Genetic Basis for \u3cem\u3eRhizobium etli\u3c/em\u3e CE3 O-Antigen O-Methylated Residues That Vary According to Growth Conditions

The Rhizobium etli CE3 O antigen is a fixed-length heteropolymer with O methylation being the predominant type of sugar modification. There are two O-methylated residues that occur, on average, once per complete O antigen: a multiply O-methylated terminal fucose and 2-O methylation of a fucose residue within a repeating unit. The amount of the methylated terminal fucose decreases and the amount of 2-O-methylfucose increases when bacteria are grown in the presence of the host plant, Phaseolus vulgaris, or its seed exudates. Insertion mutagenesis was used to identify open reading frames required for the presence of these O-methylated residues. The presence of the methylated terminal fucose required genes wreA, wreB, wreC, wreD, and wreF, whereas 2-O methylation of internal fucoses required the methyltransferase domain of bifunctional gene wreM. Mutants lacking only the methylated terminal fucose, lacking only 2-O methylation, or lacking both the methylated terminal fucose and 2-O methylation exhibited no other lipopolysaccharide structural defects. Thus, neither of these decorations is required for normal O-antigen length, transport, or assembly into the final lipopolysaccharide. This is in contrast to certain enteric bacteria in which the absence of a terminal decoration severely affects O-antigen length and transport. R. etli mutants lacking only the methylated terminal fucose were not altered in symbiosis with host Phaseolus vulgaris, whereas mutants lacking only 2-O-methylfucose exhibited a delay in nodule development during symbiosis. These results support previous conclusions that the methylated terminal fucose is dispensable for symbiosis, whereas 2-O methylation of internal fucoses somehow facilitates early events in symbiosis

Genetic Locus and Structural Characterization of the Biochemical Defect in the O-Antigenic Polysaccharide of the Symbiotically Deficient \u3cem\u3eRhizobium etli\u3c/em\u3e Mutant, CE166

The O-antigen polysaccharide (OPS) of Rhizobium etli CE3 lipopolysaccharide (LPS) is linked to the core oligosaccharide via an N-acetylquinovosaminosyl (QuiNAc) residue. A mutant of CE3, CE166, produces LPS with reduced amounts of OPS, and a suppressed mutant, CE166α, produces LPS with nearly normal OPS levels. Both mutants are deficient in QuiNAc production. Characterization of OPS from CE166 and CE166α showed that QuiNAc was replaced by its 4-keto derivative, 2-acetamido-2,6-dideoxyhexosyl-4-ulose. The identity of this residue was determined by NMR and mass spectrometry, and by gas chromatography-mass spectrometry analysis of its 2-acetamido-4-deutero-2,6-dideoxyhexosyl derivatives produced by reduction of the 4-keto group using borodeuteride. Mass spectrometric and methylation analyses showed that the 2-acetamido-2,6-dideoxyhexosyl-4-ulosyl residue was 3-linked and attached to the core-region external Kdo III residue of the LPS, the same position as that of QuiNAc in the CE3 LPS. DNA sequencing revealed that the transposon insertion in strain CE166 was located in an open reading frame whose predicted translation product, LpsQ, falls within a large family of predicted open reading frames, which includes biochemically characterized members that are sugar epimerases and/or reductases. A hypothesis to be tested in future work is that lpsQ encodes UDP-2-acetamido-2,6-dideoxyhexosyl-4-ulose reductase, the second step in the synthesis of UDP-QuiNAc from UDP-GlcNAc

Genetic Locus Required for Antigenic Maturation of \u3cem\u3eRhizobium etli\u3c/em\u3e CE3 Lipopolysaccharide

Rhizobium etli modifies lipopolysaccharide (LPS) structure in response to environmental signals, such as low pH and anthocyanins. These LPS modifications result in the loss of reactivity with certain monoclonal antibodies. The same antibodies fail to recognize previously isolated R. etli mutant strain CE367, even in the absence of such environmental cues. Chemical analysis of the LPS in strain CE367 demonstrated that it lacked the terminal sugar of the wild-type O antigen, 2,3,4-tri-O-methylfucose. A 3-kb stretch of DNA, designated as lpe3, restored wild-type antigenicity when transferred into CE367. From the sequence of this DNA, five open reading frames were postulated. Site-directed mutagenesis and complementation analysis suggested that the genes were organized in at least two transcriptional units, both of which were required for the production of LPS reactive with the diagnostic antibodies. Growth in anthocyanins or at low pH did not alter the specific expression of gusA from the transposon insertion of mutant CE367, nor did the presence of multiple copies of lpe3 situated behind a strong, constitutive promoter prevent epitope changes induced by these environmental cues. Mutations of the lpe genes did not prevent normal nodule development on Phaseolus vulgaris and had very little effect on the occupation of nodules in competition with the wild-type strain

Determination of the Joint Confidence Region of Optimal Operating Conditions in Robust Design by Bootstrap Technique

Robust design has been widely recognized as a leading method in reducing variability and improving quality. Most of the engineering statistics literature mainly focuses on finding "point estimates" of the optimum operating conditions for robust design. Various procedures for calculating point estimates of the optimum operating conditions are considered. Although this point estimation procedure is important for continuous quality improvement, the immediate question is "how accurate are these optimum operating conditions?" The answer for this is to consider interval estimation for a single variable or joint confidence regions for multiple variables. In this paper, with the help of the bootstrap technique, we develop procedures for obtaining joint "confidence regions" for the optimum operating conditions. Two different procedures using Bonferroni and multivariate normal approximation are introduced. The proposed methods are illustrated and substantiated using a numerical example.Comment: Two tables, Three figure

Six Peaks Visible in the Redshift Distribution of 46,400 SDSS Quasars Agree with the Preferred Redshifts Predicted by the Decreasing Intrinsic Redshift Model

The redshift distribution of all 46,400 quasars in the Sloan Digital Sky Survey (SDSS) Quasar Catalog III, Third Data Release, is examined. Six Peaks that fall within the redshift window below z = 4, are visible. Their positions agree with the preferred redshift values predicted by the decreasing intrinsic redshift (DIR) model, even though this model was derived using completely independent evidence. A power spectrum analysis of the full dataset confirms the presence of a single, significant power peak at the expected redshift period. Power peaks with the predicted period are also obtained when the upper and lower halves of the redshift distribution are examined separately. The periodicity detected is in linear z, as opposed to log(1+z). Because the peaks in the SDSS quasar redshift distribution agree well with the preferred redshifts predicted by the intrinsic redshift relation, we conclude that this relation, and the peaks in the redshift distribution, likely both have the same origin, and this may be intrinsic redshifts, or a common selection effect. However, because of the way the intrinsic redshift relation was determined it seems unlikely that one selection effect could have been responsible for both.Comment: 12 pages, 12 figure, accepted for publication in the Astrophysical Journa

Political opportunism and countercyclical fiscal policy in election-year recessions

Political budget cycles (PBCs) have been well documented in the literature, albeit not for all circumstances. Similarly, there is clear evidence on the positive effect of economic growth on electoral success. However, no work has been done on the impact of economic growth on the magnitude of PBCs. The theoretical model argues that a government has an incentive to increase fiscal manipulations when a recession is expected to hit and curtail reelection chances; this amounts to countercyclical policy for opportunistic rather than Keynesian motives. Very robust evidence for this behavior is found in Portuguese municipalities; in election years, budget deficits go up even more and significantly so, when a recession is expected.Fundação para a Ciência e Tecnologia (FCT

Causal connectivity of evolved neural networks during behavior

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics

Activity Recognition and Prediction in Real Homes

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201

Analyzing the House Fly's Exploratory Behavior with Autoregression Methods

This paper presents a detailed characterization of the trajectory of a single housefly with free range of a square cage. The trajectory of the fly was recorded and transformed into a time series, which was fully analyzed using an autoregressive model, which describes a stationary time series by a linear regression of prior state values with the white noise. The main discovery was that the fly switched styles of motion from a low dimensional regular pattern to a higher dimensional disordered pattern. This discovered exploratory behavior is, irrespective of the presence of food, characterized by anomalous diffusion.Comment: 20 pages, 9 figures, 1 table, full pape

Ensemble Sales Forecasting Study in Semiconductor Industry

Sales forecasting plays a prominent role in business planning and business strategy. The value and importance of advance information is a cornerstone of planning activity, and a well-set forecast goal can guide sale-force more efficiently. In this paper CPU sales forecasting of Intel Corporation, a multinational semiconductor industry, was considered. Past sale, future booking, exchange rates, Gross domestic product (GDP) forecasting, seasonality and other indicators were innovatively incorporated into the quantitative modeling. Benefit from the recent advances in computation power and software development, millions of models built upon multiple regressions, time series analysis, random forest and boosting tree were executed in parallel. The models with smaller validation errors were selected to form the ensemble model. To better capture the distinct characteristics, forecasting models were implemented at lead time and lines of business level. The moving windows validation process automatically selected the models which closely represent current market condition. The weekly cadence forecasting schema allowed the model to response effectively to market fluctuation. Generic variable importance analysis was also developed to increase the model interpretability. Rather than assuming fixed distribution, this non-parametric permutation variable importance analysis provided a general framework across methods to evaluate the variable importance. This variable importance framework can further extend to classification problem by modifying the mean absolute percentage error(MAPE) into misclassify error. Please find the demo code at : https://github.com/qx0731/ensemble_forecast_methodsComment: 14 pages, Industrial Conference on Data Mining 2017 (ICDM 2017