Primena nove kulture - kvinoje u ishrani riba

Kvinoja (Chenopodium quinoa, Willd.) je pseudocerealija koja se tradicionalno gaji na malim plantažama u ruralnim oblastima Južne Amerike u regionu Anda. Kvinoja je ratarska kultura, gajena za domaću upotrebu siromašnog stanovništva. Zbog sposobnosti prilagođavanja različitim agro-ekološkim uslovima, kvinoja može da se gaji u različitim regionima. Agrotehnika zasnovana na principima organske poljoprivrede i primenjena je u Danskoj, Italiji i Makedoniji. Zahvaljujući visokoj nutritivnoj vrednosti seme se koristi u ljudskoj ishrani, usled čega je poslednjih godina porastao interes za gajenje i preradu kvinoje, kao funkcionalne hrane. Poznato je da i lišće biljke poseduje značajne nutritivne vrednosti, pa se koristi kao zamena za spanać. Biljka može da poraste 1-3m visine, a plodovi su u obliku okruglog, malog semena, koje je obavijeno perigoniumom različite boje (bledo žute, do svetlo crvene). Perigonium se mehanički lako odvaja, kada je seme suvo. U perikarpu semena nalaze se saponini, nosioci karakterističnog gorkog ukusa semena kvinoje, zbog čega je potrebno iste odstraniti, pre upotrebe u ljudskoj ishrani. U ovom radu predmet istraživanja je bila danska sorta KVL 37, gajena u okolini Beograda. Ispitivan je nutritivi sastav sirovog i oljušćenog semena kvinoje, kao novog useva i mogućnost njegove primene u ishrani riba. Poznato je da viskoka nutritivna vrednost semena kvinoje potiče od sadržaja proteina, različitih minerala i vitamina, i to E vitamin i vitamini B grupe. Prosečan sadržaj proteina varira od 8%-22%, a glavne proteinske frakcije čine albumini i globulini (44-47% ukupnih proteina). U ovom radu sadržaj proteina je varirao od 15,5% do 16,8%, u zavisnosti stepena čistoće semena. Seme poseduje odličano izbalansiran sastav amino kiselina, a izdvajaju se lizin, treonin i metionin, amino kiseline koje su uglavnom deficitarne u biljnim sirovinama. Glavnu komponentu semena kvinoje čine ugljeni hidrati, čiji sadržaj varira od 67% do 74%. Skrob čini oko 52-63%, dok su ostali ugljeni hidrati, kao i sirova vlakna malo zastupljeni. Kvinoja sadrži 2% do 10% lipida, a dokazano je i prisustvo esencijalnih masnih kiselina, kao sto su linolenska, oleinska i palmitinska. Značajan je sadržaj minerala tj. kalcijuma, gvožđa i cinka, ali se njihov sadržaj kvantitativno smanjuje u daljim postupcima ljuštenja, pranja i poliranja semena. U humanoj ishrani saponini i fitinska kiselina predstavljaju glavne nedostatke kvinoje. Ljuštenjem i daljim prečišćavanjem seme kvinoje je našlo primenu u ishrani ljudi kao varivo, hrana za doručak, za kolače za proizvodnju brašna, kao i za ishranu životinja u formi mekinja ili pogača. U našem radu, kod oljuštenog zrna, dokazano je značajno povećanje sadržaja ulja, dok je sadržaj sirovih vlakana i ukupnog pepela smanjen za oko tri-puta. Sadržaj skroba je u očekivanim graničnim vrednostima. U pogledu sadržaja minerala nije bilo većih promena. Imajući u vidu veličinu zrna, laku pripremu, nutrtitivni potencijal, kao i novu kulturu u našoj regiji, pokazano je da seme kvinoje može da nađe primenu i kao hrana za ribe

Eeg-Derived Estimators of Present and Future Cognitive Performance

Previous electroencephalography (EEG)-based fatigue-related research primarily focused on the association between concurrent cognitive performance and time-locked physiology. The goal of this study was to investigate the capability of EEG to assess the impact of fatigue on both present and future cognitive performance during a 20-min sustained attention task, the 3-choice active vigilance task (3CVT), that requires subjects to discriminate one primary target from two secondary non-target geometric shapes. The current study demonstrated the ability of EEG to estimate not only present, but also future cognitive performance, utilizing a single, combined reaction time (RT), and accuracy performance metric. The correlations between observed and estimated performance, for both present and future performance, were strong (up to 0.89 and 0.79, respectively). The models were able to consistently estimate “unacceptable” performance throughout the entire 3CVT, i.e., excessively missed responses and/or slow RTs, while acceptable performance was recognized less accurately later in the task. The developed models were trained on a relatively large dataset (n = 50 subjects) to increase stability. Cross-validation results suggested the models were not over-fitted. This study indicates that EEG can be used to predict gross-performance degradations 5–15 min in advance

The Effect of Partial Root Drying on Antioxidant Activity in Different Agricultural Crops

Partial root drying (PRD) is a new irrigation strategy which applies alternating regimes of irrigation to half the root system while the other half dries out. Many published results showed that PRD may save water without significant effect on yield. The aim of this work was to compare the effects of PRD with full irrigation (FI) on yield and antioxidant activity in grape berry, potato tuber and tomato fruit. in both experimental conditions (field and polytunnel), the soil water content in FI treatment was kept close to field capacity, although in PRD treatment, 70o/o of the irrigation water in FI was uppti"a to one half of the root system, and irrigation was shifted according to soil water content decrease in the dry side of the root zone. At the end of the vegetation season, analyses of total yield of fruit and tubers and their quality were carried out. Antioxidant activity of tomato fruit ethanolic extract was evaluated against 2,2'-azinobis (3-eyhylbenzothiazoline-6-sulfonic acid) radical cation (ABTS'*) and expressed as Trolox (6-hydroxy-2,5,7,8-tetramethylchtoman-2- carboxylic acid) equivalent antioxidant capacity (TEAC)' In general, treatment differences in yield were not significant for either crop although WUE and anti,oxidant activity in the PRD treatments were higher than in the FI treatment for investigated crops. These results for all PRD-treated crops showed that PRD could be a useful strategy to save irrigation water without significantly sacrificing either the quantity or quality of yield

Izbor genotipova paradajza na tolerantnost prema suši

Screening collections aimed to make the selection of genotypes tolerant to drought in the vegetative stage of intensive growth of plants, which would start the program selection to obtain recombinant genotypes according to the abiotic factor. The criteria for screening were the diversity of genotypes for the number of sheets of the first flower branches and number of flower branches into the optimal mode of irrigation regime and reduced by 40 % . On the basis of the analysis genotypes: the number of leaves to the first floral branch G 106, 114, 121 and 122 ( 0 % ), and genotype G102, 114, and 125 for the characteristic number of lateral branches.Paradajz je široko prilagođen različitim podnebljima gajenja, međutim, njegov rast i razviće je prilično osetljiv na različite uslove spoljne sredine, uključujući salinitet, sušu, vlagu, ekstremne temperature, mineralne toksičnosti, kao i zagađenje životne sredine. Postoji ograničenje genetske varijacije za abiotičku toleranciju na stres u okviru kultivisanih vrsta i većina komercijalnih sorti se smatraju umereno do veoma osetljive na različite vrste stresa. Ispitivanje je izvršeno na 11 genotipa paradajza poreklom iz populacije domaćih i odomaćenih genotipova prikupljenih iz Srbije, a pripadaju kolekciji paradajza Instituta za povrtarstvo u Smederevskoj Palanci. Skrining kolekcije imao je za cilj da se izvrši izbor genotipova tolerantnih na sušu u vegetativnoj fazi intenzivnog porasta biljaka, čime bi se započeo program selekcije na dobijanje rekombinovanih genotipova prema ovom abiotskom faktoru. Kriterijumi za skrining bili su divergentnost genotipova za broj listova do prve cvetne grane i broj cvetnih grana u: optimalnom režimu navodnjavanja i redukovanom režimu za 40%. Na osnovu izvedenih analiza izdvojeni su genotipovi: za broj listova do prve cvetne grane G 106, 114, 121 i 122 (0%), kao i genotipovi G102, 114, i 125 za osobinu broja bočnih grana koji će predstavljati bazu za dobijanje rekombinovanih genotipova i početak selekcije na otpornost na sušu

DEFICIT IRRIGATION TECHNIQUE FOR REDUCING WATER USE OF TOMATO UNDER POLYTUNNEL CONDITIONS

The aim of paper was to asses the use of regulated deficit irrigation (RDI) for production of two tomato cultivars (Cedrico and Abellus) in polytunnels in Serbia. RDI plants received 60% of the water that was applied to FI plants and significant saving of water for irrigation and increased in irrigation water use efficiency (IWUE) were achieved. Yield data for Cedrico cultivar showed no differences between RDI and FI, while due to the bigger sensitivity to drought, yield of Abellus was reduced under RDI. In general, fruit quality (soluble solids, titrable acidity) was sustained or improved in both cultivars under RDI. Economic analyses showed that due to the current low prices of water and electricity in Serbia, the profit increase of Cedrico, similarly to the previously trialed cultivar Amati, was not high under RDI comparing to FI. Reduction of yield and consequent profit for Abellus, indicated that for future commercial growing of tomato under RDI should be used drought resistant cultivars

Towards Using Unlabeled Data in a Sparse-coding Framework for Human Activity Recognition

We propose a sparse-coding framework for activity recognition in ubiquitous and mobile computing that alleviates two fundamental problems of current supervised learning approaches. (i) It automatically derives a compact, sparse and meaningful feature representation of sensor data that does not rely on prior expert knowledge and generalizes extremely well across domain boundaries. (ii) It exploits unlabeled sample data for bootstrapping effective activity recognizers, i.e., substantially reduces the amount of ground truth annotation required for model estimation. Such unlabeled data is trivial to obtain, e.g., through contemporary smartphones carried by users as they go about their everyday activities. Based on the self-taught learning paradigm we automatically derive an over-complete set of basis vectors from unlabeled data that captures inherent patterns present within activity data. Through projecting raw sensor data onto the feature space defined by such over-complete sets of basis vectors effective feature extraction is pursued. Given these learned feature representations, classification backends are then trained using small amounts of labeled training data. We study the new approach in detail using two datasets which differ in terms of the recognition tasks and sensor modalities. Primarily we focus on transportation mode analysis task, a popular task in mobile-phone based sensing. The sparse-coding framework significantly outperforms the state-of-the-art in supervised learning approaches. Furthermore, we demonstrate the great practical potential of the new approach by successfully evaluating its generalization capabilities across both domain and sensor modalities by considering the popular Opportunity dataset. Our feature learning approach outperforms state-of-the-art approaches to analyzing activities in daily living.Comment: 18 pages, 12 figures, Pervasive and Mobile Computing, 201

Application of data fusion techniques and technologies for wearable health monitoring

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market