Zygomatic abscess with temporomandibular joint effusion complicating acute otitis media

WOS: 000386676300007The incidences of extracranial and intracranial complications of acute otitis media (AOM) in children have markedly decreased in the postantibiotic era. Zygomatic abscesses are the rarest type of abscesses originating from mastoiditis. This paper presents a case with a zygomatic abscess as a complication of acute coalescent mastoiditis in a 7-year-old girl who underwent cortical mastoidectomy and myringotomy-ventilation tube insertion

Exploration of video e-learning content with smartphones

Nowadays computer users prefer to learn or complement their studies with video materials. While there are many video e-learning resources available on the internet, video sharing platforms such as YouTube which provide these resources, do not structure the presented material in the prerequisite order. Furthermore, they do not track the background of the users when recommending the next material to watch. Our aim is to overcome this limitation of the existing video on demand systems. In this paper we describe the architecture of the e-learning system that we are developing which allows users to search and watch video materials organized with respect to their background and presented in prerequisite order. One of the key features of our e-learning platform is to enable users to explore the video content with mobile devices. We propose a new visual metaphor based on lists for mobile devices which reflect the prerequisite graph structure, utilizing the limited screen size more effectively

Transfer learning for drug–target interaction prediction

MotivationUtilizing AI-driven approaches for drug–target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for the prediction of interactions between drug candidate compounds and understudied target proteins with scarce training data. The idea here is to first train a deep neural network classifier with a generalized source training dataset of large size and then to reuse this pre-trained neural network as an initial configuration for re-training/fine-tuning purposes with a small-sized specialized target training dataset. To explore this idea, we selected six protein families that have critical importance in biomedicine: kinases, G-protein-coupled receptors (GPCRs), ion channels, nuclear receptors, proteases, and transporters. In two independent experiments, the protein families of transporters and nuclear receptors were individually set as the target datasets, while the remaining five families were used as the source datasets. Several size-based target family training datasets were formed in a controlled manner to assess the benefit provided by the transfer learning approach.ResultsHere, we present a systematic evaluation of our approach by pre-training a feed-forward neural network with source training datasets and applying different modes of transfer learning from the pre-trained source network to a target dataset. The performance of deep transfer learning is evaluated and compared with that of training the same deep neural network from scratch. We found that when the training dataset contains fewer than 100 compounds, transfer learning outperforms the conventional strategy of training the system from scratch, suggesting that transfer learning is advantageous for predicting binders to under-studied targets.Availability and implementationThe source code and datasets are available at https://github.com/cansyl/TransferLearning4DTI. Our web-based service containing the ready-to-use pre-trained models is accessible at https://tl4dti.kansil.org

Parental psychological distress associated with COVID-19 outbreak: A large-scale multicenter survey from Turkey

Aims: Pandemics can cause substantial psychological distress; however, we do not know the impact of the COVID-19 related lockdown and mental health burden on the parents of school age children. We aimed to comparatively examine the COVID-19 related the stress and psychological burden of the parents with different occupational, locational, and mental health status related backgrounds. Methods: A large-scale multicenter online survey was completed by the parents (n = 3,278) of children aged 6 to 18 years, parents with different occupational (health care workers—HCW [18.2%] vs. others), geographical (İstanbul [38.2%] vs. others), and psychiatric (child with a mental disorder [37.8%]) backgrounds. Results: Multivariable logistic regression analysis showed that being a HCW parent (odds ratio 1.79, p <.001), a mother (odds ratio 1.67, p <.001), and a younger parent (odds ratio 0.98, p =.012); living with an adult with a chronic physical illness (odds ratio 1.38, p <.001), having an acquaintance diagnosed with COVID-19 (odds ratio 1.22, p =.043), positive psychiatric history (odds ratio 1.29, p <.001), and living with a child with moderate or high emotional distress (odds ratio 1.29, p <.001; vs. odds ratio 2.61, p <.001) were independently associated with significant parental distress. Conclusions: Parents report significant psychological distress associated with COVID-19 pandemic and further research is needed to investigate its wider impact including on the whole family unit. © The Author(s) 2020

Recent Advances in Health Biotechnology During Pandemic

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in 2019, cut the epoch that will make profound fluctuates in the history of the world in social, economic, and scientific fields. Urgent needs in public health have brought with them innovative approaches, including diagnosis, prevention, and treatment. To exceed the coronavirus disease 2019 (COVID-19) pandemic, various scientific authorities in the world have procreated advances in real time polymerase chain reaction (RT-PCR) based diagnostic tests, rapid diagnostic kits, the development of vaccines for immunization, and the purposing pharmaceuticals for treatment. Diagnosis, treatment, and immunization approaches put for- ward by scientific communities are cross-fed from the accrued knowledge of multidisciplinary sciences in health biotechnology. So much so that the pandemic, urgently prioritized in the world, is not only viral infections but also has been the pulsion in the development of novel approaches in many fields such as diagnosis, treatment, translational medicine, virology, mi- crobiology, immunology, functional nano- and bio-materials, bioinformatics, molecular biol- ogy, genetics, tissue engineering, biomedical devices, and artificial intelligence technologies. In this review, the effects of the COVID-19 pandemic on the development of various scientific areas of health biotechnology are discussed

Effect of Group Based Synchronization on User Anonymity in Mix Networks

In so-called closed environments, the MIX network can theoretically provide perfect security, i.e. if perfect protection is envisaged, all senders and receivers should be perfectly synchronized and participate equally in each communication round of the MIX technique. In the context of open environments (e.g., the Internet), there is no synchronization between the participants and here the technique is vulnerable to known analyses such as (statistical) disclosure attacks. In short, the Mix technology is highly dependent on its application context in which it involves the participants. In this work, we study the effect of context in terms of synchronization rate, present two different synchronization approaches and evaluate their protection against disclosure attacks

Changes in the Heavy Metal Levels in Highway Landscaping and Protective Effect of Vegetative Materials

Anthropogenic activities due to increasing population and traffic density are responsible for a great portion of highway pollution. The heavy metal accumulation in highway routes poses a risk both for agricultural areas and residential areas. The study investigated the changes in heavy metal accumulation along a 200 km long portion of the D300 highway passing through Elazığ, Bingöl, and Muş, cities located in the Eastern Anatolia region of Turkey. The heavy metal accumulation in 46 soil samples collected in 2018 and 2019 from 5 different land classes was analyzed using the ICP-MS device in an accredited laboratory. The analysis results were explained using different statistical methods depending on the standard, annual change, land class, and vegetation. Although the majority of the soil samples were within acceptable levels, the chromium (Cr) and nickel (Ni) levels of certain samples were above the standard levels. Considering the land classes, compared with other areas, residential areas (RA) contained higher levels of zinc (Zn); agricultural areas (AA) contained higher levels of chromium (Cr), cobalt (Co), nickel (Ni), zinc (Zn), cadmium (Cd), and lead (Pb); and unqualified areas (UA) contained higher levels of copper (Cu). Considering vegetation, the tree- and bush-covered soil samples contained lower amounts of Cr, Co, Ni, Cu, and Cd but higher levels of Zn and Pb compared with herbaceous or bare soil samples. A similar case also applies to the soil samples that were covered with Quercus sp., a natural plant cover on the route. The results and other similar studies have shown that there should be at least 15 m long ecological corridors (pollution-resistant tree-bush vegetation) between highway routes and both agricultural and residential areas