bluuebunny/crossref_metadata_2025_split huggingface dataset:
An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
Authors: Vincent Amala Mary , P. Jidesh 
Published: July 2022

Abstract
Machine learning strategy has changed the face of automated models by integrating themselves into many application domains. The spectrum of applications ranges over various domains, from atmospheric analysis to medical diagnosis. All these applications are design-sensitive, implying that the model's performance depends highly on the selected machine learning algorithm, training procedures, regularization methods, and most importantly, how the hyperparameters are tuned. With the advent of AutoML systems, all these processes including hyperparameter optimization can be automated, producing better performance and faster results. The ideal hyperparameter setting for machine learning models has a direct and significant impact on the model's performance. This paper studies different AutoML models and the hyperparameter techniques used by them for image classification problems. We also discuss some of the libraries for hyperparameter optimization and analyse how they work for different image classification problems. Moreover, the paper proposes a framework for hyperparameter optimization that combines Bayesian optimization and evolutionary algorithms. Experiments are carried out on image classification benchmark datasets to assess the performance of different optimization approaches including the proposed model.
Learning an Improved Object Detection Approach Based on the YOLO Algorithm to Identify Circulating Tumor Cells
Authors: Li Xin , Zhang Lingzhe , Lin Chenyu , Chen Jingyao , Li Jun , Zhu Yuqian , Zhu Jinfeng , Chen Lijin , Li Xiaofeng , Chen Bingdi 
Published: July 2022

Abstract
In the undertaken study, a recognition system for circulating tumor cells was developed based on the YOLOv4 algorithm. The algorithm possesses the features of multi-layer high-level stacking, weight sharing, local connection and pooling that can be used to identify and category CTC tumor cells. The study is designed following five steps. The first step enriches many circulating tumor cells by targeting Negative Surface Charges of Cancer Cells by Multifunctional Nanoprobes. The second step screens the collected 1573 biological image samples based on circulating tumor cells according to the abnormal morphology of the nucleus and abnormal cytoplasm. The third step contains image preprocessing, unscrambling the training set and the test set, and training the YOLOv4 model along with improving the YOLOv4 model based on the test results. The fourth step has a circulating tumor cell feature extraction system based on the optimized YOLOv4 model to organize images of circulating tumor cells. The last step inputs the simulated blood sample cell image and the real blood sample cell image into the system for comparison and verification. When the proportion of the training set is 90% and the proportion of the test set is 10%, the model of IOU is the best, which is 98.88%. When the simulated blood sample image containing internal reference cells has put into the system, the average accuracy rate obtained from 10 experiments is 93.26%. When the real blood sample image has participated into the system, the average accuracy rate obtained from the 10 experiments is 85.67%. Comparing the current traditional pathology recognition, the artificial intelligence recognition system based on circulating tumor cells shows excellent computing performance and a lot time can be spared.
Reciprocal Analysis of Groundwater Potentiality and Vulnerability Modeling in the Bahabad Plain, Iran
Authors: Yazdi Seyedeh Sofia Atashi , Motamedvaziri Baharak , Hosseini Seyed Zeynalabedin , Ahmadi Hassan 
Published: July 2022

Abstract
Heuristic and statistical groundwater quality assessment models are efficient tools in the zoning of groundwater vulnerability to contamination. An important reciprocal methodology, yet neglected in Iran, was conducted to assess the performance of three groundwater vulnerability models, namely GODS, SI and DRASTIC, and a data mining model for groundwater potential, maximum entropy (MaxEnt). For both the training and validation stages for the MaxEnt model, the Mahalanobis distance technique was adopted. The vulnerability rates obtained from the DRASTIC model with a coefficient of determination (R2) value of 0.76 had a statistically significant correlation with nitrate concentrations in the 21 wells, compared to SI and GODS. The DRASTIC model can better reflect the vulnerability of groundwater resources to contamination. The impact of the vadose zone with an average effective weight of 33 is more important to other parameters, followed by depth to groundwater (D) (32.01), net recharge (R) (28.95) and the aquifer media (A) (18.1). These weights may not be changed. MaxEnt showed significant performance in both the training and validation stages with the respective area under the receiver operating characteristic curve (AUROC) values of 0.907 and 0.901. A reciprocal analysis between the vulnerability map in the superior model and the groundwater potential map derived from MaxEnt revealed that areas with high groundwater potential are still in safe state, but require more attention as the top priority for amendment practices. In addition, approximately 8.7% of the entire study area has a high vulnerability to contamination, which requires immediate pragmatic actions.
Novel extracellular role of REIC/Dkk-3 protein in PD-L1 regulation in cancer cells
Authors: Gohara Yuma , Tomonobu Nahoko , Kinoshita Rie , Futami Junichiro , Audebert Léna , Chen Youyi , Komalasari Ni Luh Gede Yoni , Jiang Fan , Yoshizawa Chikako , Murata Hitoshi , Yamamoto Ken-ichi , Watanabe Masami , Kumon Hiromi , Sakaguchi Masakiyo 
Published: July 2022

Abstract
The adenovirus-REIC/Dkk-3 expression vector (Ad-REIC) is a clinical study's focus, with the goal of using this gene to exploit the quenching of several cancer types. REIC/DKK-3 gene's cancer-suppressing mechanisms depend on multiple pathways that exert both direct and indirect effects on cancers. The direct effect is triggered by REIC/Dkk-3-mediated ER-stress that causes cancer-selective apoptosis, and the indirect effect can be classified in two ways: (i) Ad-REIC-mis-infected cancer-associated fibroblasts that lead to the production of IL-7, an important activator of T cells and NK cells, and (ii) the secretory REIC/Dkk-3 protein that promotes dendritic cell polarization from monocytes. With these unique features, Ad-REIC exerts an effective and selective prevention of cancer as if it were acting as an anticancer vaccine. However, the question of how REIC/Dkk-3 protein leverages an anticancer immunity has remained to be answered. We herein report a novel function of the extracellular REIC/Dkk-3 that regulates an immune checkpoint via a modulation of PD-L1 on the cancer-cell surface. We identified novel interactions of REIC/Dkk-3 with the membrane proteins C5aR, CXCR2, CXCR6 and CMTM6. These proteins all functioned to stabilize PD-L1 on the cell surface. Due to the dominant expression of CMTM6 among the proteins in cancer cells, we focused on CMTM6 and observed that REIC/Dkk-3 competitively bound with CMTM6 to PD-L1, which induced the liberation of PD-L1 from CMTM6 binding; the released PD-L1 immediately underwent endocytosis-mediated degradation. These results increase our understanding of not only the physiological nature of the extracellular REIC/Dkk-3 protein but also the Ad-REIC-mediated anticancer effects.
Biochar as a carbon dioxide removal strategy in integrated long-run climate scenarios
Authors: Bergero Candelaria , Wise Marshall , Lamers Patrick , Wang Yong , Weber Maridee 
Published: July 2022

Abstract

Limiting global warming to under 2°C would require stringent mitigation and likely additional carbon dioxide removals (CDR) to compensate for hard-to-avoid emissions. The relatively low cost and potential co-benefits of biochar as a soil amendment has gained attention in this context. We use a global multisector model to analyze biochar deployment in the context of energy system uses of biomass with CDR under different carbon price trajectories. We find that biochar can create an annual sink of up to 2.8 GtCO2, reducing global mean temperature increases by an additional 0.5-1.8% across scenarios by 2100 for a given carbon price path. In our scenarios, biochar’s deployment is dependent on potential crop yield gains and application rates, and the competition for resources with other CDR measures. We find that biochar can serve as a competitive CDR strategy, especially at lower carbon prices when bioenergy with carbon capture and storage is not economical.
Using sediment geochemical records to infer past lake water total phosphorus concentrations at a site with high internal P loading, Lake Søbygaard, Denmark
Authors: Boyle John , Moyle Madeleine , Søndergaard Martin , Jeppesen Erik 
Published: July 2022

Abstract
In lakes where phosphorus (P) supply is dominated by external loads, long-term mean lake water TP concentrations can be successfully reconstructed from sediment P profiles and dating using the SI-TP (Sediment Inferred lake water Total Phosphorus) mass balance model. However, it has not yet been shown that the model is applicable at lakes with high internal P loading, where sediment diagenesis releases P to the water column and degrades the sediment P record. Here we apply the SI-TP model to Lake Søbygaard in Denmark, a shallow lake with exceptionally high internal P loading for several decades since the external loading was reduced by 80-90% in 1982. Our aim was to test the impact of this sediment degradation on P record integrity. Using six published sediment P records collected over the period 1985 to 2004, we applied the model without site specific calibration. Degradation of the sediment P record by diagenesis was observed, and occurred at a rate consistent with theoretical models, offering a potential approach to correct for degradation Even neglecting degradation, the resulting sediment inferred lake water TP (SI-TP) record was found to be similar in magnitude and temporal trend to the corresponding monitored TP values. From this test of SI-TP model performance at Lake Søbygaard we conclude that the model has the potential to be more widely applied at lakes with high internal P loading.
Intelligent tunicate swarm information system for regression neuron modelling - novel state estimation learning
Authors: Reddy K. Harinadha 
Published: July 2022

Abstract
Intelligent information systems are essentially needed to identify netwok state during a operation, especially abnormal state. Gravitational force of tuncate swarm optimization (gTSO) intelligent system is proposed to obtain the optimal intelligent system control weight matrix (CWM) in a general regression neural net/network (GRNN) training. Optimized CWM computed based on the variation of in gTSO intelligent control ∆g(t) and GRNN learning parameter u(t). Now gTSO based GRNN control is computed and used for state identification either in normal operation or abnormal operation. Hereafter proposed gTSO based GRNN learning optimization applied for identification of system state by considering the different operational events like normal operational event (NoE) and abnormal operational event (ANoE). Especially ANoEs are tested with all possible test conditions to proper identification of system state.
Complex Complementary Sequence Real Orthogonal Spreading OFDM Scheme for HF Communication Link
Authors: Wang Sang-Dok , Cha Yong-Suk , Pak Myong-Hak , Choe Hyok 
Published: July 2022

Abstract
The complementary M-ary orthogonal spreading with orthogonal frequency division multiplexing (CMOS-OFDM) is an efficient scheme for providing high bit error rate (BER) performance and low peak to mean envelop power ratio (PMEPR) in high frequency (HF) communications system. In the CMOS-OFDM, orthogonal spreading OFDM is implemented based on orthogonal complementary sequence pairs where there is an issue that one data symbol is transmitted through two OFDM symbols. In this paper, we present the M-ary orthogonal spreading OFDM scheme using complex complementary sequences which satisfy real domain orthogonality. The complementary real orthogonal spreading OFDM (CROS-OFDM) is restrict the PMEPR to 3dB and provides two times the bit rate of CMOS-OFDM. The CROS-OFDM scheme uses zero-center complementary sequences like the CMOS-OFDM and hence it is suitable to the HF OFDM and Wi-Fi system where the direct current subcarrier is not used. The simulation results show that the CROS-OFDM provides higher bit rate and BER performance.
Clinical Association of Viral Tropism and Serum Cytokines in Untreated HIV-1 Infection Patients
Authors: Zhang Na , Yu Shuang-Mei , Yan Chang-Xin , Wang Xiao-Xiong , Teng Lei , Wang Jia-Ye , Zhou HaiZhou 
Published: July 2022

Abstract
Objective
Human immunodeficiency virus type 1 (HIV-1) infection disturbs the balance of CD4+ T cells and monocytes in the immune system. In the early stage of infection, the virus stimulates the activation and proliferation of immune cells, induces the release of cytokines, destroys CD4+ T cells, and accelerates HIV-1 replication and AIDS progression. It is essential to explore cytokine changes after HIV-1 infection and further understand the underlying mechanism of HIV infection.
Materials and methods
In this study, we enrolled 38 HIV-infected subjects and 30 healthy subjects. We measured and compared CD4+ T cell counts, the tropism of HIV and the serum cytokine levels in different groups.
Results
Our results showed significantly higher serum levels of IL-1β, IL-2, IL-4, IL-7, IL-10, IL-17, IFN-γ, and TNF-α in HIV-infected patients. Higher levels of IL-6 and IL-17 were observed in the < 200/mL CD4+ T cell count group, and higher levels of IL-2 were observed in the CCR5-tropic HIV strain group.
Conclusion
In conclusion, we found that HIV infection-induced activation of the immune system. IL-6 and IL-17 may predict the severity of HIV disease and regulate HIV infection. The level of IL-2 depended on the type of virus strain.
Rewiring of the N-Glycome with Prostate Cancer Progression and Therapy Resistance
Authors: Butler William , McDowell Colin , He Yiping , Yang Qing , Hauck Spencer , Zhou Yinglu , Armstrong Andrew , George Daniel , Drake Richard , Huang Jiaoti 
Published: July 2022

Abstract
An understanding of the molecular features associated with prostate cancer progression (PCa) and resistance to hormonal therapy is crucial for the identification of new targets that can be utilized to treat advanced disease and prolong patient survival. Many studies have investigated changes to the levels of various biomolecules such as RNA, protein, and small molecule metabolites. The glycome, which encompasses all sugar polymers (glycans) synthesized by cells, has remained relatively unexplored in the context of advanced PCa despite the fact that glycans have great potential value as biomarkers and therapeutic targets due to their high density on the cell surface. Using imaging mass spectrometry (IMS), we profiled the N-linked glycans in tumor tissue derived from 131 patients representing the entire spectrum of PCa to identify glycosylation changes associated with loss of tumor cell differentiation, disease remission, therapy resistance and disease recurrence, as well as neuroendocrine (NE) differentiation which is a major mechanism for therapy failure. Our results indicate significant changes to the glycosylation patterns in various stages of PCa, notably a decrease in tri- and tetraantennary glycans correlating with disease remission, a subsequent increase in these structures with the transition to therapy-resistant PCa, and downregulation of complex N-glycans correlating with NE differentiation. Furthermore, both nonglucosylated and monoglucosylated mannose 9 demonstrate aberrant upregulation in therapy-resistant PCa which may be useful therapeutic targets as these structures are not normally presented in healthy tissue. Our findings, for the first time, characterize changes to the tumor glycome throughout the entire evolution of PCa, identifying several glycan markers and signatures which may be useful for diagnostic or therapeutic purposes.
Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation
Authors: Khan Tariq M. , Naqvi Syed S. , Meijering Erik 
Published: July 2022

Abstract
Recent progress in encoder-decoder neural network architecture design has led to significant performance improvements in a wide range of medical image segmentation tasks. However, state-of-the-art networks for a given task may be too computationally demanding to run on affordable hardware, and thus users often resort to practical workarounds by modifying various macro-level design aspects. Two common examples are downsampling of the input images and reducing the network depth or size to meet computer memory constraints. In this paper, we investigate the effects of these changes on segmentation performance and show that image complexity can be used as a guideline in choosing what is best for a given dataset. We consider four statistical measures to quantify image complexity and evaluate their suitability on ten different public datasets. For the purpose of our illustrative experiments, we use DeepLabV3+ (deep large-size), M2U-Net (deep lightweight), U-Net (shallow large-size), and U-Net Lite (shallow lightweight). Our results suggest that median frequency is the best complexity measure when deciding on an acceptable input down-sampling factor and using a deep versus shallow, large-size versus lightweight network. For high-complexity datasets, a lightweight network running on the original images may yield better segmentation results than a large-size network running on downsampled images, whereas the opposite may be the case for low-complexity images.
Barriers and facilitators to naloxone uptake in Ohio: implications for community-driven overdose reduction interventions
Authors: Estadt Angela T. , Lancaster Kathryn E. , Sanchez Sabrina D. , Ricks JaNelle 
Published: July 2022

Abstract
Background
Naloxone is a life-saving and easy-to-administer opioid antagonist medicine. Despite naloxone's legality in Ohio, unintentional drug overdoses remain the leading cause of injury deaths. Understanding the barriers and facilitators to naloxone's community uptake will help inform interventions to increase the awareness, accessibility, and use of naloxone. Our study aimed to identify barriers and facilitators to opioid overdose reversal uptake to inform community-driven interventions.
Methods
We conducted in-depth interviews and focus group discussions with people who use opioids, community members, and key stakeholders in Central Ohio from February 2019 to October 2019. We used qualitative thematic analysis to identify barriers and facilitators to the uptake of naloxone in a community setting. We classified barriers and facilitators to community naloxone uptake using the socio-ecological model's exterior levels – organizational, community, and societal.
Results
A total of 47 Central Ohio residents participated in five focus group discussions and fifteen in-depth interviews. Community members (n = 23), harm reduction service providers (n = 4), and religious organizational staff (n = 5) participated in focus group discussions ranging in size from 4 to 10 participants. We conducted in-depth interviews with law enforcement officers (n = 3), pharmacists (n = 2), and people who use opioids (n = 10). At the organizational level, access to naloxone, lack of resources, organizational stigma, and misinformation emerged as barriers. In contrast, naloxone awareness and availability, safe spaces for people who use opioids, and organizational collaboration emerged as facilitators. We identified the following community-level barriers: naloxone misinformation, knowledge, awareness, and substance use stigma. Perspectives on collective responsibility to administer naloxone was identified as both a barrier and facilitator. At the societal level, despite Ohio's Good Samaritan law and the legality of naloxone, poor communication of naloxone laws was a prevalent barrier to naloxone uptake, as was the burden of law enforcement to respond to and manage overdoses.
Conclusions
Community-based interventions that develop collaborations among local organizations to provide naloxone information, training, and distribution may address prominent barriers to naloxone uptake and reduce the current burden of law enforcement to respond to overdoses. Future interventions should also dispel naloxone misinformation, substance-use stigma, and confusion about the legal consequences of administering naloxone.
FRIBAS database for better characterization of RC and URM buildings: Towards specific T-H relationships
Authors: Gallipoli Maria Rosaria , Gallipoli Maria Rosaria , Petrovic Bojana , Calamita Giuseppe , Tragni Nicola , Scaini Chiara , Barnaba Carla , Vona Marco , Parolai Stefano 
Published: July 2022

Abstract
FRIBAS database is an open access database (https://doi.org/10.5281/zenodo.6505442) made up of the characteristics of 313 buildings (71 masonry, 238 reinforced concrete and 4 mixed types). It collects and harmonizes data from different surveys performed on buildings in the Basilicata and Friuli Venezia Giulia regions (Southern and North-eastern Italy, respectively). Each building is defined by 37 parameters related to the building and foundation soil characteristics. Building and soil fundamental periods were estimated experimentally based on ambient noise measurements. FRIBAS gave us the opportunity to study the influence of the main characteristics of buildings to their structural response and soil-building interaction effect. In this study, we have used FRIBAS dataset to investigate how the building period varies as a function of building typologies and soil conditions. Our results motivate the need of going beyond a ‘one-fits-all’ numerical period-height (T-H) relationship for generic building typologies provided by seismic code, towards a specific characterization of T-H relationships that account for both soil and building typologies.