Kuhu suundub püsivus? Verbidega jääma ja jätma väljendatud sündmuste kujutamine katses

Kokkuvõte. Uurimus põhineb autorite välja töötatud joonistamise ja häälega mõtlemise katse tulemustel. Katses paluti katseisikutel (21 eesti keelt emakeelena kõnelejat) kujutada ja selgitada katse läbiviijale 24 verbi suunda (näiteks nooltega). Selles artiklis esitame täpsemalt neist kahe – jääma ja jätma – analüüsi. Verb jätma on jääma kausatiivtuletis. See mõjutab ka nende tähenduserinevust – jääma on passiivsem, jätma aga aktiivsem ja agenti rõhutav. Katseisikute kujutiste ja selgituste põhjal kirjeldame, milline oli noolte suund kujutistel ja millistest elementidest koosneb mõlema uuritava verbi skeem. Selgus, et katseisikud kujutasid verbi jätma suunda nooltega, verbi jääma oli kujutatud pigem punktide, täppide, ringide või kaarjate joontega. Kujutistel ja selgitustes avaldusid ka tüüpilised skeemi osalised: JÄÄJA, JÄTJA ja JÄETU. Mõlema verbi tegevuse aega kujutati ja selgitati minevikulisusega. Sarnane on ka nende verbide mõistemetafoorsus: mõlemad kajastavad pigem negatiivset hinnangut. Verbi jääma tähenduses on olulisel kohal see, et potentsiaalne muutus ei toimu. Verb jätma väljendab aga seda, et potentsiaalse muutuse mittetoimumine põhjustatakse. Abstract. Ilona Tragel, Jane Klavan: The direction and participants of the events expressed by the verbs jääma and jätma: a drawing experiment. We use an innovative experimental design to extract the regularities of the general conceptual structure from the speakers’ mind: a drawing task with a thinkaloud protocol. 21 native speakers of Estonian provided schematic representations of 20 experimental verbs and 4 control verbs. Our discussion focuses on jääma ‘stay, remain’ and jätma ‘leave something somewhere’. jääma typically expresses intransitive events and jätma transitive events. We zoom in on the following topics: transitivity and causativity, the positive/negative evaluation of the activity, the schematic representation of the direction of verbs and the elements belonging to the verb schema. Our study shows that the differences in the transitivity of the two verbs are reflected in the drawings and explanations given by the participants. Our results confirm the general prediction that abstract verbs have an image-schematic direction, but the specifics of the direction vary according to the type of verb.

A long post-reproductive lifespan is a shared trait among genetically distinct killer whale populations

The extended female post-reproductive lifespan found in humans and some toothed whales remains an evolutionary puzzle. Theory predicts demographic patterns resulting in increased female relatedness with age (kinship dynamics) can select for a prolonged post-reproductive lifespan due to the combined costs of inter-generational reproductive conflict and benefits of late-life helping. Here we test this prediction using >40 years of longitudinal demographic data from the sympatric yet genetically distinct killer whale ecotypes: resident and Bigg’s killer whales. The female relatedness with age is predicted to increase in both ecotypes, but with a less steep increase in Bigg’s due to their different social structure. Here, we show that there is a significant post-reproductive lifespan in both ecotypes with >30% of adult female years being lived as post-reproductive, supporting the general prediction that an increase in local relatedness with age predisposes the evolution of a post-reproductive lifespan. Differences in the magnitude of kinship dynamics however, did not influence the timing or duration of the post-reproductive lifespan with females in both ecotypes terminating reproduction before their mid-40s followed by an expected post-reproductive period of ~20 years. Our results highlight the important role of kinship dynamics in the evolution of a long post-reproductive lifespan in long-lived mammals, while further implying that the timing of menopause may be a robust trait that is persistent despite substantial variation in demographic patterns among population.

PSVII-8 Equations for prediction of empty body weight of water buffaloes finished in feedlot

This study aimed to develop equations to predict the empty body weight (EBW) using the shrunk body weight (SBW) of water buffaloes of three genetic groups (GG:Jafarabadi, Mediterranean, and Murrah; n = 65 for each GG), considering possible variations among GG. One-hundred-ninety-five non-castrated males (390±32 days of age; 327±51.96 kg of initial body weight - BW) from two years of similar experiments were used. Animals of each GG were allocated in collective pens for 28 days of adaptation period. Diet and water were offered ad libitum. Animal SBW were recorded at the beginning and every 28 days until the averages SBW reached the values determined (420, 480, and 540kg of SBW). After slaughter and to obtain the EBW, the non-carcass components of each animal [blood, paws, head, leather, tail, gastrointestinal tract (GIT), liver, kidneys, internal fat, and other internal organs] were weighed. The full GIT of each animal was weighed to obtain the total weight, and then emptied, washed, drained, and weighed to obtain the weight of the GIT content. Initially, data were analyzed using UNIVARIATE procedure in SAS. SBW recorded prior to slaughter were categorized according to SBW proposed considering the coefficient of variation below 10% for each GG to increase the precision of the data used, which decreased the initial n to 104 animals. Equations were developed and tested for GG effect using GLM and REG procedures in SAS. Tendency of GG effect was detected (P = 0.06). Thus, different prediction equations were determined for each GG, and a general prediction equation was developed for the three GG (Table 1). In conclusion, the results suggest it is possible to use distinct equations to predict the EBW according to GG as well as a general equation can be also used, resulting in high predictions of EBW of water buffaloes finished in feedlot.

Climate, species richness, and body size drive geographical variation in resource specialization of herbivorous butterflies

Revealing drivers of variation in resource specialization is a long-standing goal in ecological and evolutionary research. As a general prediction, the degree of resource specialization increases towards lower latitudes. Although herbivorous insects are one of the best-studied consumer groups, factors determining the degree of specialization on large spatial scales are poorly understood. Herein, we focused on the fundamental host breadth of 246 herbivorous butterfly species distributed across the Japanese archipelago. Using Bayesian Structural Equation Modeling based on information of pooled geographical occurrence and host use, we show that local butterfly communities tend to become more specialized towards higher latitudes, a pattern that is opposite to predictions from classical hypotheses. We also found that the pattern is mainly driven by factors related to climate, butterfly diversity, and body size in each community. Our results re-emphasize the importance of current climate as a regulating factor for butterfly host breadth and morphology.

Estimating the voluntary intake by sheep of tropical grasses from digestibility, regrowth-age and leaf content: a meta-analysis

Context The voluntary intake (VI) of forages by ruminants is usually estimated from diet DM digestibility (DMD), but may be related also to the age of regrowth and the leaf blade content (Leaf) of the forage. Aim To examine the reliability of the prediction of the VI of tropical grasses by sheep from the DMD, Leaf and Regrowth-age characteristics of the forage. Methods Data from eight experiments with mature sheep fed tropical grass hay diets (n = 229) were used to explore prediction of VI of DM (VIDM), digestible DM (DDM) (VIDDM) and estimated metabolisable energy from the DMD, Leaf and Regrowth-age of the forage. Key results The variables were generally correlated. In data pooled across experiments the VI (g/kg W0.75.day) of DM was poorly correlated with DMD, Leaf or Regrowth-age (r = 0.30–0.52). The regressions between VI and each of the variables differed among experiments in elevation (P < 0.001) but generally not in slope. When ‘experiment’ was included as a factor the VIDM (g/kg W0.75.day) = K + 0.0912 × DMD (R2 0.80; r.s.d. 6.8; K range –26.0 to +7.8; P < 0.001). Also VIDM = K + 0.069 × DMD + 0.020 × Leaf (R2 0.88; r.s.d. 5.4; DMD and Leaf, P < 0.001); thus inclusion of Leaf reduced the r.s.d. while K ranged widely (–20.5 to +12.0). The voluntary intake of digestible DM (VIDDM) = K + 0.081 × DMD + 0.011 × Leaf (R2 0.89; r.s.d. 3.2; DMD and Leaf P < 0.001; K range –35.0 to –16.3). Regrowth-age was correlated with both Leaf and DMD, and VIDM was predicted by Regrowth-age or Leaf with comparable error. Because numerous factors alter the composition of grasses at a specific Regrowth-age the DMD should be a more generally suitable variable to predict intakes of forage DM and DDM. Conclusions The estimation of the VI of ruminants ingesting tropical grass forages can be improved if the diet Leaf is included with diet DMD as a predictor. However, the general prediction of VI of sheep may involve large errors. Implications Knowledge of the leaf content as well as the digestibility should improve estimation of VI of tropical grasses by ruminants.

Prediction of liquid-liquid phase separation proteins using machine learning

The liquid-liquid phase separation (LLPS) of bio-molecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular functions. The dysregulation of LLPS might be implicated in a number of diseases. Although the LLPS of biomolecules has been investigated intensively in recent years, the knowledge of the prevalence and distribution of phase separation proteins (PSPs) is still lag behind. Development of computational methods to predict PSPs is therefore of great importance for comprehensive understanding of the biological function of LLPS. Here, a sequence-based prediction tool using machine learning for LLPS proteins (PSPredictor) was developed. Our model can achieve a maximum 10-CV accuracy of 96.03%, and performs much better in identifying new PSPs than reported PSP prediction tools. As far as we know, this is the first attempt to make a direct and more general prediction on LLPS proteins only based on sequence information.

Recommendation of Attributes for Heart Disease Prediction using Correlation Measure

Heart diseases are the major cause for human mortality rate. Correct diagnosis and treatment at an early stage will save people from heart disease and will decrease mortality rate due to heart problem. Since ten years various data mining techniques have been used to facilitate the prediction of heart diseases .In general prediction algorithms for trained with huge, known dataset to arrive at a classifier which then predicts the diseases for unknown data with the help of classifying attributes. These attributes also called as features. In this work relevant features are determined for heart disease prediction with known dataset using correlation measures. The results are presented.