cssi/SciSciGPT-SciSciNet huggingface dataset:
Negative binomial additive model for RNA-Seq data analysis.

High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. Existing models assume linear effect of covariates, which is restrictive and may not be sufficient for certain phenotypes. We introduce NBAMSeq, a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Based on extensive simulations and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. The vignette and source code of NBAMSeq are available at http://bioconductor.org/packages/release/bioc/html/NBAMSeq.html.
The knowledge revolution

We are on the threshold of a truly revolutionary era of discovery - ranging from the origins of the universe to new states of matter and microscopic machines, from a new understanding of the oceans and of the biological connections across the Earth's species to the functioning of the human brain and the origins of consciousness. This ‘golden age’ of discovery, with frequent breakthroughs occurring virtually in every field, is inducing far-reaching social changes. We are undergoing a social and economic revolution which matches the impact of the agricultural and industrial revolutions. This is a ‘knowledge revolution’ driven by knowledge and by the technologies for processing and communicating it. Knowledge is an intangible public good. It is privately produced, and it is replacing land and machines as the primary factor of production prevailing in the agricultural and industrial revolutions. This alters the terms of the debate between capitalism and socialism, and leads to a human-centred society with dif...
Sovereign Debt, Reputation and Credit Terms

This paper develops a model in which sovereign debtors repay debt in order to maintain a reputation for repayment. Repayment gives creditors reason to think that the debtor will suffer adverse consequences if the debtor defaults, so they continue to lend. I compare a situation in which competitive lenders earn zero profit on each loan with one in which they can make long-term commitments to individual borrowers, so that the zero-profit condition applies only in the long run. In many circumstances, a borrower benefits ex ante if lenders commit to denying credit to a borrower in default even if, at that point, a subsequent loan is profitable. Furthermore, a ‘debt overhang,’ while possibly altering credit terms, does not cause profitable investment opportunities to go unexploited.
Chapter 3 – Logistics

Publisher Summary The purpose of this chapter is to provide some guidelines for structuring the data-gathering process. These are only guidelines; they do not represent the only or necessarily the best way to structure this process. There are many approaches to gathering intake and assessment data that could be consistent with ecosystemic play therapy. The process used should meet the needs of the client, the therapist, and the system in which treatment is to be implemented. It outlines the key components of this process. The focus of this chapter is on the unique issues involved in completing an ecosystemic play therapy intake. The specific structure of the intake process depends on the developmental level of the child, the structure of the child's ecosystemic context including culture, and the therapist's approach and work setting. The chapter addresses some of the logistics involved in gathering the intake information needed for successful ecosystemic play therapy treatment planning. It presents a detailed description of the entire process right from the initial contact, to interviewing children, interviewing caregivers, to the closure of the therapy.
Theorem Proving Guided Development of Formal Assertions in a Resource-Constrained Scheduler for High-Level Synthesis

This paper presents a formal specification and a proof of correctness for the widely-used Force-Directed List Scheduling (FDLS) algorithm for resource-constrained scheduling of data flow graphs in high-level synthesis systems. The proof effort is conducted using a higher-order logic theorem prover. During the proof effort many interesting properties of the FDLS algorithm are discovered. These properties are formally stated and proved in a higher-order logic theorem proving environment. These properties constitute a detailed set of formal assertions and invariants that should hold at various steps in the FDLS algorithm. They are then inserted as programming assertions in the implementation of the FDLS algorithm in a production-strength high-level synthesis system. When turned on, the programming assertions (1) certify whether a specific run of the FDLS algorithm produced correct schedules and, (2) in the event of failure, help discover and isolate programming errors in the FDLS implementation. We present a detailed example and several experiments to demonstrate the effectiveness of these assertions in discovering and isolating errors. Based on this experience, we discuss the role of the formal theorem proving exercise in developing a useful set of assertions for embedding in the scheduler code and argue that in the absence of such a formal proof checking effort, discovering such a useful set of assertions would have been an arduous if not impossible task.
Resource-Aware Algorithms for Distributed Loop Closure Detection with Provable Performance Guarantees

Inter-robot loop closure detection, e.g., for collaborative simultaneous localization and mapping (CSLAM), is a fundamental capability for many multirobot applications in GPS-denied regimes. In real-world scenarios, this is a resource-intensive process that involves exchanging observations and verifying potential matches. This poses severe challenges especially for small-size and low-cost robots with various operational and resource constraints that limit, e.g., energy consumption, communication bandwidth, and computation capacity. This paper presents resource-aware algorithms for distributed inter-robot loop closure detection. In particular, we seek to select a subset of potential inter-robot loop closures that maximizes a monotone submodular performance metric without exceeding computation and communication budgets. We demonstrate that this problem is in general NP-hard, and present efficient approximation algorithms with provable performance guarantees. A convex relaxation scheme is used to certify near-optimal performance of the proposed framework in real and synthetic SLAM benchmarks.
Human Electromagnetic Field Exposure in 5G at 28 GHz

The fifth-generation wireless (5G) has already started showing its capability to achieve extremely fast data transfer, which makes itself considered to be a promising mobile technology. However, concerns have been raised on adverse health impacts that human users can experience in a 5G system by being exposed to electromagnetic fields (EMFs). This article investigates the human EMF exposure in a 5G system and compares them with those measured in the previous-generation cellular systems. It suggests a minimum separation distance between a transmitter and a human user for keeping the EMF exposure below the safety regulation level, which provides consumers with a general understanding on the safe use of 5G communications.
Property rights, mobile capital, and comparative advantage

Recent papers show that imperfect property rights to a natural resource – a sector-specific factor–can be a source of comparative advantage. In these models, weaker property rights attract labor–the only mobile factor – to the resource sector, increasing the country's comparative advantage for that sector. If capital in addition to labor is mobile, and if the benefits of capital are non-excludable or if the degree of property rights is endogenous, a deterioration of property rights has ambiguous effects on comparative advantage and on the equilibrium wage/rental ratio.
Association of exogenous estrogen and endometrial carcinoma

To determine the association between the incidence of endometrial cancer and the use of estrogen in menopausal and post-menopausal women, we retrospectively compared 317 patients with adenocarcinoma of the endometrium with an equal number of matched controls having other gynecologic neoplasms; 152 patients used estrogen, as compared to 54 of 317 controls. Thus, the risk of endometrial cancer was 4.5 times greater among women exposed to estrogen therapy. When estrogen use was adjusted for concomitant variables such as obesity, hypertension, diabetes, parity, referral pattern, age at diagnosis, year of diagnosis and other gynecologic neoplasms, the magnitude of the increased relative risk was associated with several of these variables, and was highest in patients without obesity and hypertension. Exogenous estrogen therapy is associated with an increased risk of endometrial carcinoma, but this increased relative risk is less apparent in patients with physiologic characteristics previously associated with an increased risk.
Stability conditions on product varieties

Given a stability condition on a smooth projective variety $X$, we construct a family of stability conditions on $X\times C$, where $C$ is a smooth projective curve. In particular, this gives the existence of stability conditions on arbitrary products of curves. The proof uses, by following an idea of Toda, the positivity lemma established by Bayer and Macri and weak stability conditions on the Abramovich-Polishchuk heart of a bounded t-structure in $D(X\times C)$.
Dual Mirror Descent for Online Allocation Problems

We consider online allocation problems with concave revenue functions and resource constraints, which are central problems in revenue management and online advertising. In these settings, requests arrive sequentially during a finite horizon and, for each request, a decision maker needs to choose an action that consumes a certain amount of resources and generates revenue. The revenue function and resource consumption of each request are drawn independently and at random from a probability distribution that is unknown to the decision maker. The objective is to maximize cumulative revenues subject to a constraint on the total consumption of resources. We design a general class of algorithms that achieve sub-linear expected regret compared to the hindsight optimal allocation. Our algorithms operate in the Lagrangian dual space: they maintain a dual multiplier for each resource that is updated using online mirror descent. By choosing the reference function accordingly, we recover dual sub-gradient descent and dual exponential weights algorithm. The resulting algorithms are simple, efficient, and shown to attain the optimal order of regret when the length of the horizon and the initial number of resources are scaled proportionally. We discuss applications to online bidding in repeated auctions with budget constraints and online proportional matching with high entropy.
Multi-Sentence Argument Linking.

We present a novel document-level model for finding argument spans that fill an event’s roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution. Because existing datasets for cross-sentence linking are small, development of our neural model is supported through the creation of a new resource, Roles Across Multiple Sentences (RAMS), which contains 9,124 annotated events across 139 types. We demonstrate strong performance of our model on RAMS and other event-related datasets.
VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions

Comprehensive visual understanding requires detection frameworks that can effectively learn and utilize object interactions while analyzing objects individually. This is the main objective in Human-Object Interaction (HOI) detection task. In particular, relative spatial reasoning and structural connections between objects are essential cues for analyzing interactions, which is addressed by the proposed Visual-Spatial-Graph Network (VSGNet) architecture. VSGNet extracts visual features from the human-object pairs, refines the features with spatial configurations of the pair, and utilizes the structural connections between the pair via graph convolutions. The performance of VSGNet is thoroughly evaluated using the Verbs in COCO (V-COCO) dataset. Experimental results indicate that VSGNet outperforms state-of-the-art solutions by 8% or 4 mAP.
The digital ethnography of law: Studying online hate speech online and offline

The ethnography of social media is still a developing field, and the anthropology of online legal topics is even more incipient. This article charts a digital ethnography of the regulation of hate speech online by examining the infrastructure of social media platforms, the content of speech acts (including coded speech) and their offline effects. These three levels can be analysed using an adapted version of Erving Goffman’s heuristic model of backstage, onstage and offstage presentations of the self in everyday life. A digital ethnography of law implies both a qualitative and quantitative study of offline effects of online speech, including harmful consequences that are direct as well as indirect. On this basis, the article presents findings that, while it is difficult to identify direct effects of online hate speech on violence, show the indirect effects include the silencing of dissent and an undermining of trust and cooperation in wider society.
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders

Recent work showed that overparameterized autoencoders can be trained to implement associative memory via iterative maps, when the trained input-output Jacobian of the network has all of its eigenvalue norms strictly below one. Here, we theoretically analyze this phenomenon for sigmoid networks by leveraging recent developments in deep learning theory, especially the correspondence between training neural networks in the infinite-width limit and performing kernel regression with the Neural Tangent Kernel (NTK). We find that overparameterized sigmoid autoencoders can have attractors in the NTK limit for both training with a single example and multiple examples under certain conditions. In particular, for multiple training examples, we find that the norm of the largest Jacobian eigenvalue drops below one with increasing input norm, leading to associative memory.
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. Moreover, we leverage recent advances in data augmentation to guess low-entropy labels for unlabeled data, hence making them as easy to use as labeled data. By mixing labeled, unlabeled and augmented data, MixText significantly outperformed current pre-trained and fined-tuned models and other state-of-the-art semi-supervised learning methods on several text classification benchmarks. The improvement is especially prominent when supervision is extremely limited. We have publicly released our code at https://github.com/GT-SALT/MixText.
Grief and the COVID-19 pandemic in older adults

In few periods in human history have bereavement and grief been on so many people's minds as they are today. As the coronavirus disease 2019 (COVID-19) ravages the world, we have seen many perish in a short time. Many have died alone because of requirements for physical distancing. Even more will succumb as COVID-19 continues to spread. Moreover, deaths from other causes, numbering over 50 million annually, are also happening amid physical distancing and other COVID-19-related challenges. The pandemic is affecting the way terminally ill patients are being cared for, when and how people are dying of other causes, and how bodies are being handled and bereavement rituals performed. The bereaved are required to grieve without the support of usual social and cultural rituals. Grieving is further encumbered by cascading life stressors deriving from policies needed to mitigate the pandemic. Though we are often heartened by human resilience in response to death and other hardships, for some, the burden of this pandemic will be too much. Among other mental health problems, we will likely see an increase in prolonged grief disorder. In this commentary, we review the new diagnosis of prolonged grief disorder and outline why we might anticipate increased rates of this condition on the heels of COVID-19, especially among older persons. The authors suggest ways that might mitigate this emerging problem.
Cluster-Based Social Reinforcement Learning

Social Reinforcement Learning considers multi-agent systems with large number of agents and relatively few interactions between them, which is challenging due to high-dimensional search space, inter-agent dependencies that increase computational complexity. Moreover sparse agent interactions produce insufficient data to capture higher-order relations (interactions) for learning accurate policies. To overcome these challenges, we present a dynamic cluster-based Social RL approach that utilizes the properties of the social network structure, agent interactions, and correlations to obtain a compact model to represent network dynamics.
Vibration-heating in ADR Kevlar suspension systems

The cryogenics group at NASA's Goddard Space Flight Center has a long-standing development and test program for laboratory and space-flight adiabatic demagnetization refrigerators (ADRs). These devices are used to cool components to temperatures as low as 0.05 K. At such low temperatures the ADR systems can provide a few micro-Watts of cooling power, so it is important to minimize the conduction of heat to these cold stages from the surroundings. The cold ADR elements are held in place by thin tensioned strings made of Kevlar, chosen for its high strength and stiffness and low thermal conductivity. During laboratory testing, we have observed that occasional significant additional heat loads on the coldest ADR stages correlate with unusually high vibration levels in the cryostat due to a noisy mechanical cryocooler. We theorized that this heat results from plastic deformation of the Kevlar fibers and frictional interactions among them, driven by the cryostat vibrations. We describe tests and calculations performed in attempt to confirm this source of the heating.
Evaluating foam stability using tailored water chemistry for gas mobility control applications

Abstract Generating in-situ foam is regarded as one of the most promising techniques to overcome gas mobility issues and improve sweep efficiency in both miscible and immiscible gas injection enhanced oil recovery (EOR) processes. Gravity override, viscous fingering and channeling through permeable zones are the major limiting factors that can impair the efficiency of gas floods, mainly due to low density and viscosity of the gas relative to reservoir fluids. Generating strong and stable foam while injecting gas is one way to achieve in-depth conformance improvement in the reservoir. In this study, a tailored water chemistry (formulated low salinity water) has been evaluated in comparison to using typical high salinity injection water (i.e. seawater) and deionized water in surfactant solutions to determine its overall effect on the produced foam. Using bulk foam tests, foam rheology apparatus and microfluidics device, the foam stabilization factors were analyzed and quantified by measuring the foam-life over time of different surfactants in varying salinity water solutions. In addition, the foam rheological properties were measured under high pressure. The microfluidics device was also used to examine the generated foam strength in porous media. The results from laboratory experiments clearly demonstrated that the use of tailored water chemistry can improve the stability of produced foam when compared to both high salinity water and deionized water. Low salinity tailored water chemistry solutions resulted in a longer lasting foam, by almost 1.8–3.0 times depending on the surfactant type. The foam rheology results showed that the produced foams with the tailored low salinity water are of higher apparent viscosity when compared to those obtained with deionized water. Both longer foam-life and higher apparent viscosity are indicative of better, stronger and more stable foam. The higher resistance to gas flow was observed in porous media with foams generated using the low salinity tailored water chemistry solutions when compared to those foams obtained with deionized water and high salinity water. This experimental study, for the first time, demonstrated substantial improvements in the foam stability by using a tailored water chemistry aqueous solution. Such huge foam stabilization improvements obtained with tailored water chemistry has the promising potential to increase the apparent viscosity of injected gas and subsequently more effectively mitigate gas mobility issues encountered in EOR applications.
Strength and Hartshorne’s Conjecture in high degree

Hartshorne conjectured that a smooth, codimension c subvariety of n-dimensional projective space must be a complete intersection, whenever c is less than n/3. We prove this in the special case when n is much larger than the degree of the subvariety. Similar results were known in characteristic zero due to Hartshorne, Barth-Van de Ven, and others. Our proof is field independent and employs quite different methods from those previous results, as we connect Hartshorne's Conjecture with the circle of ideas initiated by Ananyan and Hochster in their proof of Stillman's Conjecture.
The role of accounting quality in corporate liquidity management

This paper studies the role of accounting quality in accessing credit lines for liquidity purposes. While the literature suggests that firms should meet liquidity needs through credit lines and not cash holdings, some firms rely on cash instead because of limited access to credit markets. I find low accruals quality firms use less credit lines and hold more cash. This relationship is more pronounced when firms are financially distressed, when credit markets are tightening, and during the 2007–2008 financial crisis. The spread and the use of cash flow covenants in credit lines are two channels through which accruals quality affects liquidity choice.
Proper Learning, Helly Number, and an Optimal SVM Bound

The classical PAC sample complexity bounds are stated for any Empirical Risk Minimizer (ERM) and contain an extra logarithmic factor, which is known to be necessary for ERM in general. It has been recently shown by Hanneke (2016) that the optimal sample complexity of PAC learning for any VC class C is achieved by a particular improper learning algorithm, which outputs a specific majority-vote of hypotheses in C. This leaves the question of when this bound can be achieved by proper learning algorithms, which are restricted to always output a hypothesis from C. \n\nIn this paper we aim to characterize the classes for which the optimal sample complexity can be achieved by a proper learning algorithm. We identify that these classes can be characterized by the dual Helly number, which is a combinatorial parameter that arises in discrete geometry and abstract convexity. In particular, under general conditions on C, we show that the dual Helly number is bounded if and only if there is a proper learner that obtains the optimal joint dependence on e and δ. \n\nAs further implications of our techniques we resolve a long-standing open problem posed by Vapnik and Chervonenkis (1974) on the performance of the Support Vector Machine by proving that the sample complexity of SVM in the realizable case is Θ((n/e)+(1/e) log(1/δ)), where n is the dimension. This gives the first optimal PAC bound for Halfspaces achieved by a proper learning algorithm, and moreover is computationally efficient.
TO DOLLARS FROM SENSE: QUALITATIVE TO QUANTITATIVE TRANSLATION IN JURY DAMAGE AWARDS

This article offers a new multistage account of jury damage award decision making. Drawing on psychological and economic research on judgment, decision making, and numeracy, the model posits that jurors first make a categorical gist judgment that money damages are warranted, and then make an ordinal gist judgment ranking the damages deserved as low, medium, or high. They then construct numbers that fit the gist of the appropriate magnitude. The article employs data from jury decision-making research to explore the plausibility of the model.
Acoustofluidic assembly of 3D neurospheroids to model Alzheimer's disease

Neuroinflammation plays a central role in the progression of many neurodegenerative diseases such as Alzheimer's disease, and challenges remain in modeling the complex pathological or physiological processes. Here, we report an acoustofluidic method that can rapidly construct 3D neurospheroids and inflammatory microenvironments for modeling microglia-mediated neuroinflammation in Alzheimer's disease. By incorporating a unique contactless and label-free acoustic assembly, this cell culture platform can assemble dissociated embryonic mouse brain cells into hundreds of uniform 3D neurospheroids with controlled cell numbers, composition (e.g. neurons, astrocytes, and microglia), and environmental components (e.g. amyloid-β aggregates) in hydrogel within minutes. Moreover, this platform can maintain and monitor the interaction among neurons, astrocytes, microglia, and amyloid-β aggregates in real-time for several days to weeks, after the integration of a high-throughput, time-lapse cell imaging approach. We demonstrated that our engineered 3D neurospheroids can represent the amyloid-β neurotoxicity, which is one of the main pathological features of Alzheimer's disease. Using this method, we also investigated the microglia migratory behaviors and activation in the engineered 3D inflammatory microenvironment at a high throughput manner, which is not easy to achieve in 2D neuronal cultures or animal models. Along with the simple fabrication and setup, the acoustofluidic technology is compatible with conventional Petri dishes and well-plates, supports the fine-tuning of the cellular and environmental components of 3D neurospheroids, and enables the high-throughput cellular interaction investigation. We believe our technology may be widely used to facilitate 3D in vitro brain models for modeling neurodegenerative diseases, discovering new drugs, and testing neurotoxicity.
A systematic evaluation of single-cell RNA-sequencing imputation methods

The rapid development of single-cell RNA-sequencing (scRNA-seq) technologies has led to the emergence of many methods for removing systematic technical noises, including imputation methods, which aim to address the increased sparsity observed in single-cell data. Although many imputation methods have been developed, there is no consensus on how methods compare to each other. Here, we perform a systematic evaluation of 18 scRNA-seq imputation methods to assess their accuracy and usability. We benchmark these methods in terms of the similarity between imputed cell profiles and bulk samples and whether these methods recover relevant biological signals or introduce spurious noise in downstream differential expression, unsupervised clustering, and pseudotemporal trajectory analyses, as well as their computational run time, memory usage, and scalability. Methods are evaluated using data from both cell lines and tissues and from both plate- and droplet-based single-cell platforms. We found that the majority of scRNA-seq imputation methods outperformed no imputation in recovering gene expression observed in bulk RNA-seq. However, the majority of the methods did not improve performance in downstream analyses compared to no imputation, in particular for clustering and trajectory analysis, and thus should be used with caution. In addition, we found substantial variability in the performance of the methods within each evaluation aspect. Overall, MAGIC, kNN-smoothing, and SAVER were found to outperform the other methods most consistently.
Pain and Physical Activity Association in Critically Ill Patients

Critical care patients experience varying levels of pain during their stay in the intensive care unit, often requiring administration of analgesics and sedation. Such medications generally exacerbate the already sedentary physical activity profiles of critical care patients, contributing to delayed recovery. Thus, it is important not only to minimize pain levels, but also to optimize analgesic strategies in order to maximize mobility and activity of ICU patients. Currently, we lack an understanding of the relation between pain and physical activity on a granular level. In this study, we examined the relationship between nurse assessed pain scores and physical activity as measured using a wearable accelerometer device. We found that average, standard deviation, and maximum physical activity counts are significantly higher before high pain reports compared to before low pain reports during both daytime and nighttime, while percentage of time spent immobile was not significantly different between the two pain report groups. Clusters detected among patients using extracted physical activity features were significant in adjusted logistic regression analysis for prediction of pain report group.
Inline vector compression for computational physics

Abstract A novel inline data compression method is presented for single-precision vectors in three dimensions. The primary application of the method is for accelerating computational physics calculations where the throughput is bound by memory bandwidth. The scheme employs spherical polar coordinates, angle quantisation, and a bespoke floating-point representation of the magnitude to achieve a fixed compression ratio of 1.5. The anisotropy of this method is considered, along with companding and fractional splitting techniques to improve the efficiency of the representation. We evaluate the scheme numerically within the context of high-order computational fluid dynamics. For both the isentropic convecting vortex and the Taylor–Green vortex test cases, the results are found to be comparable to those without compression. Performance is evaluated for a vector addition kernel on an NVIDIA Titan V GPU; it is demonstrated that a speedup of 1.5 can be achieved.
Exposed circuits, linear quotients, and chordal clutters

Abstract A graph G is said to be chordal if it has no induced cycles of length four or more. In a recent preprint Culbertson, Guralnik, and Stiller give a new characterization of chordal graphs in terms of sequences of what they call ‘edge-erasures’. We show that these moves are in fact equivalent to a linear quotient ordering on I G ‾ , the edge ideal of the complement graph. Known results imply that I G ‾ has linear quotients if and only if G is chordal, and hence this recovers an algebraic proof of their characterization. We investigate higher-dimensional analogues of this result, and show that in fact linear quotients for more general circuit ideals of d-clutters can be characterized in terms of removing exposed circuits in the complement clutter. Restricting to properly exposed circuits can be characterized by a homological condition. This leads to a notion of higher dimensional chordal clutters which borrows from commutative algebra and simple homotopy theory. The interpretation of linear quotients in terms of shellability of simplicial complexes also has applications to a conjecture of Simon regarding the extendable shellability of k-skeleta of simplices. Other connections to combinatorial commutative algebra, chordal complexes, and hierarchical clustering algorithms are explored.
COVID-IP: staring down the Bayh-Dole Act with 2020 vision

As the human and economic toll of the COVID-19 coronavirus steadily escalates, there is extreme uncertainty regarding the timeframe for prevention, detection, and treatment. There is also concern about the eventual costs associated with approved products and the barriers to access created by the patent system. Industry, government, and academic collaborations are leading the charge in the discovery race, partnerships which have triggered calls for the activation of the federal governments so-called ``march-in rights'' established in the Bayh-Dole Act. The Bayh-Dole Act dramatically altered the patent protections available to federally funded institutions and initiated a 40-year debate over appropriate incentives for innovation and the scope of the government's authority. The COVID-19 pandemic provides an opportunity to reflect on the purpose and impact of the historic legislation as well as contemplate the implications for our public health future. Patent rights for therapeutic compounds, methods of delivery, and medical diagnostics will significantly impact access to and cost of life-saving innovations. This article examines current calls for the U.S. government to utilize governmental march-in rights to quell concerns about patent monopolization and product pricing in the face of our current pandemic.
Impacts of genomic networks governed by human-specific regulatory sequences and genetic loci harboring fixed human-specific neuro-regulatory single nucleotide mutations on phenotypic traits of modern humans

Recent advances in identification and characterization of human-specific regulatory DNA sequences set the stage for the assessment of their global impact on physiology and pathology of modern humans. Gene set enrichment analyses (GSEA) of 8405 genes linked with 35,074 human-specific neuro-regulatory single-nucleotide changes (hsSNCs) revealed numerous significant associations with morphological structures, physiological processes, and pathological conditions of modern humans. Significantly enriched traits include more than 1000 anatomically distinct regions of the adult human brain, many different types of cells and tissues, more than 200 common human disorders, and more than 1000 records of rare diseases. Thousands of genes connected with neuro-regulatory hsSNCs have been identified, which represent essential genetic elements of the autosomal inheritance and offspring survival phenotypes. A total of 1494 hsSNC-linked genes are associated with either autosomal dominant or recessive inheritance, and 2273 hsSNC-linked genes have been associated with premature death, embryonic lethality, as well as pre-, peri-, neo-, and post-natal lethality phenotypes of both complete and incomplete penetrance. Differential GSEA implemented on hsSNC-linked loci and associated genes identify a set of 7990 hsSNC-target genes linked to evolutionary distinct classes of human-specific regulatory sequences (HSRS). Notably, the expression of a majority of these genes (5389 genes; 67%) is regulated by stem cell-associated retroviral sequences (SCARS) and SCARS-regulated genes captured a dominant fraction (91%) of significant phenotypic associations linked with hsSNCs. Interrogations of the MGI database revealed readily available mouse models tailored for precise experimental definitions of functional effects of hsSNCs and SCARS on genes causally affecting thousands of mammalian phenotypes and implicated in hundreds of common and rare human disorders. These observations suggest that a preponderance of human-specific traits evolved under a combinatorial regulatory control of distinct classes of HSRS and neuro-regulatory loci harboring hsSNCs that are fixed in humans, distinct from other primates, and located in differentially accessible chromatin regions during brain development.
Further Insight Relative to Cavity Radiation: A Thought Experiment Refuting Kirchhoff's Law

Kirchhoff’s law of thermal emission demands that all cavities contain blackbody, or normal, radiation which is dependent solely on the temperature and the frequency of observation, while remaining independent of the nature of the enclosure. For over 150 years, this law has stood as a great pillar for those who believe that gaseous stars could emit a blackbody spectrum. However, it is well-known that, under laboratory conditions, gases emit in bands and cannot produce a thermal spectrum. Furthermore, all laboratory blackbodies are constructed from nearly ideal absorbers. This fact strongly opposes the validity of Kirchhoff’s formulation. Clearly, if Kirchhoff had been correct, then laboratory blackbodies could be constructed of any arbitrary material. Through the use of two cavities in temperature equilibrium with one another, a thought experiment is presented herein which soundly refutes Kirchhoff’s law of thermal emission.
Evolution of resistance to fluoroquinolones by dengue virus serotype 4 provides insight into mechanism of action and consequences for viral fitness

Abstract Drugs against flaviviruses such as dengue (DENV) and Zika (ZIKV) virus are urgently needed. We previously demonstrated that three fluoroquinolones, ciprofloxacin, enoxacin, and difloxacin, suppress replication of six flaviviruses. To investigate the barrier to resistance and mechanism(s) of action of these drugs, DENV-4 was passaged in triplicate in HEK-293 cells in the presence or absence of each drug. Resistance to ciprofloxacin was detected by the seventh passage and to difloxacin by the tenth, whereas resistance to enoxacin did not occur within ten passages. Two putative resistance-conferring mutations were detected in the envelope gene of ciprofloxacin and difloxacin-resistant DENV-4. In the absence of ciprofloxacin, ciprofloxacin-resistant viruses sustained a significantly higher viral titer than control viruses in HEK-293 and HuH-7 cells and resistant viruses were more stable than control viruses at 37 °C. These results suggest that the mechanism of action of ciprofloxacin and difloxacin involves interference with virus binding or entry.
Rotating neutron stars

Because of the tremendous densities that exist in the cores of neutron stars, a significant fraction of the matter in the cores of such stars is likely to exist in the form of hyperons. Depending on spin frequency, the hyperon content changes dramatically in rotating neutron stars, as discussed in this paper. The study is based on a collection of equations of state, which includes relativistic mean-field (RMF) models, relativistic Brueckner-Hartree-Fock models, and density-dependent relativistic Brueckner-Hartree-Fock (DD-RBHF) models. Particular emphasis is put on the latter.
Thermal limit for spherical accretion and X-ray bursts

A mechanism is described whereby the rate of spherical accretion on a collapsed object and the resulting X-ray luminosity can be limited by thermal heating and evaporation of the accreting gas. The luminosity limit can be a factor of the order of 100 below the corresponding Eddington limit. Steady accretion flow is not possible within a range above this limit, and X-ray bursts can be produced by accretion surges resulting from the cooling and collapse of a heated gas shell above a critical optical depth. The luminosity and approximate recurrence rate of the bursts from NGC 6624 and other burst sources may then be understood. A general model for bursters is proposed in which massive (about 10 to 100 solar masses) black holes, from massive stars and the cores of disrupted globular clusters, undergo supercritical spherical accretion in interstellar clouds. Both X-ray and gamma-ray bursts are predicted to have similar hard X-ray (about 200 keV) spectra, though high average accretion rates and central optical depths also yield a comparable luminosity at around 5 keV from a Comptonized blackbody spectrum in the X-ray bursts and associated steady sources.
Impacts of the 1918 flu on survivors' nutritional status: A double quasi-natural experiment

Robust empirical evidence supports the idea that embryonic and, more generally, intrauterine disruptions induced by the 1918-flu pandemic had long-term consequences on adult health status and other conditions. In this paper we assess the 1918-flu long-term effects not just of in utero exposure but also during infancy and early childhood. A unique set of events that took place in Puerto Rico during 1918-1919 generated conditions of a "double quasi-natural experiment". We exploit these conditions to empirically identify effects of exposure to the 1918 flu pandemic and those of the devastation left by an earthquake-tsunami that struck the island in 1918. Because the earthquake-tsunami affected mostly the Western coast of the island whereas early (in utero and postnatal) exposure to the flu was restricted to those born in the interval 1917-1920, we use geographic variation to identify the effects of the quake and timing of birth variation to identify those of the flu. We benefit from availability of information on markers of nutritional status in a nationally representative sample of individuals aged 75 and older in 2002. We make two contributions. First, unlike most fetal-origins research that singles out early nutritional status as a determinant of adult health, we hypothesize that the 1918 flu damaged the nutritional status of adult survivors who, at the time of the flu, were in utero or infants. Second, we target markers of nutritional status largely set when the adult survivors were infants and young children. Estimates of effects of the pandemic are quite large mostly among females and those who were exposed to the earthquake-tsunami. Impacts of the flu in areas less affected by the earthquake are smaller but do vary by area flu severity. These findings constitute empirical evidence supporting the conjecture that effects of the 1918 flu and/or the earthquake are associated not just with disruption experienced during the fetal period but also postnatally.
Pore-Network Stitching Method: A Pore-to-Core Upscaling Approach for Multiphase Flow

Pore-network modeling is a widely used predictive tool for pore-scale studies in various applications that deal with multiphase flow in heterogeneous natural rocks. Despite recent improvements to enable pore-network modeling on simplified pore geometry extracted from rock core images and and its computational efficiency compared to direct numerical simulation methods, there are still limitations to modeling a large representative pore-network for heterogeneous cores. These are due to the technical limits on sample size to discern void space during X-ray scanning and computational limits on pore-network extraction algorithms. Thus, there is a need for pore-scale modeling approaches that have the natural advantages of pore-network modeling and can overcome these limitations, thereby enabling better representation of heterogeneity of the 3D complex pore structure and enhancing the accuracy of prediction of macroscopic properties. This paper addresses these issues with a workflow that includes a novel pore-network stitching method to provide large-enough representative pore-network for a core. This workflow uses micro-CT images of heterogeneous reservoir rock cores at different resolutions to characterize the pore structure in order to select few signature parts of the core and extract their equivalent pore-network models. The space between these signature pore-networks is filled by using their statistics to generate realizations of pore-networks which are then connected together using a deterministic layered stitching method. The output of this workflow is a large pore-network that can be used in any flow and transport solver. We validate all steps of this method on different types of natural rocks based on single-phase and two-phase flow properties such as drainage relative permeability curves of carbon dioxide and brine flow. Then, we apply the stochastic workflow on two large domain problems, connecting distant pore-networks and modeling a heterogeneous core. We generate multiple realizations and compare the average results with properties from a defined reference pore-network for each problem. We demonstrate that signature parts of a heterogeneous core, which are a small portion of its entire volume, are sufficient inputs for the developed pore-network stitching method to construct a representative pore-network and predict flow properties.
To pass (or not to pass) through international fuel price changes to domestic fuel prices in developing countries: What are the drivers?

Abstract This paper attempts to shed light on the drivers causing international fuel prices to be passed through to domestic retail fuel prices. While many developing countries limit the international fuel price pass through to domestic fuel prices, others do not. In the former, large fuel subsidies can emerge, thereby threatening fiscal sustainability, worsening income distribution and setting back efforts to fight climate change. Against this backdrop, we examine the factors that determine whether governments allow international fuel price changes to be passed through to domestic prices in developing countries using a dataset spanning 109 developing countries from 2000 to 2014. The paper finds that the pass-through is higher when changes in international prices are moderate and less volatile. In addition, the flexibility of the pricing mechanism allows for higher pass-through while exchange rate depreciation and lower retail fuel prices in neighboring countries inhibit it. The econometric results also underscore the fact that countries with inflation tend to experience lower pass-through, whereas those with high public debt exhibit larger pass-through. Finally, no evidence is found that political variables or environmental policies matter with regard to fuel price dynamics in the short-term. These findings, which are consistent across fuel products (gasoline, diesel and kerosene), allow us to draw important policy lessons for fuel subsidy reforms.
Implications of the Anomalous Outburst in the Blazar PKS 0208-512

The flat spectrum radio quasar (FSRQ) PKS 0208–512 underwent three outbursts at the optical-near-infrared (OIR) wavelengths during 2008-2011. The second OIR outburst did not have a γ-ray counterpart despite being comparable in brightness and temporal extent to the other two. We model the time variable spectral energy distribution of PKS 0208–512 during those three flaring episodes with leptonic models to investigate the physical mechanism that can produce this anomalous flare. We show that the redder-when-brighter spectral trend in the OIR bands can be explained by the superposition of a fixed thermal component from the accretion disk and a synchrotron component of fixed shape and variable normalization. We estimate the accretion disk luminosity at L d 8 × 1045 erg s–1. Using the observed variability timescale in the OIR band t var, obs 2 days and the X-ray luminosity L X 3.5 × 1045 erg s–1, we are able to constrain the location of the emitting region to distance scales that are broadly comparable with the dusty torus. We show that variations in the Compton dominance parameter by a factor of ~4—which may result in the anomalous outburst—can be relatively easily accounted for by moderate variations in the magnetic field strength or the location of the emission region. Since such variations appear to be rare among FSRQs, we propose that most γ-ray/OIR flares in these objects are produced in jet regions where the magnetic field and external photon fields vary similarly with distance along the jet, e.g., .