bluuebunny/crossref_metadata_2025 huggingface dataset:
A transformer-based generative adversarial network for brain tumor segmentation
Authors: Huang Liqun , Zhu Enjun , Chen Long , Wang Zhaoyang , Chai Senchun , Zhang Baihai
Published: November 2022

Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary to CNNs. In this paper, we proposed a novel transformer-based generative adversarial network to automatically segment brain tumors with multi-modalities MRI. Our architecture consists of a generator and a discriminator, which is trained in min–max game progress. The generator is based on a typical “U-shaped” encoder–decoder architecture, whose bottom layer is composed of transformer blocks with Resnet. Besides, the generator is trained with deep supervision technology. The discriminator we designed is a CNN-based network with multi-scale L1 loss, which is proved to be effective for medical semantic image segmentation. To validate the effectiveness of our method, we conducted exclusive experiments on BRATS2015 dataset, achieving comparable or better performance than previous state-of-the-art methods. On additional datasets, including BRATS2018 and BRATS2020, experimental results prove that our technique is capable of generalizing successfully.
KRAS G12D targeted therapies for pancreatic cancer: Has the fortress been conquered?
Authors: Bannoura Sahar F. , Khan Husain Yar , Azmi Asfar S.
Published: November 2022

KRAS mutations are among the most commonly occurring mutations in cancer. After being deemed undruggable for decades, KRAS G12C specific inhibitors showed that small molecule inhibitors can be developed against this notorious target. At the same time, there is still no agent that could target KRAS G12D which is the most common KRAS mutation and is found in the majority of KRAS-mutated pancreatic tumors. Nevertheless, significant progress is now being made in the G12D space with the development of several compounds that can bind to and inhibit KRAS G12D, most notably MRTX1133. Exciting advances in this field also include an immunotherapeutic approach that uses adoptive T-cell transfer to specifically target G12D in pancreatic cancer. In this mini-review, we discuss recent advances in KRAS G12D targeting and the potential for further clinical development of the various approaches.
Outcomes for critical illness in children with cancer: Analysis of risk factors for adverse outcome and resource utilization from a specialized center in Mexico
Authors: Cardenas-Aguirre Adolfo , Hernandez-Garcia Montserrat , Lira-De-Leon Berenice , Munoz-Brugal Yulissa L. , Wang Huiqi , Villanueva-Diaz Ivonne , Ruiz-Perez Eduardo , Mijares-Tobias Jose M. , Giles-Gonzalez Alex O. , McArthur Jennifer , Escamilla-Aisan Gabriela , Arias Anita , Devidas Meenakshi , Agulnik Asya
Published: November 2022

Introduction

Children with cancer have a higher risk of adverse outcomes during critical illness than general pediatric populations. In Low- and middle-income countries, lack of resources can further negatively impact outcomes in critically ill children with cancer.

Methods

In this study, we describe the outcomes of a large cohort of children with cancer including mortality and resource utilization. We performed a retrospective review of all patients admitted to our PICU between December 12th, 2013 and December 31st, 2019. Outcomes were defined as recovery or death and resource utilization was described via use of critical care interventions, Length of stay as well as PICU- and Mechanical Ventilation- free days.

Results

Overall mortality was 6.9% while mortality in the unplanned admissions was 9.1%. This remained lower than expected mortality based on PIM2 scoring. Type of PICU admission, Neurological Deterioration as a cause of PICU admission, and PIM2 were significant as risk factors in univariate analysis, but only PIM2 remained significant in the multivariate analysis.

Discussion

Our Study shows that high survival rates are achievable for children with cancer with critical illness in resource-limited settings with provision of high-quality critical care. Organizational and clinical practice facilitating quality improvement and early identification and management of critical illness may attenuate the impact of known risk factors for mortality in this population.
Statistical methods for measuring trends in colorectal cancer incidence in registries: A systematic review
Authors: Alsadhan Norah , Almaiman Alaa , Pujades-Rodriguez Mar , Brennan Cathy , Shuweihdi Farag , Alhurishi Sultana A. , West Robert M.
Published: November 2022

Background

Monitoring cancer trends in a population is essential for tracking the disease’s burden, allocating resources, and informing public health policies. This review describes variations in commonly employed methods to estimate colorectal cancer (CRC) incidence trends.

Methods

We performed a systematic literature search in four databases to identify population-based studies reporting CRC incidence trends, published between January 2010 and May 2020. We extracted and described data on methods to estimate trends and assess model validity, and the software used.

Results

This review included 145 articles based on studies conducted in five continents. The majority (93%) presented visual summaries of trends combined with absolute, relative, or annual change estimates. Fourteen (10%) articles exclusively calculated the relative change in incidence over a given time interval, presented as the percentage of change in rates. Joinpoint regression analysis was the most commonly used method for assessing incidence trends (n= 65, 45%), providing estimates of the annual percentage change (APC) in rates. Nineteen (13%) studies performed Poisson regression and 18 (12%) linear regression analysis. Age-period-cohort modeling- a type of generalized linear models- was conducted in 18 (12%) studies. Thirty-nine (37%) of the studies modeling incidence trends (n=104, 72%) indicated the method used to evaluate model fitness. The joinpoint program (52%) was the statistical software most commonly used.

Conclusion

This review identified variation in the calculation of CRC incidence trends and inadequate reporting of model fit statistics. Our findings highlight the need for increasing clarity and transparency in reporting methods to facilitate interpretation, reproduction, and comparison with findings from previous studies.
Anlotinib combined with TAS-102 as the third-line treatment for a patient with metastatic colon cancer: A case report
Authors: Li Qizheng , Zhang Xia , Fan Buqun , Yang Yudie , Cui Xiaonan , Zhang Jie , Jiang Kaiteng , Zhang Chunxia , Zhang Bin
Published: November 2022

Chemotherapy combined with targeted therapy is a first-line and second-line treatment for metastatic colorectal cancer(mCRC), which has brought survival benefits to mCRC patients, however, disease progression is inevitable. More than 60% of patients still needed third-line treatment after the progress of second-line treatment. After the failure of second-line chemotherapy, treatment compliance and the physical tolerance of patients both decrease. Therefore, choosing an appropriate third-line treatment regimen is key to prolonging survival and improving quality of life. As a novel cytotoxic antitumor drug, trifluridine/tipiracil (TAS-102) is composed of trifluridine (FTD) and tipiracil hydrochloride (TPI). FTD can directly bind to the DNA of cancer cells to cause DNA dysfunction, thereby exerting antitumor effects. TPI can inhibit the degradation of FTD, thereby increasing its cytotoxicity. The few side effects of TAS-102 has become an important reason why clinicians present it as a treatment option to the patient for consideration, clinical trial data for progression free survival are lacking. The exploration of third-line treatment regimens with drug combinations has attracted much attention. This article reports a case of metastatic colon cancer (RAS/BRAF wild type, pMMR/Non-MSI-H), after failure of first-line and second-line therapies, the patient was eventually treated with anlotinib combined with TAS-102 as the third-line treatment. The treatment has shown good efficacy, with a long PFS benefit for more than 20 months and mild adverse reactions. This case reports demonstrates that anlotinib combined with TAS-102 is a promising third-line treatment regimen for refractory mCRC, and provides proof-of-concept for the clinical exploration of optimal third-line combination treatment regimens.
Abemaciclib plus fulvestrant for the treatment of hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer with cystic brain metastases: A case report and literature review
Authors: Chu Zhaohui
Published: November 2022

Cystic brain metastases (CBM) in patients with breast cancer are rare. They have a worse prognosis than solid brain metastases, and they are less sensitive to radiotherapy. We report a case of hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2−) metastatic breast cancer with CBM. The patient underwent treatment with docetaxel combined with capecitabine for 5 months, followed by anastrozole maintenance therapy for 10 months, and palbociclib combined with exemestane for 22 months. CBM emerged and bone metastases increased in number. A missense mutation in PIK3CA (exon 10, c.1633G>A [p.Glu545Lys]) was detected by whole-exome next-generation sequencing from peripheral blood samples. After whole-brain radiotherapy (40 Gy/20 fx) combined with 3 months of treatment with everolimus and fulvestrant, CBM demonstrated partial remission (PR), but extracranial bone metastases continued to increase in number. Thus, the patient underwent fourth-line treatment with abemaciclib (100 mg bid) combined with fulvestrant (500 mg). Three months later, CBM significantly demonstrated PR and extracranial bone metastases were stable. At present, the patient has above 9 months of progression-free survival time without obvious adverse effects. This is the first report of abemaciclib combined with fulvestrant in the treatment of CBM in a patient with HR+/HER2− breast cancer.
Association between high-dose methotrexate-induced toxicity and polymorphisms within methotrexate pathway genes in acute lymphoblastic leukemia
Authors: Xu Meng , Wu Shuangshuang , Wang Yue , Zhao Yundong , Wang Ximin , Wei Changhong , Liu Xueying , Hao Feng , Hu Cheng
Published: November 2022

Methotrexate (MTX) is a folic acid antagonist, the mechanism of action is to inhibit DNA synthesis, repair and cell proliferation by decreasing the activities of several folate-dependent enzymes. It is widely used as a chemotherapy drug for children and adults with malignant tumors. High-dose methotrexate (HD-MTX) is an effective treatment for extramedullary infiltration and systemic consolidation in children with acute lymphoblastic leukemia (ALL). However, significant toxicity results in most patients treated with HD-MTX, which limits its use. HD-MTX-induced toxicity is heterogeneous, and this heterogeneity may be related to gene polymorphisms in related enzymes of the MTX intracellular metabolic pathway. To gain a deeper understanding of the differences in toxicity induced by HD-MTX in individuals, the present review examines the correlation between HD-MTX-induced toxicity and the gene polymorphisms of related enzymes in the MTX metabolic pathway in ALL. In this review, we conclude that only the association of SLCO1B1 and ARID5B gene polymorphisms with plasma levels of MTX and MTX-related toxicity is clearly described. These results suggest that SLCO1B1 and ARID5B gene polymorphisms should be evaluated before HD-MTX treatment. In addition, considering factors such as age and race, the other exact predictor of MTX induced toxicity in ALL needs to be further determined.
Targeting glycolysis in non-small cell lung cancer: Promises and challenges
Authors: Xu Jia-Qi , Fu Yan-Li , Zhang Jing , Zhang Kai-Yu , Ma Jie , Tang Jing-Yi , Zhang Zhi-Wei , Zhou Zhong-Yan
Published: November 2022

Metabolic disturbance, particularly of glucose metabolism, is a hallmark of tumors such as non-small cell lung cancer (NSCLC). Cancer cells tend to reprogram a majority of glucose metabolism reactions into glycolysis, even in oxygen-rich environments. Although glycolysis is not an efficient means of ATP production compared to oxidative phosphorylation, the inhibition of tumor glycolysis directly impedes cell survival and growth. This review focuses on research advances in glycolysis in NSCLC and systematically provides an overview of the key enzymes, biomarkers, non-coding RNAs, and signaling pathways that modulate the glycolysis process and, consequently, tumor growth and metastasis in NSCLC. Current medications, therapeutic approaches, and natural products that affect glycolysis in NSCLC are also summarized. We found that the identification of appropriate targets and biomarkers in glycolysis, specifically for NSCLC treatment, is still a challenge at present. However, LDHB, PDK1, MCT2, GLUT1, and PFKM might be promising targets in the treatment of NSCLC or its specific subtypes, and DPPA4, NQO1, GAPDH/MT-CO1, PGC-1α, OTUB2, ISLR, Barx2, OTUB2, and RFP180 might be prognostic predictors of NSCLC. In addition, natural products may serve as promising therapeutic approaches targeting multiple steps in glycolysis metabolism, since natural products always present multi-target properties. The development of metabolic intervention that targets glycolysis, alone or in combination with current therapy, is a potential therapeutic approach in NSCLC treatment. The aim of this review is to describe research patterns and interests concerning the metabolic treatment of NSCLC.
Metabolomic profiling of developing perilla leaves reveals the best harvest time
Authors: Chen Jiabao , Guo Long , Yang Guiya , Yang Aitong , Zheng Yuguang , Wang Lei
Published: November 2022

Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) and gas chromatography-mass spectrometry (GC-MS) were applied to analyze metabolites in perilla leaves (PLs) during its developmental process. In total, 118 metabolites were identified, including volatile and non-volatile compounds, such as terpenoids, sugars, amino acids, organic acids, fatty acids, phenolic acids, flavonoids, and others. Principal component analysis (PCA) indicated great variations of metabolites during PLs development. Clustering analysis (CA) clarified the dynamic patterns of the metabolites. The heatmap of CA showed that most of the detected metabolites were significantly accumulated at stage 4 which is the pre anthesis period, and declined afterwards. The results of the present study provide a comprehensive overview of the metabolic dynamics of developing PLs which suggested that pre anthesis period is the best harvest time for PLs.
MASCoD—Multidimensional Assessment of Subjective Cognitive Decline
Authors: Maffoni Marina , Pierobon Antonia , Fundarò Cira
Published: November 2022

Subjective cognitive decline (SCD) is a subclinical cognitive impairment that is complained by the individual without being objectively supported at clinical, diagnostic, and neuropsychological levels. It can negatively impact on patient’s frailty and quality of life, as well as on the caregiver’s burden. Moreover, it can be prodromal to Mild Cognitive Impairment or dementia. Although the clinical manifestations of SCD can differ along with several cognitive domains, to date there are only screening tools to investigate subjective memory complaints. Thus, the first aim of this paper is to propose a preliminary English and Italian version of a new screening tool called MASCoD (Multidimensional Assessment of Subjective Cognitive Decline); the second aim is to propose its preliminary adoption on a pilot sample. This schedule is a brief test derived from the review of the literature and the clinical experience provided by an experts panelist. From pilot tests, it seems promising as it can help the professional to make differential diagnosis and to predict the risk of developing severe cognitive impairment over time, developing a personalized care path. This screening tool is brief, easily embeddable in usual clinical assessment, and administrable by different professionals. Furthermore, following validation, it will allow to collect manifold cognitive manifestations of SCD, addressing the shortage of previous validated instruments globally assessing cognition affected by this condition.
Role of digitalization in environment, social and governance, and sustainability: Review-based study for implications
Authors: Xu Jiaqi , She Shengxiang , Liu Wen
Published: November 2022

Digitalization gives people access to a vast network of untapped data, which has the potential to help society and the environment. Smart systems connected to the internet can systematically provide a unique opportunity to solve difficulties related to long-term sustainability. The goals are to create an egalitarian, ecologically sustainable, and healthy society. Technological resources are envisioned as game-changing instruments. Three key concerns integration benefits are highlighted here: (i) sustainable development goals, (ii) socio-economic development, and (iii) the role of digital technology in environmental sustainability. This viewpoint describes the potential that digitization can create a future sustainable society. The technological network would unite the urban and rural worlds under a shared banner of sustainable development, keeping all social elements in the loop. Nations that take a comprehensive strategy will be able to provide equitable growth and an efficient, sustainable, and digital existence for their citizens. As a result, digitization provides better living conditions, active public involvement, clean governance, and transparency in public welfare programs and processes. People who are well-informed, self-aware, and digitally equipped will be better learners, thinkers, reformers, participators, and change and growth agents, marching forward on sustainable progress. The advantages of digitization in hastening the transition to sustainable industrial processes and improving people’s health and happiness are explored. Finally, the perspective encapsulates the advantages of digitization by offering a holistic vision of how technology could aid in addressing major challenges such as endangered world biodiversity and climate change.
Ideological resistance to veg*n advocacy: An identity-based motivational account
Authors: De Groeve Ben , Bleys Brent , Hudders Liselot
Published: November 2022

Animal-based diets in Western countries are increasingly regarded as unsustainable because of their impact on human health, environmental and animal welfare. Promoting shifts toward more plant-based diets seems an effective way to avoid these harms in practice. Nevertheless, claims against the consumption of animal products contradict the ideology of the omnivorous majority known as carnism. Carnism supports animal-product consumption as a cherished social habit that is harmless and unavoidable and invalidates minorities with plant-based diets: vegetarians and vegans (veg*ns). In this theoretical review, we integrate socio-psychological and empirical literature to provide an identity-based motivational account of ideological resistance to veg*n advocacy. Advocates who argue against the consumption of animal products often make claims that it is harmful, and avoidable by making dietary changes toward veg*n diets. In response, omnivores are likely to experience a simultaneous threat to their moral identity and their identity as consumer of animal products, which may arouse motivations to rationalize animal-product consumption and to obscure harms. If omnivores engage in such motivated reasoning and motivated ignorance, this may also inform negative stereotyping and stigmatization of veg*n advocates. These “pro-carnist” and “counter-veg*n” defenses can be linked with various personal and social motivations to eat animal products (e.g., meat attachment, gender, speciesism) and reinforce commitment to and ambivalence about eating animal products. This does not mean, however, that veg*n advocates cannot exert any influence. An apparent resistance may mask indirect and private acceptance of advocates’ claims, priming commitment to change behavior toward veg*n diets often at a later point in time. Based on our theoretical account, we provide directions for future research.
Assessing the sustainability capacity of evidence-based programs in community and health settings
Authors: Bacon Caren , Malone Sara , Prewitt Kim , Hackett Rachel , Hastings Molly , Dexter Sarah , Luke Douglas A.
Published: November 2022

Background

Within many public health settings, there remain large challenges to sustaining evidence-based practices. The Program Sustainability Assessment Tool has been developed and validated to measure sustainability capacity of public health, social service, and educational programs. This paper describes how this tool was utilized between January 2014 and January 2019. We describe characteristics of programs that are associated with increased capacity for sustainability and ultimately describe the utility of the PSAT in sustainability research and practice.

Methods

The PSAT is comprised of 8 subscales, measuring sustainability capacity in eight distinct conceptual domains. Each subscale is made up of five items, all assessed on a 7-point Likert scale. Data were obtained from persons who used the PSAT on the online website (https://sustaintool.org/), from 2014 to 2019. In addition to the PSAT scale, participants were asked about four program-level characteristics. The resulting dataset includes 5,706 individual assessments reporting on 2,892 programs.

Results

The mean overall PSAT score was 4.73, with the lowest and highest scoring subscales being funding stability and program adaptation, respectively. Internal consistency for each subscale was excellent (average Cronbach's alpha = 0.90, ranging from 0.85 to 0.94). Confirmatory factor analysis highlighted good to excellent fit of the PSAT measurement model (eight distinct conceptual domains) to the observed data, with a comparative fit index of 0.902, root mean square error of approximation equal to 0.054, and standardized root mean square residual of 0.054. Overall sustainability capacity was significantly related to program size (F = 25.6; p < 0.001). Specifically, smaller programs (with staff sizes of ten or below) consistently reported lower program sustainability capacity. Capacity was not associated with program age and did not vary significantly by program level.

Discussion

The PSAT maintained its excellent reliability when tested with a large and diverse sample over time. Initial criterion validity was explored through the assessment of program characteristics, including program type and program size. The data collected reinforces the ability of the PSAT to assess sustainability capacity for a wide variety of public health and social programs.
Subject-invariant feature learning for mTBI identification using LSTM-based variational autoencoder with adversarial regularization
Authors: Salsabilian Shiva , Najafizadeh Laleh
Published: November 2022

Developing models for identifying mild traumatic brain injury (mTBI) has often been challenging due to large variations in data from subjects, resulting in difficulties for the mTBI-identification models to generalize to data from unseen subjects. To tackle this problem, we present a long short-term memory-based adversarial variational autoencoder (LSTM-AVAE) framework for subject-invariant mTBI feature extraction. In the proposed model, first, an LSTM variational autoencoder (LSTM-VAE) combines the representation learning ability of the variational autoencoder (VAE) with the temporal modeling characteristics of the LSTM to learn the latent space representations from neural activity. Then, to detach the subject’s individuality from neural feature representations, and make the model proper for cross-subject transfer learning, an adversary network is attached to the encoder in a discriminative setting. The model is trained using the 1 held-out approach. The trained encoder is then used to extract the representations from the held-out subject’s data. The extracted representations are then classified into normal and mTBI groups using different classifiers. The proposed model is evaluated on cortical recordings of Thy1-GCaMP6s transgenic mice obtained via widefield calcium imaging, prior to and after inducing injury. In cross-subject transfer learning experiment, the proposed LSTM-AVAE framework achieves classification accuracy results of 95.8% and 97.79%, without and with utilizing conditional VAE (cVAE), respectively, demonstrating that the proposed model is capable of learning invariant representations from mTBI data.
Antibiotic use, knowledge, and practices of milk vendors in India's informal dairy value chain
Authors: Sharma Garima , Leahy Eithne , Deka Ram Pratim , Shome Bibek R. , Bandyopadhyay Samiran , Dey Tushar K. , Goyal Naresh Kumar , Lundkvist Åke , Grace Delia , Lindahl Johanna F.
Published: November 2022

Background

Milk vendors play an important role in India's dairy value chain; however, their food safety practices are poorly understood. From a milk safety perspective, vendor behavior is significant because it has the potential to affect both consumer and producer behavior. This study describes the types of milk vendors in two Indian states, in an attempt to investigate vendors' hygienic knowledge and practices toward safety and antimicrobial resistance (AMR).

Methods

A cross-sectional study was conducted in the states of Assam and Haryana, India. In selected villages, all the milk vendors identified at the time of visit were interviewed. A questionnaire was used to assess the knowledge and practices on antibiotics, milk safety and hygiene. The milk samples were tested for presence of antibiotic resistant bacteria using antibiotic susceptibility testing.

Results

In total, 244 milk vendors were interviewed during the survey. Out of these, 146 (59.8%) of the vendors traded raw milk, while 40.2% traded pasteurized milk. Vendors were categorized depending on whom they supplied milk to. Five categories were identified: (a) those who sold at grocery shops; (b) those who sold on roadside (roadside vendors); (c) those who sold from door to door; (d) those who sold to sweet makers/tea stalls, and (e) those who sold from own home/other entity. The level of training among vendors on milk hygiene was non-existent and the knowledge related to antibiotics was low. Most of them [210/244 (86.07%)] agreed that boiled milk is always safer than raw milk but almost half [119 (48.77%)] of them admitted that sometimes they drink milk without boiling it. Most vendors believed that they could identify whether milk is safe or not for consumption just by its appearance and smell. Out of 124 milk samples collected from surveyed milk vendors and tested for the presence of antibiotic-resistant bacteria, 80 (64.52%) were tested positive.

Conclusion

This study highlights the low levels of knowledge regarding food safety among milk vendors. It shows the predominance of informal milk vendors in the surveyed states and prevalence of AMR bacteria in milk traded by them. Training may be a beneficial strategy for addressing the issue.
Socioeconomic Status, the Countries’ Socioeconomic Development and Mental Health: Observational Evidence for Persons with Spinal Cord Injury from 22 Countries
Authors: Fekete Christine , Tough Hannah , Leiulfsrud Annelie Schedin , Postma Karin , Bökel Andrea , Tederko Piotr , Reinhardt Jan D.
Published: November 2022

Objectives: Evidence on social inequalities in mental health of persons with physical impairments is limited. We therefore investigate associations of individual-level socioeconomic status (SES) and the country-level socioeconomic development (SED) with mental health in persons with spinal cord injury (SCI).

Methods: We analyzed data from 12,588 participants of the International SCI Community Survey from 22 countries. To investigate individual-level inequalities, SES indicators (education, income, financial hardship, subjective status) were regressed on the SF-36 mental health index (MHI-5), stratified by countries. Country-level inequalities were analyzed with empirical Bayes estimates of random intercepts derived from linear mixed-models adjusting for individual-level SES.

Results: Financial hardship and subjective status consistently predicted individual-level mental health inequalities. Country-level SED was inconsistently related to mental health when adjusting for individual-level SES. It however appeared that higher SED was associated with better mental health within higher-resourced countries.

Conclusion: Reducing impoverishment and marginalization may present valuable strategies to reduce mental health inequalities in SCI populations. Investigations of country-level determinants of mental health in persons with SCI should consider influences beyond country-level SED, such as cultural factors.
Packet-Level and Flow-Level Network Intrusion Detection Based on Reinforcement Learning and Adversarial Training
Authors: Yang Bin , Arshad Muhammad Haseeb , Zhao Qing
Published: November 2022

Powered by advances in information and internet technologies, network-based applications have developed rapidly, and cybersecurity has grown more critical. Inspired by Reinforcement Learning (RL) success in many domains, this paper proposes an Intrusion Detection System (IDS) to improve cybersecurity. The IDS based on two RL algorithms, i.e., Deep Q-Learning and Policy Gradient, is carefully formulated, strategically designed, and thoroughly evaluated at the packet-level and flow-level using the CICDDoS2019 dataset. Compared to other research work in a similar line of research, this paper is focused on providing a systematic and complete design paradigm of IDS based on RL algorithms, at both the packet and flow levels. For the packet-level RL-based IDS, first, the session data are transformed into images via an image embedding method proposed in this work. A comparison between 1D-Convolutional Neural Networks (1D-CNN) and CNN for extracting features from these images (for further RL agent training) is drawn from the quantitative results. In addition, an anomaly detection module is designed to detect unknown network traffic. For flow-level IDS, a Conditional Generative Adversarial Network (CGAN) and the ε-greedy strategy are adopted in designing the exploration module for RL agent training. To improve the robustness of the intrusion detection, a sample agent with a complement reward policy of the RL agent is introduced for the purpose of adversarial training. The experimental results of the proposed RL-based IDS show improved results over the state-of-the-art algorithms presented in the literature for packet-level and flow-level IDS.
An Effective Atrial Fibrillation Detection from Short Single-Lead Electrocardiogram Recordings Using MCNN-BLSTM Network
Authors: Zhang Hongpo , Gu Hongzhuang , Gao Junli , Lu Peng , Chen Guanhe , Wang Zongmin
Published: November 2022

Atrial fibrillation (AF) is an arrhythmia that may cause blood clots and increase the risk of stroke and heart failure. Traditional 12-lead electrocardiogram (ECG) acquisition equipment is complex and difficult to carry. Short single-lead ECG recordings based on wearable devices can remedy these shortcomings. However, reliable and accurate atrial fibrillation detection is still an issue because of the limited information on the short single-lead ECG recordings. In this paper, we propose a novel multi-branch convolutional neural network and bidirectional long short-term memory network (MCNN-BLSTM) to deal with the reliability and accuracy of AF detection in short single-lead ECG recordings. Firstly, to fuller extract the feature information of short single-lead ECG recordings, the MCNN module is designed to dynamically set several corresponding branches according to the number of slices of short single-lead ECG recordings. Then, the BLSTM module is designed to further enhance the feature information learned from each branch. We validated the model on the PhysioNet/CinC Challenge 2017 (CinC2017) database and verified the generalization of the model on the China Physiological Signal Challenge 2018 (CPSC2018) database. The results show that the accuracy of the model on the CinC 2017 database reaches 87.57%, and the average F1 score reaches 84.56%. The accuracy of the model on the CPSC 2018 database reaches 87.50%, and the average F1 score reaches 82.01%. Compared with other advanced methods, our model shows better performance and can meet the daily needs of atrial fibrillation detection with short ECG wearable devices.
Referral, Genetic Counselling, and BRCA Testing in the Manitoba High-Grade Serous Ovarian Cancer Population, 2004–2019
Authors: Winchar Kelcey , Lambert Pascal , McManus Kirk J. , Chodirker Bernie , Kean Sarah , Serfas Kim , Decker Kathleen , Nachtigal Mark W. , Altman Alon D.
Published: November 2022

(1) Background: The primary objective of this study was to examine the rate of genetic referral, BRCA testing, and BRCA positivity amongst all patients with high-grade serous ovarian cancers (HGSOC) from 2004–2019. The secondary objective was to analyze secondary factors that may affect the rates of referral and testing. (2) Methods: This population-based cohort study included all women diagnosed with HGSOC using the Manitoba Cancer Registry, CervixCheck registry, Medical Claims database at Manitoba Health, the Hospital Discharge abstract, the Population Registry, and Winnipeg Regional Health Authority genetics data. Data were examined for three different time cohorts (2004–2013, 2014–2016; 2017–2019) correlating to practice pattern changes. (3) Results: A total of 944 patients were diagnosed with HGSOC. The rate of genetic referrals changed over the three timeframes (20.0% → 56.7% → 36.6%) and rate of genetic testing increased over the entire timeframe. Factors found to increase rates of referral and testing included age, histology, history of oral contraceptive use, and family history of ovarian cancer. Prior health care utilization indicators did not affect genetic referral or testing. (4) Conclusion: The rate of genetic referral (2004–2016) and BRCA1/2 testing (2004–2019) for patients with a diagnosis of HGSOC increased over time. A minority of patients received a consultation for genetics counselling, and even fewer received testing for a BRCA1/2. Without a genetic result, it is difficult for clinicians to inform treatment decisions. Additional efforts are needed to increase genetics consultation and testing for Manitoban patients with HGSOC. Effects of routine tumour testing on rates of genetic referral will have to be examined in future studies.
Controlateral Symmetrisation in SRM for Breast Cancer: Now or Then? Immediate versus Delayed Symmetrisation in a Two-Stage Breast Reconstruction
Authors: Casella Donato , Fusario Daniele , Cassetti Dario , Pesce Anna Lisa , De Luca Alessandro , Guerra Maristella , Cuomo Roberto , Ribuffo Diego , Neri Alessandro , Marcasciano Marco
Published: November 2022

Introduction: The timing of contralateral symmetrisation in patients with large and ptotic breasts undergoing a unilateral skin-reducing mastectomy (SRM) is one of the most debated topics in the reconstructive field. There is no evidence to support the advantage of immediate or delayed symmetrisation to help surgeons with this decision. The aim of this study was to investigate the clinical and aesthetic outcomes of immediate symmetrisation. Methods: A randomised observational study was conducted on patients who underwent an SRM for unilateral breast cancer. Based on a simple randomisation list, patients were divided into two groups: a delayed symmetrisation group versus an immediate symmetrisation group. The postoperative complications, BREAST-Q outcomes and reoperations were compared. Results: Out of a total of 84 patients undergoing an SRM between January 2018 and January 2021, 42 patients underwent immediate symmetrisation and 42 patients had delayed symmetrisation. Three implant losses (7.2%) were observed and we reported three wound dehiscences; one of these was in a contralateral breast reconstruction in the immediate symmetrisation group. The BREAST-Q patient-reported outcome measures recorded better aesthetic outcomes and a high patient satisfaction for the immediate symmetrisation group. Conclusions: Simultaneous controlateral symmetrisation is a good alternative to achieve better satisfaction and quality of life for patients; from a surgical point of view, it does not excessively impact on the second time of reconstruction.
Towards Quantum Control with Advanced Quantum Computing: A Perspective
Authors: Ding Yongcheng , Ban Yue , Chen Xi
Published: November 2022

We propose the combination of digital quantum simulation and variational quantum algorithms as an alternative approach to numerical methods for solving quantum control problems. As a hybrid quantum–classical framework, it provides an efficient simulation of quantum dynamics compared to classical algorithms, exploiting the previous achievements in digital quantum simulation. We analyze the trainability and the performance of such algorithms based on our preliminary works. We show that specific quantum control problems, e.g., finding the switching time for bang-bang control or the digital quantum annealing schedule, can already be studied in the noisy intermediate-scale quantum era. We foresee that these algorithms will contribute even more to quantum control of high precision if the hardware for experimental implementation is developed to the next level.
Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles
Authors: Mesa-Rodríguez Ania , Gonzalez Augusto , Estevez-Rams Ernesto , Valdes-Sosa Pedro A.
Published: November 2022

The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity–entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results.
Graph Multihead Attention Pooling with Self-Supervised Learning
Authors: Wang Yu , Hu Liang , Wu Yang , Gao Wanfu
Published: November 2022

Graph neural networks (GNNs), which work with graph-structured data, have attracted considerable attention and achieved promising performance on graph-related tasks. While the majority of existing GNN methods focus on the convolutional operation for encoding the node representations, the graph pooling operation, which maps the set of nodes into a coarsened graph, is crucial for graph-level tasks. We argue that a well-defined graph pooling operation should avoid the information loss of the local node features and global graph structure. In this paper, we propose a hierarchical graph pooling method based on the multihead attention mechanism, namely GMAPS, which compresses both node features and graph structure into the coarsened graph. Specifically, a multihead attention mechanism is adopted to arrange nodes into a coarsened graph based on their features and structural dependencies between nodes. In addition, to enhance the expressiveness of the cluster representations, a self-supervised mechanism is introduced to maximize the mutual information between the cluster representations and the global representation of the hierarchical graph. Our experimental results show that the proposed GMAPS obtains significant and consistent performance improvements compared with state-of-the-art baselines on six benchmarks from the biological and social domains of graph classification and reconstruction tasks.
Nonparametric Clustering of Mixed Data Using Modified Chi-Squared Tests
Authors: Xu Yawen , Gao Xin , Wang Xiaogang
Published: November 2022

We propose a non-parametric method to cluster mixed data containing both continuous and discrete random variables. The product space of the continuous and discrete sample space is transformed into a new product space based on adaptive quantization on the continuous part. Detection of cluster patterns on the product space is determined locally by using a weighted modified chi-squared test. Our algorithm does not require any user input since the number of clusters is determined automatically by data. Simulation studies and real data analysis results show that our proposed method outperforms the benchmark method, AutoClass, in various settings.
EvoDesigner: Evolving Poster Layouts
Authors: Lopes Daniel , Correia João , Machado Penousal
Published: November 2022

Frequently, one of the goals of Graphic Design (gd) is discovering disruptive visual solutions that stand out and attract people’s attention. However, due to the increasing democratisation of gd, graphic designers tend to adopt design trends, leading to designs that many times lack innovative and catchy features. EvoDesigner is an evolutionary extension for Adobe InDesign that aims to aid gd processes by automatically evolving layout and style variations of given InDesign pages. The generated pages might be previously created and post-edited by designers, promoting co-creation. As an extension of the study EvoDesigner: Towards Aiding Creativity in Graphic Design, this article begins with a general introduction of EvoDesigner. Then, we review previous experiments on evolving pages towards the page balance of existing target posters. Furthermore, we present new experiments exploring the benefits of using grid systems to position and scale page items along with a user survey made to gather feedback about the impact of grid systems in the generated pages and showcase examples of artefacts created from the collaboration between designers and the system. The findings indicate that the presented techniques can be used to interpret current layouts in different manners, and suggest that grid systems may be a useful tool for promoting the automatic production of layouts with better organisation when compared to applying no organisational constraints. However, a conducted user survey indicates that, depending on the goals of the designers, more organised layouts might not always be synonymous with better results.
Evaluation of Yield Improvements in Machine vs. Visual Strength Grading for Softwood Species
Authors: Moltini Gonzalo , Íñiguez-González Guillermo , Cabrera Gonzalo , Baño Vanesa
Published: November 2022

The current national standard for strength grading in Spain is based on a visual classification, which, for softwood species with small cross-sections (with a thickness equa tol or less than 70 mm), establishes two different visual grades (ME-1 and ME-2). These grades are assigned to the strength classes C24 and C18, respectively, for maritime and radiata pines, and C27 and C18 for Scots pine, according to the European standard EN-1912:2012. The production of engineered wood products, such as glulam or cross-laminated timber is increasing worldwide. The machine grading of wood using non-destructive testing provides the industry with a more reliable, fast, and consistent method for grading. With this background in mind, this study presents the yield comparison of machine grading vs. visual grading for those three pine species from Spain. The machine settings were obtained according to the standard EN 14081-2:2019, providing several possible strength grade combinations. Results allow new possibilities for the industry and improve the structural yield of the studied timber, thus increasing the material optimization.
Contribution of Dry Forests and Forest Products to Climate Change Adaptation in Tigray Region, Ethiopia
Authors: Tesfaye Musse , Manaye Ashenafi , Tesfamariam Berihu , Mekonnen Zenebe , Eshetu Shibire Bekele , Löhr Katharina , Sieber Stefan
Published: November 2022

Despite their ecological importance, dry forests’ contribution to climate change adaptation is often neglected. Hence, this study was initiated to assess the socioeconomic contribution of dry forests to climate change adaptation in Tigray Region, Ethiopia. A mixed quantitative and qualitative research design was used to examine the role of dry forests in climate change adaptation. Household questionnaire survey, key informants, and a focus group discussion were used to collect data. The results indicated that 94% of all households visited a dry forest at least once a month to access the forest and forest products. While the dry forest income level varied significantly (p < 0.05), the overall dry forest income level contributed to 16.8% of the total household income. Dry forest income enabled the reduction of the area between the line of equality and the Lorenz curve by 21% in dry evergreen Afromontane Forest users, by 3.02% in Combretum–Terminalia woodland users, and by 3% in Acacia–Commiphora woodland users. Gender, occupation, wealth status, and distance from the forest to their homes are all factors that significantly affected Combretum–Terminalia woodland users’ income level. Among Acacia–Commiphora woodland users, the respondents’ age influenced the dry forest income level, whereas, among dry evergreen Afromontane Forest users, the family size of the household influenced the dry forest income level. The findings of this study could help policy makers understand the crucial role of dry forest income in the livelihood of the community and in climate change adaptation. Policymakers could reduce the pressure on dry forests by introducing policies that recognize the role of dry forest income in reducing poverty and income inequality and by establishing farmer cooperation in commercializing the non-timber forest products which support the long-term coping and adaptation strategy. Further research is needed to understand the increasing role of dry forest products in climate change adaptation over time and its contribution to the national economy at large.
Storing More Carbon by Improving Forest Management in the Acadian Forest of New England, USA
Authors: Giffen Robert Alec , Ryan Colleen M. , Belair Ethan P. , Pounch Michael A. , Brown Seth
Published: November 2022

The capacity of forests to store carbon, combined with time-tested approaches to managing forests, make forests a useful tool for atmospheric carbon mitigation. The primary goals of this study are to determine the amount of unrealized mitigation available from Improved Forest Management (IFM) in the Acadian Forest of New England in the northeastern U.S., and to demonstrate how this mitigation can feasibly be attained. This study used the Forest Vegetation Simulator (FVS) to model the impacts of IFM practices articulated by the New England Forestry Foundation on carbon storage in the Acadian Forest. Our results, together with empirical data from well-managed forests, show that if the modeled improved management is employed on privately owned timberland across the Acadian Forest of New England, carbon storage could be increased by 488 Tg CO2e. Our financial modeling shows that IFM could be funded in this region by combining income from carbon markets with the philanthropic funding of conservation easements, timber revenues, and capital investments from private investors who prioritize social and economic goals alongside financial returns. This study adds to the body of evidence from around the world that the potential for managed forests to contribute to climate change mitigation has not been fully realized.