Effects of combination of ethylenediaminetetraacetic acid and microbial phytase on the serum concentration and digestibility of some minerals in broiler chicks

This experiment was conducted to evaluate the combined effects of ethylene diamine tetraacetic acid (EDTA) and microbial phytase (MP) on the serum concentration and digestibility of some minerals in broiler chicks. This experiment was conducted using 360 Ross-308 male broiler chicks in a completely randomized design with a 3×2 factorial arrangement (0, 0.1 and 0.2% EDTA and 0 and 500 FTU MP). Fourreplicates of 15 chicks per each were fed dietary treatments which included; P-deficient basal diet [0.2% available phosphorus (aP)] (NC), NC + 500 FTU MP per kilogram of diet, NC + 0.1% EDTA per kilogram ofdiet, NC + 0.1% EDTA + 500 FTU MP per kilogram of diet, NC + 0.2% EDTA per kilogram and NC + 0.2% EDTA + 500 FTU MP per kilogram of diet. The concentration of zinc, copper and manganese of serum and their digestibility and also digestibility of apparent metabolizable energy (AMEn) was evaluated. The results showed that phytase supplementation of P-deficient diets significantly increased zinc concentration of serum (P < 0.05). Interaction effect of EDTA+MP on serum concentration of copper and manganese and also digestibility of zinc was significant (P < 0.05). EDTA supplementation of P-deficient diets significantly increased manganese digestibility in broiler chicks (P < 0.01). Keywords: Ethylene diamine tetraacetic acid, microbial phytase, zinc, copper, manganes

Effects of light regimes on growth and survival of Penaeus semisuleatus

We investigated the possible effects of light regimes on growth and survival rate of juvenile shrimp Penaeus semisuicants cultured in Bushehr Province, southern Iran. Five light regimes each with three replications were applied for 30 days. The treatments were 24 1 0, 18 / 6, 12 1 12, 6 1 18 and 0 124 hours of light and darkness. We found that the treatment 12 7 12 hours of light and darkness was better than 18 1 6 and significantly superior to other treatments (P<0.05). The lowest growth rate was seen in the treatment 24 / 0 (P<0.001 ). We did not find a significant difference in the survival rate of the shrimps cultured in the light and darkness treatments (P<0.05)

The improvement of Mo/4H-SiC Schottky diodes via a P2O5 surface passivation treatment

Molybdenum (Mo)/4H-silicon carbide (SiC) Schottky barrier diodes have been fabricated with a phosphorus pentoxide (P2O5) surface passivation treatment performed on the SiC surface prior to metallization. Compared to the untreated diodes, the P2O5-treated diodes were found to have a lower Schottky barrier height by 0.11 eV and a lower leakage current by two to three orders of magnitude. Physical characterization of the P2O5-treated Mo/SiC interfaces revealed that there are two primary causes for the improvement in electrical performance. First, transmission electron microscopy imaging showed that nanopits filled with silicon dioxide had formed at the surface after the P2O5 treatment that terminates potential leakage paths. Second, secondary ion mass spectroscopy revealed a high concentration of phosphorus atoms near the interface. While only a fraction of these are active, a small increase in doping at the interface is responsible for the reduction in barrier height. Comparisons were made between the P2O5 pretreatment and oxygen (O2) and nitrous oxide (N2O) pretreatments that do not form the same nanopits and do not reduce leakage current. X-ray photoelectron spectroscopy shows that SiC beneath the deposited P2O5 oxide retains a Si-rich interface unlike the N2O and O2 treatments that consume SiC and trap carbon at the interface. Finally, after annealing, the Mo/SiC interface forms almost no silicide, leaving the enhancement to the subsurface in place, explaining why the P2O5 treatment has had no effect on nickel- or titanium-SiC contacts

ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case Reports

Objective: To evaluate the efficiency of large language models (LLMs) such as ChatGPT to assist in diagnosing neuro-ophthalmic diseases based on detailed case descriptions. Methods: We selected 22 different case reports of neuro-ophthalmic diseases from a publicly available online database. These cases included a wide range of chronic and acute diseases that are commonly seen by neuro-ophthalmic sub-specialists. We inserted the text from each case as a new prompt into both ChatGPT v3.5 and ChatGPT Plus v4.0 and asked for the most probable diagnosis. We then presented the exact information to two neuro-ophthalmologists and recorded their diagnoses followed by comparison to responses from both versions of ChatGPT. Results: ChatGPT v3.5, ChatGPT Plus v4.0, and the two neuro-ophthalmologists were correct in 13 (59%), 18 (82%), 19 (86%), and 19 (86%) out of 22 cases, respectively. The agreement between the various diagnostic sources were as follows: ChatGPT v3.5 and ChatGPT Plus v4.0, 13 (59%); ChatGPT v3.5 and the first neuro-ophthalmologist, 12 (55%); ChatGPT v3.5 and the second neuro-ophthalmologist, 12 (55%); ChatGPT Plus v4.0 and the first neuro-ophthalmologist, 17 (77%); ChatGPT Plus v4.0 and the second neuro-ophthalmologist, 16 (73%); and first and second neuro-ophthalmologists 17 (17%). Conclusions: The accuracy of ChatGPT v3.5 and ChatGPT Plus v4.0 in diagnosing patients with neuro-ophthalmic diseases was 59% and 82%, respectively. With further development, ChatGPT Plus v4.0 may have potential to be used in clinical care settings to assist clinicians in providing quick, accurate diagnoses of patients in neuro-ophthalmology. The applicability of using LLMs like ChatGPT in clinical settings that lack access to subspeciality trained neuro-ophthalmologists deserves further research

Strong polarization-induced reduction of addition energies in single-molecule nanojunctions

We address polarization-induced renormalization of molecular levels in solid-state based single-molecule transistors and focus on an organic conjugate molecule where a surprisingly large reduction of the addition energy has been observed. We have developed a scheme that combines a self-consistent solution of a quantum chemical calculation with a realistic description of the screening environment. Our results indeed show a large reduction, and we explain this to be a consequence of both (a) a reduction of the electrostatic molecular charging energy and (b) polarization induced level shifts of the HOMO and LUMO levels. Finally, we calculate the charge stability diagram and explain at a qualitative level general features observed experimentally.Comment: 9 pages, 5 figure

Effect of yoghurt containing Bifidobacterium lactis Bb12® on faecal excretion of secretory immunoglobulin A and human beta-defensin 2 in healthy adult volunteers

<p>Abstract</p> <p>Background</p> <p>Probiotics are used to provide health benefits. The present study tested the effect of a probiotic yoghurt on faecal output of beta-defensin and immunoglobulin A in a group of young healthy women eating a defined diet.</p> <p>Findings</p> <p>26 women aged 18-21 (median 19) years residing in a hostel were given 200 ml normal yoghurt every day for a week, followed by probiotic yoghurt containing <it>Bifidobacterium lactis </it>Bb12^®(10⁹in 200 ml) for three weeks, followed again by normal yoghurt for four weeks. Stool samples were collected at 0, 4 and 8 weeks and assayed for immunoglobulin A and human beta-defensin-2 by ELISA. All participants tolerated both normal and probiotic yoghurt well. Human beta-defensin-2 levels in faeces were not altered during the course of the study. On the other hand, compared to the basal sample, faecal IgA increased during probiotic feeding (P = 0.0184) and returned to normal after cessation of probiotic yoghurt intake.</p> <p>Conclusions</p> <p><it>Bifidobacterium lactis </it>Bb12^®increased secretory IgA output in faeces. This property may explain the ability of probiotics to prevent gastrointestinal and lower respiratory tract infections.</p

Hygienic monitoring of freshwater crayfish (Astacus leptodactylus) on Aras Lake reservoir

Aras dam reservoir situated in the northwest of Iran, west Azarbaijan province, is the only water resource of Astacus leptodactylus harvest in the country that more than 250tons of this species were exported to different countries all over the world, annually. On the other hand, one of the polices of Iranian Science Fisheries Institute is the release of this species into other water resources in the country and for this purpose, the study of risky diseases such as Crayfish pest (Aphanomysis astasi) and other zoonotic diseases are considered as the research priorities of aquaculture development of the country. This study was carried out to health screening of Astacus leptodactylus at Aras dam reservoir from winter 2013 to fall 2014. In this regard, A total of 394 harvested livefreshwater crayfish Astacus leptodactylus (255males, 139females) weretested. 9 epibionts and parasites peritrich protozoans were identified. From Metazoan parasites group, Branchiobdella kozarovi with incidence rate of (100%) in obtained samples was the only isolated organism from this group that identified up to species level. There was a heavy damage in gills of samples with Aeolosoma hemprichi (Annelid) in winter with90% prevalence. Furthermore, Other Epibiont fouling organisms such as Rotatoria; free living nematods and suctoria were observed in this survey. The fungi study of the lesions and melanized spots of mentioned samples revealed their infection to Penicillium expansum; Aspergillus flavus; Alternaria sp. ; Fusarium sp. and Saprolegnia sp. The results of bacterial study confirmed the presence of pathogen bacteria in Astacus leptodactylus. The most frequency percentage (15.16%) in hepatopancrease were related to Aeromonas hydrophila and the least one (1.37%) were due to Yersinia bacteria. Also, only Aeromonas hydrophila and Staphylococcus aureus were isolated and identified from heamolymph, respectively. The results revealed that the combination of Salmonella typhi, Escherichia coli and Staphylococcus sp. has caused the most infection rate while. Yersinia ruckeri and Salmonella typhi has caused the least infections in Astacus leptodactylus. According to the isolation of 6 bacteria species from hepatopancreas and 2 species from heamplymph , it can be concluded that hepatopancreas enjoyed the higher infection rate compared to haemolymph in the obtained samples

Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology. 18(4):1-18. https://doi.org/10.1007/s13762-020-02896-6S118184Al-Dabbous A, Kumar P, Khan A (2017) Prediction of airborne nanoparticles at roadside location using a feed–forward artificial neural network. Atmos Pollut Res 8:446–454. https://doi.org/10.1016/j.apr.2016.11.004Antanasijević D, Pocajt V, Povrenović D, Ristić M, Perić-Grujić A (2013) PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511–519. https://doi.org/10.1016/j.scitotenv.2012.10.110Brink H, Richards JW, Fetherolf M (2016) Real-world machine learning. Richards JW, Fetherolf M (eds) Manning Publications Co. Berkeley, CA. https://www.manning.com/books/real-world-machine-learning. Accessed 26 Apr 2020Cervone G, Franzese P, Ezber Y, Boybeyi Z (2008) Risk assessment of atmospheric emissions using machine learning. Nat Hazard Earth Syst 8:991–1000. https://doi.org/10.5194/nhess-8-991-2008Chen S, Kan G, Li J, Liang K, Hong Y (2018) Investigating China’s urban air quality using big data, information theory, and machine learning. Pol J Environ Stud 27:565–578. https://doi.org/10.15244/pjoes/75159Corani (2005) Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. Ecol Model 185:513–529. https://doi.org/10.1016/j.ecolmodel.2005.01.008Cruz C, Gómez A, Ramírez L, Villalva A, Monge O, Varela J, Quiroz J, Duarte H (2017) Calidad del aire respecto de metales (Pb, Cd, Ni, Cu, Cr) y relación con salud respiratoria: caso Sonora, México. Rev Int Contam Ambient 33:23–34. https://doi.org/10.20937/RICA.2017.33.esp02.02de Hoogh K, Héritier H, Stafoggia M, Künzli N, Kloog I (2018) Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland. Environ Pollut 233:1147–1154. https://doi.org/10.1016/j.envpol.2017.10.025Franceschi F, Cobo M, Figueredo M (2018) Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering. Atmos Pollut Res 9:912–922. https://doi.org/10.1016/j.apr.2018.02.006García N, Combarro E, del Coz J, Montañes E (2013) A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study. Appl Math Comput 219:8923–8937. https://doi.org/10.1016/j.amc.2013.03.018Gibert K, Sànchez-Màrre M, Sevilla B (2012) Tools for environmental data mining and intelligent decision support. In iEMSs. Leipzig, Germany. http://www.iemss.org/society/index.php/iemss-2012-proceedings. Accessed 26 Nov 2018Gibert K, Sànchez-Marrè M, Izquierdo J (2016) A survey on pre-processing techniques: relevant issues in the context of environmental data mining. Ai Commun 29:627–663. https://doi.org/10.3233/AIC-160710Gounaridis D, Chorianopoulos I, Koukoulas S (2018) Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: the case of Athens. Appl Geogr 90:134–144. https://doi.org/10.1016/j.apgeog.2017.12.001Holloway J, Mengersen K (2018) Statistical machine learning methods and remote sensing for sustainable development goals: a review. Remote Sens 10:1–21. https://doi.org/10.3390/rs10091365Ifaei P, Karbassi A, Lee S, Yoo Ch (2017) A renewable energies-assisted sustainable development plan for Iran using techno-econo-socio-environmental multivariate analysis and big data. Energy Convers Manag 153:257–277. https://doi.org/10.1016/j.enconman.2017.10.014Kadiyala A, Kumar A (2017a) Applications of R to evaluate environmental data science problems. Environ Prog Sustain 36:1358–1364. https://doi.org/10.1002/ep.12676Kadiyala A, Kumar A (2017b) Vector time series-based radial basis function neural network modeling of air quality inside a public transportation bus using available software. Environ Prog Sustain 36:4–10. https://doi.org/10.1002/ep.12523Karimian H, Li Q, Wu Ch, Qi Y, Mo Y, Chen G, Zhang X, Sachdeva S (2019) Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations. Aerosol Air Qual Res 19:1400–1410. https://doi.org/10.4209/aaqr.2018.12.0450Krzyzanowski M, Apte J, Bonjour S, Brauer M, Cohen A, Prüss-Ustun A (2014) Air pollution in the mega-cities. Curr Environ Health Rep 1:185–191. https://doi.org/10.1007/s40572-014-0019-7Lässig K, Morik (2016) Computat sustainability. Springer, Berlin. https://doi.org/10.1007/978-3-319-31858-5Li Y, Wu Y-X, Zeng Z-X, Guo L (2006) Research on forecast model for sustainable development of economy-environment system based on PCA and SVM. In: Proceedings of the 2006 international conference on machine learning and cybernetics, vol 2006. IEEE, Dalian, China, pp 3590–3593. https://doi.org/10.1109/ICMLC.2006.258576Liu B-Ch, Binaykia A, Chang P-Ch, Tiwari M, Tsao Ch-Ch (2017) Urban air quality forecasting based on multi- dimensional collaborative support vector regression (SVR): a case study of Beijing-Tianjin-Shijiazhuang. PLoS ONE 12:1–17. https://doi.org/10.1371/journal.pone.0179763Lubell M, Feiock R, Handy S (2009) City adoption of environmentally sustainable policies in California’s Central Valley. J Am Plan Assoc 75:293–308. https://doi.org/10.1080/01944360902952295Ma D, Zhang Z (2016) Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. J Hazard Mater 311:237–245. https://doi.org/10.1016/j.jhazmat.2016.03.022Madu C, Kuei N, Lee P (2017) Urban sustainability management: a deep learning perspective. Sustain Cities Soc 30:1–17. https://doi.org/10.1016/j.scs.2016.12.012Mellos K (1988) Theory of eco-development. In: Perspectives on ecology. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-19598-5_4Ni XY, Huang H, Du WP (2017) Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data. Atmos Environ 150:146–161. https://doi.org/10.1016/j.atmosenv.2016.11.054Oprea M, Dragomir E, Popescu M, Mihalache S (2016) Particulate matter air pollutants forecasting using inductive learning approach. Rev Chim 67:2075–2081Paas B, Stienen J, Vorländer M, Schneider Ch (2017) Modelling of urban near-road atmospheric PM concentrations using an artificial neural network approach with acoustic data input. Environments 4:1–25. https://doi.org/10.3390/environments4020026Pandey G, Zhang B, Jian L (2013) Predicting submicron air pollution indicators: a machine learning approach. Environ Sci Proc Impacts 15:996–1005. https://doi.org/10.1039/c3em30890aPeng H, Lima A, Teakles A, Jin J, Cannon A, Hsieh W (2017) Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Qual Atmos Health 10:195–211. https://doi.org/10.1007/s11869-016-0414-3Pérez-Ortíz M, de La Paz-Marín M, Gutiérrez PA, Hervás-Martínez C (2014) Classification of EU countries’ progress towards sustainable development based on ordinal regression techniques. Knowl Based Syst 66:178–189. https://doi.org/10.1016/j.knosys.2014.04.041Phillis Y, Kouikoglou V, Verdugo C (2017) Urban sustainability assessment and ranking of cities. Comput Environ Urban 64:254–265. https://doi.org/10.1016/j.compenvurbsys.2017.03.002Saeed S, Hussain L, Awan I, Idris A (2017) Comparative analysis of different statistical methods for prediction of PM2.5 and PM10 concentrations in advance for several hours. Int J Comput Sci Netw Secur 17:45–52Sayegh A, Munir S, Habeebullah T (2014) Comparing the performance of statistical models for predicting PM10 concentrations. Aerosol Air Qual Res 14:653–665. https://doi.org/10.4209/aaqr.2013.07.0259Shaban K, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sens J 16:2598–2606. https://doi.org/10.1109/JSEN.2016.2514378Sierra B (2006) Aprendizaje automático conceptos básicos y avanzados Aspectos prácticos utilizando el software Weka. Madrid Pearson Prentice Hall, MadridSingh K, Gupta S, Rai P (2013) Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmos Environ 80:426–437. https://doi.org/10.1016/j.atmosenv.2013.08.023Song L, Pang S, Longley I, Olivares G, Sarrafzadeh A (2014) Spatio-temporal PM2.5 prediction by spatial data aided incremental support vector regression. In: International joint conference on neural networks. IEEE, Beijing, pp 623–630. https://doi.org/10.1109/IJCNN.2014.6889521Souza R, Coelho G, da Silva A, Pozza S (2015) Using ensembles of artificial neural networks to improve PM10 forecasts. Chem Eng Trans 43:2161–2166. https://doi.org/10.3303/CET1543361Suárez A, García PJ, Riesgo P, del Coz JJ, Iglesias-Rodríguez FJ (2011) Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Math Comput Model 54:453–1466. https://doi.org/10.1016/j.mcm.2011.04.017Tamas W, Notton G, Paoli C, Nivet M, Voyant C (2016) Hybridization of air quality forecasting models using machine learning and clustering: an original approach to detect pollutant peaks. Aerosol Air Qual Res 16:405–416. https://doi.org/10.4209/aaqr.2015.03.0193Toumi O, Le Gallo J, Ben Rejeb J (2017) Assessment of Latin American sustainability. Renew Sustain Energy Rev 78:878–885. https://doi.org/10.1016/j.rser.2017.05.013Tzima F, Mitkas P, Voukantsis D, Karatzas K (2011) Sparse episode identification in environmental datasets: the case of air quality assessment. Expert Syst Appl 38:5019–5027. https://doi.org/10.1016/j.eswa.2010.09.148United Nations, Department of Economic and Social Affairs (2019) World urbanization prospects The 2018 Revision. New York. https://doi.org/10.18356/b9e995fe-enWang B (2019) Applying machine-learning methods based on causality analysis to determine air quality in China. Pol J Environ Stud 28:3877–3885. https://doi.org/10.15244/pjoes/99639Wang X, Xiao Z (2017) Regional eco-efficiency prediction with support vector spatial dynamic MIDAS. J Clean Prod 161:165–177. https://doi.org/10.1016/j.jclepro.2017.05.077Wang W, Men C, Lu W (2008) Online prediction model based on support vector machine. Neurocomputing 71:550–558. https://doi.org/10.1016/j.neucom.2007.07.020WCED (1987) Report of the world commission on environment and development: our common future: report of the world commission on environment and development. WCED, Oslo. https://doi.org/10.1080/07488008808408783Weizhen H, Zhengqiang L, Yuhuan Z, Hua X, Ying Z, Kaitao L, Donghui L, Peng W, Yan M (2014) Using support vector regression to predict PM10 and PM2.5. In: IOP conference series: earth and environmental science, vol 17. IOP. https://doi.org/10.1088/1755-1315/17/1/012268WHO (2016) OMS | La OMS publica estimaciones nacionales sobre la exposición a la contaminación del aire y sus repercusiones para la salud. WHO. http://www.who.int/mediacentre/news/releases/2016/air-pollution-estimates/es/. Accesed 26 Nov 2018Yeganeh N, Shafie MP, Rashidi Y, Kamalan H (2012) Prediction of CO concentrations based on a hybrid partial least square and support vector machine model. Atmos Environ 55:357–365. https://doi.org/10.1016/j.atmosenv.2012.02.092Zalakeviciute R, Bastidas M, Buenaño A, Rybarczyk Y (2020) A traffic-based method to predict and map urban air quality. Appl Sci. https://doi.org/10.3390/app10062035Zeng L, Guo J, Wang B, Lv J, Wang Q (2019) Analyzing sustainability of Chinese coal cities using a decision tree modeling approach. Resour Policy 64:101501. https://doi.org/10.1016/j.resourpol.2019.101501Zhan Y, Luo Y, Deng X, Grieneisen M, Zhang M, Di B (2018) Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environ Pollut 233:464–473. https://doi.org/10.1016/j.envpol.2017.10.029Zhang Y, Huan Q (2006) Research on the evaluation of sustainable development in Cangzhou city based on neural-network-AHP. In: Proceedings of the fifth international conference on machine learning and cybernetics, vol 2006. pp 3144–3147. https://doi.org/10.1109/ICMLC.2006.258407Zhang Y, Shang W, Wu Y (2009) Research on sustainable development based on neural network. In: 2009 Chinese control and decision conference. IEEE, pp 3273–3276. https://doi.org/10.1109/CCDC.2009.5192476Zhou Y, Chang F-J, Chang L-Ch, Kao I-F, Wang YS (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134–145. https://doi.org/10.1016/j.jclepro.2018.10.24