The development of targeted medicine has greatly expanded treatment options and spurred new research avenues in cancer therapeutics, with monoclonal antibodies (mAbs) emerging as a prevalent treatment in recent years. With mixed clinical success, mAbs still hold significant shortcomings, as they possess limited tumor penetration, high manufacturing costs, and the potential to develop therapeutic resistance. However, the recent discovery of “nanobodies,” the smallest-known functional antibody fragment, has demonstrated significant translational potential in preclinical and clinical studies. This review highlights their various applications in cancer and analyzes their trajectory toward their translation into the clinic.

Seaweed-derived polysaccharides including agar and alginate, have found widespread applications in biomedical research and medical therapeutic applications including wound healing, drug delivery, and tissue engineering. Given the recent increases in the incidence of diabetes, obesity and hyperlipidemia, there is a pressing need for low cost therapeutics that can economically and effectively slow the progression of atherosclerosis. Marine polysaccharides have been consumed by humans for millennia and are available in large quantities at low cost. Polysaccharides such as fucoidan, laminarin sulfate and ulvan have shown promise in reducing atherosclerosis and its accompanying risk factors in animal models. However, others have been tested in very limited context in scientific studies. In this review, we explore the current state of knowledge for these promising therapeutics and discuss the potential and challenges of using seaweed derived polysaccharides as therapies for atherosclerosis.

Abstract. Information flow control allows untrusted code to access sensitive and trustworthy information without leaking this information. However, the presence of covert channels subverts this security mechanism, allowing processes to communicate information in violation of IFC policies. In this paper, we show that concurrent deterministic IFC systems that use time-based scheduling are vulnerable to a cache-based internal timing channel. We demonstrate this vulnerability with a concrete attack on Hails, one particular IFC web framework. To eliminate this internal timing channel, we implement instruction-based scheduling, a new kind of scheduler that is indifferent to timing perturbations from underlying hardware components, such as the cache, TLB, and CPU buses. We show this scheduler is secure against cache-based internal timing attacks for applications using a single CPU. To show the feasibility of instruction-based scheduling, we have implemented a version of Hails that uses the CPU retired-instruction counters available on commodity Intel and AMD hardware. We show that instruction-based scheduling does not impose significant performance penalties. Additionally, we formally prove that our modifications to Hails' underlying IFC system preserve non-interference in the presence of caches.

Many important security problems in JavaScript, such as browser extension security, untrusted JavaScript libraries and safe integration of mutually distrustful websites (mash-ups), may be effectively addressed using an efficient implementation of information flow control (IFC). Unfortunately existing fine-grained approaches to JavaScript IFC require modifications to the language semantics and its engine, a non-goal for browser applications. In this work, we take the ideas of coarse-grained dynamic IFC and provide the theoretical foundation for a language-based approach that can be applied to any programming language for which external effects can be controlled. We then apply this formalism to serverand client-side JavaScript, show how it generalizes to the C programming language, and connect it to the Haskell LIO system. Our methodology offers design principles for the construction of information flow control systems when isolation can easily be achieved, as well as compositional proofs for optimized concrete implementations of these systems, by relating them to their isolated variants.

No abstract

<p>Densely O-glycosylated mucin domains are found in a broad range of cell surface and secreted proteins, where they play key physiological roles. In addition, alterations in mucin expression and glycosylation are common in a variety of human diseases, such as cancer, cystic fibrosis, and inflammatory bowel diseases. These correlations have been challenging to uncover and establish because tools that specifically probe mucin domains are lacking. Here, we present a panel of bacterial proteases that cleave mucin domains via distinct peptide- and glycan-based motifs, generating a diverse enzymatic toolkit for mucin-selective proteolysis. By mutating catalytic residues of two such enzymes, we engineered mucin-selective binding agents with retained glycoform preferences. StcE^E447D is a pan-mucin stain derived from enterohemorrhagic <i>Escherichia coli </i>that is tolerant to a wide range of glycoforms. BT4244^E575A derived from <i>Bacteroides thetaiotaomicron</i> is selective for truncated, asialylated Core 1 structures commonly associated with malignant and pre-malignant tissues. We demonstrated that these catalytically inactive point mutants enable robust detection and visualization of mucin-domain glycoproteins by flow cytometry, Western blot, and immunohistochemistry. Application of our enzymatic toolkit to ovarian cancer patient ascites fluid and tissue slices facilitated characterization of patients based on differences in mucin cleavage and expression patterns.</p>

No abstract

In distributed applications, the transmission of non-contiguous data structures is greatly slowed down by the need to serialize them into a buffer before sending. We describe Compact Normal Forms, an API that allows programmers to explicitly place immutable heap objects into regions, which can both be accessed like ordinary data as well as efficiently transmitted over the network. The process of placing objects into compact regions (essentially a copy) is faster than any serializer and can be amortized over a series of functional updates to the data structure in question. We implement this scheme in the Glasgow Haskell Compiler and show that even with the space expansion attendant with memory-oriented data structure representations, we achieve between ×2 and ×4 speedups on fast local networks with sufficiently large data structures.