On the role of parameterization in models with a misspecified nuisance component.

The paper is concerned with inference for a parameter of interest in models that share a common interpretation for that parameter but that may differ appreciably in other respects. We study the general structure of models under which the maximum likelihood estimator of the parameter of interest is consistent under arbitrary misspecification of the nuisance part of the model. A specialization of the general results to matched-comparison and two-groups problems gives a more explicit and easily checkable condition in terms of a notion of symmetric parameterization, leading to a broadening and unification of existing results in those problems. The role of a generalized definition of parameter orthogonality is highlighted, as well as connections to Neyman orthogonality. The issues involved in obtaining inferential guarantees beyond consistency are briefly discussed.

Green Solvent Selection for All Solution-Processed Inverted Quantum Dot Light Emitting Diode.

Quantum-dot light-emitting diodes (QD-LEDs) have gained attention as potential display technologies. However, the solvents used to dissolve a polymeric hole transport layer (HTL) are hazardous to both humans and the environment. Additionally, intermixing the HTL and QD layers presents a significant challenge when fabricating inverted QD-LEDs. Here, a green solvent selection procedure to achieve good device performance and environmental safety in QD-LEDs is established. This procedure utilizes Hansen solubility parameters and surface roughness to identify a set of solvents that do not lower the device performance by avoiding interlayer mixing or a rough interface. The CHEM21 solvent selection guide is used to screen for environmentally hazardous solvents. Finally, cyclopentanone (CPO) is selected as the optimal HTL solvent from among 16 candidates. Using CPO improves the maximum luminescence by ≈1.6 times and the maximum current efficiency by ≈12.6 times, compared to that of conventional devices using hazardous chlorobenzene. Solvent selection is critical for the fabrication of green and high-performance inverted QD-LEDs, particularly for large display panels that require n-type oxide thin-film transistors.

Reflections on a Copenhagen-Minneapolis Axis in Bioorganic Chemistry.

The international peptide community rejoiced when one of its most distinguished members, Morten Meldal of Denmark, shared the 2022 Nobel Prize in Chemistry. In fact, the regiospecific solid-phase "copper(I)-catalyzed 1,3-dipolar cycloaddition of terminal alkynes to azides" (CuACC) reaction-that formed the specific basis for Meldal's recognition-was reported first at the 17th American Peptide Symposium held in San Diego in June 2001. The present perspective outlines intertwining conceptual and experimental threads pursued concurrently in Copenhagen and Minneapolis, sometimes by the same individuals, that provided context for Meldal's breakthrough discovery. Major topics covered include orthogonality in chemistry; the dithiasuccinoyl (Dts) protecting group for amino groups in α-amino acids, carbohydrates, and monomers for peptide nucleic acids (PNA); and poly(ethylene glycol) (PEG)-based solid supports such as PEG-PS, PEGA, and CLEAR [and variations inspired by them] for solid-phase peptide synthesis (SPPS), solid-phase organic synthesis (SPOS), and combinatorial chemistry that can support biological assays in aqueous media.

Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data.

Deep learning methods have achieved a lot of success in various applications involving converting wearable sensor data to actionable health insights. A common application areas is activity recognition, where deep-learning methods still suffer from limitations such as sensitivity to signal quality, sensor characteristic variations, and variability between subjects. To mitigate these issues, robust features obtained by topological data analysis (TDA) have been suggested as a potential solution. However, there are two significant obstacles to using topological features in deep learning: (1) large computational load to extract topological features using TDA, and (2) different signal representations obtained from deep learning and TDA which makes fusion difficult. In this paper, to enable integration of the strengths of topological methods in deep-learning for time-series data, we propose to use two teacher networks - one trained on the raw time-series data, and another trained on persistence images generated by TDA methods. These two teachers are jointly used to distill a single student model, which utilizes only the raw time-series data at test-time. This approach addresses both issues. The use of KD with multiple teachers utilizes complementary information, and results in a compact model with strong supervisory features and an integrated richer representation. To assimilate desirable information from different modalities, we design new constraints, including orthogonality imposed on feature correlation maps for improving feature expressiveness and allowing the student to easily learn from the teacher. Also, we apply an annealing strategy in KD for fast saturation and better accommodation from different features, while the knowledge gap between the teachers and student is reduced. Finally, a robust student model is distilled, which can at test-time uses only the time-series data as an input, while implicitly preserving topological features. The experimental results demonstrate the effectiveness of the proposed method on wearable sensor data. The proposed method shows 71.74% in classification accuracy on GENEActiv with WRN16-1 (1D CNNs) student, which outperforms baselines and takes much less processing time (less than 17 sec) than teachers on 6k testing samples.

Probabilistic PARAFAC2.

The Parallel Factor Analysis 2 (PARAFAC2) is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example, because of differences in signal sampling or batch sizes. A fully probabilistic treatment of the PARAFAC2 is desirable to improve robustness to noise and provide a principled approach for determining the number of factors, but challenging because direct model fitting requires that factor loadings be decomposed into a shared matrix specifying how the components are consistently co-expressed across samples and sample-specific orthogonality-constrained component profiles. We develop two probabilistic formulations of the PARAFAC2 model along with variational Bayesian procedures for inference: In the first approach, the mean values of the factor loadings are orthogonal leading to closed form variational updates, and in the second, the factor loadings themselves are orthogonal using a matrix Von Mises-Fisher distribution. We contrast our probabilistic formulations to the conventional direct fitting algorithm based on maximum likelihood on synthetic data and real fluorescence spectroscopy and gas chromatography-mass spectrometry data showing that the probabilistic formulations are more robust to noise and model order misspecification. The probabilistic PARAFAC2, thus, forms a promising framework for modeling multi-way data accounting for uncertainty.

Recent Developments with Organogermanes: their Preparation and Application in Synthesis and Catalysis.

Within the sphere of traditional Pd0 /PdII cross coupling reactions, organogermanes have been historically outperformed both in terms of scope and reactivity by more conventional transmetalating reagents. Subsequently, this class of compounds has been largely underutilized as a coupling partner in bond-forming strategies. Most recent studies, however, have shown that alternative modes of activation of these notoriously robust building blocks transform organogermanes into the most reactive site of the molecule-capable of outcompeting other functional groups (such as boronic acids, esters and silanes) for both C-C and C-heteroatom bond formation. As a result, over the past few years, the literature has increasingly featured methodologies that explore the potential of organogermanes as chemoselective and orthogonal coupling partners. Herein we highlight some of these recent advances in the field of organogermane chemistry both with respect to their synthesis and applications in synthetic and catalytic transformations.

Triplet-Triplet Energy Transfer: A Simple Strategy for an Efficient Visible Light-Induced Photoclick Reaction.

Photoclick reactions combine the advantages offered by light-driven processes and classical click chemistry and have found applications ranging from surface functionalization, polymer conjugation, photo-crosslinking, and protein labeling. Despite these advances, the dependency of most of the photoclick reactions on UV light poses a severe obstacle for their general implementation, as this light can be absorbed by other molecules in the system resulting in their degradation or unwanted reactivity. However, the development of a simple and efficient system to achieve bathochromically shifted photoclick transformations remains challenging. Here, we introduce triplet-triplet energy transfer as a fast and selective way to enable visible light-induced photoclick reactions. Specifically, we show that 9,10-phenanthrenequinones (PQs) can efficiently react with electron-rich alkenes (ERAs) in the presence of a catalytic amount (as little as 5 mol %) of photosensitizers. The photocycloaddition reaction can be achieved under green (530 nm) or orange (590 nm) light irradiation, representing a bathochromic shift of over 100 nm as compared to the classical PQ-ERAs system. Furthermore, by combining appropriate reactants, we establish an orthogonal, blue and green light-induced photoclick reaction system in which the product distribution can be precisely controlled by the choice of the color of light.

Precision Engineering of the Co-immobilization of Enzymes for Cascade Biocatalysis.

The design and orderly layered co-immobilization of multiple enzymes on resin particles remain challenging. In this study, the SpyTag/SpyCatcher binding pair was fused to the N-terminus of an alcohol dehydrogenase (ADH) and an aldo-keto reductase (AKR), respectively. A non-canonical amino acid (ncAA), p-azido-L-phenylalanine (p-AzF), as the anchor for covalent bonding enzymes, was genetically inserted into preselected sites in the AKR and ADH. Employing the two bioorthogonal counterparts of SpyTag/SpyCatcher and azide-alkyne cycloaddition for the immobilization of AKR and ADH enabled sequential dual-enzyme coating on porous microspheres. The ordered dual-enzyme reactor was subsequently used to synthesize (S)-1-(2-chlorophenyl)ethanol asymmetrically from the corresponding prochiral ketone, enabling the in situ regeneration of NADPH. The reactor exhibited a high catalytic conversion of 74 % and good reproducibility, retaining 80 % of its initial activity after six cycles. The product had 99.9 % ee, which that was maintained in each cycle. Additionally, the double-layer immobilization method significantly increased the enzyme loading capacity, which was approximately 1.7 times greater than that of traditional single-layer immobilization. More importantly, it simultaneously enabled both the purification and immobilization of multiple enzymes on carriers, thus providing a convenient approach to facilitate cascade biocatalysis.

Orthogonal sp3-Ge/B Bimetallic Modules: Enantioselective Construction and Enantiospecific Cross-Coupling.

In this study, a series of enantioenriched sp3-Ge/B bimetallic modules were successfully synthesized via an enantioselective copper-catalyzed hydroboration of carbagermatrane (Ge)-containing alkenes. Orthogonal cross-coupling selectivity under different Pd-catalyzed conditions was achieved in an enantiospecific manner. Notably, the chiral secondary Ge exhibited a remarkable transmetallation ability prior to primary or secondary Bpin. The effectiveness of this Ge/B bimetallic strategy was further demonstrated through the development of new functional small molecules with Aggregation-Induced Emission (AIE) and Circularly Polarized Luminescence (CPL) performance. This represents the first successful example of synthesis of enantioenriched alkylgermanium reagents that permit enantiospecific cross-coupling reactions.

Chromatic Selectivity of Coumarin-Caged Oligonucleotides.

A system of two coumarin-based caging groups is presented - one absorbing in the blue (400-450 nm) and the other absorbing in the green (480-550 nm) part of the visible spectrum. Together they form a pair, which allows to selectively photoactivate the one or the other in oligonucleotides. A numerical characterization defining the term "chromatic selectivity" was proposed, and it was shown how chromatically selective uncaging can literally be titrated in a kinetic reaction scheme.

"Toehold Switches; a foothold for Synthetic Biology".

Toehold switches are de novo designed riboregulators that contain two RNA components interacting through linear-linear RNA interactions, regulating the gene expression. These are highly versatile, exhibit excellent orthogonality, wide dynamic range, and are highly programmable, so can be used for various applications in synthetic biology. In this review, we summarized and discussed the design characteristics and benefits of toehold switch riboregulators over conventional riboregulators. We also discussed applications and recent advancements of toehold switch riboregulators in various fields like gene editing, DNA nanotechnology, translational repression, and diagnostics (detection of microRNAs and some pathogens). Toehold switches, therefore, furnished advancement in synthetic biology applications in various fields with their prominent features.

Methodology for dyes purification by preparative bidimensional offline reversed-phase high-performance liquid chromatography separation based on mobile phase pH change: Case study of Justicia spicigera.

The extensive characterization of the natural dyes involves the purification of their colored compounds for their structural analysis and stability studies. As most natural compounds being ionizable, herein is presented the optimization of an easy and affordable preparative bidimensional offline reversed-phase high-performance liquid chromatography purification based on mobile phase pH change. At the analytical scale, several combinations of stationary phases and mobile phases at different pH values were investigated first. The orthogonality between the dimensions was quantitatively evaluated using the nearest-neighbor distance approach. The optimized separation method was transferred to the preparative scale for the successful isolation of two colored compounds from Justicia spicigera, a traditional dye plant from Central America. They were identified as perisbivalvine B (2-amino-8-hydroxy-7-methoxy-3H-phenoxazin-3-one) and one of its derivatives, also found in another tinctorial plant species, Peristrophe bivalvis growing in Asia.

On Graphical Models and Convex Geometry.

A mixture-model of beta distributions framework is introduced to identify significant correlations among P features when P is large. The method relies on theorems in convex geometry, which are used to show how to control the error rate of edge detection in graphical models. The proposed 'betaMix' method does not require any assumptions about the network structure, nor does it assume that the network is sparse. The results hold for a wide class of data-generating distributions that include light-tailed and heavy-tailed spherically symmetric distributions. The results are robust for sufficiently large sample sizes and hold for non-elliptically-symmetric distributions.

An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications.

Microwave imaging is emerging as an alternative modality to conventional medical diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced in the solution of the underlying inverse scattering problem, namely non-linearity and ill-posedness. In this paper, an innovative approach for a reliable and automated solution of the inverse scattering problem is presented, which combines a qualitative imaging technique and deep learning in a two-step framework. In the first step, the orthogonality sampling method is employed to process measurements of the scattered field into an image, which explicitly provides an estimate of the targets shapes and implicitly encodes information in their contrast values. In the second step, the images obtained in the previous step are fed into a neural network (U-Net), whose duty is retrieving the exact shape of the target and its contrast value. This task is cast as an image segmentation one, where each pixel is classified into a discrete set of permittivity values within a given range. The use of a reduced number of possible permittivities facilitates the training stage by limiting its scope. The approach was tested with synthetic data and validated with experimental data taken from the Fresnel database to allow a fair comparison with the literature. Finally, its potential for biomedical imaging is demonstrated with a numerical example related to microwave brain stroke diagnosis.

Assessing the Orthogonality of Phage-Encoded RNA Polymerases for Tailored Synthetic Biology Applications in Pseudomonas Species.

The phage T7 RNA polymerase (RNAP) and lysozyme form the basis of the widely used pET expression system for recombinant expression in the biotechnology field and as a tool in microbial synthetic biology. Attempts to transfer this genetic circuitry from Escherichia coli to non-model bacterial organisms with high potential have been restricted by the cytotoxicity of the T7 RNAP in the receiving hosts. We here explore the diversity of T7-like RNAPs mined directly from Pseudomonas phages for implementation in Pseudomonas species, thus relying on the co-evolution and natural adaptation of the system towards its host. By screening and characterizing different viral transcription machinery using a vector-based system in P. putida., we identified a set of four non-toxic phage RNAPs from phages phi15, PPPL-1, Pf-10, and 67PfluR64PP, showing a broad activity range and orthogonality to each other and the T7 RNAP. In addition, we confirmed the transcription start sites of their predicted promoters and improved the stringency of the phage RNAP expression systems by introducing and optimizing phage lysozymes for RNAP inhibition. This set of viral RNAPs expands the adaption of T7-inspired circuitry towards Pseudomonas species and highlights the potential of mining tailored genetic parts and tools from phages for their non-model host.

Reversed HILIC Gradient: A Powerful Strategy for On-Line Comprehensive 2D-LC.

The aim of the present work is to evaluate the possibilities and limitations of reversed hydrophilic interaction chromatography (revHILIC) mode in liquid chromatography (LC). This chromatographic mode consists of combining a highly polar stationary phase (bare silica) with a gradient varying from very low (1-5%) to high (40%) acetonitrile content (reversed gradient compared to HILIC). The retention behavior of revHILIC was first compared with that of reversed-phase LC (RPLC) and HILIC using representative mixtures of peptides and pharmaceutical compounds. It appears that the achievable selectivity can be ranked in the order RPLC > revHILIC > HILIC with the two different samples. Next, two-dimensional liquid chromatography (2D-LC) conditions were evaluated by combining RPLC, revHILIC, or HILIC with RPLC in an on-line comprehensive (LC × LC) mode. evHILIC × RPLC not only showed impressive performance in terms of peak capacity and sensitivity, but also provided complementary selectivity compared to RPLC × RPLC and HILIC × RPLC. Indeed, both the elution order and the retention time range differ significantly between the three techniques. In conclusion, there is no doubt that revHILIC should be considered as a viable option for 2D-LC analysis of small molecules and also peptides.

Molecular Engineering To Enhance Reactivity and Selectivity in an Ultrafast Photoclick Reaction.

Light-induced 9,10-phenanthrenequinone-electron-rich alkene (PQ-ERA) photocycloadditions are an attractive new type of photoclick reaction, featuring fast conversions and high biocompatibility. However, the tunability of the reaction was hardly investigated up to now. To this end, we explored the influence of substituents on both reaction partners and the reaction rate between the PQs and ERAs. We identified new handles for functionalization and discovered that using enamines as ERAs leads to drastically enhanced rates (>5400 times faster), high photoreaction quantum yields (ΦP , up to 65 %), and multicolor emission output as well as a high fluorescence quantum yield of the adducts (ΦF , up to 97 %). Further investigation of the photophysical and photochemical properties provided insights to design orthogonal reaction systems both in solution and on nanoparticle surfaces for ultrafast chemoselective functionalization by photoclick reactions.

Wavelength Orthogonal Photodynamic Networks.

The ability of light to remotely control the properties of soft matter materials in a dynamic fashion has fascinated material scientists and photochemists for decades. However, only recently has our ability to map photochemical reactivity in a finely wavelength resolved fashion allowed for different colors of light to independently control the material properties of polymer networks with high precision, driven by monochromatic irradiation enabling orthogonal reaction control. The current concept article highlights the progress in visible light-induced photochemistry and explores how it has enabled the design of polymer networks with dynamically adjustable properties. We will explore current applications ranging from dynamic hydrogel design to the light-driven adaptation of 3D printed structures on the macro- and micro-scale. While the alternation of mechanical properties via remote control is largely reality for soft matter materials, we herein propose the next frontiers for adaptive properties, including remote switching between conductive and non-conductive properties, hydrophobic and hydrophilic surfaces, fluorescent or non-fluorescent, and cell adhesive vs. cell repellent properties.

A framework for calculating orthogonal selectivities in multimodal systems directly from cell culture fluid.

This paper presents a straightforward approach for measuring and quantifying orthogonality directly in complex cell culture fluids (CCFs) without the requirement for tracking the retention behaviors of large sets of proteins. Null-producing CCFs were fractionated using linear salt gradients at constant pH on a set of multimodal resins. Fractions were then analyzed by ultraperformance-reversed phase liquid chromatography and the resulting chromatograms provided host cell protein (HCP) "fingerprints." Using these fingerprints, an inner product vector-based approach was employed to quantify the degree of orthogonality between pairs of resins and operating conditions for these large HCP protein sets. To compare resin orthogonality behavior in different expression systems, the Chinese hamster ovary and Pichia pastoris null-producing CCFs were examined. Orthogonality in multimodal systems was found to strongly depend on the expression system and the HCPs being screened. We also identified several unexpected pairs of multimodal resins within the same family that exhibited significant orthogonality. Furthermore, "self-orthogonality" was evaluated between resins operated at different pHs, and important operating regimes were identified for maximizing orthogonal selectivities. The framework developed in this paper for calculating orthogonality without the need for labor-intensive HCP tracking has important implications for efficient process development and resin/operating condition selection for both monoclonal antibody (mAb) polishing steps and non-mAb processes. In addition, this study provides a tool to unlock the untapped potential of multimodal resins by aiding in their rational selection and incorporation. Finally, the orthogonality framework here can facilitate the development of sets of next-generation multimodal resins specifically designed to provide highly orthogonal and efficient separations tailored for different expression systems.

Biosensor-driven, model-based optimization of the orthogonally expressed naringenin biosynthesis pathway.

BACKGROUND: The rapidly expanding synthetic biology toolbox allows engineers to develop smarter strategies to tackle the optimization of complex biosynthetic pathways. In such a strategy, multi-gene pathways are subdivided in several modules which are each dynamically controlled to fine-tune their expression in response to a changing cellular environment. To fine-tune separate modules without interference between modules or from the host regulatory machinery, a sigma factor (σ) toolbox was developed in previous work for tunable orthogonal gene expression. Here, this toolbox is implemented in E. coli to orthogonally express and fine-tune a pathway for the heterologous biosynthesis of the industrially relevant plant metabolite, naringenin. To optimize the production of this pathway, a practical workflow is still imperative to balance all steps of the pathway. This is tackled here by the biosensor-driven screening, subsequent genotyping of combinatorially engineered libraries and finally the training of three different computer models to predict the optimal pathway configuration. RESULTS: The efficiency and knowledge gained through this workflow is demonstrated here by improving the naringenin production titer by 32% with respect to a random pathway library screen. Our best strain was cultured in a batch bioreactor experiment and was able to produce 286 mg/L naringenin from glycerol in approximately 26 h. This is the highest reported naringenin production titer in E. coli without the supplementation of pathway precursors to the medium or any precursor pathway engineering. In addition, valuable pathway configuration preferences were identified in the statistical learning process, such as specific enzyme variant preferences and significant correlations between promoter strength at specific steps in the pathway and titer. CONCLUSIONS: An efficient strategy, powered by orthogonal expression, was applied to successfully optimize a biosynthetic pathway for microbial production of flavonoids in E. coli up to high, competitive levels. Within this strategy, statistical learning techniques were combined with combinatorial pathway optimization techniques and an in vivo high-throughput screening method to efficiently determine the optimal operon configuration of the pathway. This "pathway architecture designer" workflow can be applied for the fast and efficient development of new microbial cell factories for different types of molecules of interest while also providing additional insights into the underlying pathway characteristics.