cssi/SciSciGPT-SciSciNet huggingface dataset:
Gene drive and resilience through renewal with next generation Cleave and Rescue selfish genetic elements

Gene drive-based strategies for modifying populations face the problem that genes encoding cargo and the drive mechanism are subject to separation, mutational inactivation, and loss of efficacy. Resilience, an ability to respond to these eventualities in ways that restore population modification with functional genes, is needed for long-term success. Here, we show that resilience can be achieved through cycles of population modification with “Cleave and Rescue” (ClvR) selfish genetic elements. ClvR comprises a DNA sequence-modifying enzyme such as Cas9/gRNAs that disrupts endogenous versions of an essential gene and a recoded version of the essential gene resistant to cleavage. ClvR spreads by creating conditions in which those lacking ClvR die because they lack functional versions of the essential gene. Cycles of modification can, in principle, be carried out if two ClvR elements targeting different essential genes are located at the same genomic position, and one of them, ClvRn+1, carries a Rescue transgene from an earlier element, ClvRn. ClvRn+1 should spread within a population of ClvRn, while also bringing about a decrease in its frequency. To test this hypothesis, we first show that multiple ClvRs, each targeting a different essential gene, function when located at a common chromosomal position in Drosophila. We then show that when several of these also carry the Rescue from a different ClvR, they spread to transgene fixation in populations fixed for the latter and at its expense. Therefore, genetic modifications of populations can be overwritten with new content, providing an ongoing point of control.
The Dynamic Electoral Returns of a Large Anti-Poverty Program

Short-term re-election strategies are widely used by governments around the world. This is problematic if governments can maximize their re-election chances by prioritizing short-term spending befo...
Technical Education for the Reintegration of Ex-Combatants: One Pathway, Different Trajectories

Reintegration programs promote technical and vocational education and training (TVET) for ex-combatants, with the aim of preparing them to participate in the labor market and assume new social roles. This in-depth interview study with ex-combatants from Colombia participating in technical programs explores the motivations they have for enrolling in technical education, critical information to identify ways in which programs could better respond to participants’ aspirations. Findings illustrated that through education, ex-combatants seek to be respected and recognized, set an example for their children, experience a moral transformation, and develop autonomy. These findings suggest that TVET’s assumptions that ex-combatants are only motivated by material factors such as money and employment are too reductionist.
Maximum Principles for Matrix-Valued Analytic Functions

To what extent is the maximum modulus principle for scalar-valued analytic functions valid for matrix-valued analytic functions? In response, we discuss some maximum norm principles for such functi...
The Diversity-Innovation Paradox in Science

Prior work finds a diversity paradox: Diversity breeds innovation, yet underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-complete population of ∼1.2 million US doctoral recipients from 1977 to 2015 and following their careers into publishing and faculty positions. We use text analysis and machine learning to answer a series of questions: How do we detect scientific innovations? Are underrepresented groups more likely to generate scientific innovations? And are the innovations of underrepresented groups adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are devalued and discounted: For example, novel contributions by gender and racial minorities are taken up by other scholars at lower rates than novel contributions by gender and racial majorities, and equally impactful contributions of gender and racial minorities are less likely to result in successful scientific careers than for majority groups. These results suggest there may be unwarranted reproduction of stratification in academic careers that discounts diversity's role in innovation and partly explains the underrepresentation of some groups in academia.
New Experimental Platform for Studies of Turbulence and Turbulent Mixing in Accelerating and Rotating Fluids at High Reynolds Numbers

We present a new experimental platform for studies of turbulence and turbulent mixing in accelerating and rotating fluids. The technology is based on the ultra-high performance optical holographic digital data storage. The state-of-the-art electro-mechanical, electronic, and laser components allow for realization of turbulent flows with high Reynolds number (>107) in a relatively small form-factor, and quantification of their properties with extremely high spatio-temporal resolutions and high data acquisition rates. The technology can be applied for investigation of a large variety of hydrodynamic problems including the fundamental properties of non-Kolmogorov turbulence and turbulent mixing in accelerating, rotating and multiphase flows, magneto-hydrodynamics, and laboratory astrophysics. Unique experimental and metrological capabilities enable the studies of spatial and temporal properties of the transports of momentum, angular momentum, and energy and the identification of scalings, invariants, and statistical properties of these complex turbulent flows.
First measurements of subpicosecond electron beam structure by autocorrelation of coherent diffraction radiation

Abstract We report the initial measurements of subpicosecond electron beam structure using a nonintercepting technique based on the autocorrelation of coherent diffraction radiation (CDR). A far infrared (FIR) Michelson interferometer with a Golay detector was used to obtain the autocorrelation. The radiation was generated by a thermionic rf gun beam at 40 MeV as it passed through a 5-mm-tall slit/aperture in a metal screen whose surface was at 45° to the beam direction. For the observed bunch lengths of about 450 fs (FWHM) with a shorter time spike on the leading edge, peak currents of about 100 A are indicated. Also a model was developed and used to calculate the CDR from the back of two metal strips separated by a 5-mm vertical gap. The demonstrated nonintercepting aspect of this method could allow on-line bunch length characterizations to be done during free-electron laser experiments.
Dopamine Buffering Capacity Imaging: A Pharmacodynamic fMRI Method for Staging Parkinson Disease.

We propose a novel pharmacological fMRI (phMRI) method for objectively quantifying disease severity in Parkinson disease (PD). It is based on the clinical observation that the benefit from a dose of levodopa wears off more quickly as PD progresses. Biologically this has been thought to represent decreased buffering capacity for dopamine as nigrostriatal cells die. Buffering capacity has been modeled based on clinical effects, but clinical measurements are influenced by confounding factors. The new method proposes to measure the effect objectively based on the timing of the known response of several brain regions to exogenous levodopa. Such responses are robust and can be quantified using perfusion MRI. Here we present simulation studies based on published clinical dose-response data and an intravenous levodopa infusion. Standard pharmacokinetic-pharmacodynamic methods were used to model the response. Then the effect site rate constant ke was estimated from simulated response data plus Gaussian noise. Predicted time - effect curves sampled at times consistent with phMRI differ substantially based on clinical severity. Estimated ke from noisy input data was recovered with good accuracy. These simulation results support the feasibility of levodopa phMRI hysteresis mapping to measure the severity of dopamine denervation objectively and simultaneously in all brain regions with a robust imaging response to exogenous levodopa.
A Theory of Pyramidal Ownership and Family Business Groups

We provide a rationale for pyramidal ownership (the control of a Þrm through a chain of ownership relations) that departs from the traditional argument that pyramids arise to separate cash sow from voting rights. With a pyramidal structure a family uses a Þrm it already controls to set up a new Þrm. This allows the family to access the entire stock of retained earnings of the Þrm it controls, and to share the security beneÞts of the new Þrm with minority shareholders of the original Þrm. Therefore, pyramids are more attractive when internal funds are important due to the poorly functioning capital markets, and when the security beneÞts of the new Þrm are low; conditions that hold in an environment with poor investor protection. Because our model departs from the traditional argument for pyramids as a device to separate cash sow from voting rights, it can differentiate between pyramids and dual-class shares even in situations in which the same deviation from one share-one vote can be achieved with either method. Unlike the traditional argument, our model is consistent with recent empirical evidence that some pyramidal Þrms are associated with small deviations between ownership and control. We also analyze the creation of family business groups (a collection of multiple Þrms under the control of a single family). Business groups also sourish when external markets are poorly developed because, in such cases, internal resources from the existing Þrms provide the family with a Þnancing advantage vis-a-vis other competing entrepreneurs. Thus, the model predicts that in countries with poor investor protection family business groups should be more common, and that they are more likely to be organized as pyramids. Other predictions of the model are consistent with systematic and anecdotal evidence on pyramidal business groups.
Modeling scale-up effects on a small pilot-scale fluidized-bed reactor for fuel ethanol production

The algorithm generally underpredicted pilot-scale performance, although in some cases, the predicted and actual profiles were similar. The model did a good job of predicting concentration profiles for the bench-scale reactor. Model refinements resulted in: Potential areas for improving the model include basing the carbon dioxide differential equation on phase density and velocities, inclusion of carbon dioxide solubility, and better correlations for liquid-phase dispersion. The pilot-scale FBR continues to demonstrate significant promise for industrial application. New model refinements are needed to account for very high productivities measured in the pilot-scale reactor. Use of a single holdup correlation and limiting the range of solid holdup improved model accuracy and will potentially allow development of analytical Jacobians for improved model stability. Error induced by applying Begovich-Watson parameters measured at the bench scale to the pilot-scale reactor could not solely account for differences in predicted and measured FBR behavior.
LEARNING AND EXCESS VOLATILITY

We introduce adaptive learning behavior into a general-equilibrium life-cycle economy with capital accumulation. Agents form forecasts of the rate of return to capital assets using least-squares autoregressions on past data. We show that, in contrast to the perfect-foresight dynamics, the dynamical system under learning possesses equilibria that are characterized by persistent excess volatility in returns to capital. We explore a quantitative case for theselearning equilibria. We use an evolutionary search algorithm to calibrate a version of the system under learning and show that this system can generate data that matches some features of the time-series data for U.S. stock returns and per-capita consumption. We argue that this finding provides support for the hypothesis that the observed excess volatility of asset returns can be explained by changes in investor expectations against a background of relatively small changes in fundamental factors.
Nonparametric Bounds on Welfare Measures: A New Tool for Nonmarket Valuation

Abstract In this research, we adapt and extend previous work on nonparametric bounds for welfare measures. We present a procedure for estimating upper and lower bounds on each consumer's individual willingness to pay for an environmental improvement. The exciting aspect of this work is that these bounds are derived using only observed quantities and prices of visits to a recreation area without resorting to any parametric assumptions on demand or utility. In Monte Carlo simulations, we find that the bounds have the potential to be narrow enough for policy purposes.
Up‐and‐Down Method

The Up-and-Down method is employed in dose–response studies where the aim is to model the probability of a binary outcome in terms of covariates, one of which is the dose being administered. The method allows for sequential choice of doses, dependent on the outcome of the previous dose. It is used in toxicology experiments in which the outcome is death: following a death, the next unit receives a lower dose, whereas a survival leads to the dose being increased. Keywords: dose-finding; dose–response; experimental design; quantal response; sequential method; toxicology
Information Asymmetry, Information Precision, and the Cost of Capital

The consequences of information differences across investors in capital markets are still much debated. This paper examines the relation between information differences across investors and the cost of capital. Our analysis makes three salient points. First, in models of perfect competition, information differences across investors affect a firm’s cost of capital through investors’ average information precision, and not information asymmetry per se. Second, the average information precision effect is unlikely to diversify away when there exist many firms whose cash flows covary. Finally, in models of imperfect competition information asymmetry affects the willingness to supply liquidity; this, in turn, affects a firm’s cost of capital. Thus, the precision of the information and information asymmetry have separate and distinct effects on the cost of capital. Failure to distinguish between them jeopardizes our understanding of the link between information and the cost of capital.
Chapter 2 – How Different Tissues Process Drug Response

Cellular signaling cascades are considered as a preface to a discussion of the nature of drug agonism (how drugs produce cellular response). The relationship between cell sensitivity and the intrinsic power of an agonist to induce response (quantified as ‘efficacy’) is used to explain the phenomena of full and partial agonism. The application of various assay formats to the measurement of agonism is then considered.
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations

Deep neural networks are susceptible to adversarial manipulations in the input domain. The extent of vulnerability has been explored intensively in cases of l p -bounded and l p -minimal adversarial perturbations. However, the vulnerability of DNNs to adversarial perturbations with specific statistical properties or frequency-domain characteristics has not been sufficiently explored. In this paper, we study the smoothness of perturbations and propose Smooth-Fool, a general and computationally efficient framework for computing smooth adversarial perturbations. Through extensive experiments, we validate the efficacy of the proposed method for both the white-box and black-box attack scenarios. In particular, we demonstrate that: (i) there exist extremely smooth adversarial perturbations for well-established and widely used network architectures, (ii) smoothness significantly enhances the robustness of perturbations against state-of-the-art defense mechanisms, (iii) smoothness improves the transferability of adversarial perturbations across both data points and network architectures, and (iv) class categories exhibit a variable range of susceptibility to smooth perturbations. Our results suggest that smooth APs can play a significant role in exploring the vulnerability extent of DNNs to adversarial examples. The code is available at https://github.com/alldbi/SmoothFool
Blind Identity-Based Encryption and Simulatable Oblivious Transfer.

In an identity-based encryption (IBE) scheme, there is a key extraction protocol where a user submits an identity string to a master authority who then returns the corresponding secret key for that identity. In this work, we describe how this protocol can be performed efficiently and in a blind fashion for several known IBE schemes; that is, a user can obtain a secret key for an identity without the master authority learning anything about this identity. We formalize this notion as blind IBE and discuss its many practical applications. In particular, we build upon the recent work of Camenisch, Neven, and shelat [CNS07] to construct oblivious transfer (OT) schemes which achieve full simulatability for both sender and receiver. OT constructions with comparable efficiency prior to Camenisch et al. were proven secure in the weaker half-simulation model. Our OT schemes are constructed from the blind IBE schemes we propose, which require only static complexity assumptions (e.g., DBDH) whereas prior comparable schemes require dynamic assumptions (e.g., q-PDDH).
Temperature compensation in a small rhythmic circuit

Temperature affects the conductances and kinetics of the ionic channels that underlie neuronal activity. Each membrane conductance has a different characteristic temperature sensitivity, which raises the question of how neurons and neuronal circuits can operate robustly over wide temperature ranges. To address this, we employed computational models of the pyloric network of crabs and lobsters. We produced multiple different models that exhibit a triphasic pyloric rhythm over a range of temperatures and explored the dynamics of their currents and how they change with temperature. Temperature can produce smooth changes in the relative contributions of the currents to neural activity so that neurons and networks undergo graceful transitions in the mechanisms that give rise to their activity patterns. Moreover, responses of the models to deletions of a current can be different at high and low temperatures, indicating that even a well-defined genetic or pharmacological manipulation may produce qualitatively distinct effects depending on the temperature.
Ethics and Best Practices for Mapping Archaeological Sites

Archaeologists are tasked with balancing a call to open data and the need to maintain confidentiality of sensitive archaeological site locations. Low-resolution mapping and data aggregation are the methods most commonly used to hide site locations; however, we understand little of the effectiveness of these practices. Trends in geomasking, obscuring observed geographic points, to anonymize public health data are suggested as a source of methods for sharing archaeological site data. Archaeologists have available to them a number of geomasking methods that balance open data and site security in different ways. Low-resolution mapping at several scales and random direction with fixed radius, random perturbation donut, and Gaussian donut techniques are tested on a set of archaeological site locations. Random perturbation donuts resulted in the best balance between obscuring archaeological locations and conveying observed spatial patterning. Researchers should carefully consider how they convey archaeological location data, as commonly used low-resolution scales may not provide the desired level of obscurity. Researchers should also be explicit as to how and why their methods of site visualization are chosen.
On Identifying Hashtags in Disaster Twitter Data

Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short-Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as $92.22%$. The dataset, code, and other resources are available on Github.1
Lifted Hybrid Variational Inference.

A variety of lifted inference algorithms, which exploit model symmetry to reduce computational cost, have been proposed to render inference tractable in probabilistic relational models. Most existing lifted inference algorithms operate only over discrete domains or continuous domains with restricted potential functions, e.g., Gaussian. We investigate two approximate lifted variational approaches that are applicable to hybrid domains and expressive enough to capture multi-modality. We demonstrate that the proposed variational methods are both scalable and can take advantage of approximate model symmetries, even in the presence of a large amount of continuous evidence. We demonstrate that our approach compares favorably against existing message-passing based approaches in a variety of settings. Finally, we present a sufficient condition for the Bethe approximation to yield a non-trivial estimate over the marginal polytope.
Bacterial metabolism rescues the inhibition of intestinal drug absorption by food and drug additives

Food and drug products contain diverse and abundant small-molecule additives (excipients) with unclear impacts on human physiology, drug safety, and response. Here, we evaluate their potential impact on intestinal drug absorption. By screening 136 unique compounds for inhibition of the key intestinal transporter OATP2B1 we identified and validated 24 potent OATP2B1 inhibitors, characterized by higher molecular weight and hydrophobicity compared to poor or noninhibitors. OATP2B1 inhibitors were also enriched for dyes, including 8 azo (R-N=N-R') dyes. Pharmacokinetic studies in mice confirmed that FD&C Red No. 40, a common azo dye excipient and a potent inhibitor of OATP2B1, decreased the plasma level of the OATP2B1 substrate fexofenadine, suggesting that FD&C Red No. 40 has the potential to block drug absorption through OATP2B1 inhibition in vivo. However, the gut microbiomes of multiple unrelated healthy individuals as well as diverse human gut bacterial isolates were capable of inactivating the identified azo dye excipients, producing metabolites that no longer inhibit OATP2B1 transport. These results support a beneficial role for the microbiome in limiting the unintended effects of food and drug additives in the intestine and provide a framework for the data-driven selection of excipients. Furthermore, the ubiquity and genetic diversity of gut bacterial azoreductases coupled to experiments in conventionally raised and gnotobiotic mice suggest that variations in gut microbial community structure may be less important to consider relative to the high concentrations of azo dyes in food products, which have the potential to saturate gut bacterial enzymatic activity.
Improving Crop Yields in Sub-Saharan Africa - What Does the East African Data Say

Recent micro level data from East Africa is used to benchmark aggregate data and assess the role of agricultural inputs in explaining variation in crop yields on smallholding plots. Fertilizer, improved seeds, protection against erosion and pesticides improve crop yields in Rwanda and Ethiopia, but not Uganda, possibly associated with lack of use there. With all positive yield determinants in place, wheat and maize yields could increase fourfold. The data hints at the negative effect of climate change on yields and the benefits of accompanying measures to mitigate its adverse impact (access to finance and protection against erosion). The adverse effect of crop damage on yields varies between 12/13 percent (Rwanda, Uganda) to 36 percent (Ethiopia). Protection against erosion and investment financing mitigate these effects considerably.
A geometric invariant of virtual n-links

Abstract For a virtual n-link K, we define a new virtual link V D ( K ) , which is invariant under virtual equivalence of K. The Dehn space of V D ( K ) , which we denote by D D ( K ) , therefore has a homotopy type which is an invariant of K. We show that the quandle and even the fundamental group of this space are able to detect the virtual trefoil. We also consider applications to higher-dimensional virtual links.
Approximate inference with Wasserstein gradient flows

We present a novel approximate inference method for diffusion processes, based on the Wasserstein gradient flow formulation of the diffusion. In this formulation, the time-dependent density of the diffusion is derived as the limit of implicit Euler steps that follow the gradients of a particular free energy functional. Existing methods for computing Wasserstein gradient flows rely on discretization of the domain of the diffusion, prohibiting their application to domains in more than several dimensions. We propose instead a discretization-free inference method that computes the Wasserstein gradient flow directly in a space of continuous functions. We characterize approximation properties of the proposed method and evaluate it on a nonlinear filtering task, finding performance comparable to the state-of-the-art for filtering diffusions.
NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion

With the recent developments in artificial intelligence and machine learning, anomalies in network traffic can be detected using machine learning approaches. Before the rise of machine learning, network anomalies which could imply an attack, were detected using well-crafted rules. An attacker who has knowledge in the field of cyber-defence, could make educated guesses to sometimes accurately predict which particular features of network traffic data the cyber-defence mechanism is looking at. With this information, the attacker can circumvent a rulebased cyber-defense system. However, after the advancements of machine learning for network anomaly, it is not easy for a human to understand how to bypass a cyber-defence system. Recently, adversarial attacks have become increasingly common to defeat machine learning algorithms. In this paper, we show that even if we build a classifier and train it with adversarial examples for network data, we can use adversarial attacks and successfully break the system. We propose a Generative Adversarial Network (GAN) based algorithm to generate data to train an efficient neural network based classifier, and we subsequently break the system using adversarial attacks.
Genes Regulated by HPV 16 E6 and High Expression of NFX1-123 in Cervical Cancers.

Purpose High-risk human papillomaviruses (HR HPV) cause cervical cancer, and in these cancers, HPV type 16 is the most common HR type. The HR viral oncogenes E6 and E7 partner with cellular proteins to drive cancer and modulate immune pathways; previously, we demonstrated in keratinocytes that HPV 16 E6 and high expression of the endogenous host protein partner NFX1-123 led to the increased expression of multiple genes, including Notch1, secretory leukocyte peptidase inhibitor (SLPI), and retinoic acid early transcript 1G (RAET1G). The present study was conducted to determine if NFX1-123 was highly expressed in cervical cancer and if genes increased by NFX1-123 and 16E6 in keratinocytes were also increased in cervical cancers. Materials and methods The Cancer Genome Atlas (TCGA) database and The Human Protein Atlas database were used to compare relative mRNA and protein gene expression, respectively, in the normal cervix and cervical cancers. Formalin-fixed paraffin-embedded (FFPE) normal cervix and HPV 16 positive cervical cancer samples were analyzed for relative protein expression by immunohistochemical staining. Protein expression of a subset of regulated genes was quantified by Western blot of HPV positive and negative cell lines. Results Immunohistochemical staining of HPV 16 positive cervical dysplasias and cancers revealed high NFX1-123, Ki67, and Notch1 expression. NFX1 and NFX1L1 mRNA levels were increased in cervical cancers compared to normal cervix in the TCGA database. Fourteen genes previously identified as upregulated in keratinocytes with 16E6 and overexpressed NFX1-123 also had high mRNA expression and selected genes had high protein expression in cervical cancers and cell lines. Conclusion In cervical cancer, NFX1-123 is highly expressed, and 16E6 and NFX1-123 together alter the expression of a wide set of genes. The involvement of these genes in cell proliferation, differentiation, invasion, and metastasis provides further insight into potential ways that HR HPVs promote cancer initiation and maintenance.
The Impact of the Coronavirus Lockdown on Domestic Violence

We use 911 call records and mobile device location data to study the impact of the coronavirus lockdown on domestic violence The percent of people at home shar
Distributed Iterative CT Reconstruction Using Multi-Agent Consensus Equilibrium

Model-Based Image Reconstruction (MBIR) methods significantly enhance the quality of computed tomographic (CT) reconstructions relative to analytical techniques, but are limited by high computational cost. In this article, we propose a multi-agent consensus equilibrium (MACE) algorithm for distributing both the computation and memory of MBIR reconstruction across a large number of parallel nodes. In MACE, each node stores only a sparse subset of views and a small portion of the system matrix, and each parallel node performs a local sparse-view reconstruction, which based on repeated feedback from other nodes, converges to the global optimum. Our distributed approach can also incorporate advanced denoisers as priors to enhance reconstruction quality. In this case, we obtain a parallel solution to the serial framework of Plug-n-play (PnP) priors, which we call MACE-PnP. In order to make MACE practical, we introduce a partial update method that eliminates nested iterations and prove that it converges to the same global solution. Finally, we validate our approach on a distributed memory system with real CT data. We also demonstrate an implementation of our approach on a massive supercomputer that can perform large-scale reconstruction in real-time.
The Cdc48 Complex Alleviates the Cytotoxicity of Misfolded Proteins by Regulating Ubiquitin Homeostasis

The accumulation of misfolded proteins is associated with multiple neurodegenerative disorders, but it remains poorly defined how this accumulation causes cytotoxicity. Here, we demonstrate that the Cdc48/p97 segregase machinery drives the clearance of ubiquitinated model misfolded protein Huntingtin (Htt103QP) and limits its aggregation. Nuclear ubiquitin ligase San1 acts upstream of Cdc48 to ubiquitinate Htt103QP. Unexpectedly, deletion of SAN1 and/or its cytosolic counterpart UBR1 rescues the toxicity associated with Cdc48 deficiency, suggesting that ubiquitin depletion, rather than compromised proteolysis of misfolded proteins, causes the growth defect in cells with Cdc48 deficiency. Indeed, Cdc48 deficiency leads to elevated protein ubiquitination levels and decreased free ubiquitin, which depends on San1/Ubr1. Furthermore, enhancing free ubiquitin levels rescues the toxicity in various Cdc48 pathway mutants and restores normal turnover of a known Cdc48-independent substrate. Our work highlights a previously unappreciated function for Cdc48 in ensuring the regeneration of monoubiquitin that is critical for normal cellular function.
Towards Improving the Performance of the RNN-based Inversion Model in Output Tracking Control

With the advantages of high modeling accuracy and large bandwidth, recurrent neural network (RNN) based inversion model control has been proposed for output tracking. However, some issues still need to be addressed when using the RNN-based inversion model. First, with limited number of parameters in RNN, it cannot model the low-frequency dynamics accurately, thus an extra linear model has been used, which can become an interference for tracking control at high frequencies. Moreover, the control speed and the RNN modeling accuracy cannot be improved simultaneously as the control sampling speed is restricted by the length of the RNN training set. Therefore, this article focuses on addressing these limitations of RNN-based inversion model control. Specifically, a novel modeling method is proposed to incorporate the linear model in a way that it does not affect the existing high-frequency control performance achieved by RNN. Additionally, an interpolation method is proposed to double the sampling frequency (compared to the RNN training sampling frequency). Analysis on the stability issues which may arise when the proposed new model is used for predictive control is presented along with the instructions on determining the parameters for ensuring the closed-loop stability. Finally, the proposed approach is demonstrated on a commercial piezo actuator, and the experiment results show that the tracking performances can be significantly improved.
Regulator Design for a Congested Continuum Traffic Model with App-Routing Instability

In this paper, we propose a control design methodology for a linearized continuum traffic model in the congested regime. The continuum traffic flow on a highway is modeled using a linearized version of the quasilinear system of hyperbolic partial differential equations known as the Aw-Rascle-Zhang (ARZ) model. The linear traffic model is augmented with a novel non-local boundary condition representing car influx due to the use of routing apps such as Google Maps and Waze. The routing apps act as real-time previews for highway traffic, introducing potentially destabilizing feedback in the app-based navigation decision process, necessitating the development of a feedback controller. We first study small-time H 1 solutions of the linearized model with the addition of the app-routing for sufficiently small initial data. We introduce an extended, multi-tiered boundary control design based upon the method of infinite-dimensional backstepping. Using an intermediate decoupling transformation, we account for the non-local boundary condition arising from routing app feedback. It is shown that for sufficiently small H 1 data, the equilibrium congestion solution is exponentially stable and guarantees the existence of closed-loop solutions on the infinite time interval. The existence of the extended backstepping method is studied by characterizing the existence of the companion kernels.
Effects of Low-Pressure Condition on Partial Discharges in WBG Power Electronics Modules

The aviation industry aims to reduce CO 2 emission by reducing energy consumption and benefitting from more electrical systems than those based on fossil fuels. The more electric aircraft (MEA) can take advantage of the drives based on wide bandgap (WBG)-based power modules that are lighter and can bear higher voltages and currents. However, the fast-rise and repetitive voltage pulses generated by WBG-based systems can endanger the insulating property of dielectric materials due to the partial discharges. PDs cause the local breakdown of insulation materials in regions with high electric field magnitude. In the case of power electronic modules, silicone gel, which is a predominant type of encapsulation material, is susceptible to these PDs. In this study, it is aimed to investigate the impact of rise time on various PD characteristics including PD true charge magnitude, inception and extinction fields, duration, and so forth. Besides, those systems that are anticipated to operate under harsh environmental conditions - such as naval or aviation industries - may expect additional threat. Therefore, this paper puts forth the combination of low-pressure conditions and fast-rise, high-frequency square wave pulses. The results demonstrate that more intense and more intense ones are going to be imposed at lower pressures. COMSOL Multiphysics interfaced with MATLAB is used to simulate the PD detection process based on the experimental data found in the literature.
SARS-CoV-2 viral load predicts COVID-19 mortality.

Abstract The need for reliable and widely available SARS-CoV-2 testing is well recognized, but it will be equally necessary to develop quantitative methods that determine viral load in order to guide patient triage and medical decision making. We are the first to report that SARS-CoV-2 viral load at the time of presentation is an independent predictor of COVID-19 mortality in a large patient cohort (n=1,145). Viral loads should be used to identify higher-risk patients that may require more aggressive care and should be included as a key biomarker in the development of predictive algorithms.
Hnf4a is required for the development of Cdh6-expressing progenitors into proximal tubules in the mouse kidney

Background Hepatocyte NF 4α (Hnf4a) is a major regulator of renal proximal tubule (PT) development. In humans, a mutation in HNF4A impairs PT functions and is associated with Fanconi renotubular syndrome (FRTS). In mice, mosaic deletion of Hnf4a in the developing kidney reduces the population of PT cells, leading to FRTS-like symptoms. The molecular mechanisms underlying the role of Hnf4a in PT development remain unclear. Methods The gene deletion tool Osr2Cre removed Hnf4a in developing nephrons in mice, generating a novel model for FRTS. Immunofluorescence analysis characterized the mutant phenotype, and lineage analysis tested whether Cadherin-6 (Cdh6)-expressing cells are PT progenitors. Genome-wide mapping of Hnf4a binding sites and differential gene analysis of Hnf4a mutant kidneys identified direct target genes of Hnf4a. Results Deletion of Hnf4a with Osr2Cre led to the complete loss of mature PT cells, lethal to the Hnf4a mutant mice. Cdh6high, lotus tetragonolobus lectin-low (LTLlow) cells serve as PT progenitors and demonstrate higher proliferation than Cdh6low, LTLhigh differentiated PT cells. Additionally, Hnf4a is required for PT progenitors to differentiate into mature PT cells. Genomic analyses revealed that Hnf4a directly regulates the expression of genes involved in transmembrane transport and metabolism. Conclusions Hnf4a promotes the differentiation of PT progenitors into mature PT cells by regulating the expression of genes associated with reabsorption, the major function of PT cells.
Nonlinear effects in high-intensity focused ultrasound power transfer systems

In the context of wireless acoustic power transfer, high-intensity focused ultrasound technology aims at the reduction of spreading losses by concentrating the acoustic energy at a specific location. Experiments are performed to determine the impact of nonlinear wave propagation on the spatially resonant conditions in a focused ultrasonic power transfer system. An in-depth analysis is performed to explain experimental observations. The results show that the efficiency of energy transfer is reduced as nonlinear effects become more prominent. Furthermore, the maximum voltage output position shifts away from the focal point and closer to the transducer as the source strength is increased. The results and analysis are relevant to the development of efficient ultrasonic power transfer devices when using focused sources.
Incidence and etiology of clinically-attended, antibiotic-treated diarrhea among children under five years of age in low- and middle-income countries: Evidence from the Global Enteric Multicenter Study

Diarrhea is a leading cause of antibiotic consumption among children in low- and middle-income countries. While vaccines may prevent diarrhea infections for which children often receive antibiotics, the contribution of individual enteropathogens to antibiotic use is minimally understood. We used data from the Global Enteric Multicenter Study (GEMS) to estimate pathogen-specific incidence of antibiotic-treated diarrhea among children under five years old residing in six countries of sub-Saharan Africa and South Asia before rotavirus vaccine implementation. GEMS was an age-stratified, individually-matched case-control study. Stool specimens were obtained from children presenting to sentinel health clinics with newly-onset, acute diarrhea (including moderate-to-severe and less-severe diarrhea) as well as matched community controls without diarrhea. We used data from conventional and quantitative molecular diagnostic assays applied to stool specimens to estimate the proportion of antibiotic-treated diarrhea cases attributable to each pathogen. Antibiotics were administered or prescribed to 9,606 of 12,109 moderate-to-severe cases and 1,844 of 3,174 less-severe cases. Across all sites, incidence rates of clinically-attended, antibiotic-treated diarrhea were 12.2 (95% confidence interval: 9.0-17.8), 10.2 (7.4-13.9) and 1.9 (1.3-3.0) episodes per 100 child-years at risk at ages 6 weeks to 11 months, 12-23 months, and 24-59 months, respectively. Based on the recommendation for antibiotic treatment to be reserved for cases with dysentery, we estimated a ratio of 12.6 (8.6-20.8) inappropriately-treated diarrhea cases for each appropriately-treated case. Rotavirus, adenovirus serotypes 40/41, Shigella, sapovirus, Shiga toxin-producing Escherichia coli, and Cryptosporidium were the leading antibiotic-treated diarrhea etiologies. Rotavirus caused 29.2% (24.5-35.2%) of antibiotic-treated cases, including the largest share in both the first and second years of life. Shigella caused 14.9% (11.4-18.9%) of antibiotic-treated cases, and was the leading etiology at ages 24-59 months. Our findings should inform the prioritization of vaccines with the greatest potential to reduce antibiotic exposure among children.
Efficient Approximations for Stationary Single-Channel Ca2+ Nanodomains across Length Scales

We consider the stationary solution for the Ca2+ concentration near a point Ca2+ source describing a single-channel Ca2+ nanodomain in the presence of a single mobile Ca2+ buffer with 1:1 Ca2+ binding. We present computationally efficient approximants that estimate stationary single-channel Ca2+ nanodomains with great accuracy in broad regions of parameter space. The presented approximants have a functional form that combines rational and exponential functions, which is similar to that of the well-known excess buffer approximation and the linear approximation but with parameters estimated using two novel, to our knowledge, methods. One of the methods involves interpolation between the short-range Taylor series of the free buffer concentration and its long-range asymptotic series in inverse powers of distance from the channel. Although this method has already been used to find Pade (rational-function) approximants to single-channel Ca2+ and buffer concentrations, extending this method to interpolants combining exponential and rational functions improves accuracy in a significant fraction of the relevant parameter space. A second method is based on the variational approach and involves a global minimization of an appropriate functional with respect to parameters of the chosen approximations. An extensive parameter-sensitivity analysis is presented, comparing these two methods with previously developed approximants. Apart from increased accuracy, the strength of these approximants is that they can be extended to more realistic buffers with multiple binding sites characterized by cooperative Ca2+ binding, such as calmodulin and calretinin.
Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.

Background Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. Method We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. Results The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. Conclusion This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.
A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer

Cell-free DNA’s (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor variants in cfDNA. Using this approach, we first generated a model to classify and score candidate variants for inclusion on a prostate cancer targeted sequencing panel. We then used this panel to screen tumor variants from prostate cancer patients with localized disease in both in silico and hybrid capture settings. Whole Genome Sequence (WGS) data from 550 prostate tumors was analyzed to build a targeted sequencing panel of single point and small (< 200 bp) indel mutations, which was subsequently screened in silico against prostate tumor sequences from 5 patients to assess performance against commonly used alternative panel designs. The panel’s ability to detect tumor-derived cfDNA variants was then assessed using prospectively collected cfDNA and tumor foci from a test set 18 prostate cancer patients with localized disease undergoing radical proctectomy. The panel generated from this approach identified as top candidates mutations in known driver genes (e.g. HRAS) and prostate cancer related transcription factor binding sites (e.g. MYC, AR). It outperformed two commonly used designs in detecting somatic mutations found in the cfDNA of 5 prostate cancer patients when analyzed in an in silico setting. Additionally, hybrid capture and 2500X sequencing of cfDNA molecules using the panel resulted in detection of tumor variants in all 18 patients of a test set, where 15 of the 18 patients had detected variants found in multiple foci. Machine learning-prioritized targeted sequencing panels may prove useful for broad and sensitive variant detection in the cfDNA of heterogeneous diseases. This strategy has implications for disease detection and monitoring when applied to the cfDNA isolated from prostate cancer patients.