Deep learning techniques for biological signal processing: Automatic detection of dolphin sounds

openConsidering the heterogeneous underwater acoustic transmission context, detecting and distinguishing vocalizations of cetaceans has been a challenging area of recent interest. A promising venue to improve current detection systems is constituted by machine learning algorithms. In particular, Convolutional Neural Networks (CNNs) are considered one of the most promising deep learning techniques, since they have already excelled in problems involving the automatic processing of biological sounds. Human-annotated spectrograms can be used to teach CNNs how to distinguish between information in the time-frequency domain, thus enabling the detection and classification of marine mammal sounds. However, despite these promising capabilities machine learning suffers from a lack of labeled data, which calls for the adoption of transfer learning to create accurate models even when the availability of human taggers is limited. In this thesis, we developed a dolphin whistle detection framework based on deep learning models. In particular, we investigated the performance of large-scale pre-trained models (VGG16) and compared it with the performance of a vanilla Convolutional Neural Network and several baselines (logistic regression and Support Vector Machines). The pre-trained VGG16 model achieved the best detection performance, with an accuracy of 98,9\% on a left-out test dataset.Considering the heterogeneous underwater acoustic transmission context, detecting and distinguishing vocalizations of cetaceans has been a challenging area of recent interest. A promising venue to improve current detection systems is constituted by machine learning algorithms. In particular, Convolutional Neural Networks (CNNs) are considered one of the most promising deep learning techniques, since they have already excelled in problems involving the automatic processing of biological sounds. Human-annotated spectrograms can be used to teach CNNs how to distinguish between information in the time-frequency domain, thus enabling the detection and classification of marine mammal sounds. However, despite these promising capabilities machine learning suffers from a lack of labeled data, which calls for the adoption of transfer learning to create accurate models even when the availability of human taggers is limited. In this thesis, we developed a dolphin whistle detection framework based on deep learning models. In particular, we investigated the performance of large-scale pre-trained models (VGG16) and compared it with the performance of a vanilla Convolutional Neural Network and several baselines (logistic regression and Support Vector Machines). The pre-trained VGG16 model achieved the best detection performance, with an accuracy of 98,9\% on a left-out test dataset

Current status of myocardial perfusion imaging radiopharmaceuticals for SPECT and PET imaging modalities

Coronary artery disease (CAD) is the leading cause of death and remains a major health problem worldwide. Myocardial perfusion imaging (MPI) with single photon emission tomography (SPECT) and positron emission tomography (PET) has been established as the main functional nuclear cardiology noninvasive technique for CAD over the past years. The studies has been shown that the use of MPI as a useful and important imaging modality for the diagnosis, risk stratification and treatment planning for CAD. The purpose of this article is to review properties of the radiopharmaceuticals used for myocardial perfusion imaging with SPECT and PET

Automated Detection of Dolphin Whistles with Convolutional Networks and Transfer Learning

Effective conservation of maritime environments and wildlife management of endangered species require the implementation of efficient, accurate and scalable solutions for environmental monitoring. Ecoacoustics offers the advantages of non-invasive, long-duration sampling of environmental sounds and has the potential to become the reference tool for biodiversity surveying. However, the analysis and interpretation of acoustic data is a time-consuming process that often requires a great amount of human supervision. This issue might be tackled by exploiting modern techniques for automatic audio signal analysis, which have recently achieved impressive performance thanks to the advances in deep learning research. In this paper we show that convolutional neural networks can indeed significantly outperform traditional automatic methods in a challenging detection task: identification of dolphin whistles from underwater audio recordings. The proposed system can detect signals even in the presence of ambient noise, at the same time consistently reducing the likelihood of producing false positives and false negatives. Our results further support the adoption of artificial intelligence technology to improve the automatic monitoring of marine ecosystems

Volatility spillover between Bitcoin and financial stress index

PURPOSE: This paper aims to test the volatility models for Bitcoin (BTC) and the financial stress index (FSI) and examine the volatility spillover among them. This aim was reached by obtaining weekly data from the 7th of January 2011 and the 24th of December 2021.METHODOLOGY: First, volatility modelling for the series is provided, and GARCH (1,1) for the BTC series and IGARC (1,2) for the FSI series are determined as the most appropriate volatility models. Then, residual volatility series are created for each variable over the IGARCH (1,2) and GARCH (1,1) models for the volatility spread between the series. The volatility spread between the series is examined with the diagonal VECH GARCH method. It is concluded that there is a positive volatility spillover effect from the FSI variable to the BTC variable. Then, impulse-response analysis is performed on the volatility residual series created for each variable. The empirical findings from impulse response analysis support a risk transfer between BTC and FSI series.RESULTS AND FINDINGS: Changes in the BTC return series and FSI series are caused mainly by themselves, and the series are most affected by their shocks. By comparing the variance decomposition of the volatility series with the analysis results, it can be said that the changes in the volatility series are caused mainly by each other.peer-reviewe

The outcome of COVID-19 in patients with hematological malignancy (Letter)

[No Abstract Available

The Role of Surface Modification Methods for Sustainable Textiles

Sustainability aims to provide a livable future for the next generations. Studies on reducing high chemical, energy, and water consumption make significant contributions to sustainability in many sectors. The textile sector consists of many processes such as fiber production, yarn and fabric production, dyeing, and finishing processes. Each of these processes consumes a significant amount of water and energy. Cotton fiber production consumes approximately 1559 kg of fresh water per kg, and polyester fiber production consumes approximately 108 kWh of electricity per kg. Clean water consumption can be up to 200 L/kg in subsequent processes such as bleaching, dyeing, printing, and finishing. Surface modification techniques in textile production can play a role in sustainability, especially in areas such as reduction, reuse, and recycling. In this chapter, we aim to investigate the effects of surface modification techniques on reducing chemical, energy, and water consumption in textile production, improving textile performance properties, and altering the service life of textiles

Outcome of COVID-19 in patients with chronic myeloid leukemia receiving tyrosine kinase inhibitors

Introduction In this study, we aim to report the outcome of COVID-19 in chronic myeloid leukemia (CML) patients receiving tyrosine kinase inhibitor (TKI). Method The data of 16 laboratory-confirmed COVID-19 patients with CML receiving TKI and age, gender, and comorbid disease matched COVID-19 patients without cancer at a 3/1 ratio (n = 48), diagnosed between March 11, 2020 and May 22, 2020 and included in the Republic of Turkey, Ministry of Health database, were analyzed retrospectively. Results The rates of intensive care unit (ICU) admission, and mechanical ventilation (MV) support were lower in CML patients compared to the control group, however, these differences did not achieve statistical significance (p = 0.1, and p = 0.2, respectively). The length of hospital stay was shorter in CML patients compared with the control group; however, it was not statistically significant (p = 0.8). The case fatality rate (CFR) in COVID-19 patients with CML was 6.3%, and it was 12.8% in the control group. Although the CFR in CML patients with COVID-19 was lower compared to the control group, this difference did not achieve statistical significance (p = 0.5). When CML patients were divided into 3 groups according to the TKI, no significant difference was observed regarding the rate of ICU admission, MV support, CFR, the length of stay in both hospital and ICU (all p > 0.05). Conclusion This study highlights that large scale prospective and randomized studies should be conducted in order to investigate the role of TKIs in the treatment of COVID-19

The outcome of COVID-19 in patients with hematological malignancy

In this study, we aim to report the outcomes for COVID-19 in patients with hematological malignancy in Turkey. Data from laboratory-confirmed 188 897 COVID-19 patients diagnosed between 11 March 2020 and 22 June 2020 included in the Republic of Turkey, Ministry of Health database were analyzed retrospectively. All COVID-19 patients with hematological malignancy (n = 740) were included in the study and an age, sex, and comorbidity-matched cohort of COVID-19 patients without cancer (n = 740) at a 1:1 ratio was used for comparison. Non-Hodgkin lymphoma (30.1%), myelodysplastic syndrome (19.7%), myeloproliferative neoplasm (15.7%) were the most common hematological malignancies. The rates of severe and critical disease were significantly higher in patients with hematological malignancy compared with patients without cancer (P = .001). The rates of hospital and intensive care unit (ICU) admission were higher in patients with hematological malignancy compared with the patients without cancer (P = .023,P = .001, respectively). The length of hospital stay and ICU stay was similar between groups (P = .7,P = .3, retrospectively). The rate of mechanical ventilation (MV) support was higher in patients with hematological malignancy compared with the control group (P = .001). The case fatality rate was 13.8% in patients with hematological malignancy, and it was 6.8% in the control group (P = .001). This study reveals that there is an increased risk of COVID-19-related serious events (ICU admission, MV support, or death) in patients with hematological malignancy compared with COVID-19 patients without cancer and confirms the high vulnerability of patients with hematological malignancy in the current pandemic

Patients with hematologic cancers are more vulnerable to COVID-19 compared to patients with solid cancers

Previous studies reported that COVID-19 patients with cancer had higher rates of severe events such as intensive care unit (ICU) admission, mechanical ventilation (MV) assistance, and death during the COVID-19 course compared to the general population. However, no randomized study compared the clinical course of COVID-19 in patients with hematologic cancers to patients with solid cancers. Thus, in this study, we intend to reveal the outcome of COVID-19 in hematologic cancer patients and compare their outcomes with COVID-19 patients with solid cancers. The data of 926 laboratory-confirmed COVID-19 patients, including 463 hematologic cancer patients and an age-gender paired cohort of 463 solid cancer patients, were investigated retrospectively. The frequencies of severe and critical disease, hospital and ICU admission, MV assistance were significantly higher in hematologic cancer patients compared with the solid cancer patients (p = 0.001, p = 0.045, p = 0.001, and p = 0.001, respectively). The hospital stay was longer in patients with hematologic cancers (p = 0.001); however, the median ICU stay was 6 days in both groups. The case fatality rate (CFR) was 14.9% in patients with hematologic cancers, and it was 4.8% in patients with solid cancers, and there was a statistically significant difference regarding CFR between groups (p = 0.001). Our study revealed that COVID-19 patients with hematologic cancers have a more aggressive course of COVID-19 and have higher CFR compared to COVID-19 patients with solid cancers and support the increased susceptibility of patients with hematologic cancers during the outbreak