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Signal digitization is a commonly overlooked part of ion mobility-mass spectrometry (IMS-MS) workflows, yet it greatly affects signal-to-noise ratio and MS resolution measurements. Here, we report on the integration of a 2 GS/s, 14-bit ADC with structures for lossless ion manipulations (SLIM-IMS-MS) and compare the performance to a commonly used 8-bit ADC. The 14-bit ADC provided a reduction in the digitized noise by a factor of ∼6, owing largely to the use of smaller bit sizes. The low baseline allowed threshold voltage levels to be set very close to the MCP baseline voltage, allowing for as much signal to be acquired as possible without overloading or excessive digitization of MCP baseline noise. Analyses of Agilent tuning mixture ions and a mixture of heavy labeled phosphopeptides showed that the 14-bit ADC provided a ∼1.5-2× signal-to-noise (S/N) increase for high intensity ions, such as the Agilent tuning mixture ions and the 2+ and 3+ charge states of many phosphopeptide constituents. However, signal enhancements were as much as 10-fold for low intensity ions, and the 14-bit ADC enabled discernible signal intensities otherwise lost using an 8-bit digitizer. Additionally, the 14-bit ADC required ∼14-fold fewer mass spectra to be averaged to produce a mass spectrum with a similar S/N as the 8-bit ADC, demonstrating ∼10× higher measurement throughput. The high resolution, low baseline, and fast speed of the new 14-bit ADC enables high performance digitization of MS, IMS-MS, and SLIM-IMS-MS spectra and provides a much better picture of analyte profiles in complex mixtures.

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed by a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza

Graphical abstract, Microbial communities organize into spatial patterns that are largely governed by interspecies interactions. This phenomenon is an important metric for understanding community functional dynamics, yet the use of spatial patterns for predicting microbial interactions is currently lacking. Here we propose supervised deep learning as a new tool for network inference. An agent-based model was used to simulate the spatiotemporal evolution of two interacting organisms under diverse growth and interaction scenarios, the data of which was subsequently used to train deep neural networks. For small-size domains (100 µm × 100 µm) over which interaction coefficients are assumed to be invariant, we obtained fairly accurate predictions, as indicated by an average R2 value of 0.84. In application to relatively larger domains (450 µm × 450 µm) where interaction coefficients are varying in space, deep learning models correctly predicted spatial distributions of interaction coefficients without any additional training. Lastly, we evaluated our model against real biological data obtained using Pseudomonas fluorescens and Escherichia coli co-cultures treated with polymeric chitin or N-acetylglucosamine, the hydrolysis product of chitin. While P. fluorescens can utilize both substrates for growth, E. coli lacked the ability to degrade chitin. Consistent with our expectations, our model predicted context-dependent interactions across two substrates, i.e., degrader-cheater relationship on chitin polymers and competition on monomers. The combined use of the agent-based model and machine learning algorithm successfully demonstrates how to infer microbial interactions from spatially distributed data, presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Lung cancer is the leading cause of cancer-related deaths in the United States, with estimates of 236,740 new cases and 118,830 deaths in 2022 secondary to the disease. Blood-based liquid biopsies hold promise to reduce morbidity and mortality from lung cancer by enabling early detection to downstage disease at diagnosis, theragnostic identification of patients most likely to be helped or harmed by therapy, monitoring of therapeutic efficacy, and detection of residual disease. PrognomiQ’s multi-omics platform comprehensively profiles proteins, metabolites, lipids, mRNA, and cfDNA in blood samples which can be used for the development of liquid biopsy tests with high sensitivity and specificity for lung cancer. We conducted a case-control study comprising 1031 subjects: 361 subjects with untreated non-small-cell lung cancer (NSCLC) and 670 matched controls which included 340 subjects with salient pulmonary and gastrointestinal co-morbidities. Blood samples from each subject were processed to provide 7 different `omics readouts. LCMS was used to detect and quantify proteins, metabolites, and lipids. In addition, cfDNA and mRNA were assayed using next-generation sequencing. cfDNA reads were analyzed to estimate fragment-lengths, copy-number variation, and CpG site methylation. All molecular data were normalized using standard methods specific to each assay. Univariate analyses of cases vs controls were performed to identify differentially abundant features on all available samples per assay. We detected 9,868 proteins, 605 lipids, 329 metabolites, and 109,070 mRNA transcripts. Of these, 3,098 proteins, 210 lipids, 57 metabolites, and 30,236 mRNA transcripts were significantly different (FWER < 0.05) in cases versus controls. Gene set enrichment analysis on statistically significant transcripts and proteins identified multiple gene-ontology terms associated with cancer including the Wnt signaling process and IgA immunoglobulin complex, respectively. From cfDNA data, we identified 234 non-contiguous genomic regions associated with the fragment-length disorder, 4,790 with copy-number variation, and 74 differentially methylated genomic regions spanning 184 CpG sites (FWER < 0.05). With the premise that deviations from copy number neutrality are more likely to indicate a tumor contribution, we then focused our examination on those differentially expressed proteins that overlap with differentially expressed mRNA transcripts as well as CNV genomic regions. We identified 52 protein coding genes including E-cadherin (associated with EMT) and related binding proteins such as RAB11B, CAPZB, EPS15, FLNB, MYH9, STK24 and YWHAE. Ongoing machine-learning-based classifier training to distinguish between cancer and non-cancer can serve as the basis for the development of high-sensitivity liquid-biopsy tests for lung cancer. Citation Format: Jinlyung Choi, Ajinkya Kokate, Ehdieh Khaledian, Manway Liu, Preethi Prasad, John Blume, Jessica Chan, Rea Cuaresma, Kevin Dai, Manoj Khadka, Thidar Khin, Yuya Kodama, Joon-Yong Lee, Hoda Malekpour, Megan Mora, Nithya Mudaliar, Sara Nouri Golmaei, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Dijana Vitko, Kavya Swaminathan, James Yee, Brian Young, Chinmay Belthangady, Bruce Wilcox, Brian Koh, Philip Ma. Biomarker discovery in non-small-cell lung cancer enabled by deep multi-omics profiling of proteins, metabolites, transcripts, and genes in blood. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6606.

Pancreatic ductal adenocarcinoma (PDAC) is currently the 3rd leading cause of cancer-related deaths in the US. Although the all-stage 5-year survival rate is ~10%, early-stage 5-year survival is markedly superior and in excess of 40%. Hence, early detection of PDAC via blood-based liquid biopsies holds promise to reduce morbidity and mortality. PrognomiQ’s multi-omics platform performs deep and unbiased molecular profiling of blood samples to detect proteins, metabolites, lipids, mRNA, miRNA, cfDNA fragmentation and copy-number, and CpG methylation. Here we report results from training and validation of a classifier on a subset of that multi-omic data with the potential to enable the development of high sensitivity and specificity tests for early detection of PDAC.We conducted a case-control study comprising 146 subjects across 16 clinical sites, including 63 pathology-confirmed, untreated PDAC cases (12 stage I, 8 stage II, 4 stage III, 36 stage IV, and 3 stage unknown) and 83 age- and gender- matched controls without any known cancer. For each subject, venous blood samples including plasma were collected. Unbiased LCMS was used to detect and quantify proteins, and targeted, multiplexed MRM-LCMS assays were used for both metabolites and lipids. After data processing, we detected 54,114 proteomic features, 898 lipids, and 373 metabolites. 445 proteomic features, 170 lipids, and 37 metabolites were found to be significantly different as determined by Bonferroni-corrected Wilcoxon tests with FWER < 0.05. For classification, the dataset was split into training (37 cases and 37 controls) and validation (26 cases and 46 controls) sets, with control for collection site and date, age, and gender. XGBoost models were constructed for each analyte class using ten repeats of 10-fold cross-validation. To improve specificity to PDAC, all proteomic features which mapped to GOBP terms associated with acute-phase response, inflammation, and immune response were excluded prior to training. The best-performing hyperparameters were used for a final model built on the full training set and then used for inference on the validation set. At 99% specificity, the proteomic classifier had sensitivities of 77%, 57%, and 88% for Stages 1-4, Stages 1-2, and Stages 3-4, respectively, estimated by bootstrap re-sampling of the validation results. Metabolomics had sensitivities of 81%, 71%, and 88%. Lipidomics had sensitivities of 65%, 71%, and 65%. A joint, multi-omic model was constructed by averaging the scaled probabilities of all models. This joint model improved performance at 99% specificity with sensitivities of 92%, 86%, and 94%, highlighting the synergy of multi-omics data, particularly phenotypically related omics such as those described here. Multi-omic classifiers such as these can serve as the foundation for blood-based liquid biopsies for the early detection of PDAC. Citation Format: John Blume, Ghristine Bundalian, Jessica Chan, Connie Chao-Shern, Jinlyung Choi, Rea Cuaresma, Kevin Dai, Sara N. Golmaei, Jun Heok Jang, Manoj Khadka, Ehdieh Khaledian, Thidar Khin, Yuya Kodama, Ajinkya Kokate, Joon-Yong Lee, Manway Liu, Hoda Malekpour, Megan Mora, Nithya Mudaliar, Preethi Prasad, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Kavya Swaminathan, Dijana Vitko, James Yee, Brian Young, Susan Zhang, Chinmay Belthangady, Bruce Wilcox, Brian Koh, Philip Ma. A multi-omics classifier achieves high sensitivity and specificity for pancreatic ductal adenocarcinoma in a case-control study of 146 subjects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6597.

Current coronavirus disease 2019 (COVID-19) vaccines effectively reduce overall morbidity and mortality and are vitally important to controlling the pandemic. Individuals who previously recovered from COVID-19 have enhanced immune responses after vaccination (hybrid immunity) compared with their naïve-vaccinated peers; however, the effects of post-vaccination breakthrough infections on humoral immune response remain to be determined. Here, we measure neutralizing antibody responses from 104 vaccinated individuals, including those with breakthrough infections, hybrid immunity, and no infection history. We find that human immune sera after breakthrough infection and vaccination after natural infection broadly neutralize SARS-CoV-2 (severe acute respiratory coronavirus 2) variants to a similar degree. Although age negatively correlates with antibody response after vaccination alone, no correlation with age was found in breakthrough or hybrid immune groups. Together, our data suggest that the additional antigen exposure from natural infection substantially boosts the quantity, quality, and breadth of humoral immune response regardless of whether it occurs before or after vaccination.

Climate change is globally affecting rainfall patterns, necessitating the improvement of drought tolerance in crops. Sorghum bicolor is a drought-tolerant cereal capable of producing high yields under water scarcity conditions. Functional stay-green sorghum genotypes can maintain green leaf area and efficient grain filling in terminal post-flowering water deprivation, a period of ~10 weeks. To obtain molecular insights into these characteristics, two drought-tolerant genotypes, BTx642 and RTx430, were grown in control and terminal post-flowering drought field plots in the Central Valley of California. Photosynthetic, photoprotective, water dynamics, and biomass traits were quantified and correlated with metabolomic data collected from leaves, stems, and roots at multiple timepoints during drought. Physiological and metabolomic data was then compared to longitudinal RNA sequencing data collected from these two genotypes. The metabolic response to drought highlights the uniqueness of the post-flowering drought acclimation relative to pre-flowering drought. The functional stay-green genotype BTx642 specifically induced photoprotective responses in post-flowering drought supporting a putative role for photoprotection in the molecular basis of the functional stay-green trait. Specific genes are highlighted that may contribute to post-flowering drought tolerance and that can be targeted in crops to maximize yields under limited water input conditions.HighlightPathways contributing to the long-term maintenance of photosynthetic activity in terminal post-flowering drought are revealed by a comprehensive approach combining in-field photosynthetic physiological analysis, metabolomics, and transcriptomics.

Pancreatic cancer is the third leading cause of cancer-related deaths in the United States. Disease biomarkers quantified from blood-based assays may help reduce mortality by enabling early detection, treatment selection, or response and resistance assessment. PrognomiQ has developed a multi-omics assay and analysis platform that comprehensively profiles blood samples to detect proteins, metabolites, lipids, mRNA, miRNA, cfDNA fragments, and methylation at CpG sites. This platform can provide deep insights into the biology of pancreatic cancer and could enable the development of high sensitivity and specificity tests for the early detection of pancreatic cancer. We conducted a case-control study comprising 196 subjects: 92 with untreated pancreatic cancer and 104 matched controls without pancreatic cancer. For each subject, blood was collected in assay-specific tubes and processed to provide 7 different `omics readouts. cfDNA and mRNA were isolated from samples and assayed following standard NGS protocols. cfDNA fragments were processed to estimate fragment-length disorder and copy-number variation along with CpG site methylation. In addition, targeted and untargeted LCMS were used to detect and quantify proteins, metabolites, and lipids. After normalization, non-parametric univariate analyses of cases versus controls were performed to identify differentially abundant features on all available samples for each assay. Unsupervised learning was used to investigate the separation of subjects into groups based on disease status for the subset of 157 subjects for which complete data on all 7 readouts were available. We detected 2,812 proteins, 811 lipids, 373 metabolites, and 110,864 mRNA transcripts in all samples where data for each assay was available. Of these, 275 proteins, 232 lipids, 49 metabolites, and 3385 mRNA transcripts were significantly different (FWER < 0.05) in cases versus controls. From cfDNA data, we identified 35 non-contiguous genomic regions associated with fragment-length disorder, 557 with copy-number variation, and 5 with multiple, differentially methylated CpGs (FWER < 0.05) that aggregately span 307 protein-coding genes; of these, the overlap with the differentially expressed proteins included E-cadherin (tumor suppressor) and N-cadherin (involved in epithelial-to-mesenchymal transition). Statistically significant genes and proteins were found to be associated with processes including Wnt signaling, regulation of focal adhesion assembly, and actin cytoskeleton organization. Multi-omics, unsupervised learning showed separation of early- and late-stage cases and controls. High-dimensional bioinformatics analyses systematically decomposed each `omics data type into joint and orthogonal components associated with pancreatic cancer. Ongoing multivariate analyses, including supervised machine learning, will further elucidate the biology of pancreatic cancer development, and serve as the foundation for high-sensitivity blood tests for the early detection and monitoring of pancreatic cancer. Citation Format: Ehdieh Khaledian, Preethi Prasad, John Blume, Ghristine Bundalian, Connie Chao-Shern, Jinlyung Choi, Rea Cuaresma, Jared Deyarmin, Jun Heok Jang, Manoj Khadka, Thidar Khin, Yuya Kodama, Ajinkya Kokate, Joon-Yong Lee, Manway Liu, Nithya Mudaliar, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Kavya Swaminathan, Preston Williams, Mi Yang, James Yee, Brian Young, Robert Zawada, Susan Zhang, Chinmay Belthangady, Bruce Wilcox, Philip Ma. High-dimensional, multi-omics analyses of proteins, metabolites, transcripts, and genes enable biomarker discovery in early- and late-stage pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A038.

Brown rot fungi release massive amounts of carbon from forest deadwood, particularly at high latitudes. These fungi degrade wood by generating small reactive oxygen species (ROS) to loosen lignocellulose, to then selectively remove carbohydrates. The ROS mechanism has long been considered the key adaptation defining brown rot wood decomposition, but recently, we found preliminary evidence that fungal glycoside hydrolases (GHs) implicated in early cell wall loosening might have been adapted to tolerate ROS stress and to synergize with ROS to loosen woody lignocellulose. In the current study, we found more specifically that side chain hemicellulases that help in the early deconstruction of the lignocellulosic complex are significantly more tolerant of ROS in the brown rot fungus Rhodonia placenta than in a white rot fungus (Trametes versicolor) and a soft rot fungus (Trichoderma reesei). Using proteomics to understand the extent of tolerance, we found that significant oxidation of secreted R. placenta proteins exposed to ROS was less than half of the oxidation observed for T. versicolor or T. reesei. The principal oxidative modifications observed in all cases were monooxidation and dioxidation/trioxidation (mainly in methionine and tryptophan residues), some of which were critical for enzyme activity. At the peptide level, we found that GHs in R. placenta were the least ROS affected among our tested fungi. These results confirm and describe underlying mechanisms of tolerance in early-secreted brown rot fungal hemicellulases. These enzymatic adaptations may have been as important as nonenzymatic ROS pathway adaptations in brown rot fungal evolution. IMPORTANCE Brown rot fungi play a critical role in carbon recycling and are of industrial interest. These fungi typically use reactive oxygen species (ROS) to indiscriminately “loosen” wood cell walls at the outset of decay. Brown rot fungi avoid oxidative stress associated with this ROS step by delaying the expression/secretion of many carbohydrate-active enzymes, but there are exceptions, notably some side chain hemicellulases, implicated in loosening lignocellulose. In this study, we provide enzyme activity and secretomic evidence that these enzymes in the brown rot model Rhodonia placenta are more ROS tolerant than the white and soft rot isolates tested. For R. placenta, and perhaps all brown rot lineages, these ROS tolerance adaptions may have played a long-overshadowed role in enabling brown rot.

Ion packets introduced from gates, ion funnel traps, and other conventional ion injection mechanisms produce ion pulse widths typically around a few microseconds or less for ion mobility spectrometry (IMS)-based separations on the order of 100 milliseconds. When such ion injection techniques are coupled with ultralong path length traveling wave (TW)-based IMS separations (i.e., on the order of seconds) using structures for lossless ion manipulations (SLIMs), typically very low ion utilization efficiency is achieved for continuous ion sources [e.g., electrospray ionization (ESI)]. Even with the ability to trap and accumulate much larger populations of ions than being conventionally feasible over longer time periods in SLIM devices, the subsequent long separations lead to overall low ion utilization. Here, we report the use of a highly flexible SLIM arrangement, enabling concurrent ion accumulation and separation and achieving near-complete ion utilization with ESI. We characterize the ion accumulation process in SLIM, demonstrate >98% ion utilization, and show both increased signal intensities and measurement throughput. This approach is envisioned to have broad utility to applications, for example, involving the fast detection of trace chemical species.

Motivation Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion’s collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required. Results We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: (i) stepped-field methods for drift tube-based IMS (DTIMS), (ii) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and (iii) linear calibration for Bruker timsTOF and non-linear calibration methods for traveling wave based-IMS in Waters Synapt and Structures for Lossless Ion Manipulations. We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes and collision gases, without requiring manual processing. Availability and implementation https://github.com/PNNL-Comp-Mass-Spec/AutoCCS. Supplementary information Supplementary data are available at Bioinformatics online. Demo datasets are publicly available at MassIVE (Dataset ID: MSV000085979).

This report presents the results from the 2016 Association of Biomolecular Resource Facilities Proteome Informatics Research Group (iPRG) study on proteoform inference and false discovery rate (FDR) estimation from bottom-up proteomics data. For this study, 3 replicate Q Exactive Orbitrap liquid chromatography-tandom mass spectrometry datasets were generated from each of 4 Escherichia coli samples spiked with different equimolar mixtures of small recombinant proteins selected to mimic pairs of homologous proteins. Participants were given raw data and a sequence file and asked to identify the proteins and provide estimates on the FDR at the proteoform level. As part of this study, we tested a new submission system with a format validator running on a virtual private server (VPS) and allowed methods to be provided as executable R Markdown or IPython Notebooks. The task was perceived as difficult, and only eight unique submissions were received, although those who participated did well with no one method performing best on all samples. However, none of the submissions included a complete Markdown or Notebook, even though examples were provided. Future iPRG studies need to be more successful in promoting and encouraging participation. The VPS and submission validator easily scale to much larger numbers of participants in these types of studies. The unique "ground-truth" dataset for proteoform identification generated for this study is now available to the research community, as are the server-side scripts for validating and managing submissions.

Mass spectrometry (MS)-based lipidomics is revolutionizing lipid research with high throughput identification and quantification of hundreds to thousands of lipids with the goal of elucidating lipid metabolism and function. Estimates of statistical confidence in lipid identification are essential for downstream data interpretation in a biological context. In the related field of proteomics, a variety of methods for estimating false-discovery are available, and understanding the statistical confidence of identifications is typically required for data analysis and hypothesis testing. However, there is no current method for estimating the false discovery rate (FDR) or statistical confidence for MS-based lipid identifications. This has slowed the adoption of MS-based lipidomics research, as all identifications require manual inspection and validation to ensure their accuracy. We present here the first generalizable method for FDR estimation, a target/decoy approach, that allows those conducting MS-based lipidomics research to confidently adjust spectral score thresholds to minimize false discovery and to enable full automation of data analysis.

Zika virus (ZIKV), an arbovirus of global concern, remodels intracellular membranes to form replication sites. How ZIKV dysregulates lipid networks to allow this, and consequences for disease, is poorly understood. Here, we perform comprehensive lipidomics to create a lipid network map during ZIKV infection. We find that ZIKV significantly alters host lipid composition, with the most striking changes seen within subclasses of sphingolipids. Ectopic expression of ZIKV NS4B protein results in similar changes, demonstrating a role for NS4B in modulating sphingolipid pathways. Disruption of sphingolipid biosynthesis in various cell types, including human neural progenitor cells, blocks ZIKV infection. Additionally, the sphingolipid ceramide redistributes to ZIKV replication sites, and increasing ceramide levels by multiple pathways sensitizes cells to ZIKV infection. Thus, we identify a sphingolipid metabolic network with a critical role in ZIKV replication and show that ceramide flux is a key mediator of ZIKV infection., Zika virus (ZIKV) remodels intracellular membranes for replication, but the role of different lipid types for infection and disease is unclear. Here, the authors perform lipidomics, show perturbation of the lipid network during ZIKV infection and show that ceramides are important for ZIKV infection.

Climate change is predicted to result in increased drought extent and intensity in the highly productive, former tallgrass prairie region of the continental United States. These soils store large reserves of carbon. The decrease in soil moisture due to drought has largely unknown consequences on soil carbon cycling and other key biogeochemical cycles carried out by soil microbiomes. In this study, we found that soil drying had a significant impact on the structure and function of soil microbial communities, including shifts in expression of specific metabolic pathways, such as those leading toward production of osmoprotectant compounds. This study demonstrates the application of an untargeted multi-omics approach to decipher details of the soil microbial community’s metaphenotypic response to environmental perturbations and should be applicable to studies of other complex microbial systems as well., Climate change is causing shifts in precipitation patterns in the central grasslands of the United States, with largely unknown consequences on the collective physiological responses of the soil microbial community, i.e., the metaphenome. Here, we used an untargeted omics approach to determine the soil microbial community’s metaphenomic response to soil moisture and to define specific metabolic signatures of the response. Specifically, we aimed to develop the technical approaches and metabolic mapping framework necessary for future systematic ecological studies. We collected soil from three locations at the Konza Long-Term Ecological Research (LTER) field station in Kansas, and the soils were incubated for 15 days under dry or wet conditions and compared to field-moist controls. The microbiome response to wetting or drying was determined by 16S rRNA amplicon sequencing, metatranscriptomics, and metabolomics, and the resulting shifts in taxa, gene expression, and metabolites were assessed. Soil drying resulted in significant shifts in both the composition and function of the soil microbiome. In contrast, there were few changes following wetting. The combined metabolic and metatranscriptomic data were used to generate reaction networks to determine the metaphenomic response to soil moisture transitions. Site location was a strong determinant of the response of the soil microbiome to moisture perturbations. However, some specific metabolic pathways changed consistently across sites, including an increase in pathways and metabolites for production of sugars and other osmolytes as a response to drying. Using this approach, we demonstrate that despite the high complexity of the soil habitat, it is possible to generate insight into the effect of environmental change on the soil microbiome and its physiology and functions, thus laying the groundwork for future, targeted studies. IMPORTANCE Climate change is predicted to result in increased drought extent and intensity in the highly productive, former tallgrass prairie region of the continental United States. These soils store large reserves of carbon. The decrease in soil moisture due to drought has largely unknown consequences on soil carbon cycling and other key biogeochemical cycles carried out by soil microbiomes. In this study, we found that soil drying had a significant impact on the structure and function of soil microbial communities, including shifts in expression of specific metabolic pathways, such as those leading toward production of osmoprotectant compounds. This study demonstrates the application of an untargeted multi-omics approach to decipher details of the soil microbial community’s metaphenotypic response to environmental perturbations and should be applicable to studies of other complex microbial systems as well.

Background: Despite major advance in surgical techniques from open surgery to robot-assisted surgery, acute kidney injury (AKI) is still major postoperative complication in rectal surgery. The purpose of this study is to compare the incidence of postoperative AKI according to different surgical techniques and also the risk factors, outcomes of AKI in patients undergoing rectal cancer surgery. Methods: A retrospective medical chart review was done in a total of 288 patients who received proctectomy because of rectal cancer from 2011 to 2013. Results: The mean patient age was 62 ± 12 years, and male was 64.2%. Preoperative creatinine was 0.91 ± 0.18 mg/dL. Open surgery was performed in 9%, and laparoscopy assisted surgery or robot assisted surgery were performed in 54.8% or 36.1% of patients, respectively. AKI developed in 11 patients (3.82%), 2 (18%) of them received acute hemodialysis. Incidence of AKI was not different according to the surgical technique, however, the presence of diabetes, intraoperative shock, and postoperative ileus was associated with the development of AKI. In addition, AKI patients showed significantly longer hospital stay and higher mortality than non-AKI patients. Conclusion: Our study demonstrated that despite advances in surgical techniques, incidence of postoperative AKI remains unchanged and also that postoperative AKI is associated with poor outcome. We also found that presence of diabetes, intraoperative shock and postoperative ileus are strongly associated with the development of AKI. More careful attention should be paid on high risk patients for the development of postoperative AKI regardless of surgical techniques.

Metabolic network modeling of microbial communities provides an in‐depth understanding of community‐wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high‐quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community‐level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph–heterotroph consortium that was used to provide data needed for a community‐level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339–2345, 2016. © 2016 The Authors. Journal of Cellular Physiology Published by Wiley Periodicals, Inc.

Uncertain external disturbances either secular or oscillating make the satellite attitude be deviated or oscillating from the mission attitude. These disturbances should be detected in short and rejected with proper counter torques of actuators. Nonlinear second order sliding mode observer is one of the good algorithms that can be used for the detection of disturbances’ magnitude and frequencies. With a control law of the integration and dipole rejection filters based on the identified disturbance information, the actuator torques can compensate the disturbances. The spacecraft attitude is then stabilized at the mission attitude within a tolerable error. In this paper, the performances of detection and rejection algorithms are checked respectively in computer simulations. The applicability is proved on a satellite ground simulator recently developed in Korea Aerospace University. The results show that the algorithms work properly with small error, which is due to the low grade of sensors and actuators.

The U.S. power grid is transforming to become “smarter,” cleaner, and more efficient. This is leading to the addition of significant distributed variable renewable generation. Due to the variable nature of renewable generation, the short- and long-term supply-demand imbalances are less predictable, and conventional approaches to mitigating the imbalance will not be efficient or cost-effective. To address this challenge, transactive control technologies have been proposed. The transactive control approach requires individual end-use loads to express flexibility as a function of price. To model flexibility while maintaining robustness to any non-linear behavior exhibited by end-use loads, machine learning approaches for load forecasting are being explored. However, certain aspects, such as how much training data is required and how deep models for load forecasting should be structured and trained are not well understood. This work explores how to apply sequence-to-sequence recurrent neural networks to short-term electrical load forecasting with a case study of four commercial office buildings. We find that it is best to start the training in the middle of a heating or cooling season with at least six months of data. We further show that models perform best when predictions are conditioned on three to 12 h of prior data, with a decrease in performance for shorter contexts. We identify recommended ranges for common hyperparameters that could be used by practitioners applying similar models to their own tasks. Finally, we find that transferability of models across buildings is highly dependent on the building pairs, but in the best case, models are highly transferable.

Metaproteomics has been increasingly utilized for high-throughput molecular characterization in complex environments and has been demonstrated to provide insights into microbial composition and functional roles in soil systems. Despite its potential for the study of microbiomes, significant challenges remain in data analysis, including the creation of a sample-specific protein sequence database as the taxonomic composition of soil is often unknown. Almost all metaproteome analysis tools require this database and their accuracy and sensitivity suffer when the database is incomplete or contains extraneous sequences from organisms which are not present. Here, we leverage a de novo peptide sequencing approach to identify sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data, and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified unsequenced soil isolates directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S ribosomal RNA sequencing of the soil samples and also uncovered several additional species which were strongly represented only in proteomic data. Our pipeline offers an alternative and complementary method for metaproteomic data analysis by creating a protein database directly from proteomic data, thus removing the need for metagenomic sequencing.Significance StatementProteomic characterization of environmental samples, or metaproteomics, reveals microbial activity critical to our understanding of climate, nutrient cycling and human health. Metaproteomic samples originate from diverse environs, such as soil and oceans. One option for data analysis is a de novo interpretation of the mass spectra. Unfortunately, the current generation of de novo algorithms were primarily trained on data originating from human proteins. Therefore, these algorithms struggle with data from environmental samples, limiting our ability to analyze metaproteomics data. To address this challenge, we trained a new algorithm with data from dozens of diverse environmental bacteria and achieved significant improvements in accuracy across a broad range of organisms. This generality opens proteomics to the world of natural isolates and microbiomes.

There has been a string of negligent accidents at the seaside. In addition, there is a shortage of professional equipment to deal with these negligent accidents at present. The purpose of this paper is to assess the development of a rescue lift using a structural analysis. The critical load of a support pipe on the rescue lift is investigated in this paper. The stability of the rescue lift is also determined in a structural analysis. In the critical load analysis, the effects of variations in the pipe length and the ratio of the moment of inertia are investigated. The total weight of the developed rescue lift is approximately 20.7kg, and the lift passed the load tests.

Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. Whether the task is to identify co-expressed genes in large-scale expression surveys or to predict combinations of gene knockouts which would elicit a similar phenotype, the underlying computational task is often a multi-dimensional similarity test. As datasets continue to grow, improvements to the efficiency, sensitivity or specificity of such computation will have broad impacts as it allows scientists to more completely explore the wealth of scientific data. A significant practical drawback of large-scale data mining is that the vast majority of pairwise comparisons are unlikely to be relevant, meaning that they do not share a signature of interest. It is therefore essential to efficiently identify these unproductive comparisons as rapidly as possible and exclude them from more time-intensive similarity calculations. The Blazing Signature Filter (BSF) is a highly efficient pairwise similarity algorithm which enables extensive data mining within a reasonable amount of time. The algorithm transforms datasets into binary metrics, allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity. As a result, the BSF can scale to high dimensionality and rapidly filter unproductive pairwise comparison. Two bioinformatics applications of the tool are presented to demonstrate the ability to scale to billions of pairwise comparisons and the usefulness of this approach.

ATLAS (Automatic Tool for Local Assembly Structures) is a comprehensive multi-omics data analysis pipeline that is massively parallel and scalable. ATLAS contains a modular analysis pipeline for assembly, annotation, quantification and genome binning of metagenomics and metatranscriptomics data and a framework for reference metaproteomic database construction. ATLAS transforms raw sequence data into functional and taxonomic data at the microbial population level and provides genome-centric resolution through genome binning. ATLAS provides robust taxonomy based on majority voting of protein-coding open reading frames (ORFs) rolled-up at the contig level using modified lowest common ancestor (LCA) analysis. ATLAS is user-friendly, easy install through bioconda maintained as open-source on GitHub, and is implemented in Snakemake for modular customizable workflows.

A short-term hourly water demand forecasting algorithm is needed in order to ensure a stable and safe supply of water. Unlike daily or monthly water demand forecasting, there are a large amount of fluctuation of hourly water demand. Hourly water demand is affected by short time period and abnormal data caused by the sensor, communication, and water treatment plant problems. An effective refinement method that detects and corrects the abnormal data among the historical data is needed to achieve accurate and practical hourly water demand forecasting. In this paper, we suggest an abnormal data refinement out of a confidence interval (ADR-CI) method and an error percentage correction (EPC) method. These methods try to distribute and revise the incoming hourly water demand and past water demand data. The proposed methods are verified by the experiments in a real water supply plant during a year.

Purpose – This paper aims to present a step-exchange strategy for balance control of a walking biped robot when a lateral impact acts suddenly. A step-out strategy has been recently proposed for balance control when an unknown lateral force acts to a biped robot during walking. This step-out strategy causes a robot to absorb the impact kinetic energy and efficiently maintain balance without falling down. Nevertheless, it was found that the previous strategies have drawbacks that the two foots should always be on the ground (double-support mode) after being balanced and the authors think it is difficult to continue walking after being balanced. Unlike the existing balance strategies, the proposed step-exchange strategy is to not only maintain balance but also to lift one leg in the air (single-support mode) after being balanced so that it is easy for a biped robot to keep walking after being balanced. Design/methodology/approach – In the proposed step-exchange strategy, forward Newton–Euler equation, angular momentum and energy conservation equation were derived. Hill-climbing algorithm is utilized for numerically finding a solution. To verify the proposed strategy, a biped robot by Open Dynamics Engine was stimulated, and experiments with a real biped robot (LRH-1) were also conducted. Findings – The proposed step-exchange strategy enables a walking biped robot under a lateral impact to keep balance and to keep a single-support mode after exchanging a leg. It is helpful for a biped robot to continue walking without any stop. It is found that the proposed step-exchange strategy can be applicable for maintaining balance even if a biped robot is moving. Even though this proposal seems immature yet, it is the first attempt to exchange the supporting foot itself. This strategy is very straightforward and intuitive because humans are also likely to exchange their supporting foot onto the opposite side when an unexpected force is acting. Research limitations/implications – The proposed step-exchange strategy described in this paper can be applicable in the situation when the external force is applied in the +Y direction, the left leg is the swing leg and the right leg is the stance leg, or it can also be applicable in the situation when the external force is applied in −Y direction, the right leg is the swing leg and the left leg is the stance leg (Figure 2 for ±Y force direction). If an impact force acts to the side of the swing leg, the other step-exchange strategy is needed. The authors are studying this issue as a future work. Originality/value – The authors have originated the proposed step-exchange strategy for balance control of a walking biped robot under lateral impact. The strategy is genuine and superior in comparison with the state-of-the-art strategy because not only can a biped robot be balanced but it can also easily continue walking by using the step-exchange strategy.

Purpose: Anorexia is a common symptom in terminal cancer patients. Some data have suggested a role of visfatin in regulating feeding behavior. We studied the relationship of a serum visfatin level and anorexia in cancer patients provided with hospice care. Methods: After informed consent was obtained, 69 cancer patients over 20 years old at a hospice center were enrolled in the study from July 2009 t o July 2010. We characterized patients by age, sex, body mass index, primary cancer site, and Eastern Cooperat ive Oncology Group (ECOG) performance status, etc. Also, blood tests were performed to measure the level of h emoglobin, white blood cell (WBC), C-reactive protein (CRP), total cholesterol, albumin, lymphocyte count, glucose, blood urea nitrogen (BUN), creatinine, tumor necrosis factor-alpha (TNF-α), interukin-6, leptin and visfatin. Results: The mean age of subjects was 65.5 years old, According to univariate analysis, pulse, ECOG performance scale, opioid use and visfatin level were different between the anorexia and non-anorexic group (P

Assessment of uncertainties of loads and resistances is prerequisite for the development of load and resistance factor design (LRFD). Many previous studies related to resistance factor calculations of piles were conducted for short or medium span bridges (span lengths less than 200m) reflecting the live load uncertainty for ordinary span bridges. In this study, by using a revised live load model and its uncertainty for long span bridges (span lengths longer than 200m and shorter than 1500m), resistance factors are recalibrated. For the estimation of nominal pile capacity (both base and shaft capacities), the Imperial College Pile (ICP) design method is used. For clayey and sandy foundation, uncertainty of resistance is assessed based on the ICP database. As long span bridges are typically considered as more important structures than short or medium span bridges, higher target reliability indices are assigned in the reliability analysis. Finally, resistance factors are calculated and proposed for the use of LRFD of driven piles for ordinary span and long span bridges.

Microbiome research has opened new frontiers in public health and environmental stewardship. However, protein sequence database for these complex microbial communities are often incomplete or unavailable, which limits options for spectral annotation. Spectral library search is an efficient method for MS/MS identification, but library sizes can be prohibitively large for microbiome research. Standard techniques apply a precursor ion window to filter candidates for an exact match, which can easily overlook many possible homologous matches. Emerging open library search methods appear promising, but have yet to be tested at the scale necessary for microbial communities. This calls for an efficient open spectral library search approach which can perform open search across a spectral library within a reasonable time frame. As a solution we present a GPU-accelerated, highly efficient pairwise similarity algorithm which can shortlist candidate spectra from a spectral library after performing all to all comparison. Our preliminary results show that open search for 35,000 spectra against a library of 1.18 million spectra takes approximately 45 mins, which is similar to a database search for a small bacterial organism.

Data explosion via the high-throughput and high-resolution technologies leads to a rapid growth of reference resources and the accumulation of considerably trustworthy knowledgebase. Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. However, as datasets continue to grow, the time involved in this simple task may be prohibitive. In large-scale data mining, the vast majority of pairwise comparisons are unlikely to be relevant, meaning that they do not share a signature of interest. It is therefore essential to efficiently identify these unproductive comparisons as rapidly as possible to and exclude them from more time-intensive similarity calculations. The Blazing Signature Filter (BSF) is a highly efficient pairwise similarity algorithm, which enables extensive data mining within a reasonable amount of time. The algorithm transforms datasets into binary metrics, allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity. As a result, the BSF can scale to high dimensionality and large comparisons and rapidly filter unproductive pairwise comparison. We present the two LINCS applications to demonstrate the ability to scale to billions of pairwise comparisons and the usefulness of this approach.

Data explosion via the high-throughput and high-resolution technologies leads to a rapid growth of reference resources and the accumulation of considerably trustworthy knowledgebase. Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. However, as datasets continue to grow, the time involved in this simple task may be prohibitive. In large-scale data mining, the vast majority of pairwise comparisons are unlikely to be relevant, meaning that they do not share a signature of interest. It is therefore essential to efficiently identify these unproductive comparisons as rapidly as possible to and exclude them from more time-intensive similarity calculations. The Blazing Signature Filter (BSF) is a highly efficient pairwise similarity algorithm, which enables extensive data mining within a reasonable amount of time. The algorithm transforms datasets into binary metrics, allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity. As a result, the BSF can scale to high dimensionality and large comparisons and rapidly filter unproductive pairwise comparison. We present the two LINCS applications to demonstrate the ability to scale to billions of pairwise comparisons and the usefulness of this approach.

Microbiome research has opened new frontiers in public health and environmental stewardship. However, protein sequence database for these complex microbial communities are often incomplete or unavailable, which limits options for spectral annotation. Spectral library search is an efficient method for MS/MS identification, but library sizes can be prohibitively large for microbiome research. Standard techniques apply a precursor ion window to filter candidates for an exact match, which can easily overlook many possible homologous matches. Emerging open library search methods appear promising, but have yet to be tested at the scale necessary for microbial communities. This calls for an efficient open spectral library search approach which can perform open search across a spectral library within a reasonable time frame. As a solution we present a GPU-accelerated, highly efficient pairwise similarity algorithm which can shortlist candidate spectra from a spectral library after performing all to all comparison. Our preliminary results show that open search for 35,000 spectra against a library of 1.18 million spectra takes approximately 45 mins, which is similar to a database search for a small bacterial organism.

In this study, we examine the effect of spreading sequence matched filtering on the power parameters of ultrawideband (UWB) multiuser interference (MUI) under different multiple access (MA) scenarios. More specifically, we investigate the manner in which the length of the sequence MF affects the average power, peak power, and the peak-to-average ratio (PAR) of the matched filtered version of an MUI signal. The results of the analysis performed for a simplified scenario are supported by the simulation results obtained for a realistic multipath environment.

Accurate modeling rainfall induced landslide and slope stability requires a detailed knowledge of the distribution of material strength characteristics and suction distribution. However, material properties obtained from the drying cycle are still used for infiltration analysis in many cases, even though material properties of wetting cycle are quite different from those of drying cycle due to hydraulic hysteresis and air occlusion. Therefore, the selection of proper material properties such as soil-water retention curve (SWRC) and the hydraulic conductivity function (HCF) reflecting characteristics of wetting cycle and air occlusion is an essential prerequisite in order to simulate the infiltration phenomena and to predict the suction and water content distribution in unsaturated soils. It is concluded that the simulation of infiltration with material properties from the drying cycle did not reasonably match with experimental outputs. Further discussion is made on how to describe the material properties considering air occlusion during wetting cycle over the entire suction range in order to simulate infiltration phenomena.