Microscopically, this is correlated with the repulsion of iodide ions from the amide moiety.The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.Through measuring the intensity of the fluorescence X-rays emitted by the elements of interest, X-ray fluorescence computed tomography (XFCT) is capable of mapping the elemental distribution inside an object without destructively sectioning it. With the recent advances in XFCT utilizing polychromatic microfocus X-ray sources, it is expected that the popularity of such imaging modality will rise further. However, XFCT suffers from self-absorption effects, which make it challenging to reconstruct the elemental distribution inside the sample accurately. For this reason, polychromatic XFCT is mainly used to retrieve the distribution of elements with a relatively high atomic number (Z) when compared to the matrix of the sample. To enable the quantitative reconstruction of trace and low Z elements with polychromatic XFCT, a novel reconstruction method has been proposed in this manuscript. Through examining the proposed method on both simulation data and experimental data, its capacity on retrieving the density distribution of relatively low Z elements has been confirmed.Carbon nanohorns (CNHs), a type of nanocarbon, have been studied for the application of drug delivery systems (DDSs) because they are easily functionalized, support bone regeneration, can be used to perform photohyperthermia, have low toxicity, and are easily phagocytosed by macrophages. To take advantage of these features of CNHs, we developed a DDS for the local treatment of bone metastasis by loading the antibone resorption drug ibandronate (IBN) onto CNHs. The poor adsorption of IBN onto CNHs due to the weak hydrophilic-hydrophobic interaction was overcome by using calcium phosphates (CaPs) as mediators. In the fabrication process, we used oxidized CNH (OxCNH), which is less hydrophobic, onto which IBN was coprecipitated with CaP from a labile supersaturated CaP solution. OxCNH-CaP-IBN composite nanoparticles exerted stronger cell-suppressive effects than OxCNH and IBN in both murine macrophages (RAW264.7 cells) and osteoclasts (differentiated from RAW264.7 cells). OxCNH-CaP-IBN composite nanoparticles were efficiently phagocytosed by macrophage cells, where they specifically accumulated in lysosomes. The stronger cell-suppressive effects were likely due to intracellular delivery of IBN, i.e., the release of IBN from OxCNH-CaP-IBN composite nanoparticles via dissociation of CaP in the acidic environment of lysosomes. Our findings suggest that OxCNH-CaP-IBN composite nanoparticles are potentially useful for the local treatment of metastatic bone destruction.The predicted liquid chromatographic retention times (RTs) of small molecules are not accurate enough for wide adoption in structural identification. In this study, we used the graph neural network to predict the retention time (GNN-RT) from structures of small molecules directly without the requirement of molecular descriptors. The predicted accuracy of GNN-RT was compared with random forests (RFs), Bayesian ridge regression, convolutional neural network (CNN), and a deep-learning regression model (DLM) on a METLIN small molecule retention time (SMRT) dataset. GNN-RT achieved the highest predicting accuracy with a mean relative error of 4.9% and a median relative error of 3.2%. Furthermore, the SMRT-trained GNN-RT model can be transferred to the same type of chromatographic systems easily. NG25 The predicted RT is valuable for structural identification in complementary to tandem mass spectra and can be used to assist in the identification of compounds. The results indicate that GNN-RT is a promising method to predict the RT for liquid chromatography and improve the accuracy of structural identification for small molecules.Uranium is an extremely abundant resource in seawater that could supply nuclear fuel for over the long-term, but it is tremendously difficult to extract. Here, a new supramolecular poly(amidoxime) (PAO)-loaded macroporous resin (PLMR) adsorbent has been explored for highly efficient uranium adsorption. Through simply immersing the macroporous resin in the PAO solution, PAOs can be firmly loaded on the surface of the nanopores mainly by hydrophobic interaction, to achieve the as-prepared PLMR. Unlike existing amidoxime-based adsorbents containing many inner minimally effective PAOs, almost all the PAOs of PLMR have high uranium adsorption efficiency because they can form a PAO-layer on the nanopores with molecular-level thickness and ultrahigh specific surface area. As a result, this PLMR has highly efficient uranium adsorbing performance. The uranium adsorption capacity of the PLMR was 157 mg/g (the UPAO in the PLMR was 1039 mg/g), in 32 ppm uranium-spiked seawater for 120 h. Additionally, uranium in 1.0 L 100 ppb U-spiked both water and seawater can be removed quickly and the recovery efficiency can reach 91.