeding 95% when the obtained results are assessed with ground-truth data. This unsupervised and automated approach can be applied to any type of plots presenting similar spatial patterns requiring only the image as input.
Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process

LiDAR occupies a vital position in self-driving as the advanced detection technology enables autonomous vehicles (AVs) to obtain much environmental information. Ground segmentation for LiDAR point cloud is a crucial procedure to ensure AVs’ driving safety. However, some current algorithms suffer from embarrassments such as unavailability on complex terrains, excessive time and memory usage, and additional pre-training requirements. The Jump-Convolution-Process (JCP) is proposed to solve these issues. JCP converts the segmentation problem of the 3D point cloud into the smoothing problem of the 2D image and takes little time to improve the segmentation effect significantly. First, the point cloud marked by an improved local feature extraction algorithm is projected onto an RGB image. Then, the pixel value is initialized with the points’ label and continuously updated according to image convolution. Finally, a jump operation is introduced in the convolution process to perform calculations only on the low-confidence points filtered by the credibility propagation algorithm, reducing the time cost. Experiments on three datasets show that our approach has a better segmentation accuracy and terrain adaptability than those of the three existing methods. Meanwhile, the average time for the proposed method to deal with one scan data of 64-beam and 128-beam LiDAR is only 8.61 ms and 15.62 ms, which fully meets the AVs’ requirement for real-time performance.
LiDAR Odometry and Mapping Based on Semantic Information for Outdoor Environment

Simultaneous Localization and Mapping (SLAM) in an unknown environment is a crucial part for intelligent mobile robots to achieve high-level navigation and interaction tasks. As one of the typical LiDAR-based SLAM algorithms, the Lidar Odometry and Mapping in Real-time (LOAM) algorithm has shown impressive results. However, LOAM only uses low-level geometric features without considering semantic information. Moreover, the lack of a dynamic object removal strategy limits the algorithm to obtain higher accuracy. To this end, this paper extends the LOAM pipeline by integrating semantic information into the original framework. Specifically, we first propose a two-step dynamic objects filtering strategy. Point-wise semantic labels are then used to improve feature extraction and searching for corresponding points. We evaluate the performance of the proposed method in many challenging scenarios, including highway, country and urban from the KITTI dataset. The results demonstrate that the proposed SLAM system outperforms the state-of-the-art SLAM methods in terms of accuracy and robustness.
Endophenotype Effect Sizes Provide Evidence Supporting Variant Pathogenicity in Monogenic Disease Susceptibility Genes

Accurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) are proxies for variant pathogenicity. Effect sizes were associated with pathogenic ClinVar assertions (P<0.001 for each trait) and discriminated pathogenic from non-pathogenic variants (area under the curve 0.82-0.83 across endophenotypes). Large effect size thresholds provided evidence supporting pathogenicity for up to 35% of rare variants of uncertain significance or not in ClinVar in disease susceptibility genes, using an effect size threshold of [≥]0.5 times the endophenotype standard deviation. We propose that variant associations with quantitative endophenotypes for monogenic diseases can provide evidence supporting pathogenicity.
Long-Term Survival of Synechococcus and Heterotrophic Bacteria without External Nutrient Supply after Changes in Their Relationship from Antagonism to Mutualism

Phytoplankton and heterotrophic bacteria have a close but usually changeable relationship. Uncovering the dynamic changes and driving factors of their interrelationships is of great significance for an in-depth understanding of the ecological processes and functions of marine microorganisms. ABSTRACT Marine phytoplankton and heterotrophic bacteria share a very close but usually changeable relationship. However, the ultimate fate of their unstable relationship on a long-term scale is unclear. Here, the relationship between Synechococcus and heterotrophic bacterial communities underwent a dramatic shift from antagonism to commensalism and eventually to mutualism during long-term cocultivation. The relationship change is attributed to the different (even opposite) effects of diverse bacterial members on Synechococcus and the ratio of beneficial to harmful bacteria. Different bacterial members also interact with each other (e.g., quorum-sensing communication, hostility, or mutual promotion) and drive a dynamic succession in the entire community structure that corresponds exactly to the shift in its relationship with Synechococcus. In the final mutualism stage, a self-sufficient nitrogen cycle, including nitrogen fixation, denitrification, and organic nitrogen degradation, contributed to the healthy survival of Synechococcus for 2 years without an exogenous nutrient supply. This natural selective trait of Synechococcus and heterotrophic bacteria toward mutualism under long-term coexistence provides a novel clue for understanding the ubiquity and competitive advantage of Synechococcus in global oceans. IMPORTANCE Phytoplankton and heterotrophic bacteria have a close but usually changeable relationship. Uncovering the dynamic changes and driving factors of their interrelationships is of great significance for an in-depth understanding of the ecological processes and functions of marine microorganisms. Here, we observed that Synechococcus and heterotrophic bacterial communities underwent a dramatic change in their relationship from antagonism to mutualism during a long-term cocultivation process. We revealed that the interactions between different members of the bacterial community and the combined effects of different bacterial individuals on Synechococcus promoted the dynamic changes of the Synechococcus-bacterium relationship. In the end, a self-sufficient nutrient cycle (especially nitrogen) established by Synechococcus and bacterial communities supported their long-term survival without any external nutrition supply. This study provides novel insight into the interaction between Synechococcus and heterotrophic bacteria in the ocean and provides a novel clue for understanding the ubiquity and competitive advantage of Synechococcus in global oceans.
Prognostic Impact of Sarcopenia on Clinical Outcomes in Malignancies Treated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis

Background The effect of sarcopenia on the clinical outcomes of patients with malignant neoplasms receiving immune checkpoint inhibitors (ICIs) is unclear. The aim of this study was to evaluate the effect and survival of patients with malignancies and sarcopenia receiving ICIs. Methods We systematically searched related studies in PubMed, Embase, and Cochrane Library up to March 2021 according to the inclusion and exclusion criteria. Information pertaining to the hazard ratio (HR) corresponding to 95% confidence interval (CI) of overall survival (OS) and progression-free survival (PFS) as determined by univariate and multivariate analyses; the odds ratio (OR) corresponding to the 95% CI of the disease control rate (DCR) and objective response rate (ORR); and immune-related adverse events (irAEs) was collected and analyzed using the RevMan 5.4 software. Study heterogeneity and sensitivity were also assessed. Results A total of 19 studies were finalized that included 1763patients with lung, gastrointestinal, and head and neck cancers as well as those with melanoma, renal cell carcinoma, urothelial carcinoma, pancreatic cancer, and soft tissue sarcoma. According to univariate and multivariate analyses, patients with sarcopenia at pre-immunotherapy had poorer PFS and OS than those without. HRs and the corresponding 95% CI of PFS were 1.91(1.55–2.34, p <0.00001) and 1.46 (1.20–1.78, p =0.0001), respectively, and HRs and the corresponding 95% CI of OS were 1.78 (1.47–2.14, p <0.00001) and 1.73 (1.36–2.19, p <0.0001), respectively. Patients with sarcopenia showed poor PFS and OS during treatment. In addition, patients with sarcopenia had worse ORR (OR 0.46, 95% CI 0.28–0.74, p = 0.001) and DCR (OR 0.44, 95% CI 0.31–0.64, p<0.0001); however, the incidence of irAEs of any grade and high-grade in patients with sarcopenia did not increase, OR and the corresponding 95% CI were 0.58(0.30–1.12, p = 0.10) and 0.46(0.19–1.09, p = 0.08). Further, we performed subgroup analysis, skeletal muscle mass index (SMI) and psoas muscle mass index (PMI) stratification. In the SMI group, patients with sarcopenia had poor ORR, DCR, PFS, and OS than those without. In the PMI group, sarcopenia had poor ORR,DCR, and was a poor prognostic factor for PFS and OS according to univariate analysis but had no effect on PFS and OS according to multivariate analysis. Conclusions Patients with malignancies and sarcopenia at pre-immunotherapy or follow-up visits had poorer clinical outcomes than those without, and sarcopenia was a poor predictive factor of ICI immunotherapy outcomes.
Facile Synthesis of Holmium-Based Nanoparticles as a CT and MRI Dual-Modal Imaging for Cancer Diagnosis

The rapid development of medical imaging has boosted the abilities of modern medicine. As single modality imaging limits complex cancer diagnostics, dual-modal imaging has come into the spotlight in clinical settings. The rare earth element Holmium (Ho) has intrinsic paramagnetism and great X-ray attenuation due to its high atomic number. These features endow Ho with good potential to be a nanoprobe in combined x-ray computed tomography (CT) and T2-weighted magnetic resonance imaging (MRI). Herein, we present a facile strategy for preparing HoF3 nanoparticles (HoF3 NPs) with modification by PEG 4000. The functional PEG-HoF3 NPs have good water solubility, low cytotoxicity, and biocompatibility as a dual-modal contrast agent. Currently, there is limited systematic and intensive investigation of Ho-based nanomaterials for dual-modal imaging. Our PEG-HoF3 NPs provide a new direction to realize in vitro and vivo CT/MRI imaging, as well as validation of Ho-based nanomaterials will verify their potential for biomedical applications.
Goal-Dependent Hippocampal Representations Facilitate Self-Control

Hippocampal activity linking past experiences and simulations of the future with current goals can play an important role in decision-making. The representation of information within the hippocampus may be especially critical in situations where one needs to overcome past rewarding experiences and exert self-control. Self-control success or failure may depend on how information is represented in the hippocampus and how effectively the representation process can be modified to achieve a specific goal. We test this hypothesis using representational similarity analyses of human (female/male) neuroimaging data during a dietary self-control task in which individuals must overcome taste temptations to choose healthy foods. We find that self-control is indeed associated with the way individuals represent taste information (valance) in the hippocampus and how taste representations there adapt to align with different goals/contexts. Importantly, individuals who were able to shift their hippocampal representations to a larger degree to align with the current motivation were better able to exert self-control when facing a dietary challenge. These results suggest an alternative or complementary neurobiological pathway leading to self-control success and indicate the need to update the classical view of self-control to continue to advance our understanding of its behavioral and neural underpinnings. SIGNIFICANCE STATEMENT The paper provides a new perspective on what leads to successful self-control at the behavioral and neurobiological levels. Our data suggest that self-control is enhanced when individuals adjust hippocampal processing to align with current goals.
An Inverse Node Graph-Based Method for the Urban Scene Segmentation of 3D Point Clouds

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.
Mapping Invasive Phragmites australis Using Unoccupied Aircraft System Imagery, Canopy Height Models, and Synthetic Aperture Radar

Invasive plant species are an increasing worldwide threat both ecologically and financially. Knowing the location of these invasive plant infestations is the first step in their control. Surveying for invasive Phragmites australis is particularly challenging due to limited accessibility in wetland environments. Unoccupied aircraft systems (UAS) are a popular choice for invasive species management due to their ability to survey challenging environments and their high spatial and temporal resolution. This study tested the utility of three-band (i.e., red, green, and blue; RGB) UAS imagery for mapping Phragmites in the St. Louis River Estuary in Minnesota, U.S.A. and Saginaw Bay in Michigan, U.S.A. Iterative object-based image analysis techniques were used to identify two classes, Phragmites and Not Phragmites. Additionally, the effectiveness of canopy height models (CHMs) created from two data types, UAS imagery and commercial satellite stereo retrievals, and the RADARSAT-2 horizontal-horizontal (HH) polarization were tested for Phragmites identification. The highest overall classification accuracy of 90% was achieved when pairing the UAS imagery with a UAS-derived CHM. Producer’s accuracy for the Phragmites class ranged from 3 to 76%, and the user’s accuracies were above 90%. The Not Phragmites class had user’s and producer’s accuracies above 88%. Inclusion of the RADARSAT-2 HH polarization caused a slight reduction in classification accuracy. Commercial satellite stereo retrievals increased commission errors due to decreased spatial resolution and vertical accuracy. The lowest classification accuracy was seen when using only the RGB UAS imagery. UAS are promising for Phragmites identification, but the imagery should be used in conjunction with a CHM.
The Role of Extracellular Vesicles Secreted From Thermal Stress-Induced Adipose-Derived Stem Cells on Bone Regeneration

Purpose: Extracellular vesicles (EVs) are responsible for intercellular communication. Mesenchymal stem cell-derived vesicles have been shown to have similar properties as functional mesenchymal stem cells. The aim of this study was to compare the therapeutic benefit of EVs obtained from adipose tissue-derived stem cells (ADSC) on bone repair whereas using ß-tricalcium phosphate (ß-TCP) biomaterial as a carrier. Materials and Method: A single critical size bone defect of 8 mm in diameter was created on the right side of rat calvarium using a custom-made punch needle. Animals were randomly divided into 5 groups: group 1 (no treatment), group 2 (bone graft), group 3 (ß-TCP + ADSC), group 4 (ß-TCP + EV), group 5 (ß-TCP). Eight weeks later, animals were sacrificed and histologic and radiologic evaluation was performed. Results: Semiquantitative histologic scoring demonstrated significantly higher bone regeneration scores for groups 2, 3, and 4 compared to group 1. Radiologic imaging showed significantly higher bone mineral density for groups 2, 3, and 5 compared to group 1. There were no significant differences between treatment groups in either histologic or radiologic scoring. Conclusions: Our data showed that EVs provided from thermally induced ADSCs did not show any significant difference in bone regeneration when compared to ADSCs themselves. Future studies should focus on determining the optimum amount and content of EV application since these vary significantly depending on the microenvironment.
Study on Deep Convolutional Neural Networks for Leaf Counting (cid:63)

The count of leaves in a plant gives valuable information re-lated to the plant phenology and plant growth stage. It characterises the development and growth of plants. Manual leaf counting is a slow and labor-intensive task, therefore research to build an automated image-based leaf counting system is underway. Several deep learning based algorithms have been proposed for the task of leaf counting. Many of these approaches require per leaf segmentation and annotation for training. In our approach we have treated leaf counting as a regression problem requiring only the total leaf count per plant as annotation. We review diﬀerent deep learning models that can be employed to leaf counting as a regression problem. Finally, a comparative study has been presented based on the experimental results on a benchmark dataset.
Extraction of Students’ Feedback in University Services using Logistic Regression and Chi-Square Automatic Interaction Detection (CHAID) Algorithm

- The ultimate goal of every Higher Education Institution is to provide quality education to their students. Quality of Education can be measured through the student’s satisfaction level based on their overall university experience. Students are the primary consumers in every Higher Education Institution services. Hence, their feedback or insights on their overall university experiences are significant for the university’s continuous quality improvement. The use of machine learning algorithm concepts can determine and extract useful information which can be used as reference in formulating necessary policies suited to improve university’s academic services. Our study focuses on the application of logistic regression through equation development and CHAID Algorithm through rules development. The study also uses text analytics concepts in which sentiment scores are computed to classify the comments according to satisfaction level