bluuebunny/crossref_metadata_2025_split huggingface dataset:
Designs and methodologies to recreate in vitro human gut microbiota models
Authors: Biagini Francesco , Daddi Costanza , Calvigioni Marco , De Maria Carmelo , Zhang Yu Shrike , Ghelardi Emilia , Vozzi Giovanni 
Published: December 2022

AbstractThe human gut microbiota is widely considered to be a metabolic organ hidden within our bodies, playing a crucial role in the host’s physiology. Several factors affect its composition, so a wide variety of microbes residing in the gut are present in the world population. Individual excessive imbalances in microbial composition are often associated with human disorders and pathologies, and new investigative strategies to gain insight into these pathologies and define pharmaceutical therapies for their treatment are needed. In vitro models of the human gut microbiota are commonly used to study microbial fermentation patterns, community composition, and host-microbe interactions. Bioreactors and microfluidic devices have been designed to culture microorganisms from the human gut microbiota in a dynamic environment in the presence or absence of eukaryotic cells to interact with. In this review, we will describe the overall elements required to create a functioning, reproducible, and accurate in vitro culture of the human gut microbiota. In addition, we will analyze some of the devices currently used to study fermentation processes and relationships between the human gut microbiota and host eukaryotic cells.
Graphic abstract
Changes in the Review Period of Drug Application and a Drug Lag from the FDA and the EMA: An Industry Survey in South Korea Between 2011 and 2020
Authors: Choi Hyeyoung , Lee Hyesung , Park Bojung , Kim Chorong , Lee Jaehyun 
Published: December 2022

Abstract
Background
The Korean regulatory authority has enacted legislation to expedite the new drug approval (NDA) process. However, the effectiveness of such efforts in reducing review time and drug approval delays between Korea and the USA/EU remains to be evaluated.

Methods
We investigated NDA trends in Korea from 2011 to 2020 using approval information from pharmaceutical companies. We compared the changes in the actual review duration according to active ingredient (chemical vs. biological), orphan status, therapeutic class, and NDA review process. We estimated the submission and approval gaps of new drugs between Korea and the US and EU across the study period.

Results
For 235 new drugs, the median NDA review time was 315 days, with a significant increase in the delay (average 15.4 days) over time. Biological drugs had a 43.2-day delay for approval than the time taken for approving chemical drugs. The median NDA review time for orphan drugs was 130.4 days faster than that for others, although the difference diminished after 2016. Good manufacturing practice reviews played a crucial role in delaying review time. The median submission and approval gaps in Korea were 493 and 551 days, respectively, compared to those of the US and EU.

Conclusions
Despite recent legislative initiatives, the delay in the NDA review timeline has steadily increased over 10 years in Korea. Delays in orphan drugs reviews increased after the enactment of the ‘Rare Disease Management Act’ in 2016. Careful enforcement of relevant laws and supplementary actions is required to increase new drug accessibility.
CSR–brand relationship, brand positioning, and investment risks driven towards climate change mitigation and next perspectives emerging from: “Litigation, projections, pathway, and models”
Authors: Adewole Olukorede 
Published: December 2022

AbstractThis study delineates the relationship between CSR and brands, unveiling pragmatic steps towards achieving the sustainable business environment, while unveiling its potential towards climate changes mitigation cognizant of investment risks, leading to an action plan-framework for proffering practical solutions coupled with establishing future paths and projection towards addressing climatic changes consequences, risen incidences in litigation trends and environmental issues. Trends manifesting in risen litigation incidences linked to industrial and economic activities that adversely impact the climate, environment and society makes it imperative to look extensively beyond prediction models while establishing and building on brand relationship with CSR, while strategically establishing a practically realistic business model for translating this relationship to value-creation and applied in abating climate change, addressing all environmental concerns, redressing litigation incidences, among other issues resulting from impacts of business and socio-economic pursuits of humans. The twenty-first century realities towards a green planet demands doing business strategically, optimize resources by imbibing investment risks as a trend and organizational culture—strategic fit, adopting brand as a potential tool for addressing climate change and environmentally related activities and adversaries from business activities and negligent practices from such, while achieving climate change mitigation as outlined and extensively inundated.
Decarbonization path of China’s public building sector from bottom to top
Authors: Peng Chenwei , Wei Qingpeng , Wei Wengang 
Published: December 2022

Abstract
The building sector is one of the three major energy consumption areas and one of the main areas responsible for carbon emissions. In 2019, carbon emissions related to construction and building operations in China accounted for 38% of the total social carbon emissions, of which construction accounted for 16% and operations accounted for 22%. Due to its large volume and high energy consumption per unit area, public buildings account for 38% of the operating energy consumption of all buildings, that is, 8% of the total national energy consumption. At this time, the building industry must take decarbonization actions to avoid a delay in realizing carbon neutrality and an emission peak. We need to form a unified process for the implementation boundary, implementation path, and index system to build a zero-carbon implementation plan for China’s public building sector. Based on bottom-up practical cases, this paper proposes the KAYA model, which is applicable to different scales and different types of public buildings/communities, and proposes specific and feasible plans. Through the implementation of demand reduction, energy efficiency improvement, and the fully-use of renewable energy in all five clear steps, this paper promotes the implementation of decarbonization in China’s building industry.
A climatic random forest model of agricultural insurance loss for the Northwest United States
Authors: Seamon Erich , Gessler Paul E. , Abatzoglou John T. , Mote Philip W. , Lee Stephen S. 
Published: December 2022

Abstract
We compared climatic relationships to insurance loss across the inland Pacific Northwest region of the United States, using a design matrix methodology, to identify optimum temporal windows for climate variables by county in relationship to wheat insurance loss due to drought. The results of our temporal window construction for water availability variables (precipitation, temperature, evapotranspiration, and the Palmer drought severity index [PDSI]) identified spatial patterns across the study area that aligned with regional climate patterns, particularly with regards to drought-prone counties of eastern Washington. Using these optimum time-lagged correlational relationships between insurance loss and individual climate variables, along with commodity pricing, we constructed a regression-based random forest model for insurance loss prediction and evaluation of climatic feature importance. Our cross-validated model results indicated that PDSI was the most important factor in predicting total seasonal wheat/drought insurance loss, with wheat pricing and potential evapotranspiration having noted contributions. Our overall regional model had a

$ {R}^2 $

of 0.49, and a RMSE of $30.8 million. Model performance typically underestimated annual losses, with moderate spatial variability in terms of performance between counties.
Sea surface height super-resolution using high-resolution sea surface temperature with a subpixel convolutional residual network
Authors: Archambault Théo , Charantonis Anastase , Béréziat Dominique , Mejia Carlos , Thiria Sylvie 
Published: December 2022

Abstract
The oceans have a very important role in climate regulation due to their massive heat storage capacity. Thus, for the past decades, oceans have been observed by satellites to better understand their dynamics. Satellites retrieve several data with various spatial resolutions. For instance, sea surface height (SSH) is a low-resolution data field where sea surface temperature (SST) can be retrieved in a much higher one. These two physical parameters are linked by a physical link that can be learned by a super-resolution machine-learning algorithm. In this work, we present a subpixel convolutional deep learning model that takes advantage of the higher resolution SST field to guide the downscaling of the SSH one. The data fields that we use are simulated by a physic-based ocean model at a higher sampling rate than the satellites provide. We compared our approach with a convolutional neural network model. Our architecture generalized well with validation performances of 3.94 cm root mean squared error (RMSE) and training performances of 2.65 cm RMSE.
Changes in food consumption from an agricultural-based economy to industrialisation: Uruguay (1900–70)
Authors: Presa Maximiliano , Román Carolina 
Published: December 2022

AbstractThe literature about the nutrition transition has been discussing the existence of different paths. The case of Uruguay is introduced as a different case of transition. We focus on the period 1900–70 when the country shifted from an agricultural-based economy to industrialisation through import substitution. We estimate the annual historical time series of per capita consumption of the main food items in the Uruguayan diet using the commodity flow approach complemented by the FAO’s Food Balance Sheets methodology. We identify the major trends in food consumption and discuss the main explanatory factors. We find that Uruguay showed a transition from a very high animal food-based diet towards a more diversified pattern with more milk, cereals, and vegetables. On top of that, we sustain that not only income is important to explain the major shifts in food diet, but also preferences, changes in relative prices, and productivity.
Recent Intensification (2004–2020) of Permafrost Mass‐Wasting in the Central Mackenzie Valley Foothills Is a Legacy of Past Forest Fire Disturbances
Authors: Young Joseph M. , Alvarez Alejandro , van der Sluijs Jurjen , Kokelj Steven V. , Rudy Ashley , McPhee Alex , Stoker Benjamin J. , Margold Martin , Froese Duane 
Published: December 2022

AbstractThe effects of recent climate change are accelerating permafrost thaw, including ice‐rich landscapes of the western Canadian Arctic. However, regional drivers of permafrost slope failure in hillslopes with warm, thin permafrost remain poorly understood. Repeat satellite imagery (1984–2020) indicates rapid increases in retrogressive thaw slumps (RTSs) and deep‐seated permafrost landslides (DSPLs) since 2004, indicating a change in slope stability thresholds in an area that otherwise appeared thaw stable. The widespread occurrence of DSPL represents a contrasting geomorphic response to the RTS‐dominated ice‐rich permafrost landscapes. In this study area, RTS and DSPL occur predominantly in areas that were burned by forest fires in the 1990s, indicating a legacy thermal disturbance that preconditioned permafrost hillslopes for failure. The relations between historic fires and the later development of widespread permafrost slope failures represent an outstanding example of the complex interactions between inherited landscape sensitivity in ice‐rich terrain and ongoing climate change.
Interannual Changes in Tidal Conversion Modulate M2 Amplitudes in the Gulf of Maine
Authors: Schindelegger Michael , Kotzian Daniel P. , Ray Richard D. , Green J. A. Mattias , Stolzenberger Sophie 
Published: December 2022

AbstractThe Gulf of Maine's lunar semidiurnal (M2) ocean tide exhibits spatially coherent amplitude changes of ∼1–3 cm on interannual time scales, though no causative mechanism has been identified. Here we show, using a specially designed numerical modeling framework, that stratification changes account for 32%–48% (Pearson coefficient 0.58–0.69) of the observed M2 variability at tide gauges from 1994 to 2019. Masking experiments and energy diagnoses reveal that the modeled variability is primarily driven by fluctuations in barotropic‐to‐baroclinic energy conversion on the continental slope south of the gulf's mouth, with a 1‐cm amplitude increase at Boston corresponding to a ∼7% (0.30 GW) drop in the area‐integrated conversion rate. Evidence is given for the same process to have caused the decade‐long M2 amplitude decrease in the Gulf of Maine beginning in 1980/81. The study has implications for nuisance flooding predictions and space geodetic analyses seeking highest accuracies.
Using Machine Learning to Predict Urban Canopy Flows for Land Surface Modeling
Authors: Lu Yanle , Zhou Xu‐Hui , Xiao Heng , Li Qi 
Published: December 2022

AbstractDeveloping urban land surface models for modeling cities at high resolutions needs to better account for the city‐specific multi‐scale land surface heterogeneities at a reasonable computational cost. We propose using an encoder‐decoder convolutional neural network to develop a computationally efficient model for predicting the mean velocity field directly from urban geometries. The network is trained using the geometry‐resolving large eddy simulation results. Systematic testing on urban structures with increasing deviations from the training geometries shows the prediction error plateaus at 15%, compared to errors sharply increasing up to 35% in the null models. This is explained by the trained model successfully capturing the effects of pressure drag, especially for tall buildings. The prediction error of the aerodynamic drag coefficient is reduced by 32% compared with the default parameterization implemented in mesoscale modeling. This study highlights the potential of combining computational fluid dynamics modeling and machine learning to develop city‐specific parameterizations.
Detection and Three‐Dimensional Reconstruction of Concentric Traveling Ionosphere Disturbances Induced by Hurricane Matthew on 7 October 2016
Authors: Chen Yutian , Yue Dongjie , Zhai Changzhi , Zhang Shun‐Rong 
Published: December 2022

AbstractThis paper reports the first high‐resolution 4‐dimensional ionospheric disturbance caused by hurricane Matthew on 7 October 2016. The temporal as well as vertical and horizontal variations of the concentric traveling ionospheric disturbances (CTIDs) were reconstructed using 3‐dimensional computerized ionospheric tomography (3DCIT) technology, based upon the Global Navigation Satellite System data from the dense receiver network over North America. The frequency range of disturbances was determined by spectrum analysis, and a Butterworth band‐pass filter was used to de‐trend the total electron content (TEC) sequences to determine TIDs. A remarkable CTID segment was detected at a distance of 1,000–1,500 km from the hurricane eye at ∼5:40–6:10 UT on 7 October 2016, moving westward with the horizontal phase velocity of ∼153.4 m/s, the period of ∼30 min and the horizontal wavelength of ∼276.1 km. The positive and negative wavefronts dominated the CITD at different times during the event. From 4:00 to 8:00 UT, the altitudinal variation of the CTIDs in electron density exhibited clear downward phase progression predominately in the range of 150–400 km altitudes; however, the percentage of electron density disturbances was larger below 250 km. The inverted cone‐like geometry of CTID wavefronts was presented. The vertical phase velocities of the CTIDs ∼1,100 km away from the hurricane eye in the northwest direction near 88°W, 34°N were ∼203.7–277.8 m/s, and at the same location, the horizontal phase velocities at 300 km altitude were ∼149.1–181.5 m/s, slightly larger than those at 200 km altitude (∼145.1–178.5 m/s).
Blanketing Sporadic‐E Layer Occurrences Over Santa Maria, a Transition Station From Low to Middle Latitude in the South American Magnetic Anomaly (SAMA)
Authors: Moro J. , Xu J. , Denardini C. M. , Stefani G. , Resende L. C. A. , Santos A. M. , Silva R. P. , Da Silva L. A. , Carmo C. S. , Carrasco A. J. , Li H. , Liu Z. , Wang C. , Schuch N. J. 
Published: December 2022

AbstractDigisonde data obtained from July 2019 to June 2020 are used to study the overall occurrences of the blanketing types (Esf, Esl, Esh, Esc) of the sporadic layers (Esb) over Santa Maria, a transition station from low to middle latitude in the South American Magnetic Anomaly (SAMA). The results show that the average occurrence of the Esb layers is 54%, being maximum in summer (66%), followed by spring (63%), autumn (55%), and winter (40%). Except during the winter, the Esf is the predominant type, while the Esc generally presents the lower occurrence. In general, the top (ftEs) and the blanketing (fbEs) frequencies range from 3 to 4 MHz between 100 and 110 km, with more pronounced values during daytime compared to nighttime. A comparison between the Esb occurrences between Santa Maria and stations outside the SAMA shows a great reduction in Santa Maria. It is caused by the low intensity of the geomagnetic field, which affects the layer formation process controlled by the ion dynamics. The performance of the E‐Region Ionospheric Model (MIRE), which use the tidal wind components provided by the Global Scale Wave Model as input, is analyzed in simulating the fbEs parameter and compared with measurements. The discrepancies between the modeled and measured fbEs are reduced when the meridional/zonal wind components are adjusted by some factor. This comparison contributes to our understanding of the role of the tidal winds over Santa Maria, a transition station from low to middle latitude in Brazil.
Observations of Mercury's Plasma Sheet Horn: Characterization and Contribution to Proton Precipitation
Authors: Glass A. N. , Raines J. M. , Jia X. , Sun W. , Imber S. , Dewey R. M. , Slavin J. A. 
Published: December 2022

AbstractThe Mercury Surface, Space ENvironment, GEochemistry and Ranging (MESSENGER) spacecraft was the first spacecraft to orbit the planet Mercury. Previous analysis of MESSENGER data has established that of all the planets in the solar system, Mercury's magnetosphere is the most like Earth's, dominated by the Dungey cycle in its dynamic response of the magnetosphere to solar wind forcing. In this work, we identify and describe for the first time Mercury's northern plasma sheet horn—a Dungey cycle feature key to plasma precipitation. We find three possible geometries for potential horn observation by MESSENGER and describe a case study of each. Two additional case studies are presented with geometries particularly favorable to estimating plasma precipitation within the horns. Estimates of proton precipitation flux are performed, which show precipitation levels on the order of 107 per cm2 per second, on the same order of magnitude as the estimated proton precipitation flux in the dayside cusp despite the higher average energy of the protons in the horn. Potential paths for future study of the horns are discussed.
The Question of Transpolar Arc Conjugacy: New Results From Comparing Solar Wind Data and Dipole Tilt Distribution of Five Different Datasets
Authors: Kullen Anita , Thor S. , Cai L. 
Published: December 2022

AbstractIn this work, we investigate the interhemispheric transpolar arc (TPA) conjugacy using four previously published datasets based on Polar UV images, DMSP particle data, and IMAGE images, and a new TPA list based on DMSP SSUSI images during 1 Sep to 15 Oct 2015. IMF and the Earth's dipole tilt have often been suggested to influence the TPA conjugacy, as both induce a north‐south asymmetry on the magnetosphere. However, by comparing these parameters at TPA formation with the background distribution for each dataset, we find that neither the dipole tilt nor plays a major role for the TPA conjugacy in four of the five datasets. The well‐known correlation between initial TPA location and IMF appears in all datasets with information about the TPA formation. In addition, we find that a minority of dawnside TPAs form during the “wrong” sign. In the northern (southern) hemisphere, dawn TPAs appear also during weakly duskward (dawnward) IMF. Due to the polar orbit of DMSP satellites, TPA conjugacy and location can be examined on a case‐by‐case basis with the new dataset. The results show that at least 73% of TPAs appear in both hemispheres simultaneously. IMF and dipole tilt values for conjugate TPAs do not differ from those for non‐conjugate TPAs. Most conjugate (isolated) TPAs appear on opposite oval sides in each hemisphere (57%). Interestingly, in case northern and southern hemisphere TPAs form on the same oval side, they appear typically at dawn during weak IMF .
Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System
Authors: Mu Longjiang , Nerger Lars , Streffing Jan , Tang Qi , Niraula Bimochan , Zampieri Lorenzo , Loza Svetlana N. , Goessling Helge F. 
Published: December 2022

AbstractA new version of the AWI Coupled Prediction System is developed based on the Alfred Wegener Institute Climate Model v3.0. Both the ocean and the atmosphere models are upgraded or replaced, reducing the computation time by a factor of 5 at a given resolution. This allowed us to increase the ensemble size from 12 to 30, maintaining a similar resolution in both model components. The online coupled data assimilation scheme now additionally utilizes sea‐surface salinity and sea‐level anomaly as well as temperature and salinity profile observations. Results from the data assimilation demonstrate that the sea‐ice and ocean states are reasonably constrained. In particular, the temperature and salinity profile assimilation has mitigated systematic errors in the deeper ocean, although issues remain over polar regions where strong atmosphere‐ocean‐ice interaction occurs. One‐year‐long sea‐ice forecasts initialized on 1 January, 1 April, 1 July and 1 October from 2003 to 2019 are described. To correct systematic forecast errors, sea‐ice concentration from 2011 to 2019 is calibrated by trend‐adjusted quantile mapping using the preceding forecasts from 2003 to 2010. The sea‐ice edge raw forecast skill is within the range of operational global subseasonal‐to‐seasonal forecast systems, outperforming a climatological benchmark for about 2 weeks in the Arctic and about 3 weeks in the Antarctic. The calibration is much more effective in the Arctic: Calibrated sea‐ice edge forecasts outperform climatology for about 45 days in the Arctic but only 27 days in the Antarctic. Both the raw and the calibrated forecast skill exhibit strong seasonal variations.