Miracle-dz/UniFAR-FTUs huggingface dataset:
Birds of the Burn: Avian Community and Functional Guild Variation Five Years Post-Fire in Warm–Dry Mixed Conifer, Southwest Colorado

Birds contribute to the trophic interactions within mixed conifer ecosystems and provide a suite of services, such as nutrient transport, seed dispersal, habitat creation, and insect regulation. Avian communities vary in response to the structure and composition of their habitat, which may be drastically altered by fire, the predominant disturbance of western mixed conifer forests. We conducted avian point count surveys during the peak breeding season, five years post-fire, across four burn severities (unburned, low, moderate, and high) within the 416 Fire perimeter, a 55,000-acre mixed-severity fire that burned near Durango, Colorado in 2018. Avian communities in each burn severity were evaluated for richness, diversity, differentiation, indicator species, and functional guild composition. Species assemblages were significantly different across all burn severities, excluding the low to moderate areas comparison, with differentiation driven by live tree and snag density. Avian species’ richness and diversity were not significantly different across burn severities, highlighting the importance of utilizing multivariate community analysis. Unburned and high-burn areas had significant variation in functional guilds and numerous indicator species. This study provides evidence of avian community differentiation by burn severity, suggesting that management practices promoting heterogenous stand structure in warm–dry mixed conifer will positively influence avian biodiversity.
Inferring Magma Dynamics at Veniaminof Volcano Via Application of Ambient Noise

Ambient noise interferometry has become an increasingly popular tool for monitoring active volcanoes. We apply this method to investigate seven past eruptive periods at Veniaminof volcano, Alaska. Two of the largest eruptions studied show seismic velocity changes associated with preeruptive , coeruptive, and posteruptive volcanic processes. We develop and implement new analysis techniques to determine how seismic velocity changes at Veniaminof are distributed with depth. Spatiotemporal examination of these seismic velocity changes reveals evidence for the distribution of magma storage and the timescale at which magmatic fluids intrude into and reside within these storage regions in the months preceding eruption. We conduct the depth analysis using data recorded on a single seismometer. The same analysis could be applied to any volcano monitored by at least one seismometer in order to detect magmatic activity indicative of impending eruption, specifically the intrusion and migration of magmatic fluids into the volcanic system.
The impact of number and spatial distribution of GCPs on the positional accuracy of geospatial products derived from low-cost UASs

ABSTRACT Currently, orthophotos and Digital Terrain Models (DTMs) are commonly being generated from low-cost consumer-grade Unmanned Aerial Systems (UASs) using well-known workflows based on photogrammetric and structure-from-motion methods. To achieve high accuracy positioning standards Ground Control Points (GCPs) have to be used in the workflow. In this paper, we assess the impact of number and spatial distribution of GCPs on the positional accuracy of these two geospatial products. By using 3 well-known mapping accuracy standards and 170 checkpoints, the planimetric and altimetric accuracy is assessed for 13 cases of GCPs configuration. The results show that in general, the UAS workflow presents a better accuracy in planimetry than in altimetry. In addition, the application of the mapping standards shows that the two products are suitable for GIS and mapping applications requiring spatial details equivalent to scales less than or equal 1:3000. We also conclude that the insertion of GCPs in the central part of the block does not contribute significantly to an increase in the planimetric accuracy of the geospatial products. Finally, for the proposed minimum photogrammetric overlaps (70% and 35%), the ideal GCP distribution consists of a uniform distribution of GCPs. First, on the central part the block with a horizontal separation of 3 to 4 ground base units for the altimetric component. Second, on the periphery of the block with a separation of 7 to 8 ground base units for the planimetric component.
EFFECT OF SERVICE QUALITY DELIVERY AND CUSTOMER SATISFACTON ON CUSTOMER LOYALTY IN SOME SELECTED HOSPITALS IN JOS METROPOLIS

The study evaluated the effect of service quality delivery and customer satisfaction on customer loyalty in selected hospitals in Jos North Metropolis. The main objective of the study was to ascertain the extent to which customer satisfaction mediates the relationship between service quality and customer loyalty in selected hospitals in Jos North Metropolis. The total populations of 456,568 customers of the selected hospitals were used for the study. The study adopted survey method of research with the use of questionnaire to collect data from 400 sampled customers, out of which 360 were duly completed and retrieved. Simple random sampling technique was adopted for the study while Co-variance Structural Equation Modeling (CB-SEM) was used in analyzing the data. From the results, hypothesis one, two and three were tested and it was found that there are significant relationship between service quality delivery and customer loyalty, there is a significant relationship between service quality delivery and customer satisfaction, there is a significant relationship between customer satisfaction and customer loyalty respectively. It was confirmed that customer satisfaction mediates the relationship between service quality delivery and customer loyalty. The study concluded that service quality delivery alone does not ensure long term loyalty of customers but paying attention to customer satisfaction in any organization will further ensure effective customer loyalty. The study recommended that health service organizations should recognized that in the midst of competition, service quality delivery is necessary to sustain loyalty, but where quality is given and satisfaction is not sustained, the tendency of loyalty of such customer will be very low since customers are rational beings; therefore, the study further recommends that health organizations should build up the satisfaction level of their customers by improving on their services, this will go a long way in strengthening the relationship between service quality delivery and customer loyalty.
The Path from Community College to Engineering Bachelor’s Degree Through Partnerships and NSF S-STEM Funded Scholarships

In the spring of 2014, Governor Bill Haslam signed Tennessee Promise into law; making it possible for Tennessee high school graduates to complete two years of community college (CC) free of charge. In the fall of 2016, the first cohort of Tennessee Promise students began the transition from two-year pre-engineering CC curricula to four-year institutions in order to complete their engineering education. As a result of the National Science Foundation S-STEM grant program, Lipscomb University has been able to provide significant scholarship assistance by way of the Enhancing Engineering Talent in Tennessee S-STEM grant that is designed to provide tuition assistance to Tennessee CC students as they transition to Lipscomb’s 4-year ABET accredited programs. During the course of this grant, there have been 20 NSF S-STEM scholarship recipients admitted to Lipscomb’s engineering college from four different public Tennessee CCs. This student population represents a wide range of academic and life experiences that present unique challenges with regard to their transition into a four-year program. An important aspect of this grant includes partnering with CCs in the Middle Tennessee area to provide STEM enrichment and early pre-transfer advising. This paper presents a comparison of the state-wide approved Tennessee CC Engineering Pathway curricula with Lipscomb University’s engineering curricula in Civil Engineering, Electrical/ Computer Engineering, and Mechanical Engineering and the harmonization of the different curricula. The effect of this program regarding perceived barriers to the success of CC students and CC transfers; changes in faculty and staff perceptions regarding CC transfers; and the effects of the program on CC student matriculation and completion of Lipscomb’s engineering program are presented.
Pharmacological Targeting of Ferroptosis in Cancer Treatment

A non-apoptotic iron-dependent form of Regulated Cell Death (RCD) known as ferroptosis is brought on by an excess of harmful lipid peroxides and iron overload. Inhibiting the antioxidant defense system results in overwhelming of GSH dependent pathway and building up iron-dependent Reactive Oxygen Species (ROS) that react with polyunsaturated fatty acids in large quantities can both cause ferroptosis. Recent research has shown that ferroptosis holds a great deal of promise for preventing tumor cell resistance and limiting growth and spread. Emerging evidence also suggests that ferroptosis plays a dual role in human cancer. However, the precise underlying molecular mechanisms and their different role in tumorigenesis are unclear. Therefore, in this review we summarize and briefly present the key pathways of ferroptosis, its dual role as an oncogenic and as a tumor suppressor event in human cancers, paying special attention to the regulation of ferroptosis along with a variety of current medications and naturally occurring substances that may one day be used to target ferroptosis in tumor cells. Thus, addressing this sort of cell death could be seen as a potentially expanding technique in cancer treatment. Consequently, this will offer crucial viewpoints for next research on ferroptosis-based cancer treatment. Keywords: Ferroptosis, antioxidant defense system, Cancer
Reinforcement learning for imitating constrained reaching movements

The goal of developing algorithms for programming robots by demonstration is to create an easy way of programming robots such that it can be accomplished by anyone. When a demonstrator teaches a task to a robot, he/she shows some ways of fulfilling the task, but not all the possibilities. The robot must then be able to reproduce the task even when unexpected perturbations occur. In this case, it has to learn a new solution. Here, we describe a system to teach the robot constrained reaching tasks. Our system is based on a dynamic system generator modulated by a learned speed trajectory. This system is combined with a reinforcement learning module to allow the robot to adapt the trajectory when facing a new situation, e.g., in the presence of obstacles.
Integrative analysis of RNA-sequencing and microarray for the identification of adverse effects of UVB exposure on human skin

Background Ultraviolet B (UVB) from sunlight represents a major environmental factor that causes toxic effects resulting in structural and functional cutaneous abnormalities in most living organisms. Although numerous studies have indicated the biological mechanisms linking UVB exposure and cutaneous manifestations, they have typically originated from a single study performed under limited conditions. Methods We accessed all publicly accessible expression data of various skin cell types exposed to UVB, including skin biopsies, keratinocytes, and fibroblasts. We performed biological network analysis to identify the molecular mechanisms and identify genetic biomarkers. Results We interpreted the inflammatory response and carcinogenesis as major UVB-induced signaling alternations and identified three candidate biomarkers (IL1B, CCL2, and LIF). Moreover, we confirmed that these three biomarkers contribute to the survival probability of patients with cutaneous melanoma, the most aggressive and lethal form of skin cancer. Conclusion Our findings will aid the understanding of UVB-induced cutaneous toxicity and the accompanying molecular mechanisms. In addition, the three candidate biomarkers that change molecular signals due to UVB exposure of skin might be related to the survival rate of patients with cutaneous melanoma.
Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test

Early diagnosis is crucial in Alzheimer’s disease both clinically and for preventing the rapid progression of the disease. Early diagnosis with awareness studies of the disease is of great importance in terms of controlling the disease at an early stage. Additionally, early detection can reduce treatment costs associated with the disease. A study has been carried out on this subject to have the great importance of detecting Alzheimer’s disease at a mild stage and being able to grade the disease correctly. This study’s dataset consisting of MRI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was split into training and testing sets, and deep learning-based approaches were used to obtain results. The dataset consists of three classes: Alzheimer’s disease (AD), Cognitive Normal (CN), and Mild Cognitive Impairment (MCI). The achieved results showed an accuracy of 98.94% for CN vs AD in the one vs one (1 vs 1) classification with the EfficientNetB0 model and 99.58% for AD vs CNMCI in the one vs All (1 vs All) classification with AlexNet model. In addition, in the study, an accuracy of 98.42% was obtained with the EfficientNet121 model in MCI vs CN classification. These results indicate the significant potential for mild stage Alzheimer’s disease detection of Alzheimer’s disease. Early detection of the disease in the mild stage is a critical factor in preventing the progression of Alzheimer’s disease. In addition, a variant of the non-parametric statistical McNemar’s Test was applied to determine the statistical significance of the results obtained in the study. Statistical significance of 1 vs 1 and 1 vs all classifications were obtained for EfficientNetB0, DenseNet, and AlexNet models.
Validity Study for Clinical Use of Hand-Held Spirometer in Patients with Chronic Obstructive Pulmonary Disease

A spirometer is a medical device frequently used clinically for the diagnosis and prediction of lung disease. This study aimed to investigate the clinical usefulness of a hand-held spirometer (The Spirokit), compared with conventional spirometry in patients with chronic obstructive pulmonary disease (COPD). This study was conducted from February 2022 to October 2022. Measurements from 80 patients with COPD (male: 53, female: 27) were obtained using The Spirokit and PC-based pulmonary function test equipment, and the resulting values were compared and analyzed. For the concurrent validity comparison of The Spirokit, the intra-class correlation (ICC 2, 1), coefficients of variation (CVME), 95% limits of agreement (95% LOA), and Cohen’s Kappa Index were analyzed. The Spirokit showed high agreement (ICC: 0.929–0.989; 95% LOA: −0.525 to 2.559; and CVME: 0.05–0.08) with the PC-based pulmonary function tester. Using the Cohen’s kappa coefficients, the device showed high sensitivity, specificity, and accuracy scores of Pa: 0.90, Pc: 0.52, and K: 0.79, respectively, indicating considerable agreement. The Spirokit, a portable pulmonary function test device, is a piece of equipment with high validity and portability, with high potential for replacing PC-based pulmonary function test equipment.
Distribution and storage of soil organic and inorganic carbon under different ecological zones in Xinjiang, China

The objective of this study was to quantify both soil organic carbon (SOC) and soil inorganic carbon (SIC) stocks in different ecological zones in Xinjiang Region, the largest arid and semi-arid region in northwest China. The specific focus was on the vertical distributions of 641 typical soil profiles (0-100 cm). The study region covered five ecological zones: I–Altai/west Junggar; II–Junggar basin; III–Tianshan mountain; IV–Tarim basin; and V–Kunlun-Altun mountains. The zones are categorized by their specific geographical locations from north to south with terrains derived from mountains to basins. The data used in the study were obtained from the first (1960s) and the second (1980s) National Soil Surveys and partially from the field survey of this study conducted in 2013. The results suggest that there are 11.74 Pg SOC and 26.71 Pg SIC total stocks in the 0-100 cm surface soil over the entire study region. The distributions of SOC and SIC were found to be non-uniform. The Tianshan mountain zone has the highest SOC stock, followed by the Tarim basin, Kunlun-Altun mountains, Altai and west Junggar (Altai/west Junggar), and Junggar basin zones. In contrast, the Tarim basin zone had the highest SIC stock, followed by the Tianshan mountain, Kunlun-Altun mountains, Junggar basin, and Altai/west Junggar zones. The SOC content decreases gradually from northwest to southeast and from mountains to deserts; while the SIC content decreases gradually from south to north. The SOC and SIC contents also change with soil depth. Within a given ecological zone, the SOC content increased with increasing soil depth, peaked at about 20-40 cm, then it decreased with the bigger depths below 40 cm. The SIC contents increased gradually from 0 to 40 cm, and then decreased gradually with increasing soil depth over the 40-100 cm depth in all ecological zones except for the Tianshan mountain area. Keywords: ecological zones, soil organic carbon, soil inorganic carbon, carbon storage, vertical distribution DOI: 10.25165/j.ijabe. 20191201.3872 Citation: Yan A, Li B G, Huang F, Zhang W T, Jiang P A, Sheng J D. Distribution and storage of soil organic and inorganic carbon under different ecological zones in Xinjiang, China. Int J Agric & Biol Eng, 2019; 12(1): 116–125.
Classifying breast cancer using multi-view graph neural network based on multi-omics data

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes. Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction. Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data. Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.
The Application of Protective Cultures in Cheese: A Review

A number of non-thermal preservation strategies have been adopted from the dairy industry to improve cheese quality and safety. The application of lactic acid bacteria cultures that produce bacteriocins has been extensively studied as a means of bio-preservation. However, the application of purified bacteriocins as a bio-protective agent is limited in cheese. The application of protective cultures is another strategy, and the aim of the current review is to provide an overview of the application of commercial and autochthonous adjunct cultures on the bio-protection of cheese; both public health and spoilage aspects are considered.
A Quality Management Method for Prefabricated Building Design Based on BIM and VR-Integrated Technology

Quality management in the design phase is crucial for determining the overall quality of prefabricated buildings. However, traditional design methods can no longer meet the complex design, component, and nodal requirements of prefabricated buildings. This study proposes a quality management framework for the prefabricated building design phase based on building information modeling (BIM) and virtual reality (VR) technologies to enhance design precision and satisfaction. Applying this framework to a prefabricated building project in Shenzhen, China, it was found that compared to traditional 2D drawing methods, the design issues feedback during drawing reviews decreased by 41.35%. Compared to solely using BIM technology, the number of design collisions identified through collision detection increased by 28.35%, and feedback on design issues during drawing reviews decreased by 15%. Furthermore, the framework was tested to prove its usability, effectiveness, and functionality. The framework, integrating the rich architectural information of BIM with the immersive experience of VR, contributes to focusing on the design process, improving design tools, optimizing design workflows, significantly reducing design errors, and enhancing the quality of prefabricated buildings.
Genetic analysis for morphological, quality and biofuel related traits in sweet sorghum [Sorghum bicolor (L.) Moench]

The present investigation was aimed to study the relationship between different methods of diallel data analysis of ethanol yield traits in sweet sorghum to select the relevant superior genotypes. Half-diallel hybrid analysis revealed that both general and specific combining abilities were significant for all the traits studied but the specific combining ability (SCA) was higher than the general combining ability (GCA), suggesting that the non-additive gene action is more important than additive one in controlling the studied traits. Graphically, traits of juice extraction percent, juice yield, and stripped stalk yield were most correlated with ethanol yield. Griffing and GT-biplot analyses produced equivalent results for GCA and SCA and were useful in estimating gene action by identifying the superior crosses across all the correlated traits and comparing the genotypes based on multiple traits. For the ethanol yield trait, genotype, Rio showed good GCA and was considered the best combiner parent, while crosses, Sugar Drib× Ramada and Sugar Drib × Honey may be used for the selection of the relevant traits in sorghum breeding programs to improve ethanol yield trait.