bluuebunny/crossref_metadata_2025 huggingface dataset:
Depletion of Conventional Type-1 Dendritic Cells in Established Tumors Suppresses Immunotherapy Efficacy
Authors: Teijeira Alvaro , Garasa Saray , Luri-Rey Carlos , de Andrea Carlos , Gato Maria , Molina Carmen , Kaisho Tsuneyasu , Cirella Assunta , Azpilikueta Arantza , Wculek Steffanie K. , Egea Josune , Olivera Irene , Rodriguez Inmaculada , Rouzaut Ana , Verkhusha Vladislav , Valencia Karmele , Sancho David , Berraondo Pedro , Melero Ignacio
Published: September 2022

The ability of conventional type-1 dendritic cells (cDC1) to cross-present tumor antigens to CD8+ T cells is critical for the induction of antitumor CTLs. Mice that are constitutively deficient in cDC1 cells have been reported to fail to respond to immunotherapy strategies based on checkpoint inhibitors. However, further work is needed to clarify the precise time during immunotherapy treatment that cDC1 cells are required for the beneficial effect of treatment. Here, we used a refined XCR1-DTR-Venus transgenic mouse model to acutely deplete cDC1 cells and trace their behavior using intravital microscopy. Diphtheria toxin–mediated cDC1 depletion prior to immunotherapy treatment with anti–PD-1 and/or anti-CD137 immunostimulatory mAbs completely ablated antitumor efficacy. The efficacy of adoptive T-cell therapy was also hampered by prior cDC1 depletion. After the onset of immunotherapy treatment, depletion of cDC1s only moderately reduced the therapeutic efficacy of anti–PD-1 and anti-CD137 mAbs. Intravital microscopy of liver-engrafted tumors revealed changes in the intratumoral behavior of cDC1 cells in mice receiving immunotherapy, and treatment with diphtheria toxin to deplete cDC1s impaired tumor T-cell infiltration and function. These results reveal that the functional integrity of the cDC1 compartment is required at the onset of various immunotherapies to successfully treat established tumors.

Significance:

These findings reveal the intratumoral behavior of cDC1 dendritic cells in transgenic mouse models and demonstrate that the efficacy of immunotherapy regimens is precluded by elimination of these cells.
Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan
Authors: Chien Li-Hsin , Chen Tzu-Yu , Chen Chung-Hsing , Chen Kuan-Yu , Hsiao Chin-Fu , Chang Gee-Chen , Tsai Ying-Huang , Su Wu-Chou , Huang Ming-Shyan , Chen Yuh-Min , Chen Chih-Yi , Liang Sheng-Kai , Chen Chung-Yu , Wang Chih-Liang , Hung Hsiao-Han , Jiang Hsin-Fang , Hu Jia-Wei , Rothman Nathaniel , Lan Qing , Liu Tsang-Wu , Chen Chien-Jen , Yang Pan-Chyr , Chang I-Shou , Hsiung Chao A.
Published: September 2022

Background:

Methods synthesizing multiple data sources without prospective datasets have been proposed for absolute risk model development. This study proposed methods for adapting risk models for another population without prospective cohorts, which would help alleviate the health disparities caused by advances in absolute risk models. To exemplify, we adapted the lung cancer risk model PLCOM2012, well studied in the west, for Taiwan.

Methods:

Using Taiwanese multiple data sources, we formed an age-matched case–control study of ever-smokers (AMCCSE), estimated the number of ever-smoking lung cancer patients in 2011–2016 (NESLP2011), and synthesized a dataset resembling the population of cancer-free ever-smokers in 2010 regarding the PLCOM2012 risk factors (SPES2010). The AMCCSE was used to estimate the overall calibration slope, and the requirement that NESLP2011 equals the estimated total risk of individuals in SPES2010 was used to handle the calibration-in-the-large problem.

Results:

The adapted model PLCOT-1 (PLCOT-2) had an AUC of 0.78 (0.75). They had high performance in calibration and clinical usefulness on subgroups of SPES2010 defined by age and smoking experience. Selecting the same number of individuals for low-dose computed tomography screening using PLCOT-1 (PLCOT-2) would have identified approximately 6% (8%) more lung cancers than the US Preventive Services Task Forces 2021 criteria. Smokers having 40+ pack-years had an average PLCOT-1 (PLCOT-2) risk of 3.8% (2.6%).

Conclusions:

The adapted PLCOT models had high predictive performance.

Impact:

The PLCOT models could be used to design lung cancer screening programs in Taiwan. The methods could be applicable to other cancer models.
RNA-Based Classification of Homologous Recombination Deficiency in Racially Diverse Patients with Breast Cancer
Authors: Walens Andrea , Van Alsten Sarah C. , Olsson Linnea T. , Smith Markia A. , Lockhart Alex , Gao Xiaohua , Hamilton Alina M. , Kirk Erin L. , Love Michael I. , Gupta Gaorav P. , Perou Charles M. , Vaziri Cyrus , Hoadley Katherine A. , Troester Melissa A.
Published: September 2022

Background:

Aberrant expression of DNA repair pathways such as homologous recombination (HR) can lead to DNA repair imbalance, genomic instability, and altered chemotherapy response. DNA repair imbalance may predict prognosis, but variation in DNA repair in diverse cohorts of breast cancer patients is understudied.

Methods:

To identify RNA-based patterns of DNA repair expression, we performed unsupervised clustering on 51 DNA repair-related genes in the Cancer Genome Atlas Breast Cancer [TCGA BRCA (n = 1,094)] and Carolina Breast Cancer Study [CBCS (n = 1,461)]. Using published DNA-based HR deficiency (HRD) scores (high-HRD ≥ 42) from TCGA, we trained an RNA-based supervised classifier. Unsupervised and supervised HRD classifiers were evaluated in association with demographics, tumor characteristics, and clinical outcomes.

Results

: Unsupervised clustering on DNA repair genes identified four clusters of breast tumors, with one group having high expression of HR genes. Approximately 39.7% of CBCS and 29.3% of TCGA breast tumors had this unsupervised high-HRD (U-HRD) profile. A supervised HRD classifier (S-HRD) trained on TCGA had 84% sensitivity and 73% specificity to detect HRD-high samples. Both U-HRD and S-HRD tumors in CBCS had higher frequency of TP53 mutant-like status (45% and 41% enrichment) and basal-like subtype (63% and 58% enrichment). S-HRD high was more common among black patients. Among chemotherapy-treated participants, recurrence was associated with S-HRD high (HR: 2.38, 95% confidence interval = 1.50–3.78).

Conclusions:

HRD is associated with poor prognosis and enriched in the tumors of black women.

Impact:

RNA-level indicators of HRD are predictive of breast cancer outcomes in diverse populations.
Properties of Patient-Reported Outcome Measures in Recessive Stargardt Disease
Authors: Terheyden Jan Henrik , Finger Robert P. , Wicharz Flora , Herrmann Philipp , Holz Frank G. , Tufail Adnan , Müller Philipp L.
Published: September 2022

Introduction: The study aimed to explore the psychometric properties of the National Eye Institute Visual Function Questionnaire (NEI VFQ) and Impact of Vision Impairment (IVI) profile in recessive Stargardt disease (STGD1). Methods: The NEI VFQ-25 and IVI-28 were administered to individuals with STGD1. Responses were analyzed following psychometrically established dimension structures of the NEI VFQ (visual function [VF] subscale; socioemotional [SE] subscale) and of the IVI (functional [F] subscale; emotional [E] subscale). We analyzed internal consistency, dimensionality, item fit, and differential item functioning (DIF), using latent trait models. Criterion validity was assessed using Pearson correlation coefficients. Results: Seventy-one participants (42 females, 29 males; mean age, 44 ± 19 years) were included. Self-reported difficulty levels were lower than the mean difficulty of items in both instruments. Person reliability and person separation index of the instruments were 0.85 and 2.40 (NEI VFQ-VF), 0.69 and 1.49 (NEI-VFQ-SE), 0.88 and 2.77 (IVI-F), and 0.72 and 1.62 (IVI-E). No items showed misfit at a level distorting the measurement system. One IVI item showed DIF by gender but was retained as person measures were largely unaffected by its removal. NEI VFQ-VF and IVI-F as well as NEI VFQ-SE and IVI-E were positively correlated (r = 0.79 and 0.64, respectively). Conclusion: The NEI VFQ and IVI have acceptable psychometric properties in STGD1 with the IVI allowing more sensitive person stratification. Targeting of questionnaires to individuals with STGD1 might be improved by including additional content domains specific to the disease.
Estradiol Replacement as a Potential Enhancer of Working Memory and Neuroplasticity in Hypogonadal Trans Women
Authors: Schneider Maiko A. , Malhotra Devon , Spritzer Poli M. , Hatchard Taylor , Minuzzi Luciano , Frey Benicio N. , Haefner Sasha A. , Nicholson Andrew , McKinnon Margaret , Syan Sabrina K. , Cardoso Taiane de Azevedo , Schwarz Karine , Anés Maurício , Santos-Díaz Alejandro , Lobato Maria I.R.
Published: September 2022

Introduction: The cognitive effects of cross-sex hormone therapy (CSHT) are not well understood. In cisgender individuals, sex hormone therapy can impact neurotransmitter levels and structural anatomy. Similarly, in gender-diverse persons, CSHT has been associated with neural adaptations, such as growth in brain structures resembling those observed in cisgender individuals of the same sex. Hormone-related changes in learning and memory, as seen in menopause, are associated with physiological hypogonadism or a decline in hormones, such as estradiol. The present study examined the effect of estradiol administration in humans on glutamate concentration in brain regions involved in semantic and working memory (i.e., the dorsolateral prefrontal cortex [DLPFC], the posterior hippocampus, and the pregenual anterior cingulate cortex) and its relationship with memory. Methods: Eighteen trans women (male biological sex assigned at birth) ceased CSHT for 30 days for a washout phase (t1) upon study enrollment to reach a hypogonadal state. Working and semantic memory, cognition, hormonal assays, and brain imaging were assessed. Participants resumed CSHT for 60 days for a replacement phase (t2), after which the same evaluations from t1 were repeated. Results: Estradiol increased among trans women after 60 days of resumed CSHT with significant improvements in semantic memory compared to the hypogonadal phase. Working memory recall was significantly and positively correlated to glutamate in the DLPFC during the reinstatement phase, although the relationship was not moderated by levels of estradiol. Discussion: These results may have clinical implications for the therapeutic effects of estradiol replacement, serving as a protective factor against cognitive decline and impairment for trans women post-gonadectomy.
Effects of hands-on instructional strategy on senior school students’ performance in waves
Authors: Akanbi Abdulrasaq Oladimeji , Mohammed Ridwan Enuwa , Yusuf Aishat Abdulqadir , Olayinka Yahaya Wasiu
Published: September 2022

The research examined the effects of hands-on teaching strategy on senior school learners’ performance waves in Ilorin, Nigeria. Senior secondary school (SSS2) science students were sampled at intact classes using a purposive sampling technique. Researcher-designed instructional packages on waves and Physics achievement test on waves (PATW) were the instruments employed. The gathered data were analysed with the use of descriptive and inferential statistics. Percentage, mean and standard deviation were employed in answering the research question. Meanwhile, Analysis of Variance (ANOVA) was utilised in testing the hypotheses formulated at a 0.05 alpha level. The study showed that students’ performance exposed to hands-on instructional strategy was very impressive, while students’ performance exposed to traditional strategy was low, there was a significant effect of hands-on instructional technique on the performance of students in waves and no statistically significant collaborative effect of hands-on instructional technique and students’ gender on performance in waves. It was suggested that hands-on instructional strategies should be integrated and used to make teaching and learning more concrete and active based for both teachers and students.
Dual techniques of load shedding and capacitor placement considering load models for optimal distribution system
Authors: Hussain Ali Nasser , Shakir Al-Jubori Waleed Khalid
Published: September 2022

Voltage stability represents one of the main issues in electrical power system. Under voltage load shedding (UVLS) has long been regarded as one of the most successful techniques to prevent the voltage collapse. However, the ordinary load shedding schemes do not consider the different load models and decreasing in the ‎economic cost that resulted from load disconnection, so the dual techniques of load shedding with reactive compensation are needed. Usually loads being modeled as constant power, while in fact of load flow the various load models are utilized. An investigation of optimal dual load shedding with reactive compensation for distribution system based on direct backward forward sweep method (DBFSM) load flow along with a comparison among the other load models are presented in this paper. The teaching learning-based optimization (TLBO) algorithm is executed in order to reduce power losses and enhance the voltage profile. This algorithm is tested and applied to IEEE-16 bus distribution test system to find the optimal superior capacitor size and placement while minimizing load shading for the network. Five different load shedding sequences are considered and the optimization performance of load models demonstrated the comparison through MATLAB program.
Proposal for a fuzzy logic-based system to determine cardiovascular risk
Authors: Chanchí Golondrino Gabriel Elías , Ospina Alarcón Manuel Alejandro , Campo Muñoz Wilmar Yesid
Published: September 2022

One of the key variables to determine the level of cardiovascular risk is the heart rate variability, which associates different metrics such as average of the RR intervals (average RR), standard deviation of the RR intervals (SDRR) and percentage of differences greater than 50 ms in RR intervals (pRR50). Given that these metrics make use of different measurement units, scales, and ranges, it is necessary to determine an output risk level in intelligible terms, taking as input the values of each one of them. Thus, this article proposes the development of a system based on fuzzy logic to determine the percentage or cardiovascular risk level. The fuzzy system is connected to an Arduino board with a heart rate sensor where the heart rate and heart rate variability values are obtained, so they are used to calculate the risk level metrics. Using the input values of each metric, as well as the 3 membership functions of the inputs, the output membership function, and a total of 18 inference rules defined from the inputs and outputs, the system obtains the output cardiovascular risk level. The fuzzy system was implemented using free hardware and software tools, making it available in medical campaigns for the early identification of heart conditions.
Number of sources estimation using a hybrid algorithm for smart antenna
Authors: Miry Mohammed Hussein , Sabri Atheer A. , Mary Ali Hussien
Published: September 2022

The number of sources estimation is one of the vital key technologies in smart antenna. The current paper adopts a new system that employs a hybrid algorithm of artificial bee colony (ABC) and complex generalized Hebbian (CGHA) neural network to Bayesian information criterion (BIC) technique, aiming to enhance the accuracy of number of sources estimation. The advantage of the new system is that no need to compute the covariance matrix, since its principal eigenvalues are computed using the CGHA neural network for the received signals. Moreover, the proposed system can optimize the training condition of the CGHA neural network, therefore it can overcome the random selection of initial weights and learning rate, which evades network oscillation and trapping into local solution. Simulation results of the offered system show good responses through reducing the required time to train the CGHA neural network, fast converge speed, effectiveness, in addition to achieving the correct number of sources.
Message queue telemetry transport and lightweight machine-to-machine comparison based on performance efficiency under various scenarios
Authors: Singh Drishti , Singh Ria , Gupta Aryan , Pawar Ambika Vishal
Published: September 2022

Internet of things (IoT) is been advancing over a long period of time in many aspects. For data transfer between IoT devices in a wireless sensor network, various IoT protocols are proposed. Among them, the most widely used are constrained application protocol (CoAP) and message queue telemetry transport (MQTT). Overcoming the limitations of CoAP, lightweight machine-to-machine (LwM2M) framework was designed above CoAP. Recent statistics show that LwM2M and MQTT are the widely used, but LwM2M is still less used than MQTT. Our paper is aimed at comparing both MQTT and LwM2M on the basis of performance efficiency, which will be achieved by sending same file through both protocols to the server. Performance efficiency will be calculated in two scenarios, i) when the client makes a connection with the server i.e., while initial connection and ii) while sending data file to server i.e., while data transfer. Both the protocols will be tested on the number of packets sent and the variability of packet size throughout the session. Experimental results indicated that LwM2M outperformed MQTT in both above scenarios by almost 69%. Therefore, we concluded by stating that LwM2M is best choice over MQTT, but MQTT can still be used in some situations if necessary.
Forecasting movie rating using k-nearest neighbor based collaborative filtering
Authors: Rokade Prakash Pandharinath , Prasad Rao PVRD , Devarakonda Aruna Kumari
Published: September 2022

Expressing reviews in the form of sentiments or ratings for item used or movie seen is the part of human habit. These reviews are easily available on different social websites. Based on interest pattern of a user, it is important to recommend him the items. Recommendation system is playing a vital role in everyone’s life as demand of recommendation for user’s interest increasing day by day. Movie recommendation system based on available ratings for a movie has become interesting part for new users. Till today, a lot many recommendation systems are designed using several machine learning algorithms. Still, sparsity problems, cold start problem, scalability, grey sheep problem are the hurdles for the recommendation systems that must be resolved using hybrid algorithms. We proposed in this paper, a movie rating system using a k-nearest neighbor (KNN-based) collaborative filtering (CF) approach. We compared user’s ratings for different movies to get top K users. Then we have used this top K set to find missing ratings by user for a movie using CF. Our proposed system when evaluated for various criteria shows promising results for movie recommendations compared with existing systems.
Forecasting stock price movement direction by machine learning algorithm
Authors: Khoa Bui Thanh , Huynh Tran Trong
Published: September 2022

Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.
Comparison study of transfer function and artificial neural network for cash flow analysis at Bank Rakyat Indonesia
Authors: Faricha Anifatul , Ulyah Siti Maghfirotul , Susanti Rika , Mardhiana Hawwin , Nanda Muhammad Achirul , Rahmayanti Ilma Amira , Andreas Christopher
Published: September 2022

The cash flow analysis is essential to examine the economic flows in the financial system. In this paper, the financial dataset at Bank Rakyat Indonesia was used, it recorded the sources of cash inflow and outflow during a particular period. The univariate time series model like the autoregressive and integrated moving average is the common approach to build the prediction based on the historical dataset. However, it is not suitable to estimate the multivariate dataset and to predict the extreme cases consisting of nonlinear pairs between independent-dependent variables. In this study, the comparison of using two types of models i.e., transfer function and artificial neural network (ANN) were investigated. The transfer function model includes the coefficient of moving average (MA) and autoregressive (AR), which allows the multivariate analysis. Furthermore, the artificial neural network allows the learning paradigm to achieve optimal prediction. The financial dataset was divided into training (70%) and testing (30%) for two types of models. According to the result, the artificial neural network model provided better prediction with achieved root mean square error (RMSE) of 0.264897 and 0.2951116 for training and testing respectively.
Brain tumor classification in magnetic resonance imaging images using convolutional neural network
Authors: Remzan Nihal , Tahiry Karim , Farchi Abdelmajid
Published: September 2022

Deep learning (DL) is a subfield of artificial intelligence (AI) used in several sectors, such as cybersecurity, finance, marketing, automated vehicles, and medicine. Due to the advancement of computer performance, DL has become very successful. In recent years, it has processed large amounts of data, and achieved good results, especially in image analysis such as segmentation and classification. Manual evaluation of tumors, based on medical images, requires expensive human labor and can easily lead to misdiagnosis of tumors. Researchers are interested in using DL algorithms for automatic tumor diagnosis. convolutional neural network (CNN) is one such algorithm. It is suitable for medical image classification tasks. In this paper, we will focus on the development of four sequential CNN models to classify brain tumors in magnetic resonance imaging (MRI) images. We followed two steps, the first being data preprocessing and the second being automatic classification of preprocessed images using CNN. The experiments were conducted on a dataset of 3,000 MRI images, divided into two classes: tumor and normal. We obtained a good accuracy of 98,27%, which outperforms other existing models.
Hybrid features and ensembles of convolution neural networks for weed detection
Authors: Kumar Kempegowda Sandeep , Rajeswari Rajeswari , Satyanarayana Lakshmikanth , Matada Basavarajaiah Siddesh
Published: September 2022

Weeds compete with plants for sunlight, nutrients and water. Conventional weed management involves spraying of herbicides to the entire crop which increases the cost of cultivation, decreasing the quality of the crop, in turn affecting human health. Precise automatic spraying of the herbicides on weeds has been in research and use. This paper discusses automatic weed detection using hybrid features which is generated by extracting the deep features from convolutional neural network (CNN) along with the texture and color features. The color and texture features are extracted by color moments, gray level co-occurrence matrix (GLCM) and Gabor wavelet transform. The proposed hybrid features are classified by Bayesian optimized support vector machine (BO-SVM) classifier. The experimental results read that the proposed hybrid features yield a maximum accuracy of 95.83%, higher precision, sensitivity and F-score. A performance analysis of the proposed hybrid features with BO-SVM classifier in terms of the evaluation parameters is made using the images from crop weed field image dataset.
Multiple inputs all-optical logic gates based on nanoring insulator-metal-insulator plasmonic waveguides
Authors: Fakhruldeen Hassan Falah , Mansour Tahreer Safa’a , Ibrahim Jabbar Feryal , Alkhayyat Ahmed
Published: September 2022

In this paper, we report new nanoscale plasmonic multiple inputs logic gates based on insulator-metal-insulator (IMI) nanoring waveguides. The proposed all-optical gates are numerically analyzed by the finite element method. NOT, AND, NAND, NOR, and EX-NOR all-optical logic gates were suitably designed and investigated based on the linear interface between the propagated waves through the waveguides. The operation wavelength was 1550 nm. The simulation results show that the optical transmission threshold of (0.26) which performs the operation of planned logic gates is accomplished. Moreover, simulation results show that our compact structure of all-optical logic gates may have potential applications in all-optical integrated networks.
Vivências práticas e educação ambiental no ensino do atelier de projetos integrados de arquitetura, urbanismo e paisagismo
Authors: Pagel Erica Coelho
Published: September 2022

Práticas metodológicas através de temas transversais, processos colaborativos e resolução de problemas reais possibilitando o estudante de arquitetura vivenciar o lugar e o usuário, se mostram de suma importância na formação do arquiteto e urbanista. O objetivo desse artigo é apresentar as experiências obtidas na concepção de espaços sustentáveis na arquitetura escolar dentro da disciplina de Atelier de Projetos. A metodologia articula ensino, pesquisa e extensão, através do contato com a comunidade, planejamento e execução de um espaço de educação ambiental. Foram realizadas visitas ao local, aplicação de questionários à equipe pedagógica e construção de uma horta didática no pátio da escola selecionada. Por fim, tem-se a elaboração de uma proposta de uma edificação de ensino com base nos conceitos estudados e experiências vivenciadas. Percebe-se que a participação na atividade de extensão motivou os graduandos a projetarem espaços que pudessem proporcionar maior integração da escola com o entorno, uso de técnicas construtivas de menor impacto ao meio ambiente e de menor consumo energético.
Nau sem rumo? O sistema partidário brasileiro pós-democratização
Authors: Melo Carlos Ranulfo
Published: September 2022

Este artigo tem como objetivo analisar as mudanças pelas quais passou o sistema partidário brasileiro inaugurado em 1982. Em um primeiro momento, mostra como e por que, após um período de extrema fluidez e mesmo mantendo alto nível de fragmentação, o sistema alcançou um determinado padrão de interação entre seus membros, o que lhe permitiu duas décadas de estabilidade. Na sequência, o artigo explica por que esse padrão se desfez, inaugurando uma fase de crise e incerteza. Os momentos de estabilidade e desarticulação têm relação com a dinâmica assumida pela competição em torno da Presidência da República.
Racial Disparities in Hospitalization Among Patients Who Receive a Diagnosis of Acute Coronary Syndrome in the Emergency Department
Authors: Islek Duygu , Ali Mohammed K. , Manatunga Amita , Alonso Alvaro , Vaccarino Viola
Published: September 2022

Background

Timely hospitalization of patients who are diagnosed with an acute coronary syndrome (ACS) at the emergency department (ED) is a crucial step to lower the risk of ACS mortality. We examined whether there are racial and ethnic differences in the risk of being discharged home among patients who received a diagnostic code of ACS at the ED and whether having health insurance plays a role.

Methods and Results

We examined 51 022 910 discharge records of ED visits in Florida, New York, and Utah in the years 2008, 2011, 2014, and 2016/2017 using state‐specific data from the Healthcare Cost and Utilization Project. We identified ED admissions for acute myocardial infarction or unstable angina using the
International Classification of Diseases, Ninth Revision
(
ICD‐9
)/
International Statistical Classification of Diseases, Tenth Revision
(
ICD‐10
) diagnostic codes. We used generalized estimating equation models to compare the risk of being discharged home across racial and ethnic groups. We used Poisson marginal structural models to estimate the mediating role of health insurance status. The proportion discharged home with a diagnostic code of ACS was 12% among Black patients, 6% among White patients, 9% among Hispanic patients, and 9% among Asian/Pacific Islander patients. The incidence risk ratio for being discharged home was 1.26 (95% CI, 1.18–1.34) in Black patients, 1.23 (95% CI, 1.15–1.32) in Hispanic patients, and 1.11 (95% CI, 0.93–1.31) in Asian/Pacific Islander patients compared with White patients. Race and ethnicity were marginally associated with discharge home via pathways not mediated by health insurance.

Conclusions

Racial and ethnic disparities exist in the hospitalization of patients who received a diagnostic code of ACS in the ED. Possible causes need to be investigated.
Depression or Anxiety According to Management Modalities in Patients With Unruptured Intracranial Aneurysms
Authors: Lim Yong Cheol , Lee Eunyoung , Song Jihye
Published: September 2022

Background:

In the treatment of unruptured intracranial aneurysms, the risk was usually estimated by objective neurological sequelae. However, their effects on depression and anxiety are rare and remain controversial. We aimed to evaluate the risk of depression and anxiety in patients with unruptured intracranial aneurysm stratified by management strategies in a population-based, longitudinal cohort study.

Methods:

Using the Korean National Health Insurance Research Database, 71 750 patients with unruptured intracranial aneurysms between 2008 and 2011 were identified and followed up until the end of 2020. The risk of depression and anxiety was compared among management strategies with respect to age, sex, and medical comorbidities.

Results:

The Kaplan-Meier survival curves indicated that the treatment (clipping and endovascular treatment) group developed depression more frequently than the observation group (
P
<0.001). The adjusted hazard ratio was 1.11 (95% CI, 1.07–1.15) in the treatment group. According to the management modality, the Kaplan-Meier survival curves indicated that clipping and endovascular treatment groups developed depression more frequently than the observation group (
P
<0.0001). The adjusted hazard ratio was 1.15 (95% CI, 1.10–1.21) for clipping and 1.07 (95% CI, 1.02–1.12) for endovascular treatment. The depression risk was higher with advanced age (hazard ratio for 45–64 years, 1.37 [95% CI, 1.29–1.45] and hazard ratio for ≥65 years, 2.04 [95% CI, 1.92–2.17]). The risk for anxiety did not differ among the management modalities.

Conclusions:

In this study, the risk of depression was slightly greater after clipping surgery than endovascular treatment. Data on treatment-related, long-term psychological outcomes, such as depression, may aid decision-making for preventive treatment of asymptomatic unruptured intracranial aneurysm patients.
Ability of Radiomics Versus Humans in Predicting First‐Pass Effect After Endovascular Treatment in the ESCAPE‐NA1 Trial
Authors: Bala Fouzi , Qiu Wu , Zhu Kairan , Kappelhof Manon , Cimflova Petra , Kim Beom Joon , McDonough Rosalie , Singh Nishita , Kashani Nima , Zhang Jianhai , Najm Mohamed , Ospel Johanna M. , Wadhwa Ankur , Nogueira Raul G. , McTaggart Ryan A. , Demchuk Andrew M. , Poppe Alexandre Y. , Zerna Charlotte , Joshi Manish , Almekhlafi Mohammed A. , Goyal Mayank , Hill Michael D. , Menon Bijoy K. ,   for the ESCAPE‐NA1 Investigators
Published: September 2022

Background

First‐pass effect (FPE), that is, achieving reperfusion with a single thrombectomy device pass, is associated with better clinical outcomes in patients with acute stroke. FPE is therefore increasingly used as a marker of device and procedural efficacy. We aimed to evaluate the ability of thrombus‐based radiomics models to predict FPE in patients undergoing endovascular thrombectomy and compare performance with experts and nonradiomics thrombus characteristics.

Methods

Patients with thin‐slice noncontrast computed tomography and computed tomography angiography from the ESCAPE‐NA1 (Efficacy and Safety of Nerinetide for the Treatment of Acute Ischemic Stroke) trial were included. Thrombi were manually segmented on all images. Data were randomly split into a derivation set that included a training and a validation subset and an independent test set. Radiomics features were extracted from the derivation set. The machine learning models were compared with 3 expert stroke physicians in predicting FPE in the test set using area under the receiver operating characteristic curves.

Results

Thin‐slice images of 554 patients were divided into a derivation set (training [n=388] and validation [n=55]) and a test set (n=111). A radiomics model using the combination of noncontrast computed tomography, computed tomography angiography, and noncontrast computed tomography–computed tomography angiography difference achieved the highest performance (area under the curve, 0.74 [95% CI, 0.64–0.84]) for prediction of FPE. This was higher than the mean area under the curve of the 3 experts (0.62 [95% CI, 0.53–0.71],
P
=0.01 for difference in area under the curves). The radiomics model also performed better than nonradiomics‐based thrombus features such as volume and permeability measurements in predicting FPE (
P
<0.05). Addition of device type did not improve the performance of the chosen radiomics model in predicting FPE.

Conclusion

A radiomics‐based machine learning model of thrombus characteristics from noncontrast computed tomography and computed tomography angiography performs better than experts and traditional nonradiomics imaging features in predicting FPE in patients with acute stroke treated with endovascular thrombectomy.
ProoFVer: Natural Logic Theorem Proving for Fact Verification
Authors: Krishna Amrith , Riedel Sebastian , Vlachos Andreas
Published: September 2022

Fact verification systems typically rely on neural network classifiers for veracity prediction, which lack explainability. This paper proposes ProoFVer, which uses a seq2seq model to generate natural logic-based inferences as proofs. These proofs consist of lexical mutations between spans in the claim and the evidence retrieved, each marked with a natural logic operator. Claim veracity is determined solely based on the sequence of these operators. Hence, these proofs are faithful explanations, and this makes ProoFVer faithful by construction. Currently, ProoFVer has the highest label accuracy and the second best score in the FEVER leaderboard. Furthermore, it improves by 13.21% points over the next best model on a dataset with counterfactual instances, demonstrating its robustness. As explanations, the proofs show better overlap with human rationales than attention-based highlights and the proofs help humans predict model decisions correctly more often than using the evidence directly.1
Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights
Authors: Sicilia Anthony , Maidment Tristan , Healy Pat , Alikhani Malihe
Published: September 2022

Investigating cooperativity of interlocutors is central in studying pragmatics of dialogue. Models of conversation that only assume cooperative agents fail to explain the dynamics of strategic conversations. Thus, we investigate the ability of agents to identify non-cooperative interlocutors while completing a concurrent visual-dialogue task. Within this novel setting, we study the optimality of communication strategies for achieving this multi-task objective. We use the tools of learning theory to develop a theoretical model for identifying non-cooperative interlocutors and apply this theory to analyze different communication strategies. We also introduce a corpus of non-cooperative conversations about images in the GuessWhat?! dataset proposed by De Vries et al. (2017). We use reinforcement learning to implement multiple communication strategies in this context and find that empirical results validate our theory.
Beyond Effectiveness
Authors: Coe Brooke , Spandler Kilian
Published: September 2022

Why do Southeast Asian states use regional mechanisms for disaster relief? From a conventional functionalist perspective, inadequate domestic-level responses to emergencies create a demand for scaled-up governance. This article offers an alternative interpretation of disaster cooperation in Southeast Asia. Drawing on theoretical insights from comparative regionalism and critical disaster studies, it argues that the raison d’être of the Association of Southeast Asian Nations (ASEAN) Coordinating Centre for Humanitarian Assistance on Disaster Management (AHA Centre) is to empower ASEAN states vis-à-vis extraregional humanitarian actors. The AHA Centre works to enable Member States to gatekeep intrusive extraregional aid and, ultimately, to transform authority relations in the international humanitarian system in favor of state actors that have traditionally found themselves in a peripheral and passive role.
Policy Advice in UN Development Work
Authors: Baumann Max-Otto
Published: September 2022

Providing high-level policy advice to developing countries with the purpose to shape national policies is a key function of the UN. Yet no official UN definition of policy advice exists and little is known on how much weight the UN gives to this support modality in contrast to capacity-building and implementation work. To address this gap, this article first articulates the case for the UN’s role in policy change. It then presents an empirical analysis of the policy advice landscape of the UN, providing a numerical estimate of the share of resources dedicated to policy advice and identifying five practical constraints on the UN’s policy advice function. Results suggest that, despite high expectations, the UN’s fieldwork is not strategically focused on policy advice. This article contributes to the underresearched field of UN development work and how it is, or should be, shaped by the UN’s multilateralism.
The plagiatus species group of the genus Geodromicus Redtenbacher, 1857 (Insecta: Coleoptera: Staphylinidae: Omaliinae), and additional data on some species from Japan
Authors: SHAVRIN ALEXEY V.
Published: September 2022

The plagiatus group of the genus Geodromicus Redtenbacher, 1857 includes eight Palaearctic species: the widespread G. plagiatus (Fabricius, 1798), the European G. corsicus Fagel, 1958, G. nigrita (P.W.J. Müller, 1821), G. pedemontanus Fagel, 1958, and G. suturalis (Lacordaire, 1835), and the East Palaearctic G. magnus Shavrin, 2020, G. paramerus Shavrin 2000, and G. shii Cheng & Peng, 2020 (in Cheng et al. 2020). The species group is reviewed, defined, and briefly discussed. The species are illustrated and a key is provided. Two additional species groups are established for four species from Japan with a strongly sclerotized flagellum: the aokii group, containing G. aokii (Watanabe, 1972) and G. sprius (Watanabe, 1990), and the sinuatus group containing G. sinuatus (Sharp, 1889) and G. suensoni Bernhauer, 1936. Both groups are defined, briefly discussed, and a key to each group is given. Several new synonymies are established: G. plagiatus = G. (s.str.) klimai A. Bordoni, 1984 syn. n., = G. (s.str.) schoenmanni A. Bordoni, 1984 syn. n.; G. nigrita = G. nigrita lohsei Jarrige, 1963 syn. n., = G. niveus Bernhauer, 1941 syn. n.; G. sinuatus = G. watanabei Hayashi, 2018 syn. n.; G. suensoni = G. takabai Hayashi, 2018 syn. n. Lectotypes of Staphylinus plagiatus Fabricius, 1798 and Anthophagus sinuatus Sharp, 1889 are designated. Distribution maps are provided for G. plagiatus, G. sinuatus, and G. suensoni. Geodromicus plagiatus is recorded from Cataluña province of Spain, Moldova, Georgia, Uzbekistan, and several regions of Russia (Pskov Area, Stavropol Area, Karachay-Cherkess and Chechen republics, Sverdlovsk Area, Khanty-Mansi Autonomous Region, Krasnoyarsk Territory, Evenkia and Yakutia), G. nigrita from Andorra and G. suturalis from Slovenia for the first time.
Systematic review of Myotis (Chiroptera, Vespertilionidae) from Chile based on molecular, morphological, and bioacoustic data
Authors: NOVAES ROBERTO LEONAN M. , PEDRO ANNIA RODRÍGUEZ-SAN , SALDARRIAGA-CÓRDOBA MÓNICA M. , AGUILERA-ACUÑA OMAYRA , WILSON DON E. , MORATELLI RICARDO
Published: September 2022

Myotis is the most diverse genus of bats in the world, with more than 30 species recognized in the Neotropics. However, many of these species represent cryptic complexes and are evidence of the existence of hidden diversity in several regions. Using an integrative approach based on molecular, morphological, and bioacoustic data, we performed a systematic review of Myotis species from Chile. Phylogenetic inference using cytochrome-b indicated the existence of three monophyletic lineages, and qualitative and quantitative morphological analyses supported these lineages as distinct and morphologically diagnosable taxa. Analysis of discriminant functions using parameters of echolocation calls also indicates the existence of three distinct bioacoustic clusters. Thus, all lines of evidence congruently indicate the existence of three distinct taxa. As a result, we recognize Myotis arescens as a valid and distinct species and define its taxonomic limits from the other species from Chile, Myotis atacamensis and Myotis chiloensis.
Climate Change and Onsite Wastewater Treatment Systems in the Coastal Carolinas: Perspectives from Wastewater Managers
Authors: Vorhees Lauren , Harrison Jane , O’Driscoll Michael , Humphrey Charles , Bowden Jared
Published: September 2022

Nearly one-half of the residents of North and South Carolina use decentralized or onsite wastewater treatment systems (OWTS). As the climate changes, coastal communities relying on OWTS are particularly vulnerable, as soil-based wastewater treatment may be reduced by water inundation from storm surge, sea level rise and associated groundwater rise, and heavy rainfall. Despite the vulnerabilities of OWTS to increased precipitation and sea level rise, there is little known about how onsite wastewater managers are responding to current and future climate risks. We conducted interviews with wastewater operators and installers and health regulators to understand the functioning, management, and regulation of OWTS in the current climate, challenges with rising sea levels and increases in extreme weather events, and what adaptation strategies could be implemented to mitigate negative impacts. Our results indicate that heavy precipitation and storm surges cause malfunctions for conventional septic systems where traditional site variables (e.g., soil type or groundwater level) are undesirable. Weather and climate are not required regulatory factors to consider in system selection and site approval, but many OWTS managers are aware of their impacts on the functioning of systems, and some are preemptively taking action to mitigate those impacts. Our findings suggest that filling gaps in the current communication structure between regulators and homeowners relying on OWTS is critical for coastal communities in the Carolinas to build climate resilience into decentralized wastewater infrastructure.

Significance Statement

This research aims to understand the functioning, management, and regulation of onsite wastewater treatment systems in the current climate, the challenges to these systems caused by rising sea levels and increases in extreme weather events, and the adaptation strategies that can be implemented to mitigate negative climate impacts. These results can be used by state government agencies, municipalities, and private sector wastewater managers to improve the resiliency of onsite wastewater treatment systems.
The Impact of Climate Disasters on the Cost of Equity Capital: Evidence from China
Authors: Chu Xiaojun , Xu Jing
Published: September 2022

Climate change increases the probability and intensity of disaster and brings adverse impacts on social and economic activities. This paper presents the impact of climate risk on the cost of equity capital (COE) and sheds light on the influence mechanisms and moderating factors between climate disaster shocks and the COE in a developing country. We first explain how climate risk represented by drought impacts the COE theoretically. Using the sample data listed in A-share market from 2004 to 2019, we find that drought leads to the rise of the COE due to the deterioration of information environment and the rise of business risk. Specifically, the influence mechanism is tested, and the results show that 1) drought increases firms’ real earnings management 2) and drought has a negative impact on the firms’ return on asset (ROA). Namely, the influence mechanism of drought on the COE is that drought changes the firms’ information environment and business activities. Further analysis shows that the impact of drought on the COE is different in a heterogeneous firm. The drought has a significant impact on the COE in firms with low-ability managers, state-owned enterprises, and politically connected firms, but the impact is not significant in firms with high-ability managers, non-state-owned enterprises, and nonpolitically connected firms. Our research helps people to understand the consequences of climate change from the microeconomic-level firm’s perspective.
Typology of Cybercrime Victimization in Europe: A Multilevel Latent Class Analysis
Authors: Lee Claire Seungeun , Wang Yan
Published: September 2022

The present study reveals hidden patterns of group membership across cybercrime victims in European countries. We used a multilevel latent class analysis of data from the 2019 Eurobarometer, a regionally representative sample of 21,908 individuals from 28 countries, to identify such subgroups and patterns of cybercrime victimization. The findings suggest there are two overarching cybercrime victim profiles in Europe based on individuals’ levels of “online activity” and “cybersecurity guardianship”: the “at-risk class” (19%, higher risk) and the “cautious class” (81%, lower risk). Ten different types of cybercrime victimization were compared, and while individual-level predictors were primarily used to produce different groups of cybercrime victims, our findings suggest that researchers consider both individual- and country-level predictors to understand cybercrime victimization patterns in greater depth.
Gendered experiences of loneliness during COVID-19 isolation: Insights for intersectional feminist social work – the case of Çankaya/Ankara, Turkey
Authors: Hatiboğlu-Kısat Burcu
Published: September 2022

The aim of this study is to understand the phenomenon of isolation due to COVID-19 through the lens of a feminist perspective. It focuses on daily life experiences of oppressed individuals living in Çankaya, whose spatial, socio-cultural, and political positions intersect with their age, disability, and gender. Semi-structured in-depth interviews were conducted with 37 people, of different ages, disabilities, and gender statements. Findings show that multi-dimensional oppressions and discriminations have undermined solidarity relations and result in more profound loneliness. The results highlight that new social work approaches involving intersectional feminist consciousness of oppressions need to be developed.