bluuebunny/crossref_metadata_2025_split huggingface dataset:
Measurements of aerosol particle concentrations in the atmosphere of the south-eastern coast of Lake Baikal (at Boyarsky station) in the summer of 2020
Authors: Tcydypov V V , Zhamsueva G S , Zayakhnov A S , Dementeva A L , Balzhanov T S 
Published: May 2022

Abstract
Atmospheric measurements of aerosol particle concentrations were made at a station named Boyarsky, the south-eastern coast of Lake Baikal (Russia) in the summer of 2020. The measurement period was conventionally divided into two ranges: 1) the smoke emission period of July 17-25; 2) the period of weakening of the influence of smoke emissions from forest fires in the neighbouring regions (from 14 August to 10 September). The diurnal behaviour of the total number concentrations of ultrafine particles in the size range from 5 to 200 nm (UFA) and submicron aerosols (SMA) from 0.2 to 1 μm was studied. The mean concentrations of UFA and SMA are 2705 (±1791) cm−3 and 40 (±19) cm−3 in July; 1247 (±780) cm−3 and 57 (±58) cm−3 in August; 1205 (±764) cm−3 and 37 (±19) cm−3 in September, respectively (the values in parentheses represent the respective standard deviations). The temporal evolution of the UFA particle number concentrations has shown a pronounced variation with lower concentrations in August-September and higher concentrations in July, whereas the diurnal patterns have been mostly found to be strongly influenced by the wildfire emissions. A strong relationship between the total particle number concentration has been established with the concentration of nitrogen dioxide NO2 (R =0.81), O3 (R = 0.66) and a weaker one with the concentration of sulphur dioxide (R =0.46). The thus obtained data on aerosol particle concentrations have been used to identify the events of bursts of nanoparticle formation in the atmosphere.
Considerable Arctic Sea ice loss as a factor of cold weather and heavy snowfalls in Eurasia
Authors: Frolov D 
Published: May 2022

Abstract
There goes climate warming on the Earth. An especially tremendous warming goes in the Arctic. This causes shrinking of the sea ice extent in the Arctic. Considerable Arctic Sea ice loss can lead to some extra evaporation of water vapor from the sea surface and saturation of the atmosphere with water vapor. Due to atmospheric circulation, extreme temperature anomalies and heavy snowfalls can appear in the following winter season in the lower latitudes, according to a number of studies. Some warm winters in Eurasia and America are associated with a situation in the Arctic where an extremely stable area of low pressure in the vicinity of the North Pole was present during a long time period and did not let cold air masses to move away from its borders. As a consequence, in most of the territory of Russia, USA, Northern Europe, and Eastern Canada the temperature of the winter months was a few degrees more than the usual one, for example in 2019/2020 winter months. The resulting destruction of the North Pole vortex and coming of cold air masses to Eurasia and America lead to a cold snowy winter.
Quality assessment of surface temperature reproduction by a model archive, the COSMO-CLM Russian Arctic hindcast, based on station data
Authors: Duntsev Sergey , Platonov Vladimir 
Published: May 2022

Abstract
The COSMO-CLM Russian Arctic hindcast data are compared with station data on monthly and long-term timescales using surface air temperature data at 146 weather stations in the study region. Some of the climatological ETCDDI indices are calculated, and the quality of the model archive reproduction of mean and extreme 2 m temperatures is evaluated. Generally, the hindcast succeeds in reproducing the thermal regime of the Arctic. The existing errors are caused by the large distances of the nearest model grid points from a station and the inaccuracies in the coastline or surface elevation description. The model underestimates the mean monthly temperatures by one degree on the average. The hindcast shows a tendency to underestimate the temperatures in the western part of the region, which has a mild climate, and to overestimate the low temperatures in the severe climate regions.
Digital Transformation of the Hydrometeorological Support for the Users - a Basis for Improving the Safety of Population and Enterprises
Authors: Viazilov E D 
Published: May 2022

Abstract
Major directions in solving problems of adaptation of enterprises and population to natural disasters are presented. This is done with an end-to-end technology of user support which begins with observations and finishes with decision-making in the business processes of enterprises. Increasing the awareness for the business leaders and the public about possible impacts of the disasters is a need. The heads of enterprises must receive recommendations for making decisions to prevent or reduce these impacts. It is proposed to solve this problem by a digital transformation and a new paradigm of hydrometeorological support for the users. The main software components of the end-to-end technology include tools for integration of distributed and heterogeneous data by autonomously detecting the anomalies of the current values from the local threshold values. This information is delivered to the users by mobile Internet devices. An Information Panel and MeteoMonitor applications have been developed. To increase the information delivery to the enterprise heads and the public, decision support tools are created. Some tools for assessing the potential damage and calculating the preventive actions cost will be provided. It is proposed to organize a knowledge base in the form of local threshold values of disaster indicators. The knowledge base is to be created on the possible impacts of climate change and disasters on enterprises and population for various levels of danger based on climatic, observed, and predictive information.
Inverse modeling of atmospheric chemistry with a differential evolution solver: Inverse problem and Data assimilation
Authors: Penenko A V , Konopleva V S , Penenko V V 
Published: May 2022

Abstract
In this paper, we numerically compare an inverse problem for an atmospheric chemistry model, when all the measurement data is available a priory, to a corresponding data assimilation problem when the data are obtained in some portions during the simulation. In both cases, we reconstruct the unobservable parts of the model state function by the observable ones (i.e., solve a continuation problem). This is done by identifying the model reaction rate parameters with the available measurement data by solving an optimization problem with a derivative-free differential evolution solver. In our numerical experiments, the inverse problem formulation of the continuation problem has not necessarily provided better results than the data assimilation formulation despite the latter being more limited in the measurement data.
Large-scale single-cell encapsulation in microgels through metastable droplet-templating combined with microfluidic-integration
Authors: Zhang Haoyue , Zhang Liyuan , An Chuanfeng , Zhang Yang , Shao Fei , Gao Yijie , Zhang Yonghao , Li Hanting , Zhang Yujie , Ren Changle , Sun Kai , He Wei , Cheng Fang , Wang Huanan , Weitz David A 
Published: May 2022

Abstract
Current techniques for the generation of cell-laden microgels are limited by numerous challenges, including poorly uncontrolled batch-to-batch variations, processes that are both labor- and time-consuming, the high expense of devices and reagents, and low production rates; this hampers the translation of laboratory findings to clinical applications. To address these challenges, we develop a droplet-based microfluidic strategy based on metastable droplet-templating and microchannel integration for the substantial large-scale production of single cell-laden alginate microgels. Specifically, we present a continuous processing method for microgel generation by introducing amphiphilic perfluoronated alcohols to obtain metastable emulsion droplets as sacrificial templates. In addition, to adapt to the metastable emulsion system, integrated microfluidic chips containing 80 drop-maker units are designed and optimized based on the computational fluid dynamics simulation. This strategy allows single cell encapsulation in microgels at a maximum production rate of 10 ml h−1 of cell suspension while retaining cell viability and functionality. These results represent a significant advance toward using cell-laden microgels for clinical-relevant applications, including cell therapy, tissue regeneration and 3D bioprinting.
Trapping multiple absorbing particles in air using an optical fiber by photophoretic forces
Authors: Sil Souvik , Pahi Anita , Punse Aman Anil , Banerjee Ayan 
Published: May 2022

Abstract
We demonstrate photophoretic force-based optical trapping of multiple absorbing particles in air by loosely focusing a Gaussian beam emanating from a single mode fiber using convex lenses of different focal lengths, and investigate the dependence of the number of trapped particles and their sizes on the focal length. We observe the formation of particle chains at a particular focal length, and measure the axial dynamic range of optical trapping for each lens system. We then develop a numerical simulation to explain this observed dynamic range by estimating the temperature distribution across a particle surface, and determining the axial photophoretic force. Our simulation results are in reasonable agreement with experimental results. Interestingly, we also observe that the average size of trapped particles reduces as we increase the lens focal lengths. This is somewhat intriguing as each lens produces the same intensity profile, albeit at different axial distances. However, the axial intensity gradient reduces as the lens focal length is increased, which suggests that such gradients may somehow be involved in the mechanism of photophoretic confinement.
Analysis of hydroclimatic trends and variability and their impacts on hydropower generation in two river basins in Côte d’Ivoire (West Africa) during 1981–2017
Authors: Obahoundje Salomon , Diedhiou Arona , Kouassi Kouakou Lazare , Ta Marc Youan , Mortey Eric Mensah , Roudier Philippe , Kouame Didier Guy Marcel 
Published: May 2022

Abstract
Climate change (CC) and variability impacts on hydroelectric generation have become critical for hydropower management. The trends of inflow, outflow, reservoir water level, and storage as well as hydraulicity indices of three main dams in Côte d’Ivoire, namely Kossou and Taabo in the Bandama basin and Buyo in the Sassandra basin were examined during 1981–2017 and their impacts on hydropower generation were analyzed. Moreover, the hydropower generation sensitivity to CC of these dams was assessed using statistical analysis. The results reveal that the inflow is highly dependent on rainfall while the water level is highly influenced by the outflow, which is a function of the inflow to the reservoirs and water management policy. Furthermore, the Mann Kendall test revealed that temperature and potential evapotranspiration have increased significantly in all three sub-basins while precipitation shows a significant upward trend only within the Taabo dam catchment area. Meanwhile, inflow to reservoir increased significantly and greatly than precipitation probably due to land use/cover change. Precipitation and inflow show a strong correlation as energy generation is significantly and strongly correlated to outflow (inflow) in all stations (except Kossou). Furthermore, the energy generation at Buyo and Taabo dams is more sensitive to reservoir inflow, while that of Kossou dam is more affected by water level. In addition, the power of a given year is also dependent on the total rainfall of that year and/or the previous year depending on the plant.
MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics
Authors: Strutz Jonathan , Shebek Kevin M , Broadbelt Linda J , Tyo Keith E J 
Published: May 2022

Abstract

Summary
Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource.

Availability and implementation
The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API) and https://github.com/tyo-nu/MINE-app (web app).

Supplementary information
Supplementary data are available at Bioinformatics online.
Powerful molecule generation with simple ConvNet
Authors: Yu Hongyang K , Yu Hongjiang C 
Published: May 2022

AbstractMotivationAutomated molecule generation is a crucial step in in-silico drug discovery. Graph-based generation algorithms have seen significant progress over recent years. However, they are often complex to implement, hard to train and can under-perform when generating long-sequence molecules. The development of a simple and powerful alternative can help improve practicality of automated drug discovery method.ResultsWe proposed a ConvNet-based sequential graph generation algorithm. The molecular graph generation problem is reformulated as a sequence of simple classification tasks. At each step, a convolutional neural network operates on a sub-graph that is generated at previous step, and predicts/classifies an atom/bond adding action to populate the input sub-graph. The proposed model is pretrained by learning to sequentially reconstruct existing molecules. The pretrained model is abbreviated as SEEM (structural encoder for engineering molecules). It is then fine-tuned with reinforcement learning to generate molecules with improved properties. The fine-tuned model is named SEED (structural encoder for engineering drug-like-molecules). The proposed models have demonstrated competitive performance comparing to 16 state-of-the-art baselines on three benchmark datasets.Availability and implementationCode is available at https://github.com/yuh8/SEEM and https://github.com/yuh8/SEED. QM9 dataset is availble at http://quantum-machine.org/datasets/, ZINC250k dataset is availble at https://raw.githubusercontent.com/aspuru-guzik-group/chemical_vae/master/models/zinc_properties/250k_rndm_zinc_drugs_clean_3.csv, and ChEMBL dataset is availble at https://www.ebi.ac.uk/chembl/.Supplementary informationSupplementary data are available at Bioinformatics online.
CeCaFLUX: the first web server for standardized and visual instationary 13C metabolic flux analysis
Authors: Liu Zhentao , Zhang Zhengdong , Liang Sheng , Chen Zhen , Xie Xiaoyao , Shen Tie 
Published: May 2022

AbstractSummaryThe number of instationary 13C-metabolic flux (INST-MFA) studies grows every year, making it more important than ever to ensure the clarity, standardization and reproducibility of each study. We proposed CeCaFLUX, the first user-friendly web server that derives metabolic flux distribution from instationary 13C-labeled data. Flux optimization and statistical analysis are achieved through an evolutionary optimization in a parallel manner. It can visualize the flux optimizing process in real-time and the ultimate flux outcome. It will also function as a database to enhance the consistency and to facilitate sharing of flux studies.Availability and implementationCeCaFLUX is freely available at https://www.cecaflux.net, the source code can be downloaded at https://github.com/zhzhd82/CeCaFLUX.Supplementary informationSupplementary data are available at Bioinformatics online.
Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks
Authors: Weitz Philippe , Wang Yinxi , Kartasalo Kimmo , Egevad Lars , Lindberg Johan , Grönberg Henrik , Eklund Martin , Rantalainen Mattias 
Published: May 2022

AbstractMotivationMolecular phenotyping by gene expression profiling is central in contemporary cancer research and in molecular diagnostics but remains resource intense to implement. Changes in gene expression occurring in tumours cause morphological changes in tissue, which can be observed on the microscopic level. The relationship between morphological patterns and some of the molecular phenotypes can be exploited to predict molecular phenotypes from routine haematoxylin and eosin-stained whole slide images (WSIs) using convolutional neural networks (CNNs). In this study, we propose a new, computationally efficient approach to model relationships between morphology and gene expression.ResultsWe conducted the first transcriptome-wide analysis in prostate cancer, using CNNs to predict bulk RNA-sequencing estimates from WSIs for 370 patients from the TCGA PRAD study. Out of 15 586 protein coding transcripts, 6618 had predicted expression significantly associated with RNA-seq estimates (FDR-adjusted P-value &lt;1×10−4) in a cross-validation and 5419 (81.9%) of these associations were subsequently validated in a held-out test set. We furthermore predicted the prognostic cell-cycle progression score directly from WSIs. These findings suggest that contemporary computer vision models offer an inexpensive and scalable solution for prediction of gene expression phenotypes directly from WSIs, providing opportunity for cost-effective large-scale research studies and molecular diagnostics.Availability and implementationA self-contained example is available from http://github.com/phiwei/prostate_coexpression. Model predictions and metrics are available from doi.org/10.5281/zenodo.4739097.Supplementary informationSupplementary data are available at Bioinformatics online.
Effect of Antiviral Treatment on Hepatitis B Virus Integration and Hepatocyte Clonal Expansion
Authors: Chow Ning , Wong Danny , Lai Ching-Lung , Mak Lung-Yi , Fung James , Ma Hoi-Tang , Lei Meng-Wai , Seto Wai-Kay , Yuen Man-Fung 
Published: May 2022

Abstract

Background
This study investigated the effect of nucleos(t)ide analogue (NUC) treatment on hepatitis B virus (HBV) DNA integration and hepatocyte clonal expansion, both of which are implicated in hepatocellular carcinoma (HCC) in chronic hepatitis B.

Methods
Twenty-eight patients receiving NUCs (11 lamivudine, 7 telbivudine, 10 entecavir) were included. All had liver biopsies at baseline and year 1, and 7 had a third biopsy at year 10. HBV DNA integration and hepatocyte clone size were assessed by inverse polymerase chain reaction.

Results
All patients had detectable HBV integration at baseline, with a median integration frequency of 1.01 × 109 per liver and hepatocyte clone size of 2.41 × 105. Neither integration frequency nor hepatocyte clone size correlated with age and HBV virologic parameters. After 1 year of treatment, HBV integration was still detectable in all patients, with a median of 5.74 × 108 integration per liver (0.22 log reduction; P = .008) and hepatocyte clone size of 1.22 × 105 (0.40 log reduction; P = .002). HBV integration remained detectable at year 10 of treatment, with a median integration frequency of 4.84 × 107 integration per liver (0.93 log reduction from baseline) and hepatocyte clone size of 2.55 × 104 (1.02 log reduction from baseline). From baseline through year 1 to year 10, there was a decreasing trend in both integration frequency and hepatocyte clone size (P = .066 and.018, respectively).

Conclusions
NUCs reduced both HBV DNA integration and hepatocyte clonal expansion, suggesting another alternative pathway besides direct viral suppression to reduce HCC risk. Our findings supported the notion for a long-term NUC treatment to prevent HCC.
Simplifying Treatment Criteria in Chronic Hepatitis B: Reducing Barriers to Elimination
Authors: Wong Robert J , Kaufman Harvey W , Niles Justin K , Kapoor Hema , Gish Robert G 
Published: May 2022

Abstract

Background
Early, sustained hepatitis B virus (HBV) DNA suppression reduces long-term risks of hepatocellular carcinoma. Chronic hepatitis B (CHB) treatment criteria are complex. Simplifying criteria will improve timely linkage to therapy. We evaluated treatment eligibility patterns among US patients with CHB and propose stepwise simplification of CHB treatment criteria.

Methods
Using 2016–2020 Quest Diagnostics data, we evaluated treatment eligibility among patients with CHB (2 positive HBV tests [HBV surface antigen, HBV e antigen, or HBV DNA] ≥6 months apart) using American Association for the Study of Liver Disease (AASLD), European Association for Study of the Liver (EASL), Asian Pacific Association for Study of the Liver (APASL), and Asian American Treatment Algorithm (AATA) criteria.

Results
Among 84 916 patients with CHB, 6.7%, 6.2%, 5.8%, and 16.4% met AASLD, EASL, APASL, and AATA criteria, respectively. Among treatment-ineligible patients with CHB, proportion with significant fibrosis (aspartate aminotransferase platelet ratio index &gt;0.5) were 10.4%, 10.4%, 10.8%, and 7.7% based on AASLD, EASL, APASL, and AATA, respectively. In the proposed treatment simplification, the proportion of patients with CHB eligible for therapy increased from 10.3% for step 1 (HBV DNA &gt;20 000 IU/mL, elevated alanine aminotransferase [ALT] level) to 14.1% for step 2 (HBV &gt;2000 IU/mL, elevated ALT level), 33.5% for step 3 (HBV DNA &gt;2000 IU/mL, any ALT level), and 87.2% for step 4 (detectable HBV DNA, any ALT level).

Conclusions
A large proportion of patients with CHB not meeting established treatment criteria have significant fibrosis. Simplifying criteria to treat all patients with detectable HBV DNA will reduce complexity and heterogeneity in assessing treatment eligibility, improving treatment rates and progress toward HBV elimination.