Maternal Aggression

This chapter explores evolutionary psychological (EP) perspectives on maternal aggression, focusing on physical aggression, both lethal and nonlethal. It argues that the psychological mechanisms underpinning such aggression held an adaptive function to our foremothers. If such mechanisms formerly did hold an adaptive function, then maternal aggression should not be expected to be a random event, nor necessarily caused by pathology. Rather, the risk factors and characteristic traits of maternal aggression should follow an ancestrally adaptive and evolutionary logic. In which case, it should be a predictable phenomenon on a societal level. And as the chapter presents, the theoretical understanding of maternal aggression that EP perspectives offer has allowed for the successful prediction of risk factors and characteristic traits for such aggression. The chapter reviews these risk factors and traits, along with the theoretical reasoning the predictions are based on and the cross-cultural empirical support for their existence.

Geomagnetic activity recurrences for predicting the amplitude and shape of solar cycle N. 25

The prediction of solar activity is one of the most challenging topics among the various Space Weather and Space Climate issues. In the last decades, the constant enhancement of Space Climate data allowed to improve the comprehension of the related physical phenomena and the statistical bases for prediction algorithms. For this purpose, we used geomagnetic indices to provide a pow erful algorithm (see Diego et al 2010) for the solar activity prediction, based on the evaluation of the recurrence rate in the geomagnetic activity. The aim of this paper is to present the validation of our algorithm over solar cycle n. 24, for which a successful prediction was made, and upgrade it to forecast the shape and time as well as the amplitude of the upcoming cycle n. 25. Contrary to the consensus, we predict it to be quite high, with a maximum sunspot number of 205 ± 29, that should be reached in the first half of 2023. This prediction is consistent with the scenario in which the long-term Gleissberg cycle has reached its minimum in cycle n. 24 and the rising phase is beginning.

Mycobacteriophage–antibiotic therapy promotes enhanced clearance of drug-resistant Mycobacterium abscessus

ABSTRACT Infection by multidrug-resistant Mycobacterium abscessus is increasingly prevalent in cystic fibrosis (CF) patients, leaving clinicians with few therapeutic options. A compassionate study showed the clinical improvement of a CF patient with a disseminated M. abscessus (GD01) infection, following injection of a phage cocktail, including phage Muddy. Broadening the use of phage therapy in patients as a potential antibacterial alternative necessitates the development of biological models to improve the reliability and successful prediction of phage therapy in the clinic. Herein, we demonstrate that Muddy very efficiently lyses GD01 in vitro, an effect substantially increased with standard drugs. Remarkably, this cooperative activity was retained in an M. abscessus model of infection in CFTR-depleted zebrafish, associated with a striking increase in larval survival and reduction in pathological signs. The activity of Muddy was lost in macrophage-ablated larvae, suggesting that successful phage therapy relies on functional innate immunity. CFTR-depleted zebrafish represent a practical model to rapidly assess phage treatment efficacy against M. abscessus isolates, allowing the identification of drug combinations accompanying phage therapy and treatment prediction in patients. This article has an associated First Person interview with the first author of the paper.

Harnessing Matrix Completion to Unify and Extend Viral Serology Studies

SummaryCharacterizing the antibody response against large panels of viral variants provides unique insight into key processes that shape viral evolution and host antibody repertoires, and has become critical to the development of new vaccine strategies. Given the enormous diversity of circulating virus strains and antibody responses, exhaustive testing of all antibody-virus interactions is unfeasible. However, prior studies have demonstrated that, despite the complexity of these interactions, their functional phenotypes can be characterized in a vastly simpler and lower-dimensional space, suggesting that matrix completion of relatively few measurements could accurately predict unmeasured antibody-virus interactions. Here, we combine available data from several of the largest-scale studies for both influenza and HIV-1 and demonstrate how matrix completion can substantially expedite experiments. We explore how prediction accuracy evolves as the number of available measurements changes and approximate the number of additional measurements necessary in several highly incomplete datasets (suggesting ∼250,000 measurements could be saved). In addition, we show how the method can be used to combine disparate datasets, even when the number of available measurements is below the theoretical limit for successful prediction. Our results suggest new approaches to improve ongoing experimental design, and could be readily generalized to other viruses or more broadly to other low-dimensional biological datasets.

UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations

The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters.

Predicting intent behind selections in scatterplot visualizations

Predicting and capturing an analyst’s intent behind a selection in a data visualization is valuable in two scenarios: First, a successful prediction of a pattern an analyst intended to select can be used to auto-complete a partial selection which, in turn, can improve the correctness of the selection. Second, knowing the intent behind a selection can be used to improve recall and reproducibility. In this paper, we introduce methods to infer analyst’s intents behind selections in data visualizations, such as scatterplots. We describe intents based on patterns in the data, and identify algorithms that can capture these patterns. Upon an interactive selection, we compare the selected items with the results of a large set of computed patterns, and use various ranking approaches to identify the best pattern for an analyst’s selection. We store annotations and the metadata to reconstruct a selection, such as the type of algorithm and its parameterization, in a provenance graph. We present a prototype system that implements these methods for tabular data and scatterplots. Analysts can select a prediction to auto-complete partial selections and to seamlessly log their intents. We discuss implications of our approach for reproducibility and reuse of analysis workflows. We evaluate our approach in a crowd-sourced study, where we show that auto-completing selection improves accuracy, and that we can accurately capture pattern-based intent.

A Computational Model of the Effect of Short−Term Monocular Deprivation on Binocular Rivalry in the Context of Amblyopia

Treatments for amblyopia focus on vision therapy and patching of one eye. Predicting the success of these methods remains difficult, however. Recent research has used binocular rivalry to monitor visual cortical plasticity during occlusion therapy, leading to a successful prediction of the recovery rate of the amblyopic eye. The underlying mechanisms and their relation to neural homeostatic plasticity are not known. Here we propose a spiking neural network to explain the effect of shortterm monocular deprivation on binocular rivalry. The model reproduces perceptual switches as observed experimentally. When one eye is occluded, inhibitory plasticity changes the balance between the eyes and leads to longer dominance periods for the eye that has been deprived. The model suggests that homeostatic inhibitory plasticity is a critical component of the observed effects and might play an important role in the recovery from amblyopia.

Growth prediction methods: A review

Growth prediction is an estimation of the amount of growth to be expected. In orthodontics the term refers to the estimation of amount and direction of growth of the bones of the craniofacial skeletal and overlying soft tissues. Successful prediction requires specifying both the amount and the direction of growth, in relation to the reference point. Estimation of dentofacial growth must consider the increments, vectors, area, duration and timing of growth accessions. All these are subjected to the changes in growth pattern

Predicting the time-to-deliver of software changes

In this thesis we examine the application of survival analysis on time-to-deliver data. Successful prediction of the time necessary to deliver a new feature or fix a reported defect can assist in various phases and aspects of software development. We identify and try to overcome limitations when dealing with time-to-event data. Our proposed methodological framework includes use of survival analysis, utilization of incomplete information that might be available as censored data, and incorporation of random-effects through mixed-effects models for identification of hierarchical/clustered data within our dataset. We explore and experiment with a dataset from a large scale commercial software over a twelve year period of time. We show that we can successfully implement survival analysis, and that incorporation of random-effects provides a considerable advantage, however, incorporation of censored information is not proven to be advantageous in this case.