Wastewater pollution of water resources takes a heavy toll on humans and on the environment. In highly polluted water bodies, self-purification is impaired, as the capacity of the riverine microbes to regenerate the ecosystem is overwhelmed. To date, information on the composition, dynamics and functions of the microbial communities in highly sewage-impacted rivers is limited, in particular in arid and semi-arid environments. In this year-long study of the highly sewage-impacted Al-Nar/Kidron stream in the Barr al-Khalil/Judean Desert east of Jerusalem, we show, using 16S and 18S rRNA gene-based community analysis and targeted qPCR, that both the bacterial and micro-eukaryotic communities, while abundant, exhibited low stability and diversity. Hydrolyzers of organics compounds, as well as nitrogen and phosphorus recyclers were lacking, pointing at reduced potential for regeneration. Furthermore, facultative bacterial predators were almost absent, and the obligate predators Bdellovibrio and like organisms were found at very low abundance. Finally, the micro-eukaryotic predatory community differed from those of other freshwater environments. The lack of essential biochemical functions may explain the stream's inability to self-purify, while the very low levels of bacterial predators and the disturbed assemblages of micro-eukaryote predators present in Al-Nar/Kidron may contribute to community instability and disfunction.

Numerous human conditions are associated with the microbiome, yet studies are inconsistent as to the magnitude of the associations and the bacteria involved, likely reflecting insufficiently employed sample sizes. Here, we collected diverse phenotypes and gut microbiota from 34,057 individuals from Israel and the U.S.. Analyzing these data using a much-expanded microbial genomes set, we derive an atlas of robust and numerous unreported associations between bacteria and physiological human traits, which we show to replicate in cohorts from both continents. Using machine learning models trained on microbiome data, we show prediction accuracy of human traits across two continents. Subsampling our cohort to smaller cohort sizes yielded highly variable models and thus sensitivity to the selected cohort, underscoring the utility of large cohorts and possibly explaining the source of discrepancies across studies. Finally, many of our prediction models saturate at these numbers of individuals, suggesting that similar analyses on larger cohorts may not further improve these predictions.

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.

Prior approaches for finding the longest simple path (LSP) in a graph used constraints solvers and genetic algorithms. In this work, we solve the LSP problem with heuristic search. We first introduce several methods for pruning dominated path prefixes. Then, we propose several admissible heuristic functions for this problem. Experimental results demonstrate the large impact of the proposed heuristics and pruning rules.

IMPORTANCE Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels. OBJECTIVES To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed. DESIGN, SETTING, AND PARTICIPANTS This cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied. MAIN OUTCOMES AND MEASURES Glycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed. RESULTS Postprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R = 0.62) than current standard-of-care approaches using nutritional content alone (R = 0.34 for calories and R = 0.40 for carbohydrates) to control postprandial glycemic levels. CONCLUSIONS AND RELEVANCE Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.

Background: Controlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of personalized approaches. Objective: We tested the efficacy of a predictive model of personalized postprandial glycemic response to foods that was developed with an Israeli cohort and that takes into consideration food components and specific features, including the microbiome, when applied to individuals from the Midwestern US. Design: We recruited 327 individuals for this study. Participants provided information regarding lifestyle, dietary habits, and health, as well as a stool sample for characterization of their gut microbiome. Participants were connected to continuous glucose monitors for 6 d, and the glycemic response to meals logged during this time was computed. The ability of a model trained using meals logged by the Israeli cohort to correctly predict glycemic responses in the Midwestern cohort was assessed and compared with that of a model trained using meals logged by both cohorts. Results: When trained on the Israeli cohort meals only, model performance for predicting responses of individuals in the Midwestern cohort was better (R = 0.596) than that observed for models taking into consideration the carbohydrate (R = 0.395) or calorie content of the meals alone (R = 0.336). Performance increased (R = 0.618) when the model was trained on meals from both cohorts, likely because of the observed differences in age distribution, diet, and microbiome. Conclusions: We show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods.

Wastewater purification is mostly performed in activated sludge reactors by bacterial and microeukaryotic communities, populating organic flocs and a watery liquor. While there are numerous molecular community studies of the bacterial fraction, those on microeukaryotes are rare. We performed a year-long parallel 16S rRNA gene and 18S rRNA-gene based analysis of the bacterial and of the microeukaryote communities, respectively, of physically separated flocs and particle-free liquor samples from three WWTPs. This uncovered a hitherto unknown large diversity of microeukaryotes largely composed of potential phagotrophs preferentially feeding on either bacteria or other microeukaryotes. We further explored whether colonization of the microhabitats was selective, showing that for both microbial communities, different but often closely taxonomically and functionally related populations exhibiting different dynamic patterns populated the microhabitats. An analysis of their between plants-shared core populations showed the microeukaryotes to be dispersal limited in comparison to bacteria. Finally, a detailed analysis of a weather-caused operational disruption in one of the plants suggested that the absence of populations common to the floc and liquor habitat may negatively affect resilience and stability.

HbA1c% is the most commonly used metric for assessing glycemic status, but only represents an averaged, indirect measure. Continuous Glucose Monitors (CGM) provides additional, elaborated assessment metrics. A recent study, [Zeevi et al., 2015, Cell ] showed that glycemic responses to foods vary across individuals and can be predicted using a machine learning framework. Moreover, it showed that personally tailored diets based on this framework improves Postprandial Glycemic Response (PPGR) in one-week interventions. Here, we tested the ability of such framework to improve longer term measures of glycemic control. A cohort of 28 diabetic individuals, not using short term insulin, were tracked. Each participant was tested for initial HbA1c, and connected to a CGM for a period of 14 days. Then, participant's anthropometrics, lifestyle, clinical parameters, and gut microbiome composition, were fed into a machine learning algorithm built into a personalized mobile application. Using the application, participants could define meals by combining foods, and obtain instant scores indicating the predicted PPGR for each meal. Participants were instructed to limit consumption to highly scoring meals. After 4-20 months of using the application, participants were re-connected to CGM, and HbA1c% levels were measured again. Results: Significant improvements in multiple endpoints: Average HbA1c% dropped from 7.2% to 6.5% (p-value: 1.2e-8). Average %time-in-range [70,140] mg/dl increased from 69.1% to 79.6% (p-value: 0.005). Average %time-in-range [70,180] mg/dl increased from 89.6% to 94.2% (p-value: 0.002). Mean glucose levels decreased from 125.6 mg/dl to 114.6 mg/dl (p-value: 0.0002). These observations, coupled with the short-term benefits shown in recent work, imply that drugless, personalized, nutrition-based interventions, may be key to achieving significant improvements in glycemic control of type 2 diabetic patients. Disclosure Y. Ben Shlomo: Employee; Self; DayTwo. S. Azulay: Employee; Self; DayTwo. T. Raveh-Sadka: Employee; Self; DayTwo. Y. Cohen: Employee; Self; DayTwo. A. Hanemann: Employee; Self; DayTwo.

A new cold neutron detector has been developed at the NIST Center for Neutron Research (NCNR) for the CANDoR (Chromatic Analysis Neutron Diffractometer or Reflectometer) project. Geometric and performance constraints dictate that this detector be exceptionally thin (similar to 2 mm). For this reason, the design of the detector consists of a (LiF)-Li-6:ZnS(Ag) scintillator with embedded wavelength shifting (WLS) fibers. We used the GEANT4 package to simulate neutron capture and light transport in the detector to optimize the composition and arrangement of materials to satisfy the competing requirements of high neutron capture probability and light production and transport. In the process, we have developed a method for predicting light collection and total neutron detection efficiency for different detector configurations. The simulation was performed by adjusting crucial parameters such as the scintillator stoichiometry, light yield, component grain size, WLS fiber geometry, and reflectors at the outside edges of the scintillator volume. Three different detector configurations were fabricated and their test results were correlated with the simulations. Through this correlation we have managed to find a common photon threshold for the different detector configurations which was then used to simulate and predict the efficiencies for many other detector configurations. New detectors that have been fabricated based on simulation results yielding the desired sensitivity of 90% for 3.27 meV (5 angstrom) cold neutrons. The simulation has proven to be a useful tool by dramatically reducing the development period and the required number of detector prototypes. It can be used to test new designs with different thicknesses and different target neutron energies.

Primary plasma cell leukemia (PPCL) is a rare form of multiple myeloma with a dismal prognosis. This retrospective multi-center study examines the national experience of PPCL in the era of novel agents. During 2002-2016, thirty-nine patients with PPCL were identified in 11 Israeli centers. One-fifth of them died in the first 2 months after diagnosis. The overall survival (OS) of those who survived the first 3 months was 22.5 months. About 70% of patients received at least one type of immunomodulatory drug (IMiD) and similarly proteasome inhibitor (PI) during treatment. There was a survival advantage for those who received IMiD but not for those who received PI or other type of standard dose chemotherapy. In multivariate analysis, low performance status and increased uric acid were also associated with shorter OS. In conclusion, this study demonstrates favorable impact of treatment with IMiDs and hematopoietic cell transplantation on the survival of PPCL patients.

Inspired by river networks and other structures formed by Laplacian growth, we use the Loewner equation to investigate the growth of a network of thin fingers in a diffusion field. We first review previous contributions to illustrate how this formalism reduces the network's expansion to three rules, which respectively govern the velocity, the direction, and the nucleation of its growing branches. This framework allows us to establish the mathematical equivalence between three formulations of the direction rule, namely geodesic growth, growth that maintains local symmetry, and growth that maximizes flux into tips for a given amount of growth. Surprisingly, we find that this growth rule may result in a network different from the static configuration that optimizes flux into tips.

The Poisson equation is associated with many physical processes. Yet exact analytic solutions for the two-dimensional Poisson field are scarce. Here we derive an analytic solution for the Poisson equation with constant forcing in a semi-infinite strip. We provide a method that can be used to solve the field in other intricate geometries. We show that the Poisson flux reveals an inverse square-root singularity at a tip of a slit, and identify a characteristic length scale in which a small perturbation, in a form of a new slit, is screened by the field. We suggest that this length scale expresses itself as a characteristic spacing between tips in real Poisson networks that grow in response to fluxes at tips.

A criterion for path selection for channels growing in a Poisson field is presented. We invoke a generalization of the principle of local symmetry. We then use this criterion to grow channels in a confined geometry. The channel trajectories reveal a self-similar shape as they reach steady state. Analyzing their paths, we identify a cause for branching that may result in a ramified structure in which the golden ratio appears.

Streams shape landscapes through headward growth and lateral migration. When these streams are primarily fed by groundwater, recent work suggests that their tips advance to maximize the symmetry of the local Laplacian field associated with groundwater flow. We explore the extent to which such forcing is responsible for the lateral migration of streams by studying two features of groundwater-fed streams in Bristol, Florida: their confluence angle near junctions and their curvature. First, we find that, while streams asymptotically form a 72° angle near their tips, they simultaneously exhibit a wide 120° confluence angle within approximately 10 m of their junctions. We show that this wide angle maximizes the symmetry of the groundwater field near the junction. Second, we argue that streams migrate laterally within valleys and present a new spectral analysis method to relate planform curvature to the surrounding groundwater field. Our results suggest that streams migrate laterally in response to fluxes from the surrounding groundwater table, providing evidence of a new mechanism that complements Laplacian growth at their tips.

Valleys that form around a stream head often develop characteristic finger-like elevation contours. We study the processes involved in the formation of these valleys and introduce a theoretical model that indicates how shape may inform the underlying processes. We consider valley growth as the advance of a moving boundary travelling forward purely through linearly diffusive erosion, and we obtain a solution for the valley shape in three dimensions. Our solution compares well to the shape of slowly growing groundwater-fed valleys found in Bristol, Florida. Our results identify a new feature in the formation of groundwater-fed valleys: a spatially variable diffusivity that can be modelled by a fixed-height moving boundary.

The most common translocation in multiple myeloma (MM) is t(11;14)(q13;q32). According to several studies, this translocation represents a unique subset of patients with relatively favorable outcomes. Using combined analyses of morphology and fluorescence in situ hybridization (I-FISH), we examined the co-occurrence rates of t(11;14) with seven chromosomal aberrations (CAs), del(13q), del(17p), del(1p), gain(1q), multiple gains(1q), del(16q), and del(IGH), and assessed the effect of the different combinations on patient outcomes, with overall survival (OS) as the main outcome measure. Bone marrow samples and clinical data from 212 patients with MM with t(11;14) were analyzed. At least two additional CAs were found in 35% (75/205) of patients and a strong correlation between specific CAs. The occurrence of three CAs [multiple gains of (1q) (HR = 6.94, P = 0.001), del(1p) (HR = 4.47, P = 0.008), and del(IGH) (HR = 2.38, P = 0.002)] exerted a profoundly deleterious effect on median OS when compared with patients with t(11;14) only. Del(17p) and del(13q) have also exerted a deleterious effect albeit to a lesser extent (HR = 2.05, P = 0.07 and HR = 1.81, P = 0.03, respectively). When compared with t(11;14) alone, the addition of certain CAs lead to worse outcomes. These findings may have important clinical and biological implications. Patients with coexisting adverse lesions and t(11;14) may be considered at high risk and managed accordingly. © 2016 Wiley Periodicals, Inc.

Low-level radioactive surface-contamination measurements require lightweight, large-area, and high-efficiency detectors. In the previous work, we utilized wavelength shifting (WLS) fibers, coupled to a beta-sensitive plastic scintillator (PS) layer on one side, and to an alpha-sensitive ZnS(Ag) layer on both sides, for detecting both alpha and beta particles. In this work, the main goal was to improve the light collection (maximizing the number of photons reaching the PMT) by optimizing the WLS fibers structure, for getting better signal-to-noise ratio and to minimize the low-energy threshold of the detector. In most cases, improving the light collection mostly influenced the detector resolution. In our case, improving the light collection will improve the detection efficiency by ability to detect more events at low-energy spectrum, which is limited by the noise level. Aiming to improve the scintillation light-collection efficiency, we investigated and compared four different detector configurations. Two of them described in the previous work presents utilization of WLS fibers, with different diameters ( 1 mmφ, 1.5 mmφ), coupled on the PS. Two other configurations present utilization of WLS fibers ( 1.5 mmφ) installed into a flat groove on the PS layer, while in one configuration we utilized straight WLS fibers and in the other we utilized bent WLS fibers. It was found that the utilization of WLS fibers in bent configuration gives the highest light-collection efficiency. Additionally, there is improved light collection achieved by using WLS fibers with wider diameter ( 1.5 mmφ), which maximizes the capture fraction. Additionally, since ZnS(Ag) and PS have different decay times (200 ns and 2.4 ns, respectively), we were able to separate alpha from beta events. An algorithm script was developed to calculate the full width at half maximum (FWHM) of each pulse and a histogram was generated of the FWHM values for the pulse shape discrimination (PSD). Efficient PSD was achieved for alpha energies above 100 keV with figure of merit (FOM) of 1.92. GEANT4 simulation was carried out and compared with experimental results. The results of both were matched, showed that the light-collection efficiency from the bent WLS fibers configuration was the best. The simulation results and the experiments, including full description of the detector structure, ionization stage, and the WLS light collection, are presented.

A survey of the Aerial Measuring System (AMS) of the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA) and Israel Atomic Energy Commission (IAEC) was done. The goal of the study was to investigate the response of the aerial radiation detection system to varied radioactive surface contamination levels, isotopic composition experienced, the data processing technique and the impact of the detector size. THE IAEC AMS System AirRAM 2000, was designed by the IAEC Nuclear Research Center Negev (NRCN) and built commercially by ROTEM Industries and incorporates two 2''diameter ×2'' long NaI crystals. The AirRAM 2000 mounted on a DOE Bell-412 helicopter for a series of aerial measurements at local test ranges. THE study included carry out operational flight activities collecting radiation data from natural background, dispersed radioactivity, and point sources. All planned flight activities followed by scientific discussions on the collected data were completed.

Introduction: Primary plasma cell leukemia (PPCL) is a rare and aggressive variant of multiple myeloma (MM), characterized by the presence of 20% and/or 2X109/L plasma cells (PCs) in the peripheral blood (PB) of the patient. Data regarding this entity are scarce and based on case reports and few retrospective case series. Most of the reports are dated prior to the incorporation of novel agents into MM treatment. The aim of this multicenter retrospective study was to evaluate the response and survival of PPCL patients in the era of the novel agents.