Odnos ızmeđu boje bazena ı prırasta mlađı evropskog brancına (dıcentrarchus labrax)

Dizajn sistema za gajenje riba je veoma bitan za održivu i visoko profitabilnu proizvodnju u akvakulturi. Različitim vrstama riba potrebani su drugačije dizajnirani sistemi i veštačke sredine. Sistemi u zatvorenom prostoru su korisni za mrestilišta a tankovi su veštačka staništa za vrste gajene u tim sistemima. Prethodna istraživanja pokazuju da boja zida bazena utiče na nivo stresa kod riba (Rotlant et al., 2003) i parametre koji utiču na rast, a dobrobit riba može da bude ugrožena u stresnim uslovima (De Silva and Anderson 1994). Cilj ovog istraživanja je da ispita efekte koje različite boje zidova tankova imaju na prirast mlađi Evropskog brancina (Dicentrarchus labrax). 480 jedinki mlađi nasumice su raspoređene u 12 identičnih plastičnih tankova (40 jedinki po tanku). Zapremina svakog bazena iznosila je 40 litara. U triplikatu su korišćene četiri različite boje bazena (crvena, zelena, plava i svetlo žuta). Riba je hranjena komercijalnom hranomn za brancina 2 puta dnevno u period od 60 dana. Najveći prirast dostigla je riba gajena u crvenim bazenima, dok je riba gajena u žutim bazenima imala najmanji prirast. Prethodna istraživanja su pokazala da boja zida bazena utiče na prirast ribe u uslovima gajenja i da je različitim vrstama riba potrebna drugačija boja bazena da bi postigle najbolji prirast (Duray et al., 1996; Rotland et al., 2003; Imanpoor and Abdollahi, 2011). Rezultati pokazuju da boja bazena utiče na prirast riba u uslovima gajenja

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Bio-economic efficiency of copper alloy mesh technology in offshore cage systems for sustainable aquaculture

2017-2024In the present study, innovative and environment friendly copper alloy mesh material was used in an offshore cage system to compare with traditional nylon nets, in a one-year grow-out cycle of European seabass (Dicentrarchuslabrax). Based on combined indicators such as growth performance, feed utilization with bio-economic assessment of initial investment costs, it was observed that copper alloy mesh performed higher productivity indices and economic benefits compared to those in the antifouling coated traditional nylon net pens. Results showed that copper alloy mesh is a promising alternative material that could be used in offshore cage aquaculture with an improved economic return

Efecto inhibidor de algunos aceites esenciales sobre el Penicillium digitatum causante de la putrefacción postcosecha de cítricos

In this study to control blue mould caused by Penicillium digitatum, essential oil of cumin was applied with filter paper discs of 6 mm diameter which were soaked in 0,04 ml oil and vapour effect inhibited completely mycelial growth and spore germination of pathogen in vitro. When filter paper discs soaked in essential oils of black thyme, dill, coriander and rosemary were placed on the culture medium (PDA), they had no effect on the mycelial growth. Their vapour effect inhibited mycelial growth of pathogen 85.8%, 82.8%, 80% and 71.4% respectively. Dill and rosemary oils also prevented mycelial colour.En este estudio para controlar las manchas azules causadas por Penicillium digitatum, se aplicó aceite esencial de comino en discos de papel de filtro de 6 mm de diámetro, los cuales fueron empapados en 0,04 ml de aceite y su vapor inhibió completamente el crecimiento micelar y la germinación de esporas del patógeno in vitro. Cuando los discos de papel de filtro empapados en aceites esenciales de tomillo, eneldo, culantro y romero se colocaron sobre el medio de cultivo (PDA), no se observó efecto sobre el crecimiento micelar. Los efectos de sus vapores inhibieron el crecimiento micelar de patógenos en un 85,8%, 82,8%, 80% y 71,4% respectivamente. Los aceites de eneldo y romero también evitaron la aparición del color micelar

Evaluation of anterior segment parameters in patients with pseudoexfoliation syndrome using Scheimpflug imaging

ABSTRACT Purpose: To evaluate anterior segment parameters in patients with pseudoexfoliation syndrome (PXS) using Scheimpflug imaging. Methods: Forty-three PXS patients and 43 healthy control subjects were included in this cross-sectional study. All participants underwent a detailed ophthalmologic examination. Anterior segment parameters were measured using a Scheimpflug system. Results: Considering the PXS and control groups, the mean corneal thicknesses at the apex point (536 ± 31 and 560 ± 31 µm, respectively, p=0.001), at the center of the pupil (534 ± 31 and 558 ± 33 µm, respectively, p=0.001), and at the thinnest point (528 ± 30 and 546 ± 27 µm, respectively, p=0.005) were significantly thinner in PXS patients. Visual acuity was significantly lower (0.52 ± 0.37 versus 0.88 ± 0.23, p<0.001) and axial length was significantly longer (23.9 ± 0.70 mm versus 23.2 ± 0.90 mm, p=0.001) in the PXS eyes than in the control eyes. There were no statistically significant differences in the mean values of keratometry, anterior chamber angle, anterior chamber depth, corneal volume, and anterior chamber volume between the PXS and control eyes. Conclusions: The patients with PXS had thinner corneas, worse visual acuity, and longer axial length compared with those in the healthy controls

DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis

Precise and quick monitoring of key cytometric features such as cell count, cell size, cell morphology, and DNA content is crucial for applications in biotechnology, medical sciences, and cell culture research. Traditionally, image cytometry relies on the use of a hemocytometer accompanied with visual inspection of an operator under a microscope. This approach is prone to error due to subjective decisions of the operator. Recently, deep learning approaches have emerged as powerful tools enabling quick and highly accurate image cytometric analysis that are easily generalizable to different cell types. Leading to simpler, more compact, and less expensive solutions, these approaches revealed image cytometry as a viable alternative to flow cytometry or Coulter counting. In this study, we demonstrate a modular deep learning system, DeepCAN, that provides a complete solution for automated cell counting and viability analysis. DeepCAN employs three different neural network blocks called Parallel Segmenter, Cluster CNN, and Viability CNN that are trained for initial segmentation, cluster separation, and cell viability analysis, respectively. Parallel Segmenter and Cluster CNN blocks achieve highly accurate segmentation of individual cells while Viability CNN block performs viability classification. A modified U-Net network, a wellknown deep neural network model for bioimage analysis, is used in Parallel Segmenter while LeNet-5 architecture and its modified version called Opto-Net are used for Cluster CNN and Viability CNN, respectively. We train the Parallel Segmenter using 15 images of A2780 cells and 5 images of yeasts cells, containing, in total, 14742 individual cell images. Similarly, 6101 and 5900 A2780 cell images are employed for training Cluster CNN and Viability CNN models, respectively. 2514 individual A2780 cell images are used to test the overall segmentation performance of Parallel Segmenter combined with Cluster CNN, revealing high Precision/Recall/F1-Score values of 96.52%/96.45%/98.06%, respectively. Overall cell counting/viability analysis performance of DeepCAN is tested with A2780 (2514 cells), A549 (601 cells), Colo (356 cells), and MDA-MB-231 (887 cells) cell images revealing high counting/viability analysis accuracies of 96.76%/99.02%, 93.82%/95.93%, and 92.18%/97.90%, 85.32%/97.40%, respectively