bluuebunny/crossref_metadata_2025_split huggingface dataset:
Peace, Equanimity and Acceptance in the Cancer Experience: Validation of the German version (PEACE-G) and associations with mental health, health-related quality of life and psychological constructs
Authors: Sauer Christina , Hansen Till , Prigerson Holly G. , Mack Jennifer W. , Bugaj Till J. , Weißflog Gregor 
Published: July 2024

Abstract

Purpose
Systematic reviews and meta-analyses reveal the importance of an accepting attitude towards cancer for mental health and functional coping. The aim of this study was to examine the psychometric properties of the German translation of the Peace, Equanimity, and Acceptance in the Cancer Experience (PEACE) questionnaire (Mack et al., 2008) and to investigate its associations with mental health, health-related quality of life (HRQoL) and related constructs.
Methods
The German version of the PEACE (PEACE-G) was created and validated with cancer patients in a cross-sectional two center questionnaire study. Construct validity was tested with confirmational factor analyses (CFA); Cronbach’s alpha was used to determine internal consistency of items. We further examined associations with depression (PHQ-8), anxiety (GAD-7), distress (NCCN distress thermometer), HRQoL (SF-12), psychological flexibility (AAQ-II), resilience (RS-11) and acceptance scales, to evaluate concurrent and divergent validity.
Results
N = 213 cancer patients with different tumor entities participated in this study. Results of the CFA replicated the two-factor solution of the original PEACE (peaceful acceptance and struggle with illness) with satisfactory psychometric properties. Peaceful acceptance showed negative associations with depression, anxiety, distress, psychological inflexibility, and positive associations with HRQoL, acceptance, resilience, and mindfulness. Results of sensitivity analyses indicate a third factor (injustice/anger).
Conclusion
The results demonstrate the validity and reliability of PEACE-G in cancer patients and point to the essential role of acceptance- and mindfulness-based interventions in psycho-oncology. Further studies are needed to investigate the different facets of struggle with the cancer illness.
Proteome-wide association study identifies novel Alzheimer's disease- associated proteins
Authors: Sun Lingyun , Wei Guikang , Ji Feiyang , Ding Yihong , Fan Jiayao , Xu Yue , He Chunfeng , Zhou Yuan , Liu Zuyun , Sun Zeyu , Zhou Dan 
Published: July 2024

Abstract

Background
Alzheimer's disease (AD) is a progressive neurodegenerative disease, with a critical shortage of effective prevention and treatment options. Here, we aimed to identify proteins whose genetically regulated plasma levels were associated with AD and its related phenotypes.
Methods
An integrative proteome-wide search using Olink-based plasma proteomes (N = 45,540) from the UK Biobank Pharma Proteomics Project (UKB-PPP) and a large-scale genome-wide association study (GWAS) for AD (N case = 111,326, N control = 677,663) was employed to identify AD-associated proteins. Cohort studies for AD or mild cognitive disorder (MCD) with average follow-ups of 13.7 years, alongside cross-sectional studies for the volume of whole hippocampus (WH) and white matter hyperintensities (WMH) were performed to provide additional supports.
Results
We identified 30 AD-associated proteins through a genetic-informed proteome-wide association study (PWAS). Among these, 14 proteins (including TREM2 and GRN) have been previously reported to be associated with AD. No clear evidence has linked the remaining 16 proteins (including PILRB, FES, and HDGF) with AD. PILRB and FES were further supported by cohort studies for AD and/or MCD. A higher plasma abundance of HDGF was found to be associated with a lower volume of whole-hippocampus and an increased risk of AD, consistent with a previous study which showed a potentially risk role of HDGF for AD in both brain tissues and cerebrospinal fluid. The protein-protein interaction analysis linked PILRB with ABCA7, an AD-related protein involved in the immune system.
Conclusions
The integrative genetic-informed proteome-wide scan provides promising AD-associated proteins for further mechanistic studies.
The challenges of evidence-based prehabilitation in a real-life context for patients preparing for colorectal surgery – a cohort study and multiple case analysis
Authors: Talen A.D. (Daniëlle) , Meeteren N.L.U. (Nico) Van , Barten J.A. (Di-Janne) , Pereboom I. (Ilona) , Krijnen W.P. (Wim) , Jager-Wittenaar H. (Harriët) , Bongers B.C. (Bart) , Sluis G. (Geert) Van Der 
Published: July 2024

Abstract

Background
Multimodal prehabilitation programs are effective in reducing complications after colorectal surgery in patients with a high risk of postoperative complications due to low aerobic capacity and/or malnutrition. However, high implementation fidelity is needed to achieve these effects in real-life practice. This study aimed to investigate the implementation fidelity of an evidence-based prehabilitation program in the real-life context of a Dutch regional hospital.
Methods
In this observational cohort study with multiple case analysis, all patients who underwent colorectal surgery from January 2023 to June 2023 were enrolled. Patients meeting criteria for low aerobic capacity or malnutrition were advised to participate in a prehabilitation program. According to recent scientific insights and the local care context, this program consisted of four exercise- and three nutrition modalities. Implementation fidelity was investigated by evaluating: 1) coverage (participation rate), 2) duration (number of days between start prehabilitation and surgery), 3) content (delivery of prescribed intervention modalities), and 4) frequency (attendance of sessions and compliance with prescribed parameters). An aggregated percentage of content and frequency was calculated to determine overall adherence.
Results
58 patients intended to follow the prehabilitation care pathway, of which 41 performed a preoperative risk assessment (coverage 80%). Ten patients (24%) were identified as high-risk and participated in the prehabilitation program (duration of 33–84 days). Adherence was high (84–100%) in five and moderate (72–73%) in two patients. Adherence was remarkably low (25%, 53%, 54%) in three patients who struggled executing the prehabilitation program due to multiple physical and cognitive impairments.
Conclusion
Implementation fidelity of an evidence-based multimodal prehabilitation program for high-risk patients preparing for colorectal surgery in real-life practice was moderate because adherence was high for most patients, but low for some patients. Patients with low adherence had multiple impairments, with consequences for their preparation for surgery. For healthcare professionals, it is recommended to pay attention to high-risk patients with multiple impairments and further personalize the prehabilitation program. More knowledge about identifying and treating high-risk patients is needed to provide evidence-based recommendations and to obtain higher effectiveness.
Trial registration
NCT06438484
Effect of Pulsed Laser Performance of ZnO / Ge / Si Photodetector Prepared by Multi methods
Authors: Fahad Othman Abed , Al-Rawi Bilal K. , Ramizy Asmiet 
Published: July 2024

Abstract

Triple-layer heterostructure of ZnO/Ge/Si was considered for establishing photodetector devices by utilizing thermal evaporation and pulse laser deposition. The structural and optical properties were obtained using XRD, SEM, Raman spectroscopic and UV–visible spectroscopy. the XRD showed patterns of the Ge layer a closed-oriented crystalline structure and results showed that zinc oxide is polycrystalline in nature, with a cubic crystalline phase. The Raman spectroscopic investigation of Ge film the figure shows three separate peaks, In particulars are corresponded to Ge phase, E(TO + LO), A1(TO), E(TO)respectively and the Raman spectra of ZnO films showed E (LO), A (TO), E (LO) + TO, and B1 vibration modes. The optical bandgaps of Ge and ZnO nanostructures were found to be 2.8 for Ge and 3.3, 3.2, and 3.1 eV for ZNO thin film as laser pulses of 400,500,600 pulses respectively. The current–voltage characteristics of the ZnO/Ge/Si heterojunction deposited over 400, 500, and 600 pulses were examined in dark and illumination conduction .The heterojunctions showed rectifying characteristics, with 600 pulses demonstrating the best rectification factor. Using more pulse lasers improved the photodetector performance and the figure of merit. When the deposition pulse was increased from 400 pulses to 600 pulses, the responsivity increased from 8.867 A/W to 13.229 A/W. When the photodetector was produced at the ideal pulse count of 600 pulses, the detectivity (D*) and external quantum efficiency (EQE) were 5.77E + 17 Jones and 40.505%, respectively, at 405 nm.
Lipids, lipid-lowering drug target genes and pancreatic cancer: a Mendelian randomization study
Authors: Zhan Yuxuan , Zhang Kai , Fan Yiqun , Lin Siyi , Du Bang , Wu Jian , Xu Hongxia 
Published: July 2024

Abstract

Background
Pancreatic cancer (PC) is a malignant tumor with a low survival rate. Lipid modifiers show potential for PC therapy, but evidence is lacking. This Mendelian Randomization (MR) study aimed to explore the relationship between lipid traits, and lipid-lowering drug target genes with PC risk.
Methods
We used genetic instrumental variables associated with lipid traits and lipid-lowering drug target genes to conduct MR analyses with PC risk. MR estimation utilized genome-wide association study (GWAS) data from two substantial sample sets, which were meta-analyzed to evaluate their influence on PC risk. To confirm the reliability of lipid modification drug targets, a Summary Data-based Mendelian Randomization (SMR) analysis was conducted. Additionally, a two-step MR (TSMR) analysis was implemented to investigate potential mediating roles.
Result
In the Discovery Dataset, HMGCR inhibition was statistically associated with a lower risk of PC (OR = 0.46, [95% CI, 0.22–0.97]; p = 0.0404), and did not show statistical significance in the Replication Dataset, but the result of the meta-analysis reached statistical significance (OR = 0.50, [95% CI, 0.25-1.00]; p = 0.0453). The SMR analysis enhanced the robustness of the results (OR = 0.51, [95% CI, 0.28–0.96]; p = 0.0369). Furthermore, mediation analysis revealed that lowering BMI levels mediated the protective effect of HMGCR inhibitors on PC (mediation effect: OR = 0.91, [95% CI,0.84–0.97], mediation proportion:11.69%, [95% CI, 10.04%-13.61%]).
Conclusions
This study found that HMGCR inhibitors were significantly associated with the risk of PC, suggesting that HMGCR has the potential to be a candidate drug target for the treatment or prevention of PC.
Energy Harvesting using Stochastic Network Calculus for Monitoring Underwater Tunnelling Applications
Authors: Vignesh S R , Sukumaran Rajeev 
Published: July 2024

Abstract

The implementation of Underwater Wireless Sensor Networks (UWSNs) for applications in oceanographic environment monitoring, marine biology and ecology, and underwater infrastructure monitoring requires frequent battery replacements due to the limited energy capacity of sensor nodes. Underwater Energy Harvesting (UEH) offers a promising solution to extend sensor node lifetimes and reduce maintenance costs. Among various techniques, piezoelectricity emerges as a particularly efficient method for UEH. This research proposes a novel approach to improve energy harvesting for underwater infrastructure monitoring, specifically for underwater tunnelling applications. By implementing piezoelectric wave energy and a Stochastic Network Calculus (SNC) mathematical model, the proposed method aims to simulate Efficient Energy Harvesting (EEH) to enhance sensor node performance parameters. This includes maximising the Energy Harvesting Rate (EHR), Delivery Probability Ratio (DPR), and overall Network Throughput (NT). This research contributes to the advancement of sustainable UWSNs by enabling efficient energy management and prolonging sensor node operation.
TNBC-Specific UGDH-AS1+ NK Cells Encode NSKM, Promoting Cancer Progression by Inhibiting NK Cells Activity
Authors: zhou yifeng , Zhang Zheng , Li Fanrong , Zhang Shenghua , Wang Yirong , Deng Jieqiong , Guo Binbin , Dai Xiaoxiao , Liu Wei , Pan Yacheng , Zhao Tong , Wang Shuang , Li Wanqiu , Jin Congnan , Zhang Hebin 
Published: July 2024

Abstract

Deactivation of immune cells is a major characteristic in various malignant tumors, including breast cancer, a complex and malignant cancer. Here, using single-cell RNA sequencing, we identified UGDH-AS1+ NK cells specific to the triple-negative breast cancer(TNBC) subtype, which encode the micropeptide NKSM promoting cancer progression by inhibiting NK cell activity. NKSM was upregulated in UGDH-AS1+ NK cells and associated with TNBC-infiltrating (TINK) NK cells antitumor activity. Conditional NKSM knock-in into NK cells of mice resulted in NK cell deactivation and increased tumor growth. Targeted NKSM therapy effectively reduced tumor growth in TNBC mouse models. We found that UGDH-AS1+ NK cells are shaped by the tumor microenvironment (TME). Upregulated by the TGF-β signaling pathway, NKSM could bind to proto-oncogene c-Myc, inhibiting ERK1/2-mediated Ser62 phosphorylation and reducing its stability, thereby modulating the transcription of T-bet, a key protein involved in NK cell function, and leading to NK cell deactivation. TGF-β signaling pathway convert TINK cells into UGDH-AS1+ NK cells and targeting the expression of NKSM restrain cancer progression in TNBC.
Towards Data Science in Agriculture with Big Data Management
Authors: Gandhi Purnima 
Published: July 2024

Abstract

The agriculture data is multivariate, impulsive, and complex; this data must be analyzed and interpreted accurately to address the challenges related to agro-advisory systems, recommendation systems, smart agriculture, information systems, etc. The complex characteristics of agriculture data have made data management more crucial and challenging. Big data management is a very important task to perform to develop data science applications. Data science in the agriculture domain aids in disseminating valuable information to the end users in consumable formats.
The goal of big data management is to offer batch, interactive, and iterative computations on a wide range of data problems with low latency and high efficiency. Implementation of data repositories can be a transformative solution not only in terms of data management but also in terms of the business process. The paper proposes an architecture for developing a big data repository for the agriculture domain as a rudimentary step in developing data science applications. The architecture is built and implemented using two levels of data abstraction. As a proof of concept, data are depicted using the web-based REST interface and dashboard. Data science applications such as weather, land usage, market price analytics, and crop price prediction are developed using collected data, as a proof of concept.
Development and application of a hybrid artificial neural network model for simulating future stream flows in catchments with limited in situ observed data
Authors: Mugume Seith N. , Murungi James , Nyenje Philip M. , Sempewo Jotham Ivan , Okedi John , Sörensen Johanna 
Published: July 2024

ABSTRACT

The need to develop new and computationally efficient artificial intelligence models that accurately simulate river flows in data-scarce regions, considering not only current but also projected future climate change conditions is vital. In this study, a hybrid artificial neural network (ANN) model that combines HEC-HMS and the feed-forward neural network (FFNN) was developed in the Python programming language and applied to simulate future stream flows in the River Mayanja catchment in Central Uganda. The study results suggest that the performance of the validated hybrid HEC-HMS-ANN model during calibration and validation (NSE and R2 &gt; 0.99) was more superior to the corresponding performance obtained using individual HEC-HMS (NSE and R2 &gt; 0.50), MIKE HYDRO (NSE and R2 &gt; 0.42), and ANN models (NSE and R2 &gt; 0.56). Using the developed hybrid ANN model, future average daily stream flows are projected to increase by up to 17.3% [2.2–39.5%] and 18.5% [0.8–42.7%] considering the SSP2-4.5 and SSP5-8.5 future climate change scenarios. The study demonstrates that well-trained hybrid ANN models could provide more computationally efficient models for the simulation of future stream flow and for undertaking water resource assessments in catchments with limited in situ observed data.
Effects of climate change and land use on the hydrologic regime using the Hydro-BID tool: A case study of the Andean mountain basin in Colombia
Authors: Darwin Mena , Peña-Guzmán Carlos , Rodriguez Manuel 
Published: July 2024

ABSTRACT

Changes on the land surface generate changes in land cover, which directly affect the availability of water in watersheds. This article evaluates the case study regarding the effects on the hydrological regime of the Andean mountain basin on the Coello river basin in Colombia due to changes in land use/land cover during the 2000–2019 period by the use of the Hydro-BID tool. The physical analysis of the land surface included the processing of Landsat 7 ETM and Landsat 8 OLI satellite images for the years 2001, 2003, 2015, and 2019. Seven types of coverage were determined based on these data using the Mixed Gaussian Method. The changes between each year were evaluated, after which the land use/land cover change for the year 2050 was predicted using a Markov chain. The multi-temporal analysis showed a decrease in forested areas during the studied period, while low vegetation significantly increased within the watershed. This trend was shown to continue in the future scenario for the year 2050, with an increase in flow on the watershed of 59.6%. Additionally, the climate change scenarios were modeled with the changes in land use. The combined effects established a progressive decrease in the modal flow.
Comparison of CMIP5 and CMIP6 models for temperature and precipitation simulation over the Yarlung Tsangpo-Brahmaputra River Basin
Authors: Chen Linyan , Ji Xuan , Xu Zhangchao , Qin Peng , Yang Chuanjian , Yan Siyi , Sun Cezong , Zheng Yangfan , Zhang Yuanping 
Published: July 2024

ABSTRACT

This study compares simulations from 13 CMIP5 and CMIP6 homologous models and their multi-model ensemble (MME) based on the generalized three-cornered hat method (TCH) for temperature and precipitation over the Yarlung Tsangpo-Brahmaputra River Basin (YBRB). The results showed that: (1) the MME estimations outperform the most single model, demonstrating the effectiveness of TCH in reducing model uncertainty. (2) CMIP5 and CMIP6 are more applicable for spring and autumn maximum temperatures (Tasmax), minimum temperatures (Tasmin) and precipitation in YBRB. Moreover, they showed better performance for precipitation on floodplain, for Tasmax on Tibetan Plateau, and for Tasmin in whole YBRB. (3) Relatively, CMIP5 and CMIP6 can better simulate the spatial distribution of temperature rather than precipitation (the Temporal Correlation Coefficient of Tasmax and Tasmin: 0.72 – 0.89; that of precipitation: 0.43–0.6). (4) The bias of CMIP6 for temperature and precipitation in YBRB is mostly lower than CMIP5, but still has a cold bias over YBRB (Tasmax: −7.98 °C to −14.88 °C, Tasmin: −6.24 °C to −21.45 °C) and wet bias for precipitation on Tibetan Plateau (0.56 mm − 2.00 mm), dry bias on Himalayan belt (−0.69 mm to −7.56 mm) and floodplain (−0.46 mm to −6.98 mm).
3D criteria to improve real-time pressure control in water distribution network system architecture
Authors: Mohammadzade Negharchi Saeid , Shafaghat Rouzbeh 
Published: July 2024

ABSTRACT

Real-time control (RTC) methods have been developed for over one decade to regulate service pressure and reduce leakage using pressure control valves in water distribution networks (WDN). The present study investigates control node selection frameworks and RTC system architecture for the field-oriented control of a rural WDN. The unique topology of the case-study area, the small size of the area, and high-pressure variety increased the considerations of the RTC system. A computer code was developed based on the method of characteristics for the hydraulic simulation of the network. The code could analyze unsteady flows through sloped pipes, implement pressure-based analysis, and regulate control valves based on the target node. Leakage reduction, pressure fluctuation reduction, and cavitation prevention were used as three criteria and constraints in the selection of control nodes. It was found that the optimal strategy would reduce the real water loss from 25 to 10%. Furthermore, key parameters in remote sensing were evaluated to minimize the number of sensors in order to further simplify the control algorithm and RTC system architecture through an artificial neural network (ANN) approach. The control function with a convergence rate of 99% was introduced.
Hotspot analysis for integrated multi-infrastructure asset management
Authors: Daulat Shamsuddin , Roghani Bardia , Rokstad Marius Møller , Tscheikner-Gratl Franz 
Published: July 2024

ABSTRACT
Urban infrastructure, important for societal functioning, faces challenges from aging assets and increasing service demands. Traditional asset management practices, often conducted in silos, fail to address the interconnected nature of these systems, leading to inefficiencies and heightened system failure risks. This article combines the spatial and temporal aspects of sewer, water, and road networks to facilitate integrated interventions and enable informed decision-making among diverse stakeholders. The outcome of this research is the creation of interactive hotspot maps on a unified platform, highlighting potential areas for integrated intervention across different infrastructures. To enhance the potential for collaboration in integrated interventions, flexibility in intervention planning was incorporated. With increased flexibility in intervention decisions, the potential for collaboration also increased. For the case study, introducing a 5-year intervention flexibility increased the number of collaborative projects between sewer, water, and roads from 0 to 18. The maps can also indicate areas where the application of trenchless technologies are justifiable. Other important information on asset characteristics for the decision-makers, including age, inspection, deterioration, and other relevant spatial and temporal details can also be obtained from the maps. The presented methodology and findings provide practical solution for utilities to manage urban infrastructure networks more efficiently.
Soft sensor for the dry solid content in thickened primary sludge
Authors: Molin Hanna , Bröndum Eric , Nilsson Sara , Mattson Per , Saagi Ramesh , Lindblom Erik , Carlsson Bengt , Jeppsson Ulf 
Published: July 2024

ABSTRACT
Software sensors, or soft sensors, can be a feasible option to monitor parameters that are difficult (or impossible) to measure with hardware sensors. At Henriksdal water resource recovery facility (WRRF), the operators have long experienced issues with a clogging sensor for the dry solids (DS) content in thickened primary sludge. A soft sensor was developed, and in the process, two methods were compared: long short-term memory (LSTM) network, and linear regression. The first is a recurrent neural network that can capture non-linear dynamics, whereas the latter is a linear static model. The LSTM network was the best at predicting the DS content, with a mean squared error (MSE) of 0.341 with respect to laboratory data. The linear regression model performed worse than estimating a long-time average of daily manual samples but outperformed the online sensor. Replacing the existing sensor with the developed soft sensor can open possibilities to more efficient control and operation of the thickener unit.