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Adaptative parallel simulators for bioinspired computing models

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 107卷 469-484页

作者： Martinez-del-Amor, Miguel A. Perez-Hurtado, Ignacio Orellana-Martin, David Perez-Jimenez, Mario J. Univ Seville Dept Comp Sci & Artificial Intelligence Res Grp Nat Comp Seville Spain

In the Membrane Computing area, P systems are unconventional devices of computation inspired by the structure and processes taking place in living cells. Main successful P system applications lie in computability and computational complexity theories, as well as in biological modelling. Given that models become too complex to deal with, simulators for P systems are essential tools and their efficiency is critical. In order to handle the diverse situations that may arise during the computation, these simulators have to take into account that worst-case scenarios can happen, even though they rarely occur. As a result, there is a significant loss of performance. In this paper, the concept of adaptative simulation for P systems is introduced to palliate this problem. This is achieved by passing high-level information provided directly by P system model designers to the simulator, helping it to better adapt to the target model. For this purpose, an existing simulator for an ecosystem modelling framework, named Population Dynamics P systems, is extended to include the information of modules, that are usually employed to define ecosystem models. Moreover, the standard description language for P systems, P-Lingua, has been re-engineered in its version 5. It now includes a new syntactical item, called feature, to express this kind of high-level semantic information. Experiments show that this simple adaptative simulator supporting modules as features doubles the performance when running on GPUs and on multicore processors. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Bioinspired computing Membrane computing P system programming language Parallel computing GPU computing

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Compiling with Command Trees

Compiling with Command Trees

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作者： Bot, Bernard Delft University of Technology

学位级别：硕士

Compilers translate high-level source code into low-level machine code. To represent source code a compiler uses a language called the intermediate representation (IR). An IR for the compilation of functional languages is continuation-passing style (CPS). It provides convenient abstractions for both data flow and control flow. However, CPS conversion is hard to write and the transformations on CPS are untyped. In this thesis we develop an IR based on CPS using the command tree data structure. Command trees allow us to express compiler transformations typically, declaratively, and modularly. The monadic nature of command trees allows us to bind commands together in a succinct manner. We test the usefulness of the new IR by building two versions of the LamToWat compiler that translates the lamdba calculus into WebAssembly. The first version will use a CPS IR and the second version a command tree IR.

关键词： compiler continuation tree programming language

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Analysis of Gut Microbiome Using Fecal Samples

Analysis of Gut Microbiome Using Fecal Samples

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作者： Ahmed Gamal Mayyadah Alabdely Kelly Steller Hilmi Al-Shakhir Mohammed Elshaer Mahmoud Ghannoum

With the gut microbiome being implicated in many diseases and even offering the potential for future therapies, its study has been gaining interest over the years. However, to gain more in-depth insight and understanding of the impact of the microbiome on our health, it is critical to have accurate microbial data that is reproducible and comparable across studies. Variation in experimental techniques, particularly DNA extraction and sequencing methods, may result in variability and the inability to compare results. In this chapter, we describe in detail our microbiome analysis methodology focusing on stool samples. less

关键词： DNA Extraction PCR Library Preparation Statistical Calculation DNA Ligation Real-time PCR Emulsion PCR programming language Data analysis Gut microbiome Fecal samples

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Studying Protista WBRWhole-body regeneration (WBR) and Repair Using

Studying Protista WBRWhole-body regeneration (WBR) and Repai...

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作者： Megan M. Sperry Nirosha J. Murugan Michael Levin

Physarum polycephalum is a protist slime mould that exhibits a high degree of responsiveness to its environment through a complex network of tubes and cytoskeletal components that coordinate behavior across its unicellular, multinucleated body. Physarum has been used to study decision making, problem solving, and mechanosensation in aneural biological systems. The robust generative and repair capacities of Physarum also enable the study of whole-body regeneration within a relatively simple model system. Here we describe methods for growing, imaging, quantifying, and sampling Physarum that are adapted for investigating regeneration and repair. less

关键词： Image Processing Dissection MATLAB programming language Regeneration Signaling Extract Networks Injury Slime mould

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De Novo Molecular Design with Chemical language Models

De Novo Molecular Design with Chemical Language Models

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作者： Francesca Grisoni Gisbert Schneider

Artificial intelligence (AI) offers new possibilities for hit and lead finding in medicinal chemistry. Several instances of AI have been used for prospective de novo drug design. Among these, chemical language models have been shown to perform well in various experimental scenarios. In this study, we provide a hands-on introduction to chemical language modeling. A technique based on recurrent neural networks is discussed in detail, together with a step-by-step guide to applying this AI method for focused compound library design. The program code is freely available at URL: ***/ETHmodlab/de_novo_design_RNN . less

关键词： Artificial Intelligence Sequence Generation Deep learning Machine Learning programming language Cheminformatics Drug design Recurrent neural network SMILES BIMODAL

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Stacking ensemble learning model to predict 6-month mortality in ischemic stroke patients

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SCIENTIFIC REPORTS 2022年第1期12卷 1-9页

作者： Hwangbo, Lee Kang, Yoon Jung Kwon, Hoon Lee, Jae Il Cho, Han-Jin Ko, Jun-Kyeung Sung, Sang Min Lee, Tae Hong Pusan Natl Univ Hosp Dept Radiol Gudeokro 179 Pusan 49241 South Korea Pusan Natl Univ Hosp Dept Neurol Pusan 49241 South Korea Pusan Natl Univ Hosp Dept Neurosurg Pusan 49241 South Korea Pusan Natl Univ Hosp Biomed Res Inst Pusan 49241 South Korea Pusan Natl Univ Coll Med Yangsan 50612 South Korea

Patients with acute ischemic stroke can benefit from reperfusion therapy. Nevertheless, there are gray areas where initiation of reperfusion therapy is neither supported nor contraindicated by the current practice guidelines. In these situations, a prediction model for mortality can be beneficial in decision-making. This study aimed to develop a mortality prediction model for acute ischemic stroke patients not receiving reperfusion therapies using a stacking ensemble learning model. The model used an artificial neural network as an ensemble classifier. Seven base classifiers were K-nearest neighbors, support vector machine, extreme gradient boosting, random forest, naive Bayes, artificial neural network, and logistic regression algorithms. From the clinical data in the International Stroke Trial database, we selected a concise set of variables assessable at the presentation. The primary study outcome was all-cause mortality at 6 months. Our stacking ensemble model predicted 6-month mortality with acceptable performance in ischemic stroke patients not receiving reperfusion therapy. The area under the curve of receiver-operating characteristics, accuracy, sensitivity, and specificity of the stacking ensemble classifier on a put-aside validation set were 0.783 (95% confidence interval 0.758-0.808), 71.6% (69.3-74.2), 72.3% (69.2-76.4%), and 70.9% (68.9-74.3%), respectively.

关键词： Data processing programming language

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ShortBOL: A language for Scripting Designs for Engineered Biological Systems Using Synthetic Biology Open language (SBOL)

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ACS SYNTHETIC BIOLOGY 2020年第4期9卷 962-966页

作者： Crowther, Matthew Grozinger, Lewis Pocock, Matthew Taylor, Christopher P. D. McLaughlin, James A. Misirli, Goksel Bartley, Bryan A. Beal, Jacob Goni-Moreno, Angel Wipat, Anil Newcastle Univ Sch Comp Newcastle Upon Tyne NE4 5TG Tyne & Wear England Univ Politecn Madrid CBGP INIA Inst Nacl Invest & Tecnol Agr & Alimentaria INIA Campus Montegancedo Madrid 28223 Spain Keele Univ Sch Comp & Math Keele ST5 5BG Staffs England Raytheon BBN Technol Cambridge MA 02138 USA

The Synthetic Biology Open language (SBOL) is an emerging synthetic biology data exchange standard, designed primarily for unambiguous and efficient machine communication. However, manual editing of SBOL is generally difficult for nontrivial designs. Here, we describe ShortBOL, a lightweight SBOL scripting language that bridges the gap between manual editing, visual design tools, and direct programming. ShortBOL is a shorthand textual language developed to enable users to create SBOL designs quickly and easily, without requiring strong programming skills or visual design tools.

关键词： programming language biodesign Synthetic Biology Open language (SBOL) synthetic biology RDF

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Shiftry: RNN Inference in 2KB of RAM

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PROCEEDINGS OF THE ACM ON programming languageS-PACMPL 2020年第OOPSLA期4卷 1–30页

作者： Kumar, Aayan Seshadri, Vivek Sharma, Rahul Microsoft Res Delhi India

Traditionally, IoT devices send collected sensor data to an intelligent cloud where machine learning (ML) inference happens. However, this course is rapidly changing and there is a recent trend to run ML on the edge IoT devices themselves. An intelligent edge is attractive because it saves network round trip (efficiency) and keeps user data at the source (privacy). However, the IoT devices are much more resource constrained than the cloud, which makes running ML on them challenging. Specifically, consider Arduino Uno, a commonly used board, that has 2KB of RAM and 32KB of read-only Flash memory. Although recent breakthroughs in ML have created novel recurrent neural network (RNN) models that provide good accuracy with KB-sized models, deploying them on tiny devices with such hard memory requirements has remained elusive. We provide, SHIFTRY, an automatic compiler from high-level floating-point ML models to fixed-point C-programs with 8-bit and 16-bit integers, which have significantly lower memory requirements. For this conversion, SHIFTRY uses a data-driven float-to-fixed procedure and a RAM management mechanism. These techniques enable us to provide first empirical evaluation of RNNs running on tiny edge devices. On simpler ML models that prior work could handle, SHIFTRY-generated code has lower latency and higher accuracy.

关键词： Machine learning IoT device Memory management programming language Compiler Fixed-point Embedded devices

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Functional Annotation of Custom Transcriptomes

Functional Annotation of Custom Transcriptomes

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作者： Fursham Hamid Kaur Alasoo Jaak Vilo Eugene Makeyev

Many eukaryotic genes can give rise to different alternative transcripts depending on stage of development, cell type, and physiological cues. Current transcriptome-wide sequencing technologies highlight the remarkable extent of this regulation in metazoans and allow for RNA isoforms to be profiled in increasingly small biological samples and with a growing confidence. Understanding biological functions of sample-specific transcripts is a major challenge in genomics and RNA processing fields. Here we describe simple bioinformatics workflows that facilitate this task by streamlining reference-guided annotation of novel transcripts. A key part of our protocol is the R package factR that rapidly matches custom-assembled transcripts to their likely host genes, deduces the sequence and domain structure of novel protein products, and predicts sensitivity of newly identified RNA isoforms to nonsense-mediated decay. less

关键词： RNA-seq Single-cell RNA-seq Genomics HISAT StringTie Western Blot programming language Protein domains Nonsense-mediated decay Custom transcriptome Alternative pre-mRNA processing

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Reconstruction of Archaeal Genomes from Short-Read Metagenomes

Reconstruction of Archaeal Genomes from Short-Read Metagenom...

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作者： Till L. Bornemann Panagiotis S. Adam Alexander J. Probst

As the majority of biological diversity remains unexplored and uncultured, investigating it requires culture-independent approaches. Archaea in particular suffer from a multitude of issues that make their culturing problematic, from them being frequently members of the rare biosphere, to low growth rates, to them thriving under very specific and often extreme environmental and community conditions that are difficult to replicate. OMICs techniques are state of the art approaches that allow direct high-throughput investigations of environmental samples at all levels from nucleic acids to proteins, lipids, and secondary metabolites. Metagenomics, as the foundation for other OMICs techniques, facilitates the identification and functional characterization of the microbial community members and can be combined with other methods to provide insights into the microbial activities, both on the RNA and protein levels. In this chapter, we provide a step-by-step workflow for the recovery of archaeal genomes from metagenomes, starting from raw short-read sequences. This workflow can be applied to recover bacterial genomes as well. less

关键词： Metagenomics Statistical Calculation Bowtie Coding Region Prediction Sequence Editing CRISPR DNA Extraction Microscopy DNA Fragmentation Assay programming language Python SNP Calling Genome Annotation Base-calling Prokaryotes Genome curation Short-read sequencing Rare biosphere

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