bluuebunny/crossref_metadata_2025_split huggingface dataset:
Seasonal Shift in Storm Surges at Brest Revealed by Extreme Value Analysis
Authors: Reinert Markus , Pineau‐Guillou Lucia , Raillard Nicolas , Chapron Bertrand 
Published: December 2021

AbstractGlobal warming changes the Earth's climate in different ways, in particular it influences extreme weather events like storms. Strong storms cause large surges and thus have a signature in the sea level record. While previous studies focused on long‐term changes of storm surge amplitude or frequency, changes in the timing of extreme surge events have not been investigated so far. We employed the more than 150 yr long tide gauge record of Brest (France) and found a distinct shift of storm surge timing between the years 1950 and 2000. This caused extreme events to happen about three weeks earlier during the year. We developed for this study two different methods based on statistical extreme value analysis; both methods show this shift of the seasons consistently. Furthermore, by analyzing eight additional stations, we found evidence that this timing shift happened similarly over a large part of the European Atlantic coast. Therefore, we speculate that our measured shift is part of a large‐scale climate process.
Atmospheric Pressure and Snowball Earth Deglaciation
Authors: Edkins Nicholas J. , Davies Roger 
Published: December 2021

AbstractWhen a large amount of CO2 is added to the atmosphere, both the mixing ratio and the surface pressure increase. This causes the troposphere to expand in two directions. The greenhouse effect from the increased mixing ratio causes the tropopause pressure to decrease, so the troposphere expands upward. The increased surface pressure causes the troposphere to expand downward. The first effect is radiative and well known, while the second is nonradiative and unexplored. Here, a method is presented to compare the effect of tropospheric expansion in each direction on the surface temperature of a Snowball Earth. A series of models, from a gray model to a spectral model with realistic Snowball parameters, are used to illustrate this concept. It is shown that near the deglaciation threshold, most of the increase in surface temperature that follows an increase in CO2 is due to the nonradiative downward tropospheric expansion at the surface, not the radiative upward expansion at the tropopause. The increased atmospheric mass due to the CO2, entirely apart from its radiative effect, causes an additional increase in surface temperature and therefore aids Snowball deglaciation.
To What Extent Can UAV Photogrammetry Replicate UAV LiDAR to Determine Forest Structure? A Test in Two Contrasting Tropical Forests
Authors: McNicol Iain M. , Mitchard Edward T. A. , Aquino Chiara , Burt Andrew , Carstairs Harry , Dassi Creck , Modinga Dikongo Anaick , Disney Mathias I. 
Published: December 2021

AbstractTropical forests are complex multi‐layered systems, with the height and three‐dimensional (3D) structure of trees influencing the carbon and biodiversity they contain. Fine‐resolution 3D data on forest structure can be collected reliably with Light Detection and Ranging (LiDAR) sensors mounted on aircraft or Unoccupied Aerial Vehicles (UAVs), however, they remain expensive to collect and process. Structure‐from‐Motion (SfM) Digital Aerial Photogrammetry (SfM‐DAP), which relies on photographs taken of the same area from multiple angles, is a lower‐cost alternative to LiDAR for generating 3D data on forest structure. Here, we evaluate how SfM‐DAP compares to LiDAR data acquired concurrently using a fixed‐wing UAV, over two contrasting tropical forests in Gabon and Peru. We show that SfM‐DAP data cannot be used in isolation to measure key aspects of forest structure, including canopy height (%Bias: 40%–50%), fractional cover, and gap fraction, due to difficulties measuring ground elevation, even under low tree cover. However, we find even in complex forests, SfM‐DAP is an effective means of measuring top‐of‐canopy structure, including surface heterogeneity, and is capable of producing similar measurements of vertical structure as LiDAR. Thus, in areas where ground height is known, SfM‐DAP is an effective method for measuring important aspects of forest structure, including canopy height, and gaps, however, without ground data, SfM‐DAP is of more limited utility. Our results support the growing evidence base pointing to photogrammetry as a viable complement, or alternative, to LiDAR, capable of providing much needed information to support the mapping and monitoring of biomass and biodiversity.
A Hidden Climate Indices Modeling Framework for Multivariable Space‐Time Data
Authors: Renard B. , Thyer M. , McInerney D. , Kavetski D. , Leonard M. , Westra S. 
Published: December 2021

AbstractRisk assessment for climate‐sensitive systems often relies on the analysis of several variables measured at many sites. In probabilistic terms, the task is to model the joint distribution of several spatially distributed variables, and how it varies in time. This paper describes a Bayesian hierarchical framework for this purpose. Each variable follows a distribution with parameters varying in both space and time. Temporal variability is modeled by means of hidden climate indices (HCIs) that are extracted from observed variables. This is to be contrasted with the usual approach using predefined standard climate indices (SCIs) for this purpose. In the second level of the model, the HCIs and their effects are assumed to follow temporal and spatial Gaussian processes, respectively. Both intervariable and intersite dependencies are induced by the strong effect of common HCIs. The flexibility of the framework is illustrated with a case study in Southeast Australia aimed at modeling “hot‐and‐dry” summer conditions. It involves three physical variables (streamflow, precipitation, and temperature) measured on three distinct station networks, with varying data availability and representing hundreds of sites in total. The HCI model delivers reliable and sharp time‐varying distributions for individual variables and sites. In addition, it adequately reproduces intervariable and intersite dependencies, whereas a corresponding SCI model (where hidden climate indices are replaced with standard ones) strongly underestimates them. It is finally suggested that HCI models may be used as downscaling tools to estimate the joint distribution of several variables at many stations from climate models or reanalyzes.
The association between depression and metabolic syndrome and its components: a bidirectional two-sample Mendelian randomization study
Authors: Zhang Min , Chen Jing , Yin Zhiqun , Wang Lanbing , Peng Lihua 
Published: December 2021

AbstractObservational studies suggested a bidirectional correlation between depression and metabolic syndrome (MetS) and its components. However, the causal associations between them remained unclear. We aimed to investigate whether genetically predicted depression is related to the risk of MetS and its components, and vice versa. We performed a bidirectional two-sample Mendelian randomization (MR) study using summary-level data from the most comprehensive genome-wide association studies (GWAS) of depression (n = 2,113,907), MetS (n = 291,107), waist circumference (n = 462,166), hypertension (n = 463,010) fasting blood glucose (FBG, n = 281,416), triglycerides (n = 441,016), high-density lipoprotein cholesterol (HDL-C, n = 403,943). The random-effects inverse-variance weighted (IVW) method was applied as the primary method. The results identified that genetically predicted depression was significantly positive associated with risk of MetS (OR: 1.224, 95% CI: 1.091–1.374, p = 5.58 × 10−4), waist circumference (OR: 1.083, 95% CI: 1.027–1.143, p = 0.003), hypertension (OR: 1.028, 95% CI: 1.016–1.039, p = 1.34 × 10−6) and triglycerides (OR: 1.111, 95% CI: 1.060–1.163, p = 9.35 × 10−6) while negative associated with HDL-C (OR: 0.932, 95% CI: 0.885–0.981, p = 0.007) but not FBG (OR: 1.010, 95% CI: 0.986–1.034, p = 1.34). No causal relationships were identified for MetS and its components on depression risk. The present MR analysis strength the evidence that depression is a risk factor for MetS and its components (waist circumference, hypertension, FBG, triglycerides, and HDL-C). Early diagnosis and prevention of depression are crucial in the management of MetS and its components.
Glide symmetry protected higher-order topological insulators from semimetals with butterfly-like nodal lines
Authors: Zhou Xiaoting , Hsu Chuang-Han , Huang Cheng-Yi , Iraola Mikel , Mañes Juan L. , Vergniory Maia G. , Lin Hsin , Kioussis Nicholas 
Published: December 2021

AbstractMost topological insulators (TIs) discovered today in spinful systems can be transformed from topological semimetals (TSMs) with vanishing bulk gap via introducing the spin-orbit coupling (SOC), which manifests the intrinsic links between the gapped topological insulator phases and the gapless TSMs. Recently, we have discovered a family of TSMs in time-reversal invariant spinless systems, which host butterfly-like nodal-lines (NLs) consisting of a pair of identical concentric intersecting coplanar ellipses (CICE). In this Communication, we unveil the intrinsic link between this exotic class of nodal-line semimetals (NLSMs) and a $${{\mathbb{Z}}}_{4}$$

Z

4

= 2 topological crystalline insulator (TCI), by including substantial SOC. We demonstrate that in three space groups (i.e., Pbam (No.55), P4/mbm (No.127), and P42/mbc (No.135)), the TCI supports a fourfold Dirac fermion on the (001) surface protected by two glide symmetries, which originates from the intertwined drumhead surface states of the CICE NLs. The higher order topology is further demonstrated by the emergence of one-dimensional helical hinge states, indicating the discovery of a higher order topological insulator protected by a glide symmetry.
High-speed programmable photonic circuits in a cryogenically compatible, visible–near-infrared 200 mm CMOS architecture
Authors: Dong Mark , Clark Genevieve , Leenheer Andrew J. , Zimmermann Matthew , Dominguez Daniel , Menssen Adrian J. , Heim David , Gilbert Gerald , Englund Dirk , Eichenfield Matt 
Published: December 2021

AbstractRecent advances in photonic integrated circuits have enabled a new generation of programmable Mach–Zehnder meshes (MZMs) realized by using cascaded Mach–Zehnder interferometers capable of universal linear-optical transformations on N input/output optical modes. MZMs serve critical functions in photonic quantum information processing, quantum-enhanced sensor networks, machine learning and other applications. However, MZM implementations reported to date rely on thermo-optic phase shifters, which limit applications due to slow response times and high power consumption. Here we introduce a large-scale MZM platform made in a 200 mm complementary metal–oxide–semiconductor foundry, which uses aluminium nitride piezo-optomechanical actuators coupled to silicon nitride waveguides, enabling low-loss propagation with phase modulation at greater than 100 MHz in the visible–near-infrared wavelengths. Moreover, the vanishingly low hold-power consumption of the piezo-actuators enables these photonic integrated circuits to operate at cryogenic temperatures, paving the way for a fully integrated device architecture for a range of quantum applications.
Thermodynamics of free and bound magnons in graphene
Authors: Pierce Andrew T. , Xie Yonglong , Lee Seung Hwan , Forrester Patrick R. , Wei Di S. , Watanabe Kenji , Taniguchi Takashi , Halperin Bertrand I. , Yacoby Amir 
Published: December 2021

AbstractSymmetry-broken electronic phases support neutral collective excitations. For example, monolayer graphene in the quantum Hall regime hosts a nearly ideal ferromagnetic phase at specific filling factors that spontaneously breaks the spin-rotation symmetry1–3. This ferromagnet has been shown to support spin-wave excitations known as magnons that can be electrically generated and detected4,5. Although long-distance magnon propagation has been demonstrated via transport measurements, important thermodynamic properties of such magnon populations—including the magnon chemical potential and density—have not been measured. Here we present local measurements of electron compressibility under the influence of magnons, which reveal a reduction in the gap associated with the ν = 1 quantum Hall state by up to 20%. Combining these measurements with the estimates of temperature, our analysis reveals that the injected magnons bind to electrons and holes to form skyrmions, and it enables the extraction of free magnon density, magnon chemical potential and average skyrmion spin. Our methods provide a means of probing the thermodynamic properties of charge-neutral excitations that are applicable to other symmetry-broken electronic phases.
Development of novel robotic platforms for mechanical stress induction, and their effects on plant morphology, elements, and metabolism
Authors: Kurtser Polina , Castro-Alves Victor , Arunachalam Ajay , Sjöberg Viktor , Hanell Ulf , Hyötyläinen Tuulia , Andreasson Henrik 
Published: December 2021

AbstractThis research evaluates the effect on herbal crops of mechanical stress induced by two specially developed robotic platforms. The changes in plant morphology, metabolite profiles, and element content are evaluated in a series of three empirical experiments, conducted in greenhouse and CNC growing bed conditions, for the case of basil plant growth. Results show significant changes in morphological features, including shortening of overall stem length by up to 40% and inter-node distances by up to 80%, for plants treated with a robotic mechanical stress-induction protocol, compared to control groups. Treated plants showed a significant increase in element absorption, by 20–250% compared to controls, and changes in the metabolite profiles suggested an improvement in plants’ nutritional profiles. These results suggest that repetitive, robotic, mechanical stimuli could be potentially beneficial for plants’ nutritional and taste properties, and could be performed with no human intervention (and therefore labor cost). The changes in morphological aspects of the plant could potentially replace practices involving chemical treatment of the plants, leading to more sustainable crop production.
Cardiovascular outcomes of type 2 diabetic patients treated with DPP‑4 inhibitors versus sulphonylureas as add-on to metformin in clinical practice
Authors: Bazo-Alvarez Juan Carlos , Pal Kingshuk , Pham Tra My , Nazareth Irwin , Petersen Irene , Sharma Manuj 
Published: December 2021

AbstractDPP-4 inhibitors (DPP-4i) and sulphonylureas remain the most widely prescribed add-on treatments after metformin. However, there is limited evidence from clinical practice comparing major adverse cardiovascular events (MACE) in patients prescribed these treatments, particularly among those without prior history of MACE and from vulnerable population groups. Using electronic health records from UK primary care, we undertook a retrospective cohort study with people diagnosed type-2 diabetes mellitus, comparing incidence of MACE (myocardial infarction, stroke, major cardiovascular surgery, unstable angina) and all-cause mortality among those prescribed DPP-4i versus sulphonylureas as add-on to metformin. We stratified analysis by history of MACE, age, social deprivation and comorbidities and adjusted for HbA1c, weight, smoking-status, comorbidities and medications. We identified 17,570 patients prescribed sulphonylureas and 6,267 prescribed DPP-4i between 2008–2017. Of these, 16.3% had pre-existing MACE. Primary incidence of MACE was similar in patients prescribed DPP-4i and sulphonylureas (10.3 vs 8.5 events per 1000 person-years; adjusted Hazard Ratio (adjHR): 0.94; 95%CI 0.80–1.14). For those with pre-existing MACE, rates for recurrence were higher overall, but similar between the two groups (21.8 vs 17.2 events per 1000 person-years; adjHR: 0.93; 95%CI 0.69–1.24). For those aged over 75 and with BMI less than 25 kg/m2there was a protective effect for DPP-I, warranting further investigation. Patients initiating a DPP-4i had similar risk of cardiovascular outcomes to those initiating a sulphonylurea. This indicates the choice should be based on safety and cost, not cardiovascular prognosis, when deciding between a DPP-4i or sulphonylurea as add-on to metformin.
Deep learning data augmentation for Raman spectroscopy cancer tissue classification
Authors: Wu Man , Wang Shuwen , Pan Shirui , Terentis Andrew C. , Strasswimmer John , Zhu Xingquan 
Published: December 2021

AbstractRecently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of tissue samples for RS are important for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a Generative Adversarial Network (GAN) to generate synthetic RS data resembling the training data classes. The original tissue samples and the generated data are concatenated to train classification modules. Experiments on real-world RS data demonstrate that (1) data augmentation can help improve skin cancer tissue classification accuracy, and (2) generative adversarial network can be used to generate reliable synthetic Raman spectroscopic data.
Evolutionary analysis of angiosperm dehydrin gene family reveals three orthologues groups associated to specific protein domains
Authors: Melgar Alejandra E. , Zelada Alicia M. 
Published: December 2021

AbstractDehydrins (DHNs) are a family of plant proteins that play important roles on abiotic stress tolerance and seed development. They are classified into five structural subgroups: K-, SK-, YK-, YSK-, and KS-DHNs, according to the presence of conserved motifs named K-, Y- and S- segments. We carried out a comparative structural and phylogenetic analysis of these proteins, focusing on the less-studied KS-type DHNs. A search for conserved motifs in DHNs from 56 plant genomes revealed that KS-DHNs possess a unique and highly conserved N-terminal, 15-residue amino acid motif, not previously described. This novel motif, that we named H-segment, is present in DHNs of angiosperms, gymnosperms and lycophytes, suggesting that HKS-DHNs were present in the first vascular plants. Phylogenetic and microsynteny analyses indicate that the five structural subgroups of angiosperm DHNs can be assigned to three groups of orthologue genes, characterized by the presence of the H-, F- or Y- segments. Importantly, the hydrophilin character of DHNs correlate with the phylogenetic origin of the DHNs rather than to the traditional structural subgroups. We propose that angiosperm DHNs can be ultimately subdivided into three orthologous groups, a phylogenetic framework that should help future studies on the evolution and function of this protein family.
Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies
Authors: Molnar David , Enqvist Olof , Ulén Johannes , Larsson Måns , Brandberg John , Johnsson Åse A. , Björnson Elias , Bergström Göran , Hjelmgren Ola 
Published: December 2021

AbstractTo develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered.
Application and toxicity studies of arabinoxylan and β-d-glucan stearic acid ester composite coatings in extending postharvest storage of peach
Authors: Ali Usman , Kaur Prabhjot , Kanwar Swati , Kumar Vibhu , Maurya Rohit , Bishnoi Mahendra , Basu Santanu , Mazumder Koushik 
Published: December 2021

AbstractPeaches are good source of nutrients and known for their taste and aroma. The highly perishable nature of the peaches tends to decay rapidly during transportation and storage is a serious constraint for efficient transportation and storage. Therefore, the effect of arabinoxylan (AX) and β-D-glucan stearic acid ester (SABG) composite coating material was examined for the postharvest storage quality of peach under storage at 22 ± 2 °C with 85% relative humidity (RH). Both, AX-SABG and shellac (1–2%) coatings significantly reduced the change in the quality attributes like weight loss (1.2–1.4 fold), respiration rate (1.1–1.2 fold), ripening index (1.3–1.5 fold) and firmness (1.3–1.5 fold) during 6 days storage as compared to the uncoated peaches. In addition, AX-SABG (1–2%) coating was more effective in retaining aroma volatiles and reducing disease incidence compared to shellac. Further, acute and chronic toxicological studies have shown no tissue related toxicity and mortality in mice. Our results suggest that AX-SABG as an edible coating has the potential to preserve the fruit quality during 6 days storage at 22 ± 2 °C and extend the postharvest shelf life of peach during storage.
Antimicrobial activity of a novel Spanish propolis against planktonic and sessile oral Streptococcus spp
Authors: Navarro-Pérez M. Luisa , Vadillo-Rodríguez Virginia , Fernández-Babiano Irene , Pérez-Giraldo Ciro , Fernández-Calderón M. Coronada 
Published: December 2021

AbstractIncreased bacterial resistance to traditional antimicrobial agents has prompted the use of natural products with antimicrobial properties such as propolis, extensively employed since ancient times. However, the chemical composition of propolis extracts is extremely complex and has been shown to vary depending on the region and season of collection, due to variations in the flora from which the pharmacological substances are obtained, being therefore essential for their antimicrobial activity to be checked before use. For this purpose, we evaluate the in vitro antimicrobial and anti-biofilm activity of a new and promising Spanish ethanolic extract of propolis (SEEP) on Streptococcus mutans and Streptococcus sanguinis, responsible, as dominant ‘pioneer’ species, for dental plaque. Results reveal that S. sanguinis is more sensitive to SEEP, slowing and retarding its growth considerably with lower concentrations than those needed to produce the same effect in S. mutans. SEEP presents concentration- and time-dependent killing activity and, furthermore, some of the subinhibitory concentrations employed increased biofilm formation even when bacterial growth decreased. Mono and dual-species biofilms were also inhibited by SEEP. Findings obtained clearly show the relevance of using biofilm and subinhibitory concentration models to determine optimal treatment concentrations.