Protocol for a randomized placebo-controlled clinical trial using pure palmitoleic acid to ameliorate insulin resistance and lipogenesis in overweight and obese subjects with prediabetes

Palmitoleic acid (POA), a nonessential, monounsaturated omega-7 fatty acid (C16:1n7), is a lipid hormone secreted from adipose tissue and has beneficial effects on distant organs, such as the liver and muscle. Interestingly, POA decreases lipogenesis in toxic storage sites such as the liver and muscle, and paradoxically increases lipogenesis in safe storage sites, such as adipose tissue. Furthermore, higher POA levels in humans are correlated with better insulin sensitivity, an improved lipid profile, and a lower incidence of type-2 diabetes and cardiovascular pathologies, such as myocardial infarction. In preclinical animal models, POA improves glucose intolerance, dyslipidemia, and steatosis of the muscle and liver, while improving insulin sensitivity and secretion. This double-blind placebo-controlled clinical trial tests the hypothesis that POA increases insulin sensitivity and decreases hepatic lipogenesis in overweight and obese adult subjects with pre-diabetes. Important to note, that this is the first study ever to use pure (>90%) POA with < 0.3% palmitic acid (PA), which masks the beneficial effects of POA. The possible positive findings may offer a therapeutic and/or preventative pathway against diabetes and related immunometabolic diseases

PAMOGK: A pathway graph kernel based multi-omics clustering approach for discovering cancer patient subgroups

Accurate classification of patients into homogeneous molecular subgroups is critical for the developmentof effective therapeutics and for deciphering what drives these different subtypes to cancer. However, the extensivemolecular heterogeneity observed among cancer patients presents a challenge. The availability of multi-omic datacatalogs for large cohorts of cancer patients provides multiple views into the molecular biology of the tumorswith unprecedented resolution. In this work, we develop PAMOGK, which integrates multi-omics patient data andincorporates the existing knowledge on biological pathways. PAMOGK is well suited to deal with the sparsity ofalterations in assessing patient similarities. We develop a novel graph kernel which we denote as smoothed shortestpath graph kernel, which evaluates patient similarities based on a single molecular alteration type in the contextof pathway. To corroborate multiple views of patients evaluated by hundreds of pathways and molecular alterationcombinations, PAMOGK uses multi-view kernel clustering. We apply PAMOGK to find subgroups of kidney renalclear cell carcinoma (KIRC) patients, which results in four clusters with significantly different survival times (p-value =7.4e-10). The patient subgroups also differ with respect to other clinical parameters such as tumor stage andgrade, and primary tumor and metastasis tumor spreads. When we compare PAMOGK to 8 other state-of-the-artexisting multi-omics clustering methods, PAMOGK consistently outperforms these in terms of its ability to partitionpatients into groups with different survival distributions. PAMOGK enables extracting the relative importance ofpathways and molecular data types. PAMOGK is available at github.com/tastanlab/pamog

Automatic Segmentation of Land Cover in Satellite Images

Semantic segmentation problems such as landcover segmentation rely on large amounts of annotated images to excel. Without such data for target regions, transfer learning methods are widely used to incorporate knowledge from other areas and domains to improve performance. In this study, we analyze the performance of landcover segmentation models trained on low-resolution images with insufficient data for the targeted region or zoom level. In order to boost performance on target data, we experiment with models trained with unsupervised, semi-supervised, and supervised transfer learning approaches, including satellite images from public datasets and other unlabeled sources.According to experimental results, transfer learning improves segmentation performance by 3.4% MIoU (mean intersection over union) in rural regions and 12.9% MIoU in urban regions. We observed that transfer learning is more effective when two datasets share a comparable zoom level and are labeled with identical rules; otherwise, semi-supervised learning is more effective using unlabeled data. Pseudo labeling based unsupervised domain adaptation method improved building detection performance in urban cities. In addition, experiments showed that HRNet outperformed building segmentation approaches in multi-class segmentation

Semi-Supervised Domain Adaptation for Semantic Segmentation of Roads from Satellite Images

This paper presents the preliminary findings of a semi-supervised segmentation method for extracting roads from sattelite images. Artificial Neural Networks and image segmentation methods are among the most successful methods for extracting road data from satellite images. However, these models require large amounts of training data from different regions to achieve high accuracy rates. In cases where this data needs to be of more quantity or quality, it is a standard method to train deep neural networks by transferring knowledge from annotated data obtained from different sources. This study proposes a method that performs path segmentation with semi-supervised learning methods. A semi-supervised field adaptation method based on pseudo-labeling and Minimum Class Confusion method has been proposed, and it has been observed to increase performance in targeted datasets.Comment: in Turkish languag

COVID-19 ile eş zamanlı gelişen uzamış postpartum katatonik psikoz olgusunun elektrokonvülzif terapi ile tedavisi: Bir olgu sunumu

GİRİŞ VE AMAÇ: Postpartum psikoz doğum sonrasında yaygın olarak görülebilen psikiyatrik fenomenlerden biridir. İnsidansı 1000 canlı doğumda 1-2 olarak raporlanmıştır. Bir çalışmada postpartum psikoz olgularının beşte birine katatoni tablosunun eşlik ettiği gösterilmiştir. Gerek postpartum psikoz gerekse katatoni olguları acil psikiyatrik müdahale gerektirmesi bakımından oldukça önemlidir. Biz de bu olguda COVID-19 enfeksiyonu ile eş zamanlı olarak postpartum katatonik psikoz gelişen vakamızı sunuyoruz. Bu olgu sunumu için hastamızdan onam alınmıştır

Building Segmentation on Satellite Images and Performance of Post-Processing Methods

Researchers are doing intensive work on satellite images due to the information it contains with the development of computer vision algorithms and the ease of accessibility to satellite images. Building segmentation of satellite images can be used for many potential applications such as city, agricultural, and communication network planning. However, since no dataset exists for every region, the model trained in a region must gain generality. In this study, we trained several models in China and post-processing work was done on the best model selected among them. These models are evaluated in the Chicago region of the INRIA dataset. As can be seen from the results, although state-of-art results in this area have not been achieved, the results are promising. We aim to present our initial experimental results of a building segmentation from satellite images in this study.Comment: in Turkish languag

Bu müzik nereden geliyor? Müzikal halüsinoz olgu sunumu

GİRİŞ VE AMAÇ: Müzikal halüsinasyonlar (MH), harici akustik uyaranın yokluğunda sürekli veya aralıklı müzik tonları ve melodiler duyulan işitsel halüsinasyonların özel bir türüdür. Bu durum Müzikal Kulak Sendromu, Müzikal Halüsinoz, Oliver Sacks sendromu ve İşitsel Charles Bonnet sendromu gibi isimlerle bilinmektedir. İdiyopatik ve semptomatik olmak üzere iki gruba ayrılırlar. Risk faktörleri arasında işitme kaybı, ileri yaş, beyin hastalığı, kadın cinsiyet ve sosyal izolasyon sayılabilir. Bu yazıda hafif işitme kaybı ve anormal ’de aktivitesiyle ilişkili müzikal halüsinasyonları olan ve oral lamotrijinle transdermal rivastigmin tedavisine yanıt veren yaşlı bir kadın olgu sunulmaktadır

Turkish music generation using deep learning

This is an accepted manuscript of an article published by IEEE in 28th IEEE Conference on Signal Processing and Communications Applications (SIU), available online at: https://ieeexplore.ieee.org/document/9302283 The accepted version of the publication may differ from the final published version.Bu çalı¸smada derin ögrenme ile Türkçe ¸sarkı bes- ˘ teleme üzerine yeni bir model tanıtılmaktadır. ¸Sarkı sözlerinin Tekrarlı Sinir Agları kullanan bir dil modeliyle otomatik olarak ˘ olu¸sturuldugu, melodiyi meydana getiren notaların da benzer ˘ ¸sekilde nöral dil modeliyle olu¸sturuldugu ve sözler ile melodinin ˘ bütünle¸stirilerek ¸sarkı sentezlemenin gerçekle¸stirildigi bu çalı¸sma ˘ Türkçe ¸sarkı besteleme için yapılan ilk çalı¸smadır. In this work, a new model is introduced for Turkish song generation using deep learning. It will be the first work on Turkish song generation that makes use of Recurrent Neural Networks to generate the lyrics automatically along with a language model, where the melody is also generated by a neural language model analogously, and then the singing synthesis is performed by combining the lyrics with the melody. It will be the first work on Turkish song generation

Expression of the Splicing Factor Gene SFRS10 Is Reduced in Human Obesity and Contributes to Enhanced Lipogenesis

SummaryAlternative mRNA splicing provides transcript diversity and may contribute to human disease. We demonstrate that expression of several genes regulating RNA processing is decreased in both liver and skeletal muscle of obese humans. We evaluated a representative splicing factor, SFRS10, downregulated in both obese human liver and muscle and in high-fat-fed mice, and determined metabolic impact of reduced expression. SFRS10-specific siRNA induces lipogenesis and lipid accumulation in hepatocytes. Moreover, Sfrs10 heterozygous mice have increased hepatic lipogenic gene expression, VLDL secretion, and plasma triglycerides. We demonstrate that LPIN1, a key regulator of lipid metabolism, is a splicing target of SFRS10; reduced SFRS10 favors the lipogenic β isoform of LPIN1. Importantly, LPIN1β-specific siRNA abolished lipogenic effects of decreased SFRS10 expression. Together, our results indicate that reduced expression of SFRS10, as observed in tissues from obese humans, alters LPIN1 splicing, induces lipogenesis, and therefore contributes to metabolic phenotypes associated with obesity