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Objectives: To evaluate the anatomical and visual outcomes of the multilayered inverted internal limiting membrane (ML-ILM) flap technique in the treatment of optic disc pit maculopathy (ODPM). Materials and Methods: In this retrospective interventional case series, medical records and macular spectral-domain optical coherence tomography images of patients who underwent combined pars plana vitrectomy with ML-ILM flap surgery for ODPM were analyzed. Best-corrected visual acuity (BCVA) and central macular thickness (CMT) at postoperative 6 months were compared with baseline findings. Intraoperative and postoperative complications, fluid resolution time, and recurrence during follow-up were recorded. Results: Five eyes of 5 patients with ODPM were included in the study. According to the preoperative macular fluid characteristics, 2 patients had only intraretinal fluid, while 3 patients had intraretinal and subretinal fluid. The preoperative median BCVA was 1.0 logarithm of the minimum angle of resolution (logMAR) (range, 1.0-1.3 logMAR), and the CMT was 560 µm (range, 452-667 µm). At the 6-month postoperative follow-up, the median BCVA was 0.40 logMAR (range, 0.1-0.7 logMAR), and CMT was 315 µm (range, 265-326 µm) (p=0.042 and p=0.043, respectively). During the 6-month follow-up period, no recurrence or full-thickness macular hole formation was observed. Conclusion: The ML-ILM flap technique is a preferable surgical option to achieve both high anatomical and functional success and flap stabilization. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agriculture & Nature / Kahramanmaraş Sütçü İmam Üniversitesi Tarım & Doğa Dergisi is the property of Kahramanmaras Sutcu Imam Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A thin metal plate called a drop wire is placed on warp yarns during a weaving process in order to stop the loom in case of a broken warp yarn and to fix it. Drop wires are placed on each warp yarn to ensure the flow of warp yarns at a certain tension level and to stop the loom when a warp yarn is broken. In some cases, the drop wire may not fall due to dirtiness or abrasion of the drop wire and the weaving process continues with a broken warp yarn which is an unwanted situation. In this study, an alternative method to detect broken warp yarn is proposed based on contact image sensor. Therefore the need for drop wires is eliminated. The contact image sensor is not at full scale of the weaving machine, it can only acquire a linear image only a small portion of the total warp yarns, therefore a carrier module is implemented on the machine to carry the sensor and cover all the warp yarns. A complete image is then formed at the end of each cycle, and the total number of warp yarns is evaluated. If this value is less than the expected number of warp yarns, a broken warp yarn is detected, and the system generates an alarm. The system is implemented on an industrial scale weaving machine and the warp yarns are detected with 99% performance. [ABSTRACT FROM AUTHOR]

Automatic systems are needed to recognize the fabric defects in textile manufacturing. For many years, systems that detect defects in the shortest time and with high accuracy have been researched. However, the multiplicity of the type of defect and the variability of the background made it difficult to recognize the defects. In this study, seven different feature sets based on Discrete Cosine Transform, Principal Component Analysis, Gray Level Co-occurrence Matrix, and Deep Learning Architectures have been used to detect defects. The extracted features have been classified by three different classification techniques (K-Nearest Neighbor, Support Vector Machine, and Decision Tree). The aim of this study is to investigate the effects of different feature extraction methods. The paper provides examination of the performances of different feature extraction methods and different classifiers. Methods have been evaluated in terms of precision, recall, F1-measure, and accuracy on two different datasets. mRMR feature selection after ResNet18 based feature extraction has enabled to obtain the features with the highest results in the study (0.831). Experimental results show that CNN-based feature extraction is more successful than statistical-based feature extraction methods. [ABSTRACT FROM AUTHOR]

Copyright of Bezmialem Science is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fabric analysis is done to determine fabric weave patterns used in the weaving process. The analysis of fabric-weave patterns still relied on human inspection until the middle of the 1980s. Since then, a number of studies have been made of automatic pattern recognition of woven fabric. One of the major problems in automatic woven fabric recognition is bow to detect the areas of interlacing warp and weft yams. This problem is termed 'crossed-points detection', the difficult problem. In this work, new and fully automatic methods containing Otsu, Isodata, Li, Mean Minimum, Triangle and Yen thresholding algorithms and based on Fourier image-analysis techniques as well are proposed. The application of these methods to plain woven fabric demonstrates the ability to solve such crossed-points-detection problems. Finally, the algorithms are evaluated visually by superposing the detected grid image on the initial woven-fabric image. Grid images obtained by Otsu and Isodata thresholding methods involve the crossed points better than other methods. Besides, vertical lines that extracted with Li method are more left than lines that detected using other methods. Moreover, results from Mean method is close results of Otsu and Isodata. Similarly, Mean and Minimum method’s result is close to results of Otsu and Isodata. In Triangle and Yen methods, while horizontal lines are extracted, vertical lines are not extracted. All the detected crossed-points are displayed independently and may be saved as file-format images for further processing.

There are many studies in the literature to find and identify woven fabric defects. However, the systems developed have been designed to identify only certain defects due to the large number of defect types. Moreover, a method that works well for identifying a defect type may not work for another defect type. In addition, some mistakes are easy to recognize, while others are difficult to recognize. In this study, different clustering and classification techniques have been investigated and studies detecting and recognizing fabric defects using these techniques have been investigated. Clustering algorithms are often used to detect defects while classification methods are used to recognize the defect types. When we look at the literature, the most common clustering algorithm for fabric defect recognition is K-means algorithm, while the most common classification technique is neural networks. In general, neural networks have been used in the vast majority of studies. The automatic recognition of fabric defects has not yet achieved the desired level of success. Approximately 80 percent of studies conducted on this field have only developed a model, but have not compared the method they used with other methods. So, very little work has been tested in more than one method.

Introduction: Stimulation of liver regeneration after hepatectomy may provide important advantages, especially in cases where liver tissue is at a critical level. Some studies have reported that vascular endothelial growth factor (VEGF) might ameliorate liver regeneration in animals. In this study, we aimed to examine the effects of exogenous VEGF164 on the regeneration of liver tissue after a 70% hepatectomy in rats. Methods: Twenty Wistar Albino male rats, weighing 300-350 g, were randomly divided into four groups, each having five animals. Group 1 and Group 3 were designated as control groups, and Group 2 and Group 4 as experimental groups. All rats underwent laparotomy and 70% partial hepatectomy. Postoperatively, control groups were administered saline through the tail vein at 0 and 6 hours, and VEGF164 was administered to the experimental groups in the same manner at 0 and 6 hours. Blood samples were collected by sacrificing the rats at 24 hours for Group 1 and Group 2, and at 72 hours for Group 3 and Group 4. Subsequently, relaparotomy was performed and the remaining liver tissues were completely removed in all groups. Then, the resected liver tissues were used to determine morphological regeneration rates. The histopathological proliferation of the liver was evaluated by calculating the Ki-67 proliferative index. Results: In the histopathological evaluation, it was revealed that the liver proliferation index was significantly higher in the VEGF164 group at 72 hours (p=0.028). Although liver weights were higher in the experimental groups, no statistically significant results were obtained in terms of morphological liver regeneration rates (p>0.05). Discussion and Conclusion: Although the positive effects of exogenous VEGF on liver regeneration were not found in this rat model, statistically significant increases in the liver proliferation index are promising. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Selcuk University Social Sciences Institute / Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Journal of Selcuk University Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: This study aimed to evaluate the efficacy and safety of titanium-platelet rich fibrin (T-PRF) membranes in primary pterygium surgery. Methods: Twenty-three patients diagnosed with primary pterygium were included in our retrospective study. Patients underwent pterygium excision between January 2017 and October 2017 after T-PRF membrane autograft to close the scleral bed. Blood samples from the patients were centrifuged in titanium tubes and mechanically compressed to prepare T-PRF membranes. In this study, preoperative patient characteristics, spread of pterygium, T-PRF resorption time in postoperative controls, and pterygium recurrence and complications were evaluated. Results: The mean follow-up period was noted as 8.9±3.1 months (4-14 months). The mean extension of the pterygium was 2.5±1.0 mm. Eleven patients had pterygium recurrence (42.3%). The mean time to recurrence was 3.9±1.8 months. No complications were observed during surgery. The T-PRF resorption time was noted as <7 days in seven patients. Postoperative follow-up revealed suture reaction in 7 (26.9%) patients, loss of graft in 1 (3.8%), conjunctival granuloma formation in 1 (3.8%), and Tenon's cyst formation in 1 (3.8%) patient. Conclusion: The T-PRF membrane is easily available, cost-effective, can be prepared with the desired size and thickness, and thus can be an alternative method for patients who are not eligible to receive conjunctival autograft for pterygium surgery. The surgical technique could be improved to be accepted as a standard method. [ABSTRACT FROM AUTHOR]

In this research, thermal and water vapor resistance, components of thermal comfort of 65/35 and 33/67% polyester/ cotton (PES/CO) blend fabrics woven with 2/2 twill, matt twill, cellular and diced weaves, which are commonly used for clothing, were determined. The results indicate that both the fabric construction and the constituent fiber properties affect thermal comfort properties of clothing woven fabrics. Cellular weave, which is derivative of sateen weave and diced weave, which is compound weave, has the highest thermal resistance appropriating for cold climatic conditions. On the other hand, the 2/2 twill weave and matt twill weave, which is derivative of sateen weave, depicted the lowest water vapor thermal resistance, making it convenient for hot climatic conditions. Besides, fabrics woven with 65/35% PES/CO blend yarns have higher thermal resistance, so they are suitable for cold climatic conditions. Fabrics woven with 33/67% PES/CO blend yarns have lower water vapor resistance, so they are convenient for hot climatic conditions.

Background: Histopathological examination of appendectomy specimens may reveal malignancies. Based on these results, either appendectomy is sufficient or sometimes a further treatment protocol can be needed. In this study, malignancy-diagnosed cases on appendectomy specimen were examined. Methods: Patients who underwent appendectomy between January 2013 and December 2018 with a pre-diagnosis of acute appendicitis were evaluated retrospectively and those cases with malignancy were included in the study. Patients' age, sex, tumor type, tumor diameter, tumor grade, tumor localization, surgical margin, Ki-67 index, state of lymphovascular invasion, state of peri-neural invasion, and follow-up period duration were recorded. Results: On examination of 2336 appendectomy specimens, 16 patients (0.7%) were found to have neuroendocrine tumors (NET), 11 patients (0.5%) were found to have low-grade mucinous neoplasm (LAMN), and five patients (0.2%) were found to have primary appendix carcinomas. Appendix tumors usually present with acute appendicitis symptoms. Despite re-operation with right hemicolectomy (RHC) being required in the treatment of adenocarcinoma cases, appendectomy provides adequate treatment in most cases with NET and LAMN. With these tumors, which usually have a benign prognosis, it is important to perform the necessary screening in the postoperative period and not to interrupt follow-up. [ABSTRACT FROM AUTHOR]

Merino lambs' wool fiber has a unique chemical structure that gives the wool many unique properties and technical benefits. For example, the small fiber diameters mean that Merino wool is soft, countering the scratchiness commonly associated with wool. It is also biodegradable, organic, and environmentally friendly, making it a popular choice of sustainable fabric material. Unfortunately, some fiber sellers unduly sell coarse wool tops to wool yarn factories as Merino wool tops. It is, therefore, an important task to identify the actual wool type for quality assurance in the textile manufacturing process. This paper describes applying the spectral-domain optical coherence tomography (OCT) imaging and automatic machine learning (AutoML) techniques for distinguishing Merino wool from coarse wool. We present the results of wool measurements that were performed by the OCT scans and AutoML algorithms. We conclude that OCT imaging and AutoML algorithms can be applied to distinguish Merino wool from coarse wool in a simple, non-destructive, and contactless manner. [ABSTRACT FROM AUTHOR]

The portable and wearable instrumented fabrics capable of measuring biothermal variable is essential for drivers, especially long-distance drivers. Here we report on portable and wearable devices that are able to read the temperature of human body within the woven fabric. The sensory function of the fabric is achieved by temperature sensors, soldered on conductive threads coated with cotton. The presence of stainless steel wires gives these materials conductive properties, enabling the detection of human body temperature and transmitting the signal form sensors to the motors on the fabric. When body temperature decreases, hardware/software platforms send a signal to the vibration motors in order to stimulate the driver. The ‘smart woven fabric’-sensing architecture can be divided into two parts: a textile platform, where portable and wearable devices acquire thermal signals, and hardware/software platforms, to which a sensor sends the acquired data, which send the signals to the vibration motors.