Infinite Feature Selection on Shore-Based Biomarkers Reveals Connectivity Modulation after Stroke

Connectomics is gaining increasing interest in the scientific and clinical communities. It consists in deriving models of structural or functional brain connections based on some local measures. Here we focus on structural connectivity as detected by diffusion MRI. Connectivity matrices are derived from microstructural indices obtained by the 3D-SHORE. Typically, graphs are derived from connectivity matrices and used for inferring node properties that allow identifying those nodes that play a prominent role in the network. This information can then be used to detect network modulations induced by diseases. In this paper we take a complementary approach and focus on link as opposed to node properties. We hypothesize that network modulation can be better described by measuring the connectivity alteration directly in the form of modulation of the properties of white matter fiber bundles constituting the network communication backbone. The goal of this paper is to detect the paths that are most altered by the pathology by exploiting a feature selection paradigm. Temporal changes on connection weights are treated as features and those playing a leading role in a patient versus healthy controls classification task are detected by the Infinite Feature Selection (Inf-FS) method. Results show that connection paths with high discriminative power can be identified that are shared by the considered microstructural descriptors allowing a classification accuracy ranging between 83% and 89%

In-situ fluorescence spectroscopy indicates total bacterial abundance and dissolved organic carbon

We explore in-situ fluorescence spectroscopy as an instantaneous indicator of total bacterial abundance and faecal contamination in drinking water. Eighty-four samples were collected outside of the recharge season from groundwater-derived water sources in Dakar, Senegal. Samples were analysed for tryptophan-like (TLF) and humic-like (HLF) fluorescence in-situ, total bacterial cells by flow cytometry, and potential indicators of faecal contamination such as thermotolerant coliforms (TTCs), nitrate, and in a subset of 22 samples, dissolved organic carbon (DOC). Significant single-predictor linear regression models demonstrated that total bacterial cells were the most effective predictor of TLF, followed by on-site sanitation density; TTCs were not a significant predictor. An optimum multiple-predictor model of TLF incorporated total bacterial cells, nitrate, nitrite, on-site sanitation density, and sulphate (r2 0.68). HLF was similarly related to the same parameters as TLF, with total bacterial cells being the best correlated (ρs 0.64). In the subset of 22 sources, DOC clustered with TLF, HLF, and total bacterial cells, and a linear regression model demonstrated HLF was the best predictor of DOC (r2 0.84). The intergranular nature of the aquifer, timing of the study, and/or non-uniqueness of the signal to TTCs can explain the significant associations between TLF/HLF and indicators of faecal contamination such as on-site sanitation density and nutrients but not TTCs. The bacterial population that relates to TLF/HLF is likely to be a subsurface community that develops in-situ based on the availability of organic matter originating from faecal sources. In-situ fluorescence spectroscopy instantly indicates a drinking water source is impacted by faecal contamination but it remains unclear how that relates specifically to microbial risk in this setting

Tryptophan-like and humic-like fluorophores are extracellular in groundwater: implications as real-time faecal indicators

Fluorescent natural organic matter at tryptophan-like (TLF) and humic-like fluorescence (HLF) peaks is associated with the presence and enumeration of faecal indicator bacteria in groundwater. We hypothesise, however, that it is predominantly extracellular material that fluoresces at these wavelengths, not bacterial cells. We quantified total (unfiltered) and extracellular (filtered at < 0.22 µm) TLF and HLF in 140 groundwater sources across a range of urban population densities in Kenya, Malawi, Senegal, and Uganda. Where changes in fluorescence occurred following filtration they were correlated with potential controlling variables. A significant reduction in TLF following filtration (ΔTLF) was observed across the entire dataset, although the majority of the signal remained and thus considered extracellular (median 96.9%). ΔTLF was only significant in more urbanised study areas where TLF was greatest. Beneath Dakar, Senegal, ΔTLF was significantly correlated to total bacterial cells (ρs 0.51). No significant change in HLF following filtration across all data indicates these fluorophores are extracellular. Our results suggest that TLF and HLF are more mobile than faecal indicator bacteria and larger pathogens in groundwater, as the predominantly extracellular fluorophores are less prone to straining. Consequently, TLF/HLF are more precautionary indicators of microbial risks than faecal indicator bacteria in groundwater-derived drinking water

Depression Speaks: Automatic Discrimination between Depressed and Non-Depressed Speakers Based on Nonverbal Speech Features

This article proposes an automatic approach-based on nonverbal speech features-aimed at the automatic discrimination between depressed and non-depressed speakers. The experiments have been performed over one of the largest corpora collected for such a task in the literature (62 patients diagnosed with depression and 54 healthy control subjects), especially when it comes to data where the depressed speakers have been diagnosed as such by professional psychiatrists. The results show that the discrimination can be performed with an accuracy of over 75% and the error analysis shows that the chances of correct classification do not change according to gender, depression-related pathology diagnosed by the psychiatrists or length of the pharmacological treatment (if any). Furthermore, for every depressed subject, the corpus includes a control subject that matches age, education level and gender. This ensures that the approach actually discriminates between depressed and non depressed speakers and does not simply capture differences resulting from other factors