Electrocoagulation-Electrooxidation for Mitigating Trace Organic Compounds in Model Source Waters

Conventional coagulation and oxidation are well suited for many drinking water operations to meet regulatory requirements for safe drinking water. However, these processes require auxiliary chemicals and materials that must be transported from off-site, which increases complexity of operations, and can pose difficulties for small treatment systems. Electrochemistry offers an innovative method to induce coagulation and oxidation processes for water treatment. Electrocoagulation (EC) together with electrooxidation (EO) is an attractive option for drinking water treatment systems because these processes generate iron coagulants using iron EC electrodes and oxidants (e.g., free chlorine and reactive oxygen species) using boron-doped diamond EO electrodes. This research evaluated the performance of combined EC-EO as a water treatment process for mitigating trace organic compounds in model groundwaters and surface waters. The trace organic compounds evaluated were acyclovir, trimethoprim, and benzyldimethyldecylammonium chloride (BAC-C10). These compounds represent different classes of trace organics found in source waters for drinking water treatment facilities. EO-only removed greater than 70% of acyclovir and trimethoprim in model groundwater matrices, but negligible BAC-C10 was removed relative to control experiments. Alternately, in surface waters, EO-only treatment was effective for BAC-C10 removal, but not for acyclovir and trimethoprim removal. EC-EO for model surface water treatment removed 73.5 ± 1.25% of dissolved organic carbon and improved downstream EO treatment of acyclovir, trimethoprim, and BAC-C10 by factors of 3.4, 1.7, and 1.4, respectively based on mean removal. However, EC-EO of model groundwater improved removal for only BAC-C10 (factor of 5.2 improvement), whereas ACY and TMP removal did not improve. BAC-C10 removal via EC-EO in groundwater was attributed to the particle separation step. EO was generally more energy efficient in treating model groundwaters than model surface waters. EC-EO improved the energy demands for treating model river water

Understanding asteroid collisional history through experimental and numerical studies

Asteroids can lose angular momentum due to so called splash effect, the analog to the drain effect for cratering impacts. Numerical code with the splash effect incorporated was applied to study the simultaneous evolution of asteroid sized and spins. Results are presented on the spin changes of asteroids due to various physical effects that are incorporated in the described model. The goal was to understand the interplay between the evolution of sizes and spins over a wide and plausible range of model parameters. A single starting population was used both for size distribution and the spin distribution of asteroids and the changes in the spins were calculated over solar system history for different model parameters. It is shown that there is a strong coupling between the size and spin evolution, that the observed relative spindown of asteroids approximately 100 km diameter is likely to be the result of the angular momentum splash effect

Sequential Electrocoagulation-Electrooxidation For Virus Mitigation in Drinking Water

Electrochemical water treatment is a promising alternative for small-scale and remote water systems that lack operational capacity or convenient access to reagents for chemical coagulation and disinfection. In this study, the mitigation of viruses was investigated using electrocoagulation as a pretreatment prior to electrooxidation treatment using boron-doped diamond electrodes. This research is the first to investigate a sequential electrocoagulation-electrooxidation treatment system for virus removal. Bench-scale, batch reactors were used to evaluate mitigation of viruses in variable water quality via: a) electrooxidation, and b) a sequential electrocoagulation-electrooxidation treatment train. Electrooxidation of two bacteriophages, MS2 and ΦX174, was inhibited by natural organic matter and turbidity, indicating the probable need for pretreatment. However, the electrocoagulation-electrooxidation treatment train was beneficial only in the model surface waters employed. In model groundwaters, electrocoagulation alone was as good or better than the combined electrocoagulation-electrooxidation treatment train. Reduction of human echovirus was significantly lower than one or both bacteriophages in all model waters, though bacteriophage ΦX174 was a more representative surrogate than MS2 in the presence of natural organic matter and turbidity. Compared to conventional treatment by ferric salt coagulant and free chlorine disinfection, the electrocoagulation-electrooxidation system was less effective in model surface waters but more effective in model groundwaters. Sequential electrocoagulation-electrooxidation was beneficial for some applications, though practical considerations may currently outweigh the benefits

Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum

We have developed an algorithm called Q5 for probabilistic classification of healthy vs. disease whole serum samples using mass spectrometry. The algorithm employs Principal Components Analysis (PCA) followed by Linear Discriminant Analysis (LDA) on whole spectrum Surface-Enhanced Laser Desorption/Ionization Time of Flight (SELDI-TOF) Mass Spectrometry (MS) data, and is demonstrated on four real datasets from complete, complex SELDI spectra of human blood serum. Q5 is a closed-form, exact solution to the problem of classification of complete mass spectra of a complex protein mixture. Q5 employs a novel probabilistic classification algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efficient; it is non-iterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and verified for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classification method achieves excellent performance. We achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classification techniques, and can provide clues as to the molecular identities of differentially-expressed proteins and peptides

High-Throughput Inference of Protein-Protein Interaction Sites from Unassigned NMR Data by Analyzing Arrangements Induced By Quadratic Forms on 3-Manifolds

We cast the problem of identifying protein-protein interfaces, using only unassigned NMR spectra, into a geometric clustering problem. Identifying protein-protein interfaces is critical to understanding inter- and intra-cellular communication, and NMR allows the study of protein interaction in solution. However it is often the case that NMR studies of a protein complex are very time-consuming, mainly due to the bottleneck in assigning the chemical shifts, even if the apo structures of the constituent proteins are known. We study whether it is possible, in a high-throughput manner, to identify the interface region of a protein complex using only unassigned chemical shift and residual dipolar coupling (RDC) data. We introduce a geometric optimization problem where we must cluster the cells in an arrangement on the boundary of a 3-manifold. The arrangement is induced by a spherical quadratic form, which in turn is parameterized by SO(3)xR^2. We show that this formalism derives directly from the physics of RDCs. We present an optimal algorithm for this problem that runs in O(n^3 log n) time for an n-residue protein. We then use this clustering algorithm as a subroutine in a practical algorithm for identifying the interface region of a protein complex from unassigned NMR data. We present the results of our algorithm on NMR data for 7 proteins from 5 protein complexes and show that our approach is useful for high-throughput applications in which we seek to rapidly identify the interface region of a protein complex

Within-Home versus Between-Home Variability of House Dust Endotoxin in a Birth Cohort

Endotoxin exposure has been proposed as an environmental determinant of allergen responses in children. To better understand the implications of using a single measurement of house dust endotoxin to characterize exposure in the first year of life, we evaluated room-specific within-home and between-home variability in dust endotoxin obtained from 470 households in Boston, Massachusetts. Homes were sampled up to two times over 5–11 months. We analyzed 1,287 dust samples from the kitchen, family room, and baby’s bedroom for endotoxin. We fit a mixed-effects model to estimate mean levels and the variation of endotoxin between homes, between rooms, and between sampling times. Endotoxin ranged from 2 to 1,945 units per milligram of dust. Levels were highest during summer and lowest in the winter. Mean endotoxin levels varied significantly from room to room. Cross-sectionally, endotoxin was moderately correlated between family room and bedroom floor (r = 0.30), between family room and kitchen (r = 0.32), and between kitchen and bedroom (r = 0.42). Adjusting for season, the correlation of endotoxin levels within homes over time was 0.65 for both the bedroom and kitchen and 0.54 for the family room. The temporal within-home variance of endotoxin was lowest for bedroom floor samples and highest for kitchen samples. Between-home variance was lowest in the family room and highest for kitchen samples. Adjusting for season, within-home variation was less than between-home variation for all three rooms. These results suggest that room-to-room and home-to-home differences in endotoxin influence the total variability more than factors affecting endotoxin levels within a room over time

Flowering Phenology Change and Climate Warming in Southwestern Ohio

Global surface temperature has increased markedly over the last 100 years. This increase has a variety of implications for human societies, and for ecological systems. One of the most obvious ways ecosystems are affected by global climate change is through alteration of organisms’ developmental timing (phenology). We used annual botanical surveys that documented the first flowering for an array of species from 1976 to 2003 to examine the potential implications of climate change for plant development. The overall trend for these species was a progressively earlier flowering time. The two earliest flowering taxa (Galanthus and Crocus) also exhibited the strongest shift in first flowering. We detected a significant trend in climate suggesting higher temperatures in winter and spring over the sampling interval and found a significant relationship between warming temperatures and first flowering time for some species. Although 60% of the species in our study flowered earlier over the sampling interval, the remaining species exhibited no statistically detectable change. This variation in response is ostensibly associated with among-species variation in the role of climate cues in plant development. Future work is needed to isolate specific climate cues, and to link plant phenology to the physiological processes that trigger plant development