bluuebunny/crossref_metadata_2025 huggingface dataset:
FCM with Spatial Constraint Multi-Kernel Distance-Based Segmentation and Optimized Deep Learning for Flood Detection
Authors: Prasad Rajesh S. , Prasad Jayashree Rajesh , Chaudhari Bhushan S. , Ranjan Nihar M. , Srivastava Rajat
Published: June 2023

Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-Shuffled Shepherd Optimization Algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus Optimization (ACVO) and Shuffled Shepherd Optimization Algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively.
Analyzing the Role of Class Rebalancing Techniques in Software Defect Prediction
Authors: Alqasrawi Yousef , Azzeh Mohammad , Elsheikh Yousef
Published: June 2023

Predicting software defects is an important task during software testing phase, especially for allocating appropriate resources and prioritizing testing tasks. Typically, classification algorithms are used to accomplish this task by using previously collected datasets. However, these datasets suffer from imbalanced label distribution where clean modules outnumber defective modules. Traditional classification algorithms cannot handle this nature in defect datasets because they assume the datasets are balanced. Failing to address this problem, the classification algorithm will produce a prediction biased towards the majority label. In the literature, there are several techniques designed to address this problem and most of them focus on data re-balancing. Recently, ensemble class imbalance techniques have emerged as an opposing approach to data rebalancing approaches. Regarding the software defect prediction, there are no studies examining the performance of ensemble class imbalance learning against data re-balancing approaches. This paper investigates the efficiency of ensemble class imbalance learning for software defect prediction. We conducted a comprehensive experiment that involved 12 datasets, six classifiers, nine class imbalance techniques, and 10 evaluation metrics. The experiments showed that ensemble approaches, particularly the Under Bagging technique, outperform traditional data re-balancing approaches, particularly when dealing with datasets that have high defect ratios.
SSTSA: A Self-Supervised Topic Sentiment Analysis Using Semantic Similarity Measures and Transformers
Authors: Seilsepour Azam , Ravanmehr Reza , Nassiri Ramin
Published: June 2023

The exponentially increasing amount of data generated by the public on social media platforms is a precious source of information. It can be used to find the topics and analyze the comments. Some researchers have extended the Latent Dirichlet Allocation (LDA) method by adding a sentiment layer to simultaneously find the topics and their related sentiments. However, most of these approaches do not achieve admirable accuracy in Topic Sentiment Analysis (TSA), particularly when there is insufficient training data or the texts are complex, ambiguous, and short. In this paper, a self-supervised novel approach called SSTSA is proposed for TSA that extracts the hidden topics and analyzes the total sentiment related to each topic. The SSTSA proposes a new method called Pseudo-label Generator. For this purpose, first, it employs semantic similarity and Word Mover’s Distance (WMD) measures. Then, the document embedding technique is employed to semantically estimate the sentiment orientation of samples and generate the pseudo-labels (positive or negative). Afterward, a hybrid classifier composed of a pre-trained Robustly Optimized BERT (RoBERTa) and a Long Short-Term Memory (LSTM) model is trained to predict the sentiment of unseen data. The evaluation results on different datasets of various domains demonstrate that the SSTSA outperforms similar unsupervised/self-supervised methods.
SOCIAL ENTREPRENEURSHIP ECOSYSTEMS IN THE SCIENTIFIC LITERATURE: PATTERNS AND GROUPS, FACTORS AND ECOLOGICAL PROBLEM-SOLVING
Authors: BOZHIKIN IVAN
Published: June 2023

Research on social entrepreneurship (SE) has increased significantly over the past ten years, with growing interest in the ecosystems that support initiatives to address social problems with innovative solutions. Such ecosystems can facilitate solutions to society’s wicked problems on a much larger scale. However, there is a lack of research on the nature of such ecosystems, the essential players in them and the factors driving their development and sustainability. The current study aims to fulfill this research gap. Based on a systematic literature review, the study analyzes 43 peer-reviewed papers related to SE ecosystems and SE efforts that address environmental problems. It is found that there are at least 24 patterns/ types of SE ecosystems classified into five general groups. Moreover, a diverse set of factors support SE ecosystems and facilitate them to thrive faster and more sustainably. These ecosystems often develop around particular social problems. Using environmental challenges as an example, four groups of significant environmental problems solved by SE ecosystems and SE are identified. The key findings are presented as a comprehensive study framework, which is the basis for a recommended research agenda.
THE EFFECT OF ENTREPRENEURIAL ECOSYSTEMS IN EMERGING MARKETS—A CASE STUDY OF MID-TWENTIETH CENTURY BANGLADESH
Authors: HAJJAJ BOBBY
Published: June 2023

Discussions on entrepreneurial ecosystems have been a relatively recent addition to the corpus of entrepreneurship literature and have focused heavily on fostering aggressive growth, often technology-based, ventures. Here, we tune the ecosystem model to fit the non-technologically innovative entrepreneurial spaces of emerging economies. We propose a new framework for viewing the cultural effect on entrepreneurship through interactions between the individual entrepreneur’s identity, and networks within specific infrastructural and institutional regimes wrought by predominant culture. In applying the model to mid-twentieth century Bangladesh, we find a culturally predominant negative perception of entrepreneurial activity. We show this contributed to the growth of certain types of entrepreneurship in the country that were rife with (i) higher risk-tolerant behavior among entrepreneurs and (ii) the rise of entrepreneurs having strong links with specific social networks, which together led to an increase in institutional decay and the rise of corruption.
β-Unsubstituted 5-monoazaporphyrin and its derivatives
Authors: Yuan Rong , Liu Le , Yin Bangshao , Xu Ling , Zhou Mingbo , Rao Yutao , Song Jianxin
Published: June 2023

[Formula: see text]-Unsubstituted 5-monoazaporphyrin (MAP) copper complex 10,15,20-tritolyl-5-monoazaporphyrinato copper(II) (Cu-MAP) 3 was synthesized by reaction of 1,19-dibromotetrapyrrane with NaN3-CuI in good yield. Following successive reactions of demetallation, metallation and bromination, the important precursor 3,7-dibromo-10,15,20-tritolyl-5-monoazaporphyrinato nickel(II) ([Formula: see text],[Formula: see text]-dibromo Ni-MAP) 6 was prepared smoothly. Suzuki-Miyaura coupling reactions of 6 with three organic borides resulted in the “earring” Ni-MAP 7, pyrrole bridged Ni-MAP oligomers 8 and 9, and benzene bridged Ni-MAP oligomers 10 and 11, respectively. The structures of MAPs 3, 6, 7, 8, 10 and 11 were confirmed by X-ray diffraction analysis. Optical and electrochemical studies were performed to understand their electronic properties.
Control of a Double-Pendulum Overhead Crane System Based on Hierarchical Sliding Mode Control Techniques
Authors: Idrees Muhammad
Published: June 2023

The double-pendulum overhead crane system belongs to a class of underactuated mechanical systems, which is challenging to control due to its strong nonlinearities. This study deals with the nonlinear controller design for a double-pendulum overhead crane based on hierarchical sliding mode control with state-dependent switching gain. The equivalent and switching controls are obtained with the aid of first-level and second-level sliding surfaces. The asymptotic stability of sliding surfaces is proved theoretically and also shown graphically. Numerical simulations show that the proposed controller performs better than conventional hierarchical sliding mode control. The proposed controller effectively suppresses the load and hook swing and precisely controls the trolley’s displacement. It has strong robustness to the payload displacement and the change of the crane parameters.
X-Ray Photoelectron Spectroscopy Study of Host/Dopant Cation’s Valence State and Formation of F+-Centers in Pr-Doped CeO2 Nanomaterials
Authors: Khakhal H. R. , Kumar Sudhish , Dolia S. N. , Vats V. S. , Dalela B. , Alvi P. A. , Kumar Shalendra , Dalela S.
Published: June 2023

Ce[Formula: see text]Pr[Formula: see text]O2 ([Formula: see text], 0.02, 0.04, 0.06, and 0.08) nano-materials synthesized using co-precipitation method have been investigated mainly for electronic structure properties in this manuscript. Findings and supporting studies are presented to understand the role of valence states of host and dopant cations information of F[Formula: see text] centers through X-ray photoelectron spectroscopy (XPS). Sustained cubic fluorite system confirmed by X-ray diffraction (XRD) analysis and red-shifting of energy gap by UV–Vis spectroscopy in all the samples are our findings. Samples further implored by XPS indicate incidence of cerium and Pr cations in both the oxidation states of 4[Formula: see text] and 3[Formula: see text], respectively. Finally, it has been observed that optical, electronic structure and magnetic properties of CeO2 nanomaterials can be modified by Pr-doping, promising better yield samples with good amount of ferromagnetism for potential uses in the technological applications like spintronics, optoelectronics, and photocatalytic.
Numerical invariants for two-component virtual spatial graphs
Authors: Negi Komal , Prabhakar Madeti
Published: June 2023

In this paper, we study two-component virtual spatial graphs, and define numerical invariants for the class of two-component virtual spatial graphs having some special conditions. These numerical invariants detect the non-amphichirality for this specific class of two component virtual spatial graphs. Further, we establish that one of these invariants is a Vassiliev type invariant.
Whole-Community Water Management: Inspiration for Water Utilities from Around the Globe
Authors: Zimmermann Karl , Chaudhuri Shreya , Antell Edmund , Schwartz Sarah L. , Dixit Fuhar
Published: June 2023

Changing climate threatens our water systems: Droughts, fires, and floods disrupt access to safe water and no country is immune. The aging infrastructure crisis offers an opportunity to build resiliency into our future water systems with approaches like (non-)potable reuse. Such strategies require participation by end-users, which we propose can be championed by water utilities through: (1) Raising awareness of water challenges, (2) education about current water realities and opportunities, and (3) providing resources to implement and sustain water solutions.
Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring
Authors: Qiu Xinying , Liao Shuxuan , Xie Jiajun , Nie Jian-Yun
Published: June 2023

In the prompt-specific holistic score prediction task for Automatic Essay Scoring (AES), the general approaches include pre-trained neural model, coherence model, and hybrid model that incorporate syntactic features with neural model. In this paper, we propose a novel approach to extract and represent essay coherence features with NSP that matches the state-of-the-art (SOTA) AES coherence model, and achieves the best performance for long essays. We apply syntactic feature dense embedding to augment BERT-based model and achieve the best performance for hybrid methodology for AES. In addition, we explore various ideas to combine coherence, syntactic information, and semantic embeddings, which no previous study has done. Our combined model also performs better than the SOTA available for combined model, even though it does not outperform our syntactic-enhanced neural model. We further compare with the pure neural models and analyze the strengths and weaknesses of our methodologies.
The Effects of Chinese Perfective Aspect Marker le on the Simple Past Use in English Interlanguage
Authors: Zhang Peicui , Zhuang Huibin
Published: June 2023

This study focuses on the effects of Chinese aspect marker le on English simple past use based on 90 English narratives in simple past context, and aims to address three questions: (1) Does Chinese aspect marker le significantly affect the English simple past use for Chinese EFL learners? (2) Does Chinese aspect marker le significantly affect the English simple past use for Chinese EFL learners of different proficiency levels? And (3) Does the effect of le display a balanced development with the increase of the proficiency level of the Chinese EFL learners? Through quantitative analysis of simple past variation, we mainly scrutinized the frequency and distribution features of the simple past, and the influence of Chinese perfective aspect marker le on the simple past forms and marking. The analysis shows that the presence of the aspect marker le can enhance the acquisition of English simple past. And the influence of the aspect marker le displays a balanced development: the influence of le decreases with the increase of the proficiency level of the Chinese EFL learners. This research tries to explain this phenomenon from the perspective of Universal Grammar.
Network Science-based Analysis of Urban Green Spaces in Singapore
Authors: Srikanth Anjanaa Devi , Schroepfer Thomas
Published: June 2023

Future resilient cities need to integrate increasing density with the development of urban infrastructure and systems, especially natural and green systems. In Singapore’s post-Independence history, urban green spaces have evolved from leftover manicured grassy areas to biodiverse spaces with recreational areas and facilities. Singapore has recognized the need to provide varying access to the public to protect the more natural areas from too much public access. In urban green spaces, different areas have varying access depending on the scale and significance of the natural habitat. This paper uses Spatial Network Analysis — or the study of topological spatial relationships — to inform the planning and design of varying levels of access in urban green spaces in Singapore. It investigates the influence of spatial configuration of urban green spaces on pedestrian movement, by studying two types of urban green spaces in Singapore: one-north Park and Jurong Lakeside Garden. In both cases, the research utilizes weighted Spatial Network Analysis using sDNA, an analytical tool, to gain insights into the correlations between spatial connectivity and pedestrian movement distribution patterns. Empirical quantitative data are collected using infrared-sensor devices and manual pedestrian count and activity mapping. The methodology can provide the basis for the future planning and design of pedestrian infrastructure in urban green spaces in Singapore, and other high-density cities.
A Constructive Approach for Threshold Function Identification
Authors: Li Meng-Jing , Yen Yu-Chuan , Li Yi-Ting , Chen Yung-Chih , Wang Chun-Yao
Published: June 2023

Threshold Function (TF) is a subset of Boolean function that can be represented with a single linear threshold gate (LTG). In the research about threshold logic, the identification of TF is an important task that determines whether a given function is a TF or not. In this article, we propose a sufficient and necessary condition for a function being a TF. With the proposed sufficient and necessary condition, we devise a TF identification algorithm. The experimental results show that the proposed approach saves 80% CPU time for identifying all the 8-input NP-class TFs as compared with the state-of-the-art. Furthermore, the LTGs corresponding to the identified TFs obtained by the proposed approach have smaller weights and threshold values than the state-of-the-art.
Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain
Authors: Bruun Simone Borg , Lioma Christina , Maistro Maria
Published: June 2023

Providing personalized recommendations for insurance products is particularly challenging due to the intrinsic and distinctive features of the insurance domain. First, unlike more traditional domains like retail, movie and so on, a large amount of user feedback is not available and the item catalog is smaller. Second, due to the higher complexity of products, the majority of users still prefer to complete their purchases over the phone instead of online. We present different recommender models to address such data scarcity in the insurance domain. We use recurrent neural networks with three different types of loss functions and architectures (cross-entropy, censored Weibull, and attention). Our models cope with data scarcity by learning from multiple sessions and different types of user actions. Moreover, differently from previous session-based models, our models learn to predict a target action that does not happen within the session. Our models outperform state-of-the-art baselines on a real-world insurance dataset, with ca. 44K users, 16 items, 54K purchases, and 117K sessions. Moreover, combining our models with demographic data boosts the performance. Analysis shows that considering multiple sessions and several types of actions are both beneficial for the models, and that our models are not unfair with respect to age, gender, and income.
Structural, Photophysical, and Transport Properties of Porous MAPbBr3 Single Nanowire and Nanodevice for Electrochemical Sensing
Authors: Li Chun-Yu , Shellaiah Muthaiah , Sun Kien Wen
Published: June 2023

In this paper, we report synthesis of porous methylammonium lead tribromide (MAPbBr3) nanowires via self-template-directed method with dimensions of 13 ± 3 μm in length and 700 ± 100 nm in width. The perovskite single nanowires with correct compositions are demonstrated uniform in crystallinity and high in porosity by X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The current-voltage (I-V) characteristics of a single nanowire display a linear behavior (Ohmic contact) from 300 K to 83 K with an average conductivity of 57.82 ± 4.8 S cm−1, a hole concentration of 5.98 × 1018 cm−3, and hole mobility of 60.33 cm2V−1S−1 at 300 K. The single porous nanowire not only displays resistivity of approximately five orders of magnitude lower than those of previous reported values but is also stable and insensitive to the environment’s effects. Moreover, conductivity of porous nanowires can be modulated via the doping or substituting mechanisms when encountering heavy metal ions. The single nanowire device is applied as an electrochemical sensor in detecting four metal ions (Cu2+, Co2+, Hg2+, and Fe3+). The wide dynamic range in electrical conductivity of this porous single nanowire makes it a potential candidate for metal ion sensing with great sensitivity.
Thin Quaternary Layer and Staggered Electron Blocking Layers for Improved Ultraviolet Light-Emitting Diodes
Authors: Rasheed Saad , Usman Muhammad , Mustafa Laraib , Ali Shazma
Published: June 2023

We have carried out numerical investigations of AlGaN-based ultraviolet light-emitting diodes (UV LEDs). We have shown that the unequal carrier distribution, which is typical in III-N LEDs, is mitigated in the active region. To improve the performance, we have used a thin AlInGaN quaternary layer (QL) between the final quantum barrier (FQB) and the electron blocking layer (EBL). We have also compared the results with a staggered or step-graded electron blocking layer which is known to show enhanced device output. The hole insertion has been found to greatly improve in all the multiquantum wells (MQWs) of our numerically designed LEDs. The radiative recombination rate has been found to be enhanced by ∼82% in comparison to reference LED.
More than just a small left ventricle: the right ventricular fibroblast and ECM in health and disease
Authors: McNair Benjamin D. , Shorthill Samantha K. , Bruns Danielle R.
Published: June 2023

Fibroblasts intricately organize and regulate the extracellular matrix (ECM) in cardiac health and disease. Excess deposition of ECM proteins causes fibrosis, resulting in disrupted signaling conduction and contributing to the development of arrhythmias and impaired cardiac function. Fibrosis is causally involved in cardiac failure in the left ventricle (LV). Fibrosis likely occurs in right ventricle (RV) failure, yet mechanisms remain unclear. Indeed, RV fibrosis is poorly understood with mechanisms often extrapolated from the LV to the RV. However, emerging data suggest that the LV and RV are distinct cardiac chambers and differ in regulation of the ECM and response to fibrotic stimuli. In the present review, we will discuss differences in ECM regulation in the healthy RV and LV. We will discuss the importance of fibrosis in the development of RV disease in pressure overload, inflammation, and aging. During this discussion, we will highlight mechanisms of fibrosis with respect to the synthesis of ECM proteins while acknowledging the importance of considering collagen breakdown. We will also discuss current knowledge of antifibrotic therapies in the RV and the need for additional research to help delineate the shared and distinct mechanisms of RV and LV fibrosis.
Sex-dependent attenuating effects of capsaicin administration on the mechanoreflex in healthy rats
Authors: Butenas Alec L. E. , Ishizawa Rie , Rollins Korynne S. , Mizuno Masaki , Copp Steven W.
Published: June 2023

Recent evidence in isolated sensory neurons indicates that capsaicin-induced stimulation of TRPV1 attenuates mechanosensitivity. Here we demonstrate for the first time that capsaicin exposure/administration reduces the reflex pressor and renal sympathetic nerve response to mechanoreflex activation in male rats, but not female rats, in vivo. Our data may carry important clinical implications for chronic diseases which have been linked to an exaggerated mechanoreflex, at least in males.
miR-1 Inhibits the Ferroptosis of Chondrocyte by Targeting CX43 and Alleviates Osteoarthritis Progression
Authors: Zhou Ming , Zhai Chenjun , Shen Kai , Liu Gang , Liu Lei , He Jian , Chen Jun , Xu Yaozeng
Published: June 2023

Dysregulation of miRNAs in chondrocytes has been confirmed to participate in osteoarthritis (OA) progression. Previous study has screen out several key miRNAs may play crucial role in OA based on bioinformatic analysis. Herein, we identified the downregulation of miR-1 in OA samples and inflamed chondrocytes. The further experiments revealed that miR-1 played an essential role in maintaining chondrocytes proliferation, migration, antiapoptosis, and anabolism. Connexin 43 (CX43) was further predicted and confirmed to be the target of miR-1, and mediated the promotion effects of miR-1 in regulating chondrocyte functions. Mechanistically, miR-1 maintained the expression of GPX4 and SLC7A11 by targeting CX43, attenuated the accumulation of intracellular ROS, lipid ROS, MDA, and Fe2+ in chondrocytes, thereby inhibiting the ferroptosis of chondrocytes. Finally, experimental OA model was constructed by anterior cruciate ligament transection surgery, and Agomir-1 was injected into the joint cavity of mice to assess the protective effect of miR-1 in OA progression. Histological staining, immunofluorescence staining and Osteoarthritis Research Society International score revealed that miR-1 could alleviate the OA progression. Therefore, our study elucidated the mechanism of miR-1 in OA in detail and provided a new insight for the treatment of OA.
A New Porcine Reproductive and Respiratory Syndrome Virus with N-Linked Glycosylation Site Deletion in GP5 44th Amino Acid from JXA1, NADC30-Like, and JM Triparental Recombination
Authors: Zhou Xingdong , Bian Sushu , Kan Enxi , Zhou Lujia , Zhang Xiaohui , Xiao Min , Lu Chang , Hua Ji , Wu Yuan , Zhang Cheng , Zhou Yingshan , Dong Wanyu , Du Jing , Wang Xiaodu , Song Houhui
Published: June 2023

Porcine reproductive and respiratory syndrome virus (PRRSV) is a significant pathogen causing substantial financial losses in the global swine industry. The prevention of PRRSV is hampered due to frequent gene recombination among different strains of PRRSV. In this study, a new PRRSV strain, PRRSV-HQ-2020, was identified from nursery piglets in Yunnan Province, China, in 2020. The complete genome analysis revealed that PRRSV-HQ-2020 is highly similar to JXA1-like (lineage 8.7 PRRSV, isolated from China in 2008) in the 5′UTR, nsp1–9, and nsp11 coding regions. Additionally, it has a resemblance to JM (lineage 3 PRRSV, isolated from Taiwan, China, in 2010) in the nsp12-M coding region and NADC30 (lineage 1.8 PRRSV, isolated from North American in 2008) in the nsp10, N, and 3′UTR, suggesting a natural recombination event. Furthermore, recombination analyses showed three interlineage recombination events among lineages 8.7, 1.8, and 3. Notably, the GP5 protein of PRRSV-HQ-2020 exhibited a crucial mutation at position 44, leading to the deletion of a key glycosylation site. These findings provide direct evidence for the natural occurrence of recombination events among three lineages of PRRSV-2 in Chinese swine herds, leading to the emergence of unique genetic properties of PRRSV variants, and providing a theoretical basis for developing better PRRSV prevention strategies.
Single-Phase Wireless Electric Vehicle Charger Using EF2 Inverter
Authors: Meher Soumya Ranjan , Singh Rajeev Kumar
Published: June 2023

This paper proposes a wireless battery charger topology for electric vehicle (EV) application that can be installed in public charging infrastructure as well as in home premises. The proposed topology can maintain constant current as well as constant voltage at the battery terminal and simultaneously can maintain near unity power factor at the input side using power factor correction (PFC) operation and thus satisfy all the criteria of a standard EV charger. The heart of the wireless power transfer (WPT) scheme is a high-frequency (HF) inverter. In this work, a class EF2 inverter is used to generate HF AC in such a way that it can deliver power with variable loading condition while maintaining constant current (CC) or constant voltage (CV) according to the requirement of CC-CV charging profile. To supply power to this EF2 inverter, an AC-DC front-end converter (stage-1) is integrated with the charger. The stage-1 is operated as a constant voltage source to the EF2 inverter for CC mode and constant current source for CV mode operation. CC-CV at the battery end and PFC at the input end are achieved only by controlling the gate pulse of stage-1. The WPT coils are first simulated using Ansys Maxwell package, and the complete charger is simulated with PSIM simulation software. A scaled-down 200 W laboratory prototype of the proposed charger is developed and tested with a resistive load to validate the idea. The wireless power transfer is achieved for a maximum distance of 12 cm between the transmitting and receiving coil. Finally, the charger is tested to charge both 12 V and 24 V battery packs and the CC-CV charging profile is presented for the 24 V, 30 Ah battery pack.
Estimation of Abnormal Cell Growth and MCG‐Based Discriminative Feature Analysis of Histopathological Breast Images
Authors: Saha Priya , Das Puja , Nath Niharika , Bhowmik Mrinal Kanti
Published: June 2023

The accurate prediction of cancer from microscopic biopsy images has always been a major challenge for medical practitioners and pathologists who manually observe the shape and structure of the cells from tissues under a microscope. Mathematical modelling of cell proliferation helps to predict tumour sizes and optimizes the treatment procedure. This paper introduces a cell growth estimation function that uncovers the growth behaviour of benign and malignant cells. To analyse the cellular level information from tissue images, we propose a minimized cellular graph (MCG) development method. The method extracts cells and produces different features that are useful in classifying benign and malignant tissues. The method’s graphical features enable a precise and timely exploration of huge amounts of data and can help in making predictions and informed decisions. This paper introduces an algorithm for constructing a minimized cellular graph which reduces the computational complexity. A comparative study is performed based on the state‐of‐the‐art classifiers, SVM, decision tree, random forest, nearest neighbor, LDA, Naive Bayes, and ANN. The experimental data are obtained from the BreakHis dataset, which contains 2480 benign and 5429 malignant histopathological images. The proposed technique achieves a 97.7% classification accuracy which is 7% higher than that of the other graph feature‐based classification methods. A comparative study reveals a performance improvement for breast cancer classification compared to the state‐of‐the‐art techniques.
A Novel Triple Reassortment H3N8 Avian Influenza Virus: Characteristics, Pathogenicity, and Transmissibility
Authors: Chen Sheng , Shen Shuqun , Teng Yutao , Li Ruoying , Zhang Xinheng , Liu Jiajia , Wu Zhiqiang , Yan Zhuanqiang , Chen Feng , Xie Qingmei
Published: June 2023

An increasing number of new subtypes of avian influenza viruses (AIVs) are reported to be infecting humans, including H3N2, H5N1, H7N9, H10N8, and the recently emerged H3N8 virus in China in 2022. However, the genetic and biological properties of the currently prevalent H3N8 AIVs are not yet fully understood. This study reports the isolation of a novel triple reassortment H3N8 virus (GD-H3N8) from chicken flocks in Guangdong province, China, in 2022. The GD-H3N8 virus contains the Eurasian avian duck-origin H3 gene, the North American avian N8 gene, and dynamic internal genes of the H9N2 virus, and shows high homology with human H3N8 strains. The GD-H3N8 isolate has multiple mammalian adaptive mutations associated with receptor binding and virulence. Growth kinetics assays demonstrate that the GD-H3N8 isolate is capable of efficient replication in avian, mammalian, and human cells in vitro. In vivo, the GD-H3N8 isolate can replicate efficiently in mice without preadaptation, in addition to establishing systemic infection and transmission by direct contact in chickens. These findings underscore the need for continued surveillance of H3N8 viruses to identify circulating strains that may potentially threaten human health.
A Two-Stage Sequential Framework for Traffic Accident Post-Impact Prediction Utilizing Real-Time Traffic, Weather, and Accident Data
Authors: Abdi Amirhossein , Seyedabrishami Seyedehsan , O’Hern Steve
Published: June 2023

Detecting road accident impacts as promptly as possible is essential for intelligent traffic management systems. This paper presents a sequential two-stage framework for predicting the most congested traffic level that appears after an accident and the recovery time required for returning to the level of service that existed at the accident report time. As fewer accident characteristics are available at the report time, stage one models rely on real-time traffic and weather variables. With the arrival of the responders at the accident scene, more information is gained; therefore, the second stage model is activated, which updates the remaining accident duration time. We used eXtreme Gradient Boosting (XGBoost), a machine learning algorithm, and Shapley Additive exPlanations (SHAP) for making predictions and interpreting results, respectively. The results show that our framework predicts traffic levels with overall accuracies of around 80%, and duration models have high forecast accuracy with mean absolute percentage errors ranging between 7.26% and 21.59%. Overall, in the absence of accident information, SHAP values identified that weather factors, the traffic speed difference before and after an accident, traffic volume, and the percentage of heavy vehicles before the accident are the most important variables. However, accident variables, including the occurrence of injury or fatal accidents, rear-end collisions, and the number of involved vehicles, are among the most important variables in the second stage of the framework. The findings have practical implications for real-time traffic management of accident events. Road operators could manage post-accident traffic conditions more effectively, and road users could be alerted to take another route or manage their trip.
Effects of Harvesting Age and Barley Varieties on Morphological Characteristics, Biomass Yield, Chemical Composition, and Economic Benefits under Hydroponic Conditions in Fogera District, Ethiopia
Authors: Alemnew Yeshambel , Mekuriaw Yeshambel
Published: June 2023

The objectives of this study were to evaluate the effects of harvesting age at 6, 8, 10, and 12 days and four barley varieties HB-1307, Debark-1, Tila, and local varieties on morphological characteristics, biomass yield, chemical composition, and economic benefits under hydroponics in the Fogera district at Fogera National Rice Research and Training Center of Amhara Region, Ethiopia. The interaction effects of harvesting age and barley variety significantly influenced growth parameters of plant height, shoot length, leaf length, and overall chemical composition (

P
<
0.05

). All morphological characteristics were significantly different (

P
<
0.01

) among harvesting ages, and the stem weight, leaf to stem ratio (LSR), and number of leaves per plant were not significantly influenced (

P
>
0.05

) by the different barley varieties. The highest plant height (21.26 cm) and crude protein (CP) content (21.39%) were obtained from Debark-1 at 12 days of harvesting. The highest fresh fodder biomass yield, 203.50 t/ha, and dry matter (DM) yield, 36.21 t/ha, were obtained at 12 days of harvesting. In the case of harvesting age, all morphological parameters increased with the progress harvesting age, except for the LSR and DM content. The highest net return of 2,923,002.25 ETB/ha was obtained from Debark-1 at the 12 days harvesting age, and the lowest 941,201.13 ETB/ha was obtained for the Tila variety at the six days of harvesting age. From the study, it can be concluded that based on fresh fodder biomass yield, DM yield, CP, and economic benefits, Debark-1 was the recommended barley variety on the 12-day harvesting age, followed by HB-1307, local, and Tila barley varieties.
A Study on the Bionomics of Primary Malaria Vectors Anopheles minimus and Anopheles baimaii in Some States of North East Region of India
Authors: Khan Siraj Ahmed , Sarmah Biplob , Bhattacharyya Dibya Ranjan , Raghavendra Kamaraju , Rahi Manju , Dutta Prafulla
Published: June 2023

Northeast region (NER) states of India remain highly malarious due to their geographical and ecotype diversity. Furthermore, rapid urbanization and change in climate are also affecting the vector biology and behavior of the existing species. Hence, a study is conducted in the states of Tripura and Meghalaya to generate data on the bionomics of the prevalent malaria vector species. The data from this study show that primary vectors of malaria An. minimus and An. baimaii were anthropophagic. However, An. baimaii showed a behavioral shift towards zoophagicity (∼14%). Insecticide bioassays confirm that these two major vector species are reportedly susceptible to DDT, Malathion, and indicate that intervention by DDT-IRS is effective. Thus, the implementation of appropriate strategies based on this recent information on the bionomics of malaria vectors in NE region of India will provide an opportunity to achieve malaria elimination by date in these states.
Data from Targeting BCL2 Overcomes Resistance and Augments Response to Aurora Kinase B Inhibition by AZD2811 in Small Cell Lung Cancer
Authors: Ramkumar Kavya , Tanimoto Azusa , Della Corte Carminia M. , Stewart C. Allison , Wang Qi , Shen Li , Cardnell Robert J. , Wang Jing , Polanska Urszula M. , Andersen Courtney , Saeh Jamal , Pease J. Elizabeth , Travers Jon , Fabbri Giulia , Gay Carl M. , Urosevic Jelena , Byers Lauren A.
Published: June 2023

AbstractPurpose:
Therapeutic resistance to frontline therapy develops rapidly in small cell lung cancer (SCLC). Treatment options are also limited by the lack of targetable driver mutations. Therefore, there is an unmet need for developing better therapeutic strategies and biomarkers of response. Aurora kinase B (AURKB) inhibition exploits an inherent genomic vulnerability in SCLC and is a promising therapeutic approach. Here, we identify biomarkers of response and develop rational combinations with AURKB inhibition to improve treatment efficacy.
Experimental Design:
Selective AURKB inhibitor AZD2811 was profiled in a large panel of SCLC cell lines (n = 57) and patient-derived xenograft (PDX) models. Proteomic and transcriptomic profiles were analyzed to identify candidate biomarkers of response and resistance. Effects on polyploidy, DNA damage, and apoptosis were measured by flow cytometry and Western blotting. Rational drug combinations were validated in SCLC cell lines and PDX models.
Results:
AZD2811 showed potent growth inhibitory activity in a subset of SCLC, often characterized by, but not limited to, high cMYC expression. Importantly, high BCL2 expression predicted resistance to AURKB inhibitor response in SCLC, independent of cMYC status. AZD2811-induced DNA damage and apoptosis were suppressed by high BCL2 levels, while combining AZD2811 with a BCL2 inhibitor significantly sensitized resistant models. In vivo, sustained tumor growth reduction and regression was achieved even with intermittent dosing of AZD2811 and venetoclax, an FDA-approved BCL2 inhibitor.
Conclusions:
BCL2 inhibition overcomes intrinsic resistance and enhances sensitivity to AURKB inhibition in SCLC preclinical models.
Data from Thyroid cancers exhibit oncogene-enhanced macropinocytosis that is restrained by IGF1R and promotes albumin-drug-conjugate response
Authors: Hu Huiyu , Ng Thomas S.C. , Kang Mikyung , Scott Ella , Li Ran , Quintana Jeremy M. , Matvey Dylan , Vantaku Venkata R. , Weissleder Ralph , Parangi Sareh , Miller Miles A.
Published: June 2023

Abstract
Purpose: Oncogene-driven macropinocytosis fuels nutrient scavenging in some cancer types, yet whether this occurs in thyroid cancers with prominent MAPK-ERK and PI3K pathway mutations remains unclear. We hypothesized that understanding links between thyroid cancer signaling and macropinocytosis might uncover new therapeutic strategies. Experimental Design: Macropinocytosis was assessed across cells derived from papillary thyroid cancer (PTC), follicular thyroid cancer (FTC), non-malignant follicular thyroid, and aggressive anaplastic thyroid cancer (ATC), by imaging fluorescent dextran and serum albumin. The impacts of ectopic BRAF V600E and mutant RAS, genetic PTEN silencing, and inhibitors targeting RET, BRAF, and MEK kinases were quantified. Braf V600E p53-/- ATC tumors in immunocompetent mice were used to measure efficacy of an albumin-drug-conjugate comprising microtubule-destabilizing monomethyl auristatin E (MMAE) linked to serum albumin via a cathepsin-cleavable peptide (Alb-vc-MMAE). Results: FTC and ATC cells showed greater macropinocytosis than non-malignant and PTC cells. ATC tumors accumulated albumin at 8.8% injected dose per gram tissue. Alb-vc-MMAE, but not MMAE alone, reduced tumor size by >90% (P<0.01). ATC macropinocytosis depended on MAPK/ERK activity and nutrient signaling, and increased by up to 230% with metformin, phenformin, or inhibition of insulin-like growth factor 1 receptor (IGF1Ri) in monoculture but not in vivo. Macrophages also accumulated albumin and express the cognate IGF1R ligand, IGF1, which reduced ATC responsiveness to IGF1Ri. Conclusions: These findings identify regulated oncogene-driven macropinocytosis in thyroid cancers and demonstrate the potential of designing albumin-bound drugs to efficiently treat them.
Data from Thyroid Cancers Exhibit Oncogene-Enhanced Macropinocytosis that Is Restrained by IGF1R and Promote Albumin–Drug Conjugate Response
Authors: Hu Huiyu , Ng Thomas S.C. , Kang Mikyung , Scott Ella , Li Ran , Quintana Jeremy M. , Matvey Dylan , Vantaku Venkata R. , Weissleder Ralph , Parangi Sareh , Miller Miles A.
Published: June 2023

AbstractPurpose:
Oncogene-driven macropinocytosis fuels nutrient scavenging in some cancer types, yet whether this occurs in thyroid cancers with prominent MAPK–ERK and PI3K pathway mutations remains unclear. We hypothesized that understanding links between thyroid cancer signaling and macropinocytosis might uncover new therapeutic strategies.
Experimental Design:
Macropinocytosis was assessed across cells derived from papillary thyroid cancer (PTC), follicular thyroid cancer (FTC), non-malignant follicular thyroid, and aggressive anaplastic thyroid cancer (ATC), by imaging fluorescent dextran and serum albumin. The impacts of ectopic BRAFV600E and mutant RAS, genetic PTEN silencing, and inhibitors targeting RET, BRAF, and MEK kinases were quantified. BrafV600E p53−/− ATC tumors in immunocompetent mice were used to measure efficacy of an albumin–drug conjugate comprising microtubule-destabilizing monomethyl auristatin E (MMAE) linked to serum albumin via a cathepsin-cleavable peptide (Alb-vc-MMAE).
Results:
FTC and ATC cells showed greater macropinocytosis than non-malignant and PTC cells. ATC tumors accumulated albumin at 8.8% injected dose per gram tissue. Alb-vc-MMAE, but not MMAE alone, reduced tumor size by >90% (P < 0.01). ATC macropinocytosis depended on MAPK/ERK activity and nutrient signaling, and increased by up to 230% with metformin, phenformin, or inhibition of IGF1Ri in monoculture but not in vivo. Macrophages also accumulated albumin and express the cognate IGF1R ligand, IGF1, which reduced ATC responsiveness to IGF1Ri.
Conclusions:
These findings identify regulated oncogene-driven macropinocytosis in thyroid cancers and demonstrate the potential of designing albumin-bound drugs to efficiently treat them.
The European Portuguese Version of the Brief Negative Symptom Scale
Authors: Santos Henrique Castro , Rodrigues Alexandra , Ferreira Sara , Martins João Malhadas , Baptista Tiago , Gama Marques João , Kirkpatrick Brian , Prata Diana
Published: June 2023

Negative symptoms reflect a currently much-untreated loss of normal functioning and are frequently found in psychotic disorders. We present the first translation of the Brief Negative Symptom Scale (BNSS) to European Portuguese and evaluate its validity in a sample of Portuguese male patients with a psychotic spectrum disorder. The Portuguese BNSS showed excellent internal consistency, high convergent validity (i.e., strong correlation with the PANSS negative factor), and high discriminant validity (i.e., a lack of association with the PANSS positive factor). In sum, the present European Portuguese BNSS has shown to be reliable, thus extending this instrument’s clinical availability worldwide.
Network Preservation Analysis Reveals Dysregulated Metabolic Pathways in Human Vascular Smooth Muscle Cell Phenotypic Switching
Authors: Perry R. Noah , Albarracin Diana , Aherrahrou Redouane , Civelek Mete
Published: June 2023

BACKGROUND:

Vascular smooth muscle cells are key players involved in atherosclerosis, the underlying cause of coronary artery disease. They can play either beneficial or detrimental roles in lesion pathogenesis, depending on the nature of their phenotypic changes. An in-depth characterization of their gene regulatory networks can help better understand how their dysfunction may impact disease progression.

METHODS:

We conducted a gene expression network preservation analysis in aortic smooth muscle cells isolated from 151 multiethnic heart transplant donors cultured under quiescent or proliferative conditions.

RESULTS:

We identified 86 groups of coexpressed genes (modules) across the 2 conditions and focused on the 18 modules that are least preserved between the phenotypic conditions. Three of these modules were significantly enriched for genes belonging to proliferation, migration, cell adhesion, and cell differentiation pathways, characteristic of phenotypically modulated proliferative vascular smooth muscle cells. The majority of the modules, however, were enriched for metabolic pathways consisting of both nitrogen-related and glycolysis-related processes. Therefore, we explored correlations between nitrogen metabolism-related genes and coronary artery disease–associated genes and found significant correlations, suggesting the involvement of the nitrogen metabolism pathway in coronary artery disease pathogenesis. We also created gene regulatory networks enriched for genes in glycolysis and predicted key regulatory genes driving glycolysis dysregulation.

CONCLUSIONS:

Our work suggests that dysregulation of vascular smooth muscle cell metabolism participates in phenotypic transitioning, which may contribute to disease progression, and suggests that AMT (aminomethyltransferase) and MPI (mannose phosphate isomerase) may play an important role in regulating nitrogen and glycolysis-related metabolism in smooth muscle cells.
The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness
Authors: Carvalho Jonata Tyska , Nolfi Stefano
Published: June 2023

Exposing an evolutionary algorithm that is used to evolve robot controllers to variable conditions is necessary to obtain solutions which are robust and can cross the reality gap. However, we do not yet have methods for analyzing and understanding the impact of the varying morphological conditions which impact the evolutionary process, and therefore for choosing suitable variation ranges. By morphological conditions, we refer to the starting state of the robot, and to variations in its sensor readings during operation due to noise. In this paper, we introduce a method that permits us to measure the impact of these morphological variations and we analyze the relation between the amplitude of variations, the modality with which they are introduced, and the performance and robustness of evolving agents. Our results demonstrate that (i) the evolutionary algorithm can tolerate morphological variations which have a very high impact, (ii) variations affecting the actions of the agent are tolerated much better than variations affecting the initial state of the agent or of the environment, and (iii) improving the accuracy of the fitness measure through multiple evaluations is not always useful. Moreover, our results show that morphological variations permit generating solutions which perform better both in varying and non-varying conditions.