Enhanced anthocyanin production by repeated-batch culture of strawberry cells with medium shift

Repeated-batch cultures of strawberry cells (Fragaria ananassa cv. Shikinari) subjected to four medium-shift procedures (constant LS medium, constant B5 medium, alternation between LS and B5 starting from LS and alternation between LS and B5 starting from B5) were investigated for the enhanced anthocyanin productivity. To determine the optimum period for repeated batch cultures, two medium-shift periods of 9 and 14 days were studied, which represent the end of the exponential growth phase and the stationary phase. By comparison with the corresponding batch cultures, higher anthocyanin productivity was achieved for all the repeated-batch cultures at a 9-day medium-shift period. The average anthocyanin productivity was enhanced 1.7-and 1.76-fold by repeated-batch cultures in constant LS and constant B5 medium at a 9-day shift period for 45 days, respectively. No further improvement was observed when the medium was alternated between LS (the growth medium) and B5 (the production medium). Anthocyanin production was unstable at a 14-day shift period regardless of the medium-shift procedures. The results show that it is feasible to improve anthocyanin production by a repeated-batch culture of strawberry cells.

社会形势预警的几种建模方法比较

Based on social survey data conducted by local research group in some counties executed in the nearly past five years in China, the author proposed and solved two kernel problems in the field of social situation forecasting: i) How can the attitudes’ data on individual level be integrated with social situation data on macrolevel; ii) How can the powers of forecasting models’ constructed by different statistic methods be compared? Five integrative statistics were applied to the research: 1) algorithm average (MEAN); 2) standard deviation (SD); 3) coefficient variability (CV); 4) mixed secondary moment (M2); 5) Tendency (TD). To solve the former problem, the five statistics were taken to synthesize the individual and mocrolevel data of social situations on the levels of counties’ regions, and form novel integrative datasets, from the basis of which, the latter problem was accomplished by the author: modeling methods such as Multiple Regression Analysis (MRA), Discriminant Analysis (DA) and Support Vector Machine (SVM) were used to construct several forecasting models. Meanwhile, on the dimensions of stepwise vs. enter, short-term vs. long-term forecasting and different integrative (statistic) models, meta-analysis and power analysis were taken to compare the predicting power of each model within and among modeling methods. Finally, it can be concluded from the research of the dissertation: 1) Exactly significant difference exists among different integrative (statistic) models, in which, tendency (TD) integrative models have the highest power, but coefficient variability (CV) ones have the lowest; 2) There is no significant difference of the power between stepwise and enter models as well as short-term and long-term forecasting models; 3) There is significant difference among models constructed by different methods, of which, support vector machine (SVM) has the highest statistic power. This research founded basis in all facets for exploring the optimal forecasting models of social situation’s more deeply, further more, it is the first time methods of meta-analysis and power analysis were immersed into the assessments of such forecasting models.

Development of a LCST membrane forming system for cellulose acetate ultrafiltration hollow fiber

A novel lower critical solution temperature (LCST) membrane forming system containing cellulose acetate (CA)/poly (vinyl pyrrolidone) (PVP 3 60K)/N-methyl-2-pyrrolidone (NMP)/1,2-propanediol with a weight ratio of 24.0:5.0:62.6:8.4 had been developed. CA hollow fiber ultrafiltration (UF) membranes were fabricated using the dry-wet spinning technique. The fibers were post-treated with a 200 mg/L hypochlorite solution over a period of 6 It at pH 7. The experimental results showed that water flux of a membrane decreased while retention increased with increasing CA concentration in a dope. It was concluded that the membrane pore size decreased with increasing CA concentration. The membrane fouling tendency for BSA was 3 times higher than that for PVP 24K. (C) 2004 Elsevier B.V. All rights reserved.

A survey of fingerprint classification Part II: experimental analysis and ensemble proposal

In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models.

Teaching Competences and their Development Via Video-Recording

The article deals with use of case studies for professional preparation of teachers to be. One of the suitable ways to develop professional teaching competences is to apply the method of a case study. A case study means a complex and creative solution for a given teaching situation in simulated teaching conditions. It is based on interactive and situational education and decision taking. A case study improves not only professional and teaching competences for becoming teachers – it also fulfi ls the task to develop at students their auto-evaluating and auto-refl exing skills. To increase professional competences it is mandatory to do a complex analysis of the video-record for the implemented study. A complex analysis is a subject of the research project of a student grant agency at the University of West Bohemia in Pilsen.

Regulacja karboksylazy/oksygenazy rybulozo-1,5-bisfosforanu (Rubisco) u Arabidopsis thaliana w odpowiedzi na zmianę natężenia światła z umiarkowanego na niskie

Wydział Biologii

Multiplicative Kernels: Object Detection, Segmentation and Pose Estimation

Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. Detector training can be accomplished via standard SVM learning. The resulting detectors are tuned to specific variations in the foreground class. They also serve to evaluate hypotheses of the foreground state. When the foreground parameters are provided in training, the detectors can also produce parameter estimate. When the foreground object masks are provided in training, the detectors can also produce object segmentation. The advantages of our method over past methods are demonstrated on data sets of human hands and vehicles.

Evaluation of Speaker Normalization Methods for Vowel Recognition Using Fuzzy ARTMAP and K-NN

A procedure that uses fuzzy ARTMAP and K-Nearest Neighbor (K-NN) categorizers to evaluate intrinsic and extrinsic speaker normalization methods is described. Each classifier is trained on preprocessed, or normalized, vowel tokens from about 30% of the speakers of the Peterson-Barney database, then tested on data from the remaining speakers. Intrinsic normalization methods included one nonscaled, four psychophysical scales (bark, bark with end-correction, mel, ERB), and three log scales, each tested on four different combinations of the fundamental (Fo) and the formants (F1 , F2, F3). For each scale and frequency combination, four extrinsic speaker adaptation schemes were tested: centroid subtraction across all frequencies (CS), centroid subtraction for each frequency (CSi), linear scale (LS), and linear transformation (LT). A total of 32 intrinsic and 128 extrinsic methods were thus compared. Fuzzy ARTMAP and K-NN showed similar trends, with K-NN performing somewhat better and fuzzy ARTMAP requiring about 1/10 as much memory. The optimal intrinsic normalization method was bark scale, or bark with end-correction, using the differences between all frequencies (Diff All). The order of performance for the extrinsic methods was LT, CSi, LS, and CS, with fuzzy AHTMAP performing best using bark scale with Diff All; and K-NN choosing psychophysical measures for all except CSi.

Speaker Normalization Methods for Vowel Cognition: Comparative Analysis Using Neural Network and Nearest Neighbor Classifiers

Intrinsic and extrinsic speaker normalization methods are systematically compared using a neural network (fuzzy ARTMAP) and L1 and L2 K-Nearest Neighbor (K-NN) categorizers trained and tested on disjoint sets of speakers of the Peterson-Barney vowel database. Intrinsic methods include one nonscaled, four psychophysical scales (bark, bark with endcorrection, mel, ERB), and three log scales, each tested on four combinations of F0 , F1, F2, F3. Extrinsic methods include four speaker adaptation schemes, each combined with the 32 intrinsic methods: centroid subtraction across all frequencies (CS), centroid subtraction for each frequency (CSi), linear scale (LS), and linear transformation (LT). ARTMAP and KNN show similar trends, with K-NN performing better, but requiring about ten times as much memory. The optimal intrinsic normalization method is bark scale, or bark with endcorrection, using the differences between all frequencies (Diff All). The order of performance for the extrinsic methods is LT, CSi, LS, and CS, with fuzzy ARTMAP performing best using bark scale with Diff All; and K-NN choosing psychophysical measures for all except CSi.

Na marbhnaí do Shéamas Óg Mac Coitir (1689-1720)

Go príomha, is tráchtas é seo a dhéanann staidéar ar ghné de litríocht iar-chlasaiceach na Gaeilge. Baineann sé go háirithe leis an sraith chaointe nó marbhnaí i bhfoirm véarsaíochta a cumadh do Shéamas Óg Mac Coitir (1689-1720), duine uasal Caitliceach ó Charraig Tuathail, Co. Chorcaí, nuair a ciontaíodh é in éigniú Elizabeth Squibb, bean de Chumann na gCarad; nuair a cuireadh pionós an bháis air; agus nuair a crochadh é i gCathair Chorcaí an 7 Bealtaine, 1720. Ó thaobh na staire de, scrúdaítear Clann Choitir mar shampla de theaghlach nár cheil a ndílseacht do chúis pholaitiúil na Stíobhartach agus a sheas an fód go cróga faoi mar a bhí a ngreim polaitiúil á dhaingniú ag an gCinsealacht Phrotastúnach ó dheireadh an 17ú haois amach. Tagraítear do sheicteachas na sochaí comhaimseartha agus don teannas idir an pobal Caitliceach agus an pobal Protastúnach ag an am. Déantar scagadh ar an véarsaíocht mar fhoinse luachmhar do dhearcadh míshásta an mhóraimh Chaitlicigh ar struchtúr polaitiúil chontae Chorcaí (agus na hÉireann) i dtosach an 18ú haois. Is feiniméan liteartha an dlús véarsaíochta seo a bhaineann go háirithe le traidisiún liteartha Chorcaí. Tá na dánta curtha in eagar agus aistriúchán go Béarla curtha ar fáil: is é seo croí an tráchtais. Tá an t-eagrán bunaithe ar scrúdú cuimsitheach ar thraidisiún na lsí; pléitear modheolaíocht na heagarthóireachta. Déantar iarracht ar na dánta a shuíomh sa traidisiún casta liteartha sa tráchtaireacht tosaigh; sa chuid eile den bhfearas scoláiriúil, scrúdaítear ceisteanna a bhaineann le cúrsaí teanga, foclóra, meadarachta agus stíle. Tá innéacsanna agus liosta foinsí le fáil i ndeireadh an tráchtais.

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

The dopamine metabolite 3-methoxytyramine is a neuromodulator.

Dopamine (3-hydroxytyramine) is a well-known catecholamine neurotransmitter involved in multiple physiological functions including movement control. Here we report that the major extracellular metabolite of dopamine, 3-methoxytyramine (3-MT), can induce behavioral effects in a dopamine-independent manner and these effects are partially mediated by the trace amine associated receptor 1 (TAAR1). Unbiased in vivo screening of putative trace amine receptor ligands for potential effects on the movement control revealed that 3-MT infused in the brain is able to induce a complex set of abnormal involuntary movements in mice acutely depleted of dopamine. In normal mice, the central administration of 3-MT caused a temporary mild hyperactivity with a concomitant set of abnormal movements. Furthermore, 3-MT induced significant ERK and CREB phosphorylation in the mouse striatum, signaling events generally related to PKA-mediated cAMP accumulation. In mice lacking TAAR1, both behavioral and signaling effects of 3-MT were partially attenuated, consistent with the ability of 3-MT to activate TAAR1 receptors and cause cAMP accumulation as well as ERK and CREB phosphorylation in cellular assays. Thus, 3-MT is not just an inactive metabolite of DA, but a novel neuromodulator that in certain situations may be involved in movement control. Further characterization of the physiological functions mediated by 3-MT may advance understanding of the pathophysiology and pharmacology of brain disorders involving abnormal dopaminergic transmission, such as Parkinson's disease, dyskinesia and schizophrenia.

Indication of electron neutrino appearance from an accelerator-produced off-axis muon neutrino beam.

The T2K experiment observes indications of ν(μ) → ν(e) appearance in data accumulated with 1.43×10(20) protons on target. Six events pass all selection criteria at the far detector. In a three-flavor neutrino oscillation scenario with |Δm(23)(2)| = 2.4×10(-3)  eV(2), sin(2)2θ(23) = 1 and sin(2)2θ(13) = 0, the expected number of such events is 1.5±0.3(syst). Under this hypothesis, the probability to observe six or more candidate events is 7×10(-3), equivalent to 2.5σ significance. At 90% C.L., the data are consistent with 0.03(0.04) < sin(2)2θ(13) < 0.28(0.34) for δ(CP) = 0 and a normal (inverted) hierarchy.

A C-terminal motif found in the beta2-adrenergic receptor, P2Y1 receptor and cystic fibrosis transmembrane conductance regulator determines binding to the Na+/H+ exchanger regulatory factor family of PDZ proteins.

The Na+/H+ exchanger regulatory factor (NHERF) binds to the tail of the beta2-adrenergic receptor and plays a role in adrenergic regulation of Na+/H+ exchange. NHERF contains two PDZ domains, the first of which is required for its interaction with the beta2 receptor. Mutagenesis studies of the beta2 receptor tail revealed that the optimal C-terminal motif for binding to the first PDZ domain of NHERF is D-S/T-x-L, a motif distinct from those recognized by other PDZ domains. The first PDZ domain of NHERF-2, a protein that is 52% identical to NHERF and also known as E3KARP, SIP-1, and TKA-1, exhibits binding preferences very similar to those of the first PDZ domain of NHERF. The delineation of the preferred binding motif for the first PDZ domain of the NHERF family of proteins allows for predictions for other proteins that may interact with NHERF or NHERF-2. For example, as would be predicted from the beta2 receptor tail mutagenesis studies, NHERF binds to the tail of the purinergic P2Y1 receptor, a seven-transmembrane receptor with an intracellular C-terminal tail ending in D-T-S-L. NHERF also binds to the tail of the cystic fibrosis transmembrane conductance regulator, which ends in D-T-R-L. Because the preferred binding motif of the first PDZ domain of the NHERF family of proteins is found at the C termini of a variety of intracellular proteins, NHERF and NHERF-2 may be multifunctional adaptor proteins involved in many previously unsuspected aspects of intracellular signaling.