bluuebunny/crossref_metadata_2025_split huggingface dataset:
President’s Column: Maximizing Your Membership by Leveraging SPE’s Multimedia Content
Authors: Palisch Terry 
Published: March 2024

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Editor’s Note: This month we are introducing a summary of the full transcript. We encourage you to listen to the podcast episode to hear the full conversation on your favorite podcast platform under “The SPE Podcast.”

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In this podcast episode, I am joined by Dana Otillio, vice president of marketing and communications at SPE, to discuss how members can maximize their membership by leveraging multimedia content. The episode covers various aspects, including SPE Live, webinars, online training, and the Distinguished Lecturer (DL) Program. The conversation delves into the history and evolution of SPE’s multimedia content and its impact on connecting members globally.
I introduce the episode by highlighting the goal of helping members understand how to get the most out of their SPE membership. As I like to do, I liken an SPE membership to a gym membership, emphasizing the importance of exploring all available offerings to create your energy future.
Our discussion delves into the birth of SPE Live during the pandemic, emphasizing its role in overcoming the challenges posed by the inability to hold in-person events. Its success in delivering timely, relevant technical knowledge led to it becoming a series on LinkedIn providing conversational content on global topics.
I also highlight its popularity and mention the addition of Tech Talks, sponsored content sessions that have garnered significant views. To date these have been very popular, streaming four to five SPE Lives a month and garnering 1,600 to 1,700 views live and on-demand per presentation. It’s one of our many growing outlets for multimedia.

Evolution of Multimedia Content
Our discussion then turns to the accessibility of multimedia content through the SPE Energy Stream website, launched in 2022. I describe how it is a one-stop shop for all multimedia content, easily accessible from the spe.org homepage. The website offers webinars, online training, SPE Live, and podcast episodes showcasing the organization’s commitment to providing a comprehensive multimedia experience. I like to think of SPE Energy Stream as the “OnePetro” for multimedia content.
The conversation explores the integration of the DL program with SPE Live, allowing DLs to reach a broader audience through live broadcasts. We take a moment to review the DL program’s significance in connecting with sections worldwide, and the addition of SPE Live as a feature expands the program’s reach.
We distinguish between SPE Live, which focuses on conversational content, and webinars, which are more technical, lasting 60 to 90 minutes. The availability of webinars on-demand and the opportunity to earn continuing education credits are emphasized. Online training courses, both SPE-developed and in partnership with IHRDC, are discussed as more extensive, allowing members to learn at their own pace. In 2019, SPE partnered with IHRDC to link their training content to SPE members. It’s offered through the SPE website and provides a reduced rate for our members.
MicroCT scanning and analysis in the zebrafish skeleton
Authors:   Arianna Ericka Gómez (1,2),   Phil Salmon (4), Baumgarten Nell , Rai Jyoti , Huber Philippe , Simmonds Susana P. , Yase Rohda A. , Reynolds Kiana A. , Tang W. Joyce , Kwon Ronald Young 
Published: March 2024

Abstract

Zebrafish and other small laboratory fish have emerged as important model systems for bone biomedical research. Micro-computed tomography (microCT) is a powerful approach to non-destructively visualize and quantify bone morphology, microarchitecture, and mineral density. While consensus methods for microCT scanning analysis have been established in other vertebrates including small rodents, the small size of zebrafish, their different anatomy, and their amenability to rapid-throughput and high-content approaches call for unique considerations. In this chapter, we describe key considerations for performing microCT scans in the zebrafish skeleton. We also describe different approaches for sample mounting, scan acquisition, and analysis that can be used depending on experimental objectives and outcomes. Finally, we describe a step-by-step protocol for microCT scanning and analysis of adult zebrafish using FishCuT, resulting in hundreds of descriptors of vertebral morphology and mineral density.
Geospatial Mapping Technique: A Study on Morphometry in the Gomti River Basin
Authors: Arvind Arvind , Kumar Surender Kumar Surender , Veer Vikas , Rohtash Rohtash , Anil Anil , Ripudaman Ripudaman 
Published: March 2024

Abstract
Geospatial application is a greater, recently emerging interdisciplinary technology with significant potential for studying geomorphic challenges. The study analysed the morphometric characteristics of the Gomti River basin in Uttar Pradesh, India, using topographical sheet and SRTM data. The linear (stream order and number, mean stream length and ratio, length of overland flow, bifurcation ratio and mean bifurcation ratio; areal (stream density, stream frequency, stream texture and ratio, form factor, elongated ratio, circulatory ratio, shape factor), and relief (basin relief and ratio, gradient ratio) aspects with asymmetric factors have been calculated using various methods. The catchment area of the Gomti River is 31009 km2, while its perimeter is 1435 km2. The results showed that the Gomati River has sub dendric to dendric drainage pattern followed with the natural gradient of the area. The low drainage density estimates of the basin also imply that it has a highly permeable subsurface covered by dense vegetation. The basin is substantially extended, as evidenced by the circularity ratio. The asymmetric factor analysis revealed the tilt and uplift of the Gomati Basin, indicating tectonic control over the basin, particularly near the confluence. The study's goal was to identify the major variables that contributed to the establishment of the watershed.
Antioxident and Anticancer Potency of Hydro-Alcoholic Ingudi (Balanites aegyptiaca Linn. Delile) Seed Extract
Authors: Kumar Sanjeev , Das Manoj Kumar , Singh Neelu , Pal Poonam , Tripathi Nancy , Sharma Rajesh , Jain Shreyans K. , Ranjan Amit , Singh Rajesh Kumar , Mishra Anurag 
Published: March 2024

Abstract
Balanites aegyptiaca is a well-known medicinal plant, which has been traditionally used by local communities to treat various human ailments such as antibacterial, skin diseases, wound healing, and antioxidant properties. This study, examined the phytochemical composition and cell toxicity of Balanites aegyptiaca using established methodologies. The main objectives of this research work are:
1. Evaluation of hydro-alcoholic seed extract in cell viability in breast cancer cell lines.
2. The study significance antioxidant activity.
3. Phytochemical analysis of Balanites aegyptiaca extract.
4. Anticancer activity of Balanites aegyptiaca extract.
The seeds were extracted using a hydro-alcoholic solvent (4:6 ratio), concentrated, and then lyophilized to obtain a dry crude extract. The radical scavenging capacity was determined using the DPPH method, while cytotoxicity was assessed through cell proliferation assays (MTT) involving MCF-7, MDA-MB-231, FaDu, and human PBMCs cells (2×103 cells). Our findings revealed that the hydro-alcoholic extract of the seeds exhibited significant antioxidant and anti-cancer properties. The novelty of this research work is the phytochemicals present in the plant extract that inhibit the migratory ability of breast cancer cells. The recommendations of this study can be adopted in the experimental and clinical trials.
Minimizing the training dataset for efficient crystal structure prediction by graph neural network
Authors: Lin Zijing , Li Chuannan , Liang Hanpu , Xu Si-Yuan , Zhang Xie , Wei Su-Huai 
Published: March 2024

Abstract
Machine learning has boosted the remarkable development of crystal structure prediction (CSP), greatly accelerating modern materials design.The state-of-the-art methods, however, rely on a giant training dataset, and there is a trend to keep increasing the dataset, as triggered by the great success of artificial intelligence models.Here, we show that a giant training dataset is not really necessary.Only around 100 carefully selected training samples for a target composition is already sufficient for highly accurate CSP, which is demonstrated in our newly developed CSP framework named SCCOP.This achievement stems from: (i) graph-deep-learning-based potential energy surface (PES) slicing, which classifies structures into different prototypes; and (ii) a multi-start optimization algorithm to divide-and-conquer the PES.
The accuracy and efficiency of our framework is validated on 27 typical compounds.The powerful capability of SCCOP is further demonstrated in two challenging problems: searching for the lowest-energy structures in boron allotropes and determining the structure of ordered vacancy compound CuIn5Se8.Our method discovers several new boron allotropes, and identifies the puzzling crystal structures of the ordered vacancy compound CuIn5Se8.This study establishes a new paradigm for CSP and offers a highly efficient and generally applicable CSP framework.
Urban Active Transportation Behaviour is Sensitive to the Fresh Start Effect
Authors: McGavock Jonathan , Fast Isaak , Sobhan Shamsia , Kl;aprat Nika , George Tyler , Vik Nils , Prowse Dan , Colllett Jacqueline 
Published: March 2024

Abstract
Municipal governments are investing billions of dollars to facilitate urban active transportation (AT), however, little is known about population-level AT behaviours. Triangulating real world data for cycling traffic (n=1.2M counts), corporate bicycle parking (n=56,307 counts), vehicular traffic (n=6.3M counts) and fitness centre attendance (n=563,290 counts) in a large Canadian city, we show a significant 20% decline in AT-based cycling traffic over the course of a work week, replicated over 5 consecutive years. The decline AT (-147 cyclists/day; 95% CI: -199.0 to -94.6 cyclists/day) and use of occupational bicycle parking (-12 cyclists/day; 95% CI: -17 to -7 cyclists/day) were similar to the weekly decline in fitness centre attendance. In contrast, daily vehicular traffic increased 7% from the beginning to the end of a work week (+2248 cars/day; 95% CI: 2022 to +3674 cars/day). This study provides population-level evidence that urban AT behaviour is susceptible to the “Fresh Start Effect” heuristic.
Industrial agglomeration, university-enterprise cooperation and enterprise innovation
Authors: li chaojing , Li Qiming 
Published: March 2024

Abstract
This paper constructs a fixed-effects model to investigate the relationship between industrial agglomeration, university-enterprise cooperation, and firm innovation using data from 2002–2019 on Chinese A-share listed companies in non-financial industries and the degree of regional agglomeration. The results of the benchmark regressions find that an increase in the level of regional industrial agglomeration reduces the innovation ability of firms, while university-enterprise cooperation enhances the innovation ability. At the same time, we find that university-enterprise cooperation plays a moderating role between industrial agglomeration and firm innovation, and university-enterprise cooperation can mitigate the decrease in innovation caused by industrial agglomeration. The above findings still hold after a series of robustness tests. Further, the results of heterogeneity tests show that the effects of industrial agglomeration and university-enterprise cooperation on firm innovation are influenced by the property rights and the location of the firm. The negative effect of industrial agglomeration on state-owned enterprises' innovation capability is more prominent in the eastern regions. However, the effect of university-enterprise cooperation on improving innovation capacity of non-state-owned enterprises is more significant in the western region.
A tension offloading patch mitigates dermal fibrosis induced by pro-fibrotic skin injections
Authors: Wan Derrick , Talbott Heather , Griffin Michelle , Mascharak Shamik , Parker Jennifer , Kuhnert Maxwell , Guo Jason , Deleon Nestor Diaz , Lavin Christopher , Abbas Darren , Guardino Nicholas , Morgan Annah , Valencia Caleb , Cotterell Asha , Longaker Michael 
Published: March 2024

Abstract

Skin fibrosis is a clinical problem with devastating impacts but limited treatment options. In the setting of diabetes, insulin administration often causes local dermal fibrosis, leading to a range of clinical sequelae including impeded insulin absorption. Mechanical forces are important drivers of fibrosis and, clinically, physical tension offloading at the skin level using an elastomeric patch significantly reduces wound scarring. However, it is not known whether tension offloading could similarly prevent skin fibrosis in the setting of pro-fibrotic injections. Here, we develop a porcine model using repeated local injections of bleomycin to recapitulate key features of insulin-induced skin fibrosis. Using histologic, tissue ultrastructural, and biomechanical analyses, we show that application of a tension-offloading patch both prevents and rescues existing skin fibrosis from bleomycin injections. By applying single-cell transcriptomic analysis, we find that the fibrotic response to bleomycin involves shifts in myeloid cell dynamics from favoring putatively pro-regenerative to pro-fibrotic myeloid subtypes; in a mechanomodulatory in vitro platform, we show that these shifts are mechanically driven and reversed by exogenous IL4. Finally, using a human foreskin xenograft model, we show that IL4 treatment mitigates bleomycin-induced dermal fibrosis. Overall, this study highlights that skin tension offloading, using an FDA cleared, commercially available patch, could have significant potential clinical benefit for the millions of patients dependent on insulin.
A Human Health Risk Assessment (HHRA) associated with gaseous pollutants in the ambient environment at Coimbatore City's High Traffic Road
Authors: karthikeyan Shivaranjani Salem , Arunachalam Gandhimathi 
Published: March 2024

Abstract

Adverse gaseous environmental pollutants are more prevalent due to the exponential rise of motorized road transportation, facilitated by the development of medical centers, IT sectors, and expanding urbanization. Therefore, to calculate the Air Quality Index, it is necessary to analyze and quantify the concentration of pollutants, including CO, NOX, SOX, and PM10, in the ambient air at the intersections of traffic zones along National Highway 544 (Avinashi Road), which runs through the city of Coimbatore, Southern part of India (11.0168°N, 76.9558°E). Additionally, it is essential to predict the dispersion of these pollutants using Quantum GIS. Between May and October of 2023, a Respirable dust sampler is used to gather ambient air samples at 15 locations based on zones with heavy traffic signals. Air quality is determined by comparing quantitative data with the National Ambient Air Quality Standards (NAAQs). The dispersion rate in terms of concentration of CO, NOX, and SOX in the sampling locations of National Highway 544 revealed that At the Sampling Location (SL5), the mean NOX concentration in May was higher (85 µg/m³) than in other sampling months. May 2023 observed a high mean SOX concentration value at SL7 (54 µg/m³), and NH 544's SL7 (2.14 mg/m3) had a significantly high mean CO concentration because of increased temperature. Comparing SL5, SL7, and SL12 to other sampling locations, these three spots rank lower in the Very Poor category mainly because of improper roadway alignment, traffic congestion, violations of traffic laws, poor vehicle maintenance, and end-of-life cars. The Hazard Quotient (HQ) for CO pollutants was less than 1, whereas the HQ for NOX and SOX was more significant than 1 for all exposed groups. In humans, low levels can result in hospitalization for respiratory infections and acute and obstructive lung diseases.
A low complexity contextual stacked ensemble-learning approach for pedestrian intent prediction
Authors: Chiang Chia-Yen , Jaber Mona , Slabaugh Gregory , Fathy Yasmin 
Published: March 2024

Abstract
Smart cities harness data and technology to enhance the sustainability and efficiency of urban areas and communities. Walking as a form of active travel is essential in promoting sustainable transport. It is thus crucial to accurately predict pedestrian crossing intention and avoid collisions, especially with the advent of autonomous and advanced driver-assisted vehicles. Current research leverages computer vision and machine learning advances to predict near-misses; however, this often requires high computation power to yield reliable results. In contrast, this work proposes a low-complexity ensemble-learning approach that employs contextual data for predicting the pedestrian's intent for crossing. Given a single scene, the pedestrian is first detected, and their image is then compressed using skeleton-isation, and contextual information is added into a stacked ensemble-learning approach. Our conducted experiments on different datasets achieve up to 15.8% gain in predictive accuracy compared to state-of-the-art approaches with 19.1% reduction in model complexity.
An Inner-Inter City Graph Neural Network for Predicting the Course of COVID-19 Cases
Authors: Ndiaye Moussa , Liu Tong , Cui Yangguang , Chen Jie , Yu Hang 
Published: March 2024

Abstract

Highly infectious diseases, for instance, the recent novel corona virus pandemic (COVID-19) have impacted millions of lives in the globe. From its early stage, it's clear that its transmission is closely related to the crowd mobility. However, research on the predictive power of city level mobility is lacking. To fill this gap, in this paper, we propose the Inner-Inter city Learner (IIL), a method based on the high correlation between human interaction and the new cases, to conduct short and midterm predictions of the COVID-19 cases at a city level. In term of design, IIL is composed of two main components: an inter city transmission learner and an inner city propagation pattern learner. In the first module, we specially generate graphs based on the mobility between cities. Using graph neural networks fed with the city level mobility graphs, the model learns how the transmission pattern in a city is impacted by its neighborhood cities. And in the second part, capitalizing in the highly contagious nature of the virus, we use the human interactions within the cities to capture the invariant features that directly correlate with the spread. In addition, to account for the limited inner mobility data, we employed the model agnostic meta learning to transfer the features common to all cities. We conduct various experiments and compare our methods with the state-of-art baselines. The results show the superiority of our method in various forecast horizons.
Optimization of tall fescue (Festuca arundinacea) callus induction and regeneration in culture medium containing ZnO and Ag nanoparticles
Authors: Moradi Meysam , amini fatemeh , Darbandi Ali Izadi 
Published: March 2024

Abstract

This experiment was conducted to optimize in vitro regeneration of tall fescue (Festuca arundinacea) and study of ZnO and Ag nanoparticles effects on it. The effect of six combinations of growth regulators of auxin (2.4-D) and cytokinin (BAP and Kinetin) on stem and seeds halved explant (Iranian and Foreign) was evaluated in a completely randomized design with three replications. To investigate the effect of nanoparticles on callus induction and regeneration, four concentrations of silver nanoparticles (0, 20, 40, 60 mg l-1) and zinc oxide nanoparticles (0, 25, 50, 100 mg l-1) in a completely randomized design with three replications were performed. The reults showed that the halved seed micro-sample, the Iranian ecotype, and the MS/2 culture medium had the best results for this part of experiment. The culture medium containing 1 mg l-1 2.4-D and 0.1 mg l-1 kinetin was the most suitable culture medium for callus formation, number of days to callus formation, callus length and width, callus perimeter and area, fresh and dry weight of callus. Also, the culture medium containing 0.9 mg l-1 2.4-D and 0.5 mg l-1 BAP had a higher amount than other culture mediums in terms of indirect and direct regeneration. In rooting medium with the concentration of 0.25 mg l-1 NAA and 0.1 mg l-1 2.4-D, the maximum and minimum root length and days to rooting were observed, respectively. The analysis of variance showed that the effect of silver nanoparticles and zinc oxide nanoparticles on the number of days to callus induction was significant. The effects of silver nanoparticles on the percentage of regeneration also were significant. The results of mean comparisons of silver nanoparticles showed that in the concentration of 20 mg l-1 callus formation was induced more rapidly. However, the treatment of 60 mg l-1 had the lowest average of callus induction. The results of the mean comparison showed the positive effect of zinc oxide nanoparticles on concentration of 25, 50, and 100 mg l-1 on callus formation rate compared to the non-application of zinc oxide nanoparticles.
Human limits in Machine Learning: Prediction of plant phenotypes using soil microbiome data
Authors: Aghdam Rosa , Tang Xudong , Shan Shan , Lankau Richard , Solis-Lemus Claudia 
Published: March 2024

Abstract
The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide the first deep investigation of the predictive potential of machine learning models to understand the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant phenotypes from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. We show that prediction is improved when incorporating environmental features like soil physicochemical properties and microbial population density into the models, in addition to the microbiome information. Exploring various data preprocessing strategies confirms the significant impact of human decisions on predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is not the optimal strategy to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level or model characteristics. In cases where humans are unable to classify samples accurately, machine learning model performance is limited. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power.