bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intraoperative Real-time Near-infrared Image-guided Endoscopic Endonasal Surgery for Pituitary Tumors
Authors: Muto Jun , Mine Yutaka , Nishiyama Yuya , Murayama Kazuhiro , Hayakawa Motoharu , Hasegawa Mitsuhiro , Lee John K.Y. , Hirose Yuichi 
Published: November 2022

Abstract
Background: In endoscopic endonasal surgery of pituitary tumors, the intraoperative judgment and tissue identification depend largely on surgeons’ surgical experience.
Objective: To assess whether the delayed-window indocyanine green (ICG) (DWIG) technique can visualize and distinguish the normal pituitary gland and tumor under the endoscope in real-time during surgery.
Methods: Eight patients with pituitary adenoma who received 12.5 mg ICG were observed by near-infrared (NIR) fluorescence during the surgery to identify the pituitary gland and tumors.
Results: The normal pituitary gland and pituitary adenoma were visualized by NIR fluorescence in all 8 patients. The relative ratio of the fluorescence emission of the normal gland to that of the tumor (signal-to-background ratio [SBR] normal gland/tumor) increased after 15 min, peaking at 5.8±4.9 at 90 min. It suggested that pituitary gland was more clearly visualized during that period. The tumor/blood (SBR tumor) and normal gland/ blood (SBR gland) NIR fluorescence was significantly positively correlated with each Ktrans on dynamic contrast-enhanced MRI, indicating blood–brain barrier (BBB) permeability.
Conclusions: This study showed the utility of the DWIG technique for identifying a normal pituitary gland from a tumor in endoscopic endonasal surgery from 15 to 90 min following ICG administration, “negative tumor staining”. Permeability can contribute to gadolinium enhancement on MRI and to ICG retention and NIR fluorescence in a normal pituitary gland and tumor.
CEM and MR Radiomics-based Biomarkers to Predict Immunohistochemistry Breast Cancer Subtypes: A comparative study
Authors: Iquebal Ashif , Zhou Siqiong , Pfeiffer Nicholaus , Ranjbar Sara , Banerjee Imon , Swanson Kristin , Batalini Felipe , Anderson Karen S. , Murtaza Muhammad , Pockaj Barbara A. , Patel Bhavika K. 
Published: November 2022

Abstract
Purpose Accurately predicting the clinical breast cancer subtypes could be extremely helpful for radiologists, pathologists, surgeons, and clinicians and inform future treatment prediction algorithms. Therefore, we evaluate and compare the accuracy of radiomic features extracted from contrast enhanced mammography (CEM) and magnetic resonance imaging (MRI) scans to make predictions to subtypes of breast cancer.
Methods This HIPAA-compliant prospective single institution study was approved by the local institutional review board with written informed consent. Women with breast tumors 2 cm or larger underwent dynamic contrast-enhanced MRI and/or CEM for surgical staging. Semi-manual regions of interest were drawn by radiologist using Cancer Imaging Phenomics Toolkit (CaPTk). Radiomic features were obtained using PyRadiomics and MR- and CEM- based classification models were built on a low-dimensional representation of the features obtained via kernel principal component analysis. We subscribed to an ensemble tree-based learning approach called extremely randomized trees (ERT) to predict cancer subtypes captured via immunohistochemistry markers.
Results For MRI analysis, 124 women with newly diagnosed breast cancer were included in the analysis comprising 49 HR+HER2-, 37 HR+HER2+, 11 HR-HER2+, and 27 HR-HER2- cases. For CEM analysis, models were built using data from 170 female patients including 74 HR+HER2-, 41 HR+HER2+, 14 HR-HER2+, and 43 HR-HER2-. CEM based model resulted in accuracies of 55%, 72%, 88%, and 71% respectively for HR+HER2-, HR+HER2+, HR-HER2+, and HR-HER2- whereas MRI based model alone led to accuracies of 54%, 62%, 89%, and 76% respectively for HR+HER2-, HR+HER2+, HR-HER2+, and HR-HER2-.
Conclusions Radiomic features extracted from CEM and MR were strong predictors of breast cancer subtypes with CEM-based radiomic features performing slightly better, though not statistically significantly better (p = 0.82), than its MRI counterpart.
An Activity Concentration-Based Proposal for Radon Management in Workplaces Involving NORM
Authors: Chen Jing , Quayle Debora 
Published: November 2022

Abstract
The Canadian Guidelines for the Management of Naturally Occurring Radioactive Materials (NORM) have been developed to manage radiation doses received from NORM in workplaces involving NORM, such as mineral extraction and processing, oil and gas production, metal recycling or water treatment facilities. This management strategy works well for most naturally occurring radioactive materials in workplaces, with the exception of radon. Because background radon cannot be distinguished from radon generated by a practice, the need for radon management programs cannot be decided using thresholds based on incremental dose. Drawing from lessons learned through implementing the current NORM Guidelines, we propose decoupling the decision thresholds for NORM management (excluding radon) and radon management so that the two are considered separately, and quantifying decision-points for managing occupational radon exposure as average annual activity concentrations, with no requirement for dose calculations. Proposed application of this approach in the updated Canadian NORM Guidelines is described.
On the exact regions determined by Kendall's tau and other concordance measures
Authors: Stopar Nik , Bukovšek Damjana Kokol 
Published: November 2022

Abstract
We determine the upper and lower bound for possible values of Kendall's tau of a bivariate copula given that the value of its Spearman's footrule or Gini's gamma is known, and show that these bounds are always attained.
Mathematics Subject Classification (2020). 62H20, 62H05, 60E05.
The Cost of Freshwater Charging in Brazil: suppliers’ main alternatives
Authors: Teixeira Telma , Veiga Karine , Julien Denis , Souza João Paulo , Pavan Frank 
Published: November 2022

Abstract
Charging for freshwater use raises debate on water suppliers' financial sustainability and, social and operational sector efficiency problems. The issue stands out when suppliers' income flows are insufficient to cover production costs or household revenues are low, making it difficult for tariffs to increase. Moreover, it is even worse with the combination of these factors in watersheds with water scarcity. The present work analyzes the freshwater use charging from the perspective of cost increment destination in the sanitation sector. We compared a scenario of cost absorption with the possibility of transfer to final consumers in the first block tariff, fixed by volume ( #2a), and the second block tariff charged by additional m3 consumed (#2b). Results show that scenario #1 reduces resource availability for investments, compromising infrastructure maintenance and service expansion. Alternatively, as a natural monopoly sector, the pass-through to consumers is the most likely. Both #2a and #2b scenarios nullify the encouragement of rational use and operational efficiency, add risk to environmental sustainability, and compromise basin committee plans. However, in the #2b scenario, the possibility of minor water consumption by households, changing for the first block tariff, brings back the #1 scenario possibilities. Scenario #2a, as a plausible choice, demands an accurate discussion of governance in freshwater charging.
Women’s Expectations About Birth, Requests for Pain Relief in Labor and the Subsequent Development of Birth Dissonance and Trauma
Authors: Sutton Elizabeth , Detering Karen , East Christine , Whittaker Andrea 
Published: November 2022

Abstract
Background
Birth is a significant event in women’s lives. Many women aim for a birth that avoids pharmacological pain relief because they are advised it is better for them and their baby. For women having their first baby, this may not be realistic as 3/4 of primiparous women in Australia will use pharmacological pain relief. This study examines the expectations that a group of women had regarding pain relief, how these expectations developed and what happened to their requests for pain relief in labour.
Methods
A longitudinal prospective study design was used to recruit 15 women who were having their first baby. Women having low risk pregnancies, hoping for a ‘natural birth’ (vaginal, no/minimal pharmacological pain relief) were eligible. A semi-structured interview tool was used across all three interviews that asked women about their expectations, then actual labour experience, pain management requests and how these were responded to by carers. Women were interviewed three times – at 36 weeks gestation; as soon after delivery of their baby as possible, then six months post-delivery (N = 43 interviews). Interviews were recorded and transcribed and coded by ES using NVivo software with hierarchical thematic analysis used.
Results
The study found that women appear to experience a mismatch between expectations they have developed pre-birth, versus actual experience. This appears to cause a specific form of dissonance – which we have termed ‘birth dissonance’ leaving them feeling traumatised post birth. This is because what women expected to happen in birth was often not realised. In particular, when women request pain relief in birth and that request is not responded to as hoped, women are more likely to develop post-birth trauma as a result of this dissonance arising from their expectations about birth being able to be done without significant pain relief. Interventions and technology may also contribute to this sense of mismatch and post-birth trauma.
Conclusions
Low risk birthing women delivering in a hospital may have to engage with higher levels of technology, intervention and pain relief than that which they expected pre-birth. This could possibly be avoided with four simple changes. Firstly, better pre-birth education for women about how painful labor is likely to be. Secondly, pre-birth education which includes a detailed explanation of the utility of pain relief and that new evidence shows epidurals do not lead to higher rates of interventions or surgical births. Thirdly, more egalitarian decision-making during labour, and finally delivering upon women’s requests for pain relief in labor, at the time that they ask for it. Further research is required to determine the extent of birth dissonance and how women making the transition to motherhood can avoid it.
The distribution of vertebrate roadkill varies by season, surrounding environment and animal class
Authors: Arca-Rubio Jesús , Moreno-Rueda Gregorio , Ortega Zaida 
Published: November 2022

Abstract
Due to rapid human expansion in the last century, wildlife roadkill is becoming a concerning threat to biodiversity and human safety. The frequency of roadkill events depends on factors related to specific traits of the road –fencing, tortuosity, or type of habitat, among others– and the animal ecology –such as activity patterns, reproductive season, thermoregulation– which, in turn, are related to environmental factors (with seasonal variations). Here we assessed roadkill mortality of terrestrial vertebrates over the year. To do this, we sampled 10 road sections (of 3 km, by walk) in the south of Spain for a full year, registering the carcasses of run-over vertebrates. Then, we analysed the spatiotemporal patterns of roadkill events for the four vertebrates’ classes and the effects of road traits (presence of fence, tortuosity, distance to water body) and environmental variables (mean temperature and precipitation). Mammals suffered the highest mortality by roadkill (45.72%). Tortuosity of the road section and precipitation were not related to the number of collisions, while the presence of fences was related to an increase in roadkill events, and mean temperature significantly increased the probability of collision of mammals, birds, and reptiles. There was a seasonal effect in the number of collisions, which spatial pattern depended on the class of vertebrates. All this leads us to conclude that, to reduce the impact caused by roadkill mortality on the wildlife we need specific measures to be taken in each critical place for each vertebrate group.
Duplex Baseband Signal Transmission Over Free Space Optical Link Employing Carrier Reuse
Authors: Shamsi Zaheer 
Published: November 2022

Abstract
With increasing IoT and a plethora of multimedia applications being used, there is an ever-increasing demand for bandwidth-intensive applications. Communication networks employing optical fiber had been the de facto standard for such communication systems. The Optical Fiber network's high cost and man-hours required for laying demand wireless solutions. Therefore, an optimum and cost-effective duplex optical link for two channels baseband signal data transmission has been designed and validated using a single laser source. An optical link for 20 Gbps duplex data transmission is designed based upon single laser source utilization. A cost-efficient and simple design have been accomplished with a transmission distance of 1.5 km by utilizing Differential Quadrature Phase Shift Keying (DQPSK) and On-Off Keying (OOK) modulation techniques. The design allowed us to recycle the downlink signal in uplink transmission over free Space, thus eradicating the requirement for a separate laser at the other end. Each channel's transmission data rate was 10 Gb/sec. At the former end, the DQPSK technique was employed for data modulation and the data was transmitted into free space. The data was then split at the latter end for downlink reception along with reusing the downlink received signal for uplink transmission to the former end utilizing the OOK modulation technique. The received data was compared with the transmitted one to verify and validate the system's accuracy through signal waveforms and BER (Bit Error Rate). The eye diagrams and the Bit error rate (BER) graphs of the received downlink and uplink signals over a duplex Free Space Optical (FSO) transmission link are discussed and presented.
ECU-Net: an efficient cascade U-Net for retinal vessel segmentation
Authors: Yang Xin , Li Zhiqiang , Guo Yingqing , Zhou Dake , Li Tao 
Published: November 2022

Abstract
Early detection of retinal vessel lesions by fundus image examination is essential to prevent and screen the related diseases. However, due to retinal vessels’ complex structure, it is time-consuming and laborious to extract retinal vessels manually by professionals. Therefore, retinal vessel automatic segmentation has great application value in modern medicine. In recent years, deep learning technology has achieved remarkable results in medical image segmentation, and the segmentation accuracy and speed are significantly improved compared with traditional methods. In this paper, we propose an efficient retinal vessel segmentation model ECU-Net. In this model, a feature extraction module combining edge attention module and efficient channel attention mechanism is designed to improve the vessel structure’s recognition ability. To improve the fusion efficiency of the texture information in the shallow layers and the semantic information in the deep layers, ECU-Net constructs an up-sampling fusion module. In addition, we design a multi-scale channel attention module to connect U-Net’s subnetwork to achieve efficient feature extraction. Compared with the state-of-the-art models, our ECU-Net has reached the highest level in some classical evaluation criteria and has a certain application value in the medical field.
A Novel Multi Drone Architecture with Cellular Communication for Disaster Management
Authors: Doraswamy Birasalapati , Krishna K. Lokesh , Prasad M. N. Giri 
Published: November 2022

Abstract
Purpose: Usually, the natural disaster has increased hazard rates such as distresses, injuries, and deaths of the living organism. So, recovering activities are digitalized by today’s modern technology called Unmanned Aerial Vehicles (UAV). However, in some cases, the prediction and communication process of UAVs became insufficient.
Results: To overcome this problem, a novel Wolf-based Power Efficient Information Gathering System (WbPEIGS) was proposed in this article to sense the disaster events with high accuracy rate. Moreover, to enable better communication 5G model is adopted. Hence, the main motive of this research is to predict the traffic scenario like parade, gathering, and stadium, because in this traffic location many people are often available at all times. Moreover, here UAVs with the use of sensing model was adopted to monitor the disaster tragedy.
Conclusions: Initially, the UAVs parameters are activated then types of crowd traffic places are predicted using the fixed distance threshold using WbPEIGS model. Subsequently, the key metrics are validated with other existing schemes and have attained better throughput and disaster sensing accuracy.
Fruit Image Classification using Deep Learning
Authors: Gill Harmandeep Singh , Khehra Baljit Singh 
Published: November 2022

Abstract
Fruit classification is noticed as the one of the looming sectors in computer vision and image classification. A fruit classification may be adopted in the fruit market for consumers to determine the variety and grading of fruits. Fruit quality is a prerequisite property from health view position. Classification systems described so far are not adequate for fruit classification during accuracy and quantitative analysis. Thus, the examination of new proposals for fruit classification is worthwhile. In the present time, automatic fruit classification is though a demanding task.Deep learning is a powerful state of the art approach for image classification [1] This task incorporates deep learning models: Convolution Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) for classification of fruits based on chosen optimal and derived features. As preliminary arises, it has been recognized that the recommended procedure has effective accuracy and quantitative analysis results. Moreover, the comparatively high computational momentum of the proposed scheme will promote in the future for the real time classification operations
Selective Ensemble Learning Algorithm for Imbalanced Dataset
Authors: Hongle Du , Yan Zhang , Gang Ke , Lin Zhang , Yeh-Cheng Chen 
Published: November 2022

Abstract
Under the imbalanced dataset, the performance of the base-classifier, the computingmethod of weight of base-classifier and the selection method of the base-classifier have a great impact on the performance of the ensemble classifier. In order to solve above problem toimprove the generalization performance of ensemble classifier, a selective ensemble learning algorithm based on cluster under-sampling for imbalanced dataset is proposed. First, the proposed algorithm calculates the number K of under-sampling samples according to the relationship between class sample density. Then, we use the improved K-means clustering algorithm to under-sample the majority class samples and obtain K cluster centers. Then, all cluster centers (or the sample of the nearest cluster center) are regarded as new majority samples to construct a new balanced training subset combine with the minority class’s samples. Repeat those processes to generate multiple training subsets and get multiple base-classifiers. However, with the increasing of iterations, the number of base-classifiers increase, and the similarity among the base-classifiers will also increase. Therefore, it is necessary to select some base-classifier with good classification performance and large difference for ensemble. In the stage of selecting base-classifiers, according to the difference and performance of base-classifiers, we use the idea of maximum correlation and minimum redundancy to select base-classifiers. In the ensemble stage, G-mean or F-mean, which is selected to evaluate the classification performance of base-classifier forimbalanced dataset, is selected to compute the weight of each base-classifier, and then the weighted voting method is used for ensemble. Finally, the simulation results on the artificial dataset, UCI dataset and KDDCUP dataset show that the algorithm has good generalization performance on imbalanced dataset, especially on the dataset with high imbalance degree.
Role of macrophages in the immunopathogenesis of adenomyosis
Authors: Shcherbina Nikolay , Chekhunova Anastasia 
Published: November 2022

The aim of the research: to study pathophysiological mechanisms of adenomyosis development by determining the role of macrophages in the uterine microenvironment.
Materials and methods: a prospective study has been conducted on 35 women. There were 20 (57.1 %) who had adenomyosis of I degree. The control group consisted of 15 (34.3 %) gynecologically healthy women. The patients underwent general clinical, instrumental (ultrasound, hysteroscopy) examinations. Fragments of the uterine wall obtained by hysterorectoscopy were used for morphological study. The method of immunohistochemical determination of CD68+ and CD163+ macrophages was used to analyze the characteristics of phenotypic equivalents of M1 and M2 macrophages in uterine tissue samples.
Results: The increase in the number of macrophages in the myometrium of patients with adenomyosis revealed in this study, which is found in large numbers in the areas of infiltration of the stroma of myometrial cells in close association with the perivascular region, can be regarded as the basis of the mechanism for the formation of endometrioid heterotopia. Furthermore, distortion of the CD68/CD163 ratio of macrophages is characterized by proinflammatory shift.
Conclusions: The study's main result is an increase in the quantitative indicators of CD68+ macrophages associated with adenomyosis, which indicates an immunopathological process in adenomyosis.
Realitas adalah Berjejaring: Jejaring Allah, Manusia, dan Non-Manusia Melalui Perspektif ANT Latourian pada Sains dan Teologi
Authors: Sukarno Sukarno 
Published: November 2022

AbstractThe Covid-19 pandemic and its impact through a phenomenological approach have revealed human and non-human networks. The ANT Latourian perspective associates human and non-human networks. The non-human network is revealed in the sub-atomic particle network in the EPR Effect according to science, and the non-human network is also revealed in the Father, Son and Holy Spirit network in the Trinitarian God according to theology. God and sub-atomic particles are non-human, but God is the Creator and sub-atomic particles are creation. God is distinguished from sub-atomic particles. The ANT Latourian perspective reveals the network ofGod, human and non-human. The network of God, human and non-human reveals that reality is a network.
AbstrakPandemi Covid-19 dan dampaknya melalui pendekatan fenomenologis telah mengungkapkan jejaring manusia dan nonmanusia. Perspektif ANT Latourian mengasosiasikan jejaring manusia dan nonmanusia. Jejaring non-manusia terungkap pada jejaring partikel-partikel subatomik di dalam EPR Effect menurut sains, dan jejaring nonmanusia juga terungkap pada jejaring Bapa, Putra dan Roh Kudus di dalam Allah Trinitarian menurut teologi. Allah dan partikel subatomik merupakan nonmanusia, namun Allah adalah Pencipta dan partikel subatomik adalah ciptaan. Allah dibedakan dengan partikel subatomik. Perspektif ANT Latourian mengungkap jejaring Allah, manusia dan nonmanusia. Jejaring Allah, manusia dan nonmanusia mengungkapkanrealitas adalah berjejaring.