bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthetic Forecast Ensembles for Evaluating Forecast Informed Reservoir Operations
Authors: Brodeur Zachary P. , Delaney Chris , Whitin Brett , Steinschneider Scott 
Published: February 2024

AbstractForecast informed reservoir operations (FIRO) is an important advance in water management, but the design and testing of FIRO policies is limited by relatively short (10–35 year) hydro‐meteorological hindcasts. We present a novel, multisite model for synthetic forecast ensembles to overcome this limitation. This model utilizes parametric and non‐parametric procedures to capture complex forecast errors and maintain correlation between variables, lead times, locations, and ensemble members. After being fit to data from the hindcast period, this model can generate synthetic forecast ensembles in any period with observations. We demonstrate the approach in a case study of the FIRO‐based Ensemble Forecast Operations (EFO) control policy for the Lake Mendocino—Russian River basin, which conditions release decisions on ensemble forecasts from the Hydrologic Ensemble Forecast System (HEFS). We explore two generation strategies: (a) simulation of synthetic forecasts of meteorology to force HEFS; and (b) simulation of synthetic HEFS streamflow forecasts directly. We evaluate the synthetic forecasts using ensemble verification techniques and event‐based validation, finding good agreement with the actual ensemble forecasts. We then evaluate EFO policy performance using synthetic and actual forecasts over the hindcast period (1985–2010) and synthetic forecasts only over the pre‐hindcast period (1948–1984). Results show that the synthetic forecasts highlight important failure modes of the EFO policy under plausible forecast ensembles, but improvements are still needed to fully capture FIRO policy behavior under the actual forecast ensembles. Overall, the methodology advances a novel way to test FIRO policy robustness, which is key to building institutional support for FIRO.
Enhancing the explanation of household water consumption through the water-energy nexus concept
Authors: Li Zonghan , Wang Chunyan , Liu Yi , Wang Jiangshan 
Published: February 2024

AbstractEstimating household water consumption can facilitate infrastructure management and municipal planning. The relatively low explanatory power of household water consumption, although it has been extensively explored based on various techniques and assumptions regarding influencing features, has the potential to be enhanced based on the water-energy nexus concept. This study attempts to explain household water consumption by establishing estimation models, incorporating energy-related features as inputs and providing strong evidence of the need to consider the water-energy nexus to explain water consumption. Traditional statistical (OLS) and machine learning techniques (random forest and XGBoost) are employed using a sample of 1320 households in Beijing, China. The results demonstrate that the inclusion of energy-related features increases the coefficient of determination (R2) by 34.0% on average. XGBoost performs the best among the three techniques. Energy-related features exhibit higher explanatory power and importance than water-related features. These findings provide a feasible modelling basis and can help better understand the household water-energy nexus.
Anthropogenic climate and land-use change drive short- and long-term biodiversity shifts across taxa
Authors: Montràs-Janer Teresa , Suggitt Andrew J. , Fox Richard , Jönsson Mari , Martay Blaise , Roy David B. , Walker Kevin J. , Auffret Alistair G. 
Published: February 2024

AbstractClimate change and habitat loss present serious threats to nature. Yet, due to a lack of historical land-use data, the potential for land-use change and baseline land-use conditions to interact with a changing climate to affect biodiversity remains largely unknown. Here, we use historical land use, climate data and species observation data to investigate the patterns and causes of biodiversity change in Great Britain. We show that anthropogenic climate change and land conversion have broadly led to increased richness, biotic homogenization and warmer-adapted communities of British birds, butterflies and plants over the long term (50+ years) and short term (20 years). Biodiversity change was found to be largely determined by baseline environmental conditions of land use and climate, especially over shorter timescales, suggesting that biodiversity change in recent periods could reflect an inertia derived from past environmental changes. Climate–land-use interactions were mostly related to long-term change in species richness and beta diversity across taxa. Semi-natural grasslands (in a broad sense, including meadows, pastures, lowland and upland heathlands and open wetlands) were associated with lower rates of biodiversity change, while their contribution to national-level biodiversity doubled over the long term. Our findings highlight the need to protect and restore natural and semi-natural habitats, alongside a fuller consideration of individual species’ requirements beyond simple measures of species richness in biodiversity management and policy.
Numerical algorithm for environmental/economic load dispatch with emissions constraints
Authors: Bakos Christos , Giakoumis Angelos 
Published: February 2024

AbstractThis paper presents a numerical algorithm for environmental/economic load dispatch (EELD) with emissions constraints, which takes into account the emissions trading system’s effect on electricity generation cost and is implemented using a Python computer program. The developed program is applied to a power system of six (6) fossil-fueled electricity generating units with NOx, SO2and CO2constraints and proved to be significantly beneficial not only for the environment but also for the power company and the consumers. The proposed algorithm uses multi-objective optimization and incorporates both fuel and emissions allowances costs. The schedule of generating units is calculated and the testing of all possible weighting factor combinations with resolution of 0.01 is carried out showing that the proposed algorithm is fast, cost effective and environmentally friendly.
Exergy-energy, sustainability, and emissions assessment of Guizotia abyssinica (L.) fuel blends with metallic nano additives
Authors: Abishek M. S. , Kachhap Sabindra , Rajak Upendra , Verma Tikendra Nath , Giri Nimay Chandra , AboRas Kareem M. , ELrashidi Ali 
Published: February 2024

AbstractThis study extensively examined the impact of aluminium oxide (Al2O3) and titanium dioxide (TiO2) nanoparticles addition in the biodiesel fuel derived from Guizotiaabyssinica (L.) oil. The assessment of fuel blends, which were created by combining nanoparticles and biodiesel was conducted using energy, exergy, and sustainability indices. The highest recorded power output of 2.81 kW was observed for the GAB20A engine operating at 1800 rpm. The experimental results revealed that the GAB20A exhibited the lowest fuel consumption, with a recorded value of 203 g/kWh, when operated at 1600 rpm among all the tested blend fuels. The blend GAB20A exhibited the highest level of energy efficiency at 1600 rpm of 29.5%, as determined by the study. Simultaneously, it was observed that GAB20 exhibited the lowest energy efficiency at 1200 rpm among all the blend fuels at 25%. The emission levels of nitrogen oxides (NOx) and carbon monoxide (CO) were observed to be quite low, although a little rise in carbon dioxide (CO2) was detected. For validation of results the artificial neural network (ANN) was used and an average of 1.703% difference in energy efficiency, 2.246% decrease in exergy efficiency, and 1.416% difference in sustainability index was found.
The survival of multi-drug resistant bacteria on raw Douglas fir material
Authors: Taisne A. , Aviat F. , Essono Mintsa M. , Belloncle C. , Pailhoriès H. 
Published: February 2024

AbstractIn today’s age of ecological transition, the use of materials such as renewable wood in construction is particularly relevant, but also a challenge in the healthcare sector where the hygiene dimension also comes into play. In this study we have investigated the survival of multi-resistant bacteria commonly responsible for healthcare-associated infections (HAIs) (ESBL-positive Klebsiella pneumoniae and glycopeptide-resistant Enterococcus faecalis) on two different types of wood (Douglas fir : Pseudotsuga menziesii and Maritime Pine : Pinus pinaster) compared to other materials (smooth: stainless steel and rough: pumice stone) and the effect of a disinfection protocol on the bacterial survival on Pseudotsuga menziesii. Approximately 108 bacteria were inoculated on each material and bacterial survival was observed over several days (D0, D1, D2, D3, D6, D7 and D15). Each analysis was performed in triplicate for each time and material. The results show an important reduction of the bacterial inoculum for Klebsiella pneumoniae and Enterococcus faecalis on Douglas fir, in contrast with the results obtained on maritime pine, stainless steel and pumice stone. No bacterial survival was detected on Douglas fir after application of a hospital disinfection protocol. These different results show that wood may have a place in the future of healthcare construction. Further studies would be interesting to better understand the different properties of wood.
Magneto-optical heterostructures with second resonance of transverse magneto-optical Kerr effect
Authors: Rezaeian Amene , Hosseini Farzad Mahmood 
Published: February 2024

AbstractTwo conventional magneto-plasmonic (MP) structures are firstly superimposed with mirror symmetry to form a symmetric MP heterostructure. These two MP components are separated from each other by a noble metallic layer. The unique feature of this novel heterostructure is that both magneto-plasmon modes of the up and down MP portions can be coupled as the spacer becomes thinner. This intertwining effect leads to appearance of a new peak in the angular transverse magneto-optical Kerr effect (TMOKE) curve of the heterostructure. This new peak which is reported for the first time in the TMOKE signal, is generally similar to plasmon induced transparency (PIT) phenomenon observed in plasmonic multilayered structures. We entitle this novel effect as “second resonance of TMOKE signal”. More importantly, the occurrence angle and magnitude of the second peak can be controlled by varying the thickness and material of separating layer between two MP parts. Also, the dispersion diagram of the heterostructure shows this coupling so that two branches convert into four branches by reducing the thickness of spacer. Furthermore, coupled oscillators model confirms emergence of the second peak in the TMOKE signal. These results can offer great promise for increasing sensitivity of conventional magneto-optical refractive index sensors.
Urinary cancer detection by the target urine volatile organic compounds biosensor platform
Authors: Suthat Na Ayutaya Vichayut , Tantisatirapoon Chaianan , Aekgawong Sekdusit , Anakkamatee Watcharapong , Danjittrong Taechasit , Kreepala Chatchai 
Published: February 2024

AbstractVolatile organic compounds (VOCs) have grown due to their crucial role in transitioning from invasive to noninvasive cancer diagnostic methods. This study aimed to assess the feasibility of the metal oxide biosensor platform using urine VOCs for detecting genitourinary cancers. Five different commercially available semiconductor sensors were chosen to detect specific VOCs (methane, iso-butane, hydrogen, ethanol, hydrogen sulfide, ammonia, toluene, butane, propane, trimethylamine, and methyl-mercaptan). Changes in electrical resistance due to temperature variations from the voltage heater were examined to characterize VOC metabolism. Logistic regression and ROC analysis were employed to evaluate potential urine VOCs for genitourinary cancer determination. This study involved 64 participants which were categorized into a cancer and a non-cancer group. The genitourinary cancer (confirmed by tissue pathology) comprised 32 patients, including renal cell carcinoma (3.1%), transitional cell carcinoma (46.9%), and prostate cancer (50%). The non-cancer comprised 32 patients, with 9 healthy subjects and 23 individuals with other genitourinary diseases. Results indicated that VOC sensors for methane, iso-butane, hydrogen, and ethanol, at a voltage heater of 2000 mV, demonstrated a significant predictive capability for genitourinary cancer with P = 0.013. The ROC of these biomarkers also indicated statistical significance in predicting the occurrence of the disease (P < 0.05). This report suggested that methane, iso-butane, hydrogen, and ethanol VOCs exhibited potential for diagnosing genitourinary cancer. Developing gas metal oxide sensors tailored to these compounds, and monitoring changes in electrical resistance, could serve as an innovative tool for identifying this specific type of cancer.
Analysis of progression after elective distal ureterectomy and effects of salvage radical nephroureterectomy in patients with distal ureteral urothelial carcinoma
Authors: Lee Chung Un , Lee Jong Hoon , Lee Hye Won , Chung Jae Hoon , Song Wan , Kang Minyong , Jeon Hwang Gyun , Jeong Byong Chang , Seo Seong Il , Jeon Seong Soo , Sung Hyun Hwan 
Published: February 2024

AbstractWe compared the progression patterns after radical nephroureterectomy (RNU) and elective distal ureterectomy (DU) in patients with urothelial carcinoma of the distal ureter. Between Jan 2011 and Dec 2020, 127 patients who underwent RNU and 46 who underwent elective DU for distal ureteral cancer were enrolled in this study. The patterns of progression and upper tract recurrence were compared between the two groups. Progression was defined as a local recurrence and/or distant metastasis after surgery. Upper tract recurrence and subsequent treatment in patients with DU were analyzed. Progression occurred in 35 (27.6%) and 10 (21.7%) patients in the RNU and DU groups, respectively. The progression pattern was not significantly different (p = 0.441), and the most common progression site was the lymph nodes in both groups. Multivariate logistic regression analysis revealed that pT2 stage, concomitant lymphovascular invasion, and nodal stage were significant predictors of disease progression. Upper tract recurrence was observed in nine (19.6%) patients with DU, and six (66.7%) patients had a prior history of bladder tumor. All patients with upper tract recurrence after DU were managed with salvage RNU. Elective DU with or without salvage treatment was not a risk factor for disease progression (p = 0.736), overall survival (p = 0.457), cancer-specific survival (p = 0.169), or intravesical recurrence-free survival (p = 0.921). In terms of progression patterns and oncological outcomes, there was no difference between patients who underwent RNU and elective DU with/without salvage treatment. Elective DU should be considered as a therapeutic option for distal ureter tumor.
Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis
Authors: Creagh Andrew P. , Hamy Valentin , Yuan Hang , Mertes Gert , Tomlinson Ryan , Chen Wen-Hung , Williams Rachel , Llop Christopher , Yee Christopher , Duh Mei Sheng , Doherty Aiden , Garcia-Gancedo Luis , Clifton David A. 
Published: February 2024

AbstractDigital measures of health status captured during daily life could greatly augment current in-clinic assessments for rheumatoid arthritis (RA), to enable better assessment of disease progression and impact. This work presents results from weaRAble-PRO, a 14-day observational study, which aimed to investigate how digital health technologies (DHT), such as smartphones and wearables, could augment patient reported outcomes (PRO) to determine RA status and severity in a study of 30 moderate-to-severe RA patients, compared to 30 matched healthy controls (HC). Sensor-based measures of health status, mobility, dexterity, fatigue, and other RA specific symptoms were extracted from daily iPhone guided tests (GT), as well as actigraphy and heart rate sensor data, which was passively recorded from patients’ Apple smartwatch continuously over the study duration. We subsequently developed a machine learning (ML) framework to distinguish RA status and to estimate RA severity. It was found that daily wearable sensor-outcomes robustly distinguished RA from HC participants (F1, 0.807). Furthermore, by day 7 of the study (half-way), a sufficient volume of data had been collected to reliably capture the characteristics of RA participants. In addition, we observed that the detection of RA severity levels could be improved by augmenting standard patient reported outcomes with sensor-based features (F1, 0.833) in comparison to using PRO assessments alone (F1, 0.759), and that the combination of modalities could reliability measure continuous RA severity, as determined by the clinician-assessed RAPID-3 score at baseline (r2, 0.692; RMSE, 1.33). The ability to measure the impact of the disease during daily life—through objective and remote digital outcomes—paves the way forward to enable the development of more patient-centric and personalised measurements for use in RA clinical trials.
Genomic attributes of airway commensal bacteria and mucosa
Authors: Cuthbertson Leah , Löber Ulrike , Ish-Horowicz Jonathan S. , McBrien Claire N. , Churchward Colin , Parker Jeremy C. , Olanipekun Michael T. , Burke Conor , McGowan Aisling , Davies Gwyneth A. , Lewis Keir E. , Hopkin Julian M. , Chung Kian Fan , O’Carroll Orla , Faul John , Creaser-Thomas Joy , Andrews Mark , Ghosal Robin , Piatek Stefan , Willis-Owen Saffron A. G. , Bartolomaeus Theda U. P. , Birkner Till , Dwyer Sarah , Kumar Nitin , Turek Elena M. , William Musk A. , Hui Jennie , Hunter Michael , James Alan , Dumas Marc-Emmanuel , Filippi Sarah , Cox Michael J. , Lawley Trevor D. , Forslund Sofia K. , Moffatt Miriam F. , Cookson William. O. C. 
Published: February 2024

AbstractMicrobial communities at the airway mucosal barrier are conserved and highly ordered, in likelihood reflecting co-evolution with human host factors. Freed of selection to digest nutrients, the airway microbiome underpins cognate management of mucosal immunity and pathogen resistance. We show here the initial results of systematic culture and whole-genome sequencing of the thoracic airway bacteria, identifying 52 novel species amongst 126 organisms that constitute 75% of commensals typically present in heathy individuals. Clinically relevant genes encode antimicrobial synthesis, adhesion and biofilm formation, immune modulation, iron utilisation, nitrous oxide (NO) metabolism and sphingolipid signalling. Using whole-genome content we identify dysbiotic features that may influence asthma and chronic obstructive pulmonary disease. We match isolate gene content to transcripts and metabolites expressed late in airway epithelial differentiation, identifying pathways to sustain host interactions with microbiota. Our results provide a systematic basis for decrypting interactions between commensals, pathogens, and mucosa in lung diseases of global significance.
SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism
Authors: Kambhampati Shrikaar , Hubbard Allen H. , Koley Somnath , Gomez Javier D. , Marsolais Frédéric , Evans Bradley S. , Young Jamey D. , Allen Doug K. 
Published: February 2024

AbstractThe capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.
SHMT2 reduces fatty liver but is necessary for liver inflammation and fibrosis in mice
Authors: Chen Guohua , Zhou Guoli , Zhai Lidong , Bao Xun , Tiwari Nivedita , Li Jing , Mottillo Emilio , Wang Jian 
Published: February 2024

AbstractNon-alcoholic fatty liver disease is associated with an irregular serine metabolism. Serine hydroxymethyltransferase 2 (SHMT2) is a liver enzyme that breaks down serine into glycine and one-carbon (1C) units critical for liver methylation reactions and overall health. However, the contribution of SHMT2 to hepatic 1C homeostasis and biological functions has yet to be defined in genetically modified animal models. We created a mouse strain with targeted SHMT2 knockout in hepatocytes to investigate this. The absence of SHMT2 increased serine and glycine levels in circulation, decreased liver methylation potential, and increased susceptibility to fatty liver disease. Interestingly, SHMT2-deficient mice developed simultaneous fatty liver, but when fed a diet high in fat, fructose, and cholesterol, they had significantly less inflammation and fibrosis. This study highlights the critical role of SHMT2 in maintaining hepatic 1C homeostasis and its stage-specific functions in the pathogenesis of NAFLD.
Estimating the phase diagrams of deep eutectic solvents within an extensive chemical space
Authors: Fajar Adroit T. N. , Hanada Takafumi , Hartono Aditya D. , Goto Masahiro 
Published: February 2024

AbstractAssessing the formation of a deep eutectic solvent (DES) necessitates a solid-liquid equilibrium phase diagram. Yet, many studies focusing on DES applications do not include this diagram because of challenges in measurement, leading to misidentified eutectic points. The present study provides a practical approach for estimating the phase diagram of any binary mixture from the structural information, utilizing machine learning and quantum chemical techniques. The selected machine learning model provides reasonably high accuracy in predicting melting point (R2 = 0.84; RMSE = 40.53 K) and fusion enthalpy (R2 = 0.84; RMSE = 4.96 kJ mol−1) of pure compounds upon evaluation by test data. By pinpointing the eutectic point coordinates within an extensive chemical space, we highlighted the impact of the mole fractions and melting properties on the eutectic temperatures. Molecular dynamics simulations of selected mixtures at the eutectic points emphasized the pivotal role of hydrogen bonds in dictating mixture behavior.