Baseflow persistence and magnitude in oil palm, logged and primary tropical rainforest catchments in Malaysian Borneo: implications for water management under climate change

While timber harvesting has plateaued, repeat-logging and conversion into plantations (especially oil palm) are still active in the tropics. The associated hydrological impacts especially pertaining to enhanced runoff, flood, and erosion have been well-studied, but little attention has been given to water resource availability in the humid tropics. In the light of the increasing climate extremes, this paper compared baseflow values and baseflow recession constants (K) between headwater catchments of five differing land-uses in Sabah, Malaysian Borneo, namely primary forest (PF), old growth/virgin jungle reserve (VJR), twice-logged forest with 22 years regeneration (LF2), multiple-logged forest with 8 years regeneration (LF3), and oil palm plantation (OP). Hydrological and meteorological sensors and dataloggers were established in each catchment. Daily discharge was used for computing K via four estimation methods. Catchment ranks in terms of decreasing K were VJR (0.97841), LF3 (0.96692), LF2 (0.90347), PF (0.83886), and OP (0.86756). Catchment ranks in terms of decreasing annual baseflow were PF (1877 mm), LF3 (1265 mm), LF2 (812 mm), VJR (753 mm), and OP (367 mm), corresponding to 68%, 55%, 51%, 42%, and 38% of annual streamflow, respectively. Despite the low K, PF had the highest baseflow magnitude. OP had the fastest baseflow recession and lowest baseflow magnitude. Baseflow persistence decreased with increasing degree of disturbance. K showed strong association to catchment stem density instead of basal area. For dynamic catchments in this study, the Kb3 estimator is recommended based on its lowest combination of coefficient of variation (CoV) and root mean squared error (RMSE) of prediction. For wetter catchments with even shorter recession events, the Kb4 estimator may be considered. Regarding climate change, logging and oil palm agriculture should only be conducted after considering water resource availability. Forests (even degraded ones) should be conserved as much as possible in the headwaters for sustainable water resource

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Virulence Characteristics and Genetic Affinities of Multiple Drug Resistant Uropathogenic Escherichia coli from a Semi Urban Locality in India

Extraintestinal pathogenic Escherichia coli (ExPEC) are of significant health concern. The emergence of drug resistant E. coli with high virulence potential is alarming. Lack of sufficient data on transmission dynamics, virulence spectrum and antimicrobial resistance of certain pathogens such as the uropathogenic E. coli (UPEC) from countries with high infection burden, such as India, hinders the infection control and management efforts. In this study, we extensively genotyped and phenotyped a collection of 150 UPEC obtained from patients belonging to a semi-urban, industrialized setting near Pune, India. The isolates representing different clinical categories were analyzed in comparison with 50 commensal E. coli isolates from India as well as 50 ExPEC strains from Germany. Virulent strains were identified based on hemolysis, haemagglutination, cell surface hydrophobicity, serum bactericidal activity as well as with the help of O serotyping. We generated antimicrobial resistance profiles for all the clinical isolates and carried out phylogenetic analysis based on repetitive extragenic palindromic (rep)-PCR. E. coli from urinary tract infection cases expressed higher percentages of type I (45%) and P fimbriae (40%) when compared to fecal isolates (25% and 8% respectively). Hemolytic group comprised of 60% of UPEC and only 2% of E. coli from feces. Additionally, we found that serum resistance and cell surface hydrophobicity were not significantly (p = 0.16/p = 0.51) associated with UPEC from clinical cases. Moreover, clinical isolates exhibited highest resistance against amoxicillin (67.3%) and least against nitrofurantoin (57.3%). We also observed that 31.3% of UPEC were extended-spectrum beta-lactamase (ESBL) producers belonging to serotype O25, of which four were also positive for O25b subgroup that is linked to B2-O25b-ST131-CTX-M-15 virulent/multiresistant type. Furthermore, isolates from India and Germany (as well as global sources) were found to be genetically distinct with no evidence to espouse expansion of E. coli from India to the west or vice-versa

Signature-Tagged Mutagenesis in a Chicken Infection Model Leads to the Identification of a Novel Avian Pathogenic Escherichia coli Fimbrial Adhesin

The extraintestinal pathogen, avian pathogenic E. coli (APEC), known to cause systemic infections in chickens, is responsible for large economic losses in the poultry industry worldwide. In order to identify genes involved in the early essential stages of pathogenesis, namely adhesion and colonization, Signature-tagged mutagenesis (STM) was applied to a previously established lung colonization model of infection by generating and screening a total of 1,800 mutants of an APEC strain IMT5155 (O2:K1:H5; Sequence type complex 95). The study led to the identification of new genes of interest, including two adhesins, one of which coded for a novel APEC fimbrial adhesin (Yqi) not described for its role in APEC pathogenesis to date. Its gene product has been temporarily designated ExPEC Adhesin I (EA/I) until the adhesin-specific receptor is identified. Deletion of the ExPEC adhesin I gene resulted in reduced colonization ability by APEC strain IMT5155 both in vitro and in vivo. Furthermore, complementation of the adhesin gene restored its ability to colonize epithelial cells in vitro. The ExPEC adhesin I protein was successfully expressed in vitro. Electron microscopy of an afimbriate strain E. coli AAEC189 over-expressed with the putative EA/I gene cluster revealed short fimbrial-like appendages protruding out of the bacterial outer membrane. We observed that this adhesin coding gene yqi is prevalent among extraintestinal pathogenic E. coli (ExPEC) isolates, including APEC (54.4%), uropathogenic E. coli (UPEC) (65.9%) and newborn meningitic E. coli (NMEC) (60.0%), and absent in all of the 153 intestinal pathogenic E. coli strains tested, thereby validating the designation of the adhesin as ExPEC Adhesin I. In addition, prevalence of EA/I was most frequently associated with the B2 group of the EcoR classification and ST95 complex of the multi locus sequence typing (MLST) scheme, with evidence of a positive selection within this highly pathogenic complex. This is the first report of the newly identified and functionally characterized ExPEC adhesin I and its significant role during APEC infection in chickens

The GimA Locus of Extraintestinal Pathogenic E. coli: Does Reductive Evolution Correlate with Habitat and Pathotype?

IbeA (invasion of brain endothelium), which is located on a genomic island termed GimA, is involved in the pathogenesis of several extraintestinal pathogenic E. coli (ExPEC) pathotypes, including newborn meningitic E. coli (NMEC) and avian pathogenic E. coli (APEC). To unravel the phylogeny of GimA and to investigate its island character, the putative insertion locus of GimA was determined via Long Range PCR and DNA-DNA hybridization in 410 E. coli isolates, including APEC, NMEC, uropathogenic (UPEC), septicemia-associated E. coli (SEPEC), and human and animal fecal isolates as well as in 72 strains of the E. coli reference (ECOR) collection. In addition to a complete GimA (∼20.3 kb) and a locus lacking GimA we found a third pattern containing a 342 bp remnant of GimA in this strain collection. The presence of GimA was almost exclusively detected in strains belonging to phylogenetic group B2. In addition, the complete GimA was significantly more frequent in APEC and NMEC strains while the GimA remnant showed a higher association with UPEC strains. A detailed analysis of the ibeA sequences revealed the phylogeny of this gene to be consistent with that obtained by Multi Locus Sequence Typing of the strains. Although common criteria for genomic islands are partially fulfilled, GimA rather seems to be an ancestral part of phylogenetic group B2, and it would therefore be more appropriate to term this genomic region GimA locus instead of genomic island. The existence of two other patterns reflects a genomic rearrangement in a reductive evolution-like manner

Impacts of climate change on plant diseases – opinions and trends

There has been a remarkable scientific output on the topic of how climate change is likely to affect plant diseases in the coming decades. This review addresses the need for review of this burgeoning literature by summarizing opinions of previous reviews and trends in recent studies on the impacts of climate change on plant health. Sudden Oak Death is used as an introductory case study: Californian forests could become even more susceptible to this emerging plant disease, if spring precipitations will be accompanied by warmer temperatures, although climate shifts may also affect the current synchronicity between host cambium activity and pathogen colonization rate. A summary of observed and predicted climate changes, as well as of direct effects of climate change on pathosystems, is provided. Prediction and management of climate change effects on plant health are complicated by indirect effects and the interactions with global change drivers. Uncertainty in models of plant disease development under climate change calls for a diversity of management strategies, from more participatory approaches to interdisciplinary science. Involvement of stakeholders and scientists from outside plant pathology shows the importance of trade-offs, for example in the land-sharing vs. sparing debate. Further research is needed on climate change and plant health in mountain, boreal, Mediterranean and tropical regions, with multiple climate change factors and scenarios (including our responses to it, e.g. the assisted migration of plants), in relation to endophytes, viruses and mycorrhiza, using long-term and large-scale datasets and considering various plant disease control methods

Bose-Einstein Correlations of Neutral and Charged Pions in Hadronic Z Decays

Bose-Einstein correlations of both neutral and like-sign charged pion pairs are measured in a sample of 2 million hadronic Z decays collected with the L3 detector at LEP. The analysis is performed in the four-momentum difference range 300 MeV < Q < 2 GeV. The radius of the neutral pion source is found to be smaller than that of charged pions. This result is in qualitative agreement with the string fragmentation model

Lambda and Sigma0 Pair Production in Two-Photon Collisions at LEP

Strange baryon pair production in two-photon collisions is studied with the L3 detector at LEP. The analysis is based on data collected at e+e- centre-of-mass energies from 91 GeV to 208 GeV, corresponding to an integrated luminosity of 844 pb-1. The processes gamma gamma -> Lambda Anti-lambda and gamma gamma -> Sigma0 Anti-sigma0 are identified. Their cross sections as a function of the gamma gamma centre-of-mass energy are measured and results are compared to predictions of the quark-diquark model