Miracle-dz/UniFAR-FTUs huggingface dataset:
Intensity-Valued Emotions Help Stance Detection of Climate Change Twitter Data

Our study focuses on the United Nations Sustainable Development Goal 13: Climate Action, by identifying public attitudes on Twitter about climate change. Public consent and participation is the key factor in dealing with climate crises. However, discussions about climate change on Twitter are often influenced by the polarised beliefs that shape the discourse and divide it into communities of climate change deniers and believers. In our work, we propose a framework that helps identify different attitudes in tweets about climate change (deny, believe, ambiguous). Previous literature often lacks an efficient architecture or ignores the characteristics of climate-denier tweets. Moreover, the presence of various emotions with different levels of intensity turns out to be relevant for shaping discussions on climate change. Therefore, our paper utilizes emotion recognition and emotion intensity prediction as auxiliary tasks for our main task of stance detection. Our framework injects the words affecting the emotions embedded in the tweet to capture the overall representation of the attitude in terms of the emotions associated with it. The final task-specific and shared feature representations are fused with efficient embedding and attention techniques to detect the correct attitude of the tweet. Extensive experiments on our novel curated dataset, two publicly available climate change datasets (ClimateICWSM-2023 and ClimateStance-2022), and a benchmark dataset for stance detection (SemEval-2016) validate the effectiveness of our approach.
Multi-Objective Optimization Method of Industrial Workshop Layout from the Perspective of Low Carbon

A crucial measure to accelerate the low-carbon transformation of enterprises in the industrial sector involves stringent control over carbon emissions attributed to logistics and transportation activities. In this study, a multi-objective workshop layout optimization model is developed, aiming to minimize logistics cost per unit area and carbon emissions, and maximize the non-logistics relationship. The objective is to mitigate avoidable transportation-related carbon emissions during enterprise operations, while facilitating the co-development of the enterprise’s economy and environment. The model is solved utilizing an enhanced NSGA-II algorithm, with the initial solution set optimized through a combination of system layout design method, dynamic adaptive crossover, and variation strategies. Additionally, the distribution function is introduced to enhance the elite retention strategy and boost the algorithm’s search rate. By using an actual case study, the usefulness of the enhanced algorithm is demonstrated, and the plant’s initial low-carbon layout is realized in order to advance the enterprises’ sustainable growth.
Principled Offline RL in the Presence of Rich Exogenous Information

Learning to control an agent from data collected offline in a rich pixel-based visual observation space is vital for real-world applications of reinforcement learning (RL). A major challenge in this setting is the presence of input information that is hard to model and irrelevant to controlling the agent. This problem has been approached by the theoretical RL community through the lens of exogenous information, i.e, any control-irrelevant information contained in observations. For example, a robot navigating in busy streets needs to ignore irrelevant information, such as other people walking in the background, textures of objects, or birds in the sky. In this paper, we focus on the setting with visually detailed exogenous information, and introduce new offline RL benchmarks offering the ability to study this problem. We find that contemporary representation learning techniques can fail on datasets where the noise is a complex and time dependent process, which is prevalent in practical applications. To address these, we propose to use multi-step inverse models, which have seen a great deal of interest in the RL theory community, to learn Agent-Controller Representations for Offline-RL (ACRO). Despite being simple and requiring no reward, we show theoretically and empirically that the representation created by this objective greatly outperforms baselines.
Fine-scale variation reveals high structure and floristic heterogeneity in dry forests of the Cerrado

This study aimed to compare the floristic and tree structure of two dry forests patches located in a forest remnant encompassed in Cerrado biome, south Goiás state, Brazil. In semideciduous (SF) and deciduous (DF) dry forests, we allocated 25 permanent plots (20 × 20 m), including trees and palms individuals with diameter at breast height (DBH1.3m) ≥ 5.0 cm. The whole survey showed 2 614 individuals, 146 species and 46 families. SF showed higher basal area and average height, but DF had higher density. Due to the low equitability and smaller species richness found in DF (87 species) than in SF (113), the Shannon diversity in SF was significantly higher. Both vegetation types showed low qualitative (Jaccard, 36%) and quantitative (Morisita, 19%) similarity, as only three of the 25 species regarded as having the highest importance/value were common in both dry forests. Fabaceae predominated in the number of species and density, especially in DF. Zoochory was more abundant, although it was not different between vegetation types, whereas both anemochory and autochory dispersion mechanisms were higher in DF. Although the surveys are geographically close, differences in the vegetation structure between dry forests showed strong heterogeneity and environmental complexity, reflecting an increase in local diversity, and reinforcing the need for conservation of these forest remnants in Cerrado.
A Luminex Approach to Develop an Anti-Tumor-Associated Antigen Autoantibody Panel for the Detection of Prostate Cancer in Racially/Ethnically Diverse Populations

Simple Summary This study explored the use of Luminex technology to detect fourteen autoantibodies associated with cancers and optimize an autoantibody panel for the detection of prostate cancer. Twelve autoantibodies were found at significantly higher frequencies in the prostate cancer group. One specific autoantibody, anti-HSP60, was further validated using an enzyme-linked immunosorbent assay with a larger sample size. The findings from both the Luminex and ELISA methods were consistent. We then combined three autoantibodies (p16, IMP2, and HSP60) to create a panel that achieved a high accuracy rate in detecting prostate cancer, with a sensitivity of 71.4% and specificity of 95.8%. This panel was evaluated in patients from different race/ethnicity backgrounds, and it showed the best performance in differentiating Hispanic American prostate cancer patients from healthy individuals. Overall, this study presents a promising approach for the detection of prostate cancer using a panel of specific autoantibodies, offering a potential tool for high-throughput screening. Abstract (1) Background: Autoantibodies to tumor-associated antigens (TAAs) have emerged as promising cancer biomarkers. Luminex technology offers a powerful approach for the simultaneous detection of multiple anti-TAA autoantibodies. (2) Methods: We aimed to utilize Luminex technology to evaluate and optimize a panel of anti-TAAs autoantibodies for detecting prostate cancer (PCa), which included autoantibodies to fourteen TAAs. A total of 163 serum samples (91 PCa, 72 normal controls) were screened to determine the levels of the autoantibodies using the Luminex assay. (3) Results: Twelve autoantibodies exhibited significantly high frequencies ranging from 19.8% to 51.6% in the PCa group. Receiver operating characteristic (ROC) curve analysis revealed area under the curve (AUC) values ranging from 0.609 to 0.868 for the twelve autoantibodies individually. We further confirmed the performance of the HSP60 autoantibody by using an enzyme-linked immunosorbent assay (ELISA) in a larger sample comprising 200 PCa sera, 20 benign prostatic hyperplasia (BPH) sera, and 137 normal control sera. The results obtained from the Luminex assay were consistent with the ELISA findings. We developed a panel consisting of three autoantibodies (p16, IMP2, and HSP60) which achieved an impressive AUC of 0.910 with a sensitivity of 71.4% and a specificity of 95.8%. The panel was also evaluated in PCa patients from different races/ethnicities with the best performance observed in distinguishing the Hispanic American patients with PCa from normal controls. (4) Conclusions: We developed an anti-TAA autoantibody panel for the detection of PCa that exhibits promising performance. This panel holds significant potential as a high-throughput tool to facilitate PCa detection.
MUCOSITE ORAL EM PACIENTES TRANSPLANTADOS DE MEDULA ÓSSEA

O transplante de medula óssea (TMO) é uma alternativa terapêutica amplamente usada em pacientes portadores de doenças do sistema hematopoético, podendo melhor a qualidade de vida ou até mesmo a cura da doença. Porém tal procedimento acarreta diversas alterações orais como efeito colateral tais como : xerostomia (sensação de boca seca), alteração paladar, cáries, mucosite oral, infecção fúngica, leucoplasia pilosa, infecção herpética, alterações gengivais entre outros. O presente artigo teve como objetivo descrever por meio de revisão de literatura a complicação da mucosite oral em pacientes submetidos ao transplante de medula óssea (TMO), notando-se a extrema importância do cirurgião dentista no corpo clínico afim de tratamento, prevenção e acompanhamento de alterações.
High myopia control is comparable between multifocal rigid gas-permeable lenses and spectacles

Purpose Ocular pathology may be reduced by slowing myopia progression. The purpose of this study was to evaluate the potential of a novel custom-designed rigid gas permeable (RGP) contact lens to control high myopia by comparing the efficacy of multifocal RGP lenses and single-vision spectacles for high myopia control. Methods The medical records of children fitted with spectacles or multifocal rigid gas-permeable lenses between January 2018 and May 2020 were retrospectively reviewed. Children (5–17 years) with non-cycloplegic spherical equivalent refraction of ≤ −6.00 D or spherical equivalent refraction > − 6.00 D with baseline axial length ≥ 26.5 mm, and astigmatism of ≥ −2.00 D were included. Axial length and refraction were measured at baseline, before fitting the participants with multifocal rigid gas-permeable lenses or spectacles, and at 1- and 2-year follow-up visits. Changes in axial length were compared between the groups. Results Among the 77 children with 1-year follow-up data, the mean axial elongation was 0.20 ± 0.17 mm and 0.21 ± 0.14 mm in the multifocal rigid gas-permeable and control groups, respectively, without significant differences between groups (F = 0.004, p = 0.835). Among the 41 patients who completed 2 years of follow-up, the mean axial elongation values in the multifocal rigid gas-permeable and control groups were 0.21 ± 0.15 mm and 0.24 ± 0.13 mm, respectively, at the 1-year follow-up, and 0.37 ± 0.27 mm and 0.43 ± 0.23 mm, respectively, at the 2-year follow-up, without significant between-group differences at either time point (p = 0.224). Conclusion Axial length increased at a similar rate in both the control (spectacles) and multifocal rigid gas-permeable lens groups, suggesting that multifocal rigid gas-permeable lenses have no significant impact on controlling high myopia progression compared with spectacles.
Editorial: Tobacco industry accountability - Current practices, emerging issues and challenges

COPYRIGHT © 2023 Goel, Yadav, Kontsevaya and Bhatt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Tobacco industry accountability Current practices, emerging issues and challenges
On Guerrilla Gardening: A Handbook for Gardening without Boundaries

On Guerrilla Gardening is an activist's call to arms to all citizens - green-fingered, green-thinking or curious - to join the revolution of guerrilla gardening: transforming public space into oases of colour and life. The enemy: neglect, apathy and the disintegration of community spirit. The arsenal: daring, a packet of seeds and a passionate commitment to social change. When Richard Reynolds first embarked on guerrilla gardening, growing flowers by moonlight outside his tower block, he had no idea it was part of a growing global movement committed to cultivating the potential in the land regardless of all obstacles. Charting the battles fought across thirty different countries and the revolutionary history of this subculture, On Guerrilla Gardening is an inspirational take on gardening in the 21st century.
Non-contact optical wavefront shaping for focusing light and high-resolution imaging inside and behind biological scattering medium

Optical wavefront shaping is one of the most effective techniques in focusing light inside a scattering medium. Unfortunately, most of these techniques require direct access to the scattering medium or need to know the scattering properties of the medium beforehand. Through our scheme we develop a novel concept in which both the illumination and the detection is on the same side of the inspected object and the imaging process is a real time fast converging operation that does not require to capture large plurality of images. We model the scattering medium being a biological tissue as a Matrix having mathematical properties matched to the physical and biological aspects of the sample. In our adaptive optics scheme, we aim to estimate the scattering function and thus to encode the intensity of the illuminating laser light source using DMD (Digital Micromirror Device) with an inverse scattering function of the scattering medium, such that after passing its scattering function a focused beam is obtained. We optimize the pattern to be displayed on the DMD using Particle Swarm Algorithm (PSO). As first proof of concept we show validation via numerical MATLAB simulations where we obtain a focused spot behind a scattering medium in amplitude modulation scheme.
Heterogeneous programming using OpenMP and CUDA/HIP for hybrid CPU-GPU scientific applications

Hybrid computer systems combine compute units (CUs) of different nature like CPUs, GPUs and FPGAs. Simultaneously exploiting the computing power of these CUs requires a careful decomposition of the applications into balanced parallel tasks according to both the performance of each CU type and the communication costs among them. This paper describes the design and implementation of runtime support for OpenMP hybrid GPU-CPU applications, when mixed with GPU-oriented programming models (e.g. CUDA/HIP). The paper describes the case for a hybrid multi-level parallelization of the NPB-MZ benchmark suite. The implementation exploits both coarse-grain and fine-grain parallelism, mapped to compute units of different nature (GPUs and CPUs). The paper describes the implementation of runtime support to bridge OpenMP and HIP, introducing the abstractions of Computing Unit and Data Placement. We compare hybrid and non-hybrid executions under state-of-the-art schedulers for OpenMP: static and dynamic task schedulings. Then, we improve the set of schedulers with two additional variants: a memorizing-dynamic task scheduling and a profile-based static task scheduling. On a computing node composed of one AMD EPYC 7742 @ 2.250 GHz (64 cores and 2 threads/core, totalling 128 threads per node) and 2 × GPU AMD Radeon Instinct MI50 with 32 GB, hybrid executions present speedups from 1.10× up to 3.5× with respect to a non-hybrid GPU implementation, depending on the number of activated CUs.
Genome-wide association study of soybean (Glycine max [L.] Merr.) germplasm for dissecting the quantitative trait nucleotides and candidate genes underlying yield-related traits

Soybean (Glycine max [L.] Merr.) is one of the most significant crops in the world in terms of oil and protein. Owing to the rising demand for soybean products, there is an increasing need for improved varieties for more productive farming. However, complex correlation patterns among quantitative traits along with genetic interactions pose a challenge for soybean breeding. Association studies play an important role in the identification of accession with useful alleles by locating genomic sites associated with the phenotype in germplasm collections. In the present study, a genome-wide association study was carried out for seven agronomic and yield-related traits. A field experiment was conducted in 2015/2016 at two locations that include 155 diverse soybean germplasm. These germplasms were genotyped using SoySNP50K Illumina Infinium Bead-Chip. A total of 51 markers were identified for node number, plant height, pods per plant, seeds per plant, seed weight per plant, hundred-grain weight, and total yield using a multi-locus linear mixed model (MLMM) in FarmCPU. Among these significant SNPs, 18 were putative novel QTNs, while 33 co-localized with previously reported QTLs. A total of 2,356 genes were found in 250 kb upstream and downstream of significant SNPs, of which 17 genes were functional and the rest were hypothetical proteins. These 17 candidate genes were located in the region of 14 QTNs, of which ss715580365, ss715608427, ss715632502, and ss715620131 are novel QTNs for PH, PPP, SDPP, and TY respectively. Four candidate genes, Glyma.01g199200, Glyma.10g065700, Glyma.18g297900, and Glyma.14g009900, were identified in the vicinity of these novel QTNs, which encode lsd one like 1, Ergosterol biosynthesis ERG4/ERG24 family, HEAT repeat-containing protein, and RbcX2, respectively. Although further experimental validation of these candidate genes is required, several appear to be involved in growth and developmental processes related to the respective agronomic traits when compared with their homologs in Arabidopsis thaliana. This study supports the usefulness of association studies and provides valuable data for functional markers and investigating candidate genes within a diverse germplasm collection in future breeding programs.
Cognitive Contributors of Backward Walking in Persons with Multiple Sclerosis

Purpose Individuals with multiple sclerosis (MS) are at an increased fall risk due to motor and cognitive dysfunction. Our past studies suggest that backward walking (BW) velocity predicts fall risk; however, specific cognitive domains associated with BW velocity remain understudied. The goal of this study was to determine the specific contributions of cognitive functioning to BW velocity in persons with MS. We hypothesized that better visuospatial memory, verbal immediate recall, and faster information processing speed would contribute to faster BW velocity, and deficits in these domains would partially account for disease severity-related impairment in BW velocity. Methods Participants completed demographic questionnaires, walking tests, and cognitive assessments. Applied structural equation modeling was used to test our hypothesized model of competing cognitive mediators. Within the model, disease severity was a predictor of BW via three intercorrelated cognitive mediators. Results Participants included 39 individuals with relapsing-remitting MS. Results indicated that 35.3% of the significant total effect of disease severity on BW was accounted for by specific cognitive deficits. Verbal immediate recall had the largest contribution, followed by visuospatial memory and information processing speed. Conclusions When examining the unique effects of cognitive domains on disease severity-related deficits in BW, a meaningful source of impairment related to visuospatial memory and verbal immediate recall was demonstrated. Considering the utility of BW velocity as a predictor of falls, these results highlight the importance of assessing cognition when evaluating fall risk in MS. Cognitive-based intervention studies investigating fall prevention may find BW as a more specific and sensitive predictor of fall risk than forward walking.
Microbial gene expression during hibernation in arctic ground squirrels: greater differences across gut sections than in response to pre-hibernation dietary protein content

Obligate seasonal hibernators fast for 5–9 months depending on species yet resist muscle atrophy and emerge with little lean mass loss. The role of the gut microbiome in host nitrogen metabolism during hibernation is therefore of considerable interest, and recent studies support a role for urea nitrogen salvage (UNS) in host-protein conservation. We were interested in the effect of pre-hibernation diet on UNS and the microbial provision of essential amino acids (EAAs) during hibernation; therefore, we conducted a study whereby we fed arctic ground squirrels (Urocitellus parryii) pre-hibernation diets containing 9% vs. 18% protein and compared the expression of gut bacterial urease and amino acid (AA) metabolism genes in 4 gut sections (cecum mucosa, cecum lumen, small intestine [SI] mucosa, and SI lumen) during hibernation. We found that pre-hibernation dietary protein content did not affect expression of complete bacterial AA pathway genes during hibernation; however, several individual genes within EAA pathways were differentially expressed in squirrels fed 18% pre-hibernation dietary protein. Expression of genes associated with AA pathways was highest in the SI and lowest in the cecum mucosa. Additionally, the SI was the dominant expression site of AA and urease genes and was distinct from other sections in its overall microbial functional and taxonomic composition. Urease expression in the gut microbiome of hibernating squirrels significantly differed by gut section, but not by pre-hibernation dietary protein content. We identified two individual genes that are part of the urea cycle and involved in arginine biosynthesis, which were significantly more highly expressed in the cecum lumen and SI mucosa of squirrels fed a pre-hibernation diet containing 18% protein. Six bacterial genera were responsible for 99% of urease gene expression: Cupriavidus, Burkholderia, Laribacter, Bradhyrizobium, Helicobacter, and Yersinia. Although we did not find a strong effect of pre-hibernation dietary protein content on urease or AA metabolism gene expression during hibernation, our data do suggest the potential for pre-hibernation diet to modulate gut microbiota function during hibernation, and further investigations are warranted.
Metabolic Alteration of MCF-7 Cells upon Indirect Exposure to E. coli Secretome: A Model of Studying the Microbiota Effect on Human Breast Tissue

According to studies, the microbiome may contribute to the emergence and spread of breast cancer. E. coli is one of the Enterobacteriaceae family recently found to be present as part of the breast tissue microbiota. In this study, we focused on the effect of E. coli secretome free of cells on MCF-7 metabolism. Liquid chromatography–mass spectrometry (LC-MS) metabolomics was used to study the E. coli secretome and its role in MCF-7 intra- and extracellular metabolites. A comparison was made between secretome-exposed cells and unexposed controls. Our analysis revealed significant alterations in 31 intracellular and 55 extracellular metabolites following secretome exposure. Several metabolic pathways, including lactate, aminoacyl-tRNA biosynthesis, purine metabolism, and energy metabolism, were found to be dysregulated upon E. coli secretome exposure. E. coli can alter the breast cancer cells’ metabolism through its secretome which disrupts key metabolic pathways of MCF-7 cells. These microbial metabolites from the secretome hold promise as biomarkers of drug resistance or innovative approaches for cancer treatment, either as standalone therapies or in combination with other medicines.