Neurotoxic reactive astrocytes induce cell death via saturated lipids.

Astrocytes regulate the response of the central nervous system to disease and injury and have been hypothesized to actively kill neurons in neurodegenerative disease1-6. Here we report an approach to isolate one component of the long-sought astrocyte-derived toxic factor5,6. Notably, instead of a protein, saturated lipids contained in APOE and APOJ lipoparticles mediate astrocyte-induced toxicity. Eliminating the formation of long-chain saturated lipids by astrocyte-specific knockout of the saturated lipid synthesis enzyme ELOVL1 mitigates astrocyte-mediated toxicity in vitro as well as in a model of acute axonal injury in vivo. These results suggest a mechanism by which astrocytes kill cells in the central nervous system.

Structure Based Machine Learning Prediction of Retention Times for LC Method Development of Pharmaceuticals.

PURPOSE: Significant resources are spent on developing robust liquid chromatography (LC) methods with optimum conditions for all project in the pipeline. Although, data-driven computer assisted modelling has been implemented to shorten the method development timelines, these modelling approaches require project-specific screening data to model retention time (RT) as function of method parameters. Sometimes method re-development is required, leading to additional investments and redundant laboratory work. Cheminformatics techniques have been successfully used to predict the RT of metabolites & other component mixtures for similar use cases. Here we will show that these techniques can be used to model structurally diverse molecules and predictions of these models trained on multiple LC conditions can be used for downstream data-driven modelling. METHODS: The Molecular Operating Environment (MOE) was used to calculate over 800 descriptors using the strucutres of the analytes. These descriptors were used to model the RT of the analytes under four chromatographic conditions. These models were then used to create data-driven models using LC-SIM. RESULTS: A structural-based Random Forest (RF) model outperformed other techniques in cross-validation studies and predicted the RTs of a randomized test set with a median percentage error less than 4% for all LC conditions. RTs predicted by this structure-based model were used to fit a data-driven model that identifies optimum LC conditions without any additional experimental work. CONCLUSIONS: These results show that small training sets yield pharmaceutically relevant models when used in a combination of structure-based and data-driven model.

Learning Relationships Between Chemical and Physical Stability for Peptide Drug Development.

PURPOSE OR OBJECTIVE: Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. Moreover, fluorescent reporter Thioflavin-T is commonly used to measure physical stability. Executing stability studies is a lengthy process and requires extensive resources. To reduce the resources and shorten the process for stability studies during the development of a drug product, we introduce a machine learning-based model for predicting the chemical stability over time using both formulation conditions as well as aggregation curves. METHODS: In this work, we develop the relationships between the formulation, stability timepoint, and the chemical stability measurements and evaluated the performance on a random test set. We have developed a multilayer perceptron (MLP) for total degradation prediction and a random forest (RF) model for potency. RESULTS: The coefficient of determination (R2) of 0.945 and a mean absolute error (MAE) of 0.421 were achieved on the test set when using MLP for total degradation. Similarly, we achieved a R2 of 0.908 and MAE of 1.435 when predicting potency using the RF model. When physical stability measurements are included into the MLP model, the MAE of predicting TD decreases to 0.148. Using a similar strategy for potency prediction, the MAE decreases to 0.705 for the RF model. CONCLUSIONS: We conclude two important points: first, chemical stability can be modeled using machine learning techniques and second there is a relationship between the physical stability of a peptide and its chemical stability.

Portable capillary LC for in-line UV monitoring and MS detection: Comparable sensitivity and much lower solvent consumption.

Pharmaceutical development currently relies on quality separation methods from early discovery through to line-of-site manufacturing. There have been significant advancements made regarding the column particle packing, internal diameter, length connectivity, the understanding of the impact key parameters like void volume, flow rate, and temperature all that affects the resultant separation quality, that is, resolution, peak shape, peak width, run time, and signal-to-noise ratio. There is however a strong need to establish better alternatives to large bulky high-performance liquid chromatography racks either for process analytical reaction monitoring or mass spectrometry analysis in establishing product quality. Compact, portable high-pressure liquid chromatography can be a more efficient alternative to traditional ultra-high pressure liquid chromatography and traditional liquid chromatography. The compact versatile instrument evaluated here allows good separation control with either the on-board column with fixed ultra-violet wavelength cartridge or for use with a high-resolution mass spectrometry. Significant space reduction results in greener lab spaces with improved energy efficiency for smaller labs with lower energy demands. In addition, this compact liquid chromatography was used as a portable reaction monitoring solution to compare forced degradation kinetics and assess portable liquid chromatography-mass spectrometry capability for the analyses required for pharmaceutical drug product testing.

Nitrosamine acceptable intakes should consider variation in molecular weight: The implication of stoichiometric DNA damage.

N-nitrosamines (NAs) are a class of compounds of which many, especially of the small dialkyl type, are indirect acting DNA alkylating mutagens. Their presence in pharmaceuticals is subject to very strict acceptable daily intake (AI) limits, which are traditionally expressed on a mass basis. Here we demonstrate that AIs that are not experimentally derived for a specific compound, but via statistical extrapolation or read across to a suitable analog, should be expressed on a molar scale or corrected for the target substance's molecular weight. This would account for the mechanistic aspect that each nitroso group can, at maximum, account for a single DNA mutation and the number of molecules per mass unit is proportional to the molecular weight (MW). In this regard we have re-calculated the EMA 18 ng/day regulatory default AI for unknown nitrosamines on a molar scale and propose a revised default AI of 163 pmol/day. In addition, we provide MW-corrected AIs for those nitrosamine drug substance related impurities (NDSRIs) for which EMA has pre-assigned AIs by read-across. Regulatory acceptance of this fundamental scientific tenet would allow one to derive nitrosamine limits for NDSRIs that both meet the health-protection goals and are technically feasible.

Procalcitonin-Guided Use of Antibiotics for Lower Respiratory Tract Infection.

BACKGROUND: The effect of procalcitonin-guided use of antibiotics on treatment for suspected lower respiratory tract infection is unclear. METHODS: In 14 U.S. hospitals with high adherence to quality measures for the treatment of pneumonia, we provided guidance for clinicians about national clinical practice recommendations for the treatment of lower respiratory tract infections and the interpretation of procalcitonin assays. We then randomly assigned patients who presented to the emergency department with a suspected lower respiratory tract infection and for whom the treating physician was uncertain whether antibiotic therapy was indicated to one of two groups: the procalcitonin group, in which the treating clinicians were provided with real-time initial (and serial, if the patient was hospitalized) procalcitonin assay results and an antibiotic use guideline with graded recommendations based on four tiers of procalcitonin levels, or the usual-care group. We hypothesized that within 30 days after enrollment the total antibiotic-days would be lower - and the percentage of patients with adverse outcomes would not be more than 4.5 percentage points higher - in the procalcitonin group than in the usual-care group. RESULTS: A total of 1656 patients were included in the final analysis cohort (826 randomly assigned to the procalcitonin group and 830 to the usual-care group), of whom 782 (47.2%) were hospitalized and 984 (59.4%) received antibiotics within 30 days. The treating clinician received procalcitonin assay results for 792 of 826 patients (95.9%) in the procalcitonin group (median time from sample collection to assay result, 77 minutes) and for 18 of 830 patients (2.2%) in the usual-care group. In both groups, the procalcitonin-level tier was associated with the decision to prescribe antibiotics in the emergency department. There was no significant difference between the procalcitonin group and the usual-care group in antibiotic-days (mean, 4.2 and 4.3 days, respectively; difference, -0.05 day; 95% confidence interval [CI], -0.6 to 0.5; P=0.87) or the proportion of patients with adverse outcomes (11.7% [96 patients] and 13.1% [109 patients]; difference, -1.5 percentage points; 95% CI, -4.6 to 1.7; P<0.001 for noninferiority) within 30 days. CONCLUSIONS: The provision of procalcitonin assay results, along with instructions on their interpretation, to emergency department and hospital-based clinicians did not result in less use of antibiotics than did usual care among patients with suspected lower respiratory tract infection. (Funded by the National Institute of General Medical Sciences; ProACT ClinicalTrials.gov number, NCT02130986 .).

Lemon: a framework for rapidly mining structural information from the Protein Data Bank.

MOTIVATION: The Protein Data Bank (PDB) currently holds over 140 000 biomolecular structures and continues to release new structures on a weekly basis. The PDB is an essential resource to the structural bioinformatics community to develop software that mine, use, categorize and analyze such data. New computational biology methods are evaluated using custom benchmarking sets derived as subsets of 3D experimentally determined structures and structural features from the PDB. Currently, such benchmarking features are manually curated with custom scripts in a non-standardized manner that results in slow distribution and updates with new experimental structures. Finally, there is a scarcity of standardized tools to rapidly query 3D descriptors of the entire PDB. RESULTS: Our solution is the Lemon framework, a C++11 library with Python bindings, which provides a consistent workflow methodology for selecting biomolecular interactions based on user criterion and computing desired 3D structural features. This framework can parse and characterize the entire PDB in <10 min on modern, multithreaded hardware. The speed in parsing is obtained by using the recently developed MacroMolecule Transmission Format to reduce the computational cost of reading text-based PDB files. The use of C++ lambda functions and Python bindings provide extensive flexibility for analysis and categorization of the PDB by allowing the user to write custom functions to suite their objective. We think Lemon will become a one-stop-shop to quickly mine the entire PDB to generate desired structural biology features. AVAILABILITY AND IMPLEMENTATION: The Lemon software is available as a C++ header library along with a PyPI package and example functions at https://github.com/chopralab/lemon. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening.

Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking data sets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly create benchmarking sets using the protein databank. DUBS uses a simple input text based format along with the Lemon data mining framework to efficiently access and organize data to the protein databank and output commonly used inputs for virtual screening software. The simple input format used by DUBS allows users to define their own benchmarking data sets and access the corresponding information directly from the software package. Currently, it only takes DUBS less than 2 min to create a benchmark using this format. Since DUBS uses a simple python script, users can easily modify this to create more complex benchmarks. We hope that DUBS will be a useful community resource to provide a standardized representation for benchmarking data sets in virtual screening. The DUBS package is available on GitHub at https://github.com/chopralab/lemon/tree/master/dubs.

CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials.

Small-molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations such as improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g., cofactors, metal ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm, that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions, and cofactor interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind, Astex, and PINC proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions such that the statistical score of the best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best-docked pose with biological activity. CANDOCK along with all structures and scripts used for benchmarking is available at https://github.com/chopralab/candock_benchmark.

A Guideline-Based Policy to Decrease Intensive Care Unit Admission Rates for Accidental Hypothermia.

RATIONALE: Despite guidelines advising passive rewarming for mild accidental hypothermia (AH), patients are frequently admitted to intensive care unit (ICU) for active rewarming using a forced-air warming device. We implemented a new policy at our institution aimed at safely reducing ICU admissions for AH. We analyzed our practice pre- and post-policy intervention and compared our experiences with acute care hospitals in Connecticut. METHODS: A retrospective chart review was performed on 203 participants with AH identified by primary and secondary discharge codes. Our new policy recommended passive rewarming on the medical floors for mild hypothermia (>32°C) and ICU admission for moderate hypothermia (<32°C). Practices of other Connecticut hospitals were obtained by surveying ICU nurse managers and medical directors. RESULTS: Over a 3-year period, prior to rewarming policy change, 64% (n = 92) of patients with AH were admitted to ICU, with a mean ICU length of stay (LOS [SD]) of 2.75 (2.2) days. After the policy change, over a 3-year period, 15% (n = 9) were admitted to ICU (P < .001), with an ICU LOS of 2.11 (0.9) days (P = 0.005). In both groups with AH, altered mental status, infection, and acute alcohol intoxication were the most common diagnoses at presentation. Alcohol intoxication was more prevalent in the post-policy intervention group, pre 17% versus post 46% (P < .001). No complications such as dermal burns or cardiac arrhythmias were noted with forced-air warming device use during either time period. Among the 29 hospitals surveyed, 20 used active rewarming in ICU or intermediate care units and 9 cared for patients on telemetry units. Most hospitals used active external rewarming for core body temperature of <35°C; however, 37% of hospitals performed active rewarming at temperatures >35°Cor lacked a policy. CONCLUSIONS: Reserving forced-air warming devices for the treatment of moderate-to-severe hypothermia (<32°C) significantly reduced ICU admissions for AH.

A 2-Tyr-1-carboxylate Mononuclear Iron Center Forms the Active Site of a Paracoccus Dimethylformamidase.

N,N-dimethyl formamide (DMF) is an extensively used organic solvent but is also a potent pollutant. Certain bacterial species from genera such as Paracoccus, Pseudomonas, and Alcaligenes have evolved to use DMF as a sole carbon and nitrogen source for growth via degradation by a dimethylformamidase (DMFase). We show that DMFase from Paracoccus sp. strain DMF is a halophilic and thermostable enzyme comprising a multimeric complex of the α2 ß2 or (α2 ß2 )2 type. One of the three domains of the large subunit and the small subunit are hitherto undescribed protein folds of unknown evolutionary origin. The active site consists of a mononuclear iron coordinated by two Tyr side-chain phenolates and one carboxylate from Glu. The Fe3+ ion in the active site catalyzes the hydrolytic cleavage of the amide bond in DMF. Kinetic characterization reveals that the enzyme shows cooperativity between subunits, and mutagenesis and structural data provide clues to the catalytic mechanism.

Narrative Production in Arabic-speaking Adolescents with and without Hearing Loss.

The present study investigated oral personal narratives elicited from Arabic speaking adolescents with and without hearing loss. Analyses focused on macrostructure, microstructure, and Modern Standard Arabic (MSA). For macrostructure, narratives were examined for structural components (abstract, orientation, complication, evaluation, resolution, and coda) and narrative patterns: classic (a high point followed by a resolution), high point ending, chronological, and leap frogging (jumps from one event to another). Microstructure included morpho-syntactic errors and complex sentences. MSA features were lexis and syntax. The narratives of adolescents with hearing loss tended to lack an evaluation component (expressing the narrator's perspective), contained more morpho-syntactic errors, fewer complex sentences, and fewer expressions of MSA than narratives of their hearing peers. Findings are discussed in terms of dissociation between macrostructure and microstructure in an attempt to shed light on those features of narrative which might benefit clinicians and educators working with Arabic speaking adolescents with hearing loss.

Exchange-repulsion energy in QM/EFP.

The effective fragment potential (EFP) is a quantum mechanics (QM)-based model designed to accurately describe intermolecular interactions. Hybrid QM/EFP calculations combine quantum mechanical methods with an EFP embedding to study complex systems in which many-body effects are relevant. As in EFP-only calculations, non-bonded interactions between the QM region and EFP fragments are computed as a sum of electrostatic, polarization, dispersion, and exchange-repulsion energies. The exchange-repulsion term is a computational bottleneck of the EFP calculations. Here, we present a general procedure for computing the QM/EFP exchange-repulsion interactions based on one-electron contributions to the QM Hamiltonian, by using Gaussian functions to represent localized molecular orbitals of the effective fragments. The accuracy of the exchange-repulsion and total QM/EFP interaction energies is evaluated on a diverse set of dimers, including complexes from the S22 dataset of non-covalent interactions. In most cases, the QM/EFP energies are at least as accurate as corresponding EFP energies. A simple and computationally efficient form of the introduced QM/EFP exchange-repulsion term will facilitate further developments and applications of QM/EFP methods.

Community Level Association between Home Health and Nursing Home Performance on Quality and Hospital 30-day Readmissions for Medicare Patients.

We evaluated whether community-level home health agencies and nursing home performance is associated with community-level hospital 30-day all-cause risk-standardized readmission rates for Medicare patients used data from the Centers for Medicare & Medicaid Service from 2010 to 2012. Our final sample included 2,855 communities that covered 4,140 hospitals with 6,751,713 patients, 13,060 nursing homes with 1,250,648 residents, and 7,613 home health agencies providing services to 35,660 zipcodes. Based on a mixed effect model, we found that increasing nursing home performance by one star for all of its 4 measures and home health performance by 10 points for all of its 6 measures is associated with decreases of 0.25% (95% CI 0.17-0.34) and 0.60% (95% CI 0.33-0.83), respectively, in community-level risk-standardized readmission rates.

Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals.

The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set containing 67,950 of our company's proprietary analytes, these models achieved coefficients of determination of 0.84 and 3 % error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN's SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.

Amyloid ß Induces Lipid Droplet-Mediated Microglial Dysfunction in Alzheimer's Disease.

Several microglia-expressed genes have emerged as top risk variants for Alzheimer's disease (AD). Impaired microglial phagocytosis is one of the main proposed outcomes by which these AD-risk genes may contribute to neurodegeneration, but the mechanisms translating genetic association to cellular dysfunction remain unknown. Here we show that microglia form lipid droplets (LDs) upon exposure to amyloid-beta (Aß), and that their LD load increases with proximity to amyloid plaques in brains from human patients and the AD mouse model 5xFAD. LD formation is dependent upon age and disease progression and is more prominent in the hippocampus in mice and humans. Despite variability in LD load between microglia from male versus female animals and between cells from different brain regions, LD-laden microglia exhibited a deficit in Aß phagocytosis. Unbiased lipidomic analysis identified a substantial decrease in free fatty acids (FFAs) and a parallel increase in triacylglycerols (TAGs) as the key metabolic transition underlying LD formation. We demonstrate that DGAT2, a key enzyme for the conversion of FFAs to TAGs, promotes microglial LD formation, is increased in microglia from 5xFAD and human AD brains, and that inhibiting DGAT2 improved microglial uptake of Aß. These findings identify a new lipid-mediated mechanism underlying microglial dysfunction that could become a novel therapeutic target for AD.

Detection of postoperative sleep-disordered breathing using a portable monitoring device.

PURPOSE: Adverse surgical outcomes may occur more frequently in patients with sleep-disordered breathing (SDB). Despite this concern, there have been no prospective studies using objective measures of postoperative SDB to determine the scope of the problem. We designed a prospective study to determine the feasibility of identifying SDB in elective postoperative patients by the use of a type IV portable monitor (PM). METHODS: Patients >18 years old who presented for elective surgery with at least one postoperative hospital night on a non-monitored unit were enrolled and wore a type IV device that measured nasal flow, heart rate, and oxygen saturation on their first postoperative night. Respiratory disturbance index (RDI) and oxygen desaturation index (ODI) were generated for each patient. RESULTS: Data sufficient for interpretation were collected on 100/116 patients enrolled. SDB (RDI ≥5) was observed in 51% of the study group, and 17% had a RDI >15. An elevated ODI ≥5 was seen in 42%, while 17% had an ODI ≥15. Device malfunction occurred in 16% of the study participants. CONCLUSION: A type IV PM can be employed in the postoperative setting to detect and gauge the severity of SDB.

Effectiveness and acceptability of a computerized decision support system using modified Wells criteria for evaluation of suspected pulmonary embolism.

STUDY OBJECTIVE: Ready availability of computed tomography (CT) angiography for evaluation of pulmonary embolism in emergency departments (EDs) is associated with a dramatic increase in the number of CT angiography tests. The aims of this study are to determine whether a validated prediction algorithm embedded in a computerized decision support system improves the positive yield rate of CT angiography for pulmonary embolism and is acceptable to emergency physicians. METHODS: This study was conducted as a prospective interventional study with a retrospective preinterventional comparison group. RESULTS: The implementation of the computerized physician order entry-based computerized decision support system was associated with an overall increase in the positivity rate of from 8.3% (95% confidence interval [CI] 4.9% to 12.9%) preintervention to 12.7% (95% CI 8.6% to 17.7%) postintervention, with a difference of 4.4% (95% CI -1.4% to 10.1%). A total of 404 patients were eligible for inclusion. Physician nonadherence to the computerized decision support system occurred in 105 (26.7%) cases. Fifteen patients underwent CT angiography despite low Wells score and negative D-dimer result, all of whose results were negative for pulmonary embolism. Emergency physicians did not order CT angiography for 44 patients despite high pretest probability, with one receiving a diagnosis of pulmonary embolism on a subsequent visit and another, of DVT. When emergency physicians adhered to the computerized decision support system for the evaluation of suspected pulmonary embolism, a higher yield of CT angiography for pulmonary embolism occurred, with 28 positive results of 168 CT angiography tests (16.7%; 95% CI 11.4% to 23.2%) and a difference compared with preintervention of 8.4% (95% CI 1.7% to 15.4%). Physicians cited the time required to apply the computerized decision support system and a preference for intuitive judgment as reasons for not adhering to the computerized decision support system. CONCLUSION: Use of an evidence-based computerized physician order entry-based computerized decision support system for the evaluation of suspected pulmonary embolism was associated with a higher yield of CT angiography for pulmonary embolism. The computerized decision support system, however, was poorly accepted by emergency physicians (partly because of increased computer time), leading to possibly selective use, reducing the effect on overall yield, and leading to removal of the computerized decision support system from the computer order entry. These findings emphasize the importance of facilitation of rule-based decisionmaking in the ED and attentiveness to the complex demands placed on emergency physicians.

Implementation of therapeutic hypothermia after cardiac arrest--a telephone survey of Connecticut hospitals.

RATIONALE: We hypothesize that despite excellent evidence supporting use of therapeutic hypothermia (TH) after cardiac arrest, only some of Connecticut hospitals utilize this technique for cardiac arrest patients. METHODS: Telephone survey of all adult acute care Connecticut hospitals between January and April 2010. RESULTS: Among 31 adult acute care hospitals, 27 care for cardiac arrest patients. Seventeen out of 27 hospitals use TH (63%) for cardiac arrest patients. No significant association was found between use of TH and hospital size (P=0.14), ICU type (P=0.07) or BC/BE critical-care physician staffing (P= 0.22). Lack of resources and cost of TH were commonly mentioned as barriers. CONCLUSIONS: Therapeutic hypothermia is underutilized in Connecticut with almost half of all hospitals currently not employing TH. Given the slow adoption rate of TH, state-level leadership may be indicated to accelerate implementation of this life-saving technique.