The solitary median maxillary central incisor (SMMCI) syndrome: Associations,prenatal diagnosis,and outcomes

Solitary median maxillary central incisor (SMMCI) syndrome is a complex disorder consisting of multiple, developmental defects involving midline structures of the head, which includes the cranial bones, the maxilla, and its container dentition (specifically the central incisor tooth germ), together with other midline structures of the body. SMMCI may appear as an isolated trait or in association with other midline developmental anomalies. We describe the case of a patient with SMMCI. He presented with a solitary median maxillary incisor, short stature, corpus callosum anomalies and a microform of holoprosencephaly (HPE), diabetes insipidus, and neurodevelopmental delay. The diagnosis was performed postnatally based on clinical features, radiological imaging, and a comprehensive genetic study. SMMCI can be diagnosed during the prenatal or neonatal periods or during infancy. Evaluation of the superior maxillary bone is important for prenatal diagnosis. Direct evaluation through bidimensional ultrasound or the use of multiplanar ultrasound or tridimensional reconstruction should be performed in cases of brain or face malformations. Early diagnosis can contribute to improved prenatal assessment and postnatal management.     

Pollution magnet: nano-magnetite for arsenic removal from drinking water

Arsenic contamination in groundwater is a severe global problem, most notably in Southeast Asia where millions suffer from acute and chronic arsenic poisoning. Removing arsenic from groundwater in impoverished rural or urban areas without electricity and with no manufacturing infrastructure remains a significant challenge. Magnetite nanocrystals have proven to be useful in arsenic remediation and could feasibly be synthesized by a thermal decomposition method that employs refluxing of FeOOH and oleic acid in 1-octadecene in a laboratory setup. To reduce the initial cost of production, $US 2600/kg, and make this nanomaterial widely available, we suggest that inexpensive and accessible “everyday” chemicals be used. Here we show that it is possible to create functional and high-quality nanocrystals using methods appropriate for manufacturing in diverse and minimal infrastructure, even those without electricity. We suggest that the transfer of this knowledge is best achieved using an open source concept.     

The supervisor POS–LMX–subordinate POS chain: Moderation by reciprocation wariness and supervisor's organizational embodiment

We present three studies providing an increased understanding of the interdependence between perceived organizational support (POS) and leader–member exchange (LMX). Using employees from a social service agency and new hires from a variety of organizations, we report evidence for a relational chain leading from supervisors' perceptions of support by the organization (supervisor POS) to the formation of high‐quality LMX relationships with their subordinates (first link), who interpret high‐quality LMX as support from the organization (subordinate POS, second link) and, ultimately, repay the organization with increased dedication and effort (examined here in terms of reduced withdrawal behavior). The relationship between supervisor POS and LMX with subordinates was strongly moderated by supervisor fear of exploitation in exchange relationships (reciprocation wariness), holding only for supervisors with low reciprocation wariness. Consistent with the view that employees perceive the organization as partly responsible for treatment received from supervisors, LMX was found to be more strongly related to POS when employees highly identified their supervisors with the organization (supervisor's organizational embodiment), and this interaction extended to reduced withdrawal behavior. Copyright © 2013 John Wiley & Sons, Ltd.     

Implementing industrial ecology and design for environment practices: Lawrence livermore national laboratory

Lawrence Livermore National Laboratory (LLNL), one of the nation's three national security laboratories, is initiating a major research effort in industrial ecology with the goal of increasing the economic and environmental efficiency of the U.S. and global economies. Closer to home, ongoing efforts to reduce LLNL's environmental impact on the surrounding community provide an example of how the environmental efficiency of a major research facility can be systematically improved. In the late 1980s, LLNL initiated its waste minimization and pollution prevention program, changing its focus from end-of-pipe waste management to a proactive and preventive strategy—total quality environmental management (TQEM). LLNL's success in integrating pollution prevention with R&D and operational activities is well illustrated by the waste reduction and energy and water conservation accomplishments described in this article. LLNL represents a model of how industrial ecology, design for environment, and pollution prevention can be incorporated into a comprehensive R&D and operational program. This article describes LLNL's developing industrial ecology program, gives some specific technical accomplishments that have application to many industrial facilities, and presents a case study of TQEM at LLNL's remote experimental test facility, Site 300. The integration of TQEM philosophy with LLNL's scientific capabilities holds much promise for new ideas and creative solutions to national and international industrial ecology concerns.     

Can machines recognise stress in plants?

In this paper we show that chlorophyll a fluorescence signals analysed with the self-organizing map (SOM) can be used as a routine tool for the monitoring and classification of pea varieties (Pisum sativum) according to their degree of resistance against drought stress. Fluorescence kinetics measurements were obtained from non-stressed plants. The aim of this study is to evaluate the applicability of artificial intelligence techniques in eco-physiological research. Our goal is to provide a fast tool that will contribute to the knowledge needed to develop strategies that would help to decrease the impact of environmental stress in agriculture and forestry.     

Molecular identification and nanoremediation of microbial contaminants in algal systems using untreated wastewater

Wastewater-algal biomass is a promising option to biofuel production. However, microbial contaminants constitute a substantial barrier to algal biofuel yield. A series of algal strains, Nannochloris oculata and Chlorella vulgaris samples (n = 30), were purchased from the University of Texas, and were used for both stock flask cultures and flat-panel vertical bioreactors. A number of media were used for isolation and differentiation of potential contaminants according to laboratory standards (CLSI). Conventional PCR amplification was performed followed by 16S rDNA sequencing to identify isolates at the species level. Nanotherapeutics involving a nanomicellar combination of natural chitosan and zinc oxide (CZNPs) were tested against the microbial lytic groups through Minimum Inhibitory Concentration (MIC) tests and Transmission Electronic Microscopy (TEM). Results indicated the presence of Pseudomonas spp., Bacillus pumilus/ safensis, Cellulosimicrobium cellulans, Micrococcus luteus and Staphylococcus epidermidis strains at a substantial level in the wastewater-fed algal reactors. TEM confirmed the effectiveness of CZNPs on the lytic group while the average MICs (mg/mL) detected for the strains, Pseudomonas spp, Micrococcus luteus, and Bacillus pumilus were 0.417, 3.33, and 1.458, respectively. Conclusively, CZNP antimicrobials proved to be effective as inhibitory agents against currently identified lytic microbial group, did not impact algae cells, and shows promise for in situ interventions.     

Fate of sex hormones in two pilot-scale municipal wastewater treatment plants: conventional treatment

The fate of seven sex hormones (estrone (E1), estradiol (E2), estriol (E3), ethinylestradiol (EE2), testosterone, androstenedione, and progesterone) was determined in two pilot-scale wastewater treatment plants operated under conventional loading conditions. The levels of hormones in both the liquid and the solid matrixes of the plants were determined. Each of the two 20-l/h pilot-scale plants consisted of a primary clarifier followed by a three-stage aeration tank and a final clarifier. The primary sludge and the waste activated sludge (WAS) were digested anaerobically in one pilot plant and aerobically in the other. The pilot plants were fed a complex synthetic wastewater spiked with the hormones. Levels of testosterone, androstenedione and progesterone were close to method detection limit (MDL) concentrations in the final and digester effluents (both liquid and solid phases) and were considered as completely removed. Average mass flux removals from the liquid streams (plant influent minus secondary clarifier effluent) for the natural estrogens were 82% for E1, 99% for E2, and 89% for (E1+E2). An average overall removal of only 42% was achieved for EE2. These values reflect removals averaged for the two pilot plants.     

Derivation of ozone flux-yield relationships for lettuce: a key horticultural crop

Ozone flux-response relationships were derived for lettuce, employing a multiplicative approach to model the manner in which stomatal conductance is influenced by key environmental variables, using a dataset collected during field experimentation in Crete and yield-response relationships derived from parallel open-top chamber experiments. Regional agronomic practices were adopted throughout. Computed versus measured data revealed that the derived model explained 51% (P<0.001) of the observed variation in stomatal conductance. Concentration-based indices were compared with flux-based indices. Analyses revealed a significant relationship between accumulated stomatal ozone flux and yield employing flux threshold cut-offs up to 4 nmol m(-2) s(-1). Regressions employing very low or zero flux thresholds resulted in the strongest yield-flux relationships (explaining approximately 80% (P<0.05) of the variation in the dataset).