bluuebunny/crossref_metadata_2025 huggingface dataset:
Exponential Stability of Swelling Porous Elastic with a Viscoelastic Damping and Distributed Delay Term
Authors: Choucha Abdelbaki , Boulaaras Salah Mahmoud , Ouchenane Djamel , Cherif Bahri Belkacem , Abdalla Mohamed
Published: March 2021

In this paper, we consider a swelling porous elastic system with a viscoelastic damping and distributed delay terms in the second equation. The coupling gives new contributions to the theory associated with asymptotic behaviors of swelling porous elastic soils. The general decay result is established by the multiplier method.
On a Nonhomogeneous Timoshenko System with Nonlocal Constraints
Authors: Mesloub Said , Aldosari Faten
Published: March 2021

Our main concern in this paper is to prove the well posedness of a nonhomogeneous Timoshenko system with two damping terms. The system is supplemented by some initial and nonlocal boundary conditions of integral type. The uniqueness and continuous dependence of the solution on the given data follow from some established a priori bounds, and the proof of the existence of the solution is based on some density arguments.
Growth and Productivity Assessment of Short-Duration Rice (Oryza sativa L. and Upland NERICA) Genotypes in Semiarid North-Central Namibia
Authors: Awala Simon K. , Hove Kudakwashe , Shivute Vistorina , Valombola Johanna S. , Nanhapo Pamwenafye I. , Hirooka Yoshihiro , Mwandemele Osmund D. , Iijima Morio
Published: March 2021

In semiarid regions, drought is the major threat to crop production, but climate change and variability often bring floods to the regions, forming seasonal wetlands causing damage to local, drought-adapted staple grains and, hence, low yields and food deficit. Introduction of the semiaquatic crop rice (Oryza spp.) to these semiarid wetlands could complement the dryland crop low yields and overcome the food shortage problem. A field experiment was carried out at the University of Namibia-Ogongo Campus during the 2016/2017 and 2017/2018 cropping seasons to assess the growth and yield of rice genotypes. Twelve short-duration rice genotypes, nine from the International Rice Research Institute (IRRI) and three locally grown upland NERICA genotypes, were used. Parametric analysis of variance was performed to test their effects on crop growth and yield characters. The results showed significant genotype by year interaction for days to heading, plant height, the number of tillers per m2, shoot biomass, the number of panicles per m2, 1000-grain weight, harvest index, and grain yield. The IRRI genotypes produced a higher number of tillers, shoot biomass, and grain yield than the NERICA ones, which were early maturing, were taller, and had higher 1000-grain weight across the years. Generally, most of the genotypes had lower grain yield and higher shoot biomass in the first year, due to the prevailing cool, rainy, and seemingly cloudy weather conditions associated with lower temperature and lower solar radiation. Grain yield was positively correlated with most characters but negatively correlated with the 1000-grain weight. These results demonstrate the yield superiority of IRRI genotypes over their early-maturing NERICA counterparts. However, further studies on morphological characters and drought tolerance of the IRRI genotypes are warranted to ascertain production sustainability under semiarid environments.
Increased Nuclear Transporter KPNA2 Contributes to Tumor Immune Evasion by Enhancing PD-L1 Expression in PDAC
Authors: Zhou Kai-Xia , Huang Shan , Hu Li-Peng , Zhang Xue-Li , Qin Wei-Ting , Zhang Yan-Li , Yao Lin-Li , Yu Yanqiu , Zhou Yao-Qi , Zhu Lei , Ji Jianguang , Zhang Zhi-Gang
Published: March 2021

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies and is known for its high resistance and low response to treatment. Tumor immune evasion is a major stumbling block in designing effective anticancer therapeutic strategies. Karyopherin alpha 2 (KPNA2), a member of the nuclear transporter family, is elevated in multiple human cancers and accelerates carcinogenesis. However, the specific role of KPNA2 in PDAC remains unclear. In this study, we found that expression of KPNA2 was significantly upregulated in PDAC compared to adjacent nontumor tissue and its high expression was correlated with poor survival outcome by analyzing the GEO datasets. Similar KPNA2 expression pattern was also found in both human patient samples and KPC mouse models through IHC staining. Although KPNA2 knockdown failed to impair the vitality and migration ability of PDAC cells in vitro, the in vivo tumor growth was significantly impeded and the expression of immune checkpoint ligand PD-L1 was reduced by silencing KPNA2. Furthermore, we uncovered that KPNA2 modulated the expression of PD-L1 by mediating nuclear translocation of STAT3. Collectively, our data suggested that KPNA2 has the potential to serve as a promising biomarker for diagnosis in PDAC.
Being Small and Intronic: miRNAs That Count!
Authors: Calin George A.
Published: March 2021

The roles of intronic miRNAs and their functional interaction with host coding genes represent a topic that is poorly explored. The study by Kwok and colleagues in this issue of Cancer Research presents solid evidence that the FTX locus at chromosome Xq13.2 contains multiple noncoding RNAs (ncRNA) with coordinated expression and concordant functional effects in colorectal cancer cells. The long ncRNA, FTX, and its intronic miRNAs, miR-374a, -374b, -421, and -545, are all interconnected in a functional network, including the downstream protein coding targets DHX9, DICER, PTEN, and RIG-I. These data prove that multigenic loci in the human genome have a complex functional landscape that modulates key signaling pathways for cancer progression and that much knowledge with potential clinical utility remains to be revealed from the largest, noncoding portion of the genome.

See related article by Kwok et al., p. 1308
Abstract IA-15: Platforms to improve reproducibility in artificial intelligence research
Authors: Haibe-Kains Benjamin , Mammoliti Anthony , Nakano Minoru
Published: March 2021

As artificial intelligence (AI) becomes a method of choice to analyze biomedical data, the field is facing multiple challenges around research reproducibility and transparency. Given the proliferation of studies investigating the applications of AI in research and clinical studies, it is essential for independent researchers to be able to scrutinize and reproduce the results of a study using its materials, and build upon them in future studies. Computational reproducibility is achievable when the data can easily be shared and the required computational resources are relatively common. However, the complexity of AI algorithms and their implementation, the need for specific computer hardware and the use of sensitive biomedical data represent major obstacles in healthy-related AI research. In this talk, I will describe the various aspects of an AI biomedical study that are necessary for reproducibility and the platforms that exist for sharing these materials with the scientific community.

Citation Format: Benjamin Haibe-Kains, Anthony Mammoliti, Minoru Nakano. Platforms to improve reproducibility in artificial intelligence research [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-15.
Abstract IA-18: Big pediatric cancer genomic data: Discovery, precision medicine, and data sharing
Authors: Zhang Jinghui
Published: March 2021

High throughput next generation sequencing (NGS) has enabled tremendous advances in our understanding of the genomic landscape of pediatric cancer, leading to discoveries of new mechanisms of tumor initiation and progression, novel targets, and diagnostic and prognostic markers. By focusing on the development of innovative computational analysis tools, we have investigated the genomic variants in pediatric cancer in the following areas: 1) genomic landscapes of >20 subtypes of pediatric cancer; 2) a pan-cancer study of genomes and transcriptomes of pediatric cancer which unveiled that >50% of the driver genes are absent in adult cancer; 3) clonal evolution of relapsed pediatric leukemia driven by therapy-induced variants bearing novel mutational signature; and 4) pathogenic germline mutations in cancer predisposition genes. These insights led to implementation of clinical cancer genomic profiling by three-platform of whole genome, whole exome and transcriptome sequencing for all eligible pediatric cancer patients at St Jude. Use of these data sets improved molecular diagnosis of pediatric cancer. Since 2015, we have analyzed >1,200 pediatric oncology patients providing critical data that may affect patient care. The omics data generated from our research and clinical programs can be accessed on St. Jude Cloud (https://www.stjude.cloud), a cloud-based data sharing ecosystem for accessing, analyzing and visualizing genomic data generated from >10,000 pediatric cancer patients, long-term survivors of pediatric cancer and >800 pediatric sickle cell patients. Access to three interconnected Apps on St. Jude Cloud, i.e. Genomics Platform, Pediatric Cancer (PeCan) Knowledgebase and Visualization Community, provides a unique experience for simultaneously performing advanced data analysis and enhancing the knowledgebase for pediatric cancer.

Citation Format: Jinghui Zhang. Big pediatric cancer genomic data: Discovery, precision medicine, and data sharing [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-18.
Abstract IA-23: Multi-scale modeling of cancer patients
Authors: Gevaert Olivier
Published: March 2021

Recently biomedical analysis in both imaging and non-imaging data has advanced rapidly aided by the modeling power of deep learning networks. Complex diseases such as cancer may reveal different aspects of disease characteristics in different data modalities, and thus integrating imaging with non-imaging biomarkers and clinical data has the potential to discover information that is not obvious in any single data modality. We are developing methods for integrating image, genomic and clinical data to improve precision medicine applications for cancer patients. Our results suggest that the integration strategies achieve improved prediction results than using each single data modality alone. We further show that information is transferrable across age groups or tumor subtypes under the data fusion framework.

Citation Format: Olivier Gevaert. Multi-scale modeling of cancer patients [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-23.
Abstract IA-24: Towards robust image based models for cancer risk assessment
Authors: Barzilay Regina , Yala Adam
Published: March 2021

To enable targeted personalized screening, we need to advance cancer risk models. Despite significant research on this topic, statistical models used today in clinical practice routinely undeperform. In this talk, I explore the potential of AI-based models for breast cancer prediction based on mammographic images. These models have already shown significantt performance gains over their counterparts. However, to bring deep learning models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. To this end, we propose Mirai, a new risk algorithm designed to predict risk at multiple time points, leverage potentially missing risk-factor information, and produce predictions that are consistent across mammography machines. The architectture of the new model will be covered in detail in the talk. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the US and was tested on held-out test sets from MGH, Karolinska in Sweden and Chang Gung Memorial Hospital in Taiwan, obtaining C-indices of 0.76 (95% CI 0.74, 0.80), 0.81 (0.79, 0.82), 0.79 (0.79, 0.83), respectively. Mirai obtained significantly higher five-year ROC AUCs than the Tyrer-Cuzick model (p<0.001) and prior deep learning models, Hybrid DL (p<0.001) and ImageOnly DL (p<0.001), trained on the same MGH dataset.

Citation Format: Regina Barzilay, Adam Yala. Towards robust image based models for cancer risk assessment [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-24.
Abstract PO-008: Federated learning on whole slide images using weakly supervised computational pathology
Authors: Lu Ming Yang , Kong Dehan , Lipkova Jana , Chen Richard J. , Singh Rajendra , Chen Tiffany Y. , Williamson Drew F. K. , Mahmood Faisal
Published: March 2021

Deep learning has excelled in a wide range of tasks in computational pathology, including but not limited to the characterization of both common and rare morphological phenotypes and the prediction of molecular alterations from histology. However, large datasets of diverse, high-quality annotated training examples are often necessary in order to build robust and accurate deep learning-based models for real-world test scenarios. Pooling medical data from multiple institutions can alleviate this challenge but is constrained by privacy concerns associated with sharing sensitive patient information, in addition to the technical challenges of transferring and storing large whole slide image files at the terabyte or even petabyte scale. On the other hand, federated learning is a promising field of machine learning research that allows algorithms to learn from multiple decentralized data sources without requiring training data to be directly shared. We introduce a federated learning framework for computational pathology on whole slide images using attention-based multiple instance learning for weakly-supervised learning and random noise generation for preserving differential privacy. We evaluated our approach on two different classification problems using large multi-centric datasets, including renal cell carcinoma (RCC) histologic subtyping and invasive breast carcinoma (BRCA) histologic subtyping. We additionally demonstrate that our approach can be used for survival prediction and patient stratification from whole slide images. Our proposed privacy-preserving federated learning framework can be used to develop accurate, interpretable deep learning models from distributed data sources for both classification and survival prediction.

Citation Format: Ming Yang Lu, Dehan Kong, Jana Lipkova, Richard J. Chen, Rajendra Singh, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood. Federated learning on whole slide images using weakly supervised computational pathology [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-008.
Abstract PO-018: Computational staining of tumor hypoxia from H&E images using convolutional neural networks
Authors: Zaidi Mark , Cui Haotian , Wang Bo , McKee Trevor D. , Wouters Bradly G.
Published: March 2021

Heterogeneity in the tumor environment can be driven by multiple biochemical processes, such as immune infiltration, cell-to-cell genetic variation, and oxygenation gradients. Low oxygen, termed hypoxia, has been strongly correlated with metastasis, radiation resistance, and poor prognosis. One ongoing challenge is determining the magnitude of how hypoxia influences treatment response relative to other features, such as proliferation and micro vessel density. While it is possible to stain banked tissue for these markers, it is challenging to stain for endogenous markers of hypoxia. Exogenous probes have been developed to overcome this barrier; however, most clinical samples lack such probes. Previously, we have shown that spatial distributions of hypoxia correlate with morphological features of the tumor, such as necrosis and vasculature. Some of these features can be identified in hematoxylin and eosin (H&E)-stained sections, present for nearly all tumor samples. We hypothesize that a trained machine learning algorithm can be used to accurately predict hypoxia distributions given an H&E-stained section. Colorectal, pancreatic, and ovarian cancer cell line xenografts were cut and stained for markers of hypoxia, proliferation, perfusion, vasculature, and H&E. Binary masks of necrosis were generated, and together with the H&E and hypoxia images, were used to train a convolutional neural network. Once the model had been trained, a fairly precise prediction (MSE <0.012) had been obtained relative to the ground-truth hypoxia stained images. We believe a model trained on clinical H&E and hypoxia sections can be used to accurately predict hypoxia distributions from a H&E section. Predicted hypoxia distributions from existing H&E histology can be used to evaluate the influence of hypoxia on treatment outcome, by retroactively comparing a patient’s prognosis with levels of hypoxia. Correlations between hypoxia and prognostic measures such as tumor volume after irradiation, would indicate hypoxia may have modulated the efficacy of the treatment. Thus, computational stain prediction of hypoxia can serve as an invaluable tool in closing the gap between preclinical and clinical implementations of hypoxia-targeted therapies.

Citation Format: Mark Zaidi, Haotian Cui, Bo Wang, Trevor D. McKee, Bradly G. Wouters. Computational staining of tumor hypoxia from H&E images using convolutional neural networks [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-018.
Abstract PO-029: Survival prediction of non-small cell lung cancer by deep learning model integrating clinical and positron emission tomography data
Authors: Kang Sae-Ryung , Oh Seungwon , Oh In-Jae , Min Jung-Joon , Bom Hee-Seung , Yang Hyung-Jeong , Lee Guee-Sang , Kim Soo-Hyung , Kim Min Soo
Published: March 2021

The Cox proportional hazards model (CPH), the standard method for survival analysis in cancer patients, is difficult to use maximal information of positron emission tomography (PET) images containing information that reflects underlying pathophysiology. In this retrospective study, we aimed to investigate a deep learning model integrating clinical and PET image data to improve survival prediction in non-small cell lung cancer (NSCLC) patients. We developed a bimodal learning model thorough DeepSurv-based model using 3D-CNN-based 3D-Resnet model for image data and deep neural network (DNN) model for clinical data. DeepSurv-based model combining clinical and PET image data showed the best performance among the four models, the c-index of the training and testing sets reaching 0.898 and 0.768, respectively, followed by DeepSurv-based model for single modalities (clinical (0.763/0.740) or PET (0.773/0.743)) and CPH (0.726/0.736). Deep learning-based survival prediction with combination of two modalities may improve prediction accuracy in NSCLC patients.

Citation Format: Sae-Ryung Kang, Seungwon Oh, In-Jae Oh, Jung-Joon Min, Hee-Seung Bom, Hyung-Jeong Yang, Guee-Sang Lee, Soo-Hyung Kim, Min Soo Kim. Survival prediction of non-small cell lung cancer by deep learning model integrating clinical and positron emission tomography data [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-029.
Abstract PO-031: Evaluating clinical utility of organs-at-risk segmentation in head & neck cancer by simple open-source 3D CNNs
Authors: Marsilla Joseph , Haibe-Kains Benjamin
Published: March 2021

Open source code helps the scientific community converge to breakthroughs at an accelerated rate. Only one of 14 previous deep learning based OAR segmentation studies met most open-source standards. Using this study as a benchmark, we will assess the segmentation quality of each network discussed and provide the community with pre-trained weights of the top-performing models. 11 open-source 3D segmentation models originally engineered for medical image segmentation in both the 2D and 3D domains were trained to automatically segment 19 OaR classes. For this study, a large internally curated dataset from the University Health Network (UHN) of 582 patient scans was used. All models were tested on a hold out set from the 582 cohort of 59 patients. Models were also tested on 98 external patient scans from publicly available sources. 10 test set contours from the winning network were assessed by an expert radiation oncologist with 10+ years of experience to identify the observer of the contour set (whether AI or human). Preliminary results show that only 13 out of 20 scans were identified correctly. Results show that simple 3D architectures consistently outcompete more complex networks by producing more accurate, clinically acceptable contours. A more thorough clinical acceptability test is underway to establish a protocol for integrating deep learning based auto contouring models into radiation therapy planning workflows.

Citation Format: Joseph Marsilla, Benjamin Haibe-Kains. Evaluating clinical utility of organs-at-risk segmentation in head & neck cancer by simple open-source 3D CNNs [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-031.
Abstract PO-060: Individualized prediction of meningioma recurrence risk over prolonged time periods
Authors: Mamatjan Yasin , Nassiri Farshad , Salih Mira , Aldape Kenneth , Zadeh Gelareh
Published: March 2021

Rationale and purpose: Meningiomas are benign tumors, but significant number of meningiomas display risk of early tumor recurrence. The inter-observer variability among pathologists for grading and some indistinguishable features of meningioma under the microscope prevent accurate prediction of recurrence risk that critically limits appropriate treatment and management of patients who may benefit from adjuvant radiation therapy. We aim to develop individualized prediction of meningioma recurrence risk over prolonged time periods using DNA methylation signatures and other associated clinical factors. Method: The recurrence risk predictor is based on continuous survival random-forest modelling to calculate the probabilities of a recurrence for each meningioma patient over various prolonged time frames. We processed over 500 meningioma samples using our machine-learning model that predicts probability of tumor recurrence based on methylation data. We used Random Forest based Rangers package as the survival model that returns the probabilities of a recurrence not happening over various prolonged time frames. Result: Methylation-based predictor distinguishes high-low risk groups over prolonged time periods. The predictor was validated using external validation cohort based on the selected probes and original training model. We found that methylation based predictor has potential to identify in patients with grade II meningiomas especially, while it also can identify grade I patients with higher recurrence risk over prolonged time periods. Conclusion: There were limitations of standard of care classifications in meningioma especially for Grade 2 patients due to significant intra- and inter-observer variability for grading and selection of patients for treatment. The individualized risk predictor can help determining the decisions regarding adjuvant radiation treatment versus observation along for each meningioma patient in the clinic.

Citation Format: Yasin Mamatjan, Farshad Nassiri, Mira Salih, Kenneth Aldape, Gelareh Zadeh. Individualized prediction of meningioma recurrence risk over prolonged time periods [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-060.
Abstract PO-068: Developing ovarian cancer sensors using molecular perceptron
Authors: Yaari Zvi , Yang Yoona , Jagota Anand , Zheng Ming , Heller Daniel A.
Published: March 2021

Ovarian cancer patient prognosis and quality of life are significantly affected by the failure to accurately diagnose disease at early stage. Currently, less than 15% of ovarian cancer patients are diagnosed at stage 1, when 5-year survival rates are near 93%. Early detection may save more than 80% of women diagnosed with ovarian cancer. Our goal is to develop clinical long-term implantable carbon nanotube-based sensors for ovarian cancer biomarkers to transiently detect and monitor their levels in patients. The proposed sensors will harness the unique optical properties and sensitivity of single-walled carbon nanotubes. To improve sensitivity and specificity of such sensors, previously developed to detect ovarian cancer biomarkers in vivo, we used molecular perceptron—data analytics-based methods to detect specific spectroscopic fingerprints from the binding of protein biomarkers to the nanotube surface.

Citation Format: Zvi Yaari, Yoona Yang, Anand Jagota, Ming Zheng, Daniel A. Heller. Developing ovarian cancer sensors using molecular perceptron [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-068.
EP3 (E-Prostanoid 3) Receptor Mediates Impaired Vasodilation in a Mouse Model of Salt-Sensitive Hypertension
Authors: Wu Jing , Fang Shi , Lu Ko-Ting , Wackman Kelsey , Schwartzman Michal L. , Dikalov Sergey I. , Grobe Justin L. , Sigmund Curt D.
Published: March 2021

We previously showed that impaired vasodilation in systemic and renal vessels contributes to salt-sensitive hypertension in a mouse model of impaired PPARγ (peroxisome proliferator-activated receptor gamma) function. We determined the mechanisms mediating impaired salt-induced vasodilation and whether improved vasodilation attenuates augmented hypertension in response to salt. Mice selectively expressing a PPARγ dominant negative mutation in vascular smooth muscle (S-P467L) exhibited salt-sensitive hypertension and severely impaired vasodilation in systemic and renal vessels. High-salt diet–fed S-P467L and control mice displayed comparable levels of renal oxidative stress markers. Preincubation with Tempol, a superoxide dismutase mimetic, or calphostin C, a PKC (protein kinase C) inhibitor, failed to improve salt-induced impairment of vasodilation in S-P467L mice, arguing against a role of oxidative stress or PKC activity. Inhibition of Rho kinase partially rescued impaired vasodilation in high-salt diet–fed S-P467L mice suggesting a contribution of the Ras homolog family member A (RhoA)/Rho kinase pathway. High-salt diet selectively increased synthesis of PGE2 (prostaglandin E2) in S-P467L aorta. Expression of EP3 (E-prostanoid 3) receptor mRNA was increased in aorta from chow-fed and high salt–fed S-P467L mice. Pharmacological inhibition of COX (cyclooxygenase) 2 or blockade of EP3 completely normalized the impaired vasodilation, and EP3 antagonism induced larger decreases in systolic blood pressure in high-salt diet–fed S-P467L mice. In conclusion, interference with PPARγ in vascular smooth muscle causes activation of the PGE2/EP3 signaling pathway in systemic and renal vasculature resulting in salt-induced impairment of vasodilation and salt-sensitive hypertension. PGE2/EP3 axis maybe a druggable target to prevent salt-sensitive hypertension in chronic conditions associated with decreased PPARγ activity.
Economic Evaluation of Andexanet Versus Prothrombin Complex Concentrate for Reversal of Factor Xa-Associated Intracranial Hemorrhage
Authors: Micieli Andrew , Demchuk Andrew M. , Wijeysundera Harindra C.
Published: March 2021

Background and Purpose:

Andexanet was approved by the Food and Drug Administration in 2018 for reversal of life-threatening or uncontrolled bleeding associated with factor Xa anticoagulation; however, the cost-effectiveness of Andexanet compared with standard of care (ie, prothrombin complex concentrate, PCC) in patients with factor Xa–associated intracranial hemorrhage (ICrH) is unknown.

Methods:

Cost-effectiveness analysis using a Markov cohort decision analytic model with a lifetime horizon was completed to determine the costs and benefits of Andexanet compared with PCC for reversal of factor Xa–associated ICrH. The population of interest was patients living in Canada on chronic factor Xa inhibitors for prevention of ischemic stroke in nonvalvular atrial fibrillation or the prevention/treatment of venous thromboembolism, presenting with an ICrH. Outcomes of interest were life expectancy (measured in years), quality-adjusted life years (QALY), costs (reported in 2020 Canadian dollars), and the incremental cost-effectiveness ratio.

Results:

An overall reduction in fatal ICrH and increase in thromboembolic events was associated with Andexanet compared with PCC. Andexanet had the highest discounted life expectancy of 2.53 years and a discounted QALY of 1.55. PCC had a discounted life expectancy of 2.09 years and a discounted QALY of 1.28. The average discounted lifetime costs were $237 177 Canadian dollars for Andexanet and $177 871 Canadian dollars for PCC. The strategy of Andexanet had an incremental cost-effectiveness ratio was $219 652 per QALY gained compared with the comparator of PCC. The probabilistic sensitivity analyses demonstrated that Andexanet (at its current cost) was cost-effective in 19% of simulations using a willingness-to-pay threshold of $50 000/QALY and 33% of simulations at $150 000/QALY. A 1-way sensitivity analysis found that for the incremental cost-effectiveness ratio to be <$150 000/QALY gained, Andexanet high or standard dosing would require a price reduction to <$24 000 Canadian (at current baseline efficacy).

Conclusions:

Based on available evidence, Andexanet represents low value for reversal of factor Xa–associated ICrH; however, there is substantial uncertainty reflecting the currently available data. Further comparative evidence and costing data will become available in the future with randomized trials of Andexanet versus PCC.
Revision of the genus Neozavrelia Goetghebuer, Thienemann, 1941 (Diptera: Chironomidae) from Eastern Siberia and the Russian Far East, with the description of new species
Authors: OREL OKSANA V.
Published: March 2021

Seven new species of the genus Neozavrelia Goetghebuer & Thienemann, 1941, N. kedrovaya sp. nov., N. kuzmichikha sp. nov., N. nadezhdae sp. nov., N. paramushirica sp. nov., N. samarga sp. nov., N. tygda sp. nov., N. zarya sp. nov., from Eastern Siberia and the Russian Far East are described and figured. Illustrated redescription of males for four species, N. fengchengensis Wang, Wang, 1996, N. minuta (Linevich, 1963), N. oyabeparvulus (Sasa, Kawai, Ueno, 1988) and N. yakuefea (Sasa, Suzuki, 2000) is given. A description of the pupa N. yakuefea (Sasa, Suzuki, 2000) is provided for the first time. The males of N. oyabeparvulus (Sasa, Kawai, Ueno, 1988) and N. yakuefea (Sasa, Suzuki, 2000) are recorded for the first time for the fauna of Russia. Neozavrelia okamotoi (Sasa, 1989) is a junior synonym Neozavrelia oyabeparvulus (Sasa, Kawai, Ueno, 1988). Neozavrelia ginzanlemeus (Sasa, Suzuki, 2001) is a junior synonym Neozavrelia yakuefea (Sasa, Suzuki, 2000). Keys to the males and pupae of the Russian Neozavrelia species are given.
Factors Affecting Posterior Glottic Stenosis Surgery Outcomes: Systematic Review and Meta-analysis
Authors: Kremer Candice , Jiang Roy , Singh Amrita , Sukys Jordan , Brackett Alexandria , Kohli Nikita
Published: March 2021

Objectives:

To investigate surgical adjuncts (stents) and previous surgeries on outcomes from posterior glottic stenosis (PGS).

Methods:

PubMED/Medline, CINAHL, EMBASE, and Web of Science were searched for publications on adult patients undergoing surgery for PGS. Decannulation and need for additional surgeries were evaluated as outcomes. Linear mixed-effects (with random effects and fixed effects) models were used for multivariate testing.

Results:

In total, 516 abstracts were reviewed and 26 articles were considered for systematic review. Of these, 19 articles with 140 pooled patient cases were extracted for meta-analysis. On multivariate meta-analysis analysis accounting for study-specific variation and use of open procedures, prior surgeries were associated with additional surgeries (RR = 3.76 [1.39-3.86], P = .038) and the use of a stent was associated with a lower likelihood of decannulation (RR = 0.42 [0.09-0.98], P = .044).

Conclusion:

Minimizing repeat surgery is a predictor for avoiding additional future surgeries and use of a stent was correlated with poor outcomes. These 2 findings may assist providers in patient counseling regarding the need for further surgical interventions. Further, this study is the first to compare the efficacy of surgical approaches for the resolution of PGS, and highlights the importance of avoiding repeat procedures and stents for the management of PGS.
Depression among health care students in the time of COVID-19: the mediating role of resilience in the hopelessness–depression relationship
Authors: Pretorius Tammy-lee
Published: March 2021

COVID-19 spread rapidly across the world, and by March 2020, the first case of COVID-19 was identified in South Africa. Lockdown-related measures such as restricted movement and isolation were implemented to contain the virus. Combined with these measures, factors such as economic decline, job losses, and food shortages can cause numerous mental health sequelae such as depression. Feelings of hopelessness and helplessness as well as cases of suicide have been reported around the world due to the pandemic and the associated feelings of anxiety and depression. The aims of this study were to investigate levels of hopelessness and depression in a sample of health care students. A random sample of students ( N = 174) enrolled in a health sciences programme at the University of the Western Cape completed the Beck Hopelessness Scale, the Center for Epidemiological Studies Depression Scale, and a three-item Resilience Scale. The results revealed high levels of hopelessness and depression compared to previously reported normative data for these scales. In addition, the indirect effects of hopelessness on depression were significant, demonstrating the mediating role of resilience in the hopelessness–depression relationship. These results highlight a call for universities to take proactive measures in providing students with free and easily accessible resources to help them cope and manage stress during a traumatic event. More importantly, at a national level, preventive measures should be implemented to strengthen resilience in young adults.
Opportunities for Interventions That Address Socioeconomic Barriers to Type 2 Diabetes Management: Patient Perspectives
Authors: Stotz Sarah A. , Ricks Katharine A. , Eisenstat Stephanie A. , Wexler Deborah J. , Berkowitz Seth A.
Published: March 2021

Purpose

The purpose of the study was to explore patient perspectives on socioeconomic barriers related to diabetes self-management and interventions to address these barriers.

Methods

Focus groups (n = 8) were conducted with a diverse sample of adults with type 2 diabetes (T2D; n = 53). Researchers used a semistructured moderator guide; focus groups were audio recorded and transcribed verbatim. Researchers employed the constant comparison method for qualitative content analysis and utilized Atlas.ti (Version 8.1.1) to digitalize the analytic process.

Results

Findings revealed 3 primary themes: (1) Existing food and nutrition resources are insufficient to support healthy eating for diabetes; (2) healthy eating is critical for diabetes management, but socioeconomic circumstances make doing so challenging; and (3) participants supported several broad categories of preferred intervention strategies. First, they endorsed lifestyle intervention informed by socioeconomic status (SES; eg, focusing on food resource management, sensitive health coaching and nutritional counseling). Next, they expressed enthusiasm for group-based learning opportunities, such as cooking classes and support groups with similar SES peers. Finally, they suggested healthy food access resources.

Conclusions

Participant suggestions should be incorporated into intervention development. Ultimately, these interventional strategies should be tested and refined to help improve health for individuals with type 2 diabetes.
Critical buckling load of composite laminates containing delamination using differential quadrature method and ABAQUS finite element
Authors: Edalati Hamed , Daghigh Vahid , Nikbin Kamran
Published: March 2021

Differential quadrature method (DQM) was used to compute the critical buckling load (CBL) of composite laminates containing complex delamination shapes. The composite laminate was initially flat; however, it buckled under a compressive load due to weak adhesive between the outer ply and the whole composite laminate. Previous data obtained for composite laminates containing circular or elliptical delaminations by finite element and the Rayleigh-Ritz methods as well as DQM available in the literature were used to validate the accuracy of the approach. A good agreement between the results was observed. To show the ability of this approach for calculating the CBL of a composite laminate containing complex delamination shape, a crescent-shaped delamination was considered. The CBLs for various stacking sequences of such a composite laminate were then calculated and discussed. The commercial finite element package, ABAQUS was used to validate the DQM results for crescent delamination.
Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model
Authors: Qi Xiao , Ni Ying , Xu Yiming , Tian Ye , Wang Junhua , Sun Jian
Published: March 2021

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.
Development of North Carolina Department of Transportation’s CLEAR Program for Enhanced Project Performance
Authors: Fullerton Clare E. , Tamer Alyson W. , Banerjee Siddharth , Alsharef Abdullah F. , Jaselskis Edward J.
Published: March 2021

Valuable lessons learned and best practices gleaned from construction projects often do not transfer to future generations because of the lack of a formalized process. This ongoing issue gives rise to the need to impart fresh training to new North Carolina Department of Transportation (NCDOT) employees once the aging workforce retires or in the event of turnover. In addition, a platform for personnel to record pertinent project information about successes and failures in projects is needed. Such information can help solve problems and avoid repeated mistakes. The aim of this research project is to create a new program called Communicate Lessons, Exchange Advice, Record (CLEAR) to reposit knowledge gained by personnel. Integral to this program is an internal-only web-based database on NCDOT’s Connect SharePoint portal with MS Access as its backend. The North Carolina State University researchers used a Design for Six Sigma approach to identify, define, develop, optimize, and verify lessons learned/best practices to create the CLEAR database. The database fields were selected based on end-user input as well as a review of existing data, such as claims and supplemental agreements, within NCDOT data repositories. Training materials, including videos and standard operating procedures, were created to disseminate information about this new program. The CLEAR program will help the NCDOT to institutionalize knowledge and is expected to improve project cost variability and scheduling.
Toward A Framework for Assessing the Fair Distribution of Space in Urban Streets
Authors: Lefebvre-Ropars Gabriel , Morency Catherine , Negron-Poblete Paula
Published: March 2021

The increasing popularity of street redesigns highlights the intense competition for street space between their different users. More and more cities around the world mention in their planning documents their intention to rebalance streets in favor of active transportation, transit, and green infrastructure. However, few efforts have managed to formalize quantifiable measurements of the balance between the different users and usages of the street. This paper proposes a method to assess the balance between the three fundamental dimensions of the street—the link, the place, and the environment—as well as a method to assess the adequation between supply and demand for the link dimension at the corridor level. A series of open and government georeferenced datasets were integrated to determine the detailed allocation of street space for 11 boroughs of the city of Montréal, Canada. Travel survey data from the 2013 Origine-Destination survey was used to model different demand profiles on these streets. The three dimensions of the street were found to be most unbalanced in the central boroughs of the city, which are also the most dense and touristic neighborhoods. A discrepancy between supply and demand for transit users and cyclists was also observed across the study area. This highlights the potential of using a distributive justice framework to approach the question of the fair distribution of street space in an urban context.
Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region
Authors: Sun Danyang , Leurent Fabien , Xie Xiaoyan
Published: March 2021

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.
Retrospective Longitudinal Study of the Impact of Truck Weight Regulatory Changes on Operating Gross Vehicle Weights
Authors: Pushka Amanda , Regehr Jonathan D.
Published: March 2021

Three primary policy changes on truck size and weight occurred in Canada over the past five decades: the 1974 Western Canadian Highway Strengthening Program, the 1988 Roads and Transportation Association of Canada Memorandum of Understanding on Heavy Vehicle Weights and Dimensions, and ongoing special permitting of longer combination vehicles. These regulatory changes influenced the gross vehicle weight (GVW) of the predominant truck configurations operating on principal Canadian highways. Using a unique time-series of truck weight data, this retrospective longitudinal study contributes insights about the magnitude and timing of the impacts of truck weight regulatory changes on operating GVWs that address current knowledge gaps and persistent uncertainties in models used to predict and evaluate truck weight regulatory changes. The analysis reveals that carriers hauling heavy (i.e., weigh-out) commodities adapt immediately to increases in GVW limits if there is no need to purchase new vehicles. When a regulatory change coincides with the introduction of a new, more productive vehicle configuration, the uptake of the new vehicle lags behind the regulatory change by a few years. Finally, configurations exhibit different GVW distributions and responses to increased GVW limits depending on whether the configurations are well suited for hauling weigh-out or cube-out commodities. This differential response demonstrates how regulations facilitate fleet diversity within the trucking industry’s approach to the road freight transport task.