Biological monitoring in workers in a nitrobenzene reduction plant : Haemoglobin versus serum albumin adducts

The high priority of monitoring workers exposed to nitrobenzene is a consequence of clear findings of experimental carcinogenicity of nitrobenzene and the associated evaluations by the International Agency for Research on Cancer. Eighty male employees of a nitrobenzene reduction plant, with potential skin contact with nitrobenzene and aniline, participated in a current medical surveillance programme. Blood samples were routinely taken and analysed for aniline, 4-aminodiphenyl (4-ADP) and benzidine adducts of haemoglobin (Hb) and human serum albumin (HSA). Also, levels of methaemoglobin (Met-Hb) and of carbon monoxide haemoglobin (CO-Hb) were monitored. Effects of smoking were straightforward. Using the rank sum test of Wilcoxon, we found that very clear-cut and statistically significant smoking effects (about 3-fold increases) were apparent on CO-Hb (P = 0.00085) and on the Hb adduct of 4-ADP (P = 0.0006). The mean aniline-Hb adduct level in smokers was 1.5 times higher than in non-smokers; the significance (P = 0.05375) was close to the 5% level. The strongest correlation was evident between the Hb and HSA adducts of aniline (rs = 0.846). Less pronounced correlations (but with P values < 0.02) appeared between aniline-Hb and 4-ADP-Hb adducts (rs = 0.388), between 4-ADP and 4-ADP-HSA adducts (rs = 0.373), and between 4-ADP-Hb and aniline-HSA adducts (rs = 0.275). In view of the proposal for additional use of the aniline-HSA adduct for biological monitoring, particularly in cases of acute overexposures or poisonings, the strong correlation of the Hb and HSA conjugates is noteworthy; the ratio aniline-HSA:aniline-Hb was 1:42 for the entire cohort.

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Development and characterization of a novel, antimicrobial, sterile hydrogel dressing for burn wounds : single-step production with gamma irradiation creates silver nanoparticles and radical polymerization

Patients with burn wounds are susceptible to wound infection and sepsis. This research introduces a novel burn wound dressing that contains silver nanoparticles (SNPs) to treat infection in a 2-acrylamido-2-methylpropane sulfonic acid sodium salt (AMPS-Na(+) ) hydrogel. Silver nitrate was dissolved in AMPS-Na(+) solution and then exposed to gamma irradiation to form SNP-infused hydrogels. The gamma irradiation results in a cross-linked polymeric network of sterile hydrogel dressing and a reduction of silver ions to form SNPs infused in the hydrogel in a one-step process. About 80% of the total silver was released from the hydrogels after 72 h immersion in simulated body fluid solution; therefore, they could be used on wounds for up to 3 days. All the hydrogels were found to be nontoxic to normal human dermal fibroblast cells. The silver-loaded hydrogels had good inhibitory action against Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus. Results from a pilot study on a porcine burn model showed that the 5-mM silver hydrogel was efficient at preventing bacterial colonization of wounds, and the results were comparable to the commercially available silver dressings (Acticoat(TM) , PolyMem Silver(®) ). These results support its use as a potential burn wound dressing.

Available evidence does not support serosorting as an HIV risk reduction strategy

Recent data highlighted the association between penetration of antiretrovirals in the central nervous system (CNS) and neurocognitive impairment in HIVpositive patients. Existing antiretrovirals have been ranked according to a score of neuropenetration, which was shown to be a predictor of anti-HIVactivity in the CNS and improvement of neurocognitive disorders [1]. Main factors affecting drug penetration are known to be protein binding, lipophilicity and molecular weight [2]. Moreover, active translation by membrane transporters (such as p-glycoprotein) could be a key mechanism of passage [3]. The use of raltegravir (RGV), a novel antiretroviral drug targeted to inhibit the HIV preintegrase complex, is increasing worldwide due to its efficacy and tolerability. However, penetration of RGV in the CNS has not been yet elucidated. In fact, prediction of RGV neuropenetration according to molecular characteristics is controversial. Intermediate protein binding (83%) and large volume of distribution (273 l) could suggest a high distribution beyond extracellular spaces [4]. On the contrary, low lipophilicity (oil/water partition coefficient at pH 7.4 of 2.80) and intermediate molecular weight (482.51 Da) suggest a limited diffusion. Furthermore, in-vitro studies suggest that RGV is substrate of p-glycoprotein, although this efflux pump has not been identified to significantly affect plasma pharmacokinetics [5]. In any case, no data concerning RGV passage into cerebrospinal fluid of animals or humans have yet been published.

Implications of differences in technical efficiency of fishing boats for capacity measurement and reduction

Capacity measurement and reduction is a major international issue to emerge in the new millennium. However, there has been limited assessment of the success of capacity reduction schemes (CRS). In this paper, the success of a CRS is assessed for a European fishery characterised by differences in efficiency levels of individual boats. In such a fishery, given it is assumed that the least efficient producers are the first to exit through a CRS, the reduction in harvesting capacity is less than the nominal reduction in physical fleet capacity. Further, there is potential for harvesting capacity to increase if remaining vessels improve their efficiency.

Component modeling for SCADA network mapping

Supervisory Control and Data Acquisition systems (SCADA) are widely used to control critical infrastructure automatically. Capturing and analyzing packet-level traffic flowing through such a network is an essential requirement for problems such as legacy network mapping and fault detection. Within the framework of captured network traffic, we present a simple modeling technique, which supports the mapping of the SCADA network topology via traffic monitoring. By characterizing atomic network components in terms of their input-output topology and the relationship between their data traffic logs, we show that these modeling primitives have good compositional behaviour, which allows complex networks to be modeled. Finally, the predictions generated by our model are found to be in good agreement with experimentally obtained traffic.

Identification of pre-spike network in patients with mesial temporal lobe epilepsy

Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation.

A Memetic Algorithm for the location-based continuously operating reference stations placement problem in Network Real-Time Kinematic

Network Real-Time Kinematic (NRTK) is a technology that can provide centimeter-level accuracy positioning services in real time, and it is enabled by a network of Continuously Operating Reference Stations (CORS). The location-oriented CORS placement problem is an important problem in the design of a NRTK as it will directly affect not only the installation and operational cost of the NRTK, but also the quality of positioning services provided by the NRTK. This paper presents a Memetic Algorithm (MA) for the location-oriented CORS placement problem, which hybridizes the powerful explorative search capacity of a genetic algorithm and the efficient and effective exploitative search capacity of a local optimization. Experimental results have shown that the MA has better performance than existing approaches. In this paper we also conduct an empirical study about the scalability of the MA, effectiveness of the hybridization technique and selection of crossover operator in the MA.

The impact of Cool Roof applications on energy performance : results from Australian sub-tropical and tropical field studies

Cool roof coatings are identified by their solar reflectance index. They have been reported to have multiple benefits, the extent of which are strongly dependent on the peculiarities of the local climate, building stock and electricity network. This paper presents measured and simulated data from residential, educational and commercial buildings involved in recent field trials in Australia. The purpose of the field trials was to evaluate the impact of such coatings on electricity demand and load and to assess their potential application to improve comfort whilst avoiding the need for air conditioners. Measured reductions in temperature, power (kW) and energy (kWh) were used to develop a predictive model that correlates ambient temperature distribution profiles, building demand reduction profiles and electricity network peak demand times. Combined with simulated data, the study indicates the types of buildings that could be targeted in Demand Management programs for the mutual benefit of electricity networks and building occupants.

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.