Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Use of Phenomics for Differentiation of Mungbean (Vigna radiata L. Wilczek) Genotypes Varying in Growth Rates Per Unit of Water.

In Frontiers in plant science

In the human diet, particularly for most of the vegetarian population, mungbean (Vigna radiata L. Wilczek) is an inexpensive and environmentally friendly source of protein. Being a short-duration crop, mungbean fits well into different cropping systems dominated by staple food crops such as rice and wheat. Hence, knowing the growth and production pattern of this important legume under various soil moisture conditions gains paramount significance. Toward that end, 24 elite mungbean genotypes were grown with and without water stress for 25 days in a controlled environment. Top view and side view (two) images of all genotypes captured by a high-resolution camera installed in the high-throughput phenomics were analyzed to extract the pertinent parameters associated with plant features. We tested eight different multivariate models employing machine learning algorithms to predict fresh biomass from different features extracted from the images of diverse genotypes in the presence and absence of soil moisture stress. Based on the mean absolute error (MAE), root mean square error (RMSE), and R squared (R 2) values, which are used to assess the precision of a model, the partial least square (PLS) method among the eight models was selected for the prediction of biomass. The predicted biomass was used to compute the plant growth rates and water-use indices, which were found to be highly promising surrogate traits as they could differentiate the response of genotypes to soil moisture stress more effectively. To the best of our knowledge, this is perhaps the first report stating the use of a phenomics method as a promising tool for assessing growth rates and also the productive use of water in mungbean crop.

Rane Jagadish, Raina Susheel Kumar, Govindasamy Venkadasamy, Bindumadhava Hanumantharao, Hanjagi Prashantkumar, Giri Rajkumar, Jangid Krishna Kumar, Kumar Mahesh, Nair Ramakrishnan M


drought, growth rate, high throughput phenotyping, mungbean [Vigna radiata (L.) Wilczek], plant phenomics, soil moisture stress, water use index

General General

Perceived Teacher Autonomy Support and Students' Deep Learning: The Mediating Role of Self-Efficacy and the Moderating Role of Perceived Peer Support.

In Frontiers in psychology ; h5-index 92.0

The purpose of this research is to test the mediation effect of self-efficacy on college student's perception of teacher autonomy support and students' deep learning, and whether the peer support perceived by students can moderate the relationship between perceived teacher autonomy support and deep learning. A survey of 1,800 college students from a provincial undergraduate normal university in Guizhou Province in China was conducted through the revised Perceived Teacher Autonomy Support Scale, Deep Learning Scale, Self-Efficacy Scale, and Perceived Peer Support Scale (Mean age = 21 years old, SD = 1.34). Data use SPSS23.0, AMOS22.0 for descriptive analysis and correlation analysis, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), moderation effect, and mediation effect analysis. The research results show that after controlling for gender, major, and grade, self-efficacy partially moderates the connection between perceived teacher autonomy support and deep learning of college students. Moreover, perceived peer support mediates the relationship between perceived teacher autonomy support and students' self-efficacy.

Zhao Jingxian, Qin Yue


deep learning, perceived peer support, perceived teacher autonomy support, self-determination theory, self-efficacy

General General

Accurate Prediction of Children's ADHD Severity Using Family Burden Information: A Neural Lasso Approach.

In Frontiers in computational neuroscience

The deep lasso algorithm (dlasso) is introduced as a neural version of the statistical linear lasso algorithm that holds benefits from both methodologies: feature selection and automatic optimization of the parameters (including the regularization parameter). This last property makes dlasso particularly attractive for feature selection on small samples. In the two first conducted experiments, it was observed that dlasso is capable of obtaining better performance than its non-neuronal version (traditional lasso), in terms of predictive error and correct variable selection. Once that dlasso performance has been assessed, it is used to determine whether it is possible to predict the severity of symptoms in children with ADHD from four scales that measure family burden, family functioning, parental satisfaction, and parental mental health. Results show that dlasso is able to predict parents' assessment of the severity of their children's inattention from only seven items from the previous scales. These items are related to parents' satisfaction and degree of parental burden.

Laria Juan C, Delgado-Gómez David, Peñuelas-Calvo Inmaculada, Baca-García Enrique, Lillo Rosa E


ADHD, deep learning, feature selection, interpretability, lasso

General General

Neural Correlates of Attentional Modulation of Prepulse Inhibition.

In Frontiers in human neuroscience ; h5-index 79.0

Prepulse inhibition (PPI) refers to the suppression of the startle reflex when the intense startling stimulus is shortly (20-500 ms) preceded by a weak non-startling stimulus (prepulse). Although the main neural correlates of PPI lie in the brainstem, previous research has revealed that PPI can be top-down modulated by attention. However, in the previous attend-to-prepulse PPI paradigm, only continuous prepulse but not discrete prepulse (20 ms) could elicit attentional modulation of PPI. Also, the relationship between the attentional enhancement of PPI and the changes in early cortical representations of prepulse signals is unclear. This study develops a novel attend-to-prepulse PPI task, when the discrete prepulse is set at 150 ms at a lead interval of 270 ms, and reveals that the PPI with attended prepulse is larger than the PPI with ignored prepulse. In addition, the early cortical representations (N1/P2 complex) of the prepulse show dissociation between the attended and ignored prepulse. N1 component is enhanced by directed attention, and the attentional increase of the N1 component is positively correlated with the attentional enhancement of PPI, whereas the P2 component is not affected by attentional modulation. Thus, directed attention to the prepulse can enhance both PPI and the early cortical representation of the prepulse signal (N1).

Lei Ming, Ding Yu, Meng Qingxin


N1, attention, event-related potentials, prepulse inhibition, sensory gating

Radiology Radiology

Current applications and development of artificial intelligence for digital dental radiography.

In Dento maxillo facial radiology

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.

Putra Ramadhan Hardani, Doi Chiaki, Yoda Nobuhiro, Astuti Eha Renwi, Sasaki Keiichi


Artificial intelligence, deep learning, machine learning, radiography

General General

Drugs repurposed for COVID-19 by virtual screening of 6,218 drugs and cell-based assay.

In Proceedings of the National Academy of Sciences of the United States of America

The COVID-19 pandemic caused by SARS-CoV-2 is an unprecedentedly significant health threat, prompting the need for rapidly developing antiviral drugs for the treatment. Drug repurposing is currently one of the most tangible options for rapidly developing drugs for emerging and reemerging viruses. In general, drug repurposing starts with virtual screening of approved drugs employing various computational methods. However, the actual hit rate of virtual screening is very low, and most of the predicted compounds are false positives. Here, we developed a strategy for virtual screening with much reduced false positives through incorporating predocking filtering based on shape similarity and postdocking filtering based on interaction similarity. We applied this advanced virtual screening approach to repurpose 6,218 approved and clinical trial drugs for COVID-19. All 6,218 compounds were screened against main protease and RNA-dependent RNA polymerase of SARS-CoV-2, resulting in 15 and 23 potential repurposed drugs, respectively. Among them, seven compounds can inhibit SARS-CoV-2 replication in Vero cells. Three of these drugs, emodin, omipalisib, and tipifarnib, show anti-SARS-CoV-2 activities in human lung cells, Calu-3. Notably, the activity of omipalisib is 200-fold higher than that of remdesivir in Calu-3. Furthermore, three drug combinations, omipalisib/remdesivir, tipifarnib/omipalisib, and tipifarnib/remdesivir, show strong synergistic effects in inhibiting SARS-CoV-2. Such drug combination therapy improves antiviral efficacy in SARS-CoV-2 infection and reduces the risk of each drug's toxicity. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating COVID-19 and other viruses.

Jang Woo Dae, Jeon Sangeun, Kim Seungtaek, Lee Sang Yup


SARS-CoV-2, cell-based assay, docking-based virtual screening, drug combinations, drug repurposing