bluuebunny/crossref_metadata_2025_split huggingface dataset:
Denoising method of machine tool vibration signal based on variational mode decomposition and Whale-Tabu optimization algorithm
Authors: Fang Chengzhi , Chen Yushen , Deng Xiaolei , Lin Xiaoliang , Han Yue , Zheng Junjian 
Published: January 2023

AbstractThe noise from other sources is inevitably mixed in the vibration information of CNC machine tools obtained using the sensors. In this work, a de-noising method based on joint analysis is proposed. The variational mode decomposition (VMD), correlation analysis (CA), and wavelet threshold (WT) denoising are used to denoise the original signal. First, VMD decomposes noisy signals into multiple intrinsic mode functions (IMFs). The penalty factor and decomposition level of VMD parameters are selected by the optimization algorithm by combining the whale optimization algorithm (WOA) and tabu search (TS). The minimum permutation entropy of IMF is used as the fitness function of the proposed fusion algorithm. Then, the IMF is divided into three categories by using the cross-correlation number. They include the pure components, signals containing noise, and complete noise components. Then, the WT method is used to further denoise the signals, and signal reconstruction is performed with the pure component to obtain the denoised signal. This joint analysis denoising method is named TS-WOA-VMD-CA-WT. The simulation results show that the fusion optimization algorithm proposed in this work has better performance as compared to the single optimization algorithm. It performs effectively when applied to the actual machine tool vibration signal denoising. Therefore, the proposed TS-WOA-VMD-CA-WT method is superior to other existing denoising techniques and has good generality, which is expected to be popularized and applied more widely.
Resistin-like beta reduction is associated to low survival rate and is downregulated by adjuvant therapy in colorectal cancer patients
Authors: Di Rosa Michelino , Di Cataldo Antonio , Broggi Giuseppe , Caltabiano Rosario , Tibullo Daniele , Castrogiovanni Paola , Imbesi Rosa , Lanteri Raffaele , Salomone Federico , Raciti Giuseppina , Li Volti Giovanni 
Published: January 2023

AbstractColorectal Cancer (CRC) is one of the most common cancers accounting for 1.8 million new cases worldwide every year. Therefore, the identification of new potential therapeutic targets represents a continuous challenge to improve survival and quality of CRC patient’s life. We performed a microarray analysis dataset consisting of colon biopsies of healthy subjects (HS) and CRC patients. These results were further confirmed in a clinical setting evaluating a series of CRC patients to assess the expression of Resistin-Like Beta (RETNLB) and to correlate it with their clinical data. Our results showed a significant reduction of RETNLB expression in CRC biopsies compared to the HS mucosa. Furthermore, such reduction was significantly associated with the TNM grade and patients’ age. Furthermore, a significantly positive correlation was found within mutated subjects for KRAS, TP53, and BRAF. In particular, patients with poor prognosis at 5 years exhibited RETNLB lower levels. In-silico analysis data were confirmed by histochemical analysis in a series of CRC patients recruited by our group. The results obtained provided that RETNLB low levels are associated with an unfavorable prognosis in CRC patients and its expression is also dependent on adjuvant therapy. Further studies are warranted in order to evaluate the molecular mechanisms underlying the role of RETNLB in CRC progression.
Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts
Authors: Fuentes Alejandra M. , Narayan Apurva , Milligan Kirsty , Lum Julian J. , Brolo Alex G. , Andrews Jeffrey L. , Jirasek Andrew 
Published: January 2023

AbstractTumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.
Carbon responsibility allocation method based on complex structure carbon emission flow theory
Authors: Wang Wenyong , Huo Qunhai , Deng Huawei , Yin Jingyuan , Wei Tongzhen 
Published: January 2023

AbstractA carbon responsibility allocation method based on the complex structure carbon emission flow theory is proposed to address the problem posed by the unclear carbon responsibility allocation of each link in the low-carbon development of electric power. First, the calculation method, distribution characteristics, and mechanism of carbon emission flow were analyzed. The “carbon potential of complex structure” concept was introduced to track “carbon trajectory” and “green trajectory” by harnessing the ability of complex structures to retain two-dimensional information. Subsequently, the carbon responsibility allocation methods for network loss and users’ electricity consumption behavior were developed to realize the accurate carbon responsibility allocation of each system link. Finally, the effectiveness and advancement of the proposed carbon responsibility allocation method were verified using the improved IEEE 6-bus and 30-bus test systems. The application of the proposed complex structure carbon potential in the carbon emission flow theory expands the research dimension of electric power carbon emission for low-carbon development from the “carbon perspective,” provides a novel optimization space for the operation of the distribution network and realizes the carbon emission flow theory, which serves as a bridge from calculation evaluation to optimization decision.
Mapping amorphous SiO2 in Devonian shales and the possible link to marine productivity during incipient forest diversification
Authors: Corlett H. , Feng J. , Playter T. , Rivard B. 
Published: January 2023

AbstractSilica cycling in the world’s oceans is not straightforward to evaluate on a geological time scale. With the rise of radiolarians and sponges from the early Cambrian onward, silica can have two depositional origins, continental weathering, and biogenic silica. It is critical to have a reliable method of differentiating amorphous silica and crystalline silica to truly understand biogeochemical and inorganic silica cycling. In this study, opal-A is mapped across the Western Canada Sedimentary Basin in the Late Devonian Duvernay Formation shales using longwave hyperspectral imaging alongside geochemical proxies that differentiate between crystalline and amorphous SiO2, during the expansion of the world’s early forests. Signaled by several carbon isotope excursions in the Frasnian, the punctata Event corresponds to the expansion of forests when vascular land plants develop seeds and deeper root networks, likely resulting in increased pedogenesis. Nutrients from thicker soil horizons entering the marine realm are linked to higher levels of primary productivity in oceans and subsequent oxygen starvation in deeper waters at this time. The results of this study reveal, for the first time, the spatial distribution of amorphous SiO2 across a sedimentary basin during this major shift in the terrestrial realm when forests expand and develop deeper root networks.
MHCII expression on gut macrophages supports T cell homeostasis and is regulated by microbiota and ontogeny
Authors: Guillaume Joël , Leufgen Andrea , Hager Fabian T. , Pabst Oliver , Cerovic Vuk 
Published: January 2023

AbstractMacrophages are traditionally considered antigen-presenting cells. However, their ability to present antigen and the factors regulating macrophage MHCII expression are poorly understood. Here, we demonstrate that MHCII expression on murine intestinal macrophages is differentially controlled by their residence in the small intestine (SI) or the colon, their ontogeny and the gut microbiota. Monocyte-derived macrophages are uniformly MHCIIhi, independently of the tissue of residence, microbial status or the age of the mouse, suggesting a common monocyte differentiation pathway. In contrast, MHCII expression on long-lived, prenatally-derived Tim4+ macrophages is low after birth but significantly increases at weaning in both SI and colon. Furthermore, MHCII expression on colonic Tim4+, but not monocyte-derived macrophages, is dependent on recognition of microbial stimuli, as MHCII expression is significantly downregulated in germ-free, antibiotic-treated and MyD88 deficient mice. To address the function of MHCII presentation by intestinal macrophages we established two models of macrophage-specific MHCII deficiency. We observed a significant reduction in the overall frequency and number of tissue-resident, but not newly arrived, SI CD4+ T cells in the absence of macrophage-expressed MHCII. Our data suggest that macrophage MHCII provides signals regulating gut CD4+ T cell maintenance with different requirements in the SI and colon.
Monocarboxylate transporter 4 involves in energy metabolism and drug sensitivity in hypoxia
Authors: Yamaguchi Atsushi , Mukai Yuto , Sakuma Tomoya , Narumi Katsuya , Furugen Ayako , Yamada Yuma , Kobayashi Masaki 
Published: January 2023

AbstractMetabolic reprogramming of cancer cells is a potential target for cancer therapy. It is also known that a hypoxic environment, one of the tumor microenvironments, can alter the energy metabolism from oxidative phosphorylation to glycolysis. However, the relationship between hypoxia and drug sensitivity, which targets energy metabolism, is not well known. In this study, A549 cells, a cell line derived from lung adenocarcinoma, were evaluated under normoxia and hypoxia for the sensitivity of reagents targeting oxidative phosphorylation (metformin) and glycolysis (α-cyano-4-hydroxycinnamic acid [CHC]). The results showed that a hypoxic environment increased the expression levels of monocarboxylate transporter (MCT) 4 and hypoxia-induced factor-1α (HIF-1α), whereas MCT1 and MCT2 expression did not vary between normoxia and hypoxia. Furthermore, the evaluation of the ATP production ratio indicated that glycolysis was enhanced under hypoxic conditions. It was then found that the sensitivity to metformin decreased while that to CHC increased under hypoxia. To elucidate this mechanism, MCT4 and HIF-1α were knocked down and the expression level of MCT4 was significantly decreased under both conditions. In contrast, the expression of HIF-1α was decreased by HIF-1α knockdown and increased by MCT4 knockdown. In addition, changes in metformin and CHC sensitivity under hypoxia were eliminated by the knockdown of MCT4 and HIF-1α, suggesting that MCT4 is involved in the phenomenon described above. In conclusion, it was shown that the sensitivity of reagents targeting energy metabolism is dependent on their microenvironment. As MCT4 is involved in some of these mechanisms, we hypothesized that MCT4 could be an important target molecule for cancer therapy.
An innovative transactive energy architecture for community microgrids in modern multi-carrier energy networks: a Chicago case study
Authors: Daneshvar Mohammadreza , Mohammadi-Ivatloo Behnam , Zare Kazem 
Published: January 2023

AbstractAs the technology of multi-energy carbon-free systems is strikingly developed, renewable-based multi-vector energy integration has become a prevalent trend in the decarbonization procedure of multi-carrier energy networks (MCENs). This paper proposes a fair transactive energy model for structuring an innovative local multi-energy trading market to allow multi-carrier multi-microgrids (MCMGs) with 100% renewable energy sources (RESs) in Chicago for free energy exchange aiming to balance energy in the renewable-dominant environment. Indeed, the main goal of the proposed model is to facilitate the modernization of future MCENs that are targeted to be equipped with 100% RESs and require a holistic model engaged with innovative technologies for the realization. To this end, the transactive energy architecture is designed for techno-environmental-economic assessing hybrid MCMGs to increase their flexibility in unbroken energy serving, decreasing their dependency on the main grid, and improving their economic benefits by considering their contribution level in energy interactions. To effectively model uncertainties of MCENs with 100% RESs, the novel hybrid technique is proposed that considers various stochastic changes of uncertain parameters to achieve confident results. The results highlighted the capability of the proposed model in effectively utilizing fully produced clean energy as well as continuously multi-energy serving of MCMGs in the presence of 100% RESs. Moreover, MCMGs reached techno-environmental-economic benefits by operating under the proposed transactive energy-based model, in which the technical, environmental, and economic goals are respectively realized by considering all constraints of MCENs, producing 100% clean energy by RESs, and reducing the total energy cost from $1,274,742.55 in the based model to $1,159,235.89 in the proposed one.
On Markovianity and classicality in multilevel spin–boson models
Authors: Chruściński Dariusz , Hesabi Samaneh , Lonigro Davide 
Published: January 2023

AbstractWe provide a detailed discussion about the unitary and reduced evolution induced by family of Hamiltonian models describing a multilevel system, with a ground state and a possibly multilevel excited sector, coupled to a multimode boson field via a rotating-wave interaction. We prove explicitly that the system, in the limit in which the coupling is flat with respect to the boson frequencies, is Markovian under sharp measurements in arbitrary bases; we also find necessary and sufficient conditions under which the process is classical, i.e. its family of multitime joint probability distributions satisfies the Kolmogorov consistency condition, and may thus be equivalently obtained by a classical stochastic process.
In vitro antioxidant and anti-inflammatory properties of Artocarpus altilis (Parkinson) Fosberg (seedless breadfruit) fruit pulp protein hydrolysates
Authors: Dada Sodiq Oluwaseun , Ehie Great Chimsom , Osukoya Olukemi Adetutu , Anadozie Scholastica Onyebuchi , Adewale Olusola Bolaji , Kuku Adenike 
Published: January 2023

AbstractProtein hydrolysates from dietary sources possess many physiological and biological properties. Artocarpus altilis is an evergreen multipurpose plant with many benefits. Therefore, this study evaluates in vitro antioxidant and anti-inflammatory properties of A. altilis protein hydrolysates. Protein was isolated from A. altilis and hydrolysed with pepsin and trypsin separately using different enzyme: substrate ratios (1:8, 1:16, 1:32). Antioxidant properties investigated included Fe2+-chelating, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical and hydrogen peroxide radical scavenging activities. Anti-inflammatory activities were determined using effects on hypotonic solution-induced cell lysis on red blood cell membrane stabilisation and heat-induced protein denaturation. The degree of hydrolysis of trypsin hydrolysate increased with increasing enzyme–substrate ratio, while pepsin hydrolysate decreased as the enzyme–substrate ratio increased. The dominant amino acids in A. altilis protein and hydrolysates were glutamate, aspartate and leucine. Protein hydrolysates obtained from pepsin and trypsin digestion had DPPH scavenging abilities of 43.0 ± 0.01% and 22.2 ± 0.01%, respectively. However, trypsin-hydrolysed protein had a high Fe2+-chelating ability, while pepsin-hydrolysed protein had high hydrogen peroxide scavenging ability. Trypsin-hydrolysed protein showed good membrane stability and inhibition of protein denaturation. The results indicated that A. altilis protein hydrolysates possess significant antioxidant and anti-inflammatory effects and can further lend support to food industries as functional foods.
A regressive machine-learning approach to the non-linear complex FAST model for hybrid floating offshore wind turbines with integrated oscillating water columns
Authors: Ahmad Irfan , M’zoughi Fares , Aboutalebi Payam , Garrido Izaskun , Garrido Aitor J. 
Published: January 2023

AbstractOffshore wind energy is getting increasing attention as a clean alternative to the currently scarce fossil fuels mainly used in Europe’s electricity supply. The further development and implementation of this kind of technology will help fighting global warming, allowing a more sustainable and decarbonized power generation. In this sense, the integration of Floating Offshore Wind Turbines (FOWTs) with Oscillating Water Columns (OWCs) devices arise as a promising solution for hybrid renewable energy production. In these systems, OWC modules are employed not only for wave energy generation but also for FOWTs stabilization and cost-efficiency. Nevertheless, analyzing and understanding the aero-hydro-servo-elastic floating structure control performance composes an intricate and challenging task. Even more, given the dynamical complexity increase that involves the incorporation of OWCs within the FOWT platform. In this regard, although some time and frequency domain models have been developed, they are complex, computationally inefficient and not suitable for neither real-time nor feedback control. In this context, this work presents a novel control-oriented regressive model for hybrid FOWT-OWCs platforms. The main objective is to take advantage of the predictive and forecasting capabilities of the deep-layered artificial neural networks (ANNs), jointly with their computational simplicity, to develop a feasible control-oriented and lightweight model compared to the aforementioned complex dynamical models. In order to achieve this objective, a deep-layered ANN model has been designed and trained to match the hybrid platform’s structural performance. Then, the obtained scheme has been benchmarked against standard Multisurf-Wamit-FAST 5MW FOWT output data for different challenging scenarios in order to validate the model. The results demonstrate the adequate performance and accuracy of the proposed ANN control-oriented model, providing a great alternative for complex non-linear models traditionally used and allowing the implementation of advanced control schemes in a computationally convenient, straightforward, and easy way.
Hunting behavior of a solitary sailfish Istiophorus platypterus and estimated energy gain after prey capture
Authors: Logan Ryan K. , Luongo Sarah M. , Vaudo Jeremy J. , Wetherbee Bradley M. , Shivji Mahmood S. 
Published: January 2023

AbstractForaging behavior and interaction with prey is an integral component of the ecological niche of predators but is inherently difficult to observe for highly mobile animals in the marine environment. Billfishes have been described as energy speculators, expending a large amount of energy foraging, expecting to offset high costs with periodic high energetic gain. Surface-based group feeding of sailfish,Istiophorus platypterus,is commonly observed, yet sailfish are believed to be largely solitary roaming predators with high metabolic requirements, suggesting that individual foraging also represents a major component of predator–prey interactions. Here, we use biologging data and video to examine daily activity levels and foraging behavior, estimate metabolic costs, and document a solitary predation event for a 40 kg sailfish. We estimate a median active metabolic rate of 218.9 ± 70.5 mgO2kg−1 h−1which increased to 518.8 ± 586.3 mgO2kg−1 h−1during prey pursuit. Assuming a successful predation, we estimate a daily net energy gain of 2.4 MJ (5.1 MJ acquired, 2.7 MJ expended), supporting the energy speculator model. While group hunting may be a common activity used by sailfish to acquire energy, our calculations indicate that opportunistic individual foraging events offer a net energy return that contributes to the fitness of these highly mobile predators.
African coastal camera network efforts at monitoring ocean, climate, and human impacts
Authors: Abessolo G. O. , Almar R. , Angnuureng D. B. , Bonou F. , Sohou Z. , Camara I. , Diouf A. , Alory G. , Onguéné R. , Mama A. C. , Cissé C. O. T. , Sy B. A. , Sakho I. , Djakouré S. , Yao S. , Tano A. R. , Bergsma E. W. J. , Dada O. A. 
Published: January 2023

AbstractRegular and long-term monitoring of coastal areas is a prerequisite to avoiding or mitigating the impacts of climate and human-driven hazards. In Africa, where populations and infrastructures are particularly exposed to risk, there is an urgent need to establish coastal monitoring, as observations are generally scarce. Measurement campaigns and very high-resolution satellite imagery are costly, while freely available satellite observations have temporal and spatial resolutions that are not suited to capture the event scale. To address the gap, a network of low-cost, multi-variable, shore-based video camera systems has been installed along the African coasts. Here, we present this network and its principle of sharing data, methods, and results obtained, building toward the implementation of a common integrated coastal management policy between countries. Further, we list new contributions to the understanding of still poorly documented African beaches’ evolution, waves, and sea level impacts. This network is a solid platform for the development of inter-disciplinary observations for resources and ecology (such as fisheries, and sargassum landing), erosion and flooding, early warning systems during extreme events, and science-based coastal infrastructure management for sustainable future coasts.