Long non-coding RNAs (lncRNAs) play an important role in serval biological activities, including transcription, splicing, translation, and some other cellular regulation processes. lncRNAs perform ...their biological functions by interacting with various proteins. The studies on lncRNA-protein interactions are of great value to the understanding of lncRNA functional mechanisms. In this paper, we proposed a novel model to predict potential lncRNA-protein interactions using the SKF (similarity kernel fusion) and LapRLS (Laplacian regularized least squares) algorithms. We named this method the LPI-SKF. Various similarities of both lncRNAs and proteins were integrated into the LPI-SKF. LPI-SKF can be applied in predicting potential interactions involving novel proteins or lncRNAs. We obtained an AUROC (area under receiver operating curve) of 0.909 in a 5-fold cross-validation, which outperforms other state-of-the-art methods. A total of 19 out of the top 20 ranked interaction predictions were verified by existing data, which implied that the LPI-SKF had great potential in discovering unknown lncRNA-protein interactions accurately. All data and codes of this work can be downloaded from a GitHub repository (https://github.com/zyk2118216069/LPI-SKF).
This paper aims to analyze the English adjective "heavy" and its corresponding word in Chinese, "zhong", using the framework of lexical typology as suggested by François (2008). Through this article, ...we gain a comprehensive understanding of the various semantic meanings associated with "heavy" in English. These semantic meanings are derived from empirical observations and functional properties. Moreover, we compare these meanings with their corresponding counterparts in Chinese, revealing both similarities and differences with the word "zhong". In the English language, "heavy" is connected to several senses, as defined by the Oxford Dictionary. It can refer to something weighing a lot, being worse than usual, not delicate when modifying, being thick as a material, being full of something, being large and powerful when modifying machines, being busy, or being involved in physically demanding work (heavy digging/lifting). Additionally, "heavy" can modify actions like "fall" or "hit", describe a substantial amount of food, indicate excessive use, modify sounds, imply seriousness or difficulty, describe large bodies of water (e.g., sea/ocean), and pertain to weather conditions, air, and soil. Furthermore, it can connote strictness in certain contexts. Several of these meanings find parallels in the Chinese word "zhong", such as referring to something that weighs a lot, modifying machines to signify size and power, relating to physically demanding work (e.g., heavy digging/lifting), describing the fall or impact of objects, indicating a substantial amount of food, or denoting seriousness or difficulty. Moreover, both "heavy" and "zhong" share the function of modifying actions related to drinking, smoking, or sleeping. However, it is important to note that in Chinese, alternative words like "chen" or "si" can also be used to express similar ideas.
Across five experiments (four pre-registered,
= 4,431), we investigate whether emphasizing similarities between Republicans and Democrats can improve intergroup relations between the two groups. ...Members of both groups who were presented with evidence emphasizing similarities rather than differences in the psychological attitudes of both parties reported greater inclusion of the political opposition in the self, greater belief that common ground can be reached for major social issues, and warmer feelings toward the opposition. Inclusion of the political outgroup in the self mediated the effect of the similarities condition on additional outcomes, relating to more positive and less threatening perceptions of political opposition members. These findings held even when compared with a baseline condition with no information presented to participants. We conclude that by emphasizing the study of group similarities and by disseminating research in a way that highlights similarities, researchers could reduce intergroup hostilities in the political domain.
In this article, the authors present two laboratory experiments testing a group-level perspective on the role of empathy in helping. Experiment 1 tested the authors' predictions in an intercultural ...context of helping. Confirming their specific Empathy × Group Membership moderation hypothesis, empathy had a stronger effect on helping intentions when the helper and the target belonged to the same cultural group than when they belonged to different groups. Experiment 2 replicated these findings in a modified minimal group paradigm using laboratory-created groups. Moreover, this second experiment also provides evidence for the hypothesized psychological mechanisms underlying the empathy-(ingroup) helping relationship. Specifically, analyses in the ingroup condition confirmed that the strength of the empathy-(ingroup) helping relationship systematically varied as a function of perceived similarities among ingroup members. The general implications of these findings for empathy-motivated helping are discussed.
According to statistical forecasts in modern societies is trend of mental disorders growth, especially anxiety and depression. At the same time there will be probably present higher demands for ...professional psychosocial support. Psychotherapy and counselling as talking interventions are part of the psychosocial support, needed when people can’t resolve their (mental) problems by themselves. The article deals with the question about differneces between those two professions. Are those differences artificial, is there any argumented facts in favour of different proponents? For this purpose we have examined different definitions of counselling and psychotherapy, training standards, techniques and skills, ethical standards, treatment outcomes, depth of treatment and clients expiriences.
This paper addresses recognition of human actions under view changes. We explore self-similarities of action sequences over time and observe the striking stability of such measures across views. ...Building upon this key observation, we develop an action descriptor that captures the structure of temporal similarities and dissimilarities within an action sequence. Despite this temporal self-similarity descriptor not being strictly view-invariant, we provide intuition and experimental validation demonstrating its high stability under view changes. Self-similarity descriptors are also shown to be stable under performance variations within a class of actions when individual speed fluctuations are ignored. If required, such fluctuations between two different instances of the same action class can be explicitly recovered with dynamic time warping, as will be demonstrated, to achieve cross-view action synchronization. More central to the current work, temporal ordering of local self-similarity descriptors can simply be ignored within a bag-of-features type of approach. Sufficient action discrimination is still retained in this way to build a view-independent action recognition system. Interestingly, self-similarities computed from different image features possess similar properties and can be used in a complementary fashion. Our method is simple and requires neither structure recovery nor multiview correspondence estimation. Instead, it relies on weak geometric properties and combines them with machine learning for efficient cross-view action recognition. The method is validated on three public data sets. It has similar or superior performance compared to related methods and it performs well even in extreme conditions, such as when recognizing actions from top views while using side views only for training.
Spatial soil information is essential for informed decision-making in a wide range of fields. Digital soil mapping (DSM) using machine learning algorithms has become a popular approach for generating ...soil maps. DSM capitalises on the relation between environmental variables (i.e., features) and a soil property of interest. It typically needs a training dataset that covers the feature space well. Mapping in areas where there are no training data is challenging, because extrapolation in geographic space often induces extrapolation in feature space and can seriously deteriorate prediction accuracy. The objective of this study was to analyse the extrapolation effects of random forest DSM models by predicting topsoil properties (OC, clay, and pH) in four African countries using soil data from the ISRIC Africa Soil Profiles database. The study was conducted in eight experiments whereby soil data from one or three countries were used to predict in the other countries. We calculated similarities between donor and recipient areas using four measures, including soil type similarity, homosoil, dissimilarity index by area of applicability (AOA), and quantile regression forest (QRF) prediction interval width. The aim was to determine the level of agreement between these four measures and identify the method that had the strongest agreement with common validation metrics. The results indicated a positive correlation between soil type similarity, homosoil and dissimilarity index by AOA. Surprisingly, we observed a negative correlation between dissimilarity index by AOA and QRF prediction interval width. Although the cross-validation results for the trained models were acceptable, the extrapolation results were unsatisfactory, highlighting the risk of extrapolation. Using soil data from three countries instead of one increased the similarities for all measures, but it had a limited effect on improving extrapolation. Also, none of the measures had a strong correlation with the validation metrics. This was particularly disappointing for AOA and QRF, which we had expected to be strong indicators of extrapolation prediction performance. Results showed that homosoil and soil type methods had the strongest correlation with validation metrics. The results for this case study revealed limitations of using AOA and QRF as measures of extrapolation effects, highlighting the importance of not relying on these methods blindly. Further research and more case studies are needed to address the effects of extrapolation of DSM models.
•Spatial extrapolation of DSM models substantially deteriorates prediction accuracy.•Weak correlation was found between the AOA dissimilarity index and DSM prediction accuracy.•Weak correlation was found between QRF prediction interval width and DSM prediction accuracy.•AOA dissimilarity index and QRF prediction interval were poorly correlated.•Soil type similarity and homosoil were better indicators of DSM extrapolation potential.
Fungal diseases are the common cause of death in wild animals and birds of prey. This study was designed to investigate the development of fungal infections among wild birds in Denmark. In this ...study, fungal samples were isolated from such sources as Barn swallows' feathers, White stork, and birds of prey. The fungal species were isolated by direct culture of feathers on SD Agar with chloramphenicol and incubated at 28±2ºC. The fungal genomic DNA was isolated from each species, PCR reaction was performed, and the resulting fragments of the 18S rRNA DNA were sequenced and used for identification. A comparison between the resulting fragments was made to find out the percentage of similarity among the different fungal species. The multiple sequence alignment showed percentages of similarities ranging from 39% to 99%. To sum up, the 18S rRNA DNA sequence has been evolved dramatically even within the same species, while still conserved in others. It is a useful tool to be used for the identification of fungal species as it reduces time. Moreover, according to the results, there were no comprehensive high homology percentages among the species infecting the same bird.
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