bluuebunny/crossref_metadata_2025_split huggingface dataset:
Smectite Illitization Geothermometry of the Upper Indus Basin Hydrocarbon Source Rocks (Pakistan)
Authors: Ali A. , Ullah S. 
Published: July 2024

Abstract
—Smectite illitization geothermometry has been used to assess the thermal maturity and hydrocarbon generation potential of the late Paleocene to early Eocene Patala Formation in the Upper Indus Basin. X-ray diffraction (XRD) detected illite, muscovite, quartz, kaolinite, chlorite, and calcite. Comparison between air-dried (AD) and ethylene glycol (EG)-solvated XRD patterns reveals the absence of discrete smectite and interstratified illite-smectite (I-Sm). Additionally, authigenic illite-2M1 indicates that the Patala Formation has entered the late-stage diagenetic zone or the low anchizone, which lies in the R3 illitization zone. Abundant SiO2 and Al2O3 in the Patala clay fraction indicate the substitution of tetrahedral Si4+ by Al3+ within the smectite interlayers. Likewise, the relative abundance of K2O to CaO and MgO indicates the exchange of K+ with Ca2+ and Mg2+ during smectite illitization. Scanning electron microscopy (SEM) reveals in situ growth of platy illite crystals that form within the R3 zone. Furthermore, the total organic carbon (TOC) of the Patala shale exposed in Tirah suggests a poor to good source rock. The absence of discrete smectite and I-Sm, combined with the detection of discrete illite in the Patala shale, suggests that hydrocarbon might have potentially migrated from the source to the reservoir rock during smectite illitization.
Technology Focus: Reservoir Simulation (July 2024)
Authors: Abraham Anson 
Published: July 2024

The early days of artificial intelligence (AI) and machine learning (ML) were filled with promise, but now we’re witnessing a wave of practical applications transforming reservoir engineering. The integration of AI/ML with traditional workflows marks a turning point, unleashing the immense potential of these proven techniques to address our everyday challenges in reservoir simulation.
These advancements lead to more-accurate models and faster simulation turnaround times, allowing us to model various scenarios more efficiently. These techniques enable us to understand and simulate reservoir behavior with greater fidelity and focus on the underlying physics. This deeper understanding translates to more-confident decisions regarding reservoir management and development, with a clearer picture of potential risks.
Physics-informed machine learning emerges as a significant step forward, improving our understanding and providing better models while boosting runtimes, convergence, and overall performance. Paper IPTC 23730 highlights its application in tackling computationally intensive tasks such as critical temperature prediction with high accuracy, leading to significant speed-ups in simulations, particularly for complex compositional models undergoing miscible gas injection.
Paper IPTC 23935 presents a fundamentally different approach to reservoir simulation studies. Adaptive models use multiple smaller, specialized models instead of one giant model. These models can be developed and run independently, allowing for parallel workflows and significantly reduced turnaround times. Adaptive models foster a culture of rapid experimentation and iteration, aligning perfectly with the fail-fast approach—prioritizing the quick evaluation of ideas and discarding those that don’t show promise early on.
Paper SPE 214855 provides a comprehensive overview of a complex problem—wellbore modeling. This paper offers practical insights directly relevant to my work.
These advancements represent a significant leap forward, paving the way for more-powerful and -versatile reservoir models. As research and development continue, these techniques are poised to revolutionize how we manage and optimize oil and gas reservoirs. I hope you enjoy reading this selection of papers and find them enlightening.
Recommended additional reading at OnePetro: www.onepetro.org.
SPE 216722 Developing Consistent Relative Permeability and Capillary Pressure Models for Reservoir Simulation of Carbon Capture and Storage Projects by L.S. Lun, ExxonMobil, et al.
Hybrid Swin Transformer is Gaze Estimation need
Authors: Wang Yanxia , CHENG ZHANG , Liang Wei , Huang Fenglin 
Published: July 2024

Abstract

In recent years, models based on the Swin Transformer architecture have significantly improved the performance of many computer vision tasks. However, the performance of the Swin Transformer architecture in gaze estimation tasks remains to be verified. In this paper, in order to introduce Swin Transformer into gaze estimation, Pure Swin Transformer(PST-Net) is proposed by improving Swin-T. Based on this, in order to further improve the performance of PSTNet and reduce the computational complexity, a new lightweight model, Hybrid Swin Transformer(HST-Net), is proposed. To the best of our knowledge, we are the first to apply Swin Transformer in gaze estimation. HST-Net significantly improves performance by adding CNNs before and after the Swin Transformer Block. Experiments show that HST-Net significantly outperforms PST-Net and that the model’s generalization performance is significantly better than previous methods. We further perform experiments to explore the effect of HST-Net based on a range of publicly available datasets and analyze the impact of the depthwise separable convolution after the Swin Transformer Block on the final results. The experiments demonstrate that after pre-training, HST-Net outperforms GazeTRHybrid in all benchmark tests, achieving optimal performance while reducing parameters by 56.4% and FLOPs by 75.4%.
Synergistic impacts of clean energy demonstration policies on pollution and carbon reduction
Authors: Cui LiZhi , Sun Wang 
Published: July 2024

Abstract

The promotion of synergies between pollution and carbon reduction is a key strategic direction for building a beautiful China, with the development of clean energy being a positive step towards achieving this goal. In this study, we used China's provincial panel data from 2005 to 2020 to examine the clean energy demonstration province (CEDP) pilot as a quasi-natural experiment and utilized the synthetic control and difference-in-differences model to assess the synergistic effect of CEDP formulation on pollution and carbon reduction. We found that CEDP formulation synergistically reduced pollution and carbon and that policy implementation effectively reduced the PM2.5 emission intensity and carbon emission intensity of the pilot provinces. These were robust findings even after multiple tests. There was regional heterogeneity in the synergistic effect of CEDPs, and the policy effect of conducting the CEDP pilots was more significant in the central and western regions than in the eastern regions. Further mechanism tests showed that industrial upgrading and energy efficiency improvement were important channels for CEDP formulation to achieve the synergistic effect of pollution and carbon reduction. We proposed to enhance the evaluation system for CEDP formulation and implementation, systematically expand the coverage of CEDP pilots, and reinforce the mechanism for industrial upgrading and energy efficiency improvement.
The challenge of uncertainty: can sustainability be achieved under unpredictable ecological dynamics?
Authors: Chiaravalloti Rafael , Dyble Mark , Homewood Katherine , Burgman Mark , Canty Steven , Moreau Marie-Annick , Galuccio Malena , Balet Domenec Ruiz I , Wadt Lucia Helena , Nunes André , Mills Morena 
Published: July 2024

Abstract

Unpredictable environmental dynamics may be the greatest challenge of our time1,2. However, sustainability initiatives are designed with the assumption that people’s access to natural resources is relatively predictable3. This assumption is based on the principle that to use resources sustainably users must have the ability to exclude free riders through clear boundaries4. A lack of boundaries is linked to open access and the tragedy of the commons5. However, clear and enforceable boundaries are not always possible, especially in areas with unpredictable natural resource dynamics6,7. How sustainability can be achieved in unpredictable systems is still poorly explored8. Here we show that many socio-ecological systems have developed customary systems to deal with unpredictability and achieve sustainability. We found that predictability levels below 50% lead socio-ecological systems to display broad reciprocal access across socially and spatially fuzzy boundaries and high levels of mobility. Our results demonstrate that functioning governance structures do not occur only when people have predictable access to resources and clear boundaries to resource extraction are imposed. We show that to deal with the increasing level of unpredictability around the world9, sustainability science and practice must recognise the possibility of governance structures based on mobility and information/resource sharing6.
Sustainable Construction in Nigeria: A Socioeconomic Impact Analysis
Authors: Unegbu H. C. O. , Yawas D. S. , Dan-asabe B. , Alabi A. A. 
Published: July 2024

Abstract

Sustainable construction practices represent a critical pathway towards fostering economic development and social well-being within the Nigerian construction industry. This research aims to elucidate the socioeconomic impacts of sustainable construction practices in Nigeria, employing a quantitative approach and utilizing simulated data for analyses. Through a comprehensive survey of 370 construction industry professionals, this study investigates the perceptions of sustainable practices such as energy efficiency, cost reduction, and local economic development. Findings revealed a strong consensus among respondents regarding the positive socioeconomic impacts of sustainable strategies, showcasing correlations between these practices and economic growth, job creation, and the appreciation of property values. The regression analysis highlighted the significant influence of these sustainable practices on socioeconomic aspects, emphasizing the interconnectedness between sustainable construction and economic advancement. The implications of this study underscore the importance of integrating sustainable strategies within the Nigerian construction sector, advocating for policy adjustments to leverage the benefits of these practices. This research contributes to the growing discourse on sustainable construction practices, emphasizing their potential to drive economic growth and enhance community well-being within Nigeria.
Food contamination with fipronil alters gene expression associated with foraging in Africanized honey bees
Authors: Lima Yan Souza , Lippi Isabella Cristina de Castro , Scheffer Jaine da Luz , Lunardi Juliana Sartori , Alvarez Marcus Vinícius Niz , Kadri Samir Moura , Orsi Ricardo de Oliveira 
Published: July 2024

Abstract

Taking into consideration that bees can be contaminated by pesticides through the ingestion of contaminated floral resources, we can utilize genetic techniques to assess effects that are scarcely observed in behavioral studies. This study aimed to investigate the genetic effects of ingesting lethal and sublethal doses of the insecticide fipronil in foraging honey bees during two periods of acute exposure. Bees were exposed to fipronil through contaminated honey syrup at two dosages (LD50 = 0.19 μg/bee; LD50/100 = 0.0019 μg/bee) and for two durations (one and four hours). Following exposure, we measured syrup consumption per bee, analyzed the transcriptome of bee brain tissue, and identified differentially expressed genes (DEGs), categorizing them functionally based on Gene Ontology (GO). The results revealed a significant genetic response in honey bees after exposure to fipronil, regardless of the dosage used. Fipronil affected various metabolic, transport, and cellular regulation pathways, as well as detoxification processes and xenobiotic substance detection. Additionally, downregulation of several DEGs belonging to the Olfactory Binding Protein (OBP) family was observed, suggesting potential physiological alterations in bees that may lead to disoriented behaviors and reduced foraging efficiency.
Clustering temporal step-counting patterns for 24 hours with machine learning revealed potential heterogeneity in the categorization by a traditional tertile procedure
Authors: Nawrin Saida Salima , Inada Hitoshi , Momma Haruki , nagatomi Ryoichi 
Published: July 2024

Abstract

Background
Physical activity is a crucial aspect of health benefits in the public society. Although studies on the temporal physical activity patterns might lead to the protocol for efficient intervention/program, a standardized procedure to determine and analyze the temporal physical activity patterns remains to be developed. Here, we attempted to develop a procedure to cluster 24-hour patterns of physical activity as step counts measured with an accelerometer-based wearable sensor.
Methods
Data was collected from 42 healthy participants, comprising 35 males and 7 females, at the Sendai Oroshisho center in 2008. This is a cross sectional study using unsupervised machine learning, specifically the kernel k-means algorithm with the global alignment kernel was applied on a total of 815 days from 42 participants, and 6 activity patterns were identified. Further, the probability of each 24-hour step-counting pattern was calculated for every participant., and was used in MATLAB to apply spectral clustering, and 5 activity behaviors were identified.
Results
We could identify six 24-hour step-counting patterns and five daily step-behavioral clusters. When the amount of physical activity was categorized into tertile groups reflecting highly active, moderately active, and low active, each tertile group consisted of different proportions of six 24-hour step-counting patterns.
Conclusions
Our study introduces a novel approach using an unsupervised machine learning method to categorize daily hourly activity, revealing six distinct step counting patterns and five clusters representing daily step behaviors. Our procedure would be reliable for finding and clustering physical activity patterns/behaviors and reveal heterogeneity in the categorization by a traditional tertile procedure using total step amount.
Oxytocin Supplementation Demonstrates Potential in Reducing Breast Cancer Growth via Regulation of the PIK3CA/ULK1/Autophagy Axis
Authors: Xu Shuo-Han , Zhou Feng-Jia , Liu Shuai , Fan Xin-Yu , Yan Ying 
Published: July 2024

Abstract

Breast cancer stands as a leading cause of morbidity and mortality among patients worldwide. The emerging trend in cancer prevention and treatment involves harnessing endogenous substances to regulate disease occurrence and progression. This study aimed to decipher the inhibitory effect of oxytocin on breast cancer and its underlying mechanism. Leveraging network pharmacology and molecular docking, we screened oxytocin's key targets in breast cancer. Our in vitro analysis revealed oxytocin's anti-proliferative, anti-migratory, and apoptosis-inducing properties in human breast cancer cell lines, MCF-7 and ZR-75-1. Additionally, oxytocin significantly suppressed the growth of MCF-7 transplanted tumors in nude mice. Western blot analysis detected oxytocin's modulation of PIK3CA expression and autophagy pathway factors, including ULK1, p62, LC3-Ⅰ, and LC3-Ⅱ, leading us to propose a mechanism involving the PIK3CA/ULK1/autophagy axis. The findings indicate that oxytocin effectively inhibits breast cancer cell proliferation and migration, promotes apoptosis, and reduces tumor growth, potentially through regulation of the PIK3CA/ULK1/autophagy pathway. Low-dose oxytocin supplementation emerges as a promising strategy for breast cancer prevention and treatment.
GCN2 inhibition reduces mutant SOD1 clustering and toxicity and delays disease progression in an Amyotrophic Lateral Sclerosis mouse model
Authors: Ortiz Didio Alberto , Peregrín Nuria , Valencia Miguel , Vinueza-Gavilanes Rodrigo , Marín-Ordovas Elisa , Ferrero Roberto , Nicolás María Jesús , González-Aseguinolaza Gloria , Arrasate Montserrat , Aragón Tomás 
Published: July 2024

Abstract

The disruption of protein folding homeostasis in motoneurons (MNs), and the ensuing accumulation of protein aggregates, is one of the main molecular hallmarks of amyotrophic lateral sclerosis (ALS) pathology, and has been recapitulated in cellular and animal disease models. The loss of proteostasis and other stresses in the MN trigger the activation of a general stress mechanism, the integrated stress response (ISR). The ISR is initiated by either of four stress-sensing kinases (GCN2, HRI, PERK and PKR) which, upon activation by distinct insults, promote a dramatic remodeling of gene expression to combat stress and promote survival. Paradoxically, in pathologies where stress is chronic or overwhelming, the ISR can also promote neuronal death. In ALS experimental models, extensive evidence demonstrates a key role of this mechanism in the progression of disease, and has inspired many attempts to develop ALS therapies based on ISR modulation.
In our group, we recently discovered that the downstream ISR inhibitor ISRIB increases survival of a neuronal ALS model based on the expression of the neurotoxic ALS allele, SOD1 G93A. In the current study, we found that ISR inhibition is sufficient to prevent the concentration of mutant SOD1 into cytosolic foci, suggesting that ISR is required for SOD1 protein aggregation. Through a systematic CRISPR Cas9 approach and pharmacological inhibition, we demonstrate that, unexpectedly, the ISR kinase GCN2 is required for SOD1 clustering in cell lines and primary neuronal cultures. Moreover, genetic or pharmacological GCN2 inhibition strongly enhances survival of neurons overexpressing mutant SOD1. Finally, GCN2 pharmacological inhibition in fALS SOD1G93A transgenic mice delayed muscle denervation, strength loss, weight loss, and the appearance of ALS symptoms. Based on these findings, we propose GCN2 as a new potential therapeutic target for ALS.
Tyrer-Cuzick Lifetime Risk is Not Associated with Non-BRCA1/2 Pathogenic Variants for Breast Carcinoma
Authors: Rao Divya , Wernecke Chloe , Baron Lisa , Cai Steven , Beitsch Peter , Patel Rakesh , Whitworth Pat , Rosen Barry , Tran Nhan Nguyen , Hughes Kevin , Baron Paul L. 
Published: July 2024

Abstract

Purpose: The Tyrer-Cuzick lifetime risk score (TC LR) is used to estimate the probability of an unaffected female patient developing breast cancer as well as their likelihood of carrying a BRCA1/BRCA2 pathogenic variant (PV), considering factors such as family history, personal history, and breast density. The purpose of this study is to determine if TC LR is associated with non-BRCA1/2 PVs.
Methods: A population of 964 patients, enrolled from 2019-2022, were evaluated for 12 PVs and variants of unknown significance (VUS) using lab agnostic genetic testing. TC LR scores were compared between the three patient cohorts - BRCA PVs, non-BRCA PVs, and PV negative, using Kruskal-Wallis test and DSCF adjustment for multiple comparisons. Chi-square test and logistic regression were used to compare the groups.
Results: This study found an average TC LR of 7.71%. The presence of PVs and VUS was evaluated, with 12.03% of patients were found to have a PV (average TC LR 8.98%). Out of those with PVs, 52% had non-BRCA1/2 PVs (average TC LR 5.47%). A total of 102 patients (10.58%) had a VUS (average TC LR 8.29%).
Conclusion: TC LR scores were found to be low in patients with non-BRCA1/2 PVs. The study suggests that the decision for genetic testing should not be influenced by TC LR but should be based on individual patient’s family history, NCCN guidelines, or the TC genetic risk score.
Vulnerability Assessment and Decision-Making in Older Patients with Head and Neck Cancers Planned for Chemoradiation: Experience from a Tertiary Care Cancer Centre in India
Authors: Noronha Vanita , Sarkar Laboni , Pillai Anupa , Rao Abhijith Rajaram , Kumar Anita , Dhekale Ratan , Mahajan Sarika , Daptardar Anuradha , Sonkusare Lekhika , Vagal Manjusha , Mahajan Purabi , Timmanpyati Shivshankar , Gota Vikram , Laskar Sarbani , Budrukar Ashwini , Swain Monali , Shetake Ankush , Chowdhury Oindrila Roy , Pawar Akash , Prabhash Kumar 
Published: July 2024

Abstract

Objectives
We aimed to evaluate clinical characteristics and outcomes of patients with head and neck squamous cell cancers (HNSCC) planned for chemoradiation enrolled in our geriatric clinic and the role of geriatric assessment (GA) in treatment decision-making.
Methods
Patients aged over 60 years with locally advanced HNSCC planned for chemoradiation referred to the geriatric clinic from 2018 to 2023 were identified from the clinic database.
Results
Of 164 patients identified, 36/148 (24.3%) patients were vulnerable in the domain of function and falls, 41/84 (48.8%) in comorbidities, 88/148 (55.4%) in nutrition, 35.148 (23.6%) in psychological, 6/87 (6.9%) in cognition, 25 (11.8%) in polypharmacy and 8 (5.4%) in social domains. 76 (51.4%) were frail, with 2 or more vulnerabilities.
35 (21.3%) patients were planned for radiotherapy alone, and 117 (71.3%) patients ultimately received chemoradiation. Decision on chemotherapy regimen was made awaiting GA in 99 (84.6%) patients. Among them, tentative chemotherapy regimen was changed after GA in 13 (13.1%) patients. 24-month OS was 64.7% (95% CI: 52.2%-80.3%). Factors significantly associated with poorer overall status (OS) were Eastern Cooperative Oncology Group performance status (PS) >=2 (hazard ratio (HR) 5.38, CI 2.39-12.1), vulnerability in nutrition (HR 2.52, CI 1.17-5.39), higher CARG score (HR 9.92, CI 3.91-25.16) and frailty (HR 10.51, CI 2.89-38.22). Factors impacting grade 3/4 toxicity included vulnerability in nutrition, higher CARG score and PS >=2.
Conclusion
71.3% patients planned for chemoradiation went on to receive concurrent chemotherapy after GA, with vulnerabilities in nutrition, frailty, performance status and CARG score being predictive for OS.
Optimization of Subcritical Water Pretreatment on Glutinous Rice Husk for Efficient Sugar and Hydrogen Production
Authors: Aziz Izzah Farhana Ab , Man Hasfalina Che , Dau Anelka , Hashim Norhashila , Hamzah Muhammad Hazwan , Hamzah Adila Fazliyana Aili 
Published: July 2024

Abstract

There are thousands of different rice cultivars available worldwide, and glutinous rice (Oryza sativa var. glutinosa), sometimes known as sticky rice, is one of the most well-known varieties. Glutinous Rice Husk (GRH) is categorized as lignocellulosic biomass obtained as a by-product of rice manufacturing. The GRH is residue with a substantial amount of cellulose, hemicelluloses, and lignin, but it is difficult to degrade GRH during anaerobic digestion or fermentation. This research aims to investigate the factors affecting the sugar production of GRH by subcritical water (SCW) pretreatment. It is expected that the sugar produced could increase biohydrogen production. The biohydrogen yield was compared between raw and SCW-treated GRH as a fermentation substrate. Response Surface Methodology (RSM) was utilized to determine the ideal parameters for the subcritical water pretreatment of GRH using a Central Composite Design in the Design Expert Software. SCW pretreatment with temperature (195°C), solid-to-liquid ratio (1:10), and reaction time (17.5 minutes) were selected for the optimum sugar production (647.2 mg/L total sugar) of GRH. This pretreatment improves hydrogen production during dark fermentation by increasing the availability of easily accessible simple sugars for microbes. Additionally, it improves the lignocellulosic structure of GRH to enhance the fermentation process. Dark fermentation with SCW-treated GRH (0.73 mL H2/g-TS) obtained higher hydrogen yield than raw GRH (0.09 mL H2/g-TS). These findings highlight the potential of SCW pretreatment as an effective strategy for maximizing hydrogen production from GRH and contributing to using lignocellulosic biomass for renewable energy applications.