bluuebunny/crossref_metadata_2025_split huggingface dataset:
Confining Pyrrhotite Fe7S8 in Carbon Nanotubes Covalently Bonded onto 3D Few‐Layer Graphene Boosts Potassium‐Ion Storage and Full‐Cell Applications
Authors: Han Kun , An Fuqiang , Wan Qi , Xing Lidong , Wang Lei , Liu Qiang , Wang Wei (Alex) , Liu Yongchang , Li Ping , Qu Xuanhui 
Published: March 2021

AbstractThe pyrrhotite Fe7S8 with mixed Fe‐valence possesses high theoretical capacity, high conductivity, low discharge/charge voltage plateaus, and superior redox reversibility but suffers from structural degradation upon (de)potassiation process due to severe volume variations. Herein, to conquer this issue, a novel hierarchical architecture of confining nano‐Fe7S8 in carbon nanotubes covalently bonded onto 3D few‐layer graphene (Fe7S8@CNT@3DFG) is designed for potassium storage. Notably, CNTs could successfully grow on the surface of 3DFG via a tip‐growth model under the catalytic effect of Fe3C. Such structure enables the hierarchical confinement of 0D nano‐Fe7S8 to 1D CNTs and further 1D CNTs to 3DFG, effectively buffering the volume variations, prohibiting the agglomeration of Fe7S8 nanograins, and boosting the ionic/electronic transportation through the stable and conductive CNTs‐grafted 3DFG framework. The as‐prepared Fe7S8@CNT@3DFG electrode delivers an exceptional rate capability (502 mAh g−1 at 50 mA g−1 with 277 mAh g−1 at 1000 mA g−1) and an excellent long‐term cyclic stability up to 1300 cycles. Besides, the in‐situ XRD and ex‐situ XPS/HRTEM results first elucidate the highly reversible potassium‐storage mechanism of Fe7S8. Furthermore, the designed potassium full‐cell employing Fe7S8@CNT@3DFG anode and potassium Prussian blue (KPB) cathode delivers a promising energy density of ≈120 Wh kg−1, demonstrating great application prospects.
Approaches to Cyclophane‐Types of Cyclopeptide Alkaloids
Authors: Wang Yuanhao , Joullié Madeleine M. 
Published: March 2021

AbstractThe cyclopeptide alkaloids are cyclic depsipeptides incorporating cyclophanes with polyamide units 13‐, 14‐ and 15‐membered macrocyclic systems. Although various pharmacological activities have been ascribed to cyclopeptide alkaloids from plants of the Rhamnacea family, these studies have been hampered by their low availability due to the lack of reasonable amounts distributed in nature. Therefore, novel and efficient synthetic approaches should be an important aim, which inspired us to examine how to diversely construct the unique structures of this type of natural products. In this account, several typical strategies are presented in terms of efficient, stereocontrolled and regioselective synthesis of cyclopeptide alkaloids.
An implicit–explicit time discretization scheme for second-order semilinear wave equations with application to dynamic boundary conditions
Authors: Hochbruck Marlis , Leibold Jan 
Published: March 2021

AbstractWe construct and analyze a second-order implicit–explicit (IMEX) scheme for the time integration of semilinear second-order wave equations. The scheme treats the stiff linear part of the problem implicitly and the nonlinear part explicitly. This makes the scheme unconditionally stable and at the same time very efficient, since it only requires the solution of one linear system of equations per time step. For the combination of the IMEX scheme with a general, abstract, nonconforming space discretization we prove a full discretization error bound. We then apply the method to a nonconforming finite element discretization of an acoustic wave equation with a kinetic boundary condition. This yields a fully discrete scheme and a corresponding a-priori error estimate.
Systematic reviews in sentiment analysis: a tertiary study
Authors: Ligthart Alexander , Catal Cagatay , Tekinerdogan Bedir 
Published: March 2021

AbstractWith advanced digitalisation, we can observe a massive increase of user-generated content on the web that provides opinions of people on different subjects. Sentiment analysis is the computational study of analysing people's feelings and opinions for an entity. The field of sentiment analysis has been the topic of extensive research in the past decades. In this paper, we present the results of a tertiary study, which aims to investigate the current state of the research in this field by synthesizing the results of published secondary studies (i.e., systematic literature review and systematic mapping study) on sentiment analysis. This tertiary study follows the guidelines of systematic literature reviews (SLR) and covers only secondary studies. The outcome of this tertiary study provides a comprehensive overview of the key topics and the different approaches for a variety of tasks in sentiment analysis. Different features, algorithms, and datasets used in sentiment analysis models are mapped. Challenges and open problems are identified that can help to identify points that require research efforts in sentiment analysis. In addition to the tertiary study, we also identified recent 112 deep learning-based sentiment analysis papers and categorized them based on the applied deep learning algorithms. According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis.
Conditional particle filters with diffuse initial distributions
Authors: Karppinen Santeri , Vihola Matti 
Published: March 2021

AbstractConditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which are common in statistical applications. We propose a simple but generally applicable auxiliary variable method, which can be used together with the CPF in order to perform efficient inference with diffuse initial distributions. The method only requires simulatable Markov transitions that are reversible with respect to the initial distribution, which can be improper. We focus in particular on random walk type transitions which are reversible with respect to a uniform initial distribution (on some domain), and autoregressive kernels for Gaussian initial distributions. We propose to use online adaptations within the methods. In the case of random walk transition, our adaptations use the estimated covariance and acceptance rate adaptation, and we detail their theoretical validity. We tested our methods with a linear Gaussian random walk model, a stochastic volatility model, and a stochastic epidemic compartment model with time-varying transmission rate. The experimental findings demonstrate that our method works reliably with little user specification and can be substantially better mixing than a direct particle Gibbs algorithm that treats initial states as parameters.
Bayesian ODE solvers: the maximum a posteriori estimate
Authors: Tronarp Filip , Särkkä Simo , Hennig Philipp 
Published: March 2021

AbstractThere is a growing interest in probabilistic numerical solutions to ordinary differential equations. In this paper, the maximum a posteriori estimate is studied under the class of $$\nu $$
ν
times differentiable linear time-invariant Gauss–Markov priors, which can be computed with an iterated extended Kalman smoother. The maximum a posteriori estimate corresponds to an optimal interpolant in the reproducing kernel Hilbert space associated with the prior, which in the present case is equivalent to a Sobolev space of smoothness $$\nu +1$$

ν
+
1

. Subject to mild conditions on the vector field, convergence rates of the maximum a posteriori estimate are then obtained via methods from nonlinear analysis and scattered data approximation. These results closely resemble classical convergence results in the sense that a $$\nu $$
ν
times differentiable prior process obtains a global order of $$\nu $$
ν
, which is demonstrated in numerical examples.
Associations Between Endometriosis and Gut Microbiota
Authors: Svensson Agnes , Brunkwall Louise , Roth Bodil , Orho-Melander Marju , Ohlsson Bodil 
Published: March 2021

AbstractThe gut microbiota has been associated with many diseases, including endometriosis. However, very few studies have been conducted on this topic in human. This study aimed to investigate the association between endometriosis and gut microbiota. Women with endometriosis (N=66) were identified at the Department of Gynaecology and each patient was matched with three controls (N=198) from the general population. All participants answered questionnaires about socioeconomic data, medical history, and gastrointestinal symptoms and passed stool samples. Gut bacteria were analyzed using 16S ribosomal RNA sequencing, and in total, 58 bacteria were observed at genus level in both patients with endometriosis and controls. Comparisons of the microbiota between patients and controls and within the endometriosis cohort were performed. Both alpha and beta diversities were higher in controls than in patients. With the false discovery rate q<0.05, abundance of 12 bacteria belonging to the classes Bacilli, Bacteroidia, Clostridia, Coriobacteriia, and Gammaproteobacter differed significantly between patients and controls. Differences observed between patients with or without isolated ovarian endometriosis, involvement of the gastrointestinal tract, gastrointestinal symptoms, or hormonal treatment disappeared after calculation with false discovery rate. These findings indicate that the gut microbiota may be altered in endometriosis patients.
Deep Sleep and Beeps: Sleep Quality Improvement Project in General Surgery Patients
Authors: Allen Robert W. , Burney Charles P. , Davis Amy , Henkin Jessica , Kelly Julia , Judd Brooke G. , Ivatury Srinivas Joga 
Published: March 2021

BACKGROUND:
Poor sleep leads to poor health outcomes. Inpatient sleep disturbance has been studied primarily in the ICU. Minimal research exists on sleep in surgical populations.

METHODS:
We recruited patients undergoing elective, inpatient general surgery procedures. Participants wore Fitbit trackers while inpatient to measure total sleep time (CDC recommendation is 7 or more hours per night). At discharge, patients completed the Richards-Campbell Sleep Questionnaire (RCSQ) to measure inpatient sleep quality. The RCSQ combines 5 domains into a cumulative score (0 to 100); a higher score means better sleep quality. Patients also completed the outpatient Pittsburgh Sleep Quality Index preoperatively and postoperatively. The primary end point was percentage of patients with total sleep score ≥ 50. Secondary outcomes included mean RCSQ domain scores, Fitbit total sleep time, and percentage with Pittsburgh Sleep Quality Index Score indicating poor sleep.

RESULTS:
We included 64 patients (mean ± SD age 55.0 ± 14.1 years). Mean ± SD RCSQ total sleep score was 49 ± 20.5 and 53.1% with total sleep score < 50. Mean ± SD RCSQ domain scores were Awakenings: 40.4 ± 22.8, Sleep Quality: 49.1 ± 27.9, Sleep Latency: 49.2 ± 25.3, Sleep Depth: 50.2 ± 26.5, Returning to Sleep: 55.9 ± 28.1, and Noise Disturbance: 59.1 ± 27.9. On night one, 25 devices (40%) had recorded sleep data due to enough sleep. Mean ± SD total sleep time on night 1 was 4.7 ± 2.8 hours. Mean total sleep time for nights 2, 3, and 4 remained fewer than 7 hours. Percentages for each night that achieved the CDC goal of 7 or more hours were as follows: night one 10.9%, night two 32.8%, night three 35.3%, and night four 27.6%. Per the Pittsburgh Sleep Quality Index, 88.1% of patients were poor sleepers preoperatively and 84.5% were poor sleepers at follow-up (p = 0.6).

CONCLUSIONS:
Elective general surgery patients experience a severe inpatient sleep disturbance, worse than in similarly studied ICU cohorts. This disturbance is driven primarily by nighttime awakenings.
Extending the Atmospheric River Concept to Aerosols: Climate and Air Quality Impacts
Authors: Chakraborty Sudip , Guan Bin , Waliser Duane E. , da Silva Arlindo M. , Uluatam Sophie , Hess Peter 
Published: March 2021

AbstractDespite strong impacts that aerosols have on climate and air quality, significant gaps remain in our knowledge concerning their long‐range transport, especially extreme transport events. With this consideration in mind and by leveraging the “atmospheric river” concept, this work develops an objective global algorithm for detecting aerosol atmospheric rivers (AARs), shows a climatology of AARs, elucidates their contributions to major global aerosol transport pathways, and illustrates how AARs can drive extreme cases of poor air quality conditions. Our methodology separately accounts for dust, carbonaceous (accounting for organic and black carbon separately where appropriate), sea salt, and sulfate aerosols. Findings show there are a number of long‐range regional transport pathways where AARs account for a sizable fraction (40% to >100%) of the total transport in relatively few events (20–40 AAR days/year). This study highlights the role of AARs in establishing source‐receptor relationships that can drive regional air‐quality and extremes.
Meridional Asymmetry in Recent Decadal Sea‐Level Trends in the Subtropical Pacific Ocean
Authors: Schloesser F. , Thompson P. R. , Piecuch C. G. 
Published: March 2021

AbstractRecent sea surface height (SSH) trends in the South Pacific are substantially greater than trends in the North Pacific. Here, we use the Estimating the Climate and Circulation of the Ocean Version 4 Release 4 ocean state estimate and the Ocean Reanalysis System 5 to identify the forcing and mechanisms underlying that meridional asymmetry during 2005–2015. Thermosteric contributions dominate the spatial structure in Pacific SSH trends, but contributions from local surface heat fluxes are small. Wind stress trends drive a spin‐up of the South Pacific subtropical gyre and a northward shift of the North Pacific subtropical gyre. A reduced gravity model forced with reanalysis winds qualitatively reproduces the meridional seesaw in sea level, suggesting that asymmetric trends in subtropical wind stress drive a cross‐equatorial heat transport. A reversal in forcing associated with this process could impact near‐term rates of coastal sea‐level change, particularly in Pacific Island communities.
Competition between strains of Borrelia afzelii in the host tissues and consequences for transmission to ticks
Authors: Genné Dolores , Rossel Marika , Sarr Anouk , Battilotti Florian , Rais Olivier , Rego Ryan O M , Voordouw Maarten J 
Published: March 2021

Abstract
Pathogen species often consist of genetically distinct strains, which can establish mixed infections or coinfections in the host. In coinfections, interactions between pathogen strains can have important consequences for their transmission success. We used the tick-borne bacterium Borrelia afzelii, which is the most common cause of Lyme disease in Europe, as a model multi-strain pathogen to investigate the relationship between coinfection, competition between strains, and strain-specific transmission success. Mus musculus mice were infected with one or two strains of B. afzelii, strain transmission success was measured by feeding ticks on mice, and the distribution of each strain in six different mouse organs and the ticks was measured using qPCR. Coinfection and competition reduced the tissue infection prevalence of both strains and changed their bacterial abundance in some tissues. Coinfection and competition also reduced the transmission success of the B. afzelii strains from the infected hosts to feeding ticks. The ability of the B. afzelii strains to establish infection in the host tissues was strongly correlated with their transmission success to the tick vector. Our study demonstrates that coinfection and competition between pathogen strains inside the host tissues can have major consequences for their transmission success.
Anaerobic endosymbiont generates energy for ciliate host by denitrification
Authors: Graf Jon S. , Schorn Sina , Kitzinger Katharina , Ahmerkamp Soeren , Woehle Christian , Huettel Bruno , Schubert Carsten J. , Kuypers Marcel M. M. , Milucka Jana 
Published: March 2021

AbstractMitochondria are specialized eukaryotic organelles that have a dedicated function in oxygen respiration and energy production. They evolved about 2 billion years ago from a free-living bacterial ancestor (probably an alphaproteobacterium), in a process known as endosymbiosis1,2. Many unicellular eukaryotes have since adapted to life in anoxic habitats and their mitochondria have undergone further reductive evolution3. As a result, obligate anaerobic eukaryotes with mitochondrial remnants derive their energy mostly from fermentation4. Here we describe ‘Candidatus Azoamicus ciliaticola’, which is an obligate endosymbiont of an anaerobic ciliate and has a dedicated role in respiration and providing energy for its eukaryotic host. ‘Candidatus A. ciliaticola’ contains a highly reduced 0.29-Mb genome that encodes core genes for central information processing, the electron transport chain, a truncated tricarboxylic acid cycle, ATP generation and iron–sulfur cluster biosynthesis. The genome encodes a respiratory denitrification pathway instead of aerobic terminal oxidases, which enables its host to breathe nitrate instead of oxygen. ‘Candidatus A. ciliaticola’ and its ciliate host represent an example of a symbiosis that is based on the transfer of energy in the form of ATP, rather than nutrition. This discovery raises the possibility that eukaryotes with mitochondrial remnants may secondarily acquire energy-providing endosymbionts to complement or replace functions of their mitochondria.
Trends in early initiation of breastfeeding in Bangladesh and a multilevel analysis approach to find its determinants
Authors: Ahmmed Foyez , Manik Muhammad Mahabub Rahaman 
Published: March 2021

AbstractEarly initiation of breastfeeding (EIBF) is an essential practice for child health as well as for maternal health. This study aims to determine trends, prevalence, and factors associated with EIBF in Bangladesh. Data for this study were extracted from Bangladesh demographic and health surveys (BDHS) 2004, 2007, 2011, and 2014. This study found an increasing trend in EIBF in Bangladesh irrespective of the different characteristics of mothers and children. Chi-square test was conducted to find the association between EIBF and different factors. Multilevel logistic regression analysis was used to consider the hierarchical structure of the data. Regression result showed that educated parents [Adjusted odds ratio (AOR) = 1.14, 95% Confidence Interval (CI) = 1.04, 1.26 ], exposure to media [AOR = 1.13, CI = 1.05, 1.21], 2nd or 3rd birth order [AOR = 1.13, CI = 1.04, 1.23], wanted child [AOR = 1.12, CI = 1.02, 1.23], antenatal visit [AOR = 1.07, CI = 1.00, 1.15], antenatal visit by medically trained provider [AOR = 1.06, CI = 1.00,1.13] and rich wealth index [AOR = 1.10, CI = 1.01, 1.20] were positively associated with EIBF. In contrast, mothers with caesarian delivery [AOR = 0.36, CI = 0.31, 0.40], delivery in private facility [AOR = 0.83, CI = 0.73, 0.95], multiple birth, and higher maternal age were less likely to EIBF.
Experimental validation of new self-voltage balanced 9L-ANPC inverter for photovoltaic applications
Authors: Sathik M. Jagabar , Almakhles Dhafer J. , Sandeep N. , Siddique Marif Daula 
Published: March 2021

AbstractMultilevel inverters play an important role in extracting the power from renewable energy resources and delivering the output voltage with high quality to the load. This paper proposes a new single-stage switched capacitor nine-level inverter, which comprises an improved T-type inverter, auxiliary switch, and switched cell unit. The proposed topology effectively reduces the DC-link capacitor voltage and exhibits superior performance over recently switched-capacitor inverter topologies in terms of the number of power components and blocking voltage of the switches. A level-shifted multilevel pulse width modulation scheme with a modified triangular carrier wave is implemented to produce a high-quality stepped output voltage waveform with low switching frequency. The proposed nine-level inverter’s effectiveness, driven by the recommended modulation technique, is experimentally verified under varying load conditions. The power loss and efficiency for the proposed nine-level inverter are thoroughly discussed with different loads.
Quantitative evaluation of SARS-CoV-2 inactivation using a deep ultraviolet light-emitting diode
Authors: Minamikawa Takeo , Koma Takaaki , Suzuki Akihiro , Mizuno Takahiko , Nagamatsu Kentaro , Arimochi Hideki , Tsuchiya Koichiro , Matsuoka Kaoru , Yasui Takeshi , Yasutomo Koji , Nomaguchi Masako 
Published: March 2021

AbstractInactivation technology for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is certainly a critical measure to mitigate the spread of coronavirus disease 2019 (COVID-19). A deep ultraviolet light-emitting diode (DUV-LED) would be a promising candidate to inactivate SARS-CoV-2, based on the well-known antiviral effects of DUV on microorganisms and viruses. However, due to variations in the inactivation effects across different viruses, quantitative evaluations of the inactivation profile of SARS-CoV-2 by DUV-LED irradiation need to be performed. In the present study, we quantify the irradiation dose of DUV-LED necessary to inactivate SARS-CoV-2. For this purpose, we determined the culture media suitable for the irradiation of SARS-CoV-2 and optimized the irradiation apparatus using commercially available DUV-LEDs that operate at a center wavelength of 265, 280, or 300 nm. Under these conditions, we successfully analyzed the relationship between SARS-CoV-2 infectivity and the irradiation dose of the DUV-LEDs at each wavelength without irrelevant biological effects. In conclusion, total doses of 1.8 mJ/cm2 for 265 nm, 3.0 mJ/cm2 for 280 nm, and 23 mJ/cm2 for 300 nm are required to inactivate 99.9% of SARS-CoV-2. Our results provide quantitative antiviral effects of DUV irradiation on SARS-CoV-2, serving as basic knowledge of inactivation technologies against SARS-CoV-2.
Resequencing and SNP discovery of Amur ide (Leuciscus waleckii) provides insights into local adaptations to extreme environments
Authors: Wang Shuangyi , Kuang Youyi , Liang Liqun , Sun Bo , Zhao Xuefei , Zhang Limin , Chang Yumei 
Published: March 2021

AbstractAmur ide (Leuciscus waleckii), a Cyprinid species, is broadly distributed in Northeast Asia. Different from its freshwater counterparts, the population in Lake Dali Nor has a strong alkalinity tolerance and can adapt to extremely alkali–saline water with bicarbonate over 50 mmol/L. To uncover the genetic basis of its alkaline adaptation, three populations, including one alkali form from Lake Dali Nor (DL), one freshwater form from its adjacent sister Lake Ganggeng Nor (GG), and one freshwater form from its historical origin, namely, the Songhua River (SH), were analyzed using genome resequencing technology. A total of 679.82 Gb clean data and 38,091,163 high-quality single-nucleotide polymorphism (SNP) loci were detected in the three populations. Nucleotide diversity and population structure analysis revealed that the DL and GG populations have lower nucleotide diversities and different genetic structures than those of the SH population. Selective sweeping showed 21 genes involved in osmoregulatory regulation (DLG1, VIPR1, AKT1, and GNAI1), inflammation and immune responses (DLG1, BRINP1, CTSL, TRAF6, AKT1, STAT3, GNAI1, SEC22b, and PSME4b), and cardiorespiratory development (TRAF6, PSME4b, STAT3, AKT1, and COL9A1) to be associated with alkaline adaption of the DL population. Interestingly, selective pressure (CodeML, MEME, and FEL) methods identified two functional codon sites of VIPR1 to be under positive selection in the DL population. The subsequent 3D protein modeling confirmed that these selected sites will incur changes in protein structure and function in the DL population. In brief, this study provides molecular evidence of population divergence and alkaline adaptation, which will be very useful for revealing the genetic basis of alkaline adaptation in Amur ide.