Calculation of the Ecological Risk Index in the José Antonio Alzate Dam, State of Mexico, Mexico

Due to the problem of contamination in the Jose Antonio Alzate dam, located in the State of Mexico, Mexico, the partition coefficient, the contamination degree, and the ecological risk index of nine metals were calculated in order to establish the water quality in different areas of the Alzate dam. The sampling sites were selected according to the river flow into the dam, via three sampling programs, the physical–chemical parameters were measured in situ, and the samples were collected to measure metals in the dissolved phase and also as suspended particulate matter. Thomann’s model was used to calculate the partition coefficient. Håkanson’s methodology was used to determine the degree of contamination and the ecological risk index. Finally, the water quality criteria for the dissolved metals were calculated. The results for suspended particulate matter suggest a moderate risk of metal contamination in the dam. Copper and lead in dissolved form exceeded the values of water quality criteria.     

Aloina and Aloinella (Bryopsida, Pottiaceae) in northern Argentina

Except for a robust habit, two specimens ofAloina collected in northern Argentina are morphologically similar and are tentatively attributed toA. rigida (Hedw.) Limpr. This represents an addition to the moss flora of Argentina. In addition, two specimens ofAloinella represent significant findings: one shows features that agree with those given in the protologue forA. galeata (Müll. Hal.) Broth. and is treated here asA. galeata var.galeata to show the relationship toA. galeta var.andina (Delgad.) Delgadillo & Schiavone stat. nov.; the other belongs inA. cucullifera (Mitt.) Steere, a new record for Argentina. A key to the known species ofAloinella with updated ranges is included.     

Monitoring ungulates in steep non-forest habitat: a comparison of faecal pellet and helicopter counts

Faecal pellet counts have been widely used to monitor the abundances of introduced ungulates in New Zealand, but ground-based sampling cannot be conducted safely in the steep non-forest habitats that are common in New Zealand's Southern Alps. Helicopter counts may be an effective technique for monitoring ungulates in steep non-forest habitat. We evaluated the relationship between faecal pellet and helicopter counts of ungulates (primarily feral goat Capra hircus) at 12 non-forest sites in the Southern Alps. Within each site we counted the numbers of ungulates from a helicopter on three occasions and the number of intact faecal pellets along 30 transects. Mean observed densities of feral goats derived from helicopter counts ranged from 0.0 to 20.2 km^?2. There was a positive curvilinear (concave down) relationship between faecal pellet and helicopter counts. Compared with faecal pellet counts, helicopter counts were cheaper, could identify ungulate species and provided estimates of absolute density. Helicopter counts are a cost-effective method for monitoring ungulates in the steep non-forest habitats of New Zealand's Southern Alps.     

Effect of cholesterol depletion on the pore dilation of TRPV1

The TRPV1 ion channel is expressed in nociceptors, where pharmacological modulation of its function may offer a means of alleviating pain and neurogenic inflammation processes in the human body. The aim of this study was to investigate the effects of cholesterol depletion of the cell on ion-permeability of the TRPV1 ion channel. The ion-permeability properties of TRPV1 were assessed using whole-cell patch-clamp and YO-PRO uptake rate studies on a Chinese hamster ovary (CHO) cell line expressing this ion channel. Prolonged capsaicin-induced activation of TRPV1 with N-methyl-D-glucamine (NMDG) as the sole extracellular cation, generated a biphasic current which included an initial outward current followed by an inward current. Similarly, prolonged proton-activation (pH 5.5) of TRPV1 under hypocalcemic conditions also generated a biphasic current including a fast initial current peak followed by a larger second one. Patch-clamp recordings of reversal potentials of TRPV1 revealed an increase of the ion-permeability for NMDG during prolonged activation of this ion channel under hypocalcemic conditions. Our findings show that cholesterol depletion inhibited both the second current, and the increase in ion-permeability of the TRPV1 channel, resulting from sustained agonist-activation with capsaicin and protons (pH 5.5). These results were confirmed with YO-PRO uptake rate studies using laser scanning confocal microscopy, where cholesterol depletion was found to decrease TRPV1 mediated uptake rates of YO-PRO. Hence, these results propose a novel mechanism by which cellular cholesterol depletion modulates the function of TRPV1, which may constitute a novel approach for treatment of neurogenic pain.     

MTV1 and MTV4 Encode Plant-Specific ENTH and ARF GAP Proteins That Mediate Clathrin-Dependent Trafficking of Vacuolar Cargo from the Trans-Golgi Network

Many soluble proteins transit through the trans-Golgi network (TGN) and the prevacuolar compartment (PVC) en route to the vacuole, but our mechanistic understanding of this vectorial trafficking step in plants is limited. In particular, it is unknown whether clathrin-coated vesicles (CCVs) participate in this transport step. Through a screen for modified transport to the vacuole (mtv) mutants that secrete the vacuolar protein VAC2, we identified MTV1, which encodes an EPSIN N-TERMINAL HOMOLOGY protein, and MTV4, which encodes the ADP ribosylation factor GTPase-activating protein NEVERSHED/AGD5. MTV1 and NEV/AGD5 have overlapping expression patterns and interact genetically to transport vacuolar cargo and promote plant growth, but they have no apparent roles in protein secretion or endocytosis. MTV1 and NEV/AGD5 colocalize with clathrin at the TGN and are incorporated into CCVs. Importantly, mtv1 nev/agd5 double mutants show altered subcellular distribution of CCV cargo exported from the TGN. Moreover, MTV1 binds clathrin in vitro, and NEV/AGD5 associates in vivo with clathrin, directly linking these proteins to CCV formation. These results indicate that MTV1 and NEV/AGD5 are key effectors for CCV-mediated trafficking of vacuolar proteins from the TGN to the PVC in plants.     

A methodological improvement for assessing petrochemical projects through life cycle assessment and eco-costs

Purpose

As the debate on how to effectively link life cycle analysis (LCA) and negative externalities of the products or processes is still unsolved, an improved methodology that involves the private and social (environment) profitability to petrochemical projects in Mexico is presented. We incorporate both environmental impacts, identified through the LCA, using the eco-costs model, within a project appraisal analysis. The eco-costs are a single LCA-based indicator of environmental burden, based on the concept of marginal prevention costs, i.e. costs required to bring back the environmental degradation to a sustainable level.

Methods

The methodology was carried out in four steps. First, the potential environmental impacts (in physical units) were assessed through life-cycle assessment. Second, the marginal prevention costs of pollution (eco-costs) of impact categories were estimated. Third, since these monetary values are given for a European income level, eco-costs were adjusted to Mexican income levels (i.e., benefit transfer). Fourth, these adjusted values were used for assessing the financial viability of two petrochemical projects at two stages of installed capacity.

Results and discussion

The costs of marginal prevention (eco-costs) changed from 1.62 USD/kg of acrylonitrile in the old design to 0.926 USD/kg in the redesign. In case of vinyl chloride monomer (VCM), the eco-costs were from 16.08 USD/kg of VCM for the stage design to 1.62 USD/kg for the operational stage (ex post). The sensitivity analysis identified that only 15 % of these eco-costs, in the case of acrylonitrile, and 1.5 % for the VCM, could be incorporated to the costs of the project without affecting the profit on the investment.

Conclusions

The developed methodology was helpful to express potential environmental impacts in monetary units as financial indicators in a project appraisal. Thus, the methodology can be used to internalize the eco-costs into product prices, shifting the financial burden to firms and consumers, in order to offset the high costs of the green net national product.     

The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.