Expanding the scope of Next Generation Maleimides for Antibody Conjugation

Antibody drug conjugates (ADCs) are increasingly promising targeted therapies for cancer treatments, due to the combination of antibodies with tumour selectivity and cytotoxic drugs. Current strategies to construct ADCs suffer from heterogeneity, complexity, and high costs. Dibromomaleimides (DBMs), a class of next generation maleimides (NGMs), have shown ability to site-selectively bridge antibody disulfide bonds, delivering robustly stable conjugates following maleimide hydrolysis. This work expands DBM scope by developing trifunctional DBMs built around a lysine core, introducing multiple functionalities (such as fluorophores) onto an antibody, which would be of significant interest for treatments of complex diseases. However, a problem associated with DBM-based disulfide bridging arises as disulfide scrambling where incorrectly bridged disulfides observed in antibody hinge region, thus forming two antibody isomers with limited homogeneity. To resolve this issue, in situ NGMbased bioconjugation has been conducted to enable simultaneous disulfide reduction and bridging. A variety of dithiomaleimides (DTMs) has been explored to attempt to minimise cross-reactivity to TCEP, a typical disulfide reducing agent. Compared to highly reactive DBMs, these DTMs have attenuated reactivity, and thus can be applied during the reduction. Upon disulfide reduction, the presence of these DTMs enables an efficient bridging and leads to a reduction in scrambled disulfide bonds compared to conventional DBM-based bioconjugation. However, the resultant conjugates also involve mis-bridged antibody species, therefore, NGM-based in situ bioconjugation still needs optimisation. Lastly, investigations on benzeneselenols as novel antibody reducing agents have been conducted. Traditional benzeneselenols show good reducing ability but suffer from the poor aqueous solubility and malodour. A new generation of benzeneselenols with improved bioconjugation properties have been developed, through the synthesis of substituted aryl derivatives with improved solubility for better compatibility with biological conditions. Overall, this work expands the scope of DBMs and explores methods to generate more homogeneous antibody conjugates using DBMs and related reagents

Trifunctional Dibromomaleimide Reagents Built Around A Lysine Scaffold Deliver Site-selective Dual-modality Antibody Conjugation

We describe the synthesis and application of a selection of trifunctional reagents for the dual-modality modification of native, solvent accessible disulfide bonds in trastuzumab. The reagents were developed from the dibromomaleimide (DBM) platform with two orthogonal clickable functional groups built around a lysine core. We also describe the development of an aryl diselenide additive which enables antibody disulfide reduction in 4 minutes and a rapid overall reduction-bridging-double click sequence

Half-Aggregation of Schnorr Signatures with Tight Reductions

An aggregate signature (AS) scheme allows an unspecified aggregator to compress many signatures into a short aggregation. AS schemes can save storage costs and accelerate verification. They are desirable for applications where many signatures need to be stored, transferred, or verified together, like blockchain systems, network routing, e-voting, and certificate chains. However, constructing AS schemes based on general groups, only requiring the hardness of the discrete logarithm problem, is quite tricky and has been a long-standing research question. Recently, Chalkias et al. (CT-RSA 2021) proposed a half-aggregate scheme for Schnorr signatures. We observe the scheme lacks a tight security proof and does not well support incremental aggregation, i.e., adding more signatures into a pre-existing aggregation. Chalkias et al. also presented an aggregate scheme for Schnorr signatures whose security can be tightly reduced to the security of Schnorr signatures in the random oracle model (ROM). However, the scheme is rather expensive and does not achieve half-aggregation. It is a fundamental question whether there exists half-aggregation of Schnorr signatures with tight reduction in the ROM, of both theoretical and practical interests. This work\u27s contributions are threefold. We first give a tight security proof for the scheme in CT-RSA 2021 in the ROM and the algebraic group model (AGM). Second, we provide a new half-aggregate scheme for Schnorr signatures that perfectly supports incremental aggregation, whose security also tightly reduces to Schnorr\u27s security in the AGM+ROM. Third, we present a Schnorr-based sequential aggregate signature (SAS) scheme that is tightly secure as Schnorr signature scheme in the ROM (without the AGM). Our work may pave the way for applying Schnorr aggregation in real-world cryptographic applications

Immune Landscape of Invasive Ductal Carcinoma Tumor Microenvironment Identifies a Prognostic and Immunotherapeutically Relevant Gene Signature

Background: Invasive ductal carcinoma (IDC) is a clinically and molecularly distinct disease. Tumor microenvironment (TME) immune phenotypes play crucial roles in predicting clinical outcomes and therapeutic efficacy. Method: In this study, we depict the immune landscape of IDC by using transcriptome profiling and clinical characteristics retrieved from The Cancer Genome Atlas (TCGA) data portal. Immune cell infiltration was evaluated via single-sample gene set enrichment (ssGSEA) analysis and systematically correlated with genomic characteristics and clinicopathological features of IDC patients. Furthermore, an immune signature was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. A random forest algorithm was applied to identify the most important somatic gene mutations associated with the constructed immune signature. A nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed by multivariate Cox regression. Results: The IDC were clustered into low immune infiltration, intermediate immune infiltration, and high immune infiltration by the immune landscape. The high infiltration group had a favorable survival probability compared with that of the low infiltration group. The low-risk score subtype identified by the immune signature was characterized by T cell-mediated immune activation. Additionally, activation of the interferon-α response, interferon-γ response, and TNF-α signaling via the NFκB pathway was observed in the low-risk score subtype, which indicated T cell activation and may be responsible for significantly favorable outcomes in IDC patients. A random forest algorithm identified the most important somatic gene mutations associated with the constructed immune signature. Furthermore, a nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed, revealing that the immune signature was an independent prognostic biomarker. Finally, the relationship of VEGFA, PD1, PDL-1, and CTLA-4 expression with the immune infiltration landscape and the immune signature was analyzed to interpret the responses of IDC patients to immunotherapy. Conclusion: Taken together, we performed a comprehensive evaluation of the immune landscape of IDC and constructed an immune signature related to the immune landscape. This analysis of TME immune infiltration landscape has shed light on how IDC respond to immunotherapy and may guide the development of novel drug combination strategies

SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process

Transformer Hawkes process models have shown to be successful in modeling event sequence data. However, most of the existing training methods rely on maximizing the likelihood of event sequences, which involves calculating some intractable integral. Moreover, the existing methods fail to provide uncertainty quantification for model predictions, e.g., confidence intervals for the predicted event's arrival time. To address these issues, we propose SMURF-THP, a score-based method for learning Transformer Hawkes process and quantifying prediction uncertainty. Specifically, SMURF-THP learns the score function of events' arrival time based on a score-matching objective that avoids the intractable computation. With such a learned score function, we can sample arrival time of events from the predictive distribution. This naturally allows for the quantification of uncertainty by computing confidence intervals over the generated samples. We conduct extensive experiments in both event type prediction and uncertainty quantification of arrival time. In all the experiments, SMURF-THP outperforms existing likelihood-based methods in confidence calibration while exhibiting comparable prediction accuracy

Memristive Non-Volatile Memory Based on Graphene Materials

Resistive random access memory (RRAM), which is considered as one of the most promising next-generation non-volatile memory (NVM) devices and a representative of memristor technologies, demonstrated great potential in acting as an artificial synapse in the industry of neuromorphic systems and artificial intelligence (AI), due its advantages such as fast operation speed, low power consumption, and high device density. Graphene and related materials (GRMs), especially graphene oxide (GO), acting as active materials for RRAM devices, are considered as a promising alternative to other materials including metal oxides and perovskite materials. Herein, an overview of GRM-based RRAM devices is provided, with discussion about the properties of GRMs, main operation mechanisms for resistive switching (RS) behavior, figure of merit (FoM) summary, and prospect extension of GRM-based RRAM devices. With excellent physical and chemical advantages like intrinsic Young’s modulus (1.0 TPa), good tensile strength (130 GPa), excellent carrier mobility (2.0 × 105 cm2∙V−1∙s−1), and high thermal (5000 Wm−1∙K−1) and superior electrical conductivity (1.0 × 106 S∙m−1), GRMs can act as electrodes and resistive switching media in RRAM devices. In addition, the GRM-based interface between electrode and dielectric can have an effect on atomic diffusion limitation in dielectric and surface effect suppression. Immense amounts of concrete research indicate that GRMs might play a significant role in promoting the large-scale commercialization possibility of RRAM devices