Quantitative Biology

Flow cytometry (FCM) is an analytic technique that is capable of detecting and recording the emission of fluorescence and light scattering of cells or particles (that are collectively called "events") in a population. A typical FCM experiment can produce a large array of data making the analysis computationally intensive. Current FCM data analysis platforms (FlowJo, etc.), while very useful, do not allow interactive data processing online due to the data size limitations. Here we report a more effective way to analyze FCM data. Freecyto is a free, easy-to-learn, Python-flask-based web application that uses a weighted k-means clustering algorithm to facilitate the interactive analysis of flow cytometry data. A key limitation of web browsers is their inability to interactively display large amounts of data. Freecyto addresses this bottleneck through the use of the k-means algorithm to quantize the data, allowing the user to access a representative set of data points for interactive visualization of complex datasets. Moreover, Freecyto enables the interactive analyses of large complex datasets while preserving the standard FCM visualization features, such as the generation of scatterplots (dotplots), histograms, heatmaps, boxplots, as well as a SQL-based sub-population gating feature. We also show that Freecyto can be applied to the analysis of various experimental setups that frequently require the use of FCM. Finally, we demonstrate that the data accuracy is preserved when Freecyto is compared to conventional FCM software.

Sleep has a profound influence on the physiology of body systems and biological processes. Molecular studies have shown circadian-regulated shifts in protein expression patterns across human tissues, further emphasizing the unique functional, behavioral and pharmacokinetic landscape of sleep. Thus, many pathological processes are also expected to exhibit sleep-specific manifestations. Nevertheless, sleep is seldom utilized for the study, detection and treatment of non-sleep-specific pathologies. Modern advances in biosensor technologies have enabled remote, non-invasive recording of a growing number of physiologic parameters and biomarkers. Sleep is an ideal time frame for the collection of long and clean physiological time series data which can then be analyzed using data-driven algorithms such as deep learning. In this perspective paper, we aim to highlight the potential of sleep as an auspicious time for diagnosis, management and therapy of nonsleep-specific pathologies. We introduce key clinical studies in selected medical fields, which leveraged novel technologies and the advantageous period of sleep to diagnose, monitor and treat pathologies. We then discuss possible opportunities to further harness this new paradigm and modern technologies to explore human health and disease during sleep and to advance the development of novel clinical applications: From sleep medicine to medicine during sleep.

The occurrence of the extensively used herbicide diuron in the environment poses a severe threat to the ecosystem and human health. Four different ligninolytic fungi were studied as biodegradation candidates for the removal of diuron. Among them, T. versicolor was the most effective species, degrading rapidly not only diuron (83%) but also the major metabolite 3,4-dichloroaniline (100%), after 7-day incubation. During diuron degradation, five transformation products (TPs) were found to be formed and the structures for three of them are tentatively proposed. According to the identified TPs, a hydroxylated intermediate 3-(3,4-dichlorophenyl)-1-hydroxymethyl-1-methylurea (DCPHMU) was further metabolized into the N-dealkylated compounds 3-(3,4-dichlorophenyl)-1-methylurea (DCPMU) and 3,4-dichlorophenylurea (DCPU). The discovery of DCPHMU suggests a relevant role of hydroxylation for subsequent N-demethylation, helping to better understand the main reaction mechanisms of diuron detoxification. Experiments also evidenced that degradation reactions may occur intracellularly and be catalyzed by the cytochrome P450 system. A response surface method, established by central composite design, assisted in evaluating the effect of operational variables in a trickle-bed bioreactor immobilized with T. versicolor on diuron removal. The best performance was obtained at low recycling ratios and influent flow rates. Furthermore, results indicate that the contact time between the contaminant and immobilized fungi plays a crucial role in diuron removal. This study represents a pioneering step forward amid techniques for bioremediation of pesticides-contaminated waters using fungal reactors at a real scale.

We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. Our model consists of an encoder, a decoder and a classifier. The encoder learns a non-linear subspace shared between the input data modalities. The classifier and the decoder act as regularizers to ensure that the low-dimensional encoding captures predictive differences between patients and controls. We use a learnable dropout layer to extract interpretable biomarkers from the data, and our unique training strategy can easily accommodate missing data modalities across subjects. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data. Using 10-fold cross validation, we demonstrate that our model achieves better classification accuracy than baseline methods, and that this performance generalizes to a second dataset collected at a different site. In an exploratory analysis we further show that the biomarkers identified by our model are closely associated with the well-documented deficits in schizophrenia.

Phytochemical composition of ethanol leaf extract of Diodella sarmentosa was profiled with GC-MS and the inhibitory property of the extract against total microbial dehydrogenases were assessed. The major constituents of the extract were squalene (29.50%), Phytol (24.68%), phenol, 3-pentadecyl- (18.58%), 1-Butanol, 3-methyl- (9.09%) and n-Hexadecanoic acid (7.78%). The minimum inhibitory concentration (MIC) of the extract against broad spectrum of microbial population was assessed. Bacillus subtilis, Candidas spp, and Penicillium spp were more sensitive to the treatment and thus; were further investigated using Dehydrogenase activity assay method. Total dehydrogenase activities of Bacillus subtilis, Candidas spp, and Penicillium spp at the extract concentration range of 0 to 2000mg/ml were progressively inhibited at increasing extract concentrations. The threshold inhibitory concentrations (IC50) of the extracts against Candidas spp, Penicillium spp and Bacillus subtilis were 275micro. g/ml, 322 micro. g/ml and 411 micro. g/ml respectively. Our findings suggested the extract as a useful source of antimicrobial phytochemicals for pharmaceutical use.

The prediction of protein interactions (CPIs) is crucial for the in-silico screening step in drug discovery. Recently, many end-to-end representation learning methods using deep neural networks have achieved significantly better performance than traditional machine learning algorithms. Much effort has focused on the compound representation or the information extraction from the compound-protein interaction to improve the model capability by taking the advantage of the neural attention mechanism. However, previous studies have paid little attention to representing the protein sequences, in which the long-range interactions of residue pairs are essential for characterizing the structural properties arising from the protein folding. We incorporate the self-attention mechanism into the protein representation module for CPI modeling, which aims at capturing the long-range interaction information within proteins. The proposed module concerning protein representation, called Protein Transformer, with an integration with an existing CPI model, has shown a significant improvement in the prediction performance when compared with several existing CPI models.

Background: Human gait exhibits complex fractal fluctuations among consecutive strides. The time series of gait parameters are long-range correlated (statistical persistence). In contrast, when gait is synchronized with external rhythmic cues, the fluctuation regime is modified to stochastic oscillations around the target frequency (statistical anti-persistence). To highlight these two fluctuation modes, the prevalent methodology is the detrended fluctuation analysis (DFA). The DFA outcome is the scaling exponent, which lies between 0.5 and 1 if the time series exhibit long-range correlations, and below 0.5 if the time series is anti-correlated. A fundamental assumption for applying DFA is that the analyzed time series results from a time-invariant generating process. However, a gait time series may be constituted by an ensemble of sub-segments with distinct fluctuation regimes (e.g., correlated and anti-correlated). Methods: Several proportions of correlated and anti-correlated time series were mixed together and then analyzed through DFA. The original (before mixing) time series were generated via autoregressive fractionally integrated moving average (ARFIMA) modelling or actual gait data. Results: Results evidenced a nonlinear sensitivity of DFA to the mix of correlated and anti-correlated series. Notably, adding a small proportion of correlated segments into an anti-correlated time series had stronger effects than the reverse. Significance: In case of changes in gait control during a walking trial, the resulting time series may be a patchy ensemble of several fluctuation regimes. When applying DFA, the scaling exponent may be misinterpreted. Cued walking studies may be most at risk of suffering this issue in cases of sporadic synchronization with external cues.

In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer.

Genetic mapping sprung in the last decade of the 20th century with the development of statistical procedures putting classical models of genetic effects together with molecular biology techniques. It eventually became clear that those models, originally developed to serve other purposes, implied limitations at different stages of the analyses-disclosing loci, measuring their effects and providing additional parameters for adequate biological/medical interpretations. The present paper is aimed to ponder whether it is realistic and worth to try and further amend classical models of genetic effects or it proves more sensible to undertake alternative theoretical strategies instead. In order to further feed into that debate, mathematical developments for gene-environment interaction stemming from the classical models of genetic effects are here revised and brought up-to-date with the prospects present-day available data bestow, particularly in the context of precision medicine. Those developments strengthen the methodology required to overcome the COVID-19 pandemic.

A central need in the field of astrobiology is generalized perspectives on life that make it possible to differentiate abiotic and biotic chemical systems. A key component of many past and future astrobiological measurements is the elemental ratio of various samples. Classic work on Earth's oceans has shown that life displays a striking regularity in the ratio of elements as originally characterized by Redfield. The body of work since the original observations has connected this ratio with basic ecological dynamics and cell physiology, while also documenting the range of elemental ratios found in a variety of environments. Several key questions remain in considering how to best apply this knowledge to astrobiological contexts: How can the observed variation of the elemental ratios be more formally systematized using basic biological physiology and ecological or environmental dynamics? How can these elemental ratios be generalized beyond the life that we have observed on our own planet? Here we expand recently developed generalized physiological models to create a simple framework for predicting the variation of elemental ratios found in various environments. We then discuss further generalizing the physiology for astrobiological applications. Much of our theoretical treatment is designed for in situ measurements applicable to future planetary missions. We imagine scenarios where three measurements can be made - particle/cell sizes, particle/cell stoichiometry, and fluid or environmental stoichiometry - and develop our theory in connection with these often deployed measurements.