Using SMOG 2 to simulate complex biomolecular assemblies

SummaryOver the last 20 years, the application of structure-based (Gō-like) models has ranged from protein folding with coarse-grained models to all-atom representations of large-scale molecular assemblies. While there are many variants that may be employed, the common feature of these models is that some (or all) of the stabilizing energetic interactions are defined based on knowledge of a particular experimentally-obtained conformation. With the generality of this approach, there was a need for a versatile computational platform for designing and implementing this class of models. To this end, the SMOG 2 software package provides an easy-to-use interface, where the user has full control of the model parameters. This software allows the user to edit XML-formatted files in order to provide definitions of new structure-based models. SMOG 2 reads these “template” files and maps the interactions onto specific structures, which are provided in PDB format. The force field files produced by SMOG 2 may then be used to perform simulations with a variety of popular molecular dynamics suites. In this chapter, we describe some of the key features of the SMOG 2 package, while providing examples and strategies for applying these techniques to complex (often large-scale) molecular assemblies, such as the ribosome.

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Who Likes What? — SplitLBI in Exploring Preferential Diversity of Ratings

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5359 ◽

2020 ◽

Vol 34 (01) ◽

pp. 262-269

Author(s):

Qianqian Xu ◽

Jiechao Xiong ◽

Zhiyong Yang ◽

Xiaochun Cao ◽

Qingming Huang ◽

...

Keyword(s):

Large Scale ◽

Social Preference ◽

Learning To Rank ◽

User Preferences ◽

Coarse Grained ◽

Fast Analysis ◽

Fine Grained ◽

Level Model ◽

The Common ◽

Multi Level

In recent years, learning user preferences has received significant attention. A shortcoming of existing learning to rank work lies in that they do not take into account the multi-level hierarchies from social choice to individuals. In this paper, we propose a multi-level model which learns both the common preference or utility function over the population based on features of alternatives to-be-compared, and preferential diversity functions conditioning on user categories. Such a multi-level model, enables us to simultaneously learn a coarse-grained social preference function together with a fine-grained personalized diversity. It provides us prediction power for the choices of new users on new alternatives. The key algorithm in this paper is based on Split Linearized Bregman Iteration (SplitLBI) algorithm which generates a dynamic path from the common utility to personalized preferential diversity, at different levels of sparsity on personalization. A synchronized parallel version of SplitLBI is proposed to meet the needs of fast analysis of large-scale data. The validity of the methodology are supported by experiments with both simulated and real-world datasets such as movie and dining restaurant ratings which provides us a coarse-to-fine grained preference learning.

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Late Pleistocene plant macro-fossils from the Gokarna Formation of the Kathmandu Valley, Central Nepal

Bulletin of the Department of Geology ◽

10.3126/bdg.v12i0.2252 ◽

1970 ◽

Vol 12 ◽

pp. 75-88 ◽

Cited By ~ 10

Author(s):

Sudarshan Bhandari ◽

Arata Momohara ◽

Khum N Paudayal

Keyword(s):

Late Pleistocene ◽

Large Scale ◽

Fine Sand ◽

Middle Part ◽

Silty Sand ◽

Coarse Grained ◽

Temperate Climate ◽

Kathmandu Valley ◽

The Common ◽

Warm Temperate

The Kathmandu Valley offers the best archive to study the Late Pleistocene climate in Nepal. The Gokarna Formation, constituting the middle part of the sedimentary sequence of the Kathmandu Valley comprises alternating layers of carbonaceous clay, silt, massive to parallel and large scale cross stratified, fine to coarse grained sands and occasional gravel layers, deposited at fluvio-deltaic and lacustrine environment. The organic rich layers of clay, silt, silty-sand and micaceous fine sand consists of abundant plant macro-fossils (fruit, seed and leaves). Plant macrofossils assemblage from the Gokarna Formation (thickness 28.5 m, Dhapasi section) in the northern part of the valley consists of 56 taxa from 35 families. Depending upon the available plant, seven macrofossil assemblages, DS-I to DS-VII in ascending order were established. The common tree and shrubs discovered from this section were Eurya, Ficus, Carpinus, Quercus, Alnus, Rubus, Pyracantha, Zizyphus, Carpinus, Boehmeria etc. Carex, Scirpus triqueter, Scirpus, Polygonum, Euphorbia, Oxalis, Mosla, Viola etc. were the common herbaceous taxa. The constant occurrence of subtropical and warm temperate taxa including Eurya, Ficus, Pyracantha and Zizyphus indicated that subtropical and warm temperate climate continued during the deposition of those macrofossil assemblages. However change in the constituents of those taxa and occurrence of taxa indicating cooler climatic condition like conifers and Betula may indicate minor fluctuation of climate during the deposition of the Gokarna Formation. doi: 10.3126/bdg.v12i0.2252 Bulletin of the Department of Geology, Vol. 12, 2009, pp. 75-88

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Student Perceptions of Community: A Different Perspective

NASPA Journal ◽

10.2202/0027-6014.1064 ◽

1998 ◽

Vol 35 (4) ◽

Author(s):

Jackie Clark ◽

Joan Hirt

Keyword(s):

Student Perceptions ◽

Large Scale ◽

Research University ◽

Small Scale ◽

Social Environments ◽

Common Assumption ◽

Small Communities ◽

Public Research University ◽

The Common ◽

Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

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Common Visa Policy: Bordering from Abroad—Applying Admission Criteria before Departure

10.1093/oso/9780198701002.003.0004 ◽

2017 ◽

Author(s):

Violeta Moreno-Lax

Keyword(s):

Information System ◽

Final Section ◽

Admission Criteria ◽

Member States ◽

The Common ◽

Key Features ◽

Visa Policy ◽

Migration Flows ◽

The Eu ◽

Common Policy

Visas are specifically aimed at controlling admission at the stage of pre-departure and constitute one of the essential requirements for entry under the Schengen Borders Code. This chapter examines the common policy of the EU, conceptualizing them as pre-authorizations of entry granted before arrival in the territory of the Member States. Visa requirements, as introduced in the Visa Regulation, are perused at the outset, taking account of periodic revisions of the visa lists and the criteria for amendment considered relevant by the EU legislator. The key features of the uniform visa format and the Visa Information System (VIS) are briefly presented, highlighting their contribution to the securitisation of migration flows. Then, the visa issuing procedure, as governed by the Community Code on Visas (CCV), is examined. The final section is reserved to the analysis of the implications of the different components of the policy regarding access to asylum in the Member States.

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A High-Content Screening Assay for Small Molecules That Stabilize Mutant Triose Phosphate Isomerase (TPI) as Treatments for TPI Deficiency

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/24725552211018198 ◽

2021 ◽

pp. 247255522110181

Author(s):

Andreas Vogt ◽

Samantha L. Eicher ◽

Tracey D. Myers ◽

Stacy L. Hrizo ◽

Laura L. Vollmer ◽

...

Keyword(s):

High Throughput Screening ◽

Large Scale ◽

Protein Quality ◽

Fluorescent Protein ◽

Triose Phosphate Isomerase ◽

Pharmacological Intervention ◽

Screening Assay ◽

Triose Phosphate ◽

The Common ◽

Green Fluorescent

Triose phosphate isomerase deficiency (TPI Df) is an untreatable, childhood-onset glycolytic enzymopathy. Patients typically present with frequent infections, anemia, and muscle weakness that quickly progresses with severe neuromusclar dysfunction requiring aided mobility and often respiratory support. Life expectancy after diagnosis is typically ~5 years. There are several described pathogenic mutations that encode functional proteins; however, these proteins, which include the protein resulting from the “common” TPIE105D mutation, are unstable due to active degradation by protein quality control (PQC) pathways. Previous work has shown that elevating mutant TPI levels by genetic or pharmacological intervention can ameliorate symptoms of TPI Df in fruit flies. To identify compounds that increase levels of mutant TPI, we have developed a human embryonic kidney (HEK) stable knock-in model expressing the common TPI Df protein fused with green fluorescent protein (HEK TPIE105D-GFP). To directly address the need for lead TPI Df therapeutics, these cells were developed into an optical drug discovery platform that was implemented for high-throughput screening (HTS) and validated in 3-day variability tests, meeting HTS standards. We initially used this assay to screen the 446-member National Institutes of Health (NIH) Clinical Collection and validated two of the hits in dose–response, by limited structure–activity relationship studies with a small number of analogs, and in an orthogonal, non-optical assay in patient fibroblasts. The data form the basis for a large-scale phenotypic screening effort to discover compounds that stabilize TPI as treatments for this devastating childhood disease.

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Mean-Field Models for EEG/MEG: From Oscillations to Waves

Brain Topography ◽

10.1007/s10548-021-00842-4 ◽

2021 ◽

Author(s):

Áine Byrne ◽

James Ross ◽

Rachel Nicks ◽

Stephen Coombes

Keyword(s):

Gap Junction ◽

Large Scale ◽

Mean Field ◽

Coarse Grained ◽

Neural Mass Model ◽

Neuron Network ◽

Mass Model ◽

Mean Field Model ◽

Neural Mass ◽

The Rich

AbstractNeural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

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Why ability point estimates can be pointless: a primer on using skill measures from large-scale assessments in secondary analyses

Measurement Instruments for the Social Sciences ◽

10.1186/s42409-020-00020-5 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Clemens M. Lechner ◽

Nivedita Bhaktha ◽

Katharina Groskurth ◽

Matthias Bluemke

Keyword(s):

Measurement Error ◽

Statistical Models ◽

Test Scores ◽

Large Scale ◽

Equation Modeling ◽

Model Parameters ◽

Advantages And Disadvantages ◽

Point Estimates ◽

Secondary Analyses ◽

Large Scale Assessments

AbstractMeasures of cognitive or socio-emotional skills from large-scale assessments surveys (LSAS) are often based on advanced statistical models and scoring techniques unfamiliar to applied researchers. Consequently, applied researchers working with data from LSAS may be uncertain about the assumptions and computational details of these statistical models and scoring techniques and about how to best incorporate the resulting skill measures in secondary analyses. The present paper is intended as a primer for applied researchers. After a brief introduction to the key properties of skill assessments, we give an overview over the three principal methods with which secondary analysts can incorporate skill measures from LSAS in their analyses: (1) as test scores (i.e., point estimates of individual ability), (2) through structural equation modeling (SEM), and (3) in the form of plausible values (PVs). We discuss the advantages and disadvantages of each method based on three criteria: fallibility (i.e., control for measurement error and unbiasedness), usability (i.e., ease of use in secondary analyses), and immutability (i.e., consistency of test scores, PVs, or measurement model parameters across different analyses and analysts). We show that although none of the methods are optimal under all criteria, methods that result in a single point estimate of each respondent’s ability (i.e., all types of “test scores”) are rarely optimal for research purposes. Instead, approaches that avoid or correct for measurement error—especially PV methodology—stand out as the method of choice. We conclude with practical recommendations for secondary analysts and data-producing organizations.

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Simba

Communications of the ACM ◽

10.1145/3460227 ◽

2021 ◽

Vol 64 (6) ◽

pp. 107-116

Author(s):

Yakun Sophia Shao ◽

Jason Cemons ◽

Rangharajan Venkatesan ◽

Brian Zimmer ◽

Matthew Fojtik ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Data Locality ◽

Coarse Grained ◽

Batch Size ◽

Peak Performance ◽

Large Scale Systems ◽

High Area ◽

On Chip ◽

And Storage

Package-level integration using multi-chip-modules (MCMs) is a promising approach for building large-scale systems. Compared to a large monolithic die, an MCM combines many smaller chiplets into a larger system, substantially reducing fabrication and design costs. Current MCMs typically only contain a handful of coarse-grained large chiplets due to the high area, performance, and energy overheads associated with inter-chiplet communication. This work investigates and quantifies the costs and benefits of using MCMs with finegrained chiplets for deep learning inference, an application domain with large compute and on-chip storage requirements. To evaluate the approach, we architected, implemented, fabricated, and tested Simba, a 36-chiplet prototype MCM system for deep-learning inference. Each chiplet achieves 4 TOPS peak performance, and the 36-chiplet MCM package achieves up to 128 TOPS and up to 6.1 TOPS/W. The MCM is configurable to support a flexible mapping of DNN layers to the distributed compute and storage units. To mitigate inter-chiplet communication overheads, we introduce three tiling optimizations that improve data locality. These optimizations achieve up to 16% speedup compared to the baseline layer mapping. Our evaluation shows that Simba can process 1988 images/s running ResNet-50 with a batch size of one, delivering an inference latency of 0.50 ms.

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Mobility patterns are associated with experienced income segregation in large US cities

Nature Communications ◽

10.1038/s41467-021-24899-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Esteban Moro ◽

Dan Calacci ◽

Xiaowen Dong ◽

Alex Pentland

Keyword(s):

Large Scale ◽

Mobility Model ◽

Coarse Grained ◽

Spatial Proximity ◽

Mobility Patterns ◽

Data Set ◽

Residential Patterns ◽

Income Segregation ◽

Mobility Data ◽

Mobility Behavior

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

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Power-to-Green Methanol via CO2 Hydrogenation—A Concept Study Including Oxyfuel Fluidized Bed Combustion of Biomass

Energies ◽

10.3390/en14154638 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4638

Author(s):

Simon Pratschner ◽

Pavel Skopec ◽

Jan Hrdlicka ◽

Franz Winter

Keyword(s):

Large Scale ◽

Positive Impact ◽

Renewable Energy Sources ◽

Air Separation ◽

Model Parameters ◽

Processing Unit ◽

Fluidized Bed Combustion ◽

Separation Unit ◽

Oxyfuel Combustion ◽

Wind Park

A revolution of the global energy industry is without an alternative to solving the climate crisis. However, renewable energy sources typically show significant seasonal and daily fluctuations. This paper provides a system concept model of a decentralized power-to-green methanol plant consisting of a biomass heating plant with a thermal input of 20 MWth. (oxyfuel or air mode), a CO2 processing unit (DeOxo reactor or MEA absorption), an alkaline electrolyzer, a methanol synthesis unit, an air separation unit and a wind park. Applying oxyfuel combustion has the potential to directly utilize O2 generated by the electrolyzer, which was analyzed by varying critical model parameters. A major objective was to determine whether applying oxyfuel combustion has a positive impact on the plant’s power-to-liquid (PtL) efficiency rate. For cases utilizing more than 70% of CO2 generated by the combustion, the oxyfuel’s O2 demand is fully covered by the electrolyzer, making oxyfuel a viable option for large scale applications. Conventional air combustion is recommended for small wind parks and scenarios using surplus electricity. Maximum PtL efficiencies of ηPtL,Oxy = 51.91% and ηPtL,Air = 54.21% can be realized. Additionally, a case study for one year of operation has been conducted yielding an annual output of about 17,000 t/a methanol and 100 GWhth./a thermal energy for an input of 50,500 t/a woodchips and a wind park size of 36 MWp.

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