Enhanced aging properties of HKUST-1 in hydrophobic mixed-matrix membranes for ammonia adsorption.

Metal-organic frameworks (MOFs) in their free powder form have exhibited superior capacities for many gases when compared to other materials, due to their tailorable functionality and high surface areas. Specifically, the MOF HKUST-1 binds small Lewis bases, such as ammonia, with its coordinatively unsaturated copper sites. We describe here the use of HKUST-1 in mixed-matrix membranes (MMMs) prepared from polyvinylidene difluoride (PVDF) for the removal of ammonia gas. These MMMs exhibit ammonia capacities similar to their hypothetical capacities based on the weight percent of HKUST-1 in each MMM. HKUST-1 in its powder form is unstable toward humid conditions; however, upon exposure to humid environments for prolonged periods of time, the HKUST-1 MMMs exhibit outstanding structural stability, and maintain their ammonia capacity. Overall, this study has achieved all of the critical and combined elements for real-world applications of MOFs: high MOF loadings, fully accessible MOF surfaces, enhanced MOF stabilization, recyclability, mechanical stability, and processability. This study is a critical step in advancing MOFs to a stable, usable, and enabling technology

Accelerated Postnatal Growth Increases Lipogenic Gene Expression and Adipocyte Size in Low–Birth Weight Mice

OBJECTIVE: To characterize the hormonal milieu and adipose gene expression in response to catch-up growth (CUG), a growth pattern associated with obesity and diabetes risk, in a mouse model of low birth weight (LBW). RESEARCH DESIGN AND METHODS: ICR mice were food restricted by 50% from gestational days 12.5–18.5, reducing offspring birth weight by 25%. During the suckling period, dams were either fed ad libitum, permitting CUG in offspring, or food restricted, preventing CUG. Offspring were killed at age 3 weeks, and gonadal fat was removed for RNA extraction, array analysis, RT-PCR, and evaluation of cell size and number. Serum insulin, thyroxine (T4), corticosterone, and adipokines were measured. RESULTS: At age 3 weeks, LBW mice with CUG (designated U-C) had body weight comparable with controls (designated C-C); weight was reduced by 49% in LBW mice without CUG (designated U-U). Adiposity was altered by postnatal nutrition, with gonadal fat increased by 50% in U-C and decreased by 58% in U-U mice (P less than 0.05 vs. C-C mice). Adipose expression of the lipogenic genes Fasn, AccI, Lpin1, and Srebf1 was significantly increased in U-C compared with both C-C and U-U mice (P less than 0.05). Mitochondrial DNA copy number was reduced by greater than 50% in U-C versus U-U mice (P = 0.014). Although cell numbers did not differ, mean adipocyte diameter was increased in U-C and reduced in U-U mice (P less than 0.01). CONCLUSIONS: CUG results in increased adipose tissue lipogenic gene expression and adipocyte diameter but not increased cellularity, suggesting that catch-up fat is primarily associated with lipogenesis rather than adipogenesis in this murine model

Explaining Support Vector Machines: A Color Based Nomogram.

PROBLEM SETTING: Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. OBJECTIVE: In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. RESULTS: Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. CONCLUSIONS: This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method

Current trend in synthesis, Post-Synthetic modifications and biological applications of Nanometal-Organic frameworks (NMOFs)

Since the early reports of MOFs and their interesting properties, research involving these materials has grown wide in scope and applications. Various synthetic approaches have ensued in view of obtaining materials with optimised properties, the extensive scope of application spanning from energy, gas sorption, catalysis biological applications has meant exponentially evolved over the years. The far‐reaching synthetic and PSM approaches and porosity control possibilities have continued to serve as a motivation for research on these materials. With respect to the biological applications, MOFs have shown promise as good candidates in applications involving drug delivery, BioMOFs, sensing, imaging amongst others. Despite being a while away from successful entry into the market, observed results in sensing, drug delivery, and imaging put these materials on the spot light as candidates poised to usher in a revolution in biology. In this regard, this review article focuses current approaches in synthesis, post functionalization and biological applications of these materials with particular attention on drug delivery, imaging, sensing and BioMOFs

Building Support Vector Machines with Reduced Classifier Complexity

Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size (d max ) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as O(nd max ) where n is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors