ABINIT: Overview and focus on selected capabilities.

abinit is probably the first electronic-structure package to have been released under an open-source license about 20 years ago. It implements density functional theory, density-functional perturbation theory (DFPT), many-body perturbation theory (GW approximation and Bethe-Salpeter equation), and more specific or advanced formalisms, such as dynamical mean-field theory (DMFT) and the "temperature-dependent effective potential" approach for anharmonic effects. Relying on planewaves for the representation of wavefunctions, density, and other space-dependent quantities, with pseudopotentials or projector-augmented waves (PAWs), it is well suited for the study of periodic materials, although nanostructures and molecules can be treated with the supercell technique. The present article starts with a brief description of the project, a summary of the theories upon which abinit relies, and a list of the associated capabilities. It then focuses on selected capabilities that might not be present in the majority of electronic structure packages either among planewave codes or, in general, treatment of strongly correlated materials using DMFT; materials under finite electric fields; properties at nuclei (electric field gradient, Mössbauer shifts, and orbital magnetization); positron annihilation; Raman intensities and electro-optic effect; and DFPT calculations of response to strain perturbation (elastic constants and piezoelectricity), spatial dispersion (flexoelectricity), electronic mobility, temperature dependence of the gap, and spin-magnetic-field perturbation. The abinit DFPT implementation is very general, including systems with van der Waals interaction or with noncollinear magnetism. Community projects are also described: generation of pseudopotential and PAW datasets, high-throughput calculations (databases of phonon band structure, second-harmonic generation, and GW computations of bandgaps), and the library libpaw. abinit has strong links with many other software projects that are briefly mentioned.

Comprehensive and Reproducible Untargeted Lipidomic Workflow Using LC-QTOF Validated for Human Plasma Analysis.

The goal of this work was to develop a label-free, comprehensive, and reproducible high-resolution liquid chromatography-mass spectrometry (LC-MS)-based untargeted lipidomic workflow using a single instrument, which could be applied to biomarker discovery in both basic and clinical studies. For this, we have (i) optimized lipid extraction and elution to enhance coverage of polar and nonpolar lipids as well as resolution of their isomers, (ii) ensured MS signal reproducibility and linearity, and (iii) developed a bioinformatic pipeline to correct remaining biases. Workflow validation is reported for 48 replicates of a single human plasma sample: 1124 reproducible LC-MS signals were extracted (median signal intensity RSD = 10%), 50% of which are redundant due to adducts, dimers, in-source fragmentation, contaminations, or positive and negative ion duplicates. From the resulting 578 unique compounds, 428 lipids were identified by MS/MS, including acyl chain composition, of which 394 had RSD < 30% inside their linear intensity range, thereby enabling robust semiquantitation. MS signal intensity spanned 4 orders of magnitude, covering 16 lipid subclasses. Finally, the power of our workflow is illustrated by a proof-of-concept study in which 100 samples from healthy human subjects were analyzed and the data set was investigated using three different statistical testing strategies in order to compare their capacity in identifying the impact of sex and age on circulating lipids.

A blood-based proteomic classifier for the molecular characterization of pulmonary nodules.

Each year, millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. Because most of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. We present a 13-protein blood-based classifier that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Using a systems biology strategy, we identified 371 protein candidates and developed a multiple reaction monitoring (MRM) assay for each. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and stage IA lung cancer matched for nodule size, age, gender, and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a negative predictive value (NPV) of 90%. Validation performance on samples from a nondiscovery clinical site showed an NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, and FOS) that are associated with lung cancer, lung inflammation, and oxidative stress networks. The classifier score was independent of patient nodule size, smoking history, and age, which are risk factors used for clinical management of pulmonary nodules. Thus, this molecular test provides a potential complementary tool to help physicians in lung cancer diagnosis.