cssi/SciSciGPT-SciSciNet huggingface dataset:
CONFOLD2: improved contact-driven ab initio protein structure modeling

Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed. We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets. CONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/ .
Age-Dependence and Aging-Dependence: Neuronal Loss and Lifespan in a C. elegans Model of Parkinson’s Disease

It is often assumed, but not established, that the major neurodegenerative diseases, such as Parkinson's disease, are not just age-dependent (their incidence changes with time) but actually aging-dependent (their incidence is coupled to the process that determines lifespan). To determine a dependence on the aging process requires the joint probability distribution of disease onset and lifespan. For human Parkinson's disease, such a joint distribution is not available, because the disease cuts lifespan short. To acquire a joint distribution, we resorted to an established C. elegans model of Parkinson's disease in which the loss of dopaminergic neurons is not fatal. We find that lifespan is not correlated with the loss of individual neurons. Therefore, neuronal loss is age-dependent and aging-independent. We also find that a lifespan-extending intervention into insulin/IGF1 signaling accelerates the loss of specific dopaminergic neurons, while leaving death and neuronal loss times uncorrelated. This suggests that distinct and compartmentalized instances of the same genetically encoded insulin/IGF1 signaling machinery act independently to control neurodegeneration and lifespan in C. elegans. Although the human context might well be different, our study calls attention to the need to maintain a rigorous distinction between age-dependence and aging-dependence.
Acute diverticulitis in renal transplant patients: should we treat them differently?

Abstract Background Current guidelines suggest that transplant patients with acute diverticulitis should be managed aggressively with early operative intervention to reduce morbidity and mortality. This study compared the treatment choices and clinical outcomes between renal transplant patients and immunocompetent patients with acute diverticulitis. Methods A retrospective review was performed of all patients who were admitted with acute diverticulitis between 2002 and 2015 at a single academic institution. Patient demographics, comorbidities, physiologic and radiologic disease severity, management, and disease-specific outcomes were recorded and compared between renal transplant patients and immunocompetent patients. Predictors of complications also were analyzed. Results In the study, 20 renal transplant patients and 134 immunocompetent patients were admitted for acute diverticulitis and were followed for a median time of 36 and 40 months, respectively. Patient demographics were similar between the groups. Transplant patients had significantly more comorbidities. Overall, there were no differences in physiologic disease severity or rates of elective or urgent operation, ostomy, permanent ostomy, duration of stay, 30-day readmission, disease recurrence or disease-specific complications, organ failure, or death. Among patients with complicated disease, renal transplant patients were significantly more likely to undergo an urgent operation and had more complications. On multivariate analysis, undergoing operative therapy remained the sole predictor of complications. Conclusion Nonoperative management of renal transplant patients who present with uncomplicated diverticulitis is safe as outcomes are similar to immunocompetent patients. However, the optimal management of renal transplant patients with complicated diverticulitis remains unclear as both treatment choices and complication rates differed from immunocompetent patients.
Clustering of circular consensus sequences: accurate error correction and assembly of single molecule real-time reads from multiplexed amplicon libraries

Targeted resequencing with high-throughput sequencing (HTS) platforms can be used to efficiently interrogate the genomes of large numbers of individuals. A critical issue for research and applications using HTS data, especially from long-read platforms, is error in base calling arising from technological limits and bioinformatic algorithms. We found that the community standard long amplicon analysis (LAA) module from Pacific Biosciences is prone to substantial bioinformatic errors that raise concerns about findings based on this pipeline, prompting the need for a new method. A single molecule real-time (SMRT) sequencing-error correction and assembly pipeline, C3S-LAA, was developed for libraries of pooled amplicons. By uniquely leveraging the structure of SMRT sequence data (comprised of multiple low quality subreads from which higher quality circular consensus sequences are formed) to cluster raw reads, C3S-LAA produced accurate consensus sequences and assemblies of overlapping amplicons from single sample and multiplexed libraries. In contrast, despite read depths in excess of 100X per amplicon, the standard long amplicon analysis module from Pacific Biosciences generated unexpected numbers of amplicon sequences with substantial inaccuracies in the consensus sequences. A bootstrap analysis showed that the C3S-LAA pipeline per se was effective at removing bioinformatic sources of error, but in rare cases a read depth of nearly 400X was not sufficient to overcome minor but systematic errors inherent to amplification or sequencing. C3S-LAA uses a divide and conquer processing algorithm for SMRT amplicon-sequence data that generates accurate consensus sequences and local sequence assemblies. Solving the confounding bioinformatic source of error in LAA allowed for the identification of limited instances of errors due to DNA amplification or sequencing of homopolymeric nucleotide tracts. For research and development in genomics, C3S-LAA allows meaningful conclusions and biological inferences to be made from accurately polished sequence output.
Modeling Time to Open of Emails with a Latent State for User Engagement Level

Email messages have been an important mode of communication, not only for work, but also for social interactions and marketing. When messages have time sensitive information, it becomes relevant for the sender to know what is the expected time within which the email will be read by the recipient. In this paper we use a survival analysis framework to predict the time to open an email once it has been received. We use the Cox Proportional Hazards (CoxPH) model that offers a way to combine various features that might affect the event of opening an email. As an extension, we also apply a mixture model (MM) approach to CoxPH that distinguishes between recipients, based on a latent state of how prone to opening the messages each individual is. We compare our approach with standard classification and regression models. While the classification model provides predictions on the likelihood of an email being opened, the regression model provides prediction of the real-valued time to open. The use of survival analysis based methods allows us to jointly model both the open event as well as the time-to-open. We experimented on a large real-world dataset of marketing emails sent in a 3-month time duration. The mixture model achieves the best accuracy on our data where a high proportion of email messages go unopened.
Volumetric aberrancy to map subtle faults and flexures

AbstractOne of the key tasks of a seismic interpreter is to map lateral changes in surfaces, not only including faults, folds, and flexures, but also incisements, diapirism, and dissolution feature...
Photovoltaic inverter controllers seeking AC optimal power flow solutions

This paper considers future distribution networks featuring inverter-interfaced photovoltaic (PV) systems, and addresses the synthesis of feedback controllers that seek real- and reactive-power inverter setpoints corresponding to AC optimal power flow (OPF) solutions. The objective is to bridge the temporal gap between long-term system optimization and real-time inverter control, and enable seamless PV-owner participation without compromising system efficiency and stability. The design of the controllers is grounded on a dual ∊-subgradient method, and semidefinite programming relaxations are advocated to bypass the non-convexity of AC OPF formulations. Global convergence of inverter output powers is analytically established for diminishing stepsize rules for cases where: i) computational limits dictate asynchronous updates of the controller signals, and ii) inverter reference inputs may be updated at a faster rate than the power-output settling time.
Learning Heterogeneous Hidden Markov Random Fields

Hidden Markov random elds (HMRFs) are conventionally assumed to be homogeneous in the sense that the potential functions are invariant across dierent sites. However in some biological applications, it is desirable to make HMRFs heterogeneous, especially when there exists some background knowledge about how the potential functions vary. We formally dene heterogeneous HMRFs and propose an EM algorithm whose M-step combines a contrastive divergence learner with a kernel smoothing step to incorporate the background knowledge. Simulations show that our algorithm is eective for learning heterogeneous HMRFs and outperforms alternative binning methods. We learn a heterogeneous HMRF in a real-world study.
New Hampshire Effect: behavior in sequential and simultaneous multi-battle contests

Sequential multi-battle contests are predicted to induce lower expenditure than simultaneous contests. This prediction is a result of a “New Hampshire Effect”—a strategic advantage created by the winner of the first battle. Although our laboratory study provides evidence for the New Hampshire Effect, we find that sequential contests generate significantly higher (not lower) expenditure than simultaneous contests. This is mainly because in sequential contests, there is significant over-expenditure in all battles. We suggest sunk cost fallacy and utility of winning as two complementary explanations for this behavior and provide supporting evidence.
Insight in schizophrenia spectrum disorders: relationship with behavior, mood and perceived quality of life, underlying causes and emerging treatments

Poor insight in schizophrenia is prevalent across cultures and phases of illness. In this review, we examine the recent research on the relationship of insight with behavior, mood and perceived quality of life, on its complex roots, and on the effects of existing and emerging treatments. This research indicates that poor insight predicts poorer treatment adherence and therapeutic alliance, higher symptom severity and more impaired community function, while good insight predicts a higher frequency of depression and demoralization, especially when coupled with stigma and social disadvantage. This research also suggests that poor insight may arise in response to biological, experiential, neuropsychological, social-cognitive, metacognitive and socio-political factors. Studies of the effects of existing and developing treatments indicate that they may influence insight. In the context of earlier research and historical models, these findings support an integrative model of poor insight. This model suggests that insight requires the integration of information about changes in internal states, external circumstances, others' perspectives and life trajectory as well as the multifaceted consequences and causes of each of those changes. One implication is that treatments should, beyond providing education, seek to assist persons with schizophrenia to integrate the broad range of complex and potentially deeply painful experiences which are associated with mental illness into their own personally meaningful, coherent and adaptive picture.
Regressive effects of regulation on wages

A growing body of literature analyzing the distributive consequences of regulation suggests that regulation may have particularly detrimental effects on lower-income households. Regulation can be regressive if it represents the preferences of the wealthy while imposing costs on all households. The specific channel through which regulation may impose costs on lower-income households is its effects on prices and wages. In this issue, Chambers et al. (Public Choice. https://doi.org/10.1007/s11127-017-0479-z , 2017) investigate the impact of regulation on prices. They find that regulation raises consumer prices; regulatory interventions therefore are regressive because lower income consumers tend to spend larger percentages of their budgets on regulated goods and services. In this paper, we seek to analyze the effect of regulation on wages across different income levels and occupations.
C*-algebras generated by partial isometries

We study the interconnection between directed graphs and operators on a Hilbert space. The intuition supporting this link is the following feature shared by partial isometries (as operators on a Hilbert space) on the one hand and edges in directed graphs on the other. A partial isometry a is an operator in a Hilbert space H, i.e., a:H→H which maps a (closed) subspace in H isometrically onto a generally different subspace. The respective subspaces are called the initial space and the final space of a. Denoting the corresponding (orthogonal) projections by p i and p f , note that a partial isometry a may be thought of as an edge from one vertex to another (which are not necessarily distinct) in a directed graph. And conversely, every directed graph has such a representation. Since neither the partial isometries nor the directed edges in a fixed model allow unrestricted composition, the algebraic construct which is useful is that of a groupoid. In this paper we develop this as a representation theory, and we explore the connection between realizations in the context of C *-algebras. The building blocks in our theory are certain matricial C *-algebras which we define. We then prove how they serve to localize our global representations.
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs

Neural programming involves training neural networks to learn programs, mathematics, or logic from data. Previous works have failed to achieve good generalization performance, especially on problems and programs with high complexity or on large domains. This is because they mostly rely either on black-box function evaluations that do not capture the structure of the program, or on detailed execution traces that are expensive to obtain, and hence the training data has poor coverage of the domain under consideration. We present a novel framework that utilizes black-box function evaluations, in conjunction with symbolic expressions that define relationships between the given functions. We employ tree LSTMs to incorporate the structure of the symbolic expression trees. We use tree encoding for numbers present in function evaluation data, based on their decimal representation. We present an evaluation benchmark for this task to demonstrate our proposed model combines symbolic reasoning and function evaluation in a fruitful manner, obtaining high accuracies in our experiments. Our framework generalizes significantly better to expressions of higher depth and is able to fill partial equations with valid completions.
Remembrance of Cars and Buses Past: How Prior Life Experiences Influence Travel:

Does living in a neighborhood with high-quality public transit influence travel behavior later in life, even if you move to a neighborhood with worse transit service? To test this, we construct res...
Steroid-free three-drug maintenance regimen for pancreas transplant alone: Comparison of induction with rabbit antithymocyte globulin +/- rituximab.

Graft survival following pancreas transplant alone (PTA) is inferior to other pancreas transplants. Steroid elimination is appealing, but a two-drug maintenance strategy may be inadequate. Additionally, recipients tend to have diabetic nephropathy and do not tolerate nephrotoxic medications. A three-drug maintenance strategy permits immunosuppression through different mechanisms as well as an opportunity to use lower doses of the individual medications. Induction consisted of five doses of rabbit antithymocyte globulin (1 mg/kg/dose). As of October 2007, a single dose of rituximab (150 mg/m2 ) was added. Maintenance consisted of tacrolimus, sirolimus and mycophenolate mofetil. From 2004 to 2017, 166 PTA were performed. Graft loss at 7 and 90 days were 4% and 5%, and 1-year patient and graft survival were 97% and 91%. Comparing induction without and with rituximab, there was no significant difference in 7- or 90-day graft loss, 1-year patient or graft survival, or in the rate of rejection or infection. Rabbit antithymocyte globulin induction and steroid withdrawal followed by a three-drug immunosuppression regimen is an excellent strategy for PTA recipients.
Dead family working

Abstract In their recent book, Dead Man Working, Carl Cederstrom and Peter Fleming paint a haunting picture of the contemporary employee: sleep deprived and overworked, exhausted and strung out, unable to tell where work ends and where life begins, hardly alive and yet unable to die. In this paper, the author widens the picture by examining the systemic effects of contemporary work on the family. Drawing upon ideas from psychoanalysis and critical theory, the author reveals how the extraction of life by work reverberates across generations and seeps into the home environment. The author also reveals how new constellations of family reinforce deadening work. What emerges is a family portrait known as the “dead family working.”
Improved Magnus Form Approximation of Saturation Vapor Pressure

Algorithms, based on Magnus's form equations, are described that minimize the difference between several relationships between temperature and water vapor pressure at saturation that are commonly used in archiving data. The work was initiated in connection with the development of a unified upper-air dataset that will use measurements gathered from the late 1930s to the present and archived in several data centers. The conversion of field measurements to archived humidity values within the databases that are being used have not been consistent and in some cases are unknown. A goal of this work was to develop a uniform and accurate method to convert these data to various humidity variables without regard to the equations used in archiving the original data. Archived temperature values are recorded to 0.1°C. This precision creates a temperature dependent range in uncertainty in saturation vapor pressure. A procedure was developed to take this into account when the error minimizing equations were derived.
A New Backpressure Algorithm for Joint Rate Control and Routing With Vanishing Utility Optimality Gaps and Finite Queue Lengths

The backpressure algorithm has been widely used as a distributed solution to the problem of joint rate control and routing in multi-hop data networks. By controlling an algorithm parameter, the backpressure algorithm can achieve an arbitrarily small utility optimality gap. However, this in turn brings in a large queue length at each node and hence causes large network delay. This phenomenon is known as the fundamental utility-delay tradeoff. The best known utility-delay tradeoff for general networks is $[O(\epsilon ), O(1/\epsilon )]$ and is attained by a backpressure algorithm based on a drift-plus-penalty technique. This may suggest that to achieve an arbitrarily small utility optimality gap, backpressure-based algorithms must incur arbitrarily large queue lengths. However, this paper proposes a new backpressure algorithm that has a vanishing utility optimality gap, so utility converges to exact optimality as the algorithm keeps running, while queue lengths are bounded throughout by a finite constant. The technique uses backpressure and drift concepts with a new method for convex programming.
Subgroup identification using covariate‐adjusted interaction trees

We consider the problem of identifying subgroups of participants in a clinical trial that have enhanced treatment effect. Recursive partitioning methods that recursively partition the covariate space based on some measure of between groups treatment effect difference are popular for such subgroup identification. The most commonly used recursive partitioning method, the classification and regression tree algorithm, first creates a large tree by recursively partitioning the covariate space using some splitting criteria and then selects the final tree from all the subtrees of the large tree. In the context of subgroup identification, calculation of the splitting criteria and the evaluation measure used for final tree selection rely on comparing differences in means between the treatment and control arm. When covariates are prognostic for the outcome, covariate adjusted estimators have the ability to improve efficiency compared to using differences in averages between the treatment and control group. This manuscript develops two covariate adjusted estimators that can be used to both make splitting decisions and for final tree selection. The performance of the resulting covariate adjusted recursive partitioning algorithm is evaluated using simulations and by analyzing a clinical trial that evaluates if motivational interviews improve treatment engagement for substance abusers.
Robust Set Reconciliation via Locality Sensitive Hashing

We consider variations of set reconciliation problems where two parties, Alice and Bob, each hold a set of points in a metric space, and the goal is for Bob to conclude with a set of points that is close to Alice's set of points in a well-defined way. This setting has been referred to as robust set reconciliation. In one variation, the goal is for Bob to end with a set of points that is close to Alice's in earth mover's distance, and in another the goal is for Bob to have a point that is close to each of Alice's. The first problem has been studied before; while previous results achieved an $O(d)$ approximation, where d is the dimension of the space, we achieve an $O(log n)$ approximation, where n is the number of points. The second problem appears new, and here we find schemes that, under certain conditions, use sublinear communication. Our primary novelty is utilizing Invertible Bloom Lookup Tables in combination with locality sensitive hashing. This combination allows us to cope with the geometric setting in a communication-efficient manner.
Violence while in Utero: The Impact of Assaults During Pregnancy on Birth Outcomes

We study the effects of prenatal exposure to violent crime on infant health, using New York City crime records linked to mothers' addresses in birth records data. We address endogeneity of assault ...
Towards Composable Bias Rating of AI Services

A new wave of decision-support systems are being built today using AI services that draw insights from data (like text and video) and incorporate them in human-in-the-loop assistance. However, just as we expect humans to be ethical, the same expectation needs to be met by automated systems that increasingly get delegated to act on their behalf. A very important aspect of an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias occurs when the data distribution is not representative enough of the natural phenomenon one wants to model and reason about. The possibly biased behavior of a service is hard to detect and handle if the AI service is merely being used and not developed from scratch, since the training data set is not available. In this situation, we envisage a 3rd party rating agency that is independent of the API producer or consumer and has its own set of biased and unbiased data, with customizable distributions. We propose a 2-step rating approach that generates bias ratings signifying whether the AI service is unbiased compensating, data-sensitive biased, or biased. The approach also works on composite services. We implement it in the context of text translation and report interesting results.
From the Special Issues Editor

(1997). From the Special Issues Editor. Journal of Public Administration Education: Vol. 3, No. 2, pp. 217-217.
PhaseLink: A Deep Learning Approach to Seismic Phase Association

Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks and underlies most seismicity catalogs produced around the world. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. We present PhaseLink, a framework based on recent advances in deep learning for grid‐free earthquake phase association. Our approach learns to link phases together that share a common origin and is trained entirely on millions of synthetic sequences of P and S wave arrival times generated using a 1‐D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real‐time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training data set. We demonstrate the state‐of‐the‐art performance of PhaseLink on a challenging sequence from southern California and synthesized sequences from Japan designed to test the point at which the method fails. For the examined data sets, PhaseLink can precisely associate phases to events that occur only ∼12 s apart in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real‐time seismic monitoring, and streamline automated processing of large seismic data sets.
Oxidative Post-translational Modifications Accelerate Proteolytic Degradation of Calprotectin.

Oxidative post-translational modifications affect the structure and function of many biomolecules. Herein we examine the biophysical and functional consequences of oxidative post-translational modifications to human calprotectin (CP, S100A8/S100A9 oligomer, MRP8/MRP14 oligomer, calgranulins A/B oligomer). This abundant metal-sequestering protein contributes to innate immunity by starving invading microbial pathogens of transition metal nutrients in the extracellular space. It also participates in the inflammatory response. Despite many decades of study, little is known about the fate of CP at sites of infection and inflammation. We present compelling evidence for methionine oxidation of CP in vivo, supported by using 15N-labeled CP-Ser (S100A8(C42S)/S100A9(C3S)) to monitor for adventitious oxidation following human sample collection. To elucidate the biochemical and functional consequences of oxidative post-translational modifications, we examine recombinant CP-Ser with methionine sulfoxide modifications ...
Meet cyrus: the query by voice mobile assistant for the tutoring and formative assessment of SQL learners

Being declarative, SQL stands a better chance at being the programming language for conceptual computing next to natural language programming. We examine the possibility of using SQL as a back-end for natural language database programming. Distinctly from keyword based SQL querying, keyword dependence and SQL's table structure constraints are significantly less pronounced in our approach. We present a mobile device voice query interface, called Cyrus, to arbitrary relational databases. Cyrus supports a large type of query classes, sufficient for an entry level database class. Cyrus is also application independent, allows test database adaptation, and not limited to specific sets of keywords or natural language sentence structures. It's cooperative error reporting is more intuitive, and iOS based mobile platform is also more accessible compared to most contemporary mobile and voice enabled systems.
Precipitation Prediction Skill for the West Coast United States: From Short to Extended Range

AbstractPrecipitation variability significantly influences the heavily populated West Coast of the United States, raising the need for reliable predictions. We investigate the region’s short- to ex...
Static ecological system analysis

In this article, a new mathematical method for static analysis of compartmental systems is developed in the context of ecology. The method is based on the novel system and subsystem partitioning methodologies through which compartmental systems are decomposed to the utmost level. That is, the distribution of environmental inputs and intercompartmental system flows as well as the organization of the associated storages generated by these flows within the system is determined individually and separately. Moreover, the transient and the static direct, indirect, acyclic, cycling, and transfer (diact) flows and associated storages transmitted along a given flow path or from one compartment, directly or indirectly, to any other are analytically characterized, systematically classified, and mathematically formulated. A quantitative technique for the categorization of interspecific interactions and the determination of their strength within food webs is also developed based on the diact transactions. The proposed methodology allows for both input- and output-oriented analyses of static ecological networks. The input- and output-oriented analyses are introduced within the proposed mathematical framework and their duality is demonstrated. Major flow- and stock-related concepts and quantities of the current static network analyses are also integrated with the proposed measures and indices within this unifying framework. This comprehensive methodology enables a holistic view and analysis of ecological systems.
The Primacy of “What” over “How Much”: How Type and Quantity Shape Healthiness Perceptions of Food Portions

Healthy eating goals influence many consumer choices, such that evaluating the healthiness of food portions is important. Given that both the type and quantity of food jointly contribute to weight ...
Introducing THOR, a Model Microbiome for Genetic Dissection of Community Behavior.

ABSTRACT The quest to manipulate microbiomes has intensified, but many microbial communities have proven to be recalcitrant to sustained change. Developing model communities amenable to genetic dissection will underpin successful strategies for shaping microbiomes by advancing an understanding of community interactions. We developed a model community with representatives from three dominant rhizosphere taxa, the Firmicutes, Proteobacteria, and Bacteroidetes. We chose Bacillus cereus as a model rhizosphere firmicute and characterized 20 other candidates, including “hitchhikers” that coisolated with B. cereus from the rhizosphere. Pairwise analysis produced a hierarchical interstrain-competition network. We chose two hitchhikers, Pseudomonas koreensis from the top tier of the competition network and Flavobacterium johnsoniae from the bottom of the network, to represent the Proteobacteria and Bacteroidetes, respectively. The model community has several emergent properties, induction of dendritic expansion of B. cereus colonies by either of the other members, and production of more robust biofilms by the three members together than individually. Moreover, P. koreensis produces a novel family of alkaloid antibiotics that inhibit growth of F. johnsoniae, and production is inhibited by B. cereus. We designate this community THOR, because the members are the hitchhikers of the rhizosphere. The genetic, genomic, and biochemical tools available for dissection of THOR provide the means to achieve a new level of understanding of microbial community behavior. IMPORTANCE The manipulation and engineering of microbiomes could lead to improved human health, environmental sustainability, and agricultural productivity. However, microbiomes have proven difficult to alter in predictable ways, and their emergent properties are poorly understood. The history of biology has demonstrated the power of model systems to understand complex problems such as gene expression or development. Therefore, a defined and genetically tractable model community would be useful to dissect microbiome assembly, maintenance, and processes. We have developed a tractable model rhizosphere microbiome, designated THOR, containing Pseudomonas koreensis, Flavobacterium johnsoniae, and Bacillus cereus, which represent three dominant phyla in the rhizosphere, as well as in soil and the mammalian gut. The model community demonstrates emergent properties, and the members are amenable to genetic dissection. We propose that THOR will be a useful model for investigations of community-level interactions.
Associations between neuropsychiatric and health status outcomes in individuals with probable mTBI.

Abstract Mild traumatic brain injury (mTBI) is a common occurrence, and may impact distal outcomes in a subgroup of individuals. Improved characterization of health outcomes and identification of factors associated with poor outcomes is needed to better understand the impact of mTBI, particularly in those with co-occurring posttraumatic stress disorder (PTSD). Participants in a data repository of the Injury and Traumatic Stress (INTRuST) Clinical Consortium (n = 625) completed functional disability [FD] and health-related quality of life [HRQOL] questionnaires, and a subset completed a neuropsychological assessment. FD and HRQOL were compared among participants with probable mTBI (mTBI), probable mTBI with PTSD (mTBI/PTSD), and health comparison participants (HC). Associations between symptoms, neuropsychological performance, and health outcomes were examined in those with probable mTBI with and without PTSD (n = 316). Individuals in the mTBI/PTSD group endorsed poorer health outcomes than those in the mTBI group, who endorsed poorer outcomes than those in the HC group. Individuals in either mTBI group performed worse than those in the HC on verbal learning and memory and psychomotor speed. Health outcomes were correlated with mental health and postconcussive symptoms, as well as neuropsychological variables. mTBI may adversely impact self-reported health, with the greatest effect observed in individuals with co-occurring mTBI/PTSD.
The Goldilocks Effect of Strategic Human Resource Management? Optimizing the Benefits of a High-Performance Work System Through the Dual Alignment of Vertical and Horizontal Fit

Although vertical and horizontal fit in strategic human resource management are foundational to the links between a high-performance work system (HPWS) and organizational performance, little is kno...
Lyapunov inequalities for nabla Caputo boundary value problems

ABSTRACTWe will establish uniqueness of solutions to boundary value problems involving the nabla Caputo fractional difference under two-point boundary conditions and give an explicit expression for...
One-shot generation of near-optimal topology through theory-driven machine learning

Abstract We introduce a theory-driven mechanism for learning a neural network model that performs generative topology design in one shot given a problem setting, circumventing the conventional iterative process that computational design tasks usually entail. The proposed mechanism can lead to machines that quickly respond to new design requirements based on its knowledge accumulated through past experiences of design generation. Achieving such a mechanism through supervised learning would require an impractically large amount of problem–solution pairs for training, due to the known limitation of deep neural networks in knowledge generalization. To this end, we introduce an interaction between a student (the neural network) and a teacher (the optimality conditions underlying topology optimization): The student learns from existing data and is tested on unseen problems. Deviation of the student’s solutions from the optimality conditions is quantified, and used for choosing new data points to learn from. We call this learning mechanism “theory-driven”, as it explicitly uses domain-specific theories to guide the learning, thus distinguishing itself from purely data-driven supervised learning. We show through a compliance minimization problem that the proposed learning mechanism leads to topology generation with near-optimal structural compliance, much improved from standard supervised learning under the same computational budget.
Mechanical bound states in the continuum for macroscopic optomechanics

Bound states in the continuum (BICs), an emerging type of long-lived resonances different from the cavity-based ones, have been explored in several classical systems, including photonic crystals and surface acoustic waves. Here, we reveal symmetry-protected mechanical BICs in the structure of slab-on-substrate optomechanical crystals. Using a group theory approach, we identified all the mechanical BICs at the Γ point in optomechanical crystals with C4v and C6v symmetries as examples, and analyzed their coupling with the co-localized optical BICs and guided resonances due to both moving boundary and photo-elastic effects. We verified the theoretical analysis with numerical simulations of specific optomechanical crystals which support substantial optomechanical interactions between the mechanical BICs and optical resonances. Due to the unique features of high-Q, large-size mechanical BICs and substrate-enabled thermal dissipation, this architecture of slab-on-substrate optomechanical crystals might be useful for exploring macroscopic quantum mechanical physics and enabling new applications such as high-throughput sensing and free-space beam steering.
Is a Picture Worth a Thousand Words? Effectiveness of iPad Technology in Preclinical Dental Laboratory Courses.

Dental educators should consider alternative modalities of instruction when experiencing difficulties conveying feedback to students. The aim of this study was to determine if integrating iPad technology as a visual learning tool would enhance the exchange of assessment information and improve academic performance in Indiana University School of Dentistry’s preclinical curriculum. In 2016, the first-year Tooth Morphology (TM) and second-year Fixed Prosthodontics (Fixed) courses implemented a project using iPad images that allowed instructors to annotate acceptable and deficient areas of students’ tooth wax-ups and preparations. In the two courses, all students (TM n=106 and Fixed n=105) and instructors (TM n=21 and Fixed n=17) were given pre-intervention surveys to report their perceived effectiveness of verbal feedback and were given post-intervention surveys to rate their experiences with iPad image feedback. Response rates for students in the two courses on the pre surveys were TM 87.7% and Fixed 85.7% and on the post surveys were TM 26.4% and Fixed 76.2%. Response rates for instructors on the pre surveys were TM 52.4% and Fixed 82.4% and on the post surveys were TM 76.2% and Fixed 76.5%. The results showed that a majority of both groups preferred the combination of verbal and iPad image feedback: 53% of responding students in TM and 51% in Fixed, and 75% of instructors in TM and 77% in Fixed. In the TM course, responding instructors had a statistically significantly higher agreement than students that feedback with iPad images was superior to verbal feedback alone (p=0.008). Furthermore, a multi-year analysis of TM practical examination grades found statistically significant lower change scores for the first and second exams in 2014 and 2015 compared to the 2016 scores when the iPad intervention occurred. These results suggest that verbal feedback combined with iPad images resulted in an enhanced exchange of information and increased student grades, particularly in the first-year dental curriculum.
Modulation of Cholinergic Neurotransmission in Canine Airways by Thromboxane-Mimetic, U46619

Abstract We studied the effect of a thromboxane A 2 -mimetic, U46619, on the contractile responses of canine bronchial smooth muscle to cholinergic stimulation in vitro. U46619 (3 × 10 −10 M), at a concentration that did not cause contraction, enhanced the effect of electrical field stimulation at all frequencies; histamine (10 −7 M) and prostaglandin F 2α (10 −7 M) did not. U46619 (3 × 10 −10 M) did not affect methacholine-induced contractions. U46619 may therefore increase the prejunctional release of acetylcholine.
Metallic glasses for biodegradable implants

Abstract Metallic glasses are excellent candidates for biomedical implant applications due to their inherent strength and corrosion resistance. However, use of metallic glasses in structural applications is limited because bulk dimensions are challenging to achieve. Glass-forming ability (GFA) varies strongly with alloy composition and becomes more difficult to predict as the number of chemical species in a system increases. Here, we present a theoretical model — implemented in the AFLOW framework — for predicting GFA based on the competition between crystalline phases. The model is applied to biologically relevant binary and ternary systems. Elastic properties of Ca- and Mg-based systems are estimated for use in biodegradable orthopedic support applications. Alloys based on Ag0.33Mg0.67, Cu0.5Mg0.5, Cu0.37Mg0.63, and Cu0.25Mg0.5Zn0.25, and in the Ag-Ca-Mg and Ag-Mg-Zn systems, are recommended for further study.
Famine and the Aid Response : Evidence from the Announcement of Famine-Like Conditions in the Republic of Yemen

Using a high-frequency mobile phone survey of food security conducted by the World Food Programme, this paper investigates how food assistance and access to food changed following the announcement of famine-like conditions in the Republic of Yemen. Among the mobile phone–using population, the share of households receiving food assistance more than doubled following the announcement. The increases were largely targeted at regions identified in the announcement as being closer to famine in the original announcement, and there was improvement in access to food in regions that received the most food assistance relative to the rest of the country. Although the survey misses struggling households that do not have access to a mobile phone and are potentially more at risk of famine, the results raise questions about the need for better quality data in food emergencies that are updated more regularly for better targeting of food assistance.
Postoperative renal impairment is associated with increased length of stay for incisional hernia repair after liver transplantation.

Abstract Background Incisional hernia repair is the most common procedure after orthotopic liver transplantation. Although enhanced recovery protocols are increasingly employed, the post–orthotopic liver transplantation patient may not benefit from all aspects of these models. The aim of the present study is to assess which perioperative interventions and patient factors affect hospital length of stay in a cohort of post–orthotopic liver transplantation patients undergoing incisional hernia repair. Methods We conducted a retrospective review of a series of adult patients undergoing incisional hernia repair after orthotopic liver transplantation. The primary endpoint was length of stay. Results were stratified by demographic, intraoperative, and postoperative variables. Results Eleven percent (172/1523) of patients who received orthotopic liver transplantation during the study period underwent subsequent incisional hernia repair. Median length of stay was 5 days (range 2–50). The strongest predictor of length of stay was postoperative renal function. Despite liberal intraoperative administration of volume (median 642 mL/h) and brisk intraoperative urine output (median 72 mL/h), postoperative acute kidney injury occurred in 48% of patients. Those that developed acute kidney injury received less intraoperative volume (6 vs 8.5 mL/kg/h; P = .031) and the severity of postoperative renal injury was inversely related to the amount intraoperative volume given. Conclusions In patients undergoing incisional hernia repair after orthotopic liver transplantation, postoperative renal function is frequently impaired. Although many aspects of current ERAS protocols may be applied to post-transplant patients, restrictive intraoperative fluid administration strategies should be employed with caution given a high propensity for the development of post-operative acute kidney injury in this complex population.
Nonideal Justice as Nonideal Fairness

This article argues that diverse theorists have reasons to theorize about fairness in nonideal conditions, including theorists who reject fairness in ideal theory. It then develops a new all-purpose model of ‘nonideal fairness’. Section 1 argues that fairness is central to nonideal theory across diverse ideological and methodological frameworks. Section 2 then argues that ‘nonideal fairness’ is best modeled by a nonideal original position adaptable to different nonideal conditions and background normative frameworks (including anti-Rawlsian ones). Section 3 argues that the parties to the model have grounds to seek a variety of remedial social, legal, cultural, and economic ‘nonideal primary goods’ for combating injustice as well as grounds to distribute these goods in an equitable and inclusive manner. Finally, I illustrate how the model indexes the nonideal primary goods it justifies to different nonideal contexts and background normative frameworks, illustrating why diverse theorists should find the model and its output principles attractive.
Current models of speech motor control: A control-theoretic overview of architectures and properties

This paper reviews the current state of several formal models of speech motor control, with particular focus on the low-level control of the speech articulators. Further development of speech motor control models may be aided by a comparison of model attributes. The review builds an understanding of existing models from first principles, before moving into a discussion of several models, showing how each is constructed out of the same basic domain-general ideas and components-e.g., generalized feedforward, feedback, and model predictive components. This approach allows for direct comparisons to be made in terms of where the models differ, and their points of agreement. Substantial differences among models can be observed in their use of feedforward control, process of estimating system state, and method of incorporating feedback signals into control. However, many commonalities exist among the models in terms of their reliance on higher-level motor planning, use of feedback signals, lack of time-variant adaptation, and focus on kinematic aspects of control and biomechanics. Ongoing research bridging hybrid feedforward/feedback pathways with forward dynamic control, as well as feedback/internal model-based state estimation, is discussed.