bluuebunny/crossref_metadata_2025 huggingface dataset:
An Ultrasound‐Responsive Theranostic Cyclodextrin‐Loaded Nanoparticle for Multimodal Imaging and Therapy for Atherosclerosis
Authors: Mehta Sourabh , Bongcaron Viktoria , Nguyen Tien K , Jirwanka Yugandhara , Maluenda Ana , Walsh Aidan P G , Palasubramaniam Jathushan , Hulett Mark D , Srivastava Rohit , Bobik Alex , Wang Xiaowei , Peter Karlheinz
Published: June 2022

Atherosclerosis is a major cause of mortality and morbidity worldwide. Left undiagnosed and untreated, atherosclerotic plaques can rupture and cause cardiovascular complications such as myocardial infarction and stroke. Atherosclerotic plaques are composed of lipids, including oxidized low‐density lipoproteins and cholesterol crystals, and immune cells, including macrophages. 2‐Hydroxypropyl‐beta‐cyclodextrin (CD) is FDA‐approved for capturing, solubilizing, and delivering lipophilic drugs in humans. It is also known to dissolve cholesterol crystals and decrease atherosclerotic plaque size. However, its low retention time necessitates high dosages for successful therapy. This study reports CD delivery via air‐trapped polybutylcyanoacrylate nanoparticles (with diameters of 388 ± 34 nm) loaded with CD (CDNPs). The multimodal contrast ability of these nanoparticles after being loaded with IR780 dye in mice is demonstrated using ultrasound and near‐infrared imaging. It is shown that CDNPs enhance the cellular uptake of CD in murine cells. In an ApoE–/– mouse model of atherosclerosis, treatment with CDNPs significantly improves the anti‐atherosclerotic efficacy of CD. Ultrasound triggering further improves CD uptake, highlighting that CDNPs can be used for ultrasound imaging and ultrasound‐responsive CD delivery. Thus, CDNPs represent a theranostic nanocarrier for potential application in patients with atherosclerosis.
Insight on Cathodes Chemistry for Aqueous Zinc‐Ion Batteries: From Reaction Mechanisms, Structural Engineering, and Modification Strategies
Authors: Liu Anni , Wu Feng , Zhang Yixin , Zhou Jiahui , Zhou Yaozong , Xie Man
Published: June 2022

By virtue of low cost, eco‐friendliness, competitive gravimetric energy density, and intrinsic safety, more and more attention has increasingly focused on aqueous zinc ion batteries (AZIBs) as a promising alternative for scalable energy storage. However, plagued by a complex interfacial process, sluggish dynamics, lability of electrodes and electrolytes, insufficient energy density, and poor cycle life heavily restrict practical applications of AZIBs, indicating that profound understandings on cathode storage chemistry are necessarily needed. Hence, this paper comprehensively summarizes recent advance in cathodes with critical insight on the energy storage mechanism. Furthermore, the issues and challenges for high‐performance cathodes are meticulously explored, presenting inspiring structural engineering and modification strategies. Finally, rational evaluations on representative cathodes are rendered, suggesting the potential development direction of AZIBs.
Uptake of Functional Ultrasmall Gold Nanoparticles in 3D Gut Cell Models
Authors: Sokolova Viktoriya , Ebel Jana‐Fabienne , Kollenda Sebastian , Klein Kai , Kruse Benedikt , Veltkamp Claudia , Lange Christian M. , Westendorf Astrid M. , Epple Matthias
Published: June 2022

Ultrasmall gold nanoparticles (2 nm) easily penetrate the membranes of intestinal murine epithelial cells (MODE‐K) and colorectal cancer cells (CT‐26). They are also taken up by 3D spheroids (400 µm) of these cell types and primary gut organoids (500 µm). In contrast, dissolved dyes are not taken up by any of these cells or 3D structures. The distribution of fluorescent ultrasmall gold nanoparticles inside cells, spheroids, and gut organoids is examined by confocal laser scanning microscopy. Nanoparticles conjugated with the cytostatic drug doxorubicin and a fluorescent dye exhibit significantly greater cytotoxicity toward CT‐26 tumor spheroids than equally concentrated dissolved doxorubicin, probably because they enter the interior of a spheroid much more easily than dissolved doxorubicin. Comprehensive analyses show that the cellular uptake of ultrasmall gold nanoparticles occurs by different endocytosis pathways.
125 years of helping pets and people
Authors: Mills Georgina
Published: June 2022

Georgina Mills reports on how the charity Blue Cross is looking back on its 125‐year history
Flame‐retarded rigid polyurethane foam conferred by an amino trimethylphosphonate iron and expandable graphite compound flame retardant
Authors: Zhang Xu , Li Renzhou , Liu Bing , Wang Zhi , Xie Hua
Published: June 2022

Flame‐retarded rigid polyurethane foam (RPUF) conferred by an amino trimethylphosphonate iron (Fe2+‐ATMP) and expandable graphite (EG) compound flame retardant has been successfully fabricated by one‐step all‐water foaming method. The combustion characteristics of the RPUFs and the synergistic effect of Fe2+‐ATMP and EG on RPUF have been studied based on limiting oxygen index (LOI), smoke toxicity analysis, cone calorimeter (CONE), thermogravimetric (TG) analysis and thermal decomposition kinetics analysis. The current results indicated that the flame retardancy and thermal stability of the RPUFs modified by Fe2+‐ATMP and EG had been improved obviously and the contents of smoke toxic gases had been significantly reduced. When the ratio of Fe2+‐ATMP to EG was 1:5, LOI of the modified RPUF was the highest (27.1%). And its peak heat release rate (PHRR) and total heat release (THR) were the lowest, which were 135 kW/m2 and 15.7 MJ/m2, respectively, and were 40.5% and 17.8% lower than RPUF‐0. It also showed that the modified RPUF with a Fe2+‐ATMP to EG ratio of 1:5 had the best flame retardancy, smoke suppression, and thermal stability. The present work can provide some reference and guidance for flame retardant modification of polyurethane foam.
Economic, social, and environmental aspects of Positive Energy Districts—A review
Authors: Casamassima Luca , Bottecchia Luigi , Bruck Axel , Kranzl Lukas , Haas Reinhard
Published: June 2022

The concept of Positive Energy Districts (PEDs), introduced in the Strategic Energy Technology Plan, is one of the fundamental approaches for a successful, clean and sustainable urbanization by 2025. According to the European Commission, a PED is a set of buildings where the community controls the energy flows and aims at a net positive energy balance over a year by utilizing renewable energy sources. There are a plethora of concepts similar to PEDs, such as Positive Energy Community, Net Zero Energy Neighborhood, Plus Energy Districts, that create a need to establish a structure that can facilitate the definition, development, and precise identification of PEDs. Thus, this paper aims to fill this research gap by comparing these and other related concepts through a critical literature review based on three pillars composing the triangle of sustainability: economic, social and environmental. By doing this, the paper aims to determine the connections between these similar concepts, homogenize the use of terms and avoid the issue of repetitions, which can help draw lessons learnt from other energy‐savings concepts. This study shows how PEDs and Nearly Zero Energy Communities have similar bases, aims and omissions. They diverge in a few key concepts, which can become learning points for PEDs.

This article is categorized under:

Climate and Environment > Net Zero Planning and Decarbonization

Policy and Economics > Energy Transitions

Human and Social Dimensions > Energy and Climate Justice
Distance sampling surveys: using components of detection and total error to select among approaches
Authors: Schmidt Joshua H. , Thompson William L. , Wilson Tammy L. , Reynolds Joel H.
Published: June 2022

Wildlife population estimators often require formal adjustment for imperfect detection of individuals during surveys. Conventional distance sampling (CDS) and its extensions (mark‐recapture distance sampling [MRDS], temporary emigration distance sampling [TEDS]) are popular approaches for producing unbiased estimators of wildlife abundance. However, despite extensive discussion and development of distance sampling theory in the literature, deciding which of these alternatives is most appropriate for a particular scenario can be confusing. Some of this confusion may stem from an incomplete understanding of how each approach addresses the components of the detection process. Here we describe the proper application of CDS, MRDS, and TEDS approaches and use applied examples to help clarify their differing assumptions with respect to the components of the detection process. To further aid the practitioner, we summarize the differences in a decision tree that can be used to identify cases where a more complex alternative (e.g., MRDS or TEDS) may be appropriate for a given survey application. Although the more complex approaches can account for additional sources of bias, in practical applications one also must consider estimator precision. Therefore, we also review the concept of total estimator error in the context of comparing competing methods for a given application to aid in the selection of the most appropriate distance sampling approach. Finally, we detail how the use of more advanced techniques (i.e., informed priors, open‐population distance sampling models, and integrated modeling approaches) can further reduce total estimator error by leveraging information from existing and ongoing data collection. By synthesizing the existing literature on CDS, MRDS, TEDS and their extensions, in conjunction with the concepts of total estimator error and the components of the detection process, we provide a comprehensive guide that can be used by the practitioner to more efficiently, effectively, and appropriately apply distance sampling in a variety of settings.
Spectroscopy and in‐situ X‐ray diffraction analysis on the corrosion process at the interface Pb/H2SO4 using a low‐cost electrochemical cell
Authors: Espinola Diego O. G. , Fernandes Fabio P. , Basso Rodrigo L. O. , Hönnicke Marcelo G.
Published: June 2022

Simple and low‐cost electrochemical cells for in‐situ X‐ray powder diffraction (XPD) at 8.04 keV (CuKα) and ex‐situ scanning electronic microscope (SEM) with energy dispersive spectroscopy (EDS) on the electrode surfaces are presented. Lead (Pb) and aqueous sulfuric acid (H2SO4) solution (molar concentration of 5.35) were chosen as electrode and electrolyte, respectively. A formation of a crystalline lead sulfate (PbSO4) on the Pb electrode surface with initial precipitation/nucleation at 1 h and a posterior crystal grow (with orthorhombic crystalline structure) on the Pb electrode surface, over the time (from 4 h to 12 weeks) was observed. The result is consistent with the literature, showing the full characterization capability of the proposed cells.
A Variational Deep Synthesis Approach for Perception Validation
Authors: Grau Oliver , Hagn Korbinian , Syed Sha Qutub
Published: June 2022

This chapter introduces a novel data synthesis framework for validation of perception functions based on machine learning to ensure the safety and functionality of these systems, specifically in the context of automated driving. The main contributions are the introduction of a generative, parametric description of three-dimensional scenarios in a validation parameter space, and layered scene generation process to reduce the computational effort. Specifically, we combine a module for probabilistic scene generation, a variation engine for scene parameters, and a more realistic sensor artifacts simulation. The work demonstrates the effectiveness of the framework for the perception of pedestrians in urban environments based on various deep neural networks (DNNs) for semantic segmentation and object detection. Our approach allows a systematic evaluation of a high number of different objects and combined with our variational approach we can effectively simulate and test a wide range of additional conditions as, e.g., various illuminations. We can demonstrate that our generative approach produces a better approximation of the spatial object distribution to real datasets, compared to hand-crafted 3D scenes.
Analysis and Comparison of Datasets by Leveraging Data Distributions in Latent Spaces
Authors: Stage Hanno , Ries Lennart , Langner Jacob , Otten Stefan , Sax Eric
Published: June 2022

Automated driving is widely seen as one of the areas, where key innovations are driven by the application of deep learning. The development of safe and robust deep neural network (DNN) functions requires new validation methods. A core insufficiency of DNNs is the lack of generalization for out-of-distribution datasets. One path to overcome this insufficiency is through the analysis and comparison of the domains of training and test datasets. This is important because otherwise, deep learning cannot advance automated driving. Variational autoencoders (VAEs) are able to extract meaningful encodings from datasets in their latent space. This chapter examines various methods based on these encodings and presents a broad evaluation on different automotive datasets and potential domain shifts, such as weather changes or new locations. The used methods are based on the distance to the nearest neighbors between datasets and leverage several network architectures and metrics. Several experiments with different domain shifts on different datasets are conducted and compared with a reconstruction-based method. The results show that the presented methods can be a promising alternative to the reconstruction error for detecting automotive-relevant domain shifts between different datasets. It is also shown that VAE loss variants that focus on a disentangled latent space can improve the stability of the domain shift detection quality. Best results were achieved with nearest neighbor methods using VAE and JointVAE, a VAE variant with a discrete and a continuous latent space, in combination with a metric based on the well-known z-score, and with the NVAE, a VAE variant with optimizations regarding reconstruction quality, in combination with the deterministic reconstruction error.
Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation
Authors: Hagn Korbinian , Grau Oliver
Published: June 2022

Synthetic, i.e., computer-generated imagery (CGI) data is a key component for training and validating deep-learning-based perceptive functions due to its ability to simulate rare cases, avoidance of privacy issues, and generation of pixel-accurate ground truth data. Today, physical-based rendering (PBR) engines simulate already a wealth of realistic optical effects but are mainly focused on the human perception system. Whereas the perceptive functions require realistic images modeled with sensor artifacts as close as possible toward the sensor, the training data has been recorded. This chapter proposes a way to improve the data synthesis process by application of realistic sensor artifacts. To do this, one has to overcome the domain distance between real-world imagery and the synthetic imagery. Therefore, we propose a measure which captures the generalization distance of two distinct datasets which have been trained on the same model. With this measure the data synthesis pipeline can be improved to produce realistic sensor-simulated images which are closer to the real-world domain. The proposed measure is based on the Wasserstein distance (earth mover’s distance, EMD) over the performance metric mean intersection-over-union (mIoU) on a per-image basis, comparing synthetic and real datasets using deep neural networks (DNNs) for semantic segmentation. This measure is subsequently used to match the characteristic of a real-world camera for the image synthesis pipeline which considers realistic sensor noise and lens artifacts. Comparing the measure with the well-established Fréchet inception distance (FID) on real and artificial datasets demonstrates the ability to interpret the generalization distance which is inherent asymmetric and more informative than just a simple distance measure. Furthermore, we use the metric as an optimization criterion to adapt a synthetic dataset to a real dataset, decreasing the EMD distance between a synthetic and the Cityscapes dataset from 32.67 to 27.48 and increasing the mIoU of our test algorithm () from 40.36 to$$47.63\%$$47.63%.
Chapter 2 Smittestopp for Android and iOS
Authors: Florvaag Per Magne , Kjeldsberg Henrik Aasen , Mitusch Sebastian Kenji
Published: June 2022

Contact tracing is currently a manual and laborious task that requires individuals to recall their interactions with people many days in the past. As a remedy, phones can be used to play a significant role in the response to the COVID-19 pandemic, by easing the burden of healthcare staff. Through novel and sophisticated technology, apps can be used to track infected people, issue quarantine guidelines, and provide the latest news to the public. Along with general public measures, apps can contribute significantly to keeping infection levels low. Generally, digital contract tracing can identify and warn people who may be at risk of being infected because they were in close physical proximity of someone who later tested positive for COVID-19.
Chapter 4 Smittestopp analytics: Analysis of position data
Authors: Thambawita Vajira , Hicks Steven A. , Jaouen Ewan , Halvorsen Pål , Riegler Michael A.
Published: June 2022

Contact tracing applications generally rely on Bluetooth data. This type of data works well to determine whether a contact occurred (smartphones were close to each other) but cannot offer the contextual information GPS data can offer. Did the contact happen on a bus? In a building? And of which type? Are some places recurrent contact locations? By answering such questions, GPS data can help develop more accurate and better-informed contact tracing applications. This chapter describes the ideas and approaches implemented for GPS data within the Smittestopp contact tracing application.We will present the pipeline used and the contribution of GPS data for contextual information, using inferred transport modes and surrounding POIs, showcasing the opportunities in the use of GPS information. Finally,we discuss ethical and privacy considerations, as well as some lessons learned.
Chapter 6 Digital tracing, validation, and reporting
Authors: Elmokashfi Ahmed , Funke Simon , Klock Timo , Kuchta Miroslav , Naumova Valeriya , Uv Julie
Published: June 2022

Manual contact tracing has been a key component in controlling the outbreak of the COVID-19 pandemic. The identification and isolation of close contacts of confirmed cases have successfully interrupted transmission chains and reduced the disease spread. Even though manual contact tracing has been widely used, its practice has shown that it is slow and cannot be scaled up once the epidemic grows beyond the early phase. In this case, digital contact tracing can play a significant role in controlling the pandemic. In this chapter, based on our experience and lessons learned from the Smittestopp project, we discuss the main prerequisites for the efficient implementation and validation of digital contact tracing in a population. Specifically, we discuss how to translate a close contact defined for manual tracing to proximity events discovered by a phone, that is, how to define a meaningful risk score and validate the digital contact tracing. We discuss challenges related to each step and provide solutions to some of them, even though questions still remain.
The scale of new physics from the Higgs couplings to γγ and γZ
Authors: Abu-Ajamieh Fayez
Published: June 2022

Abstract

Measuring the Higgs couplings accurately at colliders is one of the best routes for finding physics Beyond the Standard Model (BSM). If the measured couplings deviate from the SM predictions, then this would give rise to energy-growing processes that violate tree-level unitarity at some energy scale, indicating new physics. In this paper, we extend previous work on unitarity bounds from the Higgs potential and the Higgs couplings to vector bosons and the top quark; to the Higgs couplings to γγ and γZ. We find that while the HL-LHC might be able to find new physics in the γZ sector, the scale of new physics in both sectors is mostly beyond its reach. However, accurate measurements of the leading couplings of the two sectors in the HL-LHC can place stringent limits on both the scale of new physics and on other Higgs couplings that are difficult to measure. In addition, the scale of new physics is mostly within the reach of the 100 TeV collider.
Replica wormholes and holographic entanglement negativity
Authors: Dong Xi , McBride Sean , Weng Wayne W.
Published: June 2022

Abstract

Recent work has shown how to understand the Page curve of an evaporating black hole from replica wormholes. However, more detailed information about the structure of its quantum state is needed to fully understand the dynamics of black hole evaporation. Here we study entanglement negativity, an important measure of quantum entanglement in mixed states, in a couple of toy models of evaporating black holes. We find four phases dominated by different types of geometries: the disconnected, cyclically connected, anti-cyclically connected, and pairwise connected geometries. The last of these geometries are new replica wormholes that break the replica symmetry spontaneously. We also analyze the transitions between these four phases by summing more generic replica geometries using a Schwinger-Dyson equation. In particular, we find enhanced corrections to various negativity measures near the transition between the cyclic and pairwise phase.
Mechanisms of chronic alcohol exposure-induced aggressiveness in cellular model of HCC and recovery after alcohol withdrawal
Authors: Marié Constance , Fouquet Grégory , Courtois Anoïsia , Amrathlal Rabbind Singh , Jankovsky Nicolas , Ouled-Haddou Hakim , Tebbakha Riad , Bouhlal Hicham , Nguyen-Khac Éric , Naassila Mickaël , Marcq Ingrid
Published: June 2022

Alcohol-related liver disease is the most prevalent chronic liver disease worldwide, accounting for 30% of hepatocellular carcinoma (HCC) cases and HCC-specific deaths. However, the knowledge on mechanisms by which alcohol consumption leads to cancer progression and its aggressiveness is limited. Better understanding of the clinical features and the mechanisms of alcohol-induced HCC are of critical importance for prevention and the development of novel treatments. Early stage Huh-7 and advanced SNU449 liver cancer cell lines were subjected to chronic alcohol exposure (CAE), at different doses for 6 months followed by 1-month alcohol withdrawal period. ADH activity and ALDH expression were much lower in SNU449 compared with Huh-7 cells and at the 270 mM dose, CAE decreased cell viability by about 50% and 80%, respectively, in Huh-7 and SNU449 cells but induced mortality only in Huh-7 cells. Thus, Huh-7 may be more vulnerable to ethanol toxicity because of the higher levels of acetaldehyde. CAE induced a dose-dependent increase in cell migration and invasion and also in the expression of cancer stem cells markers (CD133, CD44, CD90). CAE in Huh-7 cells selectively activated ERK1/2 and inhibited GSK3β signaling pathways. Most of the changes induced by CAE were reversed after alcohol withdrawal. Interestingly, we confirmed the increase in CD133 mRNA levels in the tumoral tissue of patients with ethanol-related HCC compared to other HCC etiologies. Our results may explain the benefits observed in epidemiological studies showing a significant increase of overall survival in abstinent compared with non-abstinent patients.
Beurteilung des neuromuskulären Funktionszustands bei Berufssportlern
Authors: Bartels Thomas , Schwesig René , Brehme Kay , Pyschik Martin , Pröger Stefan , Laube Wolfgang , Kurz Eduard
Published: June 2022

In den letzten Jahren wurden bedeutende Fortschritte in den chirurgischen Techniken, der postoperativen Rehabilitation und der Identifizierung von Risikofaktoren für eine zweite Verletzung des vorderen Kreuzbands (VKB) gemacht. Dies führte jedoch nicht zu einer Verringerung des sekundären VKB-Verletzungsrisikos. Die Wiederherstellung der anatomischen (operative Rekonstruktion) und anschließend insbesondere der trainingsbedingten funktionellen Stabilität sollte eine grundlegende Voraussetzung für die Rückkehr zu Pivot-Sportarten nach einer VKB-Verletzung sein. Das VKB hat eine bedeutende Rolle bei der Kniepropriozeption mit der Konsequenz einer eingeschränkten sensomotorischen Regulation im Verletzungsfall. Das Perturbationstraining stellt eine große Herausforderung in der spätrehabilitativen Phase nach VKB-Rekonstruktion dar. Die Diagnostik der Muskelaktivierung im Rehabilitationsverlauf nach VKB-Rekonstruktion hat deshalb eine enorme Bedeutung. Mit der entwickelten Software können Charakteristika der motorischen Einheiten knieführender Muskeln zeitnah objektiviert und somit eine mögliche Überlastung bei hochreaktivem Perturbationstraining im Rahmen der spätrehabilitativen Phase nach VKB-Rekonstruktion vermieden werden.
Contributions of oceanic and continental AAM to interannual variation in ΔLOD with the detection of 2020–2021 La Nina event
Authors: Xu Xue-Qing , Zhou Yong-Hong , Duan Peng-Shuo , Fang Ming , Kong Zhao-Yang , Xu Can-Can , An Xian-Ran
Published: June 2022

As a strong climate element on interannual scales, the El Nino-Southern Oscillation (ENSO) is a major component of global weather and climate change, and it is also closely related to the interannual atmospheric angular momentum (AAM) and length-of-day changes (ΔLOD). Here, we reprocess and compare the interannual variations of AAM, ΔLOD with ENSO indices, with AAM mass and motion terms calculated over land separately from those over the ocean. Three oscillatory components (at ~ 6, ~ 7, ~ 8 years), due to angular momentum changes in Earth's interior, are removed to obtain the interannual ΔLOD solely related to climatic variations. Our results show that the AAM motion term over the ocean contributes the most to interannual ΔLOD, and that the oceanic AAM has larger variability than that over land, especially during the periods of strong ENSO events. After subtracting contributions associated with interior processes, the interannual ΔLOD anomalies corresponding to extreme ENSO events (1982–1983 ~ 0.43, 1997–1998 ~ 0.36, 2015–2016 ~ 0.42 ms) are about half as strong as those found in previous studies (~ 0.91, ~ 0.76, ~ 0.81 ms). Furthermore, we detect an intermediate La Nina event that occurred from August 2020 to May 2021, forcing the interannual ΔLOD to a minimum value of approximately -0.21 ms.
Ethnicity evaluation of ferric pyrophosphate citrate among Asian and Non-Asian populations: a population pharmacokinetics analysis
Authors: Zhang Lingxiao , Gan Liangying , Li Kexin , Xie Panpan , Tan Yan , Wei Gang , Yuan Xiaojuan , Pratt Raymond , Zhou Yongchun , Hui Ai-Min , Fang Yi , Zuo Li , Zheng Qingshan
Published: June 2022

Purpose

To evaluate the potential ethnic differences of ferric pyrophosphate citrate (FPC, Triferic) in healthy subjects and patients with hemodialysis-dependent stage 5 chronic kidney disease (CKD-5HD) and identify covariates that may influence pharmacokinetics (PK) of FPC.

Methods

Data were collected from 2 Asian and 4 non-Asian clinical studies involving healthy subjects and CKD-5HD patients. Three population PK models were developed: M1 for intravenous (IV) administration of FPC in healthy subjects; M2 for dialysate administration of FPC in CKD-5HD patients; M3 for pre-dialyzer administration of FPC in CKD-5HD patients. All the models were fitted to concentration versus time data of FPC using the nonlinear mixed effect approach with the NONMEM® program. All statistical analyses were performed using SAS version 9.4.

Results

In total, 26 Asians and 65 non-Asians were included in the final model analysis database. Forty healthy subjects were administered FPC via intravenous (IV) route and 51 patients with CKD-5HD via dialysate (N = 50) and pre-dialyzer blood circuit administration (N = 51). The PK parameters of FPC IV were similar. The population PK model showed good parameter precision and reliability as shown by model evaluation, and no relevant influence of ethnicity on PK parameters was observed. In healthy subjects, the maximum observed plasma concentration (Cmax) and area under the plasma concentration–time curve (AUC) decreased with increase in lean body mass (LBM) and the average serum total iron at 6 h before the baseline period (Feav), whereas, in both patient populations, Cmax and AUC decreased with increase in LBM and decrease in Febaseline. Other factors such as gender, age, Feav, and ethnicity had no influence on PK exposures in patients. The influence of LBM on PK exposures in patients was smaller than that in healthy subjects (ratio of AUC0-24 for the 5th [68 kg] and 95th [45 kg] patient’s LBM was almost 1). The influence of Feav and LBM on PK exposures was < 50%.

Conclusion

The population pharmacokinetics model successfully described the PK parameters of FPC in healthy subjects and CKD-5HD patients and were comparable between Asian and non-Asian populations.
Coherence resonance in paleoclimatic modeling
Authors: Bosio Aurelien , Salizzoni Pietro , Camporeale Carlo
Published: June 2022

Through a unified mathematical framework, the stochastic behavior of three celebrated low-order lumped models, previously proposed for paleoclimate simulations, is considered. Due to the coherence resonance mechanism, the feedbacks between noise and the dynamical system reproduce the hallmark of the Pleistocene climate, i.e. the 100 ky pulsation, in a range of the model parameters that is unexpectedly wide and far from the original modeling setting. In this way, the issue of arbitrary coefficient tuning of lumped approaches in paleoclimatology can be partially bypassed. A stability analysis of the considered dynamical systems allowed the parameter space to be exploited, in order to separate the deterministic-dominated region from the stochastic-dominated region. Noise intensity is varied and the closeness in the parameter space to Hopf bifurcations and/or bistable conditions is investigated in order to understand what conditions make the models prone to coherence resonance with a 100-ky pulsation, with or without the forcing induced by varying astronomical parameters.
Inflammation of the rectal remnant endangers the outcome of ileal pouch-anal anastomosis: a case–control study
Authors: Frese Jan P. , Gröne Jörn , Lauscher Johannes C. , Kreis Martin E. , Weixler Benjamin , Beyer Katharina , Seifarth Claudia
Published: June 2022

Background

Inflammation of the rectal remnant may affect the postoperative outcome of ileal pouch-anal anastomosis (IPAA) in patients with ulcerative colitis (UC). We aimed to determine the extent of inflammation in the anastomotic area during IPAA and to investigate the impact of proctitis on postoperative complications and long-term outcomes.

Methods

Three hundred thirty-four UC patients with primary IPAA were included in this retrospective case–control study. The histopathologic degree of inflammation in the anastomotic area was graded into three stages of no proctitis (“NOP”), mild to medium proctitis (“MIP”), and severe proctitis (“SEP”). Preoperative risk factors, 30-day morbidity, and follow-up data were assessed. Kaplan–Meier analysis was performed in the event of pouch failure.

Results

The prevalence of proctitis was high (MIP 40.4%, and SEP 42.8%). During follow-up, the incidence of complications was highest among SEP: resulting in re-intervention (n = 40; 28.2%, p = 0.017), pouchitis (n = 36; 25.2%, p < 0.01), and pouch failure (n = 32; 22.4%, p = 0.032). The time interval to pouch failure was 5.0 (4.0–6.9) years among NOP, and 1.2 (0.5–2.3) years in SEP (p = 0.036). ASA 3, pouchitis, and pouch fistula were independent risk factors for pouch failure.

Conclusion

Proctitis at the time of IPAA is common. A high degree of inflammation is associated with poor long-term outcomes, an effect that declines over time. In addition, a higher degree of proctitis leads to earlier pouch failure.
Incidence and risk factors of spinal cord stimulation for persistent or recurrent pain after lumbar spine surgery: a population-based study
Authors: Huttunen Jukka , Fraunberg Mikael von und zu , Ikäheimo Tiina-Mari , Jyrkkänen Henna-Kaisa , Nissen Mette , Leinonen Ville , Salmenkivi Jyrki , Malmivaara Antti , Sirola Joonas , Sund Reijo
Published: June 2022

Purpose

This study aims to elucidate the incidence of and independent risk factors for spinal cord stimulator implantations for patients who underwent lumbar spine surgery.

Methods

The PERFormance, Effectiveness, and Cost of Treatment (PERFECT) episodes database, which was established for selected diseases and procedures in Finland, includes all patients who underwent lumbar spine surgery for degenerative spine conditions or spinal cord stimulation (SCS) in Finland from 1986 to 2018. The data on age, sex, hospital diagnoses, surgical procedures, and causes of death were imported from the Finnish national registers into the PERFECT database.

Results

Between 1986 and 2018, 157,824 patients had their first lumbar spine procedure and for 1769 (1.1%) of them, a subsequent SCS procedure was observed during the follow-up. The cumulative incidence of SCS for persistent or recurrent pain after lumbar disk herniation, spinal stenosis, degenerative disk disease, and spondylolysis and spondylolisthesis surgery at 15 years was 1.2%, 1.0%, 2.7%, and 2.6% respectively. At 15 years, the cumulative incidence of SCS for persistent or recurrent pain after lumbar spine surgery after five or more lumbar spinal operations was 11.9%.

Conclusion

Repeated surgery was the most prominent significant risk factor for SCS for persistent or recurrent pain after lumbar spine surgery. The risk of SCS for persistent or recurrent pain after lumbar spine surgery increases significantly along with the number of lumbar spine procedures. When considering repeated lumbar spine surgery, careful evaluation of treatment options should take place to ensure good patient outcomes.
A Micro–macro Damage Mechanics-based Model for Fatigue Damage and Life Prediction of Fiber-reinforced Composite Laminates
Authors: Qi Wenxuan , Yao Weixing , Shen Haojie
Published: June 2022

A multidirectional damage model was proposed to predict fatigue damage evolution and final failure of composite laminates in this paper. A damage characterization model for composite laminates was established to characterize the influence of three main damage modes on the damaged mechanical behavior of composite laminates at micro–macro level. The damage evolution model was also established based on damage mechanics to predict the evolution of the three damage modes and stiffness degradation of composite laminates by means of damage characterization model. Then, a relationship between residual stiffness and residual strength was introduced, from which the residual strength could be obtained according to the predicted residual stiffness. When the residual strength is calculated to decrease to the maximum applied stress of fatigue loading after several cycles, the composite laminate was assumed to fail, and accordingly the fatigue life could be obtained. In order to verify the model, the predicted stiffness degradation and fatigue life of two cross-ply laminates under fatigue loadings with different stress levels were compared to experimental results. The standard derivation of stiffness degradation and average errors of fatigue between prediction results and experimental results were less than 0.1 and 8.26%, respectively, indicating the effectiveness and reliability of proposed model.
Understanding the application of handwritten text recognition technology in heritage contexts: a systematic review of Transkribus in published research
Authors: Nockels Joe , Gooding Paul , Ames Sarah , Terras Melissa
Published: June 2022

Handwritten Text Recognition (HTR) technology is now a mature machine learning tool, becoming integrated in the digitisation processes of libraries and archives, speeding up the transcription of primary sources and facilitating full text searching and analysis of historic texts at scale. However, research into how HTR is changing our information environment is scant. This paper presents a systematic literature review regarding how researchers are using one particular HTR platform, Transkribus, to indicate the domains where HTR is applied, the approach taken, and how the technology is understood. 381 papers from 2015 to 2020 were gathered from Google Scholar, Scopus, and Web of Science, then grouped and coded into categories using quantitative and qualitative approaches. Published research that mentions Transkribus is international and rapidly growing. Transkribus features primarily in archival and library science publications, while a long tail of broad and eclectic disciplines, including history, computer science, citizen science, law and education, demonstrate the wider applicability of the tool. The most common paper categories were humanities applications (67%), technological (25%), users (5%) and tutorials (3%). This paper presents the first overarching review of HTR as featured in published research, while also elucidating how HTR is affecting the information environment.
Data mining to detect variables associated with the occupational aspirations of Spanish 15-year-old students
Authors: Expósito-Casas Eva , González-Benito Ana , López-Martín Esther
Published: June 2022

The purpose of this work is to identify contextual variables that help to explain the occupational aspirations of Spanish 15-year-old students. This is done by performing a secondary analysis of the PISA2018 test. Data have been analysed using decision trees introducing the students’ expected occupational status as a dependent variable (DV), and the other items and indicators considered as predictors. The results show that the variables that contributed most to explaining DV were grade repetition, mastery goal orientation, enjoying reading and self-concept of reading. The findings of this exploratory work can be used as a basis for further studies aimed at establishing causal relationships between these variables and students' occupational aspirations.
Multi-band PCA based ear recognition technique
Authors: Zarachoff Matthew Martin , Sheikh-Akbari Akbar , Monekosso Dorothy
Published: June 2022

Principal Component Analysis (PCA) has been successfully applied to many applications, including ear recognition. This paper presents a Two Dimensional Multi-Band PCA (2D-MBPCA) method, inspired by PCA based techniques for multispectral and hyperspectral images, which have demonstrated significantly higher performance to that of standard PCA. The proposed method divides the input image into a number of images based on the intensity of the pixels. Three different methods are used to calculate the pixel intensity boundaries, called: equal size, histogram, and greedy hill climbing based techniques. Conventional PCA is then applied on the resulting images to extract their eigenvectors, which are used as features. The optimal number of bands was determined using the intersection of number of features and total eigenvector energy. Experimental results on two benchmark ear image datasets demonstrate that the proposed 2D-MBPCA technique significantly outperforms single image PCA by up to 56.41% and the eigenfaces technique by up to 29.62% with respect to matching accuracy on images from two benchmark datasets. Furthermore, it gives very competitive results to those of learning based techniques at a fraction of their computational cost and without a need for training.
A novel image dataset for source camera identification and image based recognition systems
Authors: Bruno Andrea , Capasso Paola , Cattaneo Giuseppe , Petrillo Umberto Ferraro , Improta Riccardo
Published: June 2022

Multimodal emotion recognition has attracted a great deal of attention in recent years, with new interesting applications now being considered. One promising application is in the digital image forensics fields where, for example, it gives the possibility to automatically highlight subjects that are in pain, in digital images under examination, by analyzing their facial expressions. However, finding an image that represents a possible crime leaves the problem of identifying the device used to take the image open. Such a problem has been addressed by Source Camera Identification algorithms (SCI, for short). These algorithms analyze some features hidden in a target image to find traces left by the sensor that captured the image. A particularly challenging case is when the candidate source cameras for an image under investigation are of the same manufacturer and model. A fair and universal assessment of these algorithms is only possible if standard datasets are used for their benchmarking. However, our comprehensive analysis has shown that the majority of the datasets proposed so far contain a collection of images taken with different types of cameras, mostly smartphones. We fill this gap by presenting UNISA2020, a novel image dataset that contains a large collection of real-world images taken with multiple conventional digital cameras of the same type. The images in our dataset have been assembled so as to avoid artifacts that could negatively affect the identification process. To validate our dataset, we also performed a comparative experimental analysis to investigate the performance of an SCI reference algorithm when running on our dataset as well as on other SCI standard datasets.
Plasmonic photonic crystal fiber sensor for optical partial discharge detection
Authors: Saber Asmaa M. , Hameed Mohamed Farhat O. , El-Azab Jala , Amer Rabah Y. , Ismail Tawfik , Obayya S. S. A.
Published: June 2022

Detection of partial discharge (PD) is vital to reassure the operation reliability of high voltage (HV) electrical power systems. Optical detection methods have been developed in recent years due to their high sensitivity and immunity to electromagnetic interference. In this work, D-shaped photonic crystal fiber sensor based on surface plasmon resonance is reported for optical PD detection in HV equipment. A photochromic spirooxazine dye-doped silica sol–gel is utilized for sensing the UV radiation emitted from the PD. The sol–gel refractive index depends on the radiation intensity which affects the resonance wavelength where coupling occurs between the core and surface plasmon modes. The sensing performance is demonstrated with the help of full vectorial finite element method (FVFEM). The geometrical parameters are studied to maximize the sensor sensitivity where high sensitivity of 2.4 nm/mW.cm−2is achieved. To the best of our knowledge, it is the first time to use a simple design of plasmonic PCF for optical PD detection.
Experimental Study on the Expended Energy on Structural Degradation of Lubricating Greases
Authors: Ahme Leif , Kuhn Erik , Delgado Canto Miguel Ángel
Published: June 2022

One of the keystones of tribological studies is the energetical approach to the lubrication process. In the particular case of lubricating greases, part of the lubrication process's energy dissipates due to a shear-induced structural rearrangement of the 3D network of the thickening agent dispersed in the base oil. This fact confers them a particular consistency, mechanical stability, rheological and tribological behaviour. In this research work, we investigate the mechanical structural degradation induced by shear stress applied in rheological tests (rotational and oscillation mode) and the influence of thickener (type and composition) and base oil on both the degradation process and the expended mechanical energies. For this purpose, lithium, calcium and polyurea-based greases of NLGI grade 2 were used. These greases have been manufactured with a different base oil (mineral, synthetic and vegetable oils) and kinematic viscosity of 48 or 240 mm2/s. Some biogenic greases were also included in this research. The optical microscopy analysis revealed thickener particles-based agglomerates with different shapes and sizes that reduced notably, if not almost completely destroyed, after stress. Due to the thickener particles-based agglomerates distribution, significant differences in the shear-induced frictional energy inside the bulk grease during the shear process were detected. The size of agglomerates depended on both the thickener content and the base oil viscosity and not the type of base oil.
Extraction and separation feasibility of cerium (III) and lanthanum (III) from aqueous solution using modified graphite adsorbent
Authors: Youssef Maha A. , Sami Nesreen M. , Hassan Hisham S.
Published: June 2022

Graphite (GR) and graphite/alginate (GRA) composite were synthesized utilizing the thermal annealing technique and used as a new adsorbent material for the selective separation and removal of La(III) and Ce(III) from aqueous solutions. Fourier transform infrared (FTIR) spectroscopy, thermal analysis (DTA, TGA), X-ray diffraction (XRD), surface area, porosity, and scanning electron microscope (SEM) were also used to characterize the generated material. Distinct experiments were performed to test the ability of the GRA to La(III) and Ce(III) removal, which include the effect of pH, shaken time, initial concentration of La(III), and Ce(III) at different temperatures range. After 20 min, both ions have reached equilibrium. The pseudo second-order kinetic model was chosen as one which best fits the experimental evidence and better reflects the chemical sorption process. Adsorption isotherm was studied using the Langmuir, Freundlich, and D-R models. The Langmuir model was used to better fit the results obtained. At 25 °C, Ce(III) and La(III) have maximum monolayer capacities of 200 and 83.3 mg/g, respectively. The sorption was endothermic reaction and spontaneous, as illustrated by the data of thermodynamics studies. GRA has the ability to be used as a novel lanthanide adsorbent material, especially for selective separation between Ce(III) and La(III).
Connecting the Dots in Self-Supervised Learning: A Brief Survey for Beginners
Authors: Fang Peng-Fei , Li Xian , Yan Yang , Zhang Shuai , Kang Qi-Yue , Li Xiao-Fei , Lan Zhen-Zhong
Published: June 2022

The artificial intelligence (AI) community has recently made tremendous progress in developing self-supervised learning (SSL) algorithms that can learn high-quality data representations from massive amounts of unlabeled data. These methods brought great results even to the fields outside of AI. Due to the joint efforts of researchers in various areas, new SSL methods come out daily. However, such a sheer number of publications make it difficult for beginners to see clearly how the subject progresses. This survey bridges this gap by carefully selecting a small portion of papers that we believe are milestones or essential work. We see these researches as the “dots” of SSL and connect them through how they evolve. Hopefully, by viewing the connections of these dots, readers will have a high-level picture of the development of SSL across multiple disciplines including natural language processing, computer vision, graph learning, audio processing, and protein learning.