bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effects of long-term molybdenum exposure in drinking water on molybdenum metabolism and production performance of beef cattle consuming a high forage diet
Authors: Thorndyke M. P. , Guimaraes O. , Medrado M. , Loh H. Y. , Tangredi B. V. , Reyes A. , Barrington B. K. , Schmidt K. , Tilquist N. M. , Li L. , Ippolito J. A. , Zervoudakis J. T. , Wagner J. J , Engle T. E. 
Published: September 2022

Abstract
Fifty-four multiparous beef cows with calves were used to evaluate the effects of Mo source (feed or water) on reproduction, mineral status, and performance over two cow-calf production cycles (553 d). Cows were stratified by age, body weight, liver Cu and Mo status, and were then randomly assigned to one of six treatment groups. Treatments were: 1) Negative control (NC; basal diet with no supplemental Mo or Cu); 2) Positive control (NC + Cu; 3 mg of supplemental Cu/kg DM); 3) NC + 500 µg Mo/L from Na2MoO4·2H2O supplied in drinking water; 4) NC + 1000 µg Mo/L of Na2MoO4·2H2O supplied in drinking water; 5) NC + Mo 1000-water + 3 mg of supplemental Cu/kg DM; and 6) NC + 3.0 mg of supplemental Mo/kg diet DM from Na2MoO4·2H2O. Animals were allowed ad libitum access to both harvested grass hay (DM basis: 6.6% crude protein; 0.15% S, 6.7 mg Cu/kg, 2.4 mg Mo/kg) and water throughout the experiment. Calves were weaned at approximately 6 months of age each year. Dietary Cu concentration below 10.0 mg Cu/kg DM total diet reduced liver and plasma Cu concentrations to values indicative of a marginal Cu deficiency in beef cows. However, no production parameters measured in this experiment were affected by treatment. Results suggest that Mo supplemented in water or feed at the concentrations used in this experiment had minimal impact on Cu status and overall performance.
Dimensionally Reduced Machine Learning Model for Predicting Single Component Octanol-Water Partition Coefficients
Authors: Kenney David H. , Paffenroth Randy C. , Timko Michael T. , Teixeira Andrew R. 
Published: September 2022

Abstract
MF-LOGP, a new method for determining a single component octanol-water partition coefficients LogP is presented which uses molecular formula as the only input. Octanol-water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,420 data points, using 10 features derived from the molecular formula to make LogP predictions. Using an independent validation set of 2,722 data points, MF-LOGP was found to have an average RMSE = 0.77 ± 0.007, MAE = 0.52 ± 0.003, and R2 = 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models RMSE = 0.42–1.54, MAE = 0.09–1.07, and R2 = 0.32–0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures.
Modulatory role of curcumin on cobalt-induced cognitive deficit, hippocampal oxidative damage, astrocytosis, and Nrf2 expression
Authors: Oria Rademene , Emeka Anyanwu , Esom Emmanuel , Nto Johnson , Katchy Amechi , Agu Augustine , Ijomone Omamuyovwi 
Published: September 2022

Abstract
Chemical overexposure is a growing environmental risk factor for many medical issues. Cobalt toxicity from environmental, industrial, and medical exposure, has previously been linked to neurological impairment. Hence, the current study looked into the neuroprotective potential of curcumin, a natural polyphenol contained in spice turmeric, against cobalt-induced neurotoxicity. Adult rats were randomly divided into six groups as follows: control, 40 mg/kg cobalt chloride (CoCl2) only, 240 mg/kg curcumin only, 120 mg/kg or 240 mg/kg curcumin or 100 mg/kg Vitamin C co-administered with CoCl2. The administration was via oral route daily for four weeks. After that, neurobehavioral tests were undertaken to evaluate short-term spatial memory. Biochemical investigation was performed to determine the hippocampal levels of status via measures of SOD, CAT, GST, and LPO). Furthermore, immunohistochemical assessment of the expression of GFAP and Nrf2 in the hippocampus was carried out. In the CoCl2 group, the results showed altered behavioral responses, a decrease in antioxidant activities, increased expression of GFAP and the number of activated astrocytes, and decreased immunoexpression of Nrf2. These effects were mitigated in the Curcumin and Vitamin C treated groups. These results collectively imply that curcumin enhances cognitive functions in rats exposed to cobalt possibly by attenuating oxidative responses, mitigating astrocytosis, and modulating Nrf2 signaling.
Histo-Fusion: A novel domain specific learning to identify Invasive Ductal Carcinoma (IDC) from Histopathological Images
Authors: Jawad M Abdul , Khursheed Farida 
Published: September 2022

Abstract
Today breast cancer is the leading type of cancer among women undergoing cancer screening. A slight delay in detecting and diagnosing this disease may result in irreversible convolutions. Histopathological images from the biopsy examination present a large amount of structural information that can significantly improve the prognosis for breast cancer. The pathological analysis, which involves the microscopic examination of the histopathological slides, is a challenging task. An automated computer-aided detection (CAD) procedure is inevitable, as it may decrease the pathologist examination time and help detect the disease at an early stage. Lately, deep learning methods using artificial neural networks are consistently in use to improve the performance of CAD methods. A common practice among recent studies is to use the transfer learning approach of training deep neural network architectures. Transfer Learning is an established learning approach that facilitates a deep neural network to train quickly on a specific dataset and resolve an interdisciplinary problem. Deep Learning methods employing the transfer learning approach have provided highly competitive results on the datasets consisting of the whole slide images, which are captured generally at high resolutions. However, the performance is not remarkably appreciable on the small and low-resolution image datasets, in particular the datasets that include patch samples. In this direction, the present study proposes a novel domain-specific learning strategy, Breast Histo-Fusion, which aims to detect breast cancer even from images of low resolution and small size. Further, four state-of-the-art deep CNNs (AlexNet, VGG19, ResNet, and DenseNet) are trained using both learning approaches: Transfer Learning and Histo-Fusion on the IDC dataset. The proposed Histo-Fusion learning approach has improved the discriminating abilities and performance of each deep CNN by providing better results of (AlexNet – 95.75%, VGG19–95.96%, ResNet34–96.17%, ResNet50–96.67%, and DenseNet121–97.49%) compared to (AlexNet – 90.41, VGG19–90.51%, ResNet34–90.83%, ResNet50–92.27%, and DenseNet121–93.05%) using the transfer learning strategy. As a result, the procedure can help expert pathologists to perform accurate diagnoses and reduce false-positive rates.
An Automated Method For Stem Diameter Measurement Based on Laser Module and Deep Learning
Authors: Wang Sheng , Li Rao , Li Huan , Ma Xiaowen , Ji Qiang , Xu Fu , Fu Hongping 
Published: September 2022

Abstract
Background: Stem diameter (SD) measuring is a crucial foundation for forest resource management. SD measuring in various scenarios necessitates the involvement of expert personnel. As a result, the adoption of automatic measurement methods is greatly expected. In this study, we built a device with an integrated image sensor and laser module to measure the SD of trees of varied diameters. The spot generated by the laser module on the tree stem can be utilized as reference information for measuring height and SD with this device. An end-to-end model is performed to identify the trunk in the panchromatic image, then SD is calculated from the linear relationship between the trunk contour and the spot diameter in pixels.
Results: We measured in three scenarios with different land cover types, i.e., transitional woodland/shrub, mixed forest, and green urban area. The overall SD ranges from 2.00 cm to 89.00 cm. To investigate the differences in measurements in different natural scenarios, we analyzed the accuracy of the device for measuring trees of different diameters. The results showed a high consistency between our method and the tape measure measurements. The absolute mean error was 0.36 cm and the root mean square error was 0.45 cm. Compared to most studies, our method demonstrates better versatility and exhibits higher performance. Our integrated device is low cost, portable, and without the assistance of a tripod.
Conclusion: Our method achieves the objective of automated measurements and is experimentally proven to possess the potential of efficient and accurate measurement of SD in natural scenarios. Although some limitations remain at present, the device can be employed as a means of forest inventory. In the future, the device can be further explored to be integrated into autonomous mobile robots for more scenarios.
Pediatric Central Nervous System Tumor Survivor and Caregiver Experiences with Multidisciplinary Telehealth
Authors: Cacciotti Chantel , Chua Isaac S. , Cuadra Jennifer , Ullrich Nicole J. , Cooney Tabitha M. 
Published: September 2022

Abstract
Purpose: Telehealth use to facilitate cancer survivorship care is accelerating; however, patient satisfaction and barriers to facilitation have not been studied amongst pediatric central nervous system (CNS) tumor survivors. We assessed the telehealth experiences of survivors and caregivers in the Pediatric Neuro-Oncology Outcomes Clinic at Dana-Farber/ Boston Children’s Hospital.
Methods: Cross-sectional study of completed surveys among patients and caregivers with ≥ 1 telehealth multidisciplinary survivorship appointment from January 2021 through March 2022.
Results: Thirty-three adult survivors and 41 caregivers participated. The majority agreed or strongly agreed that telehealth visits started on time [65/67 (97%)], scheduling was convenient [59/61 (97%)], clinician’s explanations were easy-to-understand [59/61 (97%)], listened carefully/addressed concerns [56/60 (93%)], and spent enough time with them [56/59 (95%)]. However, only 58% (n=35/60) of respondents agreed or strongly agreed they would like to continue with telehealth and 48% (n=32/67) agreed telehealth was as effective as in person office visits. Adult survivors were more likely than caregivers to prefer office visits for personal connection [23/32 (72%) vs 18/39 (46%), p=0.027].
Conclusion: Offering telehealth multi-disciplinary services may provide more efficient and accessible care for a subset of pediatric CNS tumor survivors. Despite some advantages, patients and caregivers were divided on whether they would like to continue with telehealth and whether telehealth was as effective as office visits. To improve survivor and caregiver satisfaction, initiatives to refine patient selection as well as enhance personal communication through telehealth systems should be undertaken.
The extent of trees in the tropics
Authors: Brandt John , Ertel Jessica , Spore Justine , Stolle Fred 
Published: September 2022

Abstract
Remote sensing-based assessments of tree cover and extent provide insight into global and local land use planning. However, there remains considerable uncertainty about the extent of drylands forests and trees in areas with non-forest land use. Here, we present a 10-meter tree extent map for 4.35 billion hectares in the tropics (-23.44° to 23.44° latitude) based on multitemporal composites of Sentinel-1 and Sentinel-2 analyzed with convolutional neural networks. We identify 2,719 million hectares of tropical land with ≥10% tree cover, including 680 million hectares in drylands. We find that 52% of tropical cropland and 46% of tropical urban areas had ≥10% tree cover in 2020. Compared to previous datasets, we demonstrate improved ability to map fragmented and open canopy forests at small spatial scales, especially in drylands, urban areas, and on cropland. As such, this data is expected to enable improved monitoring of tree dynamics at local scales.
Elucidating Precipitation in FeCrAl Alloys through Explainable AI: A Case Study
Authors: Roy Indranil , Ravi Sandipp Krishnan , Roychowdhury Subhrajit , Feng Bojun , Ghosh Sayan , Reynolds Christopher , Umretiya Rajnikant V. , Rebak Raul B. , Hoffman Andrew K. 
Published: September 2022

Abstract
Primary challenge of using FeCrAl in high temperature industrial setting is the formation of α’- precipitates that causes brittleness in the alloy, resulting in failure through fracture. The precipitation causes hardness change during thermal aging which is sensitive to both alloy composition and experimental condition (i.e., temperature and time of heat treatment). A Gaussian Process Regression (GPR) model is built on the hardness data collected at GE Research. Subsequently, for the first time, SHapley Additive exPlanations (SHAP) built upon the GPR is used as an Explainable Artificial Intelligence (XAI) tool to understand the effect of feature values in driving the hardness change. Several key insights are affirmed and obtained from a material science perspective.
Solid state fermentation as a comprehensive tool in producing low cost shrimp feed
Authors: Jannathulla R. 
Published: September 2022

Abstract

Fermented plant proteins are being used as a potential alternative in shrimp feed with varying degrees of success due to the demand and high cost of fishmeal. However, the greater part of earlier reports restricted in assessing their nutrient utilization rather than addressing the economic benefits. Hence, the present study is aimed to derive the economic efficiency of solid-state fermentation (SSF) using our earlier reports. Fishmeal, in our earlier studies, was gradually replaced (w/w) by using both various untreated and fermented plant proteins. According to our earlier results, fermentation significantly (P < 0.05) increased the range of inclusion level of plant proteins from 2.5–25 to 5–35%. The formulation cost was computed in the present study based on ingredients cost obtained from three different feed mills, in India, and was INR 63.5/kg for a diet containing no fishmeal replacement (control). Substitution of fishmeal using both untreated and fermented ingredients reduced the formulation cost and the reduction was INR 3.5–6.1/kg for fermented ingredients, while it was 1.5-3.0/kg for untreated ones. Results conclude that SSF could be a comprehensive tool in producing low-cost shrimp feed.
A tri-level fortification model for edge protection against intentional attacks in the capacitated vehicle routing problem
Authors: Nobari E. , Shirazi Mohsen Akbarpour , MirHassani S.A. 
Published: September 2022

Abstract
In recent decades, the issue of interdiction in human-made infrastructures has become increasingly important. The world, after 9/11, recognized the importance of addressing major infrastructures vulnerabilities and protecting them against intentional disruptions. Networks such as transportation networks or electrical grids are one of the most important network infrastructures in which any disruption can cause irreparable costs and customer service declination. Therefore, the phenomenon of interdiction and fortification of network components against possible attacks such as terrorist acts or military operations has been of great importance in the field of research. In this research we develop a tri-level model to obtain optimal edge protect plan for existing network against deliberate attacks on the capacitated vehicle routing problem (CVRP). The vehicle routing problem is one of the most important issues in the distribution and transportation field, where a fleet of vehicles tries to meet demand points in the least possible cost. Protecting edges from deliberate disturbances in this issue has not been addressed in the literature so far. The problem is strictly NP-Hard and we employ the heuristic approaches to solve it. The main idea behind our approach is to iteratively sample the tri-level solution space so that instead of solving the CVRP. This approach called backward sampling framework. We also introduce a Nested Genetic Algorithm (NGA) to accelerate algorithm implementation which numerical experiments prove the effectiveness of this approach in solving the problem.
Apple leaf disease identification via improved CycleGAN and Convolutional Neural Network
Authors: Chen Yiping , Pan Jinchao , Wu Qiufeng 
Published: September 2022

Abstract
The identification of apple leaf diseases is crucial to reduce yield reduction and timely take disease control measures. Employing machine learning-based methods, such as deep learning for accurate identification of multiple apple leaf diseases is challenging because of the limited availability of samples for supervised training and the serious class imbalance. Hence, this paper proposes an accurate deep learning-based pipeline to solve the problem of limited data sets on farms and reduce the partiality due to serious class imbalance. Firstly, this paper proposes the improved cycle-consistent adversarial networks (CycleGAN) to generate synthetic samples to improve the learning of data distribution and solve the problems of small data sets and class imbalance. On the basis of CycleGAN, two discriminators are introduced: one to judge whether the image is true or false, and one to judge whether the leaf image has disease. Specifically, healthy leaves are converted into disease-carrying leaves using two models, health-to-scab and health-to-rust to balance apple leaf disease datasets. Secondly, ResNet is trained as a baseline convolutional neural network classifier to classify apple leaf diseases. In experimental results, this paper carries out some experiments on evaluation of quality of the generated images by the improved CycleGAN and the performance of ResNet in terms of datasets and metrics. First of all, through qualitative observation of generated images and quantitative metrics, such as GAN-train and GAN-test, the generated diseases images from healthy image by the improved CycleGAN are superior to state of the art. Second, some experiments were carried out to verify the performance of the classification model, such as comparing five mainstream classification models and comparing them with traditional data augmentation methods. The results show that ResNet has the highest recognition accuracy on the test set, reaching 97.78%, and the classification accuracy is significantly improved by the generated synthetic samples (+14.7%). Finally, the experiment result of t-Distributed Stochastic Neighbor Embedding (t-SNE) visually confirmed that the images generated by improved CycleGAN have much better quality and are more convincing. Beyond that, a Visual Turing Test with three botanists showed that the generated images are indistinguishable from real apple leaf images.
Factors predicting proper handwashing practice amidst the COVID-19 pandemic in India: a field-based cross-sectional study
Authors: Gupta Shibaji , Das Debasis , Chakraborty Arup , Banerjee Rudradeep 
Published: September 2022

Abstract
Proper handwashing is one of the effective ways to prevent many communicable diseases, including COVID-19. We explored the handwashing practices in a rural Indian population before the probable third wave of the COVID-19 pandemic. A data collection schedule was administered to eligible adult members of a rural community, selected by multi-stage sampling, to assess their pattern and practice of handwashing. All 176 respondents washed their hands after defecation, 82.4 and 80.7% washed hands after urination and before taking food, respectively, while 68.2% of respondents washed hands after coming back from outdoors. Among those who handwashed, 82.9% used soap water after defecation; 46.2, 45.8 and 50.8% washed hands with soap water after urination, before taking food and after visiting outdoors, respectively. Only a quarter (24.4%) of all the participants used soap water for handwashing consistently after defecation, after urination, before taking food and after coming home. The more educated, those coming from higher socioeconomic stratum and working from home, were more likely to report proper handwashing practice. Handwashing, as recommended by health agencies, for restraining COVID-19 infection, was not noticed in the majority of the participants. Better awareness of handwashing is recommended to help restrain COVID-19 in the Indian population.
On the calculation of a set of spare parts that have two types of failures
Authors: Antonov A. V. , Chepurko V. A. 
Published: September 2022

Aim.The paper aims to develop an algorithm that would allow finding the requirednumber of items (SPTA) for a complex system, whose elements may or may not be maintainable.Methods.Markov models were used for describing the system. The final probabilities wereobtained using the Kolmogorov equation. The Kolmogorov system of equations has a stationarysolution. Classical methods of the probability theory and mathematical dependability theory wereused.Conclusions.The paper formalizes the problem of determining the required number ofSPTAsfor a system with elements that have a probability of being repaired. A Markov graph is constructed. Using induction, a stationary solution was found for the Kolmogorov system ofequations. An example of finding the required number of SPTAs is given.