STAP: An Architecture and Design Tool for Automata Processing on Memristor TCAMs

Accelerating finite-state automata benefits several emerging application domains that are built on pattern matching. In-memory architectures, such as the Automata Processor (AP), are efficient to speed them up, at least for outperforming traditional von-Neumann architectures. In spite of the AP’s massive parallelism, current APs suffer from poor memory density, inefficient routing architectures, and limited capabilities. Although these limitations can be lessened by emerging memory technologies, its architecture is still the major source of huge communication demands and lack of scalability. To address these issues, we present STAP , a Scalable TCAM-based architecture for Automata Processing . STAP adopts a reconfigurable array of processing elements, which are based on memristive Ternary CAMs (TCAMs), to efficiently implement Non-deterministic finite automata (NFAs) through proper encoding and mapping methods. The CAD tool for STAP integrates the design flow of automata applications, a specific mapping algorithm, and place and route tools for connecting processing elements by RRAM-based programmable interconnects. Results showed 1.47× higher throughput when processing 16-bit input symbols, and improvements of 3.9× and 25× on state and routing densities over the state-of-the-art AP, while preserving 10 4 programming cycles.

Current and Future Methodology for Quantitation and Site-specific Mapping the Location of DNA Adducts

Formation of DNA adducts is a key event for a genotoxic mode of action and its formation is often use as surrogate for mutation and cancer. Interest in DNA adducts are twofold, first, to demonstrate exposure, and second, to link DNA adduct location to subsequent mutations or altered gene regulation. High chemically specific mass spectrometry methods have been established for DNA adduct quantitation and elegant bio-analytic methods utilizing enzymes, various chemistries, and molecular biology methods to visualize the location of DNA adducts. Traditionally, these highly specific methods cannot be combined, and the results are incomparable. Initially developed for single-molecule DNA sequencing, nanopore-type technologies are expected to enable simultaneous quantitation and location of DNA adducts across the genome. We will briefly summarize the current methodologies for state-of-the-art quantitation of DNA adduct levels and mapping of DNA adducts and describe novel single-molecule DNA sequencing technology that is expected to achieve both measures simultaneously. Emerging technologies are expected to soon provide a comprehensive picture of the exposome and identify gene regions susceptible to DNA adduct formation.

Facade-X: An Opinionated Approach to SPARQL Anything

The Semantic Web research community understood since its beginning how crucial it is to equip practitioners with methods to transform non-RDF resources into RDF. Proposals focus on either engineering content transformations or accessing non-RDF resources with SPARQL. Existing solutions require users to learn specific mapping languages (e.g. RML), to know how to query and manipulate a variety of source formats (e.g. XPATH, JSON-Path), or to combine multiple languages (e.g. SPARQL Generate). In this paper, we explore an alternative solution and contribute a general-purpose meta-model for converting non-RDF resources into RDF: Facade-X. Our approach can be implemented by overriding the SERVICE operator and does not require to extend the SPARQL syntax. We compare our approach with the state of art methods RML and SPARQL Generate and show how our solution has lower learning demands and cognitive complexity, and it is cheaper to implement and maintain, while having comparable extensibility and efficiency.

Professional actors demonstrate variability, not stereotypical expressions, when portraying emotional states in photographs

It is long hypothesized that there is a reliable, specific mapping between certain emotional states and the facial movements that express those states. This hypothesis is often tested by asking untrained participants to pose the facial movements they believe they use to express emotions during generic scenarios. Here, we test this hypothesis using, as stimuli, photographs of facial configurations posed by professional actors in response to contextually-rich scenarios. The scenarios portrayed in the photographs were rated by a convenience sample of participants for the extent to which they evoked an instance of 13 emotion categories, and actors’ facial poses were coded for their specific movements. Both unsupervised and supervised machine learning find that in these photographs, the actors portrayed emotional states with variable facial configurations; instances of only three emotion categories (fear, happiness, and surprise) were portrayed with moderate reliability and specificity. The photographs were separately rated by another sample of participants for the extent to which they portrayed an instance of the 13 emotion categories; they were rated when presented alone and when presented with their associated scenarios, revealing that emotion inferences by participants also vary in a context-sensitive manner. Together, these findings suggest that facial movements and perceptions of emotion vary by situation and transcend stereotypes of emotional expressions. Future research may build on these findings by incorporating dynamic stimuli rather than photographs and studying a broader range of cultural contexts.

Distinct progressions of neuronal activity changes underlie the formation and consolidation of a gustatory associative memory

Acquiring new memories is a multi-stage process. Ample of studies have convincingly demonstrated that initially acquired memories are labile, and only stabilized by later consolidation processes. These multiple phases of memory formation are known to involve modification of both cellular excitability and synaptic connectivity, which in turn change neuronal activity at both the single neuron and ensemble levels. However, the specific mapping between the known phases of memory and the observed changes in neuronal activity remains unknown. Here we address this unknown in the context of conditioned taste aversion learning by continuously tracking gustatory cortex (GC) neuronal taste responses from alert rats in the 24 hours following a taste-malaise pairing. We found that the progression of neuronal activity changes in the GC depend on the neuronal organizational level. The population response changed continuously; these changes, however, were only reflected in the population mean amplitude during the acquisition and consolidation phases, and in the known quickening of the ensemble state dynamics after the time of consolidation. Together our results demonstrate how complex dynamics in different representational level of cortical activity underlie the formation and stabilization of memory within the cortex.

AID and APOBECs as Multifaceted Intrinsic Virus-Restricting Factors: Emerging Concepts in the Light of COVID-19

The AID (activation-induced cytidine deaminase)/APOBEC (apolipoprotein B mRNA editing enzyme catalytic subunit) family with its multifaceted mode of action emerges as potent intrinsic host antiviral system that acts against a variety of DNA and RNA viruses including coronaviruses. All family members are cytosine-to-uracil deaminases that either have a profound role in driving a strong and specific humoral immune response (AID) or restricting the virus itself by a plethora of mechanisms (APOBECs). In this article, we highlight some of the key aspects apparently linking the AID/APOBECs and SARS-CoV-2. Among those is our discovery that APOBEC4 shows high expression in cell types and anatomical parts targeted by SARS-CoV-2. Additional focus is given by us to the lymphoid structures and AID as the master regulator of germinal center reactions, which result in antibody production by plasma and memory B cells. We propose the dissection of the AID/APOBECs gene signature towards decisive determinants of the patient-specific and/or the patient group-specific antiviral response. Finally, the patient-specific mapping of the AID/APOBEC polymorphisms should be considered in the light of COVID-19.

Percutaneous Two-Dimensional Shear Wave Elastography for Diagnosis of Pancreatic Tumor

Background: To investigate the efficacy of two-dimensional shear wave elastography (2D-SWE) for the diagnosis of pancreatic mass lesions. Methods: This ethics committee–approved cross-sectional study included 52 patients with histologically-proven pancreatic tumors (pancreatic ductal adenocarcinoma (PDAC), 36; tumor-forming pancreatitis (TFP), 15; neuroendocrine tumor, 1) and 33 control subjects. The 2D-SWE was performed for the tumor/non-tumor tissues, and SWE-mapping patterns and propagation quality were assessed. Results: Three mapping patterns were detected based on the size and distribution of the coloring areas. Pattern A (whole coloring) was detected in all non-tumor tissues and TFP, whereas pattern C (multiple small coloring spots) was detected in PDAC only. Pattern B (partial coloring with smaller spots) was detected in other lesions. The specificity and positive predictive value of pattern A for non-PDAC and those of pattern C for PDAC were 100%. The SWE value was higher in tumor lesions than in the non-tumor tissues (38.1 vs. 9.8 kPa; p < 0.001) in patients with PDAC. The SWE value in the non-tumor lesion was higher in patients with PDAC than in control (9.8 vs. 7.5 kPa; p < 0.001). Conclusions: 2D-SWE may play a role as a novel diagnostic tool for PDAC to detect a specific mapping pattern with quantitative assessment.

Learning Japanese-English Bilingual Word Embeddings by Using Language Specificity

Cross-lingual word embeddings have been gaining attention because they can capture the semantic meaning of words across languages, which can be applied to cross-lingual tasks. Most methods learn a single mapping (e.g., a linear mapping) to transform a word embedding space from one language to another. To improve bilingual word embeddings, we propose an advanced method that adds a language-specific mapping. We focus on learning Japanese-English bilingual word embedding mapping by considering the specificity of the Japanese language. We evaluated our method by comparing it with single mapping-based-models on bilingual lexicon induction between Japanese and English. We determined that our method was more effective, with significant improvements on words of Japanese origin.