cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21744149.v1
title: Antineoplastic properties and pharmacological applications of Crotalus durissus terrificus snake venom

description: ABSTRACT Snake toxins are widely studied owing to their importance in snakebite accidents, a serious public health issue in tropical countries, and their broad therapeutic potential. Isolated fractions from venom produced by snakes of the genus Crotalus sp. present a wide variety of pharmacological uses such as antifungal, antiviral, antibacterial, and antitumor properties, among other therapeutic potentialities. Given the direct effect of this venom on tumor cells, isolation of its compounds is important for the characterization of its anticarcinogenic actions. Crotalus durissus terrificus venom and its toxins have been widely evaluated as potential candidates for the development of new antineoplastic therapies that are efficient against different tumor lines and cellular targets. This review highlights the venom toxins of this species, with a focus on their antineoplastic properties.
DOI: 10.6084/m9.figshare.21744150
title: A HEATMAP APPROACH FOR MASTER DATA MANAGEMENT PROGRAMS

description: Abstract Master data management programs are large by nature since the aim is to provide the entire enterprise with a shared trusted view of the organisation’s most critical data assets. In this paper, we present what dimensions and activities a master data management program in a large organisation should consider and how to monitor such a program once it is up and running. Our findings result from an autoethnography study where personal experiences are connected to previous research. A heatmap approach is used to visualize the inherent complexity of a master data management program. Our approach is derived from participating in four different master data management programs in four different global organisations during 2007-2020.
DOI: 10.6084/m9.figshare.21744150.v1
title: A HEATMAP APPROACH FOR MASTER DATA MANAGEMENT PROGRAMS

description: Abstract Master data management programs are large by nature since the aim is to provide the entire enterprise with a shared trusted view of the organisation’s most critical data assets. In this paper, we present what dimensions and activities a master data management program in a large organisation should consider and how to monitor such a program once it is up and running. Our findings result from an autoethnography study where personal experiences are connected to previous research. A heatmap approach is used to visualize the inherent complexity of a master data management program. Our approach is derived from participating in four different master data management programs in four different global organisations during 2007-2020.
DOI: 10.6084/m9.figshare.21679094
title: Ergonomic risks and musculoskeletal symptoms in Instituto Federal Catarinense administrative technicians during telework in the COVID-19 pandemic

description: ABSTRACT This study aimed to evaluate musculoskeletal symptoms and ergonomic risks in telework environments of administrative technicians at the Instituto Federal Catarinense. In total, 142 administrative technicians who answered an online questionnaire about sociodemographic information, task performance, work environment, and musculoskeletal pain participated in the study. Data were analyzed by binary logistic regression separately for each outcome, using pain in the neck, right shoulder and low back pain as dependent variables. The prevalence of pain among administrative technicians was 92.7% and the most frequent regions were the neck, lumbar spine, and right shoulder. Mental overload (stress), inadequate worktable, monitor and work chair, lack of guidance on ergonomic risks and adaptations in the work environment were the main ergonomic risks. An association was observed between neck pain and administrative technicians who were mentally overloaded, did not practice any physical activity, did not have the table at elbow level, had neither forearms support nor used a mouse (but a touchpad instead); and right shoulder pain in administrative technicians who had no forearm support and used a touchpad. Also, low back pain was associated with mentally overloaded women who did not have footrest, table at the elbow level, or a chair with lumbar support and upholstery. The institution should provide adequate equipment and furniture and training the employees on the ergonomic risks at work.
DOI: 10.6084/m9.figshare.21679094.v1
title: Ergonomic risks and musculoskeletal symptoms in Instituto Federal Catarinense administrative technicians during telework in the COVID-19 pandemic

description: ABSTRACT This study aimed to evaluate musculoskeletal symptoms and ergonomic risks in telework environments of administrative technicians at the Instituto Federal Catarinense. In total, 142 administrative technicians who answered an online questionnaire about sociodemographic information, task performance, work environment, and musculoskeletal pain participated in the study. Data were analyzed by binary logistic regression separately for each outcome, using pain in the neck, right shoulder and low back pain as dependent variables. The prevalence of pain among administrative technicians was 92.7% and the most frequent regions were the neck, lumbar spine, and right shoulder. Mental overload (stress), inadequate worktable, monitor and work chair, lack of guidance on ergonomic risks and adaptations in the work environment were the main ergonomic risks. An association was observed between neck pain and administrative technicians who were mentally overloaded, did not practice any physical activity, did not have the table at elbow level, had neither forearms support nor used a mouse (but a touchpad instead); and right shoulder pain in administrative technicians who had no forearm support and used a touchpad. Also, low back pain was associated with mentally overloaded women who did not have footrest, table at the elbow level, or a chair with lumbar support and upholstery. The institution should provide adequate equipment and furniture and training the employees on the ergonomic risks at work.
DOI: 10.6084/m9.figshare.7513610
title: Symptoms of depression in patients with mild cognitive impairment in Parkinson's disease

description: ABSTRACT Objective: To investigate the most frequent depressive symptoms and their association with cognition in Parkinson's disease (PD) patients with mild cognitive impairment (MCI). Methods: 48 patients with PD and 44 controls (CG), aged between 50 and 80 years and with at least 4 years of formal education, all with MCI and none diagnosed with depression, were assessed. Patients and controls were matched for age, education, and Mini-Mental State Examination (MMSE) score. Participants underwent clinical evaluation with a neurologist followed by neuropsychological assessment employing the instruments: MMSE, Clock Drawing Test, Verbal Fluency Test (semantic and phonemic), Figures Memory Test (FMT), Stroop Test, Trail Making Test, Digit Span (WAIS III), Rey Auditory Verbal Learning Test (RAVLT), Hooper Visual Organization Test, and Beck Depression Inventory (BDI). Results: The most frequent depressive symptoms in the PD group were: difficulty working, fatigue and sleep disorders (the latter also being present in CG). BDI score correlated negatively with learning and recognition memory in both groups. Episodic memory, evaluated by the FMT and RAVLT tests, was the cognitive function showing greatest impairment. Conclusion: Some of the depressive symptoms observed in PD patients with MCI seem to be attributable to complications of PD, while others are common to both PD and MCI, making differential diagnoses complex but crucial.
DOI: 10.6084/m9.figshare.7513610.v1
title: Symptoms of depression in patients with mild cognitive impairment in Parkinson's disease

description: ABSTRACT Objective: To investigate the most frequent depressive symptoms and their association with cognition in Parkinson's disease (PD) patients with mild cognitive impairment (MCI). Methods: 48 patients with PD and 44 controls (CG), aged between 50 and 80 years and with at least 4 years of formal education, all with MCI and none diagnosed with depression, were assessed. Patients and controls were matched for age, education, and Mini-Mental State Examination (MMSE) score. Participants underwent clinical evaluation with a neurologist followed by neuropsychological assessment employing the instruments: MMSE, Clock Drawing Test, Verbal Fluency Test (semantic and phonemic), Figures Memory Test (FMT), Stroop Test, Trail Making Test, Digit Span (WAIS III), Rey Auditory Verbal Learning Test (RAVLT), Hooper Visual Organization Test, and Beck Depression Inventory (BDI). Results: The most frequent depressive symptoms in the PD group were: difficulty working, fatigue and sleep disorders (the latter also being present in CG). BDI score correlated negatively with learning and recognition memory in both groups. Episodic memory, evaluated by the FMT and RAVLT tests, was the cognitive function showing greatest impairment. Conclusion: Some of the depressive symptoms observed in PD patients with MCI seem to be attributable to complications of PD, while others are common to both PD and MCI, making differential diagnoses complex but crucial.
DOI: 10.6084/m9.figshare.21744155
title: Mechanical properties of the macaw palm endocarp aiming seedling production process

description: ABSTRACT Background: The Macaw palm ( Acrocomia aculeata) is a palm tree native to the Tropical America Forests. This palm has stood out due to its high potential for the production of oil for biofuels, being an excellent source of renewable energy. However, due to the rudimentary way of exploring the Macaw palm, mostly using native people labor, it is not possible to extract the maximum economical potential that this plant can provide. In order to use the Macaw palm in industrial scale, new strategies using high-performance machinery in several segments of the production chain should be employed. Results: For that, its seedlings production must be broadly expanded, using modern techniques for its propagation and production on an industrial scale, since its present low germination rates when the natural way is employed. In this way, the present paper has the objective of establishing a database of the mechanical properties of the endocarp of the Macaw palm when submitted to compression efforts, typically used for removing almonds from the endocarp. The inherent variability of its mechanical properties was quantified, using three different crops sites. Conclusion: Important information about the mechanical properties of the Macaw palm endocarp is presented, serving as guideline for future works to genetic improvement of the Macaw palm, in order to obtain fruits with less variability in the mechanical properties of the endocarp and consequently improve the production on an industrial scale.
DOI: 10.6084/m9.figshare.21744155.v1
title: Mechanical properties of the macaw palm endocarp aiming seedling production process

description: ABSTRACT Background: The Macaw palm ( Acrocomia aculeata) is a palm tree native to the Tropical America Forests. This palm has stood out due to its high potential for the production of oil for biofuels, being an excellent source of renewable energy. However, due to the rudimentary way of exploring the Macaw palm, mostly using native people labor, it is not possible to extract the maximum economical potential that this plant can provide. In order to use the Macaw palm in industrial scale, new strategies using high-performance machinery in several segments of the production chain should be employed. Results: For that, its seedlings production must be broadly expanded, using modern techniques for its propagation and production on an industrial scale, since its present low germination rates when the natural way is employed. In this way, the present paper has the objective of establishing a database of the mechanical properties of the endocarp of the Macaw palm when submitted to compression efforts, typically used for removing almonds from the endocarp. The inherent variability of its mechanical properties was quantified, using three different crops sites. Conclusion: Important information about the mechanical properties of the Macaw palm endocarp is presented, serving as guideline for future works to genetic improvement of the Macaw palm, in order to obtain fruits with less variability in the mechanical properties of the endocarp and consequently improve the production on an industrial scale.
DOI: 10.6084/m9.figshare.21744156
title: EPIDEMIOLOGICAL PROFILE AND EVOLUTION OF ANKLE MUSCULOSKELETAL TUMORS

description: ABSTRACT Objective: Characterizing ankle tumors, presenting the epidemiological profile of these lesions. Methods: Retrospective observational case series study to evaluate the results of clinical and/or surgical treatments of patients with ankle tumors whose first visit occurred from 1990 to 2020. The dependent variables were: benign bone tumor, malignant bone tumor, benign soft tissue tumor, malignant soft tissue tumor, and infection. The independent variables were: sex, age; presence of symptoms (pain/local volume increase/fracture), duration of symptoms until treatment, diagnosis, treatment, and recurrence. Results: In total, 70 patients were included-58.5% were women, with a mean age at the time of diagnosis of 21.66 years. Among all cases, 76% were bone tumor, 14% were soft tissue tumor, and 10% were infection. The mean age at the time of diagnosis was 21.7 ± 2.29 years. The overall prevalence of pain was 77.1%. In total, 55.6% patients had a general local volume increase 13.4% had fractures. The mean time from symptoms to treatment was 17.4 ± 4.61 months and the mean diagnosis time was 10.13 ± 0.86 months. Of all cases, 73.44% underwent surgical treatment and 22.64% had recurrence. Conclusion: In this series, ankle tumors corresponded mainly to bone tumors. Benign tumors were the most prevalent type of tumor and the highest occurrence was among young people. Level of Evidence IV, Case Series.
DOI: 10.6084/m9.figshare.21744156.v1
title: EPIDEMIOLOGICAL PROFILE AND EVOLUTION OF ANKLE MUSCULOSKELETAL TUMORS

description: ABSTRACT Objective: Characterizing ankle tumors, presenting the epidemiological profile of these lesions. Methods: Retrospective observational case series study to evaluate the results of clinical and/or surgical treatments of patients with ankle tumors whose first visit occurred from 1990 to 2020. The dependent variables were: benign bone tumor, malignant bone tumor, benign soft tissue tumor, malignant soft tissue tumor, and infection. The independent variables were: sex, age; presence of symptoms (pain/local volume increase/fracture), duration of symptoms until treatment, diagnosis, treatment, and recurrence. Results: In total, 70 patients were included-58.5% were women, with a mean age at the time of diagnosis of 21.66 years. Among all cases, 76% were bone tumor, 14% were soft tissue tumor, and 10% were infection. The mean age at the time of diagnosis was 21.7 ± 2.29 years. The overall prevalence of pain was 77.1%. In total, 55.6% patients had a general local volume increase 13.4% had fractures. The mean time from symptoms to treatment was 17.4 ± 4.61 months and the mean diagnosis time was 10.13 ± 0.86 months. Of all cases, 73.44% underwent surgical treatment and 22.64% had recurrence. Conclusion: In this series, ankle tumors corresponded mainly to bone tumors. Benign tumors were the most prevalent type of tumor and the highest occurrence was among young people. Level of Evidence IV, Case Series.
DOI: 10.6084/m9.figshare.21744159
title: Near infrared spectroscopy for estimating properties of kraft paper reinforced with cellulose nanofibrils

description: ABSTRACT Background: The aim of this study was to investigate near infrared (NIR) spectroscopy ability to estimate nanofibril concentration, physical and mechanical properties of Kraft paper reinforced with cellulose nanofibrils (CNF). For this purpose, paper sheets were prepared by mixing unbleached Eucalyptus fibers and cellulose nanofibrils. Twelve treatments result from the combination of the nominal grammages 75, 85, 95 and 105 g m-² with the nanofibril concentrations of 1, 5 and 10wt%, with 7 sheets per treatment. NIR spectra measured directly on paper specimens were correlated with physical and mechanical properties values obtained through conventional laboratory analyzes. Results: Principal component analysis (PCA) revealed no separation among specimens related to nanofibril content. Partial least squares regression (PLS-R) models for estimating nanofibril content, tensile index, stretch and resistance to air passage yielded R 2cv ranging from 0.73 to 0.98. Partial least squares - discriminant analysis (PLS-DA) correctly classified up to 93% of the paper specimens both by grammage and nanofibril content using NIR spectra. Conclusion: This approach appears to be suitable for predicting physical and mechanical properties of Kraft papers and can detect cellulose nanofibril content in the cellulose handsheets.
DOI: 10.6084/m9.figshare.21744159.v1
title: Near infrared spectroscopy for estimating properties of kraft paper reinforced with cellulose nanofibrils

description: ABSTRACT Background: The aim of this study was to investigate near infrared (NIR) spectroscopy ability to estimate nanofibril concentration, physical and mechanical properties of Kraft paper reinforced with cellulose nanofibrils (CNF). For this purpose, paper sheets were prepared by mixing unbleached Eucalyptus fibers and cellulose nanofibrils. Twelve treatments result from the combination of the nominal grammages 75, 85, 95 and 105 g m-² with the nanofibril concentrations of 1, 5 and 10wt%, with 7 sheets per treatment. NIR spectra measured directly on paper specimens were correlated with physical and mechanical properties values obtained through conventional laboratory analyzes. Results: Principal component analysis (PCA) revealed no separation among specimens related to nanofibril content. Partial least squares regression (PLS-R) models for estimating nanofibril content, tensile index, stretch and resistance to air passage yielded R 2cv ranging from 0.73 to 0.98. Partial least squares - discriminant analysis (PLS-DA) correctly classified up to 93% of the paper specimens both by grammage and nanofibril content using NIR spectra. Conclusion: This approach appears to be suitable for predicting physical and mechanical properties of Kraft papers and can detect cellulose nanofibril content in the cellulose handsheets.
DOI: 10.6084/m9.figshare.21744161
title: Assessment of alternative forest road routes and landslide susceptibility mapping using machine learning

description: ABSTRACT Background: Forest roads are among the most basic infrastructure used for forestry activities and services. To facilitate the increased amount of biomass harvesting adequately, the existing road network may require modifications to allow forest transportation within harvesting units that are not yet accessed by the roads. The construction of a forest road can trigger landslides, so the necessary constraints should be considered when the road is being planned to preclude such problems. Landslide Susceptibility Mapping (LSM) has become an integral part of the growing process of machine learning (ML), providing a more effective platform for practitioners, planners, and decision-makers. This study aims to reveal the most suitable alternative routes for a forest road, especially in areas susceptible to landslides, and to provide an effective tool for decision-makers. Results: For this purpose, two models were developed through ML: Logistic Regression (LR) and Random Forest (RF). Elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from the fault, the road, and the stream, and lithology were considered as the main landslide susceptibility factors in these models. The best model was obtained by the RF approach with an Area Under ROC Curve (AUC) value of 81.9%, while the LR model was 78.2%. LSM data was used as a base, and alternative routes were obtained through CostPath analysis. Conclusion: It has been shown that the ML methods used in this study can positively contribute to decision-making by providing more effective LSM calculations in studies to determine alternative routes in a forest road network.
DOI: 10.6084/m9.figshare.21744161.v1
title: Assessment of alternative forest road routes and landslide susceptibility mapping using machine learning

description: ABSTRACT Background: Forest roads are among the most basic infrastructure used for forestry activities and services. To facilitate the increased amount of biomass harvesting adequately, the existing road network may require modifications to allow forest transportation within harvesting units that are not yet accessed by the roads. The construction of a forest road can trigger landslides, so the necessary constraints should be considered when the road is being planned to preclude such problems. Landslide Susceptibility Mapping (LSM) has become an integral part of the growing process of machine learning (ML), providing a more effective platform for practitioners, planners, and decision-makers. This study aims to reveal the most suitable alternative routes for a forest road, especially in areas susceptible to landslides, and to provide an effective tool for decision-makers. Results: For this purpose, two models were developed through ML: Logistic Regression (LR) and Random Forest (RF). Elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from the fault, the road, and the stream, and lithology were considered as the main landslide susceptibility factors in these models. The best model was obtained by the RF approach with an Area Under ROC Curve (AUC) value of 81.9%, while the LR model was 78.2%. LSM data was used as a base, and alternative routes were obtained through CostPath analysis. Conclusion: It has been shown that the ML methods used in this study can positively contribute to decision-making by providing more effective LSM calculations in studies to determine alternative routes in a forest road network.
DOI: 10.6084/m9.figshare.21744162
title: Microscopic identification of brazilian commercial wood species via machine-learning

description: ABSTRACT Background: Multiple challenges are faced by industry and certification agencies when commercializing tropical species. Anatomical similarities of tropical hardwoods impair identification. Deep learning models can facilitate microscopic identification of wood by using sophisticated techniques such as deep convolutional networks (DCNN). Our objective was to microscopically identify 23 commercially available Brazilian wood species using a custom DCNN model. Results: Photographs from microscopic slides of each wood species were processed, and the final data set contained 2,448 images. We applied stratified k-fold cross-validation technique during training to increase model’s robustness and trustworthiness. Thus, the dataset was divided into approximately 80% training (1,958 images) and 20% validation (490 images) for each fold. A series of augmentations were performed only on training data to include variations in rotation, zoom, and perspective. Image augmentation was performed on-the-fly. The network consisted of convolutions, max pooling, global average pooling, and fully connected layers. We tested the performance of the DCNN against accuracy, precision, recall, and F1-score on the validation set for each fold. Conclusion: The custom machine learned model accuracy was higher than 0.90. The model’s worst performance was identified in distinguishing between Toona ciliate and Khaya ivorensis, which was due more to wood variability than to a machine learning deficiency. Future studies should focus on integration, verification/monitoring, and updating of current models for end user manipulation, trust, ethics, and security.