Kinetoplastid-specific pATOM36 mediates membrane insertion of a subset of mitochondrial outer membrane proteins

In trypanosomes, as in other eukaryotes, more than 95% of all mitochondrial proteins are imported into the mitochondrion. The recently characterized multisubunit ATOM complex mediates import of essentially all proteins across the outer mitochondrial membrane in T. brucei. Moreover, an additional protein termed pATOM36, which is loosely associated with the ATOM complex, has been implicated in the import of only a subset of mitochondrial matrix proteins. Here we have investigated more precisely which role pATOM36 plays in mitochondrial protein import. RNAi mediated ablation of pATOM36 specifically depletes a subset of ATOM complex subunits and as a consequence results in the collapse of the ATOM complex as shown by Blue native PAGE. In addition, a SILAC-based global proteomic analysis of uninduced and induced pATOM36 RNAi cells together with in vitro import experiments suggest that pATOM36 might be a novel protein insertase acting on a subset of alpha-helically anchored mitochondrial outer membrane proteins. Identification of pATOM36 interaction partners by co-immunoprecipitation together with immunofluorescence analysis furthermore shows that unexpectedly a fraction of the protein is associated with the tripartite attachment complex (TAC). This complex is essential for proper inheritance of the kDNA as it forms a physical connection between the kDNA and the basal body of the flagellum throughout the cell cycle. Thus, the presence of pATOM36 in the TAC provides an exciting link between mitochondrial protein import and kDNA inheritance.

Increased Resting State Network Connectivity in Synesthesia: Evidence for a Neural Basis of Synesthetic Consistency

Studying individual differences in conscious awareness can potentially lend fundamental insights into the neural bases of binding mechanisms and consciousness (Cohen Kadosh and Henik, 2007). Partly for this reason, considerable attention has been devoted to the neural mechanisms underlying grapheme–color synesthesia, a healthy condition involving atypical brain activation and the concurrent experience of color photisms in response to letters, numbers, and words. For instance, the letter C printed in black on a white background may elicit a yellow color photism that is perceived to be spatially colocalized with the inducing stimulus or internally in the “mind's eye” as, for instance, a visual image. Synesthetic experiences are involuntary, idiosyncratic, and consistent over time (Rouw et al., 2011). To date, neuroimaging research on synesthesia has focused on brain areas activated during the experience of synesthesia and associated structural brain differences. However, activity patterns of the synesthetic brain at rest remain largely unexplored. Moreover, the neural correlates of synesthetic consistency, the hallmark characteristic of synesthesia, remain elusive.

Evidence of secondary dengue infection and potential for association with clinical depression in a subset of a randomly selected cohort in South Texas

Globally, dengue is an emerging disease resulting in an estimated 50 million new cases and 22, 000 deaths each year. Anecdotally, depression has been reported as a possible sequelae of dengue virus infection. To test the association, we performed a cross-sectional analysis in a selected sub-set of participants from the Cameron County Hispanic Cohort (CCHC) in South Texas. All study subjects in the analysis had Center for Epidemiological Studies Depression scale (CES-D) scores and were tested for dengue antibodies using stored plasma. We found that 5.0% of participants tested either positive or equivocal for anti-dengue IgG antibodies using the capture antibody test, which detects acute secondary infections. Logistic regression identified that evidence of acute secondary dengue infection was not associated with depression (Odds Ratio [OR] = 0.97, 95%Confidence Interval [CI] 0.47-1.98); however, both being female (OR = 1.53, 95%CI 1.09-2.15) and obese body mass index (BMI > 30) (OR = 1.84, 95%CI 1.19-2.84) were associated with depression. ^

BEST SUBSET SELECTION FOR CATEGORICAL DATA

When choosing among models to describe categorical data, the necessity to consider interactions makes selection more difficult. With just four variables, considering all interactions, there are 166 different hierarchical models and many more non-hierarchical models. Two procedures have been developed for categorical data which will produce the "best" subset or subsets of each model size where size refers to the number of effects in the model. Both procedures are patterned after the Leaps and Bounds approach used by Furnival and Wilson for continuous data and do not generally require fitting all models. For hierarchical models, likelihood ratio statistics (G('2)) are computed using iterative proportional fitting and "best" is determined by comparing, among models with the same number of effects, the Pr((chi)(,k)('2) (GREATERTHEQ) G(,ij)('2)) where k is the degrees of freedom for ith model of size j. To fit non-hierarchical as well as hierarchical models, a weighted least squares procedure has been developed.^ The procedures are applied to published occupational data relating to the occurrence of byssinosis. These results are compared to previously published analyses of the same data. Also, the procedures are applied to published data on symptoms in psychiatric patients and again compared to previously published analyses.^ These procedures will make categorical data analysis more accessible to researchers who are not statisticians. The procedures should also encourage more complex exploratory analyses of epidemiologic data and contribute to the development of new hypotheses for study. ^

AN EVALUATION OF THE CONSISTENCY OF IMRT PATIENT-SPECIFIC QA TECHNIQUES

To ensure the integrity of an intensity modulated radiation therapy (IMRT) treatment, each plan must be validated through a measurement-based quality assurance (QA) procedure, known as patient specific IMRT QA. Many methods of measurement and analysis have evolved for this QA. There is not a standard among clinical institutions, and many devices and action levels are used. Since the acceptance criteria determines if the dosimetric tools’ output passes the patient plan, it is important to see how these parameters influence the performance of the QA device. While analyzing the results of IMRT QA, it is important to understand the variability in the measurements. Due to the different form factors of the many QA methods, this reproducibility can be device dependent. These questions of patient-specific IMRT QA reproducibility and performance were investigated across five dosimeter systems: a helical diode array, radiographic film, ion chamber, diode array (AP field-by-field, AP composite, and rotational composite), and an in-house designed multiple ion chamber phantom. The reproducibility was gauged for each device by comparing the coefficients of variation (CV) across six patient plans. The performance of each device was determined by comparing each one’s ability to accurately label a plan as acceptable or unacceptable compared to a gold standard. All methods demonstrated a CV of less than 4%. Film proved to have the highest variability in QA measurement, likely due to the high level of user involvement in the readout and analysis. This is further shown by how the setup contributed more variation than the readout and analysis for all of the methods, except film. When evaluated for ability to correctly label acceptable and unacceptable plans, two distinct performance groups emerged with the helical diode array, AP composite diode array, film, and ion chamber in the better group; and the rotational composite and AP field-by-field diode array in the poorer group. Additionally, optimal threshold cutoffs were determined for each of the dosimetry systems. These findings, combined with practical considerations for factors such as labor and cost, can aid a clinic in its choice of an effective and safe patient-specific IMRT QA implementation.

Identification and characterization of cellular genes differentially expressed in a subset of tumors with non -functional WT1

Wilms tumor (WT) or nephroblastoma is a genetically heterogeneous pediatric renal tumor that accounts for 6–7% of all childhood cancers in the U.S. WT1, located at 11p13, is the sole WT gene cloned to date. Additional genomic regions containing genes that play a role in the development of Wilms tumor include 11p15, 7p, 16q, 1p, 17q and 19q. This heterogeneity has made it extremely difficult to develop an understanding of the pathways involved in the development of WT, even in the 5–20% of tumors that show mutations at the WT1 locus. My research addresses this gap in our current comprehension of the development of WT. ^ I have used two complementary approaches to extend the current understanding of molecular changes involved in the development of WT. In order to minimize complexities due to genetic heterogeneity, I confined my analysis to the WT1 pathway by assessing those genetically defined tumors that carry WT1 mutations. WT1 encodes a zinc finger transcription factor, and in vitro studies have identified many genes that are potentially regulated in vivo by WT1. However, there is very little in vivo data that suggests that they are transcriptionally regulated endogenously by WT1. In one approach I assessed the role of WT1 in the in vivo regulation of PDGFA and IGF2, two genes that are strong contenders for endogenous regulation by WT1. Using primary tissue samples, I found no correlation between the level of RNA expression of WT1 with either PDGFA or IGF2, suggesting that WT1 does not play a critical role in their expression in either normal kidney or WT. ^ In a parallel strategy, using differential display analysis I compared global gene expression in a subset of tumors with known homozygous inactivating WT1 mutations (WT1-tumors) to the gene expression in a panel of appropriate control tissues (fetal kidney, normal kidney, rhabdoid tumor and pediatric renal cell carcinoma). Transcripts that are aberrantly expressed in this subset of Wilms tumors are candidates for endogenous transcriptional regulation by WT1 as well as for potentially functioning in the development of WT. By this approach I identified several differentially expressed transcripts. I further characterized two of these transcripts, identifying a candidate WT gene in the process. I then performed a detailed analysis of this WT candidate gene, which maps to 7p. Future studies will shed more light on the role of these differentially expressed genes in WT. ^

Theoretical analysis and numerical verification of the consistency of viscous smoothed-particle-hydrodynamics formulations in simulating free-surface flows

The theoretical formulation of the smoothed particle hydrodynamics (SPH) method deserves great care because of some inconsistencies occurring when considering free-surface inviscid flows. Actually, in SPH formulations one usually assumes that (i) surface integral terms on the boundary of the interpolation kernel support are neglected, (ii) free-surface conditions are implicitly verified. These assumptions are studied in detail in the present work for free-surface Newtonian viscous flow. The consistency of classical viscous weakly compressible SPH formulations is investigated. In particular, the principle of virtual work is used to study the verification of the free-surface boundary conditions in a weak sense. The latter can be related to the global energy dissipation induced by the viscous term formulations and their consistency. Numerical verification of this theoretical analysis is provided on three free-surface test cases including a standing wave, with the three viscous term formulations investigated.

An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.

Harmony: Towards automated self-adaptive consistency in cloud storage

In just a few years cloud computing has become a very popular paradigm and a business success story, with storage being one of the key features. To achieve high data availability, cloud storage services rely on replication. In this context, one major challenge is data consistency. In contrast to traditional approaches that are mostly based on strong consistency, many cloud storage services opt for weaker consistency models in order to achieve better availability and performance. This comes at the cost of a high probability of stale data being read, as the replicas involved in the reads may not always have the most recent write. In this paper, we propose a novel approach, named Harmony, which adaptively tunes the consistency level at run-time according to the application requirements. The key idea behind Harmony is an intelligent estimation model of stale reads, allowing to elastically scale up or down the number of replicas involved in read operations to maintain a low (possibly zero) tolerable fraction of stale reads. As a result, Harmony can meet the desired consistency of the applications while achieving good performance. We have implemented Harmony and performed extensive evaluations with the Cassandra cloud storage on Grid?5000 testbed and on Amazon EC2. The results show that Harmony can achieve good performance without exceeding the tolerated number of stale reads. For instance, in contrast to the static eventual consistency used in Cassandra, Harmony reduces the stale data being read by almost 80% while adding only minimal latency. Meanwhile, it improves the throughput of the system by 45% while maintaining the desired consistency requirements of the applications when compared to the strong consistency model in Cassandra.

Data consistency management in an open smart home management platform

In this paper, the authors introduce a novel mechanism for data management in a middleware for smart home control, where a relational database and semantic ontology storage are used at the same time in a Data Warehouse. An annotation system has been designed for instructing the storage format and location, registering new ontology concepts and most importantly, guaranteeing the Data Consistency between the two storage methods. For easing the data persistence process, the Data Access Object (DAO) pattern is applied and optimized to enhance the Data Consistency assurance. Finally, this novel mechanism provides an easy manner for the development of applications and their integration with BATMP. Finally, an application named "Parameter Monitoring Service" is given as an example for assessing the feasibility of the system.

Spatial analysis of geometric design consistency and road sight distance

Because of the high number of crashes occurring on highways, it is necessary to intensify the search for new tools that help in understanding their causes. This research explores the use of a geographic information system (GIS) for an integrated analysis, taking into account two accident-related factors: design consistency (DC) (based on vehicle speed) and available sight distance (ASD) (based on visibility). Both factors require specific GIS software add-ins, which are explained. Digital terrain models (DTMs), vehicle paths, road centerlines, a speed prediction model, and crash data are integrated in the GIS. The usefulness of this approach has been assessed through a study of more than 500 crashes. From a regularly spaced grid, the terrain (bare ground) has been modeled through a triangulated irregular network (TIN). The length of the roads analyzed is greater than 100 km. Results have shown that DC and ASD could be related to crashes in approximately 4% of cases. In order to illustrate the potential of GIS, two crashes are fully analyzed: a car rollover after running off road on the right side and a rear-end collision of two moving vehicles. Although this procedure uses two software add-ins that are available only for ArcGIS, the study gives a practical demonstration of the suitability of GIS for conducting integrated studies of road safety.

Anomalous Consistency in Mild Cognitive Impairment: A Complex Networks Approach

Increased variability in performance has been associated with the emergence of several neurological and psychiatric pathologies. However, whether and how consistency of neuronal activity may also be indicative of an underlying pathology is still poorly understood. Here we propose a novel method for evaluating consistency from non-invasive brain recordings. We evaluate the consistency of the cortical activity recorded with magnetoencephalography in a group of subjects diagnosed with Mild Cognitive Impairment (MCI), a condition sometimes prodromal of dementia, during the execution of a memory task. We use metrics coming from nonlinear dynamics to evaluate the consistency of cortical regions. A representation known as parenclitic networks is constructed, where atypical features are endowed with a network structure, the topological properties of which can be studied at various scales. Pathological conditions correspond to strongly heterogeneous networks, whereas typical or normative conditions are characterized by sparsely connected networks with homogeneous nodes. The analysis of this kind of networks allows identifying the extent to which consistency is affected in the MCI group and the focal points where MCI is especially severe. To the best of our knowledge, these results represent the first attempt at evaluating the consistency of brain functional activity using complex networks theory.

Rapamycin-modulated transcription defines the subset of nutrient-sensitive signaling pathways directly controlled by the Tor proteins

The immunosuppressant rapamycin inhibits Tor1p and Tor2p (target of rapamycin proteins), ultimately resulting in cellular responses characteristic of nutrient deprivation through a mechanism involving translational arrest. We measured the immediate transcriptional response of yeast grown in rich media and treated with rapamycin to investigate the direct effects of Tor proteins on nutrient-sensitive signaling pathways. The results suggest that Tor proteins directly modulate the glucose activation and nitrogen discrimination pathways and the pathways that respond to the diauxic shift (including glycolysis and the citric acid cycle). Tor proteins do not directly modulate the general amino acid control, nitrogen starvation, or sporulation (in diploid cells) pathways. Poor nitrogen quality activates the nitrogen discrimination pathway, which is controlled by the complex of the transcriptional repressor Ure2p and activator Gln3p. Inhibiting Tor proteins with rapamycin increases the electrophoretic mobility of Ure2p. The work presented here illustrates the coordinated use of genome-based and biochemical approaches to delineate a cellular pathway modulated by the protein target of a small molecule.