Advances in the Development of Phage-Based Probes for Detection of Bio-Species

Bacteriophages, abbreviated as “phages”, have been developed as emerging nanoprobes for the detection of a wide variety of biological species, such as biomarker molecules and pathogens. Nanosized phages can display a certain length of exogenous peptides of arbitrary sequence or single-chain variable fragments (scFv) of antibodies that specifically bind to the targets of interest, such as animal cells, bacteria, viruses, and protein molecules. Metal nanoparticles generally have unique plasmon resonance effects. Metal nanoparticles such as gold, silver, and magnetism are widely used in the field of visual detection. A phage can be assembled with metal nanoparticles to form an organic–inorganic hybrid probe due to its nanometer-scale size and excellent modifiability. Due to the unique plasmon resonance effect of this composite probe, this technology can be used to visually detect objects of interest under a dark-field microscope. In summary, this review summarizes the recent advances in the development of phage-based probes for ultra-sensitive detection of various bio-species, outlining the advantages and limitations of detection technology of phage-based assays, and highlighting the commonly used editing technologies of phage genomes such as homologous recombination and clustered regularly interspaced palindromic repeats/CRISPR-associated proteins system (CRISPR-Cas). Finally, we discuss the possible scenarios for clinical application of phage-probe-based detection methods.

Translates of homogeneous measures associated with observable subgroups on some homogeneous spaces

In the present article, we study the following problem. Let $\boldsymbol {G}$ be a linear algebraic group over $\mathbb {Q}$ , let $\Gamma$ be an arithmetic lattice, and let $\boldsymbol {H}$ be an observable $\mathbb {Q}$ -subgroup. There is a $H$ -invariant measure $\mu _H$ supported on the closed submanifold $H\Gamma /\Gamma$ . Given a sequence $(g_n)$ in $G$ , we study the limiting behavior of $(g_n)_*\mu _H$ under the weak- $*$ topology. In the non-divergent case, we give a rather complete classification. We further supplement this by giving a criterion of non-divergence and prove non-divergence for arbitrary sequence $(g_n)$ for certain large $\boldsymbol {H}$ . We also discuss some examples and applications of our result. This work can be viewed as a natural extension of the work of Eskin–Mozes–Shah and Shapira–Zheng.

Compositional Learning and Verification of Neural Network Controllers

Recent advances in deep learning have enabled data-driven controller design for autonomous systems. However, verifying safety of such controllers, which are often hard-to-analyze neural networks, remains a challenge. Inspired by compositional strategies for program verification, we propose a framework for compositional learning and verification of neural network controllers. Our approach is to decompose the task (e.g., car navigation) into a sequence of subtasks (e.g., segments of the track), each corresponding to a different mode of the system (e.g., go straight or turn). Then, we learn a separate controller for each mode, and verify correctness by proving that (i) each controller is correct within its mode, and (ii) transitions between modes are correct. This compositional strategy not only improves scalability of both learning and verification, but also enables our approach to verify correctness for arbitrary compositions of the subtasks. To handle partial observability (e.g., LiDAR), we additionally learn and verify a mode predictor that predicts which controller to use. Finally, our framework also incorporates an algorithm that, given a set of controllers, automatically synthesizes the pre- and postconditions required by our verification procedure. We validate our approach in a case study on a simulation model of the F1/10 autonomous car, a system that poses challenges for existing verification tools due to both its reliance on LiDAR observations, as well as the need to prove safety for complex track geometries. We leverage our framework to learn and verify a controller that safely completes any track consisting of an arbitrary sequence of five kinds of track segments.

Structural properties of multiple zeta values

We study some classical identities for multiple zeta values and show that they still hold for zeta functions built from an arbitrary sequence of nonzero complex numbers. We introduce the complementary zeta function of a system, which naturally occurs when lifting identities for multiple zeta values to identities for quasisymmetric functions.

On the $$c_0$$-equivalence and permutations of series

Assume that a convergent series of real numbers $$\sum \limits _{n=1}^\infty a_n$$ ∑ n = 1 ∞ a n has the property that there exists a set $$A\subseteq {\mathbb {N}}$$ A ⊆ N such that the series $$\sum \limits _{n \in A} a_n$$ ∑ n ∈ A a n is conditionally convergent. We prove that for a given arbitrary sequence $$(b_n)$$ ( b n ) of real numbers there exists a permutation $$\sigma :{\mathbb {N}}\rightarrow {\mathbb {N}}$$ σ : N → N such that $$\sigma (n) = n$$ σ ( n ) = n for every $$n \notin A$$ n ∉ A and $$(b_n)$$ ( b n ) is $$c_0$$ c 0 -equivalent to a subsequence of the sequence of partial sums of the series $$\sum \limits _{n=1}^\infty a_{\sigma (n)}$$ ∑ n = 1 ∞ a σ ( n ) . Moreover, we discuss a connection between our main result with the classical Riemann series theorem.

Sharp Second-Order Pointwise Asymptotics for Lossless Compression with Side Information

The problem of determining the best achievable performance of arbitrary lossless compression algorithms is examined, when correlated side information is available at both the encoder and decoder. For arbitrary source-side information pairs, the conditional information density is shown to provide a sharp asymptotic lower bound for the description lengths achieved by an arbitrary sequence of compressors. This implies that for ergodic source-side information pairs, the conditional entropy rate is the best achievable asymptotic lower bound to the rate, not just in expectation but with probability one. Under appropriate mixing conditions, a central limit theorem and a law of the iterated logarithm are proved, describing the inevitable fluctuations of the second-order asymptotically best possible rate. An idealised version of Lempel-Ziv coding with side information is shown to be universally first- and second-order asymptotically optimal, under the same conditions. These results are in part based on a new almost-sure invariance principle for the conditional information density, which may be of independent interest.

Improving Recurrent Neural Networks for Offline Arabic Handwriting Recognition by Combining Different Language Models

In handwriting recognition, the design of relevant features is very important, but it is a daunting task. Deep neural networks are able to extract pertinent features automatically from the input image. This drops the dependency on handcrafted features, which is typically a trial and error process. In this paper, we perform an exhaustive experimental evaluation of learned against handcrafted features for Arabic handwriting recognition task. Moreover, we focus on the optimization of the competing full-word based language models by incorporating different characters and sub-words models. We extensively investigate the use of different sub-word-based language models, mainly characters, pseudo-words, morphemes and hybrid units in order to enhance the full-word handwriting recognition system for Arabic script. The proposed method allows the recognition of any out of vocabulary word as an arbitrary sequence of sub-word units. The KHATT database has been used as a benchmark for the Arabic handwriting recognition. We show that combining multiple language models enhances considerably the recognition performance for a morphologically rich language like Arabic. We achieve the state-of-the-art performance on the KHATT dataset.

Computational Recipe for Designing Antibodies against the Ebola Virus

A conceptual basis for antiviral therapy is to deliver a synthetic antibody that binds to a viral surface protein, and thus prevents the virus from deploying its cell-entry mechanism. The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. In this paper, we devised a computational recipe to predict both the viral escape mutations and the possible inhibitory synthetic antibodies. We combined bioinformatics, structural biology, and molecular dynamics (MD) simulations to explore the most likely viral mutations and the candidate antibodies that can inhibit those escape mutations. Specifically, using the crystal structures of the Sudan and Zaire Ebola viral GPs in complex to their respective antibodies (ABs), we have performed an extensive set of MD simulations, both on the wild-type structures and on a large array of additional complexes designed and generated through combinatorial mutations. We discovered that our methods enabled the successful redesign of antibody sequences to essentially all likely glycoprotein mutations. Our findings and the computational methodology developed here for general antibody design can facilitate therapy of current and possibly next generations of viruses.Significance of the ManuscriptThis manuscript has high significance both methodologically and in potential biomedical application. In methodology, the manuscript combines molecular dynamics, Monte Carlo calculations, and bioinformatics in a novel way to simulate the evolutionary arms race between an evolving viral coat protein and a counter-evolving antibody against the virus. This simulation is shown to provide a method for designing a synthetic antibody against the newly emerging viral strains. This work is done in the context of ongoing work in other laboratories in which cells can be induced to produce synthetic antibodies and those synthetic antibodies can be edited (via, for example, CRISPR) to have an arbitrary sequence in the region that binds the viral coat protein. Putting those experimental methods together with the computational methods we present in this paper has the potential to provide a important approach to produce antibodies-on-demand against evolving viruses.

Double-stranded RNA bending by AU-tract sequences

ABSTRACTSequence-dependent structural deformations of the DNA double helix (dsDNA) have been extensively studied, where adenine tracts (A-tracts) provide a striking example for global bending in the molecule. In contrast to dsDNA, much less is known about how the nucleotide sequence affects bending deformations of double-stranded RNA (dsRNA). Using all-atom microsecond long molecular dynamics simulations we found a sequence motif consisting of alternating adenines and uracils, or AU-tracts, that bend the dsRNA helix by locally compressing the major groove. We experimentally tested this prediction using atomic force microscopy (AFM) imaging of long dsRNA molecules containing phased AU-tracts. AFM images revealed a clear intrinsic bend in these AU-tracts molecules, as quantified by a significantly lower persistence length compared to dsRNA molecules of arbitrary sequence. The bent structure of AU-tracts here described might play a role in sequence-specific recognition of dsRNAs by dsRNA-interacting proteins or impact the folding of RNA into intricate tertiary and quaternary structures.