Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequencies of amino acid strings in globular protein sequences indicate suppression of blocks of consecutive hydrophobic residues

Patterns of hydrophobic and hydrophilic residues play a major role in protein folding and function. Long, predominantly hydrophobic strings of 20–22 amino acids each are associated with transmembrane helices and have been used to identify such sequences. Much less attention has been paid to hydrophobic sequences within globular proteins. In prior work on computer simulations of the competition between on-pathway folding and off-pathway aggregate formation, we found that long sequences of consecutive hydrophobic residues promoted aggregation within the model, even controlling for overall hydrophobic content. We report here on an analysis of the frequencies of different lengths of contiguous blocks of hydrophobic residues in a database of amino acid sequences of proteins of known structure. Sequences of three or more consecutive hydrophobic residues are found to be significantly less common in actual globular proteins than would be predicted if residues were selected independently. The result may reflect selection against long blocks of hydrophobic residues within globular proteins relative to what would be expected if residue hydrophobicities were independent of those of nearby residues in the sequence.
MusicLand : Exploratory Browsing in Music Space

Most of the existing query-term based search tools focus on direct search activities, where the user is assumed to have a clear and precise idea about his targets. However, if the user is uncertain about his targets, he will need to define/refine them with multiple related queries. Such query behaviour, or exploratory browsing, is not well supported by current search tools. MusicLand is designed for exploratory browsing using music album data. Users input query terms to select a potentially interesting subset of the database. To provide context, the retrieved results are visually clustered based on the query terms, and are displayed on a rectangularized Venn diagram. The Boolean relationships between these regions are visually reinforced using colour encoding and perceptual layering. Refinement of old query is guided by a history of the user’s query evolution, along with a list of potential query terms based on the user’s current music album of interest. Semantic zooming maximizes MusicLand’s display capacity. Animation helps link the zoom views, and the new and old queries. A formative evaluation revealed encouraging responses, suggesting the validity of MusicLand’s design principles.
Compositions and microstructures of CB sulfides: Implications for the thermal history of the CB chondrite parent body

We studied textures and compositions of sulfide inclusions in unzoned Fe,Ni metal particles within CBa Gujba, CBa Weatherford, CBb HH 237, and CBb QUE 94411 in order to constrain formation conditions and secondary thermal histories on the CB parent body. Unzoned metal particles in all four chondrites have very similar metal and sulfide compositions. Metal particles contain different types of sulfides, which we categorize as: homogeneous low‐Cr sulfides composed of troilite, troilite‐containing exsolved daubreelite lamellae, arcuate sulfides that occur along metal grain boundaries, and shock‐melted sulfides composed of a mixture of troilite and Fe, Ni metal. Our model for formation proposes that the unzoned metal particles were initially metal droplets that formed from splashing by a partially molten impacting body. Sulfide inclusions later formed as a result of precipitation of excess S from solid metal at low temperatures, either during single stage cooling or during a reheating event by impacts. Sulfides containing exsolution lamellae record temperatures of ≪600 °C, and irregular Fe‐FeS intergrowth textures suggest localized shock melting, both of which are indicative of heterogeneous heating by impact processes on the CB parent body. Our study shows that CBa and CBb chondrites formed in a similar environment, and also experienced similar secondary impact processing.
Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks

Deep learning based techniques have been recently used with promising results for data integration problems. Some methods directly use pre-trained embeddings that were trained on a large corpus such as Wikipedia. However, they may not always be an appropriate choice for enterprise datasets with custom vocabulary. Other methods adapt techniques from natural language processing to obtain embeddings for the enterprise's relational data. However, this approach blindly treats a tuple as a sentence, thus losing a large amount of contextual information present in the tuple. We propose algorithms for obtaining local embeddings that are effective for data integration tasks on relational databases. We make four major contributions. First, we describe a compact graph-based representation that allows the specification of a rich set of relationships inherent in the relational world. Second, we propose how to derive sentences from such a graph that effectively "describe" the similarity across elements (tokens, attributes, rows) in the two datasets. The embeddings are learned based on such sentences. Third, we propose effective optimization to improve the quality of the learned embeddings and the performance of integration tasks. Finally, we propose a diverse collection of criteria to evaluate relational embeddings and perform an extensive set of experiments validating them against multiple baseline methods. Our experiments show that our framework, EmbDI, produces meaningful results for data integration tasks such as schema matching and entity resolution both in supervised and unsupervised settings.
Prediction of wind effects on buildings using computational methods — review of the state of the art

Advancements in computer software and hardware technology provide a new direction for analyzing engineering problems. Recently the field of wind engineering has gained significant momentum in the computer modelling process. This paper reviews the state of the art in computational wind engineering, including the finite element method, finite difference method, and control volume technique. A portion of this paper summarizes the research in this area carried out by the authors. Computations have been made for a variety of building configurations, including normal wind flow conditions for a building with different aspect ratios, and modelling wind environmental conditions around groups of buildings. The computer modelling technique may eventually enhance the design of buildings and structures against wind loading and supplement the current design practice of using building codes and standards or performing experiments in wind tunnels. Key words: buildings, computer modelling, pressure, velocity, wind enginee.
On optimal, minimal BRDF sampling for reflectance acquisition

The bidirectional reflectance distribution function (BRDF) is critical for rendering, and accurate material representation requires data-driven reflectance models. However, isotropic BRDFs are 3D functions, and measuring the reflectance of a flat sample can require a million incident and outgoing direction pairs, making the use of measured BRDFs impractical. In this paper, we address the problem of reconstructing a measured BRDF from a limited number of samples. We present a novel mapping of the BRDF space, allowing for extraction of descriptive principal components from measured databases, such as the MERL BRDF database. We optimize for the best sampling directions, and explicitly provide the optimal set of incident and outgoing directions in the Rusinkiewicz parameterization for n = {1, 2, 5, 10, 20} samples. Based on the principal components, we describe a method for accurately reconstructing BRDF data from these limited sets of samples. We validate our results on the MERL BRDF database, including favorable comparisons to previous sets of industry-standard sampling directions, as well as with BRDF measurements of new flat material samples acquired with a gantry system. As an extension, we also demonstrate how this method can be used to find optimal sampling directions when imaging a sphere of a homogeneous material; in this case, only two images are often adequate for high accuracy.
Modeling Per-Flow Throughput and Capturing Starvation in CSMA Multi-Hop Wireless Networks

Multi-hop wireless networks employing random access protocols have been shown to incur large discrepancies in the throughputs achieved by the flows sharing the network. Indeed, flow throughputs can span orders of magnitude from near starvation to many times greater than the mean. In this paper, we address the foundations of this disparity. We show that the fundamental cause is not merely differences in the number of contending neighbors, but a generic coordination problem of CSMA-based random access in a multi-hop environment. We develop a new analytical model that incorporates this lack of coordination, identifies dominating and starving flows and accurately predicts per-flow throughput in a large-scale network. We then propose metrics that quantify throughput imbalances due to the MAC protocol operation. Our model and metrics provide a deeper understanding of the behavior of CSMA protocols in arbitrary topologies and can aid the design of effective protocol solutions to the starvation problem.
Overload Control for μs-Scale RPCs with Breakwater

Modern datacenter applications are composed of hundreds of microservices with high degrees of fanout. As a result, they are sensitive to tail latency and require high request throughputs. Maintaining these characteristics under overload is difficult, especially for RPCs with short service times. In this paper, we consider the challenging case of microsecond-scale RPCs, where the cost of communicating information and dropping a request is similar to the cost of processing a request. We present Breakwater, an overload control scheme that can prevent overload in microsecond-scale services through a new, server-driven admission control scheme that issues credits based on server-side queueing delay. Breakwater contributes several techniques to amortize communication costs. It engages in demand speculation, where it assumes clients have unmet demand and issues additional credits when the server is not overloaded. Moreover, it piggybacks client-side demand information in RPC requests and credits in RPC responses. To cope with the occasional bursts in load caused by demand speculation, Breakwater drops requests when overloaded using active queue management. When clients’ demand spikes unexpectedly to 1.4× capacity, Breakwater converges to stable performance in less than 20 ms with no congestion collapse while DAGOR and SEDA take 500 ms and 1.58 s to recover from congestion collapse, respectively.
NeuroImage: Clinical

Alzheimer's disease is considered a disconnection syndrome, motivating the use of brain network measures to detect changes in whole-brain resting state functional connectivity (FC). We investigated changes in FC within and among resting state networks (RSN) across four different stages in the Alzheimer's disease continuum. FC changes were examined in two independent cohorts of individuals (84 and 58 individuals, respectively) each comprising control, subjective cognitive decline, mild cognitive impairment and Alzheimer's dementia groups. For each participant, FC was computed as a matrix of Pearson correlations between pairs of time series from 278 gray matter brain regions. We determined significant differences in FC modular organization with two distinct approaches, network contingency analysis and multiresolution consensus clustering. Network contingency analysis identified RSN sub-blocks that differed significantly across clinical groups. Multiresolution consensus clustering identified differences in the stability of modules across multiple spatial scales. Significant modules were further tested for statistical association with memory and executive function cognitive domain scores. Across both analytic approaches and in both participant cohorts, the findings converged on a pattern of FC that varied systematically with diagnosis within the frontoparietal network (FP) and between the FP network and default mode network (DMN). Disturbances of modular organization were manifest as greater internal coherence of the FP network and stronger coupling between FP and DMN, resulting in less segregation of these two networks. Our findings suggest that the pattern of interactions within and between specific RSNs offers new insight into the functional disruption that occurs across the Alzheimer's disease spectrum.
Improving students' understanding of greenhouse effect, acid rain and the depletion of stratospheric ozone

Global change phenomena, such as an enhancing greenhouse effect, acid rain or the depletion of stratospheric ozone, are considered as environmental problems. Contrary to the demands of Education for Sustainable Development (ESD), three recently published studies have shown that German students still struggle with the acquisition of a scientifically acceptable understanding of these issues. These findings are in accordance with results from international research. Thus, the aim of the research project described in this paper is to enhance students’ understanding of the greenhouse effect, acid rain and the depletion of stratospheric ozone. Using the model of Didactical Design Research, teaching and learning materials were developed and empirically tested in Design-Experiments with 14 students in two iterative cycles. The results show that students are able to develop a scientifically acceptable understanding of the atmospheric phenomena by changing their prior conceptions in different ways. For example they can acquire a scientifical understanding of the transformation of radiation in the process of greenhouse effect or they can describe the chemical processes of the formation of acid rain and ozone depletion adequately. Furthermore the results bring out that students struggle with obstacles, e.g. concerning the acquisition of conceptions about radiation or the correct use of technical language.
Turbulent Heat Transfer and Pressure Drop for

The effective design of viscoelastic dampers as applied to real-world complex engineering structures can be conveniently carried out by using modern multiobjective numerical optimization techniques. The large number of evaluations of the cost functions normally combined with the typically high dimensions of finite element models of industrial structures makes multiobjective optimization very costly, sometimes unfeasible. Those difficulties motivate the study reported in this paper, in which a strategy is proposed consisting in the use of evolutionary algorithms specially adapted to multiobjective optimization of viscoelastic systems, combined with robust condensation and metamodeling. After the discussion of various theoretical aspects, a numerical application is presented to illustrate the use and demonstrate the effectiveness of the methodology proposed for the optimal design of viscoelastic constrained layers.
Regeneration of Inorganic Nutrients from Diatom Decomposition

The regeneration of the inorganic nutrients (phosphorus, nitrogen and silicon) from diatom kept in the dark place while subjected to bacterial attack was studied, using three kinds of species: S. costatum, T. decipiens and Ch. gracilis. The regeneration processes were, generally speaking, divided into four stages. The active mineralization of diatom appeared to have been restricted to the 2nd stage, which was regulated by physiological characters, species of diatom, and also the temperature condition under which the sample was kept. The resolution of silica wall and the consumption of organic carbon get earlier than the regeneration of phosphorus and nitrogen. Hence, the ratio of Si: P showed typically high value at the early time, but as the mineralization went on the ratio was gradually decreased and approached to the value which the diatom had at initial. Meanwhile, the N:P ratio maintained nearly ,constant value and was close to the ratio of the starting sample. These ratios undergo a change succesively in the course of mineralization, but, seem to be regulated by the physiological characters and temperature condition. The solubility of silica frustules are finally discussed. It was inferred from the present examination that the stoichiometrical relation between oxygen consumption and regeneration of inorganic phosphorus and nitrogen, which was developed by REDFIFLD (1934) and RICHARDS (1958) for the relative distribution of nitrate, phosphate and silicate in the sea was not to be materialized at early stage of mineralization.
Communications of the Association for Information Systems

: Grounded theory method (GTM) has become popular in the information systems (IS) field despite multiple interpretations and disputes about its use and usefulness. This paper analyzes how IS researchers collaborate during the GTM process and how they report on the research process. We analyze a sample of papers from the AIS Senior Scholars’ basket of eight that use GTM as their research method to understand how researchers report collaboration in GTM research. We then draw from the previous literature and our own GTM research experiences to illustrate different alternatives of performing collaboration in GTM tasks and their pros and cons in order to help other GTM researchers. We highlight potential issues that arise from different epistemological and ontological stances and provide guidance and examples of how to avoid these issues and how to document the research process.
Role for G Protein G (cid:1)(cid:2) Isoform Speciﬁcity in Synaptic Signal Processing: A Computational Study

Role for G protein G (cid:1)(cid:2) isoform speciﬁcity in synaptic signal processing: A computational study. J Neurophysiol 87: 2612–2623, 2002; 10.1152/jn.00667. 2001. Computational modeling is used to investi-gate the functional impact of G protein–mediated presynaptic autoin- hibition on synaptic ﬁltering properties. It is demonstrated that this form of autoinhibition, which is relieved by depolarization, acts as a high-pass ﬁlter. This contrasts with vesicle depletion, which acts as a low-pass ﬁlter. Model parameters are adjusted to reproduce kinetic slowing data from different G (cid:1)(cid:2) dimeric isoforms, which produce different degrees of slowing. With these sets of parameter values, we demonstrate that the range of frequencies ﬁltered out by the autoinhibition varies greatly depending on the G (cid:1)(cid:2) isoform activated by the autoreceptors. It is shown that G protein autoinhibition can enhance the spatial contrast between a spatially distributed high-frequency signal and surrounding low-frequency noise, providing an alternate mechanism to lateral inhibition. It is also shown that autoinhibition can increase the ﬁdelity of coincidence detection by increasing the signal-to-noise ratio in the postsynaptic cell. The ﬁlter cut, the input frequency below which signals are ﬁltered, depends on several bio- physical parameters in addition to those related to G (cid:1)(cid:2) binding and unbinding. By varying one such parameter, the rate at which trans- mitter unbinds from autoreceptors, we show that the ﬁlter cut can be adjusted up or down for several of the G (cid:1)(cid:2) isoforms. This allows for great synapse-to-synapse variability in the distinction between signal and noise. 0.19 ms (cid:2) 1 so that a single presynaptic cell is incapable of evoking a postsynaptic action potential. To maintain the same level of presynaptic autoreceptor activation, the binding rate is increased by a factor of four to k a (cid:1) (cid:4) 0.8 mM (cid:2) 1 ms (cid:2) 1 . E S L T S
Teaching Hybrid Courses across Disciplines: Effectively Combining Traditional Learning and e-Learning Pedagogies

 Abstract —This paper examines the role and effectiveness of hybrid course offerings within the higher education context. Providing a review of the growing body of academic literature on the effectiveness and learning outcomes of the hybrid model, this paper also provides a look at two distinct hybrid courses; one lower-division college algebra course and one upper-division human resource management course. The strengths and weaknesses of these hybrid courses are assessed and discussed alongside a brief proposal for continued academic research examining hybrid course best practices.
Characterizing ozone production in the Mexico City Metropolitan Area: a case study using a chemical transport model

An episodic simulation is conducted to characterize midday (12:00–17:00 CDT) ozone (O 3 ) photochemical production and to investigate its sensitivity to emission changes of ozone precursors in the Mexico City Metropolitan Area (MCMA) during an "O 3 -South" meteorological episode using the Comprehensive Air Quality Model with extensions (CAMx). High O x (O 3 +NO 2 ) photochemical production rates of 10–80 ppb/h are predicted due to the high reactivity of volatile organic compounds (VOCs) in which alkanes, alkenes, and aromatics exert comparable contributions. The predicted ozone production efficiency is between 4–10 O 3 molecules per NO x molecule oxidized, and increases with VOC-to-NO 2 reactivity ratio. Process apportionment analyses indicate significant outflow of pollutants such as O 3 and peroxyacetyl nitrate (PAN) from the urban area to the surrounding regional environment. PAN is not in chemical-thermal equilibrium during the photochemically active periods. Sensitivity studies of O 3 production suggest that O 3 formation in the MCMA urban region with less chemical aging (NO z /NO y 3 production and its sensitivities to precursors suggest that midday O 3 formation during this episode is VOC-sensitive in the urban region on the basis of the current emissions inventory estimates, and current NO x levels depress the O 3 production.
Improving Denitrification Models by Including Bacterial and Periphytic Biofilm in a Shallow Water‐Sediment System

Denitrification is one of the most challenging nitrogen (N)‐cycling processes to quantify due to complex interactions among influencing factors. In shallow water‐sediment systems, ubiquitous periphytic biofilms consisting of a consortium of algae, bacteria, and nutrients account for additional interactions. The challenge in improving denitrification models is to determine how each factor directly affects denitrification and how those factors indirectly impact denitrification via interactions with other factors. We combined incubation experiments and structural equation modeling to reveal the interaction networks of biotic and abiotic factors and their roles in net denitrification. The N species in the sediment exerted much greater control over net denitrification than that exerted by the N species in the water column. In addition, the relative abundances of denitrifier and nitrifier genes in the sediment showed a dominant control over net denitrification that was similar to the control by N species but was affected by abiotic factors. Periphytic biofilm increased net denitrification both directly and indirectly by altering the pH and the dissolved oxygen, dissolved organic carbon, and N concentrations in the water column and sediment. The overall contribution of periphytic biofilm to net denitrification was less than that of the sediment but greater than that of the water column. Our findings highlight the importance of incorporating bacterial denitrifier and nitrifier genes and periphytic biofilm characteristics during model construction when predicting denitrification in a shallow and stagnant water‐sediment system.