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Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.Both short-wave infrared (SWIR 900-1700 nm) and near-infrared (NIR 650-900 nm) luminescence possess lower optical scattering and higher signal-to-noise in deep tissues than conventional luminescence, gaining increasing attention in biomedicine. Herein, we designed mesoporous silica-coated Yb-doped magnesium germanate nanoparticles (mMGOs) with excellent two-in-one NIR and SWIR persistent luminescence after X-ray irradiation by simply regulating the valence of rare-earth ions, which also possess high cargo loading and a controlled release profile in the tumor region. The investigations in vitro and in vivo showed that mMGOs were repeatedly activated to realize rechargeable persistent luminescence imaging for tracking cargo delivery in mice. Moreover, the stimulative drug-release profile inhibited tumor growth effectively. Both of the X-ray excited two-in-one NIR and SWIR persistent luminescence imaging not only allowed for rechargeable imaging of deep tumors but also achieved long-term tracking with a remarkable tumor inhibition effect.Decline in total phosphorus (TP) during lake reoligotrophication does not apparently immediately influence carbon assimilation or deep-water oxygen levels. Traditional monitoring and interpretation do not typically consider the amount of organic carbon exported from the productive zone into the hypolimnion as a measure of net ecosystem production. This research investigated the carbon-to-phosphorus ratios of suspended particles in the epilimnion, (CP)epi, as indicators of changing productivity. We report sestonic CP ratios, phytoplankton biomass, and hypolimnetic oxygen depletion rates in Lake Hallwil, a lake whose recovery from eutrophic conditions has been documented in 35 years of historic water-monitoring data. This study also interpreted long-term (CP)epi ratios from reoligotrophication occurring in four other lakes. Lake Hallwil exhibited three distinct phases. (i) The (CP)epi ratio remained low when TP concentrations did not limit production. (ii) (CP)epi increased steadily when phytoplankton began optimizing the declining P and biomass remained stable. (iii) Below a critical TP threshold of ?15 to ?20 mg P m-3, (CP)epi remained high and the biomass eventually declined. This analysis showed that the (CP)epi ratio indicates the reduction of productivity prior to classic indicators such as deep-water oxygen depletion.Breast cancer is one of the leading causes of cancer death in women. Novel in vitro tools that integrate three-dimensional (3D) tumor models with highly sensitive chemical reporters can provide useful information to aid biological characterization of cancer phenotype and understanding of drug activity. The combination of surface-enhanced Raman scattering (SERS) techniques with microfluidic technologies offers new opportunities for highly selective, specific, and multiplexed nanoparticle-based assays. Here, we explored the use of functionalized nanoparticles for the detection of estrogen receptor alpha (ERα) expression in a 3D tumor model, using the ERα-positive human breast cancer cell line MCF-7. https://www.selleckchem.com/products/ro-31-8220-mesylate.html This approach was used to compare targeted versus nontargeted nanoparticle interactions with the tumor model to better understand whether targeted nanotags are required to efficiently target ERα. Mixtures of targeted anti-ERα antibody-functionalized nanotags (ERα-AuNPs) and nontargeted (against ERα) anti-human epidermal growth factor receptor 2 (HER2) antibody-functionalized nanotags (HER2-AuNPs), with different Raman reporters with a similar SERS signal intensity, were incubated with MCF-7 spheroids in microfluidic devices and spectroscopically analyzed using SERS. MCF-7 cells express high levels of ERα and no detectable levels of HER2. 2D and 3D SERS measurements confirmed the strong targeting effect of ERα-AuNP nanotags to the MCF-7 spheroids in contrast to HER2-AuNPs (63% signal reduction). Moreover, 3D SERS measurements confirmed the differentiation between the targeted and the nontargeted nanotags. Finally, we demonstrated how nanotag uptake by MCF-7 spheroids was affected by the drug fulvestrant, the first-in-class approved selective estrogen receptor degrader (SERD). These results illustrate the potential of using SERS and microfluidics as a powerful in vitro platform for the characterization of 3D tumor models and the investigation of SERD activity.Flexible metal-organic frameworks (MOFs) are of high interest as smart programmable materials for gas sorption due to their unique structural changes triggered by external stimuli. Owing to this property, which leads to opportunities such as maximizing deliverable gas capacity, flexible MOFs sometimes offer more advantages in sorption applications compared to their more rigid counterparts. Herein, we elucidate the effect of transition metal identity of a series of isonicotinate-based flexible MOFs, M(4-PyC)2 [M?Mg, Mn, and Cu; 4-PyC = 4-pyridine carboxylic acid] on the structural dynamic response to different gases (C2H4, C2H6, Xe, Kr, and SO2). Isotherms at different temperatures show that C2H4, C2H6, and Xe can form sufficiently strong interactions with both Mg(4-PyC)2 and Mn(4-PyC)2 frameworks resulting in gate-opening behavior due to the rotation of the linker's pyridine ring, while Kr cannot induce this phenomenon for the two MOFs under the measured conditions. In contrast, the gate-opening behavior occurs for Cu(4-PyC)2 solely in the presence of C2H4, and no other measured gas, due to the open metal sites of Cu centers.
