bluuebunny/crossref_metadata_2025 huggingface dataset:
Applying interpretable machine learning algorithms to predict risk factors for permanent stoma in patients after TME
Authors: Liu Yuan , Zhao Songyun , Du Wenyi , Tian Zhiqiang , Chi Hao , Chao Cheng , Shen Wei
Published: March 2023

Objective

The purpose of this study was to develop a machine learning model to identify preoperative and intraoperative high-risk factors and to predict the occurrence of permanent stoma in patients after total mesorectal excision (TME).

Methods

A total of 1,163 patients with rectal cancer were included in the study, including 142 patients with permanent stoma. We collected 24 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination characteristics, type of surgery, and intraoperative information. Four machine learning algorithms including extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM) and k-nearest neighbor algorithm (KNN) were applied to construct the model and evaluate the model using k-fold cross validation method, ROC curve, calibration curve, decision curve analysis (DCA) and external validation.

Results

The XGBoost algorithm showed the best performance among the four prediction models. The ROC curve results showed that XGBoost had a high predictive accuracy with an AUC value of 0.987 in the training set and 0.963 in the validation set. The k-fold cross-validation method was used for internal validation, and the XGBoost model was stable. The calibration curves showed high predictive power of the XGBoost model. DCA curves showed higher benefit rates for patients who received interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.89, indicating that the XGBoost prediction model has good extrapolation.

Conclusion

The prediction model for permanent stoma in patients with rectal cancer derived from the XGBoost machine learning algorithm in this study has high prediction accuracy and clinical utility.
Exoscope and operative microscope for training in microneurosurgery: A laboratory investigation on a model of cranial approach
Authors: Calloni Tommaso , Antolini Laura , Roumy Louis-Georges , Nicolosi Federico , Carrabba Giorgio G. , Di Cristofori Andrea , Fontanella Marco M. , Giussani Carlo G.
Published: March 2023

Objective

To evaluate the viability of exoscopes in the context of neurosurgical education and compare the use of a 4k3D exoscope to a traditional operative microscope in the execution of a task of anatomic structure identification on a model of cranial approach.

Material and methods

A cohort of volunteer residents performed a task of anatomical structure identification with both devices three times across an experimental period of 2 months. We timed the residents’ performances, and the times achieved were analyzed. The volunteers answered two questionnaires concerning their opinions of the two devices.

Results

Across tries, execution speed improved for the whole cohort. When using the exoscopes, residents were quicker to identify a single anatomical structure starting from outside the surgical field when deep structures were included in the pool. In all other settings, the two devices did not differ in a statistically significant manner. The volunteers described the exoscope as superior to the microscope in all the aspects the questionnaires inquired about, besides the depth of field perception, which was felt to be better with the microscope. Volunteers furthermore showed overwhelming support for training on different devices and with models of surgical approaches.

Conclusion

The exoscope appeared to be non-inferior to the microscope in the execution of a task of timed identification of anatomical structures on a model of cranial approach carried out by our cohort of residents. In the questionnaires, the residents reported the exoscope to be superior to the microscope in eight of nine investigated domains. Further studies are needed to investigate the use of the exoscope in learning of microsurgical skills.
Muscle mineral profile of water buffaloes (Bubalus bubalis) reared in different production systems of the Brazilian Eastern Amazon
Authors: Rodrigues Laurena Silva , Silva Jamile Andréa Rodrigues da , Lourenço-Júnior José de Brito , Silva André Guimarães Maciel e , Almeida André Martinho de , Mourato Miguel Pedro , Castro Vinícius Costa Gomes de , Bezerra Andréia Santana , Silva Welligton Conceição da , Prates José António Mestre
Published: March 2023

Healthy food must have an adequate balance of macroelements, such as calcium or phosphorus and, microelements, such as iron, copper. This study aimed to evaluate the influence of three extensive systems, during the dry and rainy seasons, and an intensive (feedlot) system in the Eastern Amazon, on the muscle mineral profile of water buffaloes. In total, 12 male buffaloes, aged between 24 and 36 months, slaughtered in commercial slaughterhouses, were used in each of the systems considered: Marajó island, Santarém, Nova Timboteua, and a feedlot. Approximately 5 g of muscle was collected, stored, and frozen, until freeze-dried. The samples were analyzed for the mineral profile using inductively coupled plasma-optical emission spectrometry (ICP-OES). There were significant differences (P < 0.05) for concentrations of sodium (Na), magnesium (Mg), phosphorus (P), sulfur (S), copper (Cu), zinc (Zn), and iron (Fe). Extensive and intensive systems showed significant differences (P < 0.05) for Na, Ca, S, Cu, and Fe concentrations. The season also influenced (P < 0.05) K, Ca, P, S, Zn, and Fe concentrations. The location and season of the year had a significant interaction (P < 0.05) for K, Mg, P, Zn, and Fe concentrations. The study showed that the different Amazonian production systems and the year season influenced the levels of minerals present in buffalo muscle. The values obtained were, in general, higher in extensive production systems, and Marajó Island stood out with higher mineral values in the dry season. Therefore, the meat from animals reared in these systems is a good mineral source for daily human needs.
Selected biomarkers of oxidative stress in healthy Beagle dogs: A preliminary study
Authors: Porato Mathilde , Noël Stéphanie , Pincemail Joël , Albert Adelin , Cheramy-Bien Jean-Paul , Le Goff Caroline , Hamaide Annick
Published: March 2023

Introduction

While oxidative stress has been studied in pathologic conditions in dogs, data in presumably healthy dogs and standardized protocols are lacking. This work purposed to bridge the gap by presenting provisional physiological ranges for oxidative stress biomarkers in a group of Beagle dogs.

Methods

Based on our long-standing clinical expertise in the field of oxidative stress, nine plasma biomarkers of oxidative stress were evaluated for their concentrations (mean ± SD) in 14 healthy adult Beagle dogs.

Results

Selected biomarkers were: vitamins C (7.90 ± 1.36 μg/mL) and E (34.1 ± 6.63 μg/mL), zinc (0.80 ± 0.17 mg/L), copper (0.54 ± 0.048 mg/L), selenium (256 ± 25.7 μg/L), total and oxidized glutathione (822 ± 108 μM and 3.56 ± 1.76 μM), myeloperoxidase (67.4 ± 56.2 ng/mL), and isoprostanes (340 ± 95.3 ng/mL). Glutathione peroxidase activity and superoxide anion production in whole blood were also measured. Glutathione peroxidase activity was 473 ± 34.0 IU/g of hemoglobin and superoxide anion production in whole blood was 18,930 ± 12,742 counts per 30 min. Reduced glutathione/oxidized glutathione and copper/zinc ratios were, respectively, 280 ± 139 and 0.70 ± 0.15. Sex-related differences were recorded for zinc (p = 0.0081), copper/zinc ratio (p = 0.0036) and plasma isoprostanes (p = 0.0045).

Conclusion

Provisional physiological norms covering 95% of our group were proposed for each biomarker and should be of interest for future studies of canine oxidative stress.
Digital livestock systems and probiotic mixtures can improve the growth performance of swine by enhancing immune function, cecal bacteria, short-chain fatty acid, and nutrient digestibility
Authors: Park Sang-O , Seo Kyung-Hoon
Published: March 2023

In response to climate change, the use of digital livestock systems and probiotic mixtures as technological strategies to improve animal health and production is driving new innovations in the farm animal industry. However, there is little information available regarding the effects of digital livestock systems and probiotic mixtures (consisting of Bacillus subtillus, Streptomyces galilaeus, and Sphingobacteriaceae) on the growth performance of the growth-finishing swine. Thus, the objective of this study was to investigate the effects of digital livestock systems and probiotic mixtures on the immune function, cecal bacteria, short-chain fatty acids, nutrient digestibility, and growth performance of growth-finishing swine. A total of 64 crossbred male swine (Duroc × Landrace × Yorkshire, average body weight: 60.17 ± 1.25 kg) were randomly assigned to four treatment groups: CON (control group with a conventional livestock system without a probiotic mixture), CON0.4 (a conventional livestock system with a 0.4% probiotic mixture), DLSC (a digital livestock system without a probiotic mixture), and DLS0.4 (a digital livestock system with a 0.4% probiotic mixture). The swine were reared under standard environmental conditions until their average body weight reached 110 kg. The results indicated that the growth performance of the swine improved with an increase in nutrient digestibility and immune function via modulation of blood immune markers in the group with a digital livestock system compared to the CON group, although the growth performance of the swine was similar between the DLSC and CON0.4 groups. Moreover, the application of the digital livestock system and the probiotic mixture maintained higher levels of Lactobacillus and balanced short-chain fatty acid profiles compared to the CON group. These results suggest that a digital livestock system and a probiotic mixture can improve the growth performance of swine by enhancing their nutrient digestibility, improving their immune function, and maintaining balanced cecal bacteria and short-chain fatty acids. Therefore, this study provides insights into the application of digital livestock systems and probiotic mixtures as a climate change response strategy to improve swine production.
Chicken-origin Cluster 3.2 Tembusu virus exhibits higher infectivity than duck-origin Cluster 2 Tembusu virus in chicks
Authors: Huang Yunzhen , Hu Ziqiang , Dong Jiawen , Li Linlin , Zhang Junqin , Kuang Ruihuan , Gao Shimin , Sun Minhua , Liao Ming
Published: March 2023

In 2020, a chicken-origin Cluster 3 Tembusu virus (TMUV) caused outbreaks of a disease characterized by egg-drop syndrome in laying hens in China. In the present study, a TMUV strain, TMUV-GX, was isolated from tissue samples of laying hens with egg drop syndrome in south China. Phylogenetic analysis grouped TMUV-GX into TMUV Cluster 3.2, which was distinct from the prevalent TMUV Cluster 2 in duck flocks. To study the infectivity and pathogenicity of TMUV-GX in chickens and ducks, 7 day-old specific pathogen-free (SPF) chicks and SPF ducklings were infected with the same dose of the TMUV-GX. As a comparison, the duck-origin Cluster 2 strain, TMUV-JM, infection groups were set up in chicks and ducklings. Compared with the low infectivity and pathogenicity of TMUV-JM in chicks, the chicken-origin TMUV-GX displayed high replication competence in multiple tissues and caused tissues histopathological damage. In addition, the replication competence of TMUV-GX in ducklings was comparable to that of TMUV-JM. Our study revealed chicken-origin Cluster 3.2 TMUV exhibits high infectivity in chicks and ducklings, and suggested that chicken-origin Cluster 3.2 TMUV possesses a biological basis for widespread infection of chickens and ducks.
Relationship Between Educational Level and Attitudes Towards Alcohol Conversations in Healthcare: A Cross-Sectional Survey Conducted in Four European Countries
Authors: Karlsson Nadine , Skagerström Janna , O'Donnell Amy , Abidi Latifa , Thomas Kristin , Nilsen Per , Lid Torgeir Gilje
Published: March 2023

Objectives: To examine the association between educational level and attitudes towards alcohol conversations in healthcare using population-based surveys of adults in England, the Netherlands, Norway, and Sweden; and to compare attitudes towards alcohol conversations in healthcare between these four countries.

Methods: Cross-sectional surveys were conducted amongst adults in the general population in England (n = 3,499), the Netherlands (n = 2,173), Norway (n = 1,208), and Sweden (n = 3,000). Logistic regression analysis was used to examine associations between attitudes towards alcohol conversations in healthcare and educational level, key demographic variables, alcohol consumption, and country of residence.

Results: In all four countries, low educational level (p < 0.001) and male gender (p < 0.001) were associated with holding negative attitudes towards discussing alcohol in healthcare. Risky drinkers had more negative attitudes than low risky drinkers towards discussing alcohol in healthcare (p < 0.001) in all countries except England (p = 0.48), and also reported low levels of perceived honesty and confidence in healthcare (p < 0.001).

Conclusion: These findings highlight the importance of considering patients’ socio-economic status when developing and implementing alcohol prevention interventions in healthcare.
A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
Authors: Mahmud Md Ishtyaq , Mamun Muntasir , Abdelgawad Ahmed
Published: March 2023

Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving, computer activities with artificial intelligence include other activities. A group of algorithms called “deep learning” is used in machine learning. With the aid of magnetic resonance imaging (MRI), deep learning is utilized to create models for the detection and categorization of brain tumors. This allows for the quick and simple identification of brain tumors. Brain disorders are mostly the result of aberrant brain cell proliferation, which can harm the structure of the brain and ultimately result in malignant brain cancer. The early identification of brain tumors and the subsequent appropriate treatment may lower the death rate. In this study, we suggest a convolutional neural network (CNN) architecture for the efficient identification of brain tumors using MR images. This paper also discusses various models such as ResNet-50, VGG16, and Inception V3 and conducts a comparison between the proposed architecture and these models. To analyze the performance of the models, we considered different metrics such as the accuracy, recall, loss, and area under the curve (AUC). As a result of analyzing different models with our proposed model using these metrics, we concluded that the proposed model performed better than the others. Using a dataset of 3264 MR images, we found that the CNN model had an accuracy of 93.3%, an AUC of 98.43%, a recall of 91.19%, and a loss of 0.25. We may infer that the proposed model is reliable for the early detection of a variety of brain tumors after comparing it to the other models.
Design and Evaluation on Onboard Antenna Pointing Control System for a Wireless Relay System Using Fixed-Wing UAV
Authors: Hamajima Koki , Yasukawa Kei , Ueba Masazumi , Kanou Hisayoshi , Matsui Munehiro , Abe Junichi , Itokawa Kiyohiko , Yamashita Fumihiro
Published: March 2023

Among several usages of unmanned aerial vehicles (UAV), wireless relay systems for high altitudes using fixed-wing UAVs, or high-altitude platform stations (HAPS), are some of the most promising applications. To realize the systems by making the most of advantages of the long flight duration and endurance of fixed-wing airplanes, this paper proposes an antenna pointing control system using mechanical gimbals onboard a fixed-wing UAV continuously turning midair and describes results of the blocking analysis of the antenna driving angles of the gimbal directed to a ground station, the design of the antenna pointing control system, and the evaluation of its performance. It is confirmed by the evaluation that, though the antenna pointing control accuracy is greatly influenced by the noisy antenna pointing direction command, its accuracy is greatly improved by using the highly accurate RF sensor to detect antenna pointing direction.
Implementation of Animal Welfare: Pros and Cons
Authors: Bozzo Giancarlo , Dimuccio Michela Maria
Published: March 2023

Animal welfare is defined as a permanent state reflecting an animal’s subjective perception of its situation indicated by behavioral, postural, and physiological parameters [...]
Uptake and Translocation of Foliar-Applied L-Proline in Sweet Cherry (Prunus avium L.)
Authors: Hölzel Nadine , Close Dugald C. , Bound Sally A. , Quin Peter R. , Visentin Denis C. , Swarts Nigel D.
Published: March 2023

Foliar application of nitrogen (N) may supplement soil-applied N in sweet cherry orchards. The proteinogenic amino acid L-proline is a potential source of organic N. However, little is understood about its uptake and effects on fruit quality. In this study, 15N-labelled L-proline was spray-applied to branches of the cultivar ‘Lapins’ either pre- or post-harvest. Leaves, fruit, and whole branches were sampled to investigate the uptake and allocation of foliar-applied N. Both treatments resulted in elevated 15N levels in leaves, with N derived from proline (%NDP) comprising 0.22% and 0.45% after pre- and post-harvest applications, respectively. The fruit was a sink for pre-harvest L-proline, with the highest %NDP in the pedicel (0.21%), followed by the skin (0.17%) and flesh (0.12%). Quality outcomes of smaller, darker fruit with lower stem retention indicate advanced maturity following L-proline application. Both pre- and post-harvest treatments resulted in the recovery of 15N in branches at late dormancy, with %NDP in bark (0.12%), buds (0.15%), and wood (0.02%) of the post-harvest treatment twice as high compared with those from the pre-harvest treatment. This study demonstrates proof of concept of the uptake of L-proline into the leaves of sweet cherry plants and translocation into the fruit and storage organs of the branch.
LANZA® Tedera Is Strongly Suppressed by Competition from Lolium multiflorum and Is Best Adapted to Light-Textured Soils
Authors: Fernández-Habas Jesús , Real Daniel , Vanwalleghem Tom , Fernández-Rebollo Pilar
Published: March 2023

Bituminaria bituminosa var. albomarginata, known as Tedera, is a promising forage for Mediterranean climates. An improved variety named LANZA® has been developed. Previous research suggests that soil water saturation in heavy-textured soils might affect its initial development. Competition from grasses could also compromise its successful establishment and persistence. We investigated the effects of soils with contrasting textures (loamy sand vs. clay) with a high soil water content and the competition from Lolium multiflorum on the development of LANZA® in a pot experiment. Dry mass (DM) production of LANZA® was strongly reduced (86%) when grown with L. multiflorum. LANZA® shoot DM was 60% higher than the total shoot DM production of the mixture of LANZA® and L. multiflorum. Soil type did not significantly affect the shoot and total root DM. However, a 44% reduction of the DM of thin roots and slower development was observed in clay soils, which may indicate a preference for light-textured soils. This study provides further information on the factors limiting the establishment and persistence of LANZA®. Future research should confirm these results at field scale and investigate measures aimed at reducing early competition in monocultures and functional complementarity with partner species in mixtures to successfully establish LANZA®.
LPS Administration during Fertilization Affects Epigenetic Inheritance during Embryonic Development
Authors: Kim Sangwoo , Yoneda Erina , Tomita Kisaki , Kayano Mitsunori , Watanabe Hiroyuki , Sasaki Motoki , Shimizu Takashi , Muranishi Yuki
Published: March 2023

Intrauterine inflammation can cause infertility by disrupting reproductive function. The pathogenesis underlying this process may primarily involve endotoxins from lipopolysaccharides (LPS), which are produced by Gram-negative bacteria. However, the long-term effects of endotoxins in mammalian pregnancy following LPS exposure during fertilization have not been clarified. In this study, we performed experiments to analyze the influence of LPS on early embryonic development and fetal development in mice. Mice uteruses were examined for the expression of genes related to the inflammatory response. The expression of Il-1β and Il-6 increased following the administration of 200 and 1000 µg/kg LPS. Exposure to LPS using in vitro fertilization (IVF) significantly decreased the embryonic developmental rate. A concentration of 100 µg/kg LPS significantly increased the placental weight and fetal crown –rump length (CRL), whereas a concentration of 200 µg/kg LPS significantly decreased the placenta weight and fetal weight in vivo. These findings indicate that maternal LPS during fertilization affects fetal development until the late stage of pregnancy. Thus, maternal endotoxins may affect epigenetic inheritance during embryonic development from the early to late stages of pregnancy.
An Electrophysiological and Proteomic Analysis of the Effects of the Superoxide Dismutase Mimetic, MnTMPyP, on Synaptic Signalling Post-Ischemia in Isolated Rat Hippocampal Slices
Authors: Puzio Martina , Moreton Niamh , Sullivan Mairéad , Scaife Caitriona , Glennon Jeffrey C. , O’Connor John J.
Published: March 2023

Metabolic stress and the increased production of reactive oxygen species (ROS) are two main contributors to neuronal damage and synaptic plasticity in acute ischemic stroke. The superoxide scavenger MnTMPyP has been previously reported to have a neuroprotective effect in organotypic hippocampal slices and to modulate synaptic transmission after in vitro hypoxia and oxygen–glucose deprivation (OGD). However, the mechanisms involved in the effect of this scavenger remain elusive. In this study, two concentrations of MnTMPyP were evaluated on synaptic transmission during ischemia and post-ischemic synaptic potentiation. The complex molecular changes supporting cellular adaptation to metabolic stress, and how these are modulated by MnTMPyP, were also investigated. Electrophysiological data showed that MnTMPyP causes a decrease in baseline synaptic transmission and impairment of synaptic potentiation. Proteomic analysis performed on MnTMPyP and hypoxia-treated tissue indicated an impairment in vesicular trafficking mechanisms, including reduced expression of Hsp90 and actin signalling. Alterations of vesicular trafficking may lead to reduced probability of neurotransmitter release and AMPA receptor activity, resulting in the observed modulatory effect of MnTMPyP. In OGD, protein enrichment analysis highlighted impairments in cell proliferation and differentiation, such as TGFβ1 and CDKN1B signalling, in addition to downregulation of mitochondrial dysfunction and an increased expression of CAMKII. Taken together, our results may indicate modulation of neuronal sensitivity to the ischemic insult, and a complex role for MnTMPyP in synaptic transmission and plasticity, potentially providing molecular insights into the mechanisms mediating the effects of MnTMPyP during ischemia.
Underground Mine Road Detection Using Deep Learning Technique
Authors: Tong Zhixue , Zhang Wenda , Zhang Xuefeng
Published: March 2023

Semantic segmentation of underground mine roads is very important to efficiently obtain road information from images. The boundary of underground mine roads is not obvious, the environment is complex, and road identification is difficult. In order to effectively realize the accurate identification of underground mine roads, a network identification model using a deep learning technique is proposed. Choosing BiSeNet as the basic framework, adopting a unified attention fusion module, and using channel and spatial attention to enrich the fusion feature representation can effectively obtain feature information and reduce the loss of feature information. In addition, the lightweight network STDC is integrated into the backbone network to reduce computational complexity. Finally, experiments were carried out on underground mine roads. The experimental results show that the mean intersection over union and pixel accuracy of the proposed method reached 89.34% and 98.34%, respectively, and the recognition speed reached 23 f/s when identifying underground mine roads. In this study, the underground mine road recognition model trained by deep learning technology can solve the problem of underground mine road recognition with high accuracy.
Numerical Simulation of Ice Crystal Trajectory and Its Influencing Factors Based on Lagrangian Method
Authors: Wang Zhengzhi , Liu Chunfu , Zhong Wei , Zhao Huanyu
Published: March 2023

Ice crystals are one of the important factors causing aircraft icing, and icing prediction is a major problem in the aviation field. The calculation of ice crystal trajectory is a precondition of the accurate simulation of ice accretion. In this paper, an ice crystal trajectory simulation method based on the Lagrangian method is proposed, and the effects of various parameters on the calculation results are discussed. Control equations of the ice crystal motion trajectory were established, and the solution methods of the equations are given. The method was used to calculate the ice crystal motion characteristics under various conditions, and the calculation results were compared with experimental results and reference results, to verify the correctness of the method. The effects of different particle densities, drag coefficient models, ice crystal diameters, inflow velocities, and angles of attack on the ice crystal collection coefficient were studied. The results showed that the smaller the particle density, the smaller the collection coefficient; the collection coefficient increases with the increase of ice crystal diameter and inflow velocity.
Heat Transfer Mechanism Investigation of Bubble Growth on the Superhydrophilic Nano-Structured Surface Using Moving Particle Semi-Implicit Method
Authors: Guo Kailun , Li Sijun , Zhong Yubao , Chen Ronghua , Wang Mingjun , Qiu Suizheng , Tian Wenxi , Su Guanghui
Published: March 2023

The boiling behavior on nano-structured surfaces is a frontier research direction in nuclear engineering. However, the mechanism of boiling heat transfer on nano-structured surfaces is still unclear. In this study, a depletable micro-layer model and the nano-structure model are proposed based on the Moving Particle Semi-implicit (MPS) method coupled with Meshless-Advection using the Flow-directional Local-grid (MAFL) scheme, also known as the MPS-MAFL method. The developed method in this paper establishes a bridge between the nano-scale surface structure heat transfer and the macroscopic bubble boiling. Only by knowing the nanoparticle size, porosity, and thickness of the nano-structure, the heat transfer of the nano-structure can be considered into the macroscopic boiling bubble growth process. The accuracy of the approach is validated by benchmark cases and experiments, respectively. The present method quantitatively simulates the bubble growth behaviors on nano-structured surfaces for the first time. The results indicate that the heat transfer contribution of the micro-layer to bubble growth was not neglectable, while the proportion of heat transfer rate of the micro-layer on the bared surface was 40.55% at ΔTw = 7.22 ℃ and 32.23% at ΔTw = 10.15 ℃. The heat transfer contributions of the micro-layer and the wicked fluid to the bubble growth in the nano-structured heater were about 42.13%, the ratio of them was 14:11. The current study provides a fundamental base for further investigations.
Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns
Authors: Aydın Yaren , Bekdaş Gebrail , Nigdeli Sinan Melih , Isıkdağ Ümit , Kim Sanghun , Geem Zong Woo
Published: March 2023

CO2 emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to reduce CO2 emissions. This research proposes an optimization-machine learning pipeline and a set of models trained for the prediction of the design variables of an ecofriendly concrete column. In this research, the harmony search algorithm was used as the optimization algorithm, and different regression models were used as predictive models. Multioutput regression is applied to predict the design variables such as section width, height, and reinforcement area. The results indicated that the random forest algorithm performed better than all other machine learning algorithms that have also achieved high accuracy.
Permanent Deformation and Its Unified Model of Coal Gangue Subgrade Filler under Traffic Cyclic Loading
Authors: Zhang Zong-Tang , Wang Yan-Hao , Gao Wen-Hua , Hu Wei , Liu Shun-Kai
Published: March 2023

Using coal gangue as subgrade filler can not only solve the environmental problems of coal mine waste accumulation but also decrease the subgrade cost, which has important theoretical and practical significance. A series of cyclic triaxial tests was carried out using the large-scale dynamic and static triaxial apparatus (LSDSTA) to investigate the permanent deformation (ε) of coal gangue subgrade filler (CGSF) under cyclic loading. Experimental grading was designed by using the fractal model grading equation (FMGE), and then well-grading limits of CGSF were captured. The relationship curve between ε and the numbers of cyclic loading (N) can be divided into three stages, i.e., the rapid growth phase, the deceleration growth phase, and the approaching stability phase. N = 1000 can be used as the criterion for reaching the stable stage of CGSF. The effect of confining pressure (σ3′) on ε is related to the level of σ3′. The effect of σ3′ on ε is significant when σ3′ is smaller, whereas the influence of σ3′ on ε is smaller when σ3′ is larger. Furthermore, the influence of grading (Df) on ε of coal gangue samples is significant. With the increase of Df, ε first increases and then decreases, reflecting that there is an obvious optimal grading for coal gangue samples under cyclic loading. Moreover, the effect of compaction degree (Dc) on ε of CGSF depends on the level of Dc. ε is hardly affected when Dc is smaller, whereas increasing Dc has a significant effect on restraining ε when Dc is bigger. In addition, according to the analysis of the permanent deformation curve for CGSF, the unified calculation model of permanent deformation for CGSF under cyclic loading is established. Compared with the existing permanent deformation models, the proposed model in this paper can better describe the permanent deformation of CGSF under cyclic loading. Finally, the model parameters are analyzed, and the model is verified.
Nitrate-Polluted Waterbodies Remediation: Global Insights into Treatments for Compliance
Authors: Fernández-López José A. , Alacid Mercedes , Obón José M. , Martínez-Vives Ricardo , Angosto José M.
Published: March 2023

Nitrate (NO3−) pollution of surface and groundwater bodies is a global problem of increasing concern, which has stimulated significant research interest. Nitrogen is crucial for life as a macronutrient for living organisms on Earth, but the global nitrogen cycle has been seriously altered by intensification of human activities, leading to eutrophication and hypoxic conditions of aquatic ecosystems. Due to nitrogen overfertilization, intensive agricultural practices generate huge nitrate fluxes that inadvertently deteriorate water quality. Different industrial processes also contribute to NO3− pollution in the environment. There are multiple technologies capable of achieving effective denitrification of waterbodies to ensure safe NO3− levels. Either separation-based or transformation-based denitrification technologies must address the challenges of by-product generation, increased energy demand, and reduced environmental footprint. This paper highlights the most used approaches, along with some promising alternatives for remediation of nitrate-polluted waters.
Modelling of Energy-Dependent Electron Interactions in the Earth’s Mesosphere
Authors: Campbell Laurence , Brunger Michael J.
Published: March 2023

Electrons are produced in the Earth’s quiet nighttime mesosphere by ionization by cosmic rays and ionization of NO by Lyman-α radiation. They are removed by attachment or recombination processes that are usually assumed in modelling to occur at the ambient temperature. However, the electrons have initial energies that are much higher than at thermal equilibrium, and so must have a range of energies as they progress towards equilibrium via interactions with atoms and molecules. As attachment and recombination rates are dependent on the electron energy, it is possible that modelling that considers the actual energy of the electrons will give different results to those based on assuming that the electrons are at the ambient temperature. In this work, starting with electrons at a higher initial energy, the detailed electron interactions (including elastic scattering and vibrational excitation of molecules) are tracked in a time-step simulation. This simulation is implemented by treating electrons in subranges of the electron energy spectrum as chemical species. This allows an investigation of two phenomena in the nighttime mesosphere: the origin of the D-region ledge and the production of radiative emissions from vibrationally excited molecules. It is found that there is negligible difference in the electron densities calculated using the ambient temperature or detailed interaction models, so this study does not provide an explanation for the D-region ledge. However, in the latter model, emissions at various wavelengths are predicted due to reactions involving vibrationally excited molecules. It is also found, using the time-step calculation, that it would take several hours for the predicted electron density to approach equilibrium.
New Generalization of Geodesic Convex Function
Authors: Almutairi Ohud Bulayhan , Saleh Wedad
Published: March 2023

As a generalization of a geodesic function, this paper introduces the notion of the geodesic φE-convex function. Some properties of the φE-convex function and geodesic φE-convex function are established. The concepts of a geodesic φE-convex set and φE-epigraph are also given. The characterization of geodesic φE-convex functions in terms of their φE-epigraphs, are also obtained.
Renyi Entropy of the Residual Lifetime of a Reliability System at the System Level
Authors: Mesfioui Mhamed , Kayid Mohamed , Shrahili Mansour
Published: March 2023

The measurement of uncertainty across the lifetimes of engineering systems has drawn more attention in recent years. It is a helpful metric for assessing how predictable a system’s lifetime is. In these circumstances, Renyi entropy, a Shannon entropy extension, is particularly appealing. In this paper, we develop the system signature to give an explicit formula for the Renyi entropy of the residual lifetime of a coherent system when all system components have lived to a time t. In addition, several findings are studied for the aforementioned entropy, including the bounds and order characteristics. It is possible to compare the residual lifespan predictability of two coherent systems with known signatures using the findings of this study.
Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability
Authors: Altini Nicola , Puro Emilia , Taccogna Maria Giovanna , Marino Francescomaria , De Summa Simona , Saponaro Concetta , Mattioli Eliseo , Zito Francesco Alfredo , Bevilacqua Vitoantonio
Published: March 2023

The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work, we developed an explainable computer-aided diagnosis (CAD) system that can be used to support pathologists in the evaluation of tumor cellularity in breast histopathological slides. In particular, we compared an end-to-end DL approach that exploits the Mask R-CNN instance segmentation architecture with a two steps pipeline, where the features are extracted while considering the morphological and textural characteristics of the cell nuclei. Classifiers that are based on support vector machines and artificial neural networks are trained on top of these features in order to discriminate between tumor and non-tumor nuclei. Afterwards, the SHAP (Shapley additive explanations) explainable artificial intelligence technique was employed to perform a feature importance analysis, which led to an understanding of the features processed by the machine learning models for making their decisions. An expert pathologist validated the employed feature set, corroborating the clinical usability of the model. Even though the models resulting from the two-stage pipeline are slightly less accurate than those of the end-to-end approach, the interpretability of their features is clearer and may help build trust for pathologists to adopt artificial intelligence-based CAD systems in their clinical workflow. To further show the validity of the proposed approach, it has been tested on an external validation dataset, which was collected from IRCCS Istituto Tumori “Giovanni Paolo II” and made publicly available to ease research concerning the quantification of tumor cellularity.
Machine Learning for Brain MRI Data Harmonisation: A Systematic Review
Authors: Wen Grace , Shim Vickie , Holdsworth Samantha Jane , Fernandez Justin , Qiao Miao , Kasabov Nikola , Wang Alan
Published: March 2023

Background: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. Objective: This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. Method: This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. Results: a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). Conclusion: Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.
Deep Edge Detection Methods for the Automatic Calculation of the Breast Contour
Authors: Freitas Nuno , Silva Daniel , Mavioso Carlos , Cardoso Maria J. , Cardoso Jaime S.
Published: March 2023

Breast cancer conservative treatment (BCCT) is a form of treatment commonly used for patients with early breast cancer. This procedure consists of removing the cancer and a small margin of surrounding tissue, while leaving the healthy tissue intact. In recent years, this procedure has become increasingly common due to identical survival rates and better cosmetic outcomes than other alternatives. Although significant research has been conducted on BCCT, there is no gold-standard for evaluating the aesthetic results of the treatment. Recent works have proposed the automatic classification of cosmetic results based on breast features extracted from digital photographs. The computation of most of these features requires the representation of the breast contour, which becomes key to the aesthetic evaluation of BCCT. State-of-the-art methods use conventional image processing tools that automatically detect breast contours based on the shortest path applied to the Sobel filter result in a 2D digital photograph of the patient. However, because the Sobel filter is a general edge detector, it treats edges indistinguishably, i.e., it detects too many edges that are not relevant to breast contour detection and too few weak breast contours. In this paper, we propose an improvement to this method that replaces the Sobel filter with a novel neural network solution to improve breast contour detection based on the shortest path. The proposed solution learns effective representations for the edges between the breasts and the torso wall. We obtain state of the art results on a dataset that was used for developing previous models. Furthermore, we tested these models on a new dataset that contains more variable photographs and show that this new approach shows better generalization capabilities as the previously developed deep models do not perform so well when faced with a different dataset for testing. The main contribution of this paper is to further improve the capabilities of models that perform the objective classification of BCCT aesthetic results automatically by improving upon the current standard technique for detecting breast contours in digital photographs. To that end, the models introduced are simple to train and test on new datasets which makes this approach easily reproducible.
Development of pH-Responsive Polypills via Semi-Solid Extrusion 3D Printing
Authors: Wang Fan , Li Ling , Zhu Xiaolong , Chen Feng , Han Xiaoxiao
Published: March 2023

The low bioavailability of orally administered drugs as a result of the instability in the gastrointestinal tract environment creates significant challenges to developing site-targeted drug delivery systems. This study proposes a novel hydrogel drug carrier using pH-responsive materials assisted with semi-solid extrusion 3D printing technology, enabling site-targeted drug release and customisation of temporal release profiles. The effects of material parameters on the pH-responsive behaviours of printed tablets were analysed thoroughly by investigating the swelling properties under both artificial gastric and intestinal fluids. It has been shown that high swelling rates at either acidic or alkaline conditions can be achieved by adjusting the mass ratio between sodium alginate and carboxymethyl chitosan, enabling site-targeted release. The drug release experiments reveal that gastric drug release can be achieved with a mass ratio of 1:3, whilst a ratio of 3:1 allows for intestinal release. Furthermore, controlled release is realised by tuning the infill density of the printing process. The method proposed in this study can not only significantly improve the bioavailability of oral drugs, but also offer the potential that each component of a compound drug tablet can be released in a controlled manner at a target location.
Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks
Authors: Esmaeili Fatemeh , Cassie Erica , Nguyen Hong Phan T. , Plank Natalie O. V. , Unsworth Charles P. , Wang Alan
Published: March 2023

Anomaly detection is a significant task in sensors’ signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors’ applications. Deep learning algorithms are effective tools for anomaly detection due to their capability to address imbalanced datasets. In this study, we took a semi-supervised learning approach, utilizing normal data for training the deep learning neural networks, in order to address the diverse and unknown features of anomalies. We developed autoencoder-based prediction models to automatically detect anomalous data recorded by three electrochemical aptasensors, with variations in the signals’ lengths for particular concentrations, analytes, and bioreceptors. Prediction models employed autoencoder networks and the kernel density estimation (KDE) method for finding the threshold to detect anomalies. Moreover, the autoencoder networks were vanilla, unidirectional long short-term memory (ULSTM), and bidirectional LSTM (BLSTM) autoencoders for the training stage of the prediction models. However, the decision-making was based on the result of these three networks and the integration of vanilla and LSTM networks’ results. The accuracy as a performance metric of anomaly prediction models showed that the performance of vanilla and integrated models were comparable, while the LSTM-based autoencoder models showed the least accuracy. Considering the integrated model of ULSTM and vanilla autoencoder, the accuracy for the dataset with the lengthier signals was approximately 80%, while it was 65% and 40% for the other datasets. The lowest accuracy belonged to the dataset with the least normal data in its dataset. These results demonstrate that the proposed vanilla and integrated models can automatically detect abnormal data when there is sufficient normal data for training the models.
Non-Vitamin K Antagonist Oral Anticoagulants in Patients with β-Thalassemia
Authors: Malagù Michele , Longo Filomena , Marchini Federico , Sirugo Paolo , Capanni Andrea , Clò Stefano , Mari Elisa , Culcasi Martina , Bertini Matteo
Published: March 2023

Background. Patients with β-thalassemia have a high incidence of atrial fibrillation (AF) and other supraventricular arrhythmias. The use of non-vitamin K antagonist oral anticoagulants (NOACs) for thromboembolic prophylaxis in patients with β-thalassemia has not been systematically evaluated. Methods. We enrolled patients with transfusion-dependent β-thalassemia, who were on treatment with NOACs for thromboembolic prophylaxis of supraventricular arrhythmias. Data on thromboembolic and bleeding events were collected. Results. Eighteen patients were enrolled. The patients had a history of AF (sixteen), typical atrial flutter (five), and atypical atrial flutter (four). The patients were treated with dabigatran (seven), apixaban (five), rivaroxaban (four) or edoxaban (two). The mean follow-up duration was 22 ± 15 months. No thromboembolic events were reported. No major bleedings were observed. Three patients had non-major bleeding events. Two patients reported dyspepsia during treatment with dabigatran and were shifted to a different NOAC. Conclusions. Our study suggests the efficacy and safety of NOACs in patients affected by transfusion-dependent β-thalassemia.