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Among new antimalarials discovered over the past decade are multiple chemical scaffolds that target Plasmodium falciparum P-type ATPase ( Pf ATP4). This essential protein is a Na + pump responsible for the maintenance of Na + homeostasis. Pf ATP4 belongs to the type two-dimensional (2D) subfamily of P-type ATPases, for which no structures have been determined. To gain better insight into the structure/function relationship of this validated drug target, we generated a homology model of Pf ATP4 based on sarco/endoplasmic reticulum Ca 2+ ATPase, a P2A-type ATPase, and refined the model using molecular dynamics in its explicit membrane environment. This model predicted several residues in Pf ATP4 critical for its function, as well as those that impart resistance to various Pf ATP4 inhibitors. To validate our model, we developed a genetic system involving merodiploid states of Pf ATP4 in which the endogenous gene was conditionally expressed, and the second allele was mutated to assess its effect on the parasite. Our model predicted residues involved in Na + coordination as well as the phosphorylation cycle of Pf ATP4. Phenotypic characterization of these mutants involved assessment of parasite growth, localization of mutated Pf ATP4, response to treatment with known Pf ATP4 inhibitors, and evaluation of the downstream consequences of Na + influx. Our results were consistent with modeled predictions of the essentiality of the critical residues. Additionally, our approach confirmed the phenotypic consequences of resistance-associated mutations as well as a potential structural basis for the fitness cost associated with some mutations. Taken together, our approach provides a means to explore the structure/function relationship of essential genes in haploid organisms., Competing Interests: Competing interests statement:The authors declare no competing interest.

Cutaneous leishmaniasis (CL) affects the lives of 0.7-1 million people every year causing lesions that take months to heal. These lesions can result in disfiguring scars with psychological, social and economic consequences. Antimonials are the first line of therapy for CL, however the treatment is lengthy and linked to significant toxicities; further, its efficacy is variable and resistant parasites are emerging. Shorter or lower dose antimonial treatment regimens, which would decrease the risk of adverse events and improve patient compliance, have shown reduced efficacy and further increase the risk emergence of antimonial-resistant strains. The progression of lesions in CL is partly determined by the immune response it elicits, and previous studies showed that administration of immunomodulatory type D CpG ODNs, magnifies the immune response to Leishmania and reduces lesion severity in nonhuman primates (NHP) challenged with Leishmania major or Leishmania amazonensis. Here we explored whether the addition of a single dose of immunomodulating CpG ODN D35 augments the efficacy of a short-course, low-dose pentavalent antimonial treatment regimen. Results show that macaques treated with D35 plus 5mg/kg sodium stibogluconate (SbV) for 10 days had smaller lesions and reduced time to re-epithelization after infection with Leishmania major. No toxicities were evident during the studies, even at doses of D35 10 times higher than those used in treatment. Critically, pentavalent antimonial treatment did not modify the ability of D35 to induce type I IFNs. The findings support the efficacy of D35 as adjuvant therapy for shorter, low dose pentavalent antimonial treatment., Competing Interests: Daniela Verthelyi is named as an inventor on a patent covering D35 filed on April, 1999, US 7,960,356 B2 under the Serial number 60/128,898. WO2000/061151. The patent is owned by the US government and licensed to DNDi.

The Complexity-entropy causality plane (CECP) is a parsimonious representation space for time series. It has only two dimensions: normalized permutation entropy ([Formula: see text]) and Jensen-Shannon complexity ([Formula: see text]) of a time series. This two-dimensional representation allows for detection of slow or rapid drifts in the condition of mechanical components monitored through sensor measurements. The CECP representation can be used for both predictive analytics and visual monitoring of changes in component condition. This method requires minimal pre-processing of raw signals. Furthermore, it is insensitive to noise, stationarity, and trends. These desirable properties make CECP a good candidate for machine condition monitoring and fault diagnostics. In this work we study the effectiveness of CECP on three rotary component condition assessment applications. We use CECP representation of vibration signals to differentiate various machine component health conditions for rotary machine components, namely roller bearing and gears. The results confirm that the CECP representation is able to detect, with high accuracy, changes in underlying dynamics of machine component degradation states. From class separability perspective, the CECP representation is able to generate linearly separable classes for the classification of different fault states. This classification performance improves with increasing signal length. For signal length of 16,384 data points, the fault classification accuracy varies from 90% to 100% for bearing applications, and from 85% to 100% for gear applications. We observed that the optimum parameter for CECP representatino depends on the application. For bearing applications we found that embedding dimension D = 4, 5, 6, and embedding delay τ = 1, 2, 3 are suitable for good fault classification. For gear applications we find that embedding dimension D = 4, 5, and embedding delay τ = 1, 5 are suitable for fault classification., Competing Interests: The authors have declared that no competing interests exist.

Monitoring the performance of manufacturing equipment is critical to ensure the efficiency of manufacturing processes. Machine-monitoring data allows measuring manufacturing equipment efficiency. However, acquiring real and useful machine-monitoring data is expensive and time consuming. An alternative method of getting data is to generate machine-monitoring data using simulation. The simulation data mimic operations and operational failure. In addition, the data can also be used to fill in real data sets with missing values from real-time data collection. The mimicking of real manufacturing systems in computer-based systems is called "virtual manufacturing". The computer-based systems execute the manufacturing system models that represent real manufacturing systems. In this paper, we introduce a virtual machining model of milling operations. We developed a prototype virtual machining model that represents 3-axis milling operations. This model is a digital mock-up of a real milling machine; it can generate machine-monitoring data from a process plan. The prototype model provides energy consumption data based on physics-based equations. The model uses the standard interfaces of Step-compliant data interface for Numeric Controls (STEP-NC) and MTConnect to represent process plan and machine-monitoring data, respectively. With machine-monitoring data for a given process plan, manufacturing engineers can anticipate the impact of a modification in their actual manufacturing systems. This paper describes also how the virtual machining model is integrated into an agent-based model in a simulation environment. While facilitating the use of the virtual machining model, the agent-based model also contributes to the generation of more complex manufacturing system models, such as a virtual shop-floor model. The paper describes initial building steps towards a shop-floor model. Aggregating the data generated during the execution of a virtual shop-floor model allows one to take advantage of data analytics techniques to predict performance at the shop-floor level.

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Ribosomal protein (RP) gene mutations, mostly associated with inherited or acquired bone marrow failure, are believed to drive disease by slowing the rate of protein synthesis. Here de novo missense mutations in the RPS23 gene, which codes for uS12, are reported in two unrelated individuals with microcephaly, hearing loss, and overlapping dysmorphic features. One individual additionally presents with intellectual disability and autism spectrum disorder. The amino acid substitutions lie in two highly conserved loop regions of uS12 with known roles in maintaining the accuracy of mRNA codon translation. Primary cells revealed one substitution severely impaired OGFOD1-dependent hydroxylation of a neighboring proline residue resulting in 40S ribosomal subunits that were blocked from polysome formation. The other disrupted a predicted pi-pi stacking interaction between two phenylalanine residues leading to a destabilized uS12 that was poorly tolerated in 40S subunit biogenesis. Despite no evidence of a reduction in the rate of mRNA translation, these uS12 variants impaired the accuracy of mRNA translation and rendered cells highly sensitive to oxidative stress. These discoveries describe a ribosomopathy linked to uS12 and reveal mechanistic distinctions between RP gene mutations driving hematopoietic disease and those resulting in developmental disorders., (Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)