Search

The relatively stable MOFs Alfum, MIL-160, DUT-4, DUT-5, MIL-53-TDC, MIL-53, UiO-66, UiO-66-NH2, UiO-66(F)4, UiO-67, DUT-67, NH2-MIL-125, MIL-125, MIL-101(Cr), ZIF-8, ZIF-11 and ZIF-7 were studied for their C6 sorption properties. An understanding of the uptake of the larger C6 molecules cannot simply be achieved with surface area and pore volume (from N2 sorption) but involves the complex micropore structure of the MOF. The maximum adsorption capacity at p p0−1 = 0.9 was shown by DUT-4 for benzene, MIL-101(Cr) for cyclohexane and DUT-5 for n-hexane. In the low-pressure range from p p0−1 = 0.1 down to 0.05 the highest benzene uptake is given by DUT-5, DUT-67/UiO-67 and MIL-101(Cr), for cyclohexane and n-hexane by DUT-5, UiO-67 and MIL-101(Cr). The highest uptake capacity at p p0−1 = 0.02 was seen with MIL-53 for benzene, MIL-125 for cyclohexane and DUT-5 for n-hexane. DUT-5 and MIL-101(Cr) are the MOFs with the widest pore window openings/cross sections but the low-pressure uptake seems to be controlled by a complex combination of ligand and pore-size effect. IAST selectivities between the three binary mixtures show a finely tuned and difficult to predict interplay of pore window size with (critical) adsorptive size and possibly a role of electrostatics through functional groups such as NH2. [ABSTRACT FROM AUTHOR]

S‐alkyltetrahydrothiophenium, [CnTHT]+ bis(trifluorosulfonyl)imide, [NTf2]− room temperature ionic liquids (ILs) and tetraphenylborate, [BPh4]− salts with alkyl chain lengths from C4 to C10 have been prepared. The ILs and salts were characterized and their purity verified by 1H‐ and 13C‐nuclear magnetic resonance, elemental analysis, ion chromatography, Karl‐Fischer titration, single crystal X‐ray diffraction as well as thermogravimetric analysis. The experimentally determined density and viscosity decrease with increasing temperature. The experimental solubility of the [CnTHT][NTf2]‐ILs in water (75 to 2.2 mg/L for C4 to C10) was modelled with very good agreement by Perturbed Chain Statistical Associating Fluid Theory (PC‐SAFT), based on the extremely low vapor pressures for the [CnTHT][NTf2]‐ILs measured in this work (4.15 to 0.037 ⋅ 10−7×psat for C4 to C10). PC‐SAFT is able to predict and correlate different thermodynamic properties by estimating the Helmholtz residual energy. [ABSTRACT FROM AUTHOR]

Many Gram-negative bacteria utilize specialized secretion systems to inject proteins (effectors) directly into host cells. Little is known regarding how bacteria ensure that only small subsets of the thousands of proteins they encode are recognized as substrates of the secretion systems, limiting their identification through bioinformatic analyses. Many of these proteins require chaperones to direct their secretion. Here, using the newly described protein interaction platform assay, we demonstrate that type 3 secretion system class IB chaperones from one bacterium directly bind their own effectors as well as those from other species. In addition, we observe that expression of class IB homologs from seven species, including pathogens and endosymbionts, mediate the translocation of effectors from Shigella directly into host cells, demonstrating that class IB chaperones are often functionally interchangeable. Notably, class IB chaperones bind numerous effectors. However, as previously proposed, they are not promiscuous; rather they recognize a defined sequence that we designate the conserved chaperone-binding domain (CCBD) sequence [(LMIF)\(_{1}\)XXX(IV)\(_{5}\)XX(IV)\(_{8}\)X(N)\(_{10}\)]. This sequence is the first defined amino acid sequence to be identified for any interspecies bacterial secretion system, i.e., a system that delivers proteins directly into eukaryotic cells. This sequence provides a new means to identify substrates of type III secretion systems. Indeed, using a pattern search algorithm for the CCBD sequence, we have identified the first two probable effectors from an endosymbiont, Sodalis glossinidius.

Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically from multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H images were used to train new Cellpose models. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (across 9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new Cellpose model and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated Cellpose masks (p<0.0001) indicating greater reproducibility between Cellpose masks. There was also a high correlation (R 2 >0.9) between Cellpose and manually segmented masks for the ORR, mean NAD(P)H lifetime, and mean FAD lifetime across 2D and 3D cell culture treatment conditions. Masks generated from Cellpose and manual segmentation also maintain similar means, variances, and effect sizes between treatments for the ORR and FLIM parameters. Overall, Cellpose provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.