bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthesis, properties, and application of the new nanocatalyst of double layer hydroxides in the one-pot multicomponent synthesis of 2-amino-3-cyanopyridine derivatives
Authors: Momeni Sarieh , Ghorbani-Vaghei Ramin 
Published: January 2023

AbstractA new heterogeneous nanocatalyst LDH@3-chloropyltrimethoxysilane@1,3-benzenedisulfonyl amine@Cu (LDH@TRMS@BDSA@Cu) was synthesized and confirmed by analyzes such as Fourier transform infrared spectroscopy, Field Emission Scanning Electron Microscopy, energy scattered X-ray spectroscopy (EDX), elemental mapping, X-ray diffraction analysis, heat gravity/heat derivatization (TGA) and differential scanning calorimetry. The newly synthesized nanocatalyst effectively catalyzed the reaction between different aryl aldehydes, malononitrile, different acetophenones and ammonium acetate in solvent-free conditions and they were converted into 2-amino-3-cyanopyridine derivatives with high efficiency. The reaction showed advantages such as simplicity, high stability, environmental friendliness, excellent efficiency and short time. Also, this catalyst is recyclable and was recycled 4 times without losing significant catalytic power.
Antiviral activities of plant-derived indole and β-carboline alkaloids against human and avian influenza viruses
Authors: Hegazy Akram , Mahmoud Sara H. , Elshaier Yaseen A. M. M. , Shama Noura M. Abo , Nasr Nasr Fawzy , Ali M. A. , El-Shazly Assem Mohamed , Mostafa Islam , Mostafa Ahmed 
Published: January 2023

AbstractThe persistent evolution of drug-resistant influenza strains represents a global concern. The innovation of new treatment approaches through drug screening strategies and investigating the antiviral potential of bioactive natural-based chemicals may address the issue. Herein, we screened the anti-influenza efficacy of some biologically active indole and β-carboline (βC) indole alkaloids against two different influenza A viruses (IAV) with varied host range ranges; seasonal influenza A/Egypt/NRC098/2019(H1N1) and avian influenza A/chicken/Egypt/N12640A/2016(H5N1). All compounds were first assessed for their half-maximal cytotoxic concentration (CC50) in MDCK cells and half-maximal inhibitory concentrations (IC50) against influenza A/H5N1. Intriguingly, Strychnine sulfate, Harmalol, Harmane, and Harmaline showed robust anti-H5N1 activities with IC50 values of 11.85, 0.02, 0.023, and 3.42 µg/ml, respectively, as compared to zanamivir and amantadine as control drugs (IC50 = 0.079 µg/ml and 17.59 µg/ml, respectively). The efficacy of the predefined phytochemicals was further confirmed against influenza A/H1N1 and they displayed potent anti-H1N1 activities compared to reference drugs. Based on SI values, the highly promising compounds were then evaluated for antiviral efficacy through plaque reduction assay and consistently they revealed high viral inhibition percentages at non-toxic concentrations. By studying the modes of antiviral action, Harmane and Harmalol could suppress viral infection via interfering mainly with the viral replication of the influenza A/H5N1 virus, whilst Harmaline exhibited a viricidal effect against the influenza A/H5N1 virus. Whereas, Strychnine sulfate elucidated its anti-influenza potency by interfering with viral adsorption into MDCK cells. Consistently, chemoinformatic studies showed that all studied phytochemicals illustrated HB formations with essential peptide cleft through the NH of indole moiety. Among active alkaloids, harmalol displayed the best lipophilicity metrics including ligand efficiency (LE) and ligand lipophilic efficiency (LLE) for both viruses. Compounds geometry and their ability to participate in HB formation are very crucial.
Half-Heusler phase TmNiSb under pressure: intrinsic phase separation, thermoelectric performance and structural transition
Authors: Ciesielski Kamil , Synoradzki Karol , Szymański Damian , Tobita Kazuki , Berent Katarzyna , Obstarczyk Patryk , Kimura Kaoru , Kaczorowski Dariusz 
Published: January 2023

AbstractHalf-Heusler (HH) phase TmNiSb was obtained by arc-melting combined with high-pressure high-temperature sintering in conditions: p = 5.5 GPa, $$T_{HPHT}$$

T

HPHT

= 20, 250, 500, 750, and 1000 $$^{\circ }$$

∘

C. Within pressing temperatures 20–750 $$^{\circ }$$

∘

C the samples maintained HH structure, however, we observed intrinsic phase separation. The material divided into three phases: stoichiometric TmNiSb, nickel-deficient phase TmNi$$_{1-x}$$

1
-
x

Sb, and thulium-rich phase Tm(NiSb)$$_{1-y}$$

1
-
y

. For TmNiSb sample sintered at 1000 $$^{\circ }$$

∘

C, we report structural transition to LiGaGe-type structure (P$$6_3$$

6
3

mc, a = 4.367(3) Å, c = 7.138(7) Å). Interpretation of the transition is supported by X-ray powder diffraction, electron back-scattered diffraction, ab-initio calculations of Gibbs energy and phonon dispersion relations. Electrical resistivity measured for HH samples with phase separation shown non-degenerate behavior. Obtained energy gaps for HH samples were narrow ($$\le$$
≤
260 meV), while the average hole effective masses in range 0.8–2.5$$m_e$$

m
e

. TmNiSb sample pressed at 750 $$^{\circ }$$

∘

C achieved the biggest power factor among the series, 13 $$\upmu$$
μ
WK$$^{-2}$$

-
2

cm$$^{-1}$$

-
1

, which proves that the intrinsic phase separation is not detrimental for the electronic transport.
A comparison of manual and automated neural architecture search for white matter tract segmentation
Authors: Tchetchenian Ari , Zhu Yanming , Zhang Fan , O’Donnell Lauren J. , Song Yang , Meijering Erik 
Published: January 2023

AbstractSegmentation of white matter tracts in diffusion magnetic resonance images is an important first step in many imaging studies of the brain in health and disease. Similar to medical image segmentation in general, a popular approach to white matter tract segmentation is to use U-Net based artificial neural network architectures. Despite many suggested improvements to the U-Net architecture in recent years, there is a lack of systematic comparison of architectural variants for white matter tract segmentation. In this paper, we evaluate multiple U-Net based architectures specifically for this purpose. We compare the results of these networks to those achieved by our own various architecture changes, as well as to new U-Net architectures designed automatically via neural architecture search (NAS). To the best of our knowledge, this is the first study to systematically compare multiple U-Net based architectures for white matter tract segmentation, and the first to use NAS. We find that the recently proposed medical imaging segmentation network UNet3+ slightly outperforms the current state of the art for white matter tract segmentation, and achieves a notably better mean Dice score for segmentation of the fornix (+ 0.01 and + 0.006 mean Dice increase for left and right fornix respectively), a tract that the current state of the art model struggles to segment. UNet3+ also outperforms the current state of the art when little training data is available. Additionally, manual architecture search found that a minor segmentation improvement is observed when an additional, deeper layer is added to the U-shape of UNet3+. However, all networks, including those designed via NAS, achieve similar results, suggesting that there may be benefit in exploring networks that deviate from the general U-Net paradigm.
Progressing of a power model for electrical conductivity of graphene-based composites
Authors: Zare Yasser , Rhee Kyong Yop , Park Soo-Jin 
Published: January 2023

AbstractThis work presents a power equation for the conductivity of graphene-based polymer composites by the tunneling length, interphase deepness and filler size. The impressions of these factors on the effective concentration and percolation beginning of graphene nano-sheets in nanocomposites are also expressed. The developed equations for percolation beginning and conductivity are examined by the experimented data of some examples, which can guesstimate the interphase depth, tunneling size and percolation exponent. Besides, the impacts of numerous factors on the percolation beginning and conductivity are designed. The developed equation for percolation beginning shows the formation of thick interphase and large tunnels in the reported samples. So, disregarding of tunneling and interphase spaces in polymer graphene nanocomposites overpredicts the percolation beginning. Additionally, the developed model presents the acceptable calculations for the conductivity of samples. Among the mentioned parameters, the concentration and graphene conductivity in addition to the interphase depth induce the strongest effects on the conductivity of composites.
Impact of hydrogen gas inhalation during therapeutic hypothermia on cerebral hemodynamics and oxygenation in the asphyxiated piglet
Authors: Nakamura Shinji , Nakao Yasuhiro , Htun Yinmon , Mitsuie Tsutomu , Koyano Kosuke , Morimoto Aya , Konishi Yukihiko , Arioka Makoto , Kondo Sonoko , Kato Ikuko , Ohta Ken-ichi , Yasuda Saneyuki , Miki Takanori , Ueno Masaki , Kusaka Takashi 
Published: January 2023

AbstractWe previously reported the neuroprotective potential of combined hydrogen (H2) gas ventilation therapy and therapeutic hypothermia (TH) by assessing the short-term neurological outcomes and histological findings of 5-day neonatal hypoxic-ischemic (HI) encephalopathy piglets. However, the effects of H2 gas on cerebral circulation and oxygen metabolism and on prognosis were unknown. Here, we used near-infrared time-resolved spectroscopy to compare combined H2 gas ventilation and TH with TH alone. Piglets were divided into three groups: HI insult with normothermia (NT, n = 10), HI insult with hypothermia (TH, 33.5 ± 0.5 °C, n = 8), and HI insult with hypothermia plus H2 ventilation (TH + H2, 2.1–2.7%, n = 8). H2 ventilation and TH were administered and the cerebral blood volume (CBV) and cerebral hemoglobin oxygen saturation (ScO2) were recorded for 24 h after the insult. CBV was significantly higher at 24 h after the insult in the TH + H2 group than in the other groups. ScO2 was significantly lower throughout the 24 h after the insult in the TH + H2 group than in the NT group. In conclusion, combined H2 gas ventilation and TH increased CBV and decreased ScO2, which may reflect elevated cerebral blood flow to meet greater oxygen demand for the surviving neurons, compared with TH alone.
Immortalized murine tenocyte cells: a novel and innovative tool for tendon research
Authors: Oreff Gil Lola , Maurer Barbara , ELKhamary Ahmed N. , Gerner Iris , Sexl Veronika , Jenner Florien 
Published: January 2023

AbstractPrimary tenocytes rapidly undergo senescence and a phenotypic drift upon in vitro monolayer culture, which limits tendon research. The Ink4a/Arf locus encodes the proteins p16Ink4a/Arf and p14ARF (p19ARF in mice) that regulate cell cycle progression and senescence. We here established an immortalized cell line using tenocytes isolated from Ink4a/Arf deficient mice (Ink4a/Arf−/−). These cells were investigated at three distinct time points, at low (2–5), intermediate (14–17) and high (35–44) passages. Wild-type cells at low passage (2–5) served as controls. Ink4a/Arf−/− tenocytes at all stages were comparable to wild-type cells regarding morphology, expression of tenogeneic genes (collagen type 1, 3 and 5, Scleraxis, Tenomodulin and Tenascin-C), and surface markers (CD29, CD44 and CD105) and form 3D tendon-like structures. Importantly, Ink4a/Arf−/− tenocytes maintained their phenotypic features and proliferation potential in culture for more than 40 passages and also following freeze–thaw cycles. In contrast, wild-type tenocytes underwent senescence starting in passage 6. These data define Ink4a/Arf−/− tenocytes as novel tool for in vitro tendon research and as valuable in vitro alternative to animal experiments.
Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation
Authors: Farook Taseef Hasan , Ahmed Saif , Jamayet Nafij Bin , Rashid Farah , Barman Aparna , Sidhu Preena , Patil Pravinkumar , Lisan Awsaf Mahmood , Eusufzai Sumaya Zabin , Dudley James , Daood Umer 
Published: January 2023

AbstractThe current multiphase, invitro study developed and validated a 3-dimensional convolutional neural network (3D-CNN) to generate partial dental crowns (PDC) for use in restorative dentistry. The effectiveness of desktop laser and intraoral scanners in generating data for the purpose of 3D-CNN was first evaluated (phase 1). There were no significant differences in surface area [t-stat(df) = − 0.01 (10), mean difference = − 0.058, P > 0.99] and volume [t-stat(df) = 0.357(10)]. However, the intraoral scans were chosen for phase 2 as they produced a greater level of volumetric details (343.83 ± 43.52 mm3) compared to desktop laser scanning (322.70 ± 40.15 mm3). In phase 2, 120 tooth preparations were digitally synthesized from intraoral scans, and two clinicians designed the respective PDCs using computer-aided design (CAD) workflows on a personal computer setup. Statistical comparison by 3-factor ANOVA demonstrated significant differences in surface area (P < 0.001), volume (P < 0.001), and spatial overlap (P < 0.001), and therefore only the most accurate PDCs (n = 30) were picked to train the neural network (Phase 3). The current 3D-CNN produced a validation accuracy of 60%, validation loss of 0.68–0.87, sensitivity of 1.00, precision of 0.50–0.83, and serves as a proof-of-concept that 3D-CNN can predict and generate PDC prostheses in CAD for restorative dentistry.
Measuring accessibility to public services and infrastructure criticality for disasters risk management
Authors: Tariverdi Mersedeh , Nunez-del-Prado Miguel , Leonova Nadezda , Rentschler Jun 
Published: January 2023

AbstractComponent criticality analysis of infrastructure systems has traditionally focused on physical networks rather than infrastructure services. As an example, a key objective of transport infrastructure is to ensure mobility and resilient access to public services, including for the population, service providers, and associated supply chains. We introduce a new user-centric measure for estimating infrastructure criticality and urban accessibility to critical public services - particularly healthcare facilities without loss of generality - and the effects of disaster-induced infrastructure disruptions. Accessibility measures include individuals’ choices of all services in each sector. The approach is scalable and modular while preserving detailed features necessary for local planning decisions. It relies on open data to simulate various disaster scenarios, including floods, seismic, and compound shocks. We present results for Lima, Peru, and Manila, Philippines, to illustrate how the approach identifies the most affected areas by shocks, underserved populations, and changes in accessibility and critical infrastructure components. We capture the changes in people’s choices of health service providers under each scenario. For Lima, we show that the floods of 2020 caused an increase in average access times to all health services from 33 minutes to 48 minutes. We identify specific critical road segments for ensuring access under each scenario. For Manila, we locate the 22% of the population who lost complete access to all higher health services due to flooding of over 15 cm. The approach is used to identify and prioritize targeted measures to strengthen the resilience of critical public services and their supporting infrastructure systems, while putting the population at the center of decision-making.
Machine learning framework to segment sarcomeric structures in SMLM data
Authors: Varga Dániel , Szikora Szilárd , Novák Tibor , Pap Gergely , Lékó Gábor , Mihály József , Erdélyi Miklós 
Published: January 2023

AbstractObject detection is an image analysis task with a wide range of applications, which is difficult to accomplish with traditional programming. Recent breakthroughs in machine learning have made significant progress in this area. However, these algorithms are generally compatible with traditional pixelated images and cannot be directly applied for pointillist datasets generated by single molecule localization microscopy (SMLM) methods. Here, we have improved the averaging method developed for the analysis of SMLM images of sarcomere structures based on a machine learning object detection algorithm. The ordered structure of sarcomeres allows us to determine the location of the proteins more accurately by superimposing SMLM images of identically assembled proteins. However, the area segmentation process required for averaging can be extremely time-consuming and tedious. In this work, we have automated this process. The developed algorithm not only finds the regions of interest, but also classifies the localizations and identifies the true positive ones. For training, we used simulations to generate large amounts of labelled data. After tuning the neural network’s internal parameters, it could find the localizations associated with the structures we were looking for with high accuracy. We validated our results by comparing them with previous manual evaluations. It has also been proven that the simulations can generate data of sufficient quality for training. Our method is suitable for the identification of other types of structures in SMLM data.
Realtime user ratings as a strategy for combatting misinformation: an experimental study
Authors: Stein Jonas , Frey Vincenz , van de Rijt Arnout 
Published: January 2023

AbstractBecause fact-checking takes time, verdicts are usually reached after a message has gone viral and interventions can have only limited effect. A new approach recently proposed in scholarship and piloted on online platforms is to harness the wisdom of the crowd by enabling recipients of an online message to attach veracity assessments to it. The intention is to allow poor initial crowd reception to temper belief in and further spread of misinformation. We study this approach by letting 4000 subjects in 80 experimental bipartisan communities sequentially rate the veracity of informational messages. We find that in well-mixed communities, the public display of earlier veracity ratings indeed enhances the correct classification of true and false messages by subsequent users. However, crowd intelligence backfires when false information is sequentially rated in ideologically segregated communities. This happens because early raters’ ideological bias, which is aligned with a message, influences later raters’ assessments away from the truth. These results suggest that network segregation poses an important problem for community misinformation detection systems that must be accounted for in the design of such systems.
Analysing multi-perspective patient-related data during laparoscopic gynaecology procedures
Authors: Jalal Nour Aldeen , Abdulbaki Alshirbaji Tamer , Laufer Bernhard , Docherty Paul D. , Neumuth Thomas , Moeller Knut 
Published: January 2023

AbstractFusing data from different medical perspectives inside the operating room (OR) sets the stage for developing intelligent context-aware systems. These systems aim to promote better awareness inside the OR by keeping every medical team well informed about the work of other teams and thus mitigate conflicts resulting from different targets. In this research, a descriptive analysis of data collected from anaesthesiology and surgery was performed to investigate the relationships between the intra-abdominal pressure (IAP) and lung mechanics for patients during laparoscopic procedures. Data of nineteen patients who underwent laparoscopic gynaecology were included. Statistical analysis of all subjects showed a strong relationship between the IAP and dynamic lung compliance (r = 0.91). Additionally, the peak airway pressure was also strongly correlated to the IAP in volume-controlled ventilated patients (r = 0.928). Statistical results obtained by this study demonstrate the importance of analysing the relationship between surgical actions and physiological responses. Moreover, these results form the basis for developing medical decision support models, e.g., automatic compensation of IAP effects on lung function.
Measurement-based preparation of stable coherent states of a Kerr parametric oscillator
Authors: Suzuki Yuta , Watabe Shohei , Kawabata Shiro , Masuda Shumpei 
Published: January 2023

AbstractKerr parametric oscillators (KPOs) have attracted increasing attention in terms of their application to quantum information processing and quantum simulations. The state preparation and measurement of KPOs are typical requirements when used as qubits. The methods previously proposed for state preparations of KPOs utilize modulation of external fields such as a pump and drive fields. We study the stochastic state preparation of stable coherent states of a KPO with homodyne detection, which does not require modulation of external fields, and thus can reduce experimental efforts and exclude unwanted effects of possible imperfection in control of external fields. We quantitatively show that the detection data, if averaged over an optimal averaging time to decrease the effect of measurement noise, has a strong correlation with the state of the KPO, and therefore can be used to estimate the state (stochastic state preparation). We examine the success probability of the state estimation taking into account the measurement noise and bit flips. Moreover, the proper range of the averaging time to realize a high success probability is obtained by developing a binomial-coherent-state model, which describes the stochastic dynamics of the KPO under homodyne detection.
The role of V-shaped oceans and ribbon continents in the Brasiliano/PanAfrican assembly of western Gondwana
Authors: Caxito Fabrício de Andrade , Alkmim Fernando Flecha 
Published: January 2023

AbstractWestern Gondwana amalgamated by collision of continental blocks that did not form prior conjugated margins (extroversion), and by typical Wilson cycles, when continental blocks that rifted away giving birth to new oceans were subsequently re-joined in approximately the same position (introversion). The introverted systems are characterized by the opening of V-shaped basins through rifting and hyperextension of various continental pieces (micro- and ribbon continents) from a former Central African Block. These continental fragments lost substantial parts of their mantle lithosphere and became decratonized while drifting towards the external Goiás-Pharusian ocean. Protracted seafloor spreading and consumption through subduction of the internal and external oceans, respectively, ultimately led to multiple, diachronous collisions with other continental blocks detached from Rodinia (Amazonian, West Africa, Embu, etc.). These collisions pushed the ribbon continents back and closed the introverted basins, squeezing and incorporating the reworked basement tracts between the main colliding blocks and the rigid remainder of the Central African Block (the São Francisco-Congo craton). Continental extrusion and lateral escape tectonics ensued, generating thousands-of-km long networks of anastomosing directional shear zones (keirogens), as a consequence of both the accretionary systems developed between the involved blocks and the highly deformable nature of the decratonized ribbon continents.