Bone marrow and renal injury associated with haloalkene cysteine conjugates in calves

Almost 40 years ago, it was reported that cattle-feed which had been extracted with hot trichloroethylene and then fed to calves produced renal injury and a fatal aplastic anaemia. The toxic factor was subsequently identified as S-(1,2-dichlorovinyl)-L-cysteine (DCVC). These original findings have been confirmed, a single intravenous dose of DCVC at 4 mg/kg, or 0.4 mg/kg intravenously per day administered for 10 days to calves produced aplastic anaemia, and renal injury after a single dose of 4 mg/kg. The toxicity to calves of a number of other haloalkene cysteine conjugates has been examined to ascertain whether, like DCVC, they produce bone marrow and renal injury. Intravenous administration of the N-acetyl cysteine conjugate of DCVC produced renal but not bone marrow injury at a molar equivalent dose to DCVC, indicating that the calf can deacetylate the mercapturic acid and further that sufficient chemical had reached the kidney to be a substrate for the enzyme cysteine conjugate beta-lyase. However, intravenous administration of the alpha-methyl analogue of DCVC, which cannot undergo metabolism via the enzyme cysteine conjugate beta-lyase, was without toxicity at doses about five-fold higher than DCVC. These latter findings provide strong evidence that metabolism of DCVC via the enzyme beta-lyase is necessary for bone marrow and renal injury to occur. The cysteine conjugates of perchloroethylene and hexachloro-1,3-butadiene(HCBD) when given intravenously to calves at molar equivalent doses to DCVC, or above, did not produce either bone marrow or renal injury. In contrast, intravenous administration of the cysteine conjugate of tetrafluoroethylene (TFEC) produced severe renal tubular injury in calves without affecting the bone marrow. In vitro studies with these haloalkene cysteine conjugates showed, like DCVC, that they were good substrates for calf renal cysteine conjugate beta-lyase and toxic to renal cells as judged by their ability to reduce organic anion and cation transport by slices of calf renal cortex and inhibit the renal enzyme glutathione reductase. Calves were also dosed either orally or intravenously with HCBD to assess its toxicity. HCBD at higher molar equivalent doses than DCVC produced mid-zonal necrosis in the liver, renal tubular necrosis but no bone marrow injury in calves. The key findings emerging from these studies are (1) that none of the other cysteine conjugates, at molar equivalent doses to DCVC and above, produce bone marrow injury in calves, (2) TFEC produced only renal injury, suggesting that sufficient of the other conjugates had not reached the kidney for metabolism by beta-lyase to produce cytotoxicity and (3) that HCBD itself is more toxic than its cysteine or mercapturic acid conjugate, suggesting that pharmaco-kinetics and disposition are important factors in determining the toxicity of these conjugates to calves. Further studies are needed to understand the basis for the selective toxicity of DCVC to the bone marrow of calves.     

Spectral nephelometry for making extravirgin olive oil fingerprints

In spectral nephelometry, absorption spectroscopy and nephelometry are innovatively combined to provide simultaneous online monitoring of color and turbidity of edible oils. Spectral nephelometry instrumentation consists of an optoelectronic device that measures the absorption spectrum of the oil sample at different angles. Data processing, carried out by principal component analysis (PCA), allows identification of the oil sample and creates a two-dimensional map as a fingerprint of oil types.     

Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

Growth and formation mechanism of sea urchin-like ZnO nanostructures on Si

Sea urchin-like nanostructures of ZnO consisting of ZnO nanowires with blunt faceted ends were grown on Si (100) substrates by oxidation of metallic Zn at 600 °C. ZnO nanowires having a diameter of 30–60 nm and length of 2–4 Μm were in similar shape with uniform diameter along its entire length with well faceted blunt ends. X-ray diffraction and transmission electron microscope analysis showed that the as-grown nanostructures were highly crystalline with wurtzite hexagonal structure having lattice constants of a=b=3.25 å and c=5.21 å. Room temperature photoluminescence (PL) measurements showed a weak near band-edge emission at 380 nm, but a strong green emission at 500–530 nm. A model for vapor-solid (VS) growth mechanism of ZnO nanowires was presented, in which nucleation of ZnO is crucial for the growth of the nanostructures.     

Methanol oxidation on Au/TiO2 catalysts

We have investigated the adsorption and reaction of methanol with Au/TiO₂ catalysts using a pulsed flow reactor, DRIFTS and TPD. The TiO₂ (P25) surface adsorbed a full monolayer of methanol, much of it in a dissociative manner, forming methoxy groups associated with the cationic sites, and hydroxyl groups at the anions. The methoxy is relatively stable until 250 °C, at which point decomposition occurs, producing mainly dimethyl ether by a bimolecular surface reaction. As the concentration of methoxy on the surface diminishes, so the mechanism reverts to a de-oxygenation pathway, producing mainly methane and water (at ~330 °C in TPD), but also with some coincident CO and hydrogen. Au catalysts were prepared by the deposition-precipitation method to give Au loadings between 0.5–3 wt %. The effect of low levels of Au on the reactivity is marked. The pathway which gives methane, which is characteristic of titania, remains, but a new feature of the reaction is the evolution of CO₂ and H₂ at lower temperature (a peak is seen in TPD at 220 °C), and the elimination of the DME-producing state. Clearly this is associated with the presence of Au and appears to be due to the production of a formate species on the surface of the Au component. This formate species is mainly involved in the reaction of methanol with the Au/TiO₂ catalysts which results in a combustion pathway being followed, with complete conversion occurring by ~130 °C.     

Architectural Design,Improvement, and Challenges of Distributed Software-Defined Wireless Sensor Networks

Wireless Personal Communications - Software-defined networking (SDN) is widely perceived to simplify network management and monitoring. The introduction of the SDN model into wireless sensor...     

The Double Edge Sword Based Distributed Executor Service

Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a client-server based simulator and found that our system outperformed by sustaining smaller response times.     

Low-Dimensional Halide Perovskites and Their Advanced Optoelectronic Applications

Nano-Micro Letters - A NiFe2O4/expanded graphite (NiFe2O4/EG) nanocomposite was prepared via a simple and inexpensive synthesis method. Its lithium storage properties were studied with the goal of...     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.     

An enumeration algorithm for integer‐valued data envelopment analysis

Data envelopment analysis (DEA) models assume real‐valued inputs and outputs, but on many occasions, some inputs and/or outputs can only take integer values. In these cases, using DEA models can result in misleading efficiency assessments and inaccurate performance targets. In this paper, we propose an enumeration algorithm for computing efficiency scores and performance targets of decision‐making units with integer value inputs/outputs. In the presented algorithm, we do not use any of the mixed integer linear programming (MILP) models that are used in previous studies. We show that the result of our algorithm and that of the MILP model presented in this context is the same. We also generalize our algorithm for different types of returns to scale as well as for the hybrid setting with real‐valued data.