bluuebunny/crossref_metadata_2025_split huggingface dataset:
The WHOQOL-BREF instrument: Psychometric evaluation of the Greek version in patients with advanced cancer and pain and associations with psychological distress
Authors: Siafaka Vassiliki , Mavridis Dimitris , Tsonis Orestis , Tzamakou Eleftheria , Christogiannis Christos , Tefa Louiza , Arnaoutoglou Eleni , Tzimas Petros , Pentheroudakis George 
Published: August 2022

AbstractObjectivesAssessment of the psychometric characteristics of the Greek version of the brief World Health Organization Quality of Life Instrument (WHOQOL-BREF) in patients with advanced cancer and pain, and exploration of the association between psychological distress and quality of life (QoL).MethodThe sample consisted of 145 patients with advanced cancer and pain who completed the WHOQOL-BREF, the Symptom Checklist-90 (SCL-90), and the Pain Visual Analogue Scale (VAS). In analysis, the following methods were used: Cronbach's alpha, Item Response Theory (IRT), polychoric, Pearson and polyserial correlation, t-test, and Linear regression.ResultsThe internal consistency was high for all domains of the WHOQOL-BREF (Cronbach's α ≥ 0.731). Similarly, with the exception of three items, the WHOQOL-BREF items has large discrimination parameters suggesting that they have a high ability in differentiating subjects. On SCL-90, the three dimensions with the highest scores were Depression, Somatization, and Anxiety. The overall score for psychological distress, the Global Severity Index (GSI), showed significant negative association with all the WHOQOL-BREF factor scores (Physical Health: B = −1.488, p < 0.001, Psychological Health: B = −1.688, p < 0.001, Social Relationships: B = −0.910, p < 0.001, Environment: B = −1.064, p < 0.001). Male gender was associated with lower scores for Social Relationships (B = −0.358, p = 0.007) and Environment (B = −0.293, p = 0.026).Significance of resultsThe Greek version of the WHOQOL-BREF showed good psychometric properties in patients with advanced cancer and can be used as a reliable instrument in clinical practice. The level of psychological distress can be considered a determinant of QoL in patients with advanced cancer and pain, independently of pain intensity or other clinical characteristics. In cancer, the disease process can activate multiple physiological and psychological mechanisms that lead to a wide range of symptoms of psychological distress. To improve their QoL, psychological intervention focused on the identification and alleviation of psychological distress in patients with advanced cancer, and help in finding meaning in their experience, should be provided.
Clinical wisdom in the age of computer apps: a systematic review of four functions that may complement clinical treatment
Authors: Pacheco Catalina R. , Scheeringa Michael S. 
Published: August 2022

Abstract
Mental health clinicians perform complex tasks with patients that potentially could be improved by the massive computing power available through mobile apps. This study aimed to analyse commercially available mobile and computer applications (apps) focused on treating psychiatric disorders. Apps were analysed by two independent raters for whether they took advantage of computer power to process data in a fashion that augments four main elements of clinical treatment including (1) assessment/diagnosis, (2) treatment planning, (3) treatment fidelity monitoring, and (4) outcome tracking. The evidence base for each of these apps was also explored via PsychINFO, Research Gate and Google Scholar. Searches of the Google Play Store, the Apple App Store, and the One Mind PsyberGuide found 722 apps labelled for mental health use, of which 163 apps were judged relevant to clinical work with patients with psychiatric disorders. Fifty-nine of these were determined to contain a computer-driven function for at least one of the four main elements of clinical treatment. The most common element was assessment/diagnosis (55/59 apps), followed by outcome tracking (34/59 apps). Six apps updated treatment plans using user input. Only one app tracked treatment fidelity. None of the apps contained computer-driven functions for all four elements. Twelve apps were supported in randomized clinical trials to show greater efficacy compared with either wait-list or other active treatments. Results showed that these four clinical elements can be meaningfully augmented, but the full potential of computer processing appears unreached in mental health-related apps.

Key learning aims

(1)
To understand what apps are currently available to treat clinical-level psychiatric problems.

(2)
To understand how many of the commercially available mental health-focused apps can be used for the treatment of clinical populations.

(3)
To understand how mental health services can be complemented by utilizing computer processing power within apps.
Evaluation of the CMIP6 Precipitation Simulations Over Global Land
Authors: Li Zhengyang , Liu Tie , Huang Yue , Peng Jiabin , Ling Yunan 
Published: August 2022

AbstractPrecipitation's temporal and spatial patterns under climate change significantly impact global terrestrial ecology and human social activities. Climate models are essential tools for assessing the impacts of climate change and formulating policies to address climate change. The evaluation results of historical climate model simulations can represent the reliability of their future simulations. This study evaluated the simulation capabilities of 41 historical All‐Forcing monthly precipitation simulations and three integrated models over global land in the Coupled Model Comparison Project Phase 6 (CMIP6). The results show that the simulation capability of global climate models (GCMs) in CMIP6 is highly variable overland around the world. This variability is manifested in two aspects: the spatial variability of the comprehensive simulation ability of each model in different geographical regions and climatic zones of the world and the significant difference in the simulation ability of different models in each region. These GCMs generally overestimate global monthly precipitation over land, with the exception of southeast Asia and tropical rainforest climate (Af), where all models underestimate monthly precipitation. Some GCMS can perform well regionally but poorly on the global scale. One example shows that EC‐Earth3's best capability at Cwc climatic zone, surpassing the integrated model, but failed to rank in the top 10 in 22 of the 29 climate zones. Our results highlight the need to select appropriate models for integration when conducting climate change studies at global and regional scales as a critical factor in studying climate change predictions.
Vertical Structure in Phytoplankton Growth and Productivity Inferred From Biogeochemical‐Argo Floats and the Carbon‐Based Productivity Model
Authors: Arteaga Lionel A. , Behrenfeld Michael J. , Boss Emmanuel , Westberry Toby K. 
Published: August 2022

AbstractEstimates of marine net primary production (NPP) commonly rely on limited in situ 14C incubations or satellite‐based algorithms mainly constrained to the surface ocean. Here we combine data from biogeochemical Argo floats with a carbon‐based productivity model (CbPM) to compute vertically resolved estimates of NPP. Inferred NPP profiles derived by informing the CbPM with float‐based, depth‐resolved, bio‐optical data are able to qualitatively reproduce the vertical structure in NPP inferred from in situ 14C incubations at various ocean regions. At station ALOHA, float‐based estimates agree within uncertainty with productivity observations at depth, but underestimate surface NPP. We test the ability of the CbPM to infer the depth‐resolved structure in NPP from bio‐optical properties in the mixed layer, in similar fashion as how remote sensing algorithms of ocean productivity operate. In Southern Ocean waters, the depth‐reconstructing implementation of the CbPM overestimates phytoplankton division rates and Chl:C below the mixed layer, resulting in artificially high subsurface NPP when compared with the fully float‐informed implementation of the model. The CbPM subsurface extrapolation of phytoplankton Chl, Chl:C, division rates, and NPP improves by accounting for deep nutrient (iron) stress impacts on photoacclimation in the Southern Ocean. This improvement is also observed in vertically integrated NPP, where the mean bias between model implementations in depth‐integrated productivity south of 30°S is reduced by 62% when account for deep iron limitation. Our results demonstrate that profiling data from biogeochemical Argo floats can serve to inform regional adjustments that lead to the improvement of marine productivity algorithms.
Separating Annual, Interannual and Regional Change in Sea Surface Temperature in the Northeastern Atlantic and Nordic Seas
Authors: Saes Mischa J. M. , Gjelstrup Caroline V. B. , Visser Andre W. , Stedmon Colin A. 
Published: August 2022

AbstractSea surface temperature (SST) in the Northeastern North Atlantic and Nordic Seas exhibits pronounced variability across seasonal to decadal time scales. These changes can be expected to be driven by a combination of altered local conditions, shifts in seasonality and large‐scale regional oceanographic change. Separating the contribution from each of these offers insight into how the region is changing. Here, we present the result of an analysis of weekly satellite‐derived SST data from 1979 to 2020. An empirical orthogonal function (EOF) analysis allows us to separate observed changes in SST into independent underlying timeseries. Each timeseries explains part of the variability in SST. EOF1 can be allocated with changes in seasonality and a long‐term warming trend, with summer maxima warming with twice the rate (0.043°C year−1) compared to winter minima (0.023°C year−1). EOF2 is associated with the North Atlantic subpolar gyre and the North Atlantic Oscillation, affecting the Atlantic Water flow across the Greenland‐Scotland Ridge, imposing a dipole cooling/warming pattern. Local sea‐ice melt along the southeast Greenland shelf is represented by EOF3, and finally the influx of warmer water with the North Icelandic Irminger Current is captured by EOF4. Each of these disaggregated signals differ considerably in their contribution to driving temporal and spatial trends in SST. The isolated signals offer a high‐resolution long‐time series of valuable indicators of oceanographic change which will likely be reflected in biogeochemistry, plankton, fish, mammals, and seabirds in the region.
The Importance of Storm Surge for Sediment Delivery to Microtidal Marshes
Authors: Zhu Qingguang , Wiberg Patricia L. 
Published: August 2022

AbstractStorm surge has the potential to significantly increase suspended sediment flux to microtidal marshes. However, the overall effects of storm surge on microtidal marsh deposition have not been well quantified, with most modeling studies focusing on regular (astronomical) tidal flooding. Here we applied the Delft3D model to a microtidal bay‐marsh complex in Hog Bay, Virginia to quantify the contributions of storm surge to marsh deposition. We validated the model using spatially distributed hydrodynamic and suspended sediment data collected from the site and ran model simulations under different storm surge conditions with/without storm‐driven water level changes. Our results show that episodic storm surge events occurred 5% of the time at our study site, but contributed 40% of marsh deposition during 2009–2020. Our simulations illustrate that while wind‐driven waves control sediment resuspension on tidal flats, marsh deposition during storms was largely determined by tidal inundation associated with storm‐driven water levels. A moderate storm surge event can double sediment flux to most marshes around the bay and deliver more sediment to the marsh interior compared to simulations that include wind waves but not storm surge variations in water levels. Simulations of bay and marsh response to different storm surge events with varying magnitude of storm surge intensity reveal that total marsh deposition around the bay increased linearly with storm surge intensity, suggesting that future changes to storm magnitude and/or frequency would have significant implications for sediment supply to marshes at our study site.
Cerebrospinal fluid proteomic study of two bipolar disorder cohorts
Authors: Isgren Anniella , Göteson Andreas , Holmén-Larsson Jessica , Pelanis Aurimantas , Sellgren Carl , Joas Erik , Sparding Timea , Zetterberg Henrik , Smedler Erik , Jakobsson Joel , Landén Mikael 
Published: August 2022

AbstractThe pathophysiology of bipolar disorder remains to be elucidated and there are no diagnostic or prognostic biomarkers for the condition. In this explorative proteomic study, we analyzed 201 proteins in cerebrospinal fluid (CSF) from mood stable bipolar disorder patients and control subjects sampled from two independent cohorts, amounting to a total of 204 patients and 144 controls. We used three Olink Multiplex panels, whereof one specifically targets immune biomarkers, to assess a broad set of CSF protein concentrations. After quality control and removal of proteins with a low detection rate, 105 proteins remained for analyses in relation to case–control status and clinical variables. Only case–control differences that replicated across cohorts were considered. Results adjusted for potential confounders showed that CSF concentrations of growth hormone were lower in bipolar disorder compared with controls in both cohorts. The effect size was larger when the analysis was restricted to bipolar disorder type 1 and controls. We found no indications of immune activation or other aberrations. Growth hormone exerts many effects in the central nervous system and our findings suggest that growth hormone might be implicated in the pathophysiology of bipolar disorder.
Genomic insights into the phylogeny and biomass-degrading enzymes of rumen ciliates
Authors: Li Zongjun , Wang Xiangnan , Zhang Yu , Yu Zhongtang , Zhang Tingting , Dai Xuelei , Pan Xiangyu , Jing Ruoxi , Yan Yueyang , Liu Yangfan , Gao Shan , Li Fei , Huang Youqin , Tian Jian , Yao Junhu , Xing XvPeng , Shi Tao , Ning Jifeng , Yao Bin , Huang Huoqing , Jiang Yu 
Published: August 2022

Abstract
Understanding the biodiversity and genetics of gut microbiomes has important implications for host physiology and industrial enzymes, whereas most studies have been focused on bacteria and archaea, and to a lesser extent on fungi and viruses. One group, still underexplored and elusive, is ciliated protozoa, despite its importance in shaping microbiota populations. Integrating single-cell sequencing and an assembly-and-identification pipeline, we acquired 52 high-quality ciliate genomes of 22 rumen morphospecies from 11 abundant morphogenera. With these genomes, we resolved the taxonomic and phylogenetic framework that revised the 22 morphospecies into 19 species spanning 13 genera and reassigned the genus Dasytricha from Isotrichidae to a new family Dasytrichidae. Comparative genomic analyses revealed that extensive horizontal gene transfers and gene family expansion provided rumen ciliate species with a broad array of carbohydrate-active enzymes (CAZymes) to degrade all major kinds of plant and microbial carbohydrates. In particular, the genomes of Diplodiniinae and Ophryoscolecinae species encode as many CAZymes as gut fungi, and ~80% of their degradative CAZymes act on plant cell-wall. The activities of horizontally transferred cellulase and xylanase of ciliates were experimentally verified and were 2–9 folds higher than those of the inferred corresponding bacterial donors. Additionally, the new ciliate dataset greatly facilitated rumen metagenomic analyses by allowing ~12% of the metagenomic sequencing reads to be classified as ciliate sequences.
Confidence bands in survival analysis
Authors: Sachs Michael C. , Brand Adam , Gabriel Erin E. 
Published: August 2022

Abstract
Background
Providing estimates of uncertainty for statistical quantities is important for statistical inference. When the statistical quantity of interest is a survival curve, which is a function over time, the appropriate type of uncertainty estimate is a confidence band constructed to account for the correlation between points on the curve, we will call this a simultaneous confidence band. This, however, is not the type of confidence band provided in standard software, which is constructed by joining the confidence intervals at given time points.

Methods
We show that this type of band does not have desirable joint/simultaneous coverage properties in comparison to simultaneous bands.

Results
There are different ways of constructing simultaneous confidence bands, and we find that bands based on the likelihood ratio appear to have the most desirable properties. Although there is no standard software available in the three major statistical packages to compute likelihood-based simultaneous bands, we summarise and give code to use available statistical software to construct other simultaneous forms of bands, which we illustrate using a study of colon cancer.

Conclusions
There is a need for more user-friendly statistical software to compute simultaneous confidence bands using the available methods.
ESE3-positive PSCs drive pancreatic cancer fibrosis, chemoresistance and poor prognosis via tumour–stromal IL-1β/NF–κB/ESE3 signalling axis
Authors: Zhao Tiansuo , Xiao Di , Jin Fanjie , Sun Xugang , Yu Jie , Wang Hongwei , Liu Jing , Cai Wenrun , Huang Chongbiao , Wang Xiuchao , Gao Song , Liu Zhe , Yang Shengyu , Gao Chuntao , Hao Jihui 
Published: August 2022

Abstract
Background
Desmoplastic stroma, a feature of pancreatic ductal adenocarcinoma (PDAC), contains abundant activated pancreatic stellate cells (PSCs). How PSCs promote PDAC progression remains incompletely understood.

Methods
Effect of epithelium-specific E-twenty six factor 3 (ESE3)-positive PSCs on PDAC fibrosis and chemoresistance was examined by western blot, RT-PCR, immunofluorescence, flow cytometry assay, chromatin immunoprecipitation, luciferase assay, immunohistochemistry and subcutaneous pancreatic cancer mouse model.

Results
ESE3 expression increased in PSCs in PDAC tissues compared with those in normal PSCs. Clinical data showed that ESE3 upregulation in PSCs was positively correlated with tumour size, pTNM stage, CA19-9, carcinoembryonic antigen and serum CA242 level. ESE3 overexpression in PSCs was an independent negative prognostic factor for disease-free survival and overall survival amongst patients with PDAC. Mechanistically, the conditional medium from the loss and gain of ESE3-expressing PSCs influenced PDAC chemoresistance and tumour growth. ESE3 directly induced the transcription of α-SMA, collagen-I and IL-1β by binding to ESE3-binding sites on their promoters to activate PSCs. IL-1β upregulated ESE3 in PSCs through NF-κB activation, and ESE3 was required for PSC activation by tumour cell-derived IL-1β.

Conclusion
Inhibiting the IL-1β/ESE3 (PSCs)/IL-1β-positive feedback loop is a promising therapeutic strategy to reduce tumour fibrosis and increase chemotherapeutic efficacy in PDAC.
Accelerated prediction of atomically precise cluster structures using on-the-fly machine learning
Authors: Wang Yunzhe , Liu Shanping , Lile Peter , Norwood Sam , Hernandez Alberto , Manna Sukriti , Mueller Tim 
Published: August 2022

AbstractThe chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but predicting the stable structures of clusters can be computationally expensive. In this work, we present a procedure for rapidly predicting low-energy structures of nanoclusters by combining a genetic algorithm with interatomic potentials actively learned on-the-fly. Applying this approach to aluminum clusters with 21 to 55 atoms, we have identified structures with lower energy than any reported in the literature for 25 out of the 35 sizes. Our benchmarks indicate that the active learning procedure accelerated the average search speed by about an order of magnitude relative to genetic algorithm searches using only density functional calculations. This work demonstrates a feasible way to systematically discover stable structures for large nanoclusters and provides insights into the transferability of machine-learned interatomic potentials for nanoclusters.
Performance of glass fiber reinforced polyamide composites exposed to bioethanol fuel at high temperature
Authors: Wei Xin-Feng , Kallio Kai J. , Olsson Richard T. , Hedenqvist Mikael S. 
Published: August 2022

AbstractThe increasing use of bioethanol fuel, as a promising carbon-neutral alternative to petroleum-based gasoline, has raised concerns on compatibility issues between bioethanol and the polymer components used in the automotive fuel system. Here, the performance of two commercial glass-fiber reinforced polyamide composites (polyamide 12 (PA12) and an aromatic highly-biobased polyamide (polyphthalamide, PPA)) exposed to a mixture of ethanol and gasoline (E25 fuel), was investigated at high temperature (120 °C). The polyamide matrices showed a fast and remarkably high fuel uptake. The sorbed fuel had a strong plasticizing effect on both materials, as revealed by the large decrease in the glass transition temperature and a 60% decrease in stiffness and strength. The PPA, however, sustained degradation better than the PA12, which experienced swelling-induced surface cracking and oxidation-induced embrittlement. The results suggest that care should be taken when using polyamide composites in ethanol-based fuel applications at this high temperature level.
Plastic injection molding dies using hybrid additively manufactured 420/CX stainless steels: electrochemical considerations
Authors: Shahriari Ayda , Samei Javad , Sanjari Mehdi , Jahanbakht Mohammad , Amirkhiz Babak Shalchi , Mohammadi Mohsen 
Published: August 2022

AbstractThis research focused on the corrosion resistance and microstructure of hybrid additively manufactured (HAM) samples of AISI 420/CX (420/CX SS) stainless steels. Potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), and Mott–Schottky analyses as well as the electrochemical noise (EN) technique were used to evaluate the electrochemical behavior of the as-built and heat-treated HAM parts in NaCl solution. The results showed a more protective passive layer formed on the CX SS side. The distribution of Cr-rich M23C6carbides in matrix of 420 SS side resulted in a lower corrosion resistance compared to the CX SS side. The noise data analysis confirmed an increase in the galvanic currents of the HAM parts after heat treatment. The stochastic analysis revealed the interface in the heat-treated condition increases pit growth more than the as-built one due to the evolution of nano-sized intermetallic compounds of Al-N/ (Cr, Nb) (N, C) at the heat-treated interface area.