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The potato tuberworm, Phthorimaea operculella (Zeller), is a major pest that courses economic losses of potato Solanum tuberosum L. in both the field and the stores. In this research, the fumigant toxicity of essential oils of three Lamiaceous plants including Thymus caucasicus L., Rosmarinus officinalis L. and Satureja hortensis L. and methyl iodide (a chemical fumigant) were evaluated on P. operculella and on its demographic parameters under the laboratory conditions. Essential oils were extracted through hydro-distillation method using a Clevenger-type apparatus. The gas chromatography-mass spectroscopy (GC-MS) analysis indicated that thymol (29.76 %) in T. caucasicus, carvacrol (36.67 %) in S. hortensis and 1, 8-cineole (26.94 %) in R. officinalis essential oils were the major secondary constituents. The 50 % Lethal Concentration (LC50) values for T. caucasicus, R. officinalis, and S. hortensis essential oils as well as methyl iodide towards P. operculella eggs were 5.99, 4.58, 3.55 and 0.29 µL L− 1 air, respectively. On the other hand, methyl iodide showed the highest acute toxicity on the egg stage of P. operculella compared to the essential oils. The 30 % Lethal Concentration (LC30) values of T. caucasicus, R. officinalis, and S. hortensis essential oils and methyl iodide (4.12, 3.53, 1.96, and 0.23 µL L− 1 air, respectively) were affected the life history and demographical parameters of P. operculella. The longest development time was observed on methyl iodide and the shortest was on T. caucasicus and S. hortensis essential oils. The intrinsic rate of natural increase (rm) values obtained in the control group and treated insects were: 0.19, 0.16, 0.14, 0.13, and 0.01 female progenies per female per day for T. caucasicus, S. hortensis, R. officinalis essential oils and methyl iodide, respectively. These results suggested that methyl iodide was significantly more toxic to the P. operculella compared to the essential oils of all the three mentioned plants. Meanwhile, it was concluded that the application of these plant essential oils can be useful as an alternative method for potato tuberworm control in storage facilities. [ABSTRACT FROM AUTHOR]

Introduction: Given the high prevalence of headache that may result in lowering the quality of life and some side effects of drug treatments that may reduce the tendency of patients to medication and complete the course of treatment, and sometimes resistance of some headaches to medications, we decided to evaluate the effectiveness of hypnotherapy as a nonpharmacologic method in patients with two of the most common types of headaches (tension and migraine headaches). Methods: In this study, totally 36 patients were studied. From each type of headaches, 18 patients were randomly selected for the study and patients of both treatment groups of tension and migraine headache were divided into three categories (6 people). The first group was only subjected to the same standard drug treatment, the second group was subjected to the same drug treatment associated with hypnotherapy and the third group underwent only hypnotherapy. Considered checklists were first completed providing a full description by the patient monitored by the researcher. Hypnotherapy was conducted in five sessions spaced two weeks. Also, in categories one and two of each group, the same standard drug treatment was continued. Then checklists were completed again by patients and obtained results were analyzed. Findings: Before starting the treatment for the group treated with standard drugs associated with hypnotherapy, 69.2% of patients had severe headache and 30.8% had moderate headaches; however, after the end of treatment, only 7.7% had severe headache and 61.5% had a mild headache. In the drug treatment group, the proportion of patients was reduced from 58.3% of patients with severe headache and 41.7% of patients with moderate headache, to 50% moderate headache and 50 % mild headache, respectively. In the hypnotherapy group, 63.6% severe headache and 36.4% moderate headache were declined to 45.5% moderate and 54.5% mild headache, respectively. Comparing both types of headaches indicated that hypnosis had a significant effect on reducing the number of attacks and the work disability amount, that in the meantime, it had a significant decline in the group subjected to the standard drug therapy with hypnosis. Conclusion: This study showed that hypnotherapy can be appropriately effective as drug therapy. [ABSTRACT FROM AUTHOR]

In this paper, we report on the implementation of a novel compute-to-learn pedagogy, which is based upon the theories of situated cognition and meaningful learning. The compute-to-learn pedagogy is designed to simulate an authentic research experience as part of the undergraduate curriculum, including project development, teamwork, peer review, and publication. The compute-to-learn pedagogy was piloted during the Fall 2015 semester within a one-semester, peer-led honors studio environment that uses active learning strategies to encourage cooperation and collaboration among students as they learn how to program. The rationale behind the pedagogy, lessons learned, and adjustments made based on three iterations of its execution, and its initial assessment through end-of-semester interviews are discussed. [ABSTRACT FROM AUTHOR]

The inference of Gene Regulatory Networks (GRNs) using gene expression data in order to detect the basic cellular processes is a key issue in biological systems. Inferring GRN correctly requires inferring predictor set accurately. In this paper, a fast and accurate predictor set inference framework which linearly combines some inference methods is proposed. The purpose of the combination of various methods is to increase the accuracy of inferred GRN. The proposed framework offers a linear weighted combination of Pearson Correlation Coefficient (PCC) and two different feature selection approaches, namely: Information Gain (IG) and ReliefF. In order to set the appropriate weights, Genetic Algorithm (GA) is used. Similarity measure is considered as fitness function to guide GA. At the end, based on the obtained weights, the best predictor set of GRN using three aforementioned inference methods is selected and the network topology is formed. Due to the huge volume of gene expression data, GRN inference algorithms should infer GRN at a reasonable runtime. Hence, a novel criterion is provided to evaluate GRNs based on runtime and accuracy. The simulation results using biological data indicate that the proposed framework is fast and more reliable compared to other recent methods [1-7]. [ABSTRACT FROM AUTHOR]

The computational challenge of fast and reliable transition state and reaction path optimization requires new methodological strategies to maintain low cost, high accuracy, and systematic searching capabilities. The growing string method using internal coordinates has proven to be highly effective for the study of molecular, gas phase reactions, but difficulties in choosing a suitable coordinate system for periodic systems has prevented its use for surface chemistry. New developments are therefore needed, and presented herein, to handle surface reactions which include atoms with large coordination numbers that cannot be treated using standard internal coordinates. The double-ended and single-ended growing string methods are implemented using a hybrid coordinate system, then benchmarked for a test set of 43 elementary reactions occurring on surfaces. These results show that the growing string method is at least 45% faster than the widely used climbing image-nudged elastic band method, which also fails to converge in several of the test cases. Additionally, the surface growing string method has a unique single-ended search method which can move outward from an initial structure to find the intermediates, transition states, and reaction paths simultaneously. This powerful explorative feature of single ended-growing string method is demonstrated to uncover, for the first time, the mechanism for atomic layer deposition of TiN on Cu(111) surface. This reaction is found to proceed through multiple hydrogen-transfer and ligand-exchange events, while formation of H-bonds stabilizes intermediates of the reaction. Purging gaseous products out of the reaction environment is the driving force for these reactions. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

[Display omitted] • Modified peroxamide-based ROS-responsive doxorubicin prodrugs were synthesized. • Among five prodrug candidates, OH-mOX-Dox showed the highest ROS-sensitive response. • OH-mOX-Dox showed selective cytotoxicity toward breast cancer cells. • OH-mOX-Dox showed significantly reduced cytotoxicity toward normal breast cells. • X-ray induced increased ROS level resulted in enhanced cytotoxicity of OH-mOX-Dox. Reactive oxygen species (ROS) plays a pivotal physiological role in intracellular signaling of any living organism. Due to the elevated levels of ROS in tumor microenvironment than normal tissues, an increasing number of ROS-responsive probes and prodrugs is being studied for the fight against cancer. This study describes the design and synthesis of a panel of novel modified peroxamide-based ROS-responsive prodrugs of doxorubicin, among which the OH-mOX-Dox prodrug showed very stable and highly specific ROS sensitivity. This novel Dox prodrug exerted potent anti-proliferation effects against the two breast cancer cell lines of MDA-MB-468 and MDA-MB-231 while it showed minimal toxicity toward the normal breast cell line, MCF-12A. The cytotoxicity of the OH-mOX-Dox prodrug was significantly enhanced at elevated ROS levels after co-incubation with l -buthionine sulfoximine (BSO). Our clonogenic assay data validated that enhanced intracellular ROS level upon X-ray irradiation resulted in an increase in the efficacy of the OH-mOX-Dox prodrug against the two breast cancer cell lines. [ABSTRACT FROM AUTHOR]

• An efficient end-to-end domain adaptation network to achieve multi-modality image segmentation. • The proposed model requires fewer parameters, less memory and less training time compared to other state-of-the-art methods. • The method is implemented for 2D and 3D medical images for multi-class segmentation. • The method can be easily extended to segment images from unseen domains without model retraining. [Display omitted] In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the same organ. This is due to the significant intensity variations of different image modalities. In this paper, we propose a novel end-to-end deep neural network to achieve multi-modality image segmentation, where image labels of only one modality (source domain) are available for model training and the image labels for the other modality (target domain) are not available. In our method, a multi-resolution locally normalized gradient magnitude approach is firstly applied to images of both domains for minimizing the intensity discrepancy. Subsequently, a dual task encoder-decoder network including image segmentation and reconstruction is utilized to effectively adapt a segmentation network to the unlabeled target domain. Additionally, a shape constraint is imposed by leveraging adversarial learning. Finally, images from the target domain are segmented, as the network learns a consistent latent feature representation with shape awareness from both domains. We implement both 2D and 3D versions of our method, in which we evaluate CT and MRI images for kidney and cardiac tissue segmentation. For kidney, a public CT dataset (KiTS19, MICCAI 2019) and a local MRI dataset were utilized. The cardiac dataset was from the Multi-Modality Whole Heart Segmentation (MMWHS) challenge 2017. Experimental results reveal that our proposed method achieves significantly higher performance with a much lower model complexity in comparison with other state-of-the-art methods. More importantly, our method is also capable of producing superior segmentation results than other methods for images of an unseen target domain without model retraining. The code is available at GitHub (https://github.com/MinaJf/LMISA) to encourage method comparison and further research. [ABSTRACT FROM AUTHOR]

Finding efficient alternative to CDiC (Conventional Distillation Column) for methanol-water separation has been an attractive field of study in literature. In this work, five heat pump-assisted schemes are proposed and compared to each other to find the optimal one; (1) VRC (Vapor Recompression Column), (2) external HIDiC (Heat-Integrated Distillation Column), (3) intensified HIDiC with feed preheater, (4) double compressor intensified HIDiC-1, and (5) double compressor intensified HIDiC-2. GA (Genetic Algorithm) is then implemented for optimization of the schemes when TAC (Total Annual Cost) is its objective function. During optimization, two new variables are added for using only appropriate amount of the overhead stream in VRC and double compressor intensified HIDiCs, and another new binary variable is also used for considering feed preheating. Although TAC of the intensified HIDiC with feed preheater is found higher than CDiC by 25.0%, all optimal VRC, external HIDiC, double compressor intensified HIDiCs schemes are reached lower optimal TAC by 3.1%, 27.2%, 24.4%, and 34.2%. Introduced for the first time, the optimal scheme is the double compressor intensified HIDiC-2 with 34.2% TAC saving, 70.4% TEC (Total Energy Consumption) reduction with payback period of 3.30 years. [ABSTRACT FROM AUTHOR]

• Simvastatin treatment improve spatial memory impairment after ethanol neurotoxicity. • Simvastatin treatment has an antioxidant activity in ethanol neurotoxicity. • Simvastatin treatment reduced caspase-3 activation after ethanol neurotoxicity. • Simvastatin treatment has an anti-inflammatory activity in ethanol neurotoxicity via reducing of TNFα. • Simvastatin treatment reduced microgliosis after ethanol neurotoxicity. Ethanol is associated with oxidative stress. Exposure to ethanol during childhood may lead to neurological disorders. Congenital disorders induced by alcohol are mainly caused by an oxidative-inflammatory cascade due to extensive apoptotic neurodegeneration in the brain, particularly in the hippocampus. Simvastatin, which acts as an inhibitor of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA), is widely used to manage cardiovascular diseases. Recently, the neuroprotective effects of simvastatin against nervous system disorders have been introduced. In this study, we examined the protective effects of simvastatin on ethanol-related neurotoxicity in the hippocampus of rat pups. Ethanol (5.27 g/kg) in a milk solution (27.8 mL/kg) was administered to male rat pups via intragastric intubation at 2–10 days after birth. Also, 10 and 20 mg/kg of simvastatin were injected to the animals. By using Morris water maze task, the hippocampus-dependent memory and spatial learning was evaluated 36 days after birth. An ELISA assay was performed to investigate the antioxidant and anti-inflammatory effects of simvastatin by measuring the levels of tumor necrosis factor-α (TNF-α), and antioxidant enzymes. To assess the expression levels of Iba1 immunohistochemical staining and caspase-3 immunofluorescence staining was performed. The current study demonstrated that administration of simvastatin significantly attenuates spatial memory impairment (P < 0.01) after ethanol neurotoxicity. Also simvastatin could considerably increase the total superoxide dismutaseand glutathione levels (P < 0.01). Moreover, it was associated with a greater reduction in malondialdehyde (P < 0.05) and TNF-α levels, compared to the ethanol group (P < 0.01). Furthermore, in the simvastatin group, the hippocampal level of caspase-3 and the level of Iba1-positive cells, reduced (P < 0.01). This study demonstrated that apoptotic signaling, mediated by the oxidative-inflammatory cascade, could be inhibited by simvastatin in rat pups with ethanol exposure in the postnatal period. [ABSTRACT FROM AUTHOR]

The leading-edge technology of Digital Twin (DT) presents potential solutions for challenges associated with renewable energy resources (RER), particularly solar energy, such as optimal management, random nature and unpredictability, maintenance, security, and energy efficiency. These issues are more elevated today due to the widespread adoption of solar energy in the power system. A DT leverages advanced technologies including the Internet of Things (IoT), artificial intelligence, and computing techniques, to observe and confirm the state of physical entities, analyze data and derive valid information to monitor and optimize the entity's operation. This Perspective strives to trace the growing body of advances in the field and proposes opportunity areas for DT of RER (RERDT) and DT of solar energy (DTSE) in various application domains including forecasting, reliability analysis, security, and resiliency. Barriers that hinder the adoption of RERDT and DTSE technologies to meet its continuous operation are discussed, and also possible views as future trends to deal with the challenges are presented. RERDT and DTSE technologies promise to be transformative in enabling flexible and sustainable energy sources as well as in active real-time management since it provides a full comprehension of the resources. [ABSTRACT FROM AUTHOR]

We report two cases of corneal graft rejection following immunization with COVID-19-inactivated vaccine Sinopharm and describe the practical implications of this phenomenon in the clinical setting. A 36-year-old woman with a history of unilateral repeated PKP received the first dose of inactivated Sinopharm vaccine. Seven days later, she presented with symptoms and signs of unilateral corneal graft rejection on slit-lamp examination. A 54-year-old woman with a history of unilateral PKP secondary to herpes simplex keratitis (HSK) developed signs of unilateral corneal graft rejection just a week after injection of the first dose of the similar vaccine. Rejection in both patients was treated successfully with topical steroids. To the best of our knowledge, this is the first report of corneal graft rejection following the COVID-19-inactivated Sinopharm vaccine which occurred through the short period after the injection. We hypothesized that the rejection is the result of an allogenic reaction and the immunogenic role of aluminum hydroxide as an adjuvant of this type of vaccine. However, as the second case had a history of rejection following the HSK, a reactivation could not be definitely ruled out. Ophthalmologists should consider these types of adverse reactions after COVID-19 immunization and also monitor a close follow-up of graft health postimmunization. Patients should be informed about the signs and symptoms of rejection, urgent referral, and treatment. [ABSTRACT FROM AUTHOR]

Serious challenges such as rapid growth in population, environmental pollution, and possibility of energy shortage have motivated researchers for designing optimal energy operation strategies through the concept of smart city. Buildings and electric transport systems, especially subway systems and Plug in Electric Vehicles (PEVs), are among the major energy consumption systems in a smart city. In this paper, a linear model is proposed for the co-optimization planning and operation of distributed energy resources and transportation systems in an interconnected system that analyzes the interactions of such an interconnected system. The idea of Regenerative Breaking Energy (RBE) is deployed to improve the operation of the subway system through the interconnected subway system in the smart city. For the optimal operation of the smart city, an optimization formulation is devised to minimize the total cost of the city in the presence of subway RBE. In this paper, the traffic and length of the routes are modeled considering Vehicle to Grid (V2G) and Vehicle to Subway (V2S) located in the parking lots. Furthermore, the degradation model is developed to increase the PEVs' battery life. An unscented transformation (UT) approach is utilized to construct a stochastic framework based on the uncertainty method, to handle the uncertain behaviors of PEVs and distributed energy resource and loads in the smart city. Finally, the proposed model is implemented and its performance is analyzed in both the stochastic and deterministic frameworks. • Providing a comprehensive integrated model for planning and operation of smart grid, and transportation systems. • Assessing the role of parking lots in smart grid and subway stations for attending V2G and V2S. • Considering charging lines in the smart city incorporating the unexpected aging of PEVs' batteries. • Developing a stochastic framework based on UT to handle the correlated uncertainties in the smart city. [ABSTRACT FROM AUTHOR]

Serious challenges such as rapid growth in population, environmental pollution, and possibility of energy shortage have motivated researchers for designing optimal energy operation strategies through the concept of smart city. Buildings and electric transport systems, especially subway systems and Plug in Electric Vehicles (PEVs), are among the major energy consumption systems in a smart city. In this paper, a linear model is proposed for the co-optimization planning and operation of distributed energy resources and transportation systems in an interconnected system that analyzes the interactions of such an interconnected system. The idea of Regenerative Breaking Energy (RBE) is deployed to improve the operation of the subway system through the interconnected subway system in the smart city. For the optimal operation of the smart city, an optimization formulation is devised to minimize the total cost of the city in the presence of subway RBE. In this paper, the traffic and length of the routes are modeled considering Vehicle to Grid (V2G) and Vehicle to Subway (V2S) located in the parking lots. Furthermore, the degradation model is developed to increase the PEVs' battery life. An unscented transformation (UT) approach is utilized to construct a stochastic framework based on the uncertainty method, to handle the uncertain behaviors of PEVs and distributed energy resource and loads in the smart city. Finally, the proposed model is implemented and its performance is analyzed in both the stochastic and deterministic frameworks. • Providing a comprehensive integrated model for planning and operation of smart grid, and transportation systems. • Assessing the role of parking lots in smart grid and subway stations for attending V2G and V2S. • Considering charging lines in the smart city incorporating the unexpected aging of PEVs' batteries. • Developing a stochastic framework based on UT to handle the correlated uncertainties in the smart city. [ABSTRACT FROM AUTHOR]

• Fucoidan-conjugated doxorubicin nanoparticles were developed to target P-selectin. • Nanoparticles showed uniform sizes, stability, and pH responsiveness. • DSPE-PEG helped control and stabilize the size and stability of nanoparticles. • NPs showed enhanced cellular uptake in cell line with high P-selectin expression. • NPs showed enhanced cytotoxicity against cell line with high P-selectin expression. Fucoidan, a type of sulfated polysaccharide known for its anticoagulant, anti-tumor and anti-inflammatory effects, has been reported to have strong affinity towards P-selectin. P-selectin, which plays an important role in metastasis by enhancing the adhesion of cancer cells to endothelium and activated platelets in distant organs, is overexpressed on many cancer types. This study demonstrates the synthesis of a fucoidan-based drug delivery system for minimizing the side effects of doxorubicin (Dox) with the help of active targeting toward P-selectin. Fucoidan-doxorubicin nanoparticles (FU-Dox NPs), developed by direct conjugation of Dox to the fucoidan backbone, showed a well-controlled size distribution and sustained release. The active targeting capability of FU-Dox NPs toward P-selectin resulted in enhanced cellular uptake and cytotoxicity against the MDA-MB-231 cell line with high P-selectin expression compared to the MDA-MB-468 cell line with low P-selectin expression. [ABSTRACT FROM AUTHOR]

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model's architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system's prognostics and diagnostics without modifying the models' architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions. [ABSTRACT FROM AUTHOR]

Recent studies of signaling networks point out that an order of drugs to be administrated to the cancerous cells can be critical for optimal therapeutic outcomes of recalcitrant metastatic and drug-resistant cell types. In this study, a development of a polymeric nanoparticle system for sequential delivery is reported. The nanoparticle system can co-encapsulate and co-deliver a combination of therapeutic agents with different physicochemical properties [i.e. epidermal growth factor receptor (EGFR) inhibitor, erlotinib (Ei), and doxorubicin (Dox)]. Dox is hydrophilic and was complexed with anionic lipid, 1,2-dioleoyl- sn -glycero-3-phosphate (DOPA), via ion pairing to form a hydrophobic entity. Then it was co-encapsulated with hydrophobic Ei in a poly(L-lactide)- b -polyethylene glycol (PLA- b -PEG) nanoparticle by nanoprecipitation. The complexation of Dox with DOPA greatly helps the encapsulation of Dox, and substantially reduces the release rate of Dox. This nanoparticle system was found to burst the release of Ei with a slow and sustained profile of Dox, which is an optimal course of administration for these two drugs as previously reported. The efficacy of this sequential delivery nanoparticle system was validated in vitro and its in vivo potential applicability was substantiated by fluorescent imaging of high tumor accumulation. [ABSTRACT FROM AUTHOR]

Occupational exposure to heat stress may lead to changes in blood cell count. The objective of this retrospective descriptive-analytical field study is to investigate the relationship between heat stress exposure and some immunological parameters among foundry workers. This study was carried out on 55 subjects of which, 35 were working in a foundry unit (exposed group) and 20 were working in a computer numerical control (CNC) machining unit (control group). The measurement method consisted of taking 10 cc of the subjects' blood between 10 am and 2 pm and then performing the automated blood cell counting and enzyme-linked immunosorbent assay (ELISA) on the sample. Environmental parameters such as noise levels, lighting, and the wet-bulb globe temperature (WBGT) index were measured at the subjects' workstations. All measurements were made in the first half of the month of July. The mean WBGT index was 22.5 ± 2.16 °C for the control group and 32.97 ± 3.22 °C for the exposed group. The laboratory test results show a significant decrease in white blood cell count and lymphocyte levels and a significant increase in neutrophil levels and neutrophil to lymphocyte ratio in exposed group compared with control ones (P < 0.05). IgM levels decrease under heat stress, but we do not observe significant differences between IgG and IgA levels between two groups. A significant negative correlation was observed between white blood cell count, lymphocyte levels, and IgM concentration with WBGT index and significant positive correlation was observed between neutrophil and neutrophil to lymphocyte ratio with WBGT index. No significant correlation was observed between sound pressure levels and light intensity with studied immunological parameters. In conclusion, under heat stress conditions, leukocytes levels and immunoglobulin concentration may reduce and it may weaken and suppress the human immune system. [ABSTRACT FROM AUTHOR]

In this study, the reaction kinetics for a heterogeneous oxidation reaction of elemental mercury (Hg(0)) vapor with CuCl 2 was studied in a fixed-bed reactor using 2%(wt) CuCl 2 /α-Al 2 O 3 between 100 and 180 °C for Hg(0) oxidation after air preheater at a typical coal-fired power plant. The reaction rate expression was first order with respect to Hg(0). However, between 100 and 180 °C, CuCl 2 over α-Al 2 O 3 agglomerates and sinters. This sintering effect added significant mass-transfer resistance to the diffusion of Hg(0) vapor, and thus made the conversion of CuCl 2 incomplete. Therefore, a grain model was formulated to determine the rate constant by taking into account the mass-transfer resistance. The model constituted a two parameter estimation problem for the determination of the rate constant and product layer diffusivity. The model predictions with the two optimum parameters were in good agreement with the experimental data. The activation energy value determined from the rate constant values was significantly lower than those for other Hg(0) oxidation catalysts under HCl and O 2 gases reported in the literature. This result corroborates that CuCl 2 can enhance Hg(0) oxidation by lowering the activation energy barrier with the reduction of Cu(2+) to Cu(1+) and supplying thermally stable surface Cl sites following a Mars-Maessen mechanism. CuCl 2 -based catalyst has potential to be applied after air preheater for Hg(0) oxidation followed by the separation of Hg(2+) in wet flue gas desulfurization (FGD) system or by activated carbon (AC) injection. [ABSTRACT FROM AUTHOR]

There is an increasing interest in engineered nanoparticle (NP) conjugates for targeted and controlled drug delivery. However, the practical applications of these NP delivery vehicles remain constrained because of their reactivity with the body’s immune system defenses resulting in undesirable off-target effects. In this study, poly(D,L lactide-co-glycolide) (PLGA)- b -polyethylene glycol (PEG) NPs conjugated to different quantities of the commercial antibody Herceptin ® meant to target HER2-positive breast cancer cells were studied for their immune cell uptake and immunogenic properties (using murine macrophages and human dendritic cells). We further modified the Herceptin ® -NP conjugates with short PEG linkers with an aim to increase their biocompatibility. The 50% Herceptin ® -NP conjugate group with short PEG modification to Herceptin ® showed the best reduction in immune cell uptake by 82% along with the reduction by >50% for proinflammatory cytokine response (TNF-α and IL-6). In conclusion, optimum Herceptin ® coverage with improved hydrophilic profile results in reduced phagocytic uptake and immunogenicity of engineered NP-antibody conjugates, potentially minimizing their undesirable off-target effects as a drug delivery vehicle. [ABSTRACT FROM AUTHOR]

Background: Airborne pathogens play an important role in a hospital air quality. Respiratory infections are the most common occupational disease among the health care staff. The aim of this study was to determine the effect of ventilation system parameters and patient bed arrangements on concentration of airborne pathogens in indoor air of an isolation room. Materials and Methods: A single-bed room was considered in which a patient diagnosed with tuberculosis had been admitted. Five different ventilation types, each at four different capacities were installed in the room while two different locations for the patient's bed were assessed. A direct-impact sampling method (blood agar plate) was used in order to determine the intensity of the bio-aerosols in indoor air of the isolation room. Results: The results showed that when the air was supplied through a circular vent located on the northern wall and the vented air was exhausted via a linear vent located on the southern wall, the average concentration of the bio-aerosols in the air, (with 12 air changes per hour) was reduced to 25 colonies per cubic meter (cfu/m³) (in the range of 25-88 cfu/m³ and a 95 percent confidence interval). In accordance with the analysis applied upon the two different locations of the bed, no significant difference was observed (P>0.05). Conclusion: Installation of ventilation systems as determined by the study is recommended for tuberculosis isolation rooms. [ABSTRACT FROM AUTHOR]