Predicting Pseudogene–miRNA Associations Based on Feature Fusion and Graph Auto-Encoder

Pseudogenes were originally regarded as non-functional components scattered in the genome during evolution. Recent studies have shown that pseudogenes can be transcribed into long non-coding RNA and play a key role at multiple functional levels in different physiological and pathological processes. microRNAs (miRNAs) are a type of non-coding RNA, which plays important regulatory roles in cells. Numerous studies have shown that pseudogenes and miRNAs have interactions and form a ceRNA network with mRNA to regulate biological processes and involve diseases. Exploring the associations of pseudogenes and miRNAs will facilitate the clinical diagnosis of some diseases. Here, we propose a prediction model PMGAE (Pseudogene–MiRNA association prediction based on the Graph Auto-Encoder), which incorporates feature fusion, graph auto-encoder (GAE), and eXtreme Gradient Boosting (XGBoost). First, we calculated three types of similarities including Jaccard similarity, cosine similarity, and Pearson similarity between nodes based on the biological characteristics of pseudogenes and miRNAs. Subsequently, we fused the above similarities to construct a similarity profile as the initial representation features for nodes. Then, we aggregated the similarity profiles and associations of nodes to obtain the low-dimensional representation vector of nodes through a GAE. In the last step, we fed these representation vectors into an XGBoost classifier to predict new pseudogene–miRNA associations (PMAs). The results of five-fold cross validation show that PMGAE achieves a mean AUC of 0.8634 and mean AUPR of 0.8966. Case studies further substantiated the reliability of PMGAE for mining PMAs and the study of endogenous RNA networks in relation to diseases.

Repetition Versus Variability in Verb Learning: Sometimes Less Is More

Purpose This study examined whether 2-year-olds are better able to acquire novel verb meanings when they appear in varying linguistic contexts, including both content nouns and pronouns, as compared to when the contexts are consistent, including only content nouns. Additionally, differences between typically developing toddlers and late talkers were explored. Method Forty-seven English-acquiring 2-year-olds ( n = 14 late talkers, n = 33 typically developing) saw scenes of actors manipulating objects. These actions were labeled with novel verbs. In the varied condition, children heard sentences containing both content nouns and pronouns (e.g., “The girl is ziffing the truck. She is ziffing it!”). In the consistent condition, children heard the verb an equal number of times, but only with content nouns (e.g., “The girl is ziffing the truck. The girl is ziffing the truck!”). At test, children were shown two new scenes and were asked to find the novel verb's referent. Children's eye gaze was analyzed as a measure of learning. Results Mixed-effects regression analyses revealed that children looked more toward the correct scene in the consistent condition than the varied condition. This difference was more pronounced for late talkers than for typically developing children. Conclusion To acquire an initial representation of a new verb's meaning, children, particularly late talkers, benefit more from hearing the verb in consistent linguistic contexts than in varying contexts.

Multimorbidity and clinical reasoning through the eyes of GPs: a qualitative study

ObjectivesDespite the high prevalence of patients suffering from multimorbidity, the clinical reasoning processes involved during the longitudinal management are still sparse.This study aimed to investigate what are the different characteristics of the clinical reasoning process clinicians use with patients suffering from multimorbidity, and to what extent this clinical reasoning differs from diagnostic reasoning.DesignGiven the exploratory nature of this study and the difficulty general practitioners (GPs) have in expressing their reasoning, a qualitative methodology was therefore, chosen. The Clinical reasoning Model described by Charlin et al was used as a framework to describe the multifaceted processes of the clinical reasoning.SettingSemistructured interviews were conducted with nine GPs working in an ambulatory setting in June to September 2018, in Geneva, Switzerland.ParticipantsParticipants were GPs who came from public hospital or private practice. The interviews were transcribed verbatim and a thematic analysis was conducted.ResultsThe results highlighted how some cognitive processes seem to be more specific to the management reasoning.Thus, the main goal is not to reach a diagnosis, but rather to consider several possibilities in order to maintain a balance between the evidence-based care options, patient’s priorities and maintaining quality of life. The initial representation of the current problem seems to be more related to the importance of establishing links between the different pre-existing diseases, identifying opportunities for actions and trying to integrate the new elements from the patient’s context, rather than identifying the signs and symptoms that can lead to generating new clinical hypotheses. The multiplicity of options to resolve problems is often perceived as difficult by GPs. Furthermore, longitudinal management does not allow them to achieve a final resolution of problems and that requires continuous review and an ongoing prioritisation process.ConclusionThis study contributes to a better understanding of the clinical reasoning processes of GPs in the longitudinal management of patients suffering from multimorbidity. Through a practical and accessible model, this qualitative study offers new perspectives for identifying the components of management reasoning. These results open the path to new research projects.

Survey of Digital Tools for the Generation of Ideas

During the concept phase of the industrial design process drawings are used to represent designer’s ideas. More specifically, the designer’s goal is to put the characteristics of ideas on paper so that they can later act as pivotal points in the development of a project. Sketching is also the ideal tool to continue developing an idea: because it is imprecise, the sketch guarantees a high degree of freedom, allowing for changes to made and new ideas to be added. Another possibility is to translate ideas into sketches on computer tools. This approach can allow the designer to use the created 3D model as the basis for further developing ideas. At the present moment, however, this type of solution is not extensively used by designers during the concept phase. Some researchers have identified technical problems as the reason why these instruments have been unsuccessful on the market, while for others this is related to systems still too rigid to be adapted to the often-diverse needs of designers. The research presented in this position paper aims at analyzing what has so far been understood with respect to the process of generating ideas, their initial representation in the concept phase and the tools that have been developed so far to support this phase. Consequently, a discussion on these themes and some hypotheses from which develop new research lines will be presented.

Detailed 3D Fault Representations for the 2019 Ridgecrest, California, Earthquake Sequence

ABSTRACT We present new 3D source fault representations for the 2019 M 6.4 and M 7.1 Ridgecrest earthquake sequence. These representations are based on relocated hypocenter catalogs expanded by template matching and focal mechanisms for M 4 and larger events. Following the approach of Riesner et al. (2017), we generate reproducible 3D fault geometries by integrating hypocenter, nodal plane, and surface rupture trace constraints. We used the southwest–northeast-striking nodal plane of the 4 July 2019 M 6.4 event to constrain the initial representation of the southern Little Lake fault (SLLF), both in terms of location and orientation. The eastern Little Lake fault (ELLF) was constrained by the 5 July 2019 M 7.1 hypocenter and nodal planes of M 4 and larger aftershocks aligned with the main trend of the fault. The approach follows a defined workflow that assigns weights to a variety of geometric constraints. These main constraints have a high weight relative to that of individual hypocenters, ensuring that small aftershocks are applied as weaker constraints. The resulting fault planes can be considered averages of the hypocentral locations respecting nodal plane orientations. For the final representation we added detailed, field-mapped rupture traces as strong constraints. The resulting fault representations are generally smooth but nonplanar and dip steeply. The SLLF and ELLF intersect at nearly right angles and cross on another. The ELLF representation is truncated at the Airport Lake fault to the north and the Garlock fault to the south, consistent with the aftershock pattern. The terminations of the SLLF representation are controlled by aftershock distribution. These new 3D fault representations are available as triangulated surface representations, and are being added to a Community Fault Model (CFM; Plesch et al., 2007, 2019; Nicholson et al., 2019) for wider use and to derived products such as a CFM trace map and viewer (Su et al., 2019).

Fragmentation of the Thinking Structure of Translation in Solving Mathematical Modelling Problems

Fragmentation of the thinking structure is the process of construction of information in the brain that is inefficient, incomplete, and not interconnected, and hinders the process of mathematical problem solving. In solving mathematical modeling problems, students need to do translation thinking which is useful for changing the initial representation (source representation) into a new representation (target representation). This study aims to discover how the occurrence of the fragmentation of the thinking structure of translation within students in their solving of mathematical modeling problems. The method used is descriptive qualitative with the instrument in the form of one question for the mathematical modeling of necklace pendants and semi-structured interview sheets. The results showed that there were three errors that occurred in solving mathematical modeling problems. First, the error in changing a verbal representation to a graph. Secondly, errors in changing a graphical representation to symbols (algebraic form). Thirdly, errors in changing graphical representation and symbols into mathematical models. The three errors that occur are described based on the four categories of Bosse frameworks (Bosse, et al., 2014), namely: (1) unpacking the source (UtS), (2) preliminary coordination (PC), (3) constructing the target (CtT), and (4) determining equivalence (DE). In this study, there were 3 subjects who experienced fragmentation of the thinking structure in solving mathematical modeling problems. One of the highlights is the fragmentation of the structure of translation thinking often starts from the process of unpacking of the source due to the incompleteness of considering all the available source details.

DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System

In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it’s almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning-based CF methods try to map users and items into a common representation space. In this case, the higher similarity between a user and an item in that space implies they match better. Matching function learning-based CF methods try to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i.e., the limited expressiveness of dot product and the weakness in capturing low-rank relations respectively. To this end, we propose a general framework named DeepCF, short for Deep Collaborative Filtering, to combine the strengths of the two types of methods and overcome such flaws. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DeepCF framework.

The effect of the digital core image resolution on permeability

Digital core analysis using three-dimensional tomographic images of the internal structure of porous media has received significant development in recent years. Three-dimensional images of the core obtained with the help of x-ray computer tomography can be used to calculate the filtration properties of rocks. However, the question of the influence of the resolution quality of the three-dimensional core image on the simulation results still remains unanswered. This paper studies the influence of the resolution of the three-dimensional image of the core on the calculated absolute permeability in the case of a model porous medium consisting of axisymmetric conical constrictions of different sizes. Based on the initial representation of the model porous medium, several models with different discretization steps were generated, which correspond to images taken with different resolution. The results show that the resolution (the degree of discretization) significantly affects the calculated absolute permeability of the porous medium. The calculated permeability decreases with increasing sampling step. This is because the small channels are not visible at lower resolutions. Elimination of these channels leads to loss of connectivity of the model.

Cognition, infant

In the past thirty years developmental psychologists have developed techniques for investigating the cognitive resources of infants. These techniques show that an infant’s initial representation of the world is richer and more abstract than traditional empiricists supposed. For instance, infants seem to have at least some understanding of distance, of the continued existence of objects which are out of sight, and of the mental states of others. Such results have led philosophers to reconsider the idea – to be found in Plato – that there may be innate constraints on the way we view the world, and to examine the extent to which innate ‘knowledge’ may be revised as a result of learning.

Analysis of the offer and demand for the transportation of people from a settlement in the city of Skopje

Transportation planning is a complex task and a major challenge that plays primary role in the development of the economic processes in the exchange of people and goods. There are several approaches from which transport can be analysed. As an economic activity transport is analysed in terms of offer and demand. The offer of transport is an offer of given capacity which can transported from one place to another. The offer analysis is made in relation to the demand for transport which is made by the individuals that travel or the firms and industrial facilities that want to transport goods. An initial representation of the theoretical approach in the analysis of the transport of people through the description of a specific case is shown in this paper.