cssi/SciSciGPT-SciSciNet huggingface dataset:
Assessing the Usage of Indirect Motor Pathways Following a Hemiparetic Stroke

A hallmark impairment in a hemiparetic stroke is a loss of independent joint control resulting in abnormal co-activation of shoulder abductor and elbow flexor muscles in their paretic arm, clinically known as the flexion synergy. The flexion synergy appears while generating shoulder abduction (SABD) torques as lifting the paretic arm. This likely be caused by an increased reliance on contralesional indirect motor pathways following damage to direct corticospinal projections. The assessment of functional connectivity between brain and muscle signals, i.e., brain-muscle connectivity (BMC), may provide insight into such changes to the usage of motor pathways. Our previous model simulation shows that multi-synaptic connections along the indirect motor pathway can generate nonlinear connectivity. We hypothesize that increased usage of indirect motor pathways (as increasing SABD load) will lead to an increase of nonlinear BMC. To test this hypothesis, we measured brain activity, muscle activity from shoulder abductors when stroke participants generate 20% and 40% of maximum SABD torque with their paretic arm. We computed both linear and nonlinear BMC between EEG and EMG. We found dominant nonlinear BMC at contralesional/ipsilateral hemisphere for stroke, whose magnitude increased with the SABD load. These results supported our hypothesis and indicated that nonlinear BMC could provide a quantitative indicator for determining the usage of indirect motor pathways following a hemiparetic stroke.
Evaluating the Effect of Longitudinal Dose and INR Data on Maintenance Warfarin Dose Predictions

Warfarin, a commonly prescribed drug to prevent blood clots, has a highly variable individual response. Determining a maintenance warfarin dose that achieves a therapeutic blood clotting time, as measured by the international normalized ratio (INR), is crucial in preventing complications. Machine learning algorithms are increasingly being used for warfarin dosing; usually, an initial dose is predicted with clinical and genotype factors, and revised after a few days based on previous doses and current INR. Since sequential dose and INR better capture individual differences in warfarin response, we hypothesized that longitudinal dose response data will improve maintenance dose predictions. To test this hypothesis, we analyzed a dataset from the COAG warfarin dosing clinical trial, which includes clinical data, warfarin dosing history and INR measurements over the study period, and maintenance dose when therapeutic INR was achieved. Various regression models were trained to predict the maintenance warfarin dose using clinical factors, warfarin dosing history and INR data. Dose revision algorithms that used a single dose and INR measurement achieved comparable performance as the baseline revision algorithm. In contrast, dose revision algorithms that used longitudinal dose and INR data predicted doses that were statistically significantly much closer to the true maintenance dose. With the best performing model, gradient boosting (GB), the proportion of ideal estimated dose, i.e., within ±20% of the true dose, increased from baseline (54.92%) to that with GB with single (63.11%) and longitudinal (75.41%) INR. More accurate maintenance dose predictions with longitudinal dose response data can potentially achieve therapeutic INR faster, reduce drug-related complications and improve patient outcomes with warfarin therapy.
On the mean-field equations for ferromagnetic spin systems

We derive mean-field equations for a general class of ferromagnetic spin systems with an explicit error bound in finite volumes. The proof is based on a link between the mean-field equation and the free convolution formalism of random matrix theory, which we exploit in terms of a dynamical method. We present three sample applications of our results to Kac interactions, randomly diluted models, and models with an asymptotically vanishing external field.
Mechanical Behavior of Axonal Actin, Spectrin, and Their Periodic Structure: A Brief Review

Actin and spectrin are important constituents of axonal cytoskeleton. Periodic actin-spectrin structures are found in dendrites, initial segment of axon, and main axon. Actin-spectrin periodicity has been hypothesized to be manipulating the axon stability and mechanical behavior. Several experimental and computational studies have been performed focusing on the mechanical behavior of actin, spectrin, and actin-spectrin network. However, most of the actin studies focus on typical long F-actin and do not provide quantitative comparison between the mechanical behavior of short and long actin filaments. Also, most of the spectrin studies focus on erythrocytic spectrin and do not shed light on the behavior of structurally different axonal spectrin. Only a few studies have highlighted forced unfolding of axonal spectrin which are relevant to brain injury scenario. A comprehensive, strain rate dependent mechanical study is still absent in the literature. Moreover, the current opinions regarding periodic actin-spectrin network structure in axon are disputed due to conflicting results on actin ring organization—as argued by recent super-resolution microscopy studies. This review summarizes the ongoing limitations in this regard and provides insights on possible approaches to address them. This study will invoke further investigation into relevant high strain rate response of actin, spectrin, and actin-spectrin network—shedding light into brain pathology scenario such as traumatic brain injury (TBI).
Probing interactions of therapeutic antibodies with serum via second virial coefficient measurements.

Abstract Antibody-based therapeutics are the fastest-growing drug class on the market, used to treat aggressive forms of cancer, chronic autoimmune conditions, and numerous other disease states. Although the specificity, affinity, and versatility of therapeutic antibodies can provide an advantage over traditional small-molecule drugs, their development and optimization can be much more challenging and time-consuming. This is, in part, because the ideal formulation buffer systems used for in vitro characterization inadequately reflect the crowded biological environments (serum, endosomal lumen, etc.) that these drugs experience once administered to a patient. Such environments can perturb the binding of antibodies to their antigens and receptors, as well as homo- and hetero-aggregation, thereby altering therapeutic effect and disposition in ways that are incompletely understood. Although excluded volume effects are classically thought to favor binding, weak interactions with co-solutes in crowded conditions can inhibit binding. The second virial coefficient (B2) parameter quantifies such weak interactions and can be determined by a variety of techniques in dilute solution, but analogous methods in complex biological fluids are not well established. Here, we demonstrate that fluorescence correlation spectroscopy is able to measure diffusive B2-values directly in undiluted serum. Apparent second virial coefficient (B2,app) measurements of antibodies in serum reveal that changes in the balance between attractive and repulsive interactions can dramatically impact global nonideality. Furthermore, our findings suggest that the approach of isolating specific components and completing independent cross-term virial coefficient measurements may not be an effective approach to characterizing nonideality in serum. The approach presented here could enrich our understanding of the effects of biological environments on proteins in general and advance the development of therapeutic antibodies and other protein-based therapeutics.
Engineering the Optical Vacuum: Arbitrary Magnitude, Sign, and Order of Dispersion in Free Space Using Space–Time Wave Packets

Spatial structuring of an optical pulse can lead in some cases upon free propagation to changes in its temporal profile. For example, introducing conventional angular dispersion into the field results in the pulse encountering group-velocity dispersion in free space. However, only limited control is accessible via this strategy. Here we show that precise and versatile control can be exercised in free space over the dispersion profile of so-called `space-time' wave packets: a class of pulsed beams undergirded by non-differentiable angular dispersion. This abstract mathematical feature allows us to tune the magnitude and sign of the different dispersion orders without introducing losses, thereby realizing arbitrary dispersion profiles, and achieving dispersion values unattainable in optical materials away from resonance. Unlike optical materials and photonic structures in which the values of the different dispersion orders are not independent of each other, these orders are addressable separately using our strategy. These results demonstrate the versatility of space-time wave packets as a platform for structured light and point towards their utility in nonlinear and quantum optics.
Deep learning to accelerate Maxwell’s equations for inverse design of dielectric metasurfaces

We present a data-driven forward simulation framework for the inverse design of metasur- faces that is more accurate than methods based on the local phase approximation, a factor of 104 times faster than mesh based solvers.
Spillovers from Online Engagement: How a Newspaper Subscriber’s Activation of Digital Paywall Access Affects Her Retention and Subscription Revenue

Newspapers are increasingly reliant on subscription revenue as advertising spend shifts to online platforms. Many newspapers have implemented paywalls in an attempt to boost subscription revenue. W...
Fixed- and Partial-Node Approximations in Slater Determinant Space for Molecules.

We present a study of fixed- and partial-node approximations in Slater determinant basis sets, using full configuration interaction quantum Monte Carlo (FCIQMC) to perform sampling. Walker annihilation in the FCIQMC method allows partial-node simulations to be performed, relaxing the nodal constraint to converge to the FCI solution. This is applied to ab initio molecular systems, using symmetry-projected Jastrow mean-field wave functions for complete active space (CAS) problems. Convergence and the sign problem within the partial-node approximation are studied, which is shown to eventually be limited in its use due to the large walker populations required. However, the fixed-node approximation results in an accurate and practical method. We apply these approaches to various molecular systems and active spaces, including ferrocene and acenes. This also provides a test of symmetry-projected Jastrow mean-field wave functions in variational Monte Carlo for a new set of problems. For trans-polyacetylene molecules and acenes, we find that the time to perform a constant number of fixed-node FCIQMC iterations scales as O(N1.44) and O(N1.75), respectively, resulting in an efficient method for CAS-based problems that can be applied accurately to large active spaces.
New-onset seizures after bamlanivimab infusion.

Since the beginning of the COVID-19 pandemic, industry and healthcare providers have investigated methodologies to manage infection of SARS-CoV-2. One treatment breakthrough has been the introduction of monoclonal antibodies to prevent worsening SARS-CoV-2 infection in non-hospitalized patients diagnosed with COVID-19. These monoclonal antibodies, like bamlanivimab, bind to the SARS-CoV-2 spike protein and prevent its ability to binding to human ACE2 receptors. This is a case of a 91 year-old man with no prior seizure history who developed new-onset seizures after bamlanivimab infusion.
Treatment Patterns, Outcomes, and Costs Associated With Localized Upper Tract Urothelial Carcinoma.

Background Upper tract urothelial carcinoma (UTUC) is a heterogeneous disease that presents a clinical management challenge for the urologic surgeon. We assessed treatment patterns, costs, and survival outcomes among patients with nonmetastatic UTUC. Methods We identified 4114 patients diagnosed with nonmetastatic UTUC from 2004 to 2013 in the Survival Epidemiology, and End Results-Medicare population-based database. Patients were stratified into low- or high-risk disease groups. Median total costs from 30 days prior to diagnosis through 365 days after diagnosis were compared between groups. Overall and cancer-specific survival were evaluated using Cox proportional hazards regression. All statistical tests were 2-sided. Results After risk stratification, 1027 (24.9%) and 3087 (75.0%) patients were classified into low- vs high-risk UTUC groups. Most patients underwent at least 1 surgical intervention (95.1%); 68.4% underwent at least 1 endoscopic intervention. Patients diagnosed with high- vs low-risk UTUC were more likely to undergo nephroureterectomy (83.6% vs 72.0%; P < .001); few patients with low-risk disease were exclusively managed endoscopically (16.9%). At 365 days after diagnosis, costs of care for high- vs low-risk UTUC were statistically significantly higher ($108 520 vs $91 233; median difference $16 704, 95% confidence interval [CI] = $11 619 to $21 778; P < .001). Those with high-risk UTUC had worse cancer-specific and overall survival compared with patients with low-risk UTUC (cancer-specific survival hazard ratio [HR] = 4.14, 95% CI = 3.19 to 5.37; overall survival HR = 1.78, 95% CI = 1.62 to 1.96). Conclusions UTUC continues to be managed primarily with nephroureterectomy, regardless of risk stratification, and patients with high-risk UTUC have worse overall and cancer-specific survival. Substantial costs are associated with management of low- and high-risk UTUC, with the latter being more costly up to 1 year from diagnosis.
Attention-Based Multi-scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction

COVID-19 image analysis has mostly focused on diagnostic tasks using single timepoint scans acquired upon disease presentation or admission. We present a deep learning-based approach to predict lung infiltrate progression from serial chest radiographs (CXRs) of COVID-19 patients. Our method first utilizes convolutional neural networks (CNNs) for feature extraction from patches within the concerned lung zone, and also from neighboring and remote boundary regions. The framework further incorporates a multi-scale Gated Recurrent Unit (GRU) with a correlation module for effective predictions. The GRU accepts CNN feature vectors from three different areas as input and generates a fused representation. The correlation module attempts to minimize the correlation loss between hidden representations of concerned and neighboring area feature vectors, while maximizing the loss between the same from concerned and remote regions. Further, we employ an attention module over the output hidden states of each encoder timepoint to generate a context vector. This vector is used as an input to a decoder module to predict patch severity grades at a future timepoint. Finally, we ensemble the patch classification scores to calculate patient-wise grades. Specifically, our framework predicts zone-wise disease severity for a patient on a given day by learning representations from the previous temporal CXRs. Our novel multi-institutional dataset comprises sequential CXR scans from N = 93 patients. Our approach outperforms transfer learning and radiomic feature-based baseline approaches on this dataset.
Ultra high-dimensional multivariate posterior contraction rate under shrinkage priors

Abstract In recent years, shrinkage priors have received much attention in high-dimensional data analysis from a Bayesian perspective. Compared with widely used spike-and-slab priors, shrinkage priors have better computational efficiency. But the theoretical properties, especially posterior contraction rate, which is important in uncertainty quantification, are not established in many cases. In this paper, we apply global–local shrinkage priors to high-dimensional multivariate linear regression with unknown covariance matrix. We show that when the prior is highly concentrated near zero and has heavy tail, the posterior contraction rates for both coefficients matrix and covariance matrix are nearly optimal. Our results hold when number of features p grows much faster than the sample size n, which is of great interest in modern data analysis. We show that a class of readily implementable scale mixture of normal priors satisfies the conditions of the main theorem.
Decision Making in Joint Push-Grasp Action Space for Large-Scale Object Sorting

We present a planner for large-scale (un)labeled object sorting tasks, which uses two types of manipulation actions: overhead grasping and planar pushing. The grasping action offers completeness guarantee under mild assumptions, and the planar pushing is an acceleration strategy that moves multiple objects at once. We make two main contributions: (1) We propose a bilevel planning algorithm. Our high-level planner makes efficient, near-optimal choices between pushing and grasping actions based on a cost model. Our low-level planner computes one-step greedy pushing or grasping actions. (2) We propose a novel low-level push planner that can find one-step greedy pushing actions in a semi-discrete search space. The structure of the search space allows us to efficiently make decisions. We show that, for sorting up to 200 objects, our planner can find near-optimal actions within 10 seconds of computation on a desktop PC.
Faithful Edge Federated Learning: Scalability and Privacy

Federated learning enables machine learning algorithms to be trained over decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated learning requires ensuring that agents (e.g., mobile devices) faithfully execute the intended algorithm, which has been largely overlooked in the literature. In this study, we first use risk bounds to analyze how the key feature of federated learning, unbalanced and non-i.i.d. data, affects agents’ incentives to voluntarily participate and obediently follow traditional federated learning algorithms. To be more specific, our analysis reveals that agents with less typical data distributions and relatively more samples are more likely to opt out of or tamper with federated learning algorithms. To this end, we formulate the first faithful implementation problem of federated learning and design two faithful federated learning mechanisms which satisfy economic properties, scalability, and privacy. First, we design a Faithful Federated Learning (FFL) mechanism which approximates the Vickrey–Clarke–Groves (VCG) payments via an incremental computation. We show that it achieves (probably approximate) optimality, faithful implementation, voluntary participation, and some other economic properties (such as budget balance). Further, the time complexity in the number of agents $K$ is $\mathcal {O}(\log (K))$ . Second, by partitioning agents into several clusters, we present a scalable VCG mechanism approximation. We further design a scalable and Differentially Private FFL (DP-FFL) mechanism , the first differentially private faithful mechanism, that maintains the economic properties. Our DP-FFL mechanism enables one to make three-way performance tradeoffs among privacy, the iterations needed, and payment accuracy loss.
A generalization of decomposition in orbifolds

This paper describes a generalization of decomposition in orbifolds. In general terms, decomposition states that two-dimensional orbifolds and gauge theories whose gauge groups have trivially-acting subgroups decompose into disjoint unions of theories. However, decomposition can be, at least naively, broken in orbifolds if the orbifold has discrete torsion in the trivially-acting subgroup. (Formally, this breaks finite global one-form symmetries.) Nevertheless, even in such cases, one still sees rudiments of decomposition. In this paper, we generalize decomposition in orbifolds to include such examples of discrete torsion, which we check in numerous examples. Our analysis includes as special cases (and in one sense generalizes) quantum symmetries of abelian orbifolds.
Integrative structural dynamics probing of the conformational heterogeneity in synaptosomal-associated protein 25

Summary SNAP-25 (synaptosomal-associated protein of 25 kDa) is a prototypical intrinsically disordered protein (IDP) that is unstructured by itself but forms coiled-coil helices in the SNARE complex. With high conformational heterogeneity, detailed structural dynamics of unbound SNAP-25 remain elusive. Here, we report an integrative method to probe the structural dynamics of SNAP-25 by combining replica-exchange discrete molecular dynamics (rxDMD) simulations and label-based experiments at ensemble and single-molecule levels. The rxDMD simulations systematically characterize the coil-to-molten globular transition and reconstruct structural ensemble consistent with prior ensemble experiments. Label-based experiments using Forster resonance energy transfer and double electron-electron resonance further probe the conformational dynamics of SNAP-25. Agreements between simulations and experiments under both ensemble and single-molecule conditions allow us to assign specific helix-coil transitions in SNAP-25 that occur in submillisecond timescales and potentially play a vital role in forming the SNARE complex. We expect that this integrative approach may help further our understanding of IDPs.
An Energy-Efficient Edge Computing Paradigm for Convolution-Based Image Upsampling

State-of-the-art deep learning solutions for image upsampling are currently trained using either resize or sub-pixel convolution to learn kernels that generate high fidelity images with minimal artifacts. However, performing inference with these learned convolution kernels requires memory-intensive feature map transformations that dominate time and energy costs in real-time applications. To alleviate this pressure on memory bandwidth, we propose a novel energy-efficient edge computing paradigm that confines the use of resize or sub-pixel convolution to training in the cloud by transforming learned convolution kernels to deconvolution kernels before deploying them for inference as a functionally equivalent deconvolution. These kernel transformations, intended as a one-time cost when shifting from training to inference, enable a systems designer to use each algorithm in their optimal context by preserving the image fidelity learned when training in the cloud while minimizing data transfer penalties during inference at the edge. We compare the inference properties of these convolution-based image upsampling algorithms and introduce a novel deconvolution inference algorithm, which we refer to as REVD2. To demonstrate the benefits of our approach, we upsample images selected from the BSD300 dataset using a pre-trained single-image super resolution network provided by the PyTorch model zoo. Using quantitative models of incurred time and energy costs to analyze this deep neural network, we estimate that using REVD2 for inference at the edge improves system latency by $2.1\times $ or $2.8\times $ and energy efficiency by $2.1\times $ or $2.7\times $ when respectively compared to sub-pixel or resize convolution counterparts.
Acoustic properties across the human skull.

Transcranial ultrasound is emerging as a noninvasive tool for targeted treatments of brain disorders. Transcranial ultrasound has been used for remotely mediated surgeries, transient opening of the blood-brain barrier, local drug delivery, and neuromodulation. However, all applications have been limited by the severe attenuation and phase distortion of ultrasound by the skull. Here, we characterized the dependence of the aberrations on specific anatomical segments of the skull. In particular, we measured ultrasound propagation properties throughout the perimeter of intact human skulls at 500 kHz. We found that the parietal bone provides substantially higher transmission (average pressure transmission 31 ± 7%) and smaller phase distortion (242 ± 44 degrees) than frontal (13 ± 2%, 425 ± 47 degrees) and occipital bone regions (16 ± 4%, 416 ± 35 degrees). In addition, we found that across skull regions, transmission strongly anti-correlated (R=-0.79) and phase distortion correlated (R=0.85) with skull thickness. This information guides the design, positioning, and skull correction functionality of next-generation devices for effective, safe, and reproducible transcranial focused ultrasound therapies.
Experimenting with Apéry Limits and WZ pairs

This article, dedicated with admiration in memory of Jon and Peter Borwein,illustrates by example, the power of experimental mathematics, so dear to them both, by experimenting with so-called Apery limits and WZ pairs. In particular we prove a weaker form of an intriguing conjecture of Marc Chamberland and Armin Straub (in an article dedicated to Jon Borwein), and generate lots of new Apery limits. We also rediscovered an infinite family of cubic irrationalities, that suggested very good effective irrationalitymeasures (lower than Liouville's generic 3), and that we conjectured to go down to the optimal 2. As it turned out, as pointed out by Paul Voutier (see the postscript kindly written by him), our conjectures follow from deep results in number theory. Nevertheless we believe that further experiments with our Maple programs would lead to new and interesting results.
RASA: Efficient Register-Aware Systolic Array Matrix Engine for CPU

As AI-based applications become pervasive, CPU vendors are starting to incorporate matrix engines within the datapath to boost efficiency. Systolic arrays have been the premier architectural choice as matrix engines in offload accelerators. However, we demonstrate that incorporating them inside CPUs can introduce under-utilization and stalls due to limited register storage to amortize the fill and drain times of the array. To address this, we propose RASA, Register-Aware Systolic Array. We develop techniques to divide an execution stage into several sub-stages and overlap instructions to hide overheads and run them concurrently. RASA-based designs improve performance significantly with negligible area and power overhead.
Causal Bandits with Unknown Graph Structure

In causal bandit problems, the action set consists of interventions on variables of a causal graph. Several researchers have recently studied such bandit problems and pointed out their practical applications. However, all existing works rely on a restrictive and impractical assumption that the learner is given full knowledge of the causal graph structure upfront. In this paper, we develop novel causal bandit algorithms without knowing the causal graph. Our algorithms work well for causal trees, causal forests and a general class of causal graphs. The regret guarantees of our algorithms greatly improve upon those of standard multi-armed bandit (MAB) algorithms under mild conditions. Lastly, we prove our mild conditions are necessary: without them one cannot do better than standard MAB bandit algorithms.
Dynamics of a single polyampholyte chain

Polymers that feature both positive and negative charges along chains, known as polyampholytes, represent a class of materials that hold promise for a new generation of energy storage devices, the design of which will require knowledge of the underlying structure and dynamics. Here, we develop a theory based on the Rouse model for the dynamic structure factor of a single polyampholyte chain in the weak coupling regime (negligible intramolecular electrostatics) or subjected to weak external electric fields (governed by linear response). Neglecting effects of small ions, we find deviations in scaling from the classic Rouse theory and make predictions for scattering experiments performed on polyampholytes. We find that, under weak coupling with arbitrarily strong fields, the dynamics are highly dependent on the charge distribution and consequently look at two representative examples—random charge densities and periodic charge densities—with different scaling properties. Under weak fields, the dynamics are largely independent of charge distribution. Finally, we investigate the influence of hydrodynamic effects and the implications of including inertial effects in the model.
Improving trial generalizability using observational studies.

Complementary features of randomized controlled trials (RCTs) and observational studies (OSs) can be used jointly to estimate the average treatment effect of a target population. We propose a calibration weighting estimator that enforces the covariate balance between the RCT and OS, therefore improving the trial-based estimator's generalizability. Exploiting semiparametric efficiency theory, we propose a doubly robust augmented calibration weighting estimator that achieves the efficiency bound derived under the identification assumptions. A nonparametric sieve method is provided as an alternative to the parametric approach, which enables the robust approximation of the nuisance functions and data-adaptive selection of outcome predictors for calibration. We establish asymptotic results and confirm the finite sample performances of the proposed estimators by simulation experiments and an application on the estimation of the treatment effect of adjuvant chemotherapy for early-stage non-small cell lung patients after surgery. This article is protected by copyright. All rights reserved.
Multinomial Probability Assessment of Motorcycle Injury Severities

The analysis of motorcycle crashes that occurred in Florida has shown an average increase of 11.9% from 1999 to 2004. During that time period, 48.5% resulted in severe injury while 2.6% were fatal. Various studies have evaluated contributing causes of motorcycle crashes and factors determining injury severity. No specific conclusions have been reached on factors directly connected to injury severity. In view of these statistics and findings from previous research, this study applies the Multinomial Logit (MNL) and the Multinomial Probit (MNP) models to analyze factors associated with motorcycle crash injury severities. The multinomial models are used instead of the ordered models since the latter are not flexible in quantifying the effect of the independent variables for each injury severity category. The MNL and MNP models yield the similar results in terms of the sign and magnitude of the coefficients. However the standard errors of the coefficients in MNP are tighter than those in MNL, implying that MNP is a more efficient estimator than MNL. It was found that, increase in number of lanes, alcohol and drug use, high posted speed limit, curved areas, turning movements, ramps, and driving with no adequate daylight increase probability of severe injury. Collision with truck and buses and more than 2 vehicles in the crash also increase the probability of severe injury.
Transcriptional control of rat hepatic glutaminase expression by dietary protein level and starvation

Mammalian liver possesses a unique isozyme of phosphate-activated glutaminase that is subject to long-term regulation. In rats during starvation or after consumption of diets containing high amounts of protein (60%), hepatic glutaminase activity was 100% higher than in rats fed a 20% protein diet. Conversely, rats fed low protein diets (0 and 5%) had lower hepatic glutaminase activity when compared with rats fed the 20% protein diet. Differences in activity with different dietary protein levels were not due to differences in the amount of food consumed. The relative abundance of mRNA encoding hepatic glutaminase was lower in rats fed 0% protein and higher in those starved or fed 60% protein diet when compared with rats fed the 20% protein diet. The mRNA elongation assay in hepatic nuclei isolated from these animals demonstrated that the rate of transcription of the glutaminase gene was also different in rats starved or fed different levels of dietary protein. Overall, the results indicate that differences in hepatic glutaminase activity in rats starved or fed different levels of protein are mainly due to differences in the rate of transcription of the gene. In this way the regulation of hepatic glutaminase expression is similar to that seen for other enzymes involved in hepatic amino acid catabolism but differs markedly from that of renal glutaminase, in which changes in transcription rate are not observed and alterations of mRNA turnover are the principle mechanism of long-term regulation.
Cylinders through five points: complex and real enumerative geometry

It is known that five points in R3 generically determine a finite number of cylinders containing those points. We discuss ways in which it can be shown that the generic (complex) number of solutions, with multiplicity, is six, of which an even number will be real valued and hence correspond to actual cylinders in R3. We partially classify the case of no real solutions in terms of the geometry of the five given points. We also investigate the special case where the five given points are coplanar, as it differs from the generic case for both complex and real valued solution cardinalities.
Helping Lawyers Help Clients Make Good Decisions About Dispute Resolution

Lawyers face a great challenge in advising clients about what dispute resolution (DR) process to use. There are many DR processes and people are developing new variations all the time. The choice of process can have a huge impact on the parties. Thus it is important for lawyers to give clients good advice about the choice of process, though this is easier said than done. This article suggests that local and subject-area legal practice communities use dispute systems design strategies to help lawyers counsel clients in choosing DR options. These strategies could include development of protocols for lawyers to assess DR options with convenient checklists, educational materials for clients, and trainings. These strategies can include rules requiring lawyers to advise clients about DR options, but lawyers can easily circumvent these rules, which are not likely to be effective unless they are part of a comprehensive strategy.
Reclassification of Linearly Classified Data Using Constraint Databases

In many problems the raw data is already classified according to a variety of features using some linear classification algorithm but needs to be reclassified. We introduce a novel reclassification method that creates new classes by combining in a flexible way the existing classes without requiring access to the raw data. The flexibility is achieved by representing the results of the linear classifications in a linear constraint database and using the full query capabilities of a constraint database system. We implemented this method based on the MLPQ constraint database system. We also tested the method on a data that was already classified using a decision tree algorithm.
Uncomplicated hypospadias and anomalies of upper urinary tract. Need for screening

In a series of 200 patients with uncomplicated hypospadias, 6 (3 per cent) had an anomaly of the upper urinary tract. However, even this low figure is an overestimate of the usefulness of the intravenous pyelogram as a screening procedure since none of the patients has had related symptoms and none has required treatment for his anomaly. The implication of this review is that the intravenous urogram is not a necessary part of the evaluation, for management purposes, of the asymptomatic patient with uncomplicated hypospadias. Occasionally, some interesting information may be obtained.
Analysis of dendritic cells in tumor-free and tumor-containing sentinel lymph nodes from patients with breast cancer

Sentinel lymph node (SLN) biopsy allows identification of the first lymph node into which a primary tumor drains. In breast cancer, identification of tumor cells in the SLNs is a predictor of the tumor's metastatic potential. In the present article, we tested the hypotheses that a positive immune response can occur in tumor-free SLNs and that the activation state of dendritic cells (DCs), the major antigen presenting cells within SLNs, predicts the immune status and metastatic potential of the tumor. Fifty paraffin-embedded SLN sections, 25 tumor-free and 25 tumor-containing, from patients with breast cancer were analyzed by immunohistochemistry to determine the immune maturation state of their DCs. In addition, 12 lymph nodes from noncancer-containing breasts were analyzed. Tissues were stained with antibodies against CD3, MHC class II, CD1a, CD83, IL-10, and IL-12. Mature DCs were defined by CD83 expression and immature DCs by CD1a expression. We found a trend toward higher numbers of mature CD83-positive DCs in tumor-free SLNs than in tumor-containing SLNs (P = 0.07). In addition, tumor-free SLNs were more likely to contain cells expressing IL-10 (P = 0.02) and, to a lesser extent, IL-12 (P = 0.12). In contrast, when all SLNs, both tumor-free and tumor-containing, were compared with uninvolved lymph nodes, the numbers of mature and immature DCs were similar. Our results suggest tumor-free SLNs are immunologically competent and potentially a site of tumor-specific T-cell activation, as evidenced by the presence of greater numbers of mature DCs and cytokine-producing cells in tumor-free SLNs.
Exploring the sanction–crime relationship through a lens of procedural justice

Abstract Purpose Research overwhelmingly explores “kinds of people” as moderators of the sanction–crime relationship (Piquero et al., 2011). This work, on the other hand, focuses on the sanction experience and draws upon the procedural justice doctrine and key ideas in Sherman's (1993, 2014) defiance theory to test whether individual evaluations of procedural justice condition the effect of legal sanctions on subsequent criminal behavior. Methods Using a sample of serious adolescent offenders, generalized linear regression models with interaction terms are employed to test whether the effect of legal sanctions on involvement, variety, and frequency of offending is conditioned by procedural justice. Significant interaction effects of legal sanction and procedural justice are illustrated with graphical methods. Results Results suggest that evaluations of procedural justice condition the sanction–crime relationship. Sanctions lead to a higher likelihood of offending among individuals with low evaluations of procedural justice. However, among those with higher evaluations of procedural justice, there is no significant relationship between sanctions and subsequent offending. Conclusion Increasing perceptions of procedural justice reduces unintended consequences of sanctioning experiences. In an era of heightened focus on interactions between citizens and criminal justice professionals, enhancing procedural justice is not only ethical but also protects against deviance amplification processes.
On surrogate supervision multiview learning

In semi-supervised multi-view learning, the input vector is partitioned into two views and a classifier based on each view is sought after. In such settings, often examples which include the two views and a label are available [1]. In this paper, we are interested in the setting where a classifier for examples from one view is sought after although no labeled examples are provided for that view. Specifically, we consider the setting where labeled examples are provided only for the other view along with additional unlabeled examples of the two views jointly. To solve this problem, we present the Classification-Constrained Canonical Correlation Analysis (C4A) algorithm. We apply our algorithm to an audiovisual classification task. In comparison to two alternatives, the proposed method demonstrates superior performance.
Physics-based sea clutter model for improved detection of low radar cross-section targets

This paper studies the challenging problem of detecting a low radar cross-section target in heavy sea clutter by proposing a physics-based sea clutter generation model. The model includes a process that generates random dynamic sea clutter based on the governing physics of water gravity and capillary waves and a finite-difference time-domain electromagnetics simulations process based on Maxwells equations propagating the radar signal. A subspace clutter suppression detector is considered to remove dominant clutter eigenmodes. The improved detection performance over matched filtering is demonstrated using sea clutter model simulations.
Tumor growth in vivo and as multicellular spheroids compared by mathematical models

In vivo volume growth of two murine tumor cell lines was compared by mathematical modeling to their volume growth as multicellular spheroids. Fourteen deterministic mathematical models were studied. For one cell line, spheroid growth could be described by a model simpler than needed for description of growthin vivo. A model that explicitly included the stimulatory role for cell-cell interactions in regulation of growth was always superior to a model that did not include such a role. The von Bertalanffy model and the logistic model could not fit the data; this result contradicted some previous literature and was found to depend on the applied least squares fitting method. By the use of a particularly designed mathematical method, qualitative differences were discriminated from quantitative differences in growth dynamics of the same cells cultivated in two different three-dimensional systems.
Different Strains of Mice Exhibit Different Aortic Arch Geometry and Plaque Distribution: A Fluid Dynamic Effect

In earlier work, we observed different patterns of disease distribution in the aortic arches of apolipoprotien E (apoE)-deficient C57BL/6 (B6) and 129/SvEv (129) mouse strains [1]. In the relatively young mice (6 mo old), fatty plaques are seen mainly in the aortic root area, extending into the right innominate artery (RIA), with very little plaque in the more distal aortic arch in the B6 strain. However, in the 129 strain, plaques are found more in the inner curvature of the aortic arch but less in the sinus area than in B6 strain (Fig. 1).Copyright © 2007 by ASME
Gender stereotyping effects on entrepreneurial self‐efficacy and high‐growth entrepreneurial intention

Purpose – The number of women choosing entrepreneurship as an occupation continues to grow. However, there are very few start‐up high‐growth ventures in traditionally non‐feminine industries, such as manufacturing or technology. The purpose of this paper is to draw attention to the potential impact of implicit and explicit gender stereotypes on women's high‐growth entrepreneurial intention, and to examine the role of entrepreneurial self‐efficacy in this process. The authors aim to argue that there is a dual stereotype associated with high‐growth entrepreneurship (HGE), which negatively impacts on women's intention and self‐efficacy, thereby limiting their behavior in this arena.Design/methodology/approach – This is a conceptual paper. Through the lens of stereotype activation theory the authors call for researchers to begin examining these phenomena and to utilize more generalizable samples of entrepreneurial students in future research.Findings – The paper finds that by decreasing the masculine stereoty...
Investigation of the correlation between surface properties and surface chemistry of electroless nickel coatings

Abstract Corrosion and erosion resistance of high phosphorus (more than 10 wt.%) and low phosphorus (1–2wt.%) electroless nickel (EN) coatings are investigated using scanning Auger microscopy and X-ray photoelectron spectroscopy. The results support the observations of others showing the correlation between nickel-to-phosphorus ratios and the chemical-mechanical properties of the coatings. The present study gives further insight into the mechanisms responsible for surface chemistry modification and subsequent change in properties as a result of process variables and heat treating. Angle-dependent X-ray photoelectron spectroscopy results showed that the thickness of the passivated surface oxide formed on as-plated EN materials was proportional to the phosphorus content of the deposit. The model derived from this and Auger data implied a layered structure, with a nickel oxide over a phosphate film on the EN alloy surface. Heat treating thickened the oxide and depleted the underlying alloy of nickel.
The Dark Side of 5α-Reductase Inhibitors' Therapy: Sexual Dysfunction, High Gleason Grade Prostate Cancer and Depression

With aging, abnormal benign growth of the prostate results in benign prostate hyperplasia (BPH) with concomitant lower urinary tract symptoms (LUTS). Because the prostate is an androgen target tissue, and transforms testosterone into 5α-dihydrotestosterone (5α-DHT), a potent androgen, via 5α-reductase (5α-R) activity, inhibiting this key metabolic reaction was identified as a target for drug development to treat symptoms of BPH. Two drugs, namely finasteride and dutasteride were developed as specific 5α-reductase inhibitors (5α-RIs) and were approved by the U.S. Food and Drug Administration for the treatment of BPH symptoms. These agents have proven useful in the reducing urinary retention and minimizing surgical intervention in patients with BPH symptoms and considerable literature exists describing the benefits of these agents. In this review we highlight the adverse side effects of 5α-RIs on sexual function, high grade prostate cancer incidence, central nervous system function and on depression. 5α-Rs isoforms (types 1-3) are widely distributed in many tissues including the central nervous system and inhibition of these enzymes results in blockade of synthesis of several key hormones and neuro-active steroids leading to a host of adverse effects, including loss of or reduced libido, erectile dysfunction, orgasmic dysfunction, increased high Gleason grade prostate cancer, observed heart failure and cardiovascular events in clinical trials, and depression. Considerable evidence exists from preclinical and clinical studies, which point to significant and serious adverse effects of 5α-RIs, finasteride and dutasteride, on sexual health, vascular health, psychological health and the overall quality of life. Physicians need to be aware of such potential adverse effects and communicate such information to their patients prior to commencing 5α-RIs therapy.
Information behavior in everyday life: Research on street‐level sex workers, new immigrants, and hair stylists. Sponsored by SIG USE

Little research has focused on the information behavior of the general public (Julien & Duggan, 2000). In this session we report findings on how information is sought and used as part of everyday life by three distinct groups: (1) street-level sex workers, (2) new immigrants, and (3) hairdressers.
Structure side‐effect sorting of drugs I: Extrapyramidal syndrome

A computer program using PL/I language was developed to sort the side effects of 540 clinically useful drugs. The common structural features of the drugs that produce the extrapyramidal syndrome are: (a) the drug contains at least one tertiary amino group separated by three carbon atoms from a coplanar hydro-phobic region, and (b) the coplanar hydrophobic region can be a phenothiazine ring, its isosteres, or two benzene rings held close together (e.g., trimethobenzamide and haloperidol). Other tran-quilizers and anticonvulsants such as diazepam and diphenyl-hydantoin are shown to have entirely different structural features.