bluuebunny/crossref_metadata_2025_split huggingface dataset:
The diel vertical distribution and carbon biomass of the zooplankton community in the Caroline Seamount area of the western tropical Pacific Ocean
Authors: Tao Zhencheng , Xian Haochen , Luan Zhendong , Nan Feng , Wang Yanqing , Sun Song 
Published: November 2022

AbstractZooplankton can affect and regulate the biological carbon pump in the biogeochemical cycles of marine ecosystems through diel vertical migration (DVM) behaviour. The diel vertical distribution and migration of a zooplankton community were studied at a continuous survey station in the Caroline Seamount area of the western tropical Pacific Ocean. Using a MultiNet sampling system, 346 zooplankton species/taxa were collected and identified. The vertical distribution patterns of abundance and composition of the zooplankton community differed between daytime and nighttime. The highest biodiversity index occurred in the 100–200-m ocean depth layer, but some zooplankton species remained in the deep-water layer below 300 m. The DVM patterns of the various dominant species differed, even when the species belonged to the same order or family. Dissolved oxygen and seawater temperature were the main environmental factors affecting the diel vertical distribution of the zooplankton community. The oxygen minimum zone was identified as performing the dual role of “ecological barrier” and “refuge” for zooplankton. The active carbon flux mediated by the zooplankton DVM in the Caroline Seamount area was 14.5 mg C/(m2·d). Our findings suggest that zooplankton DVM can affect and mediate the biological carbon pump in the Caroline Seamount area.
The relationship between structural analysis of the hand and clinical characteristics in psoriatic arthritis
Authors: Pfeil Alexander , Heinz Marcus , Hoffmann Tobias , Weise Tobias , Renz Diane M. , Franz Marcus , Malich Ansgar , Driesch Dominik , Oelzner Peter , Wolf Gunter , Böttcher Joachim 
Published: November 2022

AbstractUp to now, there is only limited information available on a possible relationship between clinical characteristics and the mineralization of metacarpal bones and finger joint space distance (JSD) in patients with psoriatic arthritis (PsA). Computerized digital imaging techniques like digital X-ray radiogrammetry (DXR) and computer-aided joint space analysis (CAJSA) have significantly improved the structural analysis of hand radiographs and facilitate the recognition of radiographic damage. The objective of this study was to evaluate clinical features which potentially influence periarticular mineralization of the metacarpal bones and finger JSD in PsA-patients. 201 patients with PsA underwent computerized measurements of the metacarpal bone mineral density (BMD) with DXR and JSD of all finger joints by CAJSA. DXR-BMD and JSD were compared with clinical features such as age and sex, disease duration, C-reactive protein (CRP) as well as treatment with prednisone and disease-modifying antirheumatic drugs (DMARDs). A longer disease duration and an elevated CRP value were associated with a significant reduction of DXR-BMD, whereas JSD-parameters were not affected by both parameters. DXR-BMD was significantly reduced in the prednisone group (–0.0383 g/cm²), but prednisone showed no impact on finger JSD. Patients under the treatment with bDMARDs presented significant lower DXR-BMD (–0.380 g/cm²), JSDMCP (–0.0179 cm), and JSDPIP (–0.0121 cm) values. Metacarpal BMD was influenced by inflammatory activity, prednisone use, and DMARDs. In contrast, finger JSD showed only a change compared to baseline therapy. Therefore, metacarpal BMD as well as finger JSD represent radiographic destruction under different aspects.
The number of examinations required for the accurate prediction of the progression of the central 10-degree visual field test in glaucoma
Authors: Omoto Takashi , Asaoka Ryo , Akagi Tadamichi , Oishi Akio , Miyata Manabu , Murata Hiroshi , Fujino Yuri , Hirasawa Kazunori , Inoue Tatsuya , Tanito Masaki , Shoji Nobuyuki 
Published: November 2022

AbstractThe purpose of the study was to investigate the number of examinations required to precisely predict the future central 10-degree visual field (VF) test and to evaluate the effect of fitting non-linear models, including quadratic regression, exponential regression, logistic regression, and M-estimator robust regression model, for eyes with glaucoma. 180 eyes from 133 open angle glaucoma patients with a minimum of 13 Humphrey Field Analyzer 10-2 SITA standard VF tests were analyzed in this study. Using trend analysis with ordinary least squares linear regression (OLSLR), the first, second, and third future VFs were predicted in a point-wise (PW) manner using a varied number of prior VF sequences, and mean absolute errors (MAE) were calculated. The number of VFs needed to reach the minimum 95% confidence interval (CI) of the MAE of the OLSLR was investigated. We also examined the effect of applying other non-linear models. When predicting the first, second, and third future VFs using OLSLR, the minimum MAE was obtained using VF1–12 (2.15 ± 0.98 dB), VF1–11 (2.33 ± 1.10 dB), and VF1–10 (2.63 ± 1.36 dB), respectively. To reach the 95% CI of these MAEs, 10, 10, and 8 VFs were needed for the first, second and third future VF predictions, respectively. No improvement was observed by applying non-linear regression models. As a conclusion, approximately 8–10 VFs were needed to achieve an accurate prediction of PW VF sensitivity of the 10-degree central VF.
Machine learning based personalized drug response prediction for lung cancer patients
Authors: Qureshi Rizwan , Basit Syed Abdullah , Shamsi Jawwad A. , Fan Xinqi , Nawaz Mehmood , Yan Hong , Alam Tanvir 
Published: November 2022

AbstractLung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug resistance may affect treatment plans and treatment efficacy may be lost after about a year. Predicting the response to EGFR-TKIs for EGFR-mutated lung cancer patients is a key research area. In this study, we propose a personalized drug response prediction model (PDRP), based on molecular dynamics simulations and machine learning, to predict the response of first generation FDA-approved small molecule EGFR-TKIs, Gefitinib/Erlotinib, in lung cancer patients. The patient’s mutation status is taken into consideration in molecular dynamics (MD) simulation. Each patient’s unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. Moreover, additional clinical features were incorporated into machine learning model for drug response prediction. The complete feature set includes demographic and clinical information (DCI), geometrical properties of the drug-target binding site, and the binding free energy of the drug-target complex from the MD simulation. PDRP incorporates an XGBoost classifier, which achieves state-of-the-art performance with 97.5% accuracy, 93% recall, 96.5% precision, and 94% F1-score, for a 4-class drug response prediction task. We found that modeling the geometry of the binding pocket combined with binding free energy is a good predictor for drug response. However, we observed that clinical information had a little impact on the performance of the model. The proposed model could be tested on other types of cancers. We believe PDRP will support the planning of effective treatment regimes based on clinical-genomic information. The source code and related files are available on GitHub at: https://github.com/rizwanqureshi123/PDRP/.
Multimodal machine learning in precision health: A scoping review
Authors: Kline Adrienne , Wang Hanyin , Li Yikuan , Dennis Saya , Hutch Meghan , Xu Zhenxing , Wang Fei , Cheng Feixiong , Luo Yuan 
Published: November 2022

AbstractMachine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.
MACHETE identifies interferon-encompassing chromosome 9p21.3 deletions as mediators of immune evasion and metastasis
Authors: Barriga Francisco M. , Tsanov Kaloyan M. , Ho Yu-Jui , Sohail Noor , Zhang Amy , Baslan Timour , Wuest Alexandra N. , Del Priore Isabella , Meškauskaitė Brigita , Livshits Geulah , Alonso-Curbelo Direna , Simon Janelle , Chaves-Perez Almudena , Bar-Sagi Dafna , Iacobuzio-Donahue Christine A. , Notta Faiyaz , Chaligne Ronan , Sharma Roshan , Pe’er Dana , Lowe Scott W. 
Published: November 2022

AbstractThe most prominent homozygous deletions in cancer affect chromosome 9p21.3 and eliminate CDKN2A/B tumor suppressors, disabling a cell-intrinsic barrier to tumorigenesis. Half of 9p21.3 deletions, however, also encompass a type I interferon (IFN) gene cluster; the consequences of this co-deletion remain unexplored. To functionally dissect 9p21.3 and other large genomic deletions, we developed a flexible deletion engineering strategy, MACHETE (molecular alteration of chromosomes with engineered tandem elements). Applying MACHETE to a syngeneic mouse model of pancreatic cancer, we found that co-deletion of the IFN cluster promoted immune evasion, metastasis and immunotherapy resistance. Mechanistically, IFN co-deletion disrupted type I IFN signaling in the tumor microenvironment, leading to marked changes in infiltrating immune cells and escape from CD8+ T-cell surveillance, effects largely driven by the poorly understood interferon epsilon. These results reveal a chromosomal deletion that disables both cell-intrinsic and cell-extrinsic tumor suppression and provide a framework for interrogating large deletions in cancer and beyond.
Fundamentals and applications of N-heterocyclic carbene functionalized gold surfaces and nanoparticles
Authors: Kaur Gurkiran , Thimes Rebekah L. , Camden Jon P. , Jenkins David M. 
Published: November 2022

Improved stability and higher degree of synthetic tunability has allowed N-heterocyclic carbenes to supplant thiols as ligands for gold surface functionalization. This review article summarizes the basic science and applications of NHCs on gold.
Tensor decomposition based networks for nuclei segmentation and classification
Authors: Chen Jinhao , Chen Zhao 
Published: November 2022

AbstractNuclei segmentation and classification for Haematoxylin & Eosin stained histology images is a challenging task because of many issues, large intra‐class variability among nuclei, overlapping nuclei etc. Most of the existing methods require large computation to improve the accuracy. Moreover, the learned features in deep networks are high dimensional compared to the input microscopy images. Thus, the discriminative features may lie in low dimensional subspaces, which can be recovered by tensor analysis methods such as Tucker decomposition. Therefore, we propose two novel modules, Tucker Decomposition and Convolution Combined (TuCo) module and Tucker Decomposition and Deformable Convolution Combined (TuDe) module, for nuclei segmentation and classification. They are designed to extract the nuclear geometric information and low‐rank features, and can be plugged into existing backbones. Thus, we design TuCo‐Net and TuDe‐Net. Experimental results validate the effectiveness of the proposed networks. The code and optimized models will be available at https://github.com/dark‐ai/TuCo‐Net‐and‐TuDe‐Net.
Analytical model of a fractional slot double‐layer‐winding vernier permanent magnet machine
Authors: Wang Weibing , Cheng Ming 
Published: November 2022

AbstractThis study presents an analytical model (ANA) for predicting magnetic field and performances of a fractional slot (FS) double‐layer‐winding (DLW) vernier permanent magnet (VPM) machine. Magnetic vector potential equations of a FS DLW VPM machine are analytically solved by the technique of variable separation in three subdomains, that is, regions of permanent magnets (PM), air‐gap, and slots in 2‐D polar coordinate system. Field distributions and performances, such as flux linkage, electromotive force (EMF), torque and inductance of a FS DLW VPM machine are developed. Results from ANA of an 18‐slot/2‐stator‐pole‐pair FS DLW VPM machine prototype have been compared with those from finite element analysis (FEA), which indicates that ANA has a huge advantage in the computing speed in addition to a good accuracy. In the end, a prototype is built and its EMF and torque are tested in experiment platforms in which the results verify ANA. The necessity and importance of the research is that the model is an analytical method which provides a close solution of magnetic field and performances of a FS DLW VPM machine and its calculation time is much less than FEA on the base of a good precision.
Examining the feasibility of current spiral curve design controls for LiDAR‐based automated vehicles
Authors: Wang Shuyi , Mao Chengyang , Ma Yang , Liu Jinzhou , Yu Bin 
Published: November 2022

AbstractEnabling large‐scale deployment of automated vehicles (AVs) in the near future requires answering the question firstly: whether AVs could safely adapt to as‐built road geometry? This study aims to examine the feasibility of current spiral curve design controls for LiDAR‐based AVs (LAVs) from the perspective of the available sight distance (ASD). A series of tests featuring the design speed (Vd), lengths of tangent (LT) and spiral (LS), circular curve radii (R), and point thresholds for detection (NT) were simulated in PreScan/MATLAB/Simulink co‐simulation platform. The ASDs affected by those parameters’ combined effects were analyzed and compared with required stopping sight distances (RSDs) of human‐driven vehicles (HVs) and level 3 to 5 (L3–L5) LAVs followed by proposing the ASD‐oriented safe speeds and the corresponding speed limits. The results indicate that: (1) the combination of the tangent‐spiral curve‐circular curve causes a shorter ASD than that without the spiral curve; (2) a longer spiral curve causes a shorter ASD; and (3) only a low‐type combination of R, LS, Vd conditions is feasible for L3 LAVs while L4 or L5 LAVs have difficulties in dealing with high‐type conditions. These findings help understand the ASD for AVs and provide safety‐critical speed references for administrators.
Synthesis of thorium and uranium in asymptotic giant branch stars
Authors: Choplin A. , Goriely S. , Siess L. 
Published: November 2022

Context.The intermediate neutron capture process (i-process) operates at neutron densities between those of the slow and rapid neutron-capture processes. It is believed to be triggered by the ingestion of protons in a convective helium-burning region. One possible astrophysical site is low-mass low-metallicity asymptotic giant branch (AGB) stars.Aims.Although it has been widely believed that actinides, and most particularly Th and U, are exclusively produced by explosiver-process nucleosynthesis, we study here the possibility that actinides may also be significantly synthesized throughi-process nucleosynthesis in AGB stars.Methods.We computed a 1M⊙model at [Fe/H] = −2.5 with the stellar evolution code STAREVOL. We used a nuclear network of 1160 species from H to Cf coupled to the transport processes. Models of various resolutions (temporal and spatial) that use different nuclear datasets are also considered for the analysis.Results.During the proton ingestion event, the neutron density in our AGB model goes up to ∼1015cm−3and is shown to be high enough to give rise to the production of actinides. While most of the nuclear flow cycles in the neutron-rich Pb–Bi–Po region, a non-negligible fraction leaks towards heavier elements and eventually synthesizes actinides. The surface enrichment in Th and U is subject to nuclear and astrophysical model uncertainties that could be lowered in the future, in particular by a detailed analysis of the nuclear inputs that affect the neutron capture rates of neutron-rich isotopes between Pb and Pa, along thei-process path. One stellar candidate that may confirm the production of actinides by thei-process is the carbon-enhanced metal-poor (CEMP)r/sstarRAVE J094921.8−161722, which shows Th lines in its spectrum. Its surface abundance is shown to be reasonably well reproduced by our AGB model, though abundances of lightN ≃ 50 elements remain underestimated. Combined with cosmochronometry, this finding opens the way to dating thei-process event and thus obtaining a lower limit for the age of CEMP-r/sstars. Such a dating is expected to be accurate only if surface abundances of Th and U can be extracted simultaneously.Conclusions.We show that actinides can be synthesized in low-metallicity low-mass AGB stars through thei-process. This astrophysical site therefore potentially contributes to the Galactic enrichment of Th and U, which demonstrates that ther-process may not be the sole mechanism for the production of U and Th.
Das wachsende Interesse an Breast Implant Illness und dessen Einfluss
auf das Verhalten der Patientinnen
Authors: Weitkunat Adina , Targosinski Stefan , Bauer Benedikt , Grünert Jörg G. , Winsauer Stefan 
Published: November 2022

ZusammenfassungBreast Implant Illness (BII) bezeichnet eine Reihe von unspezifischen,
systemischen Symptomen, von denen angenommen wird, dass sie mit Brustimplantaten
in Verbindung stehen. Zu den BII-Symptomen gehören unter anderem
Müdigkeit, Haarausfall, Hautausschläge, oder chronische
Schmerzen. Schlüssige Beweise für einen kausalen Zusammenhang
zwischen Silikonimplantaten und BII fehlen jedoch nach wie vor. In Anbetracht
der jüngsten Erkenntnisse, dass texturierte Implantate in seltenen
Fällen zu einem Brustimplantat-assoziierten, anaplastischen
Großzell-Lymphom (BIA-ALCL) führen können, ist ein
möglicher Zusammenhang zwischen Brustimplantaten und BII denkbar und
rechtfertigt weitere Untersuchungen. Wir beobachten eine steigende Anzahl von
Patientinnen, die aufgrund systemischer Symptome im Zusammenhang mit
Brustimplantaten Beratung und Behandlung ersuchen, was sich in einem zunehmenden
Interesse in der Literatur und den sozialen Medien widerspiegelt. Das Ziel
dieser Arbeit war es, das wachsende Interesse an BII zu untersuchen. Wir
beschreiben die klinischen Merkmale einer Patientin, die unter Symptomen leidet,
die mit BII vereinbar sind und kontextualisieren die klinische
Präsentation mit der aktuellen Datenlage.
Prevention of Dental Biofilm Formation with Polyphenols: A Systematic Review
Authors: Schestakow Anton , Meyer-Probst Clara Theres , Hannig Christian , Hannig Matthias 
Published: November 2022

AbstractPolyphenols are plant secondary products with health-promoting properties against various degenerative or infectious diseases, and thus may help in the prevention of oral diseases. The aim of the present systematic review was to investigate polyphenols as a possible adjuvant in inhibiting dental biofilm formation, which is an important precondition for the most prevalent oral disease – caries and periodontitis. A literature search was conducted using the databases PubMed, CENTRAL and Scopus. Only studies with oral healthy participants and plaque level as outcome were included. Data search and extraction was conducted by two authors independently. Of the 211 initially identified studies, only six met all inclusion criteria. Meta-analysis was performed with five studies using the random effect model. Treatment with polyphenols reduced the plaque level in comparison to a negative control, but not significantly. Strong evidence of heterogeneity was observed. The diversity and complexity of polyphenols and their preparation need to be considered. There is no clear evidence that clinical use of polyphenols can prevent dental biofilm formation. Additional research with more and larger randomized controlled trials are required.
Feasibility study of a high spatial and time resolution beam emission spectroscopy diagnostic for localized density fluctuation measurements in Lithium Tokamak eXperiment-β (LTX-β)
Authors: Banerjee Santanu , Boyle D. P. , Maan A. , Majeski R. , Kaita R. , Smith D. , von Hellermann M. , Hansen C. , Capecchi W. , Elliott D. 
Published: November 2022

Trapped electron mode (TEM) is the main source of turbulence predicted for the unique operation regime of a flat temperature profile under low-recycling conditions in the LTX-β tokamak, while ion temperature gradient driven turbulence may also occur with gas fueling from the edge. To investigate mainly TEM scale density fluctuations, a high spatial and time resolution 2D beam emission spectroscopy (BES) diagnostic is being developed. Apart from spatially localized density turbulence measurement, BES can provide turbulence flow and flow shear dynamics. This BES system will be realized using an avalanche photodiode-based camera and narrow band interference filter. The system can acquire data at 2 MHz. Simulations with the Simulation of Spectra (SOS) code indicate that a high signal to noise ratio can be achieved with the proposed system. This will enable sampling the density fluctuations at this high time resolution. The design considerations and system optimization using the SOS code are presented.
Design and characterization of a recoil ion momentum spectrometer for investigating molecular fragmentation dynamics upon MeV energy ion impact ionization
Authors: Duley Avijit , Tyagi Rohit , Bari Sandeep B. , Kelkar A. H. 
Published: November 2022

We present the development and performance of a newly built recoil ion momentum spectrometer to study the fragmentation dynamics of ionized molecules. The spectrometer is based on the two-stage Wiley–McLaren geometry and satisfies both time and velocity focusing conditions. An electrostatic lens has been introduced in the drift region to achieve velocity imaging and higher angular collection. The spectrometer is equipped with a 2D position-sensitive detector with multi-hit coincidence electronics. Ionic fragments with kinetic energy ∼8 eV can be detected with 4π collection. The overall performance of the spectrometer has been tested by carrying out three-dimensional ion imaging measurements for diatomic (N2) and polyatomic (CH2Cl2) molecules under the impact of 1 MeV protons. Three-dimensional momentum and kinetic energy release distributions were derived from the measured position and time-of-flight spectra. The observed features of the various fragmentation channels as well as the measured kinetic energy release distributions are in complete agreement with the available data.