BUSCADOR RECOLECTA

5 pages. -- Supplementary Figure S1 (previous page): Cell-entry inhibition by 10E8 Fab mutants. -- Supplementary Figure S2: Characterization of 10E8 mutant Fabs. -- Supplementary Figure S3: Representative images of POPC vesicle binding experiments in the absence (middle) and presence (right) of epitope, and in vesicles including the anionic phospholipid POPS without epitope (left). -- Supplementary Table S1: Kinetic parameters of binding of Fab 10E8 variants to MPER-TMD671-709 peptide reconstituted in SLBs., Peer reviewed

20 pages. -- Figure S1. Bifunctional lipid probes and workflow used for in vivo photoaffinity binding studies. -- Figure S2. Photolabeling experiments of non-lipid nanodomain marker (transferrin receptor) and lipid nanodomain marker (Cav-1) with bifunctional lipids and competition experiments between bifunctional analogues and cold lipids in living cells. -- Figure S3. Role of previously described chol-binding motif in IFN-γR2-chol interaction in vivo. -- Figure S4. Physiochemical analysis of IFN-γR2TMD wild type and mutants. -- Figure S5. Validation of the novel chol-binding domain localized within the IFN-γR2TMD. -- Figure S6. Characterization of IFN-γR1 and IFN-γR2 constructs for ID-PRIME and proof of principle. -- Figure S7. Chol binding is required for IFN-γR transmembrane signal activation and PDL1 cell surface exposure in response to IFN-γ stimulation. -- Figure S8. Subcellular localization of candidate chol-binding proteins. -- Supplementary Table 1. Single-and Multi-span motifs found using MOPRO. -- Supplementary Table 2. List of chol-binding protein candidates., Peer reviewed

Since the discovery of the Higgs boson in 2012, detailed studies of its properties have been ongoing. Besides its mass, its width - related to its lifetime - is an important parameter. One way to determine this quantity is by measuring its off-shell production, where the Higgs boson mass is far away from its nominal value, and relating it to its on-shell production, where the mass is close to the nominal value. Here, we report evidence for such off-shell contributions to the production cross section of two Z bosons with data from the CMS experiment at the CERN Large Hadron Collider. We constrain the total rate of the off-shell Higgs boson contribution beyond the Z boson pair production threshold, relative to its standard model expectation, to the interval [0.0061, 2.0] at 95% confidence level. The scenario with no off-shell contribution is excluded at a p-value of 0.0003 (3.6 standard deviations). We measure the width of the Higgs boson as Th = 3.2 (+2.4 / -1.7) MeV, in agreement with the standard model expectation of 4.1 MeV. In addition, we set constraints on anomalous Higgs boson couplings to W and Z boson pairs., Peer reviewed

Supplementary Table 1. Hi-C experimental statistics. Statistics shown separated by replicates (top table) and merged datasets (middle table for valid pairs and bottom table for filtered reads). Supplementary Table 2. Mass-spectrometry analysis of histone H1 peptides after immunoprecipitation with H1 variant specific antibodies., Peer reviewed

62 pages. -- PDF file includes: Major Resources Table. -- Detailed Methods. -- Figure S1. PCSK9-LDLr binding curves showing the equation of the fitted curves, parameters used to calculate EC50 and R square of each curve. -- Figure S2. Real time PCR curves of LDLr mRNA expression in HEK293 cells transfected with wt, D374Y, L8 or L11 PCSK9 variants. -- Table S1. Detected peptides by LC-MS/MS in the higher molecular weight extra band from ER enriched extracts from HEK293 cells transfected with the L11 SP variant. -- Table S2. In silico prediction of peptide detectability using CONSeQuence, a consensus prediction system built around four independent machine learning algorithms. -- Table S3: List of proteins identified in the sample by LC-MS/MS in the higher molecular weight extra band from ER enriched extracts from HEK293 cells transfected with the L11 SP variant., Peer reviewed

FIG. 1. Nuclear modification factor of the B+c meson compared to that of light charged hadrons, and B+, Bs and D0mesons, as a function of the measured transverse momentum. The total uncertainty is shown for the B+c meson, whereasthe statistical (bars) and systematic (filled rectangles) uncertainties are shown for the other hadrons., FIG. 2. Nuclear modification factor of the B+c meson compared to that of the ground and first excited states of charmonia and bottomonia, as a function of the measured transverse momentum. The total uncertainty is shown for the B+c meson, whereas the statistical (bars) and systematic (filled rectangles) uncertainties are shown for the other hadrons., Peer reviewed

1 table., Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research., Peer reviewed

Peer reviewed

Details on simulation and systematic uncertainties., Peer reviewed

Additional figures and tables., Peer reviewed