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Host–guest interactions have been increasingly explored for use in the dynamic physical crosslinking of polymeric precursors to form hydrogel networks. However, the orientation of guest motifs is restricted upon macromolecule conjugation. The implications of such restriction on both the kinetics and thermodynamics of the resulting host–guest supramolecular crosslinks are poorly understood. Herein, guest crosslinking motifs from controlled regioisomers are demonstrated to yield distinct material properties. Moreover, the underlying phenomena point to further unexpected impact of modular guest topology manifest on the molecular scale in both the affinity and dynamics of supramolecular complex formation. 

A transient mechanism to achieve gelation in host–guest supramolecular hydrogels is demonstrated by acidification and pH correctionviaindirect control from a biocatalytic enzyme network. 

Abstract The management of diabetes in a manner offering autonomous insulin therapy responsive to glucose‐directed need, and moreover with a dosing schedule amenable to facile administration, remains an ongoing goal to improve the standard of care. While basal insulins with reduced dosing frequency, even once‐weekly administration, are on the horizon, there is still no approved therapy that offers glucose‐responsive insulin function. Herein, a nanoscale complex combining both electrostatic‐ and dynamic‐covalent interactions between a synthetic dendrimer carrier and an insulin analogue modified with a high‐affinity glucose‐binding motif yields an injectable insulin depot affording both glucose‐directed and long‐lasting insulin availability. Following a single injection, it is even possible to control blood glucose for at least one week in diabetic swine subjected to daily oral glucose challenges. Measurements of serum insulin concentration in response to challenge show increases in insulin corresponding to elevated blood glucose levels, an uncommon finding even in preclinical work on glucose‐responsive insulin. Accordingly, the subcutaneous nanocomplex that results from combining electrostatic‐ and dynamic‐covalent interactions between a modified insulin and a synthetic dendrimer carrier affords a glucose‐responsive insulin depot for week‐long control following a single routine injection. 

The field of speech emotion recognition (SER) aims to create scientifically rigorous systems that can reliably characterize emotional behaviors expressed in speech. A key aspect for building SER systems is to obtain emotional data that is both reliable and reproducible for practitioners. However, academic researchers encounter difficulties in accessing or collecting naturalistic, large-scale, reliable emotional recordings. Also, the best practices for data collection are not necessarily described or shared when presenting emotional corpora. To address this issue, the paper proposes the creation of an affective naturalistic database consortium (AndC) that can encourage multidisciplinary cooperation among researchers and practitioners in the field of affective computing. This paper’s contribution is twofold. First, it proposes the design of the AndC with a customizable-standard framework for intelligently-controlled emotional data collection. The focus is on leveraging naturalistic spontaneous record- ings available on audio-sharing websites. Second, it presents as a case study the development of a naturalistic large-scale Taiwanese Mandarin podcast corpus using the customizable- standard intelligently-controlled framework. The AndC will en- able research groups to effectively collect data using the provided pipeline and to contribute with alternative algorithms or data collection protocols. 

Modeling cross-lingual speech emotion recognition (SER) has become more prevalent because of its diverse applications. Existing studies have mostly focused on technical approaches that adapt the feature, domain, or label across languages, without considering in detail the similarities be- tween the languages. This study focuses on domain adaptation in cross-lingual scenarios using phonetic constraints. This work is framed in a twofold manner. First, we analyze emotion-specific phonetic commonality across languages by identifying common vowels that are useful for SER modeling. Second, we leverage these common vowels as an anchoring mechanism to facilitate cross-lingual SER. We consider American English and Taiwanese Mandarin as a case study to demonstrate the potential of our approach. This work uses two in-the-wild natural emotional speech corpora: MSP-Podcast (American English), and BIIC-Podcast (Taiwanese Mandarin). The proposed unsupervised cross-lingual SER model using these phonetical anchors outperforms the baselines with a 58.64% of unweighted average recall (UAR). 

Advancing speech emotion recognition (SER) de- pends highly on the source used to train the model, i.e., the emotional speech corpora. By permuting different design parameters, researchers have released versions of corpora that attempt to provide a better-quality source for training SER. In this work, we focus on studying communication modes of collection. In particular, we analyze the patterns of emotional speech collected during interpersonal conversations or monologues. While it is well known that conversation provides a better protocol for eliciting authentic emotion expressions, there is a lack of systematic analyses to determine whether conversational speech provide a “better-quality” source. Specifically, we examine this research question from three perspectives: perceptual differences, acoustic variability and SER model learning. Our analyses on the MSP- Podcast corpus show that: 1) rater’s consistency for conversation recordings is higher when evaluating categorical emotions, 2) the perceptions and acoustic patterns observed on conversations have properties that are better aligned with expected trends discussed in emotion literature, and 3) a more robust SER model can be trained from conversational data. This work brings initial evidences stating that samples of conversations may provide a better-quality source than samples from monologues for building a SER model. 

Hydrogels prepared from supramolecular cross-linking motifs are appealing for use as biomaterials and drug delivery technologies. The inclusion of macromolecules (e.g., protein therapeutics) in these materials is relevant to many of their intended uses. However, the impact of dynamic network cross-linking on macromolecule diffusion must be better understood. Here, hydrogel networks with identical topology but disparate cross-link dynamics are explored. These materials are prepared from cross-linking with host–guest complexes of the cucurbit[7]uril (CB[7]) macrocycle and two guests of different affinity. Rheology confirms differences in bulk material dynamics arising from differences in cross-link thermodynamics. Fluorescence recovery after photobleaching (FRAP) provides insight into macromolecule diffusion as a function of probe molecular weight and hydrogel network dynamics. Together, both rheology and FRAP enable the estimation of the mean network mesh size, which is then related to the solute hydrodynamic diameters to further understand macromolecule diffusion. Interestingly, the thermodynamics of host–guest cross-linking are correlated with a marked deviation from classical diffusion behavior for higher molecular weight probes, yielding solute aggregation in high-affinity networks. These studies offer insights into fundamental macromolecular transport phenomena as they relate to the association dynamics of supramolecular networks. Translation of these materials from in vitro to in vivo is also assessed by bulk release of an encapsulated macromolecule. Contradictory in vitro to in vivo results with inverse relationships in release between the two hydrogels underscores the caution demanded when translating supramolecular biomaterials into application.