The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exercises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a framework for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exercise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so.PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi
Programmed cell death (PCD) in unicellular organisms is in some instances an altruistic trait. When the beneficiaries are clones or close kin, kin selection theory may be used to explain the evolution of the trait, and when the trait evolves in groups of distantly related individuals, group or multilevel selection theory is invoked. In mixed microbial communities, the benefits are also available to unrelated taxa. But the evolutionary ecology of PCD in communities is poorly understood. Few hypotheses have been offered concerning the community role of PCD despite its far‐reaching effects. The hypothesis we consider here is that PCD is a black queen. The Black Queen Hypothesis (BQH) outlines how public goods arising from a leaky function are exploited by other taxa in the community. Black Queen (BQ) traits are essential for community survival, but only some members bear the cost of possessing them, while others lose the trait In addition, BQ traits have been defined in terms of adaptive gene loss, and it is unknown whether this has occurred for PCD. Our conclusion is that PCD fulfils the two most important criteria of a BQ (leakiness and costliness), but that more empirical data are needed for assessing the remaining two criteria. In addition, we hold that for viewing PCD as a BQ, the original BQH needs to include social traits. Thus, despite some empirical and conceptual shortcomings, the BQH provides a helpful avenue for investigating PCD in microbial communities.
Adsorptive
desulfurization (ADS) of hydrocarbon fuels using zeolite-based
adsorbents holds great promise due to the mild conditions required
to remove sulfur, thus addressing the energy and environmental concerns.
However, screening of the ever-increasing number of potential ADS
zeolites for adsorptive capacity is increasingly intractable. Furthermore,
there is no consensus on the parameters with a dominating influence;
hence, adsorbent synthesis design has remained an art. Machine learning
(ML) has gained popularity as a powerful tool for understanding the
catalytic mechanism and providing insights into catalytic design.
In this study, we used multiple linear regression (MLR) and random
forest (RF) regression to explore the process of ADS by zeolites using
data from the literature. We found better predictive performance under
the RF model (R
2 = 0.93) than the MLR
model (R
2 = 0.88), which violated the
assumption of linearity. The initial adsorbate concentration showed
the highest relative importance of the variables, followed by zeolite
properties (metal ion, mesoporous volume, pore size, Si/Al ratio,
and surface area) for ADS activity. Our RF prediction model may be
used in place of experimental ADS zeolite screening, cutting down
on time and resource requirements. This work demonstrates the utility
of ML and literature survey data as an inexpensive alternative to
experimentation when doing research to obtain mechanistic insight
into the complex process of ADS.
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