פרסומים

Abstract Because of the profound ecological and economic impacts of many non-native insect species, early detection and eradication of newly founded, isolated populations is a high priority for preventing damages. Though successful eradication is often challenging, the effectiveness of several treatment methods/tactics is enhanced by the existence of Allee dynamics in target populations. Historically, successful eradication has often relied on the application of two or more tactics. Here, we examine how to combine three treatment tactics in the most cost-effective manner, either simultaneously or sequentially in a multiple-annum process. We show that each tactic is most efficient across a specific range of population densities. Furthermore, we show that certain tactics inhibit the efficiency of other tactics and should therefore not be used simultaneously; but since each tactic is effective at specific densities, different combinations of tactics should be applied sequentially through time when a multiple-annum eradication programme is needed.

Studying the role of predator-prey interactions in food-web stability and species coexistence in the environment is arduous. Here, Cohen et al. use a combination of community and single-cell analyses to show that bacterial predators can regulate prey populations in the species-rich environments of wastewater treatment plants. A fundamental question in community ecology is the role of predator-prey interactions in food-web stability and species coexistence. Although microbial microcosms offer powerful systems to investigate it, interrogating the environment is much more arduous. Here, we show in a 1-year survey that the obligate predators Bdellovibrio and like organisms (BALOs) can regulate prey populations, possibly in a density-dependent manner, in the naturally complex, species-rich environments of wastewater treatment plants. Abundant as well as rarer prey populations are affected, leading to an oscillating predatory landscape shifting at various temporal scales in which the total population remains stable. Shifts, along with differential prey range, explain co-existence of the numerous predators through niche partitioning. We validate these sequence-based findings using single-cell sorting combined with fluorescent hybridization and community sequencing. Our approach should be applicable for deciphering community interactions in other systems.

Managing and engineering microbial communities relies on the ability to predict their composition. While progress has been made on predicting compositions on short, ecological timescales, there is still little work aimed at predicting compositions on evolutionary timescales. Therefore, it is still unknown for how long communities typically remain stable after reaching ecological equilibrium, and how repeatable and predictable are changes when they occur. Here, we address this knowledge gap by tracking the composition of 87 two- and three-species bacterial communities, with 3–18 replicates each, for ~400 generations. We find that community composition typically changed during evolution, but that the composition of replicate communities remained similar. Furthermore, these changes were predictable in a bottom-up approach—changes in the composition of trios were consistent with those that occurred in pairs during coevolution. Our results demonstrate that simple assembly rules can hold even on evolutionary timescales, suggesting it may be possible to forecast the evolution of microbial communities.

Ab Initio Multiple Spawning (AIMS) simulates the excited-state dynamics of molecular systems by representing nuclear wavepackets in a basis of coupled traveling Gaussian functions, called trajectory basis functions (TBFs). New TBFs are spawned when nuclear wavepackets enter regions of strong nonadiabaticity, permitting the description of non-Born-Oppenheimer processes. The spawning algorithm is simultaneously the blessing and the curse of the AIMS method: it allows for an accurate description of the transfer of nuclear amplitude between different electronic states, but it also dramatically increases the computational cost of the AIMS dynamics as all TBFs are coupled. Recently, a strategy coined stochastic-selection AIMS (SSAIMS) was devised to limit the ever-growing number of TBFs and tested on simple molecules. In this work, we use the photodynamics of three different molecules-cyclopropanone, fulvene, and 1,2-dithiane-to investigate (i) the potential of SSAIMS to reproduce reference AIMS results for challenging nonadiabatic dynamics, (ii) the compromise achieved by SSAIMS in obtaining accurate results while using the smallest average number of TBFs as possible, and (iii) the performance of SSAIMS in comparison to the mixed quantum/classical method trajectory surface hopping (TSH)-both in terms of its accuracy and computational cost. We show that SSAIMS can accurately reproduce the AIMS results for the three molecules considered at a much cheaper computational cost, often close to that of TSH. We deduce from these tests that an overlap-based criterion for the stochastic-selection process leads to the best agreement with the reference AIMS dynamics for the smallest average number of TBFs.

New Coronavirus Disease 2019 (COVID-19) vaccines are available to prevent the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. We compared the efficacy of new COVID-19 vaccines to prevent symptomatic and severe disease in the adult population and to prevent symptomatic COVID-19 among the elderly. Leading medical databases were searched until August 30, 2021. Published phase 3 randomized controlled trials (RCTs) evaluated efficacy of the vaccine to prevent symptomatic and sever COVID-19 in adults were included. Two reviewers independently evaluated the literature search results and independently extracted summary data. The risk of bias was evaluated using the Cochrane Risk of Bias Assessment Tool. We performed a network meta-analysis (NMA) according to PRISMA-NMA 2015 to pool indirect comparisons between different vaccines regarding their relative efficacy. The primary outcomes were the efficacy of the vaccine against symptomatic COVID-19 in adults (PROSPERO registration number: CRD42021235364). Above 200,000 adult participants from eight phase 3 RCTs were included in NMA, of whom 52% received the intervention (active COVID-19 vaccine). While each of nine vaccines was tested in the unique clinical trial as compared to control, based on indirect comparison, BNT162b2 and mRNA-1273 vaccines were ranked with the highest probability of efficacy against symptomatic COVID-19 (P-scores 0.952 and 0.843, respectively), followed by Gam-COVID-Vac (P-score 0.782), NVX-CoV23730 (P-score 0.700), CoronaVac (P-score 0.570), BN02 (P-score 0.428), WIV04 (P-score 0.327), and Ad26.COV2.S (P-score 0.198). No statistically significant difference was seen in the ability of the vaccines to prevent symptomatic disease in the elderly population. No vaccine was statistically significantly associated with a decreased risk for severe COVID-19 than other vaccines, although mRNA-1273 and Gam-COVID-Vac have the highest P-scores (0.899 and 0.816, respectively), indicating greater protection against severe disease than other vaccines. In our indirect comparison, the BNT162b2 and mRNA-1273 vaccines, which use mRNA technology, were associated with the highest efficacy to prevent symptomatic COVID-19 compared to other vaccines. This finding may have importance when deciding which vaccine to use, together with other important factors as availability of the vaccines, costs, logistics, side effects, and patient acceptability.

Bisphenol-A (BPA), 17 alpha-ethinylestradiol (EE2), and 4-nonylphenol (4NP) are endocrine-disrupting chemicals (EDCs) that are useful models for studying the potential fate and transport of EDCs in soil and water environments. Two alluvial soils with contrasting physicochemical properties were used as adsorbents for this study. The Zook soil material had more organic matter and clay than the sandy loam Hanlon soil material. Batch equilibrium experiments were performed to generate adsorption isotherms, to determine the adsorption parameters, and to assess desorption hysteresis. Adsorption of BPA to both soils followed an L-type isotherm, and 4NP adsorbed to both Hanlon and Zook soils exhibited S-shape isotherms. EE2 adsorbed to the Zook soil also followed an S-shaped isotherm, but EE2 adsorbed to the Hanlon soil showed an H-type isotherm. Overall, the Sips model fit the data well, with standard errors of prediction generally <= 6%. The adsorption affinity (K-LF) values were highest for 4NP, and BPA had the lowest hysteresis indices. The data suggest that BPA was most likely adsorbed by soil organic matter via hydrogen bonding involving its two phenolic groups. In contrast, isotherm shape, model affinity indices, lack of desorption, and molecular-scale characteristics led us to infer that 4NP was adsorbed largely by the retention of molecular clusters, perhaps in clay nanopores. Finally, the adsorption of EE2 exhibited different isotherm shapes for the two soils as well as intermediate affinity and desorption indices, suggesting that EE2 molecules could be retained both by soil organic matter and by clay.

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.