פרסומים

This study is to compare the tissue distribution and metabolism of AN1284 after subcutaneous and oral administration at doses causing maximal reductions in IL-6 in plasma and tissues of mice. Anti-inflammatory activity of AN1284 and its metabolites was detected in lipopolysaccharide (LPS) activated RAW 264.7 macrophages. Mice were given AN1284 by injection or gavage, 15 min before LPS. IL-6 protein levels were measured after 4 h. Using a liquid chromatography/mass spectrometry method we developed, we showed that AN1284 is rapidly metabolized to the indole (AN1422), a 7-OH derivative (AN1280) and its glucuronide. AN1422 has weaker anti-inflammatory activity than AN1284 in LPS-activated macrophages and in mice. AN1284 (0.5 mg/kg) caused maximal reductions in IL-6 in the plasma, brain, and liver when injected subcutaneously and after gavage only in the liver. Similar reductions in the plasma and brain required a dose of 2.5 mg/kg, which resulted in 5.5-fold higher hepatic levels than after injection of 0.5 mg/kg, but 7, 11, and 19-fold lower ones in the plasma, brain, and kidneys, respectively. Hepatic concentrations produced by AN1284 were 2.5 mg/kg/day given by subcutaneously implanted mini-pumps that were only 12% of the peak levels seen after acute injection of 0.5 mg/kg. Similar hepatic concentrations were obtained by (1 mg/kg/day), administered in the drinking fluid. These were sufficient to decrease hepatocellular damage and liver triglycerides in previous experiments in diabetic mice. AN1284 can be given orally by a method of continuous release to treat chronic liver disease, and its preferential concentration in the liver should limit any adverse effects.

This study is to compare the tissue distribution and metabolism of AN1284 after subcutaneous and oral administration at doses causing maximal reductions in IL-6 in plasma and tissues of mice. Anti-inflammatory activity of AN1284 and its metabolites was detected in lipopolysaccharide (LPS) activated RAW 264.7 macrophages. Mice were given AN1284 by injection or gavage, 15 min before LPS. IL-6 protein levels were measured after 4 h. Using a liquid chromatography/mass spectrometry method we developed, we showed that AN1284 is rapidly metabolized to the indole (AN1422), a 7-OH derivative (AN1280) and its glucuronide. AN1422 has weaker anti-inflammatory activity than AN1284 in LPS-activated macrophages and in mice. AN1284 (0.5 mg/kg) caused maximal reductions in IL-6 in the plasma, brain, and liver when injected subcutaneously and after gavage only in the liver. Similar reductions in the plasma and brain required a dose of 2.5 mg/kg, which resulted in 5.5-fold higher hepatic levels than after injection of 0.5 mg/kg, but 7, 11, and 19-fold lower ones in the plasma, brain, and kidneys, respectively. Hepatic concentrations produced by AN1284 were 2.5 mg/kg/day given by subcutaneously implanted mini-pumps that were only 12% of the peak levels seen after acute injection of 0.5 mg/kg. Similar hepatic concentrations were obtained by (1 mg/kg/day), administered in the drinking fluid. These were sufficient to decrease hepatocellular damage and liver triglycerides in previous experiments in diabetic mice. AN1284 can be given orally by a method of continuous release to treat chronic liver disease, and its preferential concentration in the liver should limit any adverse effects.

Plant root systems are exposed to spatial and temporal heterogeneity regarding water availability. In the long-term, compensation, increased uptake by roots in areas with favorable conditions in response to decreased uptake in areas under stress, is driven by root growth and distribution. In the short-term (hours-days), compensative processes are less understood. We hypothesized hydraulic compensation where local lowered water availability is accompanied by increased uptake from areas where water remains available. Our objective was to quantify instantaneous hydraulic root uptake under conditions of differential water availability. Tomato (Solanum lycopersicum L.) plants were grown in split-root weighing-drainage lysimeters in which each half of the roots could alternatively be exposed to short-term conditions of salinity. Uptake was quantified from each of the two root zone compartments. One-sided exposure to salinity immediately led to less uptake from the salt-affected compartment and increased uptake from the nontreated compartment. Compensation occurred at salinity, caused by NaCl solution of 4 dS m(-1), that did not decrease uptake in plants with entire root systems exposed. At higher salinity, 6.44 dS m(-1), transpiration decreased by similar to 50% when the total root system was exposed. When only half of the roots were exposed, total uptake was maintained at levels of nonstressed plants with as much as 85% occurring from the nontreated compartment. The extent of compensation was not absolute and apparently a function of salinity, atmospheric demand, and duration of exposure. As long as there is no hydraulic restriction in other areas, temporary reduction in water availability in some parts of a tomato's root zone will not affect plant-scale transpiration.

Close range spectra imaging of agricultural plants is widely performed to support digital plant phenotyping, a task where physicochemical changes in plants are monitored in a non-destructive way. A major step before analyzing the spectral images of plants is to distinguish the plant from the background. Usually, this is an easy task and can be performed using mathematical operations on the combinations of selected spectral bands, such as estimating the normalized difference vegetative index (NDVI). However, when the background of plants contains objects with similar spectral properties as plant then the segmentation based on the threshold of NDVI images can suffer. Another common approach is to train pixel classifiers on spectra extracted from selected locations in the spectral image, but such an approach does not take the spatial information about the plant structure into account. From a technical perspective, plant spectral imaging for digital phenotyping applications usually involves imaging several plants together for a comparative purpose, hence, the imaging scene is relatively big in terms of memory. To solve the challenge of plant segmentation and handling the memory challenge, this study proposes a novel approach, which combines chemometrics with advanced deep learning (DL) based semantic segmentation. The approach has four key steps. As a first step, the spectral image is pre-processed to reduce illumination effects present in the close-range spectral images of plants resulting from the interaction of light with complex plant geometry. Different chemometric pre-processing methods were explored to find possible improvements in the segmentation performance of the DL model. The second step was to perform a principal components analysis (PCA) to reduce the dimensionality of the images, thus drastically reducing their size so that they can be handled more easily using the available computer memory during the training of the DL model. As the third step, small random images (128 × 128) were subsampled from the tall and wide image matrices to generate the training and validation sets for training the DL models. In the last step, a U-net based deep semantic segmentation model was trained and validated on the sub-sampled spectral images. The results showed that the proposed approach allowed efficient handling and training of the DL segmentation model. The intersection over union (IoU) scores for the segmentation was 0.96 for the independent test set image. The segmentation based on variable sorting for normalization and standard normal variate pre-processed data achieved the highest IoU scores. A combination of chemometrics and DL led to an efficient segmentation of tall and wide spectral images which otherwise would have given out-of-memory errors. The developed method can facilitate digital phenotyping tasks where close-range spectral imaging is used to estimate the physicochemical properties of plants.

Convolutional Neural Networks (CNNs) are broadly used in numerous applications such as computer vision and image classification. Although CNN models deliver state-of-the-art accuracy, they require heavy computational resources that are not always affordable or available on every platform. Limited performance, system cost, and energy consumption, such as in edge devices, argue for the optimization of computations in neural networks. Toward this end, we propose herein the value-locality-based compression (VELCRO) algorithm for neural networks. VELCRO is a method to compress general-purpose neural networks that are deployed for a small subset of focused specialized tasks. Although this study focuses on CNNs, VELCRO can be used to compress any deep neural network. VELCRO relies on the property of value locality, which suggests that activation functions exhibit values in proximity through the inference process when the network is used for specialized tasks. VELCRO consists of two stages: a preprocessing stage that identifies output elements of the activation function with a high degree of value locality, and a compression stage that replaces these elements with their corresponding average arithmetic values. As a result, VELCRO not only saves the computation of the replaced activations but also avoids processing their corresponding output feature map elements. Unlike common neural network compression algorithms, which require computationally intensive training processes, VELCRO introduces significantly fewer computational requirements. An analysis of our experiments indicates that, when CNNs are used for specialized tasks, they introduce a high degree of value locality relative to the general-purpose case. In addition, the experimental results show that without any training process, VELCRO produces a compression-saving ratio in the range 13.5–30.0% with no degradation in accuracy. Finally, the experimental results indicate that, when VELCRO is used with a relatively low compression target, it significantly improves the accuracy by 2–20% for specialized CNN tasks.

Protein-based binders have become increasingly more attractive candidates for drug and imaging agent development. Such binders could be evolved from a number of different scaffolds, including antibodies, natural protein effectors and unrelated small protein domains of different geometries. While both computational and experimental approaches could be utilized for protein binder engineering, in this review we focus on various computational approaches for protein binder design and demonstrate how experimental selection could be applied to subsequently optimize computationally-designed molecules. Recent studies report a number of designed protein binders with pM affinities and high specificities for their targets. These binders usually characterized with high stability, solubility, and low production cost. Such attractive molecules are bound to become more common in various biotechnological and biomedical applications in the near future.

Most studies linking cardiovascular disease with particulate matter (PM) exposures have focused on total mass concentrations, regardless of their origin. However, the origin of an air mass is inherently linked to particle composition and possible toxicity. We examine how the concentration-response relation between hourly PM exposure and ischemic events is modified by air-mass origin and season. Using telemedicine data, we conducted a case-crossover study of 1855 confirmed ischemic cardiac events in Israel (2005–2013). Based on measurements at three fixed-sites in Tel Aviv and Haifa, ambient PM with diameter < 2.5 μm (PM2.5) and 2.5–10 μm (PM10–2.5) concentrations during the hours before event onset were compared with matched control periods using conditional logistic regression that allowed for non-linearity. We also examined effect modification of these associations based on the geographical origin of each air mass by season. Independent of the geographical origin of the air mass, we observed concentration-response curves that were supralinear. For example, the overall odds ratios (ORs) of ischemic events for an increase of 10-μg/m3 in the 2-h average of PM10–2.5 were 1.08 (95% confidence interval (CI): 1.03–1.14) and 1.00 (0.99–1.01) at the median (17.8 μg/m3) and 95th percentile (82.3 μg/m3) values, respectively. Associations were strongest at low levels of PM10–2.5 when air comes from central Europe in the summer (OR: 1.27; 95% CI: 1.06, 1.52). Our study demonstrates that hourly associations between PM2.5 and PM10–2.5 and ischemic cardiac events are supralinear during diverse pollution conditions in a single population that experiences a wide range of exposure levels.

Most studies linking cardiovascular disease with particulate matter (PM) exposures have focused on total mass concentrations, regardless of their origin. However, the origin of an air mass is inherently linked to particle composition and possible toxicity. We examine how the concentration-response relation between hourly PM exposure and ischemic events is modified by air-mass origin and season. Using telemedicine data, we conducted a case-crossover study of 1855 confirmed ischemic cardiac events in Israel (2005–2013). Based on measurements at three fixed-sites in Tel Aviv and Haifa, ambient PM with diameter < 2.5 μm (PM2.5) and 2.5–10 μm (PM10–2.5) concentrations during the hours before event onset were compared with matched control periods using conditional logistic regression that allowed for non-linearity. We also examined effect modification of these associations based on the geographical origin of each air mass by season. Independent of the geographical origin of the air mass, we observed concentration-response curves that were supralinear. For example, the overall odds ratios (ORs) of ischemic events for an increase of 10-μg/m3 in the 2-h average of PM10–2.5 were 1.08 (95% confidence interval (CI): 1.03–1.14) and 1.00 (0.99–1.01) at the median (17.8 μg/m3) and 95th percentile (82.3 μg/m3) values, respectively. Associations were strongest at low levels of PM10–2.5 when air comes from central Europe in the summer (OR: 1.27; 95% CI: 1.06, 1.52). Our study demonstrates that hourly associations between PM2.5 and PM10–2.5 and ischemic cardiac events are supralinear during diverse pollution conditions in a single population that experiences a wide range of exposure levels.

This protocol was developed based on the knowledge acquired in the framework of the Interreg MONITOOL project (EAPA\_565/2016) where extensive sampling campaigns were performed in transitional and coastal waters covering eight European countries. It provides detailed procedures and guidelines for the sampling of these waterbodies by concurrent collection of discrete water samples and the deployment of Diffusive Gradient in Thin-films (DGT) passive samplers for the measurement of trace metal concentrations. In order to facilitate the application of this protocol by end-users, it presents steps to follow in the laboratory prior to sampling campaigns, explains the procedures for field campaigns (including in situ measurement of supporting parameters) and subsequent sample processing in the laboratory in preparation for trace metal analyze by inductively coupled plasma-mass spectrometry (ICP-MS) and voltammetry. The protocol provides a systematic, coherent field sampling and sample preparation strategy that was developed in order to ensure comparability and reproducibility of the data obtained from each project Partner in different regions. • Standardization of the concurrent sampling of transitional and coastal waters by DGT passive samplers and spot sampling. • Robust procedures and tips based on existing international standards and comprehensive practical experience. • Links to demonstration videos produced within the MONITOOL project.