bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chemical chaperones ameliorate neurodegenerative disorders in Derlin-1-deficient mice via improvement of cholesterol biosynthesis
Authors: Sugiyama Takashi , Murao Naoya , Kadowaki Hisae , Nishitoh Hideki 
Published: December 2022

AbstractThere are no available therapies targeting the underlying molecular mechanisms of neurodegenerative diseases. Although chaperone therapies that alleviate endoplasmic reticulum (ER) stress recently showed promise in the treatment of neurodegenerative diseases, the detailed mechanisms remain unclear. We previously reported that mice with central nervous system-specific deletion of Derlin-1, which encodes an essential component for ER quality control, are useful as models of neurodegenerative diseases such as spinocerebellar degeneration. Cholesterol biosynthesis is essential for brain development, and its disruption inhibits neurite outgrowth, causing brain atrophy. In this study, we report a novel mechanism by which chemical chaperones ameliorate brain atrophy and motor dysfunction. ER stress was induced in the cerebella of Derlin-1 deficiency mice, whereas the administration of a chemical chaperone did not alleviate ER stress. However, chemical chaperone treatment ameliorated cholesterol biosynthesis impairment through SREBP-2 activation and simultaneously relieved brain atrophy and motor dysfunction. Altogether, these findings demonstrate that ER stress may not be the target of action of chaperone therapies and that chemical chaperone-mediated improvement of brain cholesterol biosynthesis is a promising novel therapeutic strategy for neurodegenerative diseases.
Classification with 2-D convolutional neural networks for breast cancer diagnosis
Authors: Sharma Anuraganand , Kumar Dinesh 
Published: December 2022

AbstractBreast cancer is the most common cancer in women. Classification of cancer/non-cancer patients with clinical records requires high sensitivity and specificity for an acceptable diagnosis test. The state-of-the-art classification model—convolutional neural network (CNN), however, cannot be used with such kind of tabular clinical data that are represented in 1-D format. CNN has been designed to work on a set of 2-D matrices whose elements show some correlation with neighboring elements such as in image data. Conversely, the data examples represented as a set of 1-D vectors—apart from the time series data—cannot be used with CNN, but with other classification models such as Recurrent Neural Networks for tabular data or Random Forest. We have proposed three novel preprocessing methods of data wrangling that transform a 1-D data vector, to a 2-D graphical image with appropriate correlations among the fields to be processed on CNN. We tested our methods on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets. To our knowledge, this work is novel on non-image tabular data to image data transformation for the non-time series data. The transformed data processed with CNN using VGGnet-16 shows competitive results for the WBC dataset and outperforms other known methods for the WDBC dataset.
Pathway expression analysis
Authors: Mankovich Nathan , Kehoe Eric , Peterson Amy , Kirby Michael 
Published: December 2022

AbstractThis paper introduces a pathway expression framework as an approach for constructing derived biomarkers. The pathway expression framework incorporates the biological connections of genes leading to a biologically relevant model. Using this framework, we distinguish between shedding subjects post-infection and all subjects pre-infection in human blood transcriptomic samples challenged with various respiratory viruses: H1N1, H3N2, HRV (Human Rhinoviruses), and RSV (Respiratory Syncytial Virus). Additionally, pathway expression data is used for selecting discriminatory pathways from these experiments. The classification results and selected pathways are benchmarked against standard gene expression based classification and pathway ranking methodologies. We find that using the pathway expression data along with selected pathways, which have minimal overlap with high ranking pathways found by traditional methods, improves classification rates across experiments.
Non-collinear magnetic configuration mediated exchange coupling at the interface of antiferromagnet and rare-earth nanolayers
Authors: Huang Junyu , Liu Chang , Cui Yifan , Ling Yuxiang , Chen Keming , Zhao Kunlong , Xiao Xiangshang , Yuan Bin , Paul Amitesh 
Published: December 2022

AbstractMn$$_{3}$$3Ir/CoFe bilayer is a prototypical exchange-coupled antiferromagnet (AF)–ferromagnet (FM) system. Nevertheless, a strong exchange coupling between FM and rare-earth(RE) interfaces of Fe/Dy and Fe/Tb has been established earlier. Strong coupling at the FM–RE interface originates from the number of irreversible spins owing to the imbalance in the non-collinear configuration in RE. However, exchange coupling between AF–RE could not be established due to the minimal number of irreversible spins in AF and RE. A frustrated inter-domain magnetic interaction leads to the coexistence of spin-freezing-like ordering around the temperature range of helical spin modulation at the exchange-coupled interfaces of RE-based specimens. To overcome the lack of coupling between the AF–RE interface, we use a sandwich structure of AF–FM–RE layers (Mn$$_{3}$$3Ir/CoFe/Dy) as we demonstrate establishing considerable exchange bias in the system. Changing the bias direction during field cooling introduces possible differences in non-collinear directions (helicities), which affects the number of irreversible spins and consequent exchange coupling differently for opposite directions. The non-collinear structures in RE are topologically stable; thus, their directions of orientation can be regarded as an additional degree of freedom, which can be manipulated in all-spin-based technology.
Predicting stress, strain and deformation fields in materials and structures with graph neural networks
Authors: Maurizi Marco , Gao Chao , Berto Filippo 
Published: December 2022

AbstractDeveloping accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm. Although impressive achievements have been reached, efficiently predicting complex physical phenomena in materials and structures remains a challenge. Here, we present an AI-based general framework, implemented through graph neural networks, able to learn complex mechanical behavior of materials from a few hundreds data. Harnessing the natural mesh-to-graph mapping, our deep learning model predicts deformation, stress, and strain fields in various material systems, like fiber and stratified composites, and lattice metamaterials. The model can capture complex nonlinear phenomena, from plasticity to buckling instability, seemingly learning physical relationships between the predicted physical fields. Owing to its flexibility, this graph-based framework aims at connecting materials’ microstructure, base materials’ properties, and boundary conditions to a physical response, opening new avenues towards graph-AI-based surrogate modeling.
Surgical outcomes of adnexal masses classified by IOTA ultrasound simple rules
Authors: Carballo Erica V. , Maturen Katherine E. , Li Zhanhai , Patel-Lippmann Krupa K. , Wasnik Ashish P. , Sadowski Elizabeth A. , Barroilhet Lisa M. 
Published: December 2022

AbstractIOTA (International Ovarian Tumor Analysis) Simple Rules classifies adnexal masses as benign, malignant, or indeterminate based on sonographic features. We seek to determine if IOTA inappropriately directed women to surgery, or more aggressive surgery, than their final diagnosis warranted. This is a retrospective study of sonographically detected adnexal masses with known clinical outcomes from two institutions (n = 528). Surgically managed patients (n = 172) were categorized based on pathology and compared using Chi-square and t-test for categorical and continuous variables respectively. A logistic regression was used to predict characteristics that predicted surgery or imaging follow up of indeterminate masses. Of the 528 masses imaged, 29% (n = 155) underwent surgery for benign pathology. Only 1.9% (n = 10) underwent surgery after classification as malignant by IOTA for what was ultimately a benign mass. Surgical complications occurred in 10 cases (5.8%), all benign. Fifteen (3.2%) patients went into surgically induced menopause for benign masses, one of which was inaccurately classified by IOTA as malignant. Of the 41 IOTA indeterminate masses, the presence of soft tissue nodules on ultrasound was the only statistically significant predictor of the patient being triaged directly to surgery (OR 1.79, p = 0.04). Our findings support that the IOTA ultrasound classification system can provide clinical guidance without incurring unnecessary surgeries or surgical complications.
Predicting potential global distribution and risk regions for potato cyst nematodes (Globodera rostochiensis and Globodera pallida)
Authors: He Yitong , Wang Rui , Zhao Honghai , Ren Yonglin , Agarwal Manjree , Zheng Dan , Gao Shan , McKirdy Simon J. , Chu Dong 
Published: December 2022

AbstractPotato cyst nematodes (PCNs), golden (yellow) cyst nematode (Globodera rostochiensis, gPCN) and pale (white) cyst nematode (G. pallida, pPCN), are important invasive pests in many countries and regions where they can cause significant yield and economic loss for agriculture. Prediction and identification of habitats suitable for PCNs are critical for developing biosecurity strategies, both pre and post border, to maximise the potential for early elimination should an incursion occur. To date, the potential global distribution of PCNs has not been thoroughly studied. Therefore, this study conducted a species distribution model to illustrate the potential global distribution of PCNs and risk regions. In this study, the Maximum Entropy Model (Maxent) associated with the Geographic Information System (GIS) was employed to reveal the potential distribution of the gPCN and pPCN. In addition to bioclimate, soil quality was also included in the model. The global cultivated lands, whether the susceptible hosts were present or not, were used to assess the maximum potential risk regions. The limitation factors for PCNs distribution were also assessed. Results showed that 66% of the global land surface was suitable for gPCN or pPCN or both, and both species can colonise more than 75% of the global cultivated lands. The coldest quarter’s mean temperature and precipitation were critical limitations in unsuitable regions. In summary, the global risk maps of PCNs contribute valuable additional information that complements previous national/regional distribution predictions. The results of this distribution research will contribute practical support for decision-makers and practitioners to implement biosecurity strategies from a global perspective, that incorporate prevention or promptly enforce control practices to limit the damage caused by future incursions.
Diabetes mellitus degenerates cisplatin-induced nephrotoxicity in short hydration method: a propensity score-matching analysis
Authors: Saito Yoshitaka , Sakamoto Tatsuhiko , Takekuma Yoh , Kobayashi Masaki , Okamoto Keisuke , Shinagawa Naofumi , Shimizu Yasushi , Kinoshita Ichiro , Sugawara Mitsuru 
Published: December 2022

AbstractCisplatin (CDDP)-induced nephrotoxicity (CIN) is dose-limiting. We revealed that co-administration of non-steroid anti-inflammatory drugs and baseline comorbidity of diabetes mellitus (DM) are associated with CIN development in the short hydration method; however, the results were accessorily obtained without appropriate power calculation. This study aimed to demonstrate the influence of DM complications on CIN incidence in a real-world setting. Lung cancer patients receiving CDDP (≥ 75 mg/m2)-containing regimens with a short hydration method (n = 227) were retrospectively evaluated. The patients were divided into control and baseline DM complication groups. The primary endpoint was the evaluation of CIN incidence between the groups. Propensity score-matching was performed to confirm the robustness of the primary analysis results. CIN occurred in 6.8% of control and 27.0% of DM patients, respectively, with a significant difference in all-patient populations (P = 0.001). In addition, variation of serum creatinine and creatinine clearance significantly worsened in DM patients. Similar results were obtained in a propensity-matched population. Multivariate logistic regression analysis found that DM complication is a singular risk factor for CIN development (adjusted odds ratio; 4.31, 95% confidence interval; 1.62–11.50, P = 0.003). In conclusion, our study revealed that baseline DM complications significantly worsen CIN.
Seismic inversion with L2,0-norm joint-sparse constraint on multi-trace impedance model
Authors: Dai Ronghuo , Yang Jun 
Published: December 2022

AbstractImpedance inversion of post-stack seismic data is a key technology in reservoir prediction and characterization. Compared to the common used single-trace impedance inversion, multi-trace impedance simultaneous inversion has many advantages. For example, it can take lateral regularization constraint to improve the lateral stability and resolution. We propose to use the L2,0-norm of multi-trace impedance model as a regularization constraint in multi-trace impedance inversion in this paper. L2,0-norm is a joint-sparse measure, which can not only measure the conventional vertical sparsity with L0-norm in vertical direction, but also measure the lateral continuity with L2-norm in lateral direction. Then, we use a split Bregman iteration strategy to solve the L2,0-norm joint-sparse constrained objective function. Next, we use a 2D numerical model and a real seismic data section to test the efficacy of the proposed method. The results show that the inverted impedance from the L2,0-norm constraint inversion has higher lateral stability and resolution compared to the inverted impedance from the conventional sparse constraint impedance inversion.
Quality factor measurement based on direct energy method for piezoelectric resonator
Authors: Li Jian , Wu Xiaosheng 
Published: December 2022

AbstractThis letter presents a new real‐time and direct measurement method based on energy for piezoelectric resonator. An analytical model considering the stored energy and dissipation energy for quality factor measurement is built up. Using this novel direct energy method, the quality factor for piezoelectric resonator can be measured in real time. The finite element method simulations are introduced to verify the analytical model. Experiments results agree well with the theoretical model and show that quality factor can be measured by direct energy method. This work provides a new method for quality factor measurement of piezoelectric resonator.
Pilot protection method for bipolar CSC‐HVDC transmission lines based on transient currents behaviour
Authors: Khaleghi‐Abbasabadi Ali , Mehrabi‐Kooshki Mohammad , Mirhosseini Seyed Sattar , Jamali Sadegh 
Published: December 2022

AbstractThe unnecessary prolonged operating time of conventional current differential protection under high‐impedance DC line faults is an important issue affecting the continuity of power supply. This issue originates from the extreme differential current fluctuation under fault conditions. This paper presents a reinforced pilot protection algorithm for bipolar CSC‐HVDC transmission lines to achieve a selective and sensitive performance without sacrificing the protection speed. In this regard, the characteristics of low‐frequency transient modal currents are investigated along with the impact of discharging capacitive current under various fault conditions. Based on the features arising from line‐mode compensated currents, it is shown that their opposite changing directions induces a positive high‐value differential current during DC line faults, whereas the compensated line‐mode currents follow the same changing trend under external faults. After detecting internal faults, the faulted pole is detected by utilizing the polarity of ground‐mode components. Compared to other pilot protections, the proposed protection provides more reliability, selectivity, and sensitivity with high operating speed. Moreover, it has robust performance against discharging capacitive current, asynchronous measurement, line parameters uncertainty, and noise. Various simulation tests are performed on a ±500 kV HVDC test system in PSCAD to verify effectiveness of the proposed protection.
Improved demodulation method for fibre optic sensors applied in transformer partial discharge detection
Authors: Qiao Jixian , Zhang Weichao , Wang Yanqi , Zhao Hong 
Published: December 2022

AbstractThe 2 × 2 couplers, as a demodulation core of the fibre optic sensing system, have been extensively employed in fibre optic sensors for partial discharge (PD) detection of power equipment. However, the unstable demodulation sensitivity and low signal‐to‐noise ratio (SNR) of such a system are still unsatisfactory in engineering. This work uses a distributed feedback fibre laser as the sensor and employs a common asymmetric 2 × 3 coupler instead of a 2 × 2 coupler as the demodulation core to design a PD detection system. Then, we propose a new demodulation method for PD acoustic emission signals based on the Naval Postgraduate School method. On the PD detection platform, the performance of the conventional detection system and the proposed system is compared. The experimental results show that the proposed system has better reliability and can identify the PD signals in all instances. Furthermore, the average SNR of the new demodulation method is 6.11 dB higher than the traditional 2 × 2 coupler system and can be extended to most PD detection systems using fibre optic sensors.
SDNet: Image‐based sonar detection network for multi‐scale objects
Authors: Shi Pengfei , Sun Huanru , Xin Yuanxue , He Qi , Wang Xiaotian 
Published: December 2022

AbstractAutonomous Underwater Vehicle (AUV) carrying sonar for object detection has become one of the main ways of ocean exploration. However, object detection in sonar images always faces the problems of balancing detection accuracy and efficiency. To this end, this paper proposes an efficient underwater object detection network for sonar images named SDNet. In the model, the authors construct a new feature extraction network based on RepVGG to balance the detection accuracy and speed. By combining channel attention with RepVGG, useful information of high‐order feature maps captured can be selectively paid attention to. Then, the authors design a feature fusion network to efficiently converge the location and semantic information of multi‐scale feature maps. In the network, the authors propose a lightweight cross stage partial network for sonar (CSP_S) module suitable for sonar images, which can enhance the model's feature fusion capability and simplify the model.Finally, to reduce the conflict between classification and regression tasks, the authors leverage the Decoupled Head for the sonar object classification and localization. By testing on the self‐built Underwater Sonar Dataset underwater sonar dataset (USD) and the public sonar dataset sonar common target detection dataset (SCTD), the detection accuracy of SDNet reaches 99.52% and 95.20%, respectively. Moreover, the detection speed reaches 114 frames per second (FPS) and 138 FPS, respectively. The experimental results show that SDNet can effectively balance the sonar detection accuracy and efficiency.Code is available at https://github.com/Yukino111/sdnet‐pytorch.
Analysis of reactive power loadability and management of flexible alternating current transmission system devices in a distribution grid using whale optimization algorithm
Authors: Baby Honey , Jayakumar Jayaraj , Mathew Mobi , Hussien Mohamed G. , Kumar Nallapaneni Manoj 
Published: December 2022

AbstractThe optimal injection of reactive power to minimize power loss and manage voltage profiles is a difficult task that must be addressed and managed effectively for the power system's reliability. The distribution network indices like voltage stability, loss minimization, and power quality enhancement can be improved by the effective management of reactive power regulation. Aside from its interaction with reactive power management, the best location and sizing in a cost‐effective strategy have an impact on overall performance. As a broadly accepted swarm intelligence method in various engineering fields due to its simple assembly, less entails operator constraints, wild convergence speed, and better harmonizing ability between exploration and exploitation faces, the Whale Optimization Algorithm (WOA) is used in conjunction with a Flexible Alternating Current Transmission System (FACTS) device in this paper to manage reactive power effectively. The WOA was implemented and compared with Differential Evolution (DE), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) at 100%, 150%, and 200% load in the IEEE 30 test system. The results reveal that the suggested WOA produces a greater impact on cost management, power loss, and reactive power balance than any previous evolutionary algorithm.