cssi/SciSciGPT-SciSciNet huggingface dataset:
Strain-Dependent Inhibition of Clostridioides difficile by Commensal Clostridia Carrying the Bile Acid-Inducible (bai) Operon.

ABSTRACT Clostridioides difficile is one of the leading causes of antibiotic-associated diarrhea. Gut microbiota-derived secondary bile acids and commensal Clostridia that carry the bile acid-inducible (bai) operon are associated with protection from C. difficile infection (CDI), although the mechanism is not known. In this study, we hypothesized that commensal Clostridia are important for providing colonization resistance against C. difficile due to their ability to produce secondary bile acids, as well as potentially competing against C. difficile for similar nutrients. To test this hypothesis, we examined the abilities of four commensal Clostridia carrying the bai operon (Clostridium scindens VPI 12708, C. scindens ATCC 35704, Clostridium hiranonis, and Clostridium hylemonae) to convert cholate (CA) to deoxycholate (DCA) in vitro, and we determined whether the amount of DCA produced was sufficient to inhibit the growth of a clinically relevant C. difficile strain. We also investigated the competitive relationships between these commensals and C. difficile using an in vitro coculture system. We found that inhibition of C. difficile growth by commensal Clostridia supplemented with CA was strain dependent, correlated with the production of ∼2 mM DCA, and increased the expression of bai operon genes. We also found that C. difficile was able to outcompete all four commensal Clostridia in an in vitro coculture system. These studies are instrumental in understanding the relationship between commensal Clostridia and C. difficile in the gut, which is vital for designing targeted bacterial therapeutics. Future studies dissecting the regulation of the bai operon in vitro and in vivo and how this affects CDI will be important. IMPORTANCE Commensal Clostridia carrying the bai operon, such as C. scindens, have been associated with protection against CDI; however, the mechanism for this protection is unknown. Herein, we show four commensal Clostridia that carry the bai operon and affect C. difficile growth in a strain-dependent manner, with and without the addition of cholate. Inhibition of C. difficile by commensals correlated with the efficient conversion of cholate to deoxycholate, a secondary bile acid that inhibits C. difficile germination, growth, and toxin production. Competition studies also revealed that C. difficile was able to outcompete the commensals in an in vitro coculture system. These studies are instrumental in understanding the relationship between commensal Clostridia and C. difficile in the gut, which is vital for designing targeted bacterial therapeutics.
End-to-end Learning of Waveform Generation and Detection for Radar Systems

An end-to-end learning approach is proposed for the joint design of transmitted waveform and detector in a radar system. Detector and transmitted waveform are trained alternately: For a fixed transmitted waveform, the detector is trained using supervised learning so as to approximate the Neyman-Pearson detector; and for a fixed detector, the transmitted waveform is trained using reinforcement learning based on feedback from the receiver. No prior knowledge is assumed about the target and clutter models. Both transmitter and receiver are implemented as feedforward neural networks. Numerical results show that the proposed end-to-end learning approach is able to obtain a more robust radar performance in clutter and colored noise of arbitrary probability density functions as compared to conventional methods, and to successfully adapt the transmitted waveform to environmental conditions.
Chromosome organization by one-sided and two-sided loop extrusion

SMC complexes, such as condensin or cohesin, organize chromatin throughout the cell cycle by a process known as loop extrusion. SMC complexes reel in DNA, extruding and progressively growing DNA loops. Modeling assuming two-sided loop extrusion reproduces key features of chromatin organization across different organisms. In vitro single-molecule experiments confirmed that yeast condensins extrude loops, however, they remain anchored to their loading sites and extrude loops in a 'one-sided' manner. We therefore simulate one-sided loop extrusion to investigate whether 'one-sided' complexes can compact mitotic chromosomes, organize interphase domains, and juxtapose bacterial chromosomal arms, as can be done by 'two-sided' loop extruders. While one-sided loop extrusion cannot reproduce these phenomena, variants can recapitulate in vivo observations. We predict that SMC complexes in vivo constitute effectively two-sided motors or exhibit biased loading and propose relevant experiments. Our work suggests that loop extrusion is a viable general mechanism of chromatin organization.
Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings

Involvement hot spots have been proposed as a useful concept for meeting analysis and studied off and on for over 15 years. These are regions of meetings that are marked by high participant involvement, as judged by human annotators. However, prior work was either not conducted in a formal machine learning setting, or focused on only a subset of possible meeting features or downstream applications (such as summarization). In this paper we investigate to what extent various acoustic, linguistic and pragmatic aspects of the meetings, both in isolation and jointly, can help detect hot spots. In this context, the openSMILE toolkit [1] is to used to extract features based on acoustic-prosodic cues, BERT word embeddings [2] are used for encoding the lexical content, and a variety of statistics based on speech activity are used to describe the verbal interaction among participants. In experiments on the annotated ICSI meeting corpus, we find that the lexical model is the most informative, with incremental contributions from interaction and acoustic-prosodic model components.
Free to Trade: Democracies, Autocracies, and International Trade

Relatively little research has focused on whether countries' political institutions affect their international trade relations. We address this issue by analyzing the relationship between regime type and trade policy. In a formal model of commercial policy, we establish that the ratification responsibility of the legislature in democratic states leads pairs of democracies to set trade barriers at a lower level than mixed country-pairs (composed of an autocracy and a democracy). We test this hypothesis by analyzing the effects of regime type on trade during the period from 1960 to 1990. The results of this analysis accord with our argument: Democratic pairs have had much more open trade relations than mixed pairs.
Railroads of the Raj: Estimating the Impact of Transportation Infrastructure

How large are the benefits of transportation infrastructure projects, and what explains these benefits? To shed new light on these questions, I collect archival data from colonial India and use it to estimate the impact of India's vast railroad network. Guided by six predictions from a general equilibrium trade model, I find that railroads: (1) decreased trade costs and interregional price gaps; (2) increased interregional and international trade; (3) eliminated the responsiveness of local prices to local productivity shocks (but increased the transmission of these shocks between regions); (4) increased the level of real income (but harmed neighboring regions without railroad access); (5) decreased the volatility of real income; and (6), a sufficient statistic for the effect of railroads on welfare in the model accounts for virtually all of the observed reduced-form impact of railroads on real income. I find similar results from an instrumental variable specification, no spurious effects from over 40,000 km of lines that were approved but never built, and tight bounds on the estimated impact of railroads. These results suggest that transportation infrastructure projects can improve welfare significantly, and do so because they allow regions to exploit gains from trade.
Sleep and diurnal rest-activity rhythm disturbances in a mouse model of Alzheimer’s disease

Study objectives Accumulating evidence suggests a strong association between sleep, amyloid-beta (Aβ) deposition, and Alzheimer's disease (AD). We sought to determine if (1) deficits in rest-activity rhythms and sleep are significant phenotypes in J20 AD mice, (2) metabotropic glutamate receptor 5 inhibitors (mGluR5) could rescue deficits in rest-activity rhythms and sleep, and (3) Aβ levels are responsive to treatment with mGluR5 inhibitors. Methods Diurnal rest-activity levels were measured by actigraphy and sleep-wake patterns by electroencephalography, while animals were chronically treated with mGluR5 inhibitors. Behavioral tests were performed, and Aβ levels measured in brain lysates. Results J20 mice exhibited a 4.5-h delay in the acrophase of activity levels compared to wild-type littermates and spent less time in rapid eye movement (REM) sleep during the second half of the light period. J20 mice also exhibited decreased non-rapid eye movement (NREM) delta power but increased NREM sigma power. The mGluR5 inhibitor CTEP rescued the REM sleep deficit and improved NREM delta and sigma power but did not correct rest-activity rhythms. No statistically significant differences were observed in Aβ levels, rotarod performance, or the passive avoidance task following chronic mGluR5 inhibitor treatment. Conclusions J20 mice have disruptions in rest-activity rhythms and reduced homeostatic sleep pressure (reduced NREM delta power). NREM delta power was increased following treatment with a mGluR5 inhibitor. Drug bioavailability was poor. Further work is necessary to determine if mGluR5 is a viable target for treating sleep phenotypes in AD.
Classification is a Strong Baseline for Deep Metric Learning.

Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images. Two major applications of metric learning are content-based image retrieval and face verification. For the retrieval tasks, the majority of current state-of-the-art (SOTA) approaches are triplet-based non-parametric training. For the face verification tasks, however, recent SOTA approaches have adopted classification-based parametric training. In this paper, we look into the effectiveness of classification based approaches on image retrieval datasets. We evaluate on several standard retrieval datasets such as CAR-196, CUB-200-2011, Stanford Online Product, and In-Shop datasets for image retrieval and clustering, and establish that our classification-based approach is competitive across different feature dimensions and base feature networks. We further provide insights into the performance effects of subsampling classes for scalable classification-based training, and the effects of binarization, enabling efficient storage and computation for practical applications.
Chapter 2 – Pituitary Gland

Publisher Summary Hormone secretions in the pituitary glands control the growth and activity of three other endocrine glands: the thyroid, adrenals, and gonads. This chapter describes the structure, physiology and functions of pituitary gland, and general aspects of the regulation of anterior pituitary function. It is a compound organ consisting of a neural or posterior lobe, a larger anterior portion, the adenohypophysis, and a small intermediate lobe. The hormones of pituitary are not limited in their activity to regulation of endocrine target glands; they also act directly on nonendocrine target tissues. There are six anterior pituitary hormones including thyrotropes, corticotropes, somatotropes, and lactotropes; all are proteins or glycoproteins. Secretion of anterior pituitary hormones is regulated by the central nervous system and hormones produced in peripheral target glands. Regulation of these hormones is subject to negative feedback inhibition by secretions of their target glands. In normal individuals, it is achieved through the interplay of stimulatory effects of releasing hormones and inhibitory effects of target gland hormones. The posterior pituitary gland secretes two hormones; oxytocin that increases uterine contractions during parturition and vasopressin that exerts contraction of vascular smooth muscle and thus raises blood pressure. The hormones of the posterior pituitary gland are synthesized and stored in nerve cells, and their secretion is controlled by neurotransmitters.
Neutrophils Are Required During Immunization With the Pneumococcal Conjugate Vaccine for Protective Antibody Responses and Host Defense Against Infection

Neutrophils can shape adaptive immunity; however, their role in vaccine-induced protection against infections in vivo remains unclear. Here, we tested their role in the clinically relevant polysaccharide conjugate vaccine against Streptococcus pneumoniae (pneumococcus). We antibody depleted neutrophils during vaccination, allowed them to recover, and 4 weeks later challenged mice with pneumococci. We found that while isotype-treated vaccinated controls were protected against an otherwise lethal infection in naive mice, full protection was lost upon neutrophil depletion. Compared to vaccinated controls, neutrophil-depleted mice had higher lung bacterial burdens, increased incidence of bacteremia, and lower survival rates. Sera from neutrophil-depleted mice had less antipneumococcal IgG2c and IgG3, were less efficient at inducing opsonophagocytic killing of bacteria by neutrophils in vitro, and were worse at protecting naive mice against pneumococcal pneumonia. In summary, neutrophils are required during vaccination for optimal host protection, which has important implications for future vaccine design against pneumococci and other pathogens.
An experiment protocol for a search for radio signals of extraterrestrial intelligent origin in the presence of man-made radio frequency sources

Abstract The major difficulty encountered in the conduct of a comprehensive search for radio signals of extraterrestrial intelligent origin is the vast range of fundamental parameters that must be examined to make significant inroads on the plausible signal regime. This problem is aggravated by the almost certain occurrence of mistaken detections caused by stochastic processes and by man-made radio frequency emissions. Each such mistake, unless quickly and unambiguously characterized as a false alarm, will require repetitive observations during the conduct of the program. This wasted time will, in effect, reduce the sensitivity achievable in a given search duration. This paper examines the implications of false alarms on the search system design, performance, and application and, in particular, on the choice of the data-processing approach. We show that, by an approach that limits the requirement for repeated observations, the degradation caused by false detections can be constrained to less than approximately 40%, whereas, without this restriction, the degradation might be severe. By modeling the mistaken detections caused by man-made devices as a Poisson process, we can place an upper bound on the probability of false alarms as a function of the detection threshold and of the number of interferors in the hemisphere. The understanding developed by these considerations suggests an experiment protocol consisting of a set of sequentially applied criteria to limit the range of parameters that need to be considered in declaring a false alarm, thus decreasing the time required to make this declaration and enhancing the performance of the search.
Chapter 3 – The Processor

Publisher Summary The central processing unit (CPU) guides the computer through the various steps of solving a problem. Data enters the computer through an input unit, is processed by the central processing unit, and is then made available to the user through an output unit. A logical view of a computer shows what functions the computer performs. A physical view of a computer shows how the mechanisms of the computer actually perform these functions. The three logical units that make up the central processing unit are the arithmetic and logic unit (ALU), main storage, and the control unit. Main storage retains active programs and data. It is relatively expensive, so secondary storage is used to store programs and data until they are needed in main storage. The set of a computer's built-in operations is called its “instruction set.” A computer program is a set of instructions that tells a computer how to solve a particular problem. A computer program must be in main storage for a computer to be able to perform its instructions.
Noise reduction techniques for use in determining local geomagnetic field changes.

Measurements of the difference in total field ΔF(t) have been made over a 16km N-S path near Boulder. The behavior observed is quite different from that for an E-W path. The present accuracy of the narrow line rubidium magnetometers used is about 0.01γ. The N-S variations appear to correlate mainly with variations in H rather than D, and may be associated with either gradients of external fields or currents in shallow conductivity anomalies. More recently three magnetometers have been set up on a straight E-W line so that the ‘second difference’ can be measured, and a transfer function from field component variation to the second difference can be determined. A generalization of this approach will be used for analyzing USGS tectonomagnetic data from California.
Says who? How the match between the social identity of organizations and their audiences increases perceptions of organizational authenticity:

Research shows that people systematically prefer “authentic” organizations above organizations that are not deemed authentic. Yet, people who desire authenticity may also be unable to detect what i...
Tectonic controls on rates and spatial patterns of glacial erosion through geothermal heat flux

Abstract Glacial erosion has shaped many mountain belts during the cold periods of the Late Cenozoic. The rate of glacial erosion is sensitive to the subglacial environment, including both the subglacial hydrology and the basal thermal regime. Geothermal heat from underlying bedrock is a major contributor to glacier energy budgets, controlling ice dynamics at the ice-bed interface by changing the basal temperature and the supply of meltwater. Despite the known influence of geothermal heat on the subglacial environment, its impact on glacial erosion has received little study. The geothermal heat flux in glaciated mountain ranges varies widely as a function of the tectonic setting. Therefore, if glacial erosion is sensitive to geothermal heat flux, the evolution of glaciated landscapes may depend upon tectonically-controlled geothermal gradients. We explore the impact of geothermal heat flux on the rates and spatial patterns of glacial erosion in mountain ranges using numerical models. We couple a sliding-dependent glacial erosion model with the Parallel Ice Sheet Model (PISM) to simulate the evolution of a synthetic glacial landscape. We find a robust tendency for increasing glacial erosion with increasing geothermal heat flux. The spatial pattern of erosion also varies with the magnitude of geothermal heat flux. At low geothermal heat flux, glacial erosion is consistently focused in major valleys. As geothermal heat flux increases, the area of significant glacial erosion expands into higher elevations and the rate of erosion increases. The location of maximum erosion migrates up-valley as geothermal heat flux increases, suggesting that glacial erosion tends to produce distinct landscapes as a function of geothermal heat flux. Our finding suggests that active mountain belts with high geothermal heat flux will express the glacial buzzsaw effect, in which high elevation topography is preferentially removed by glacial erosion. Glaciers at passive margins with low geothermal heat flux, in contrast, will tend to incise deep valleys at relatively low elevations. Previous work on the interaction between tectonics and landscape evolution has focused on relief generation and fracturing of rocks. Our results introduce a novel potential linkage between tectonics and erosion based on the sensitivity of glacial erosion to geothermal heat.
Fast Join Project Query Evaluation using Matrix Multiplication

In the last few years, much effort has been devoted to developing join algorithms to achieve worst-case optimality for join queries over relational databases. Towards this end, the database community has had considerable success in developing efficient algorithms that achieve worst-case optimal runtime for full join queries, i.e., joins without projections. However, not much is known about join evaluation with projections beyond some simple techniques of pushing down the projection operator in the query execution plan. Such queries have a large number of applications in entity matching, graph analytics and searching over compressed graphs. In this paper, we study how a class of join queries with projections can be evaluated faster using worst-case optimal algorithms together with matrix multiplication. Crucially, our algorithms are parameterized by the output size of the final result, allowing for choosing the best execution strategy. We implement our algorithms as a subroutine and compare the performance with state-of-the-art techniques to show they can be improved upon by as much as 50x. More importantly, our experiments indicate that matrix multiplication is a useful operation that can help speed up join processing owing to highly optimized open source libraries that are also highly parallelizable.
Peripheral myelin protein 22 preferentially partitions into ordered phase membrane domains

The ordered environment of cholesterol-rich membrane nanodomains is thought to exclude many transmembrane (TM) proteins. Nevertheless, some multispan helical transmembrane proteins have been proposed to partition into these environments. Here, giant plasma membrane vesicles (GPMVs) were employed to quantitatively show that the helical tetraspan peripheral myelin protein 22 (PMP22) exhibits a pronounced preference for, promotes the formation of, and stabilizes ordered membrane domains. Neither S-palmitoylation of PMP22 nor its putative cholesterol binding motifs are required for this preference. In contrast, Charcot-Marie-Tooth disease-causing mutations that disrupt the stability of PMP22 tertiary structure reduce or eliminate this preference in favor of the disordered phase. These studies demonstrate that the ordered phase preference of PMP22 derives from global structural features associated with the folded form of this protein, providing a glimpse at the structural factors that promote raft partitioning for multispan helical membrane proteins.
Improving Non-autoregressive Neural Machine Translation with Monolingual Data

Non-autoregressive (NAR) neural machine translation is usually done via knowledge distillation from an autoregressive (AR) model. Under this framework, we leverage large monolingual corpora to improve the NAR model’s performance, with the goal of transferring the AR model’s generalization ability while preventing overfitting. On top of a strong NAR baseline, our experimental results on the WMT14 En-De and WMT16 En-Ro news translation tasks confirm that monolingual data augmentation consistently improves the performance of the NAR model to approach the teacher AR model’s performance, yields comparable or better results than the best non-iterative NAR methods in the literature and helps reduce overfitting in the training process.
Sociality and interaction envelope organize visual action representations.

Humans observe a wide range of actions in their surroundings. How is the visual cortex organized to process this diverse input? Using functional neuroimaging, we measured brain responses while participants viewed short videos of everyday actions, then probed the structure in these responses using voxel-wise encoding modeling. Responses are well fit by feature spaces that capture the body parts involved in an action and the action’s targets (i.e. whether the action was directed at an object, another person, the actor, and space). Clustering analyses reveal five large-scale networks that summarize the voxel tuning: one related to social aspects of an action, and four related to the scale of the interaction envelope, ranging from fine-scale manipulations directed at objects, to large-scale whole-body movements directed at distant locations. We propose that these networks reveal the major representational joints in how actions are processed by visual regions of the brain. How is action perception organized in the brain? Here, the authors report evidence for five networks tuned to actions’ social content and the scale of their effect on the world and propose that sociality and interaction envelope are organizing dimensions of visual action representation.
Learning Generative Models of Shape Handles

We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations. Our model can generate handle sets with varying cardinality and different types of handles. Key to our approach is a deep architecture that predicts both the parameters and existence of shape handles and a novel similarity measure that can easily accommodate different types of handles, such as cuboids or sphere-meshes. We leverage the recent advances in semantic 3D annotation as well as automatic shape summarization techniques to supervise our approach. We show that the resulting shape representations are not only intuitive, but achieve superior quality than previous state-of-the-art. Finally, we demonstrate how our method can be used in applications such as interactive shape editing and completion, leveraging the latent space learned by our model to guide these tasks.
Basis Prediction Networks for Effective Burst Denoising With Large Kernels

Bursts of images exhibit significant self-similarity across both time and space. This motivates a representation of the kernels as linear combinations of a small set of basis elements. To this end, we introduce a novel basis prediction network that, given an input burst, predicts a set of global basis kernels --- shared within the image --- and the corresponding mixing coefficients --- which are specific to individual pixels. Compared to state-of-the-art techniques that output a large tensor of per-pixel spatiotemporal kernels, our formulation substantially reduces the dimensionality of the network output. This allows us to effectively exploit comparatively larger denoising kernels, achieving both significant quality improvements (over 1dB PSNR) and faster run-times over state-of-the-art methods.
Goal-Aware Prediction: Learning to Model What Matters

Learned dynamics models combined with both planning and policy learning algorithms have shown promise in enabling artificial agents to learn to perform many diverse tasks with limited supervision. However, one of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model (future state reconstruction), and that of the downstream planner or policy (completing a specified task). This issue is exacerbated by vision-based control tasks in diverse real-world environments, where the complexity of the real world dwarfs model capacity. In this paper, we propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space, resulting in a learning objective that more closely matches the downstream task. Further, we do so in an entirely self-supervised manner, without the need for a reward function or image labels. We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
Multiple Pursuer Multiple Evader Differential Games

In this article an $N$ -pursuer versus $M$ -evader team conflict is studied. This article extends classical differential game theory to simultaneously address weapon assignments and multiplayer pursuit-evasion scenarios. Saddle-point strategies that provide guaranteed performance for each team regardless of the actual strategies implemented by the opponent are devised. The players’ optimal strategies require the codesign of cooperative optimal assignments and optimal guidance laws. A representative measure of performance is employed and the Value function of the attendant game is obtained. It is shown that the Value function is continuously differentiable and that it satisfies the Hamilton–Jacobi–Isaacs equation—the curse of dimensionality is overcome and the optimal strategies are obtained. The cases of $N=M$ and $N>M$ are considered. In the latter case, cooperative guidance strategies are also developed in order for the pursuers to exploit their numerical advantage. This article provides a foundation to formally analyze complex and high-dimensional conflicts between teams of $N$ pursuers and $M$ evaders by means of differential game theory.
Water Assisted Proton Transport in Confined Nanochannels

Hydrated excess protons under hydrophobic confinement are a critical component of charge transport behavior and reactivity in nanoporous materials and biomolecular systems. Herein, excess proton confinement effects are computationally investigated for sub-2 nm hydrophobic nanopores by varying the diameters (d = 0.81, 0.95, 1.09, 1.22, 1.36, 1.63, and 1.90 nm), lengths (l ∼3 and ∼5 nm), curvature, and chirality of cylindrical carbon nanotube (CNT) nanopores. CNTs with a combination of different diameter segments are also explored. The spatial distribution of water molecules under confinement is diameter-dependent; however, proton solvation and transport are consistently found to occur in the water layer adjacent to the pore wall, showing an “amphiphilic” character of the hydrated excess proton hydronium-like structure. The proton transport free energy barrier also decreases significantly as the nanopore diameter increases and proton transport becomes almost barrierless in the d > 1 nm nanopores. Among the nanopores studied, the Zundel cation (H5O2+) is populated only in the d = 0.95 nm CNT (7,7) nanopore. The presence of the hydrated excess proton and K+ inside the CNT (7,7) nanopore induces a water density increase by 40 and 20%, respectively. The K+ transport through CNT nanopores is also consistently higher in the free energy barrier than proton transport. Interestingly, the evolution of excess protonic charge defect distribution reveals a “frozen” single water wire configuration in the d = 0.81 nm CNT (6,6) nanopore (or segment), through which hydrated excess protons can only shuttle via the Grotthuss mechanism. Vehicular diffusion becomes relevant to proton transport in the “flat” free energy regions and in the wider nanopores, where protons do not primarily shuttle in the axial direction.
Rabi oscillations and Ramsey-type pulses in ultracold bosons: Role of interactions

Two realizations of a two-state model in a rubidium Bose-Einstein condensate are explored through Raman coupling of hyperfine states and lattice coupling in momentum space in a joint experimental and theoretical study. The difference in the two realizations is highlighted by a particularly important role of interactions in the lattice coupling case.
Linear Stability of Higher Dimensional Schwarzschild Spacetimes: Decay of Master Quantities

In this paper, we study solutions to the linearized vacuum Einstein equations centered at higher-dimensional Schwarzschild metrics. We employ Hodge decomposition to split solutions into scalar, co-vector, and two-tensor pieces; the first two portions respectively correspond to the closed and co-closed, or polar and axial, solutions in the case of four spacetime dimensions, while the two-tensor portion is a new feature in the higher-dimensional setting. Rephrasing earlier work of Kodama-Ishibashi-Seto in the language of our Hodge decomposition, we produce decoupled gauge-invariant master quantities satisfying Regge-Wheeler type wave equations in each of the three portions. The scalar and co-vector quantities respectively generalize the Moncrief-Zerilli and Regge-Wheeler quantities found in the setting of four spacetime dimensions; beyond these quantities, we discover a higher-dimensional analog of the Cunningham-Moncrief-Price quantity in the co-vector portion. In addition, our work provides the first verification that the scalar master quantity satisfies its putative Regge-Wheeler equation. In the analysis of the master quantities, we strengthen the mode stability result of Kodama-Ishibashi to a uniform boundedness estimate in all dimensions; further, we prove decay estimates in the case of six or fewer spacetime dimensions. In the case of more than six spacetime dimensions, we discover an obstruction to Morawetz type estimates arising from negative potential terms growing quadratically in spacetime dimension. Finally, we provide a rigorous argument that linearized solutions of low angular frequency are decomposable as a sum of pure gauge solution and linearized Myers-Perry solution, the latter solutions generalizing the linearized Kerr solutions in four spacetime dimensions.
SERINC5 Potently Restricts Retrovirus Infection In Vivo.

ABSTRACT The serine incorporator (SERINC) proteins are multipass transmembrane proteins that affect sphingolipid and phosphatidylserine synthesis. Human SERINC5 and SERINC3 were recently shown to possess antiretroviral activity for a number of retroviruses, including human immunodeficiency virus (HIV), murine leukemia virus (MLV), and equine infectious anemia virus (EIAV). In the case of MLV, the glycosylated Gag (glyco-Gag) protein was shown to counteract SERINC5-mediated restriction in in vitro experiments and the viral envelope was found to determine virion sensitivity or resistance to SERINC5. However, nothing is known about the in vivo function of SERINC5. Antiretroviral function of a host factor in vitro is not always associated with antiretroviral function in vivo. Using SERINC5−/− mice that we had generated, we showed that mouse SERINC5 (mSERINC5) restriction of MLV infection in vivo is influenced not only by glyco-Gag but also by the retroviral envelope. Finally, we also examined the in vivo function of the other SERINC gene with known antiretroviral functions, SERINC3. By using SERINC3−/− mice, we found that the murine homologue, mSERINC3, had no antiretroviral role either in vivo or in vitro. To our knowledge, this report provides the first data showing that SERINC5 restricts retrovirus infection in vivo and that restriction of retrovirus infectivity in vivo is dependent on the presence of both glyco-Gag and the viral envelope. IMPORTANCE This study examined for the first time the in vivo function of the serine incorporator (SERINC) proteins during retrovirus infection. SERINC3 and SERINC5 (SERINC3/5) restrict a number of retroviruses, including human immunodeficiency virus 1 (HIV-1) and murine leukemia virus (MLV), by blocking their entry into cells. Nevertheless, HIV-1 and MLV encode factors, Nef and glycosylated Gag, respectively, that counteract SERINC3/5 in vitro. We recently developed SERINC3 and SERINC5 knockout mice to examine the in vivo function of these genes. We found that SERINC5 restriction is dependent on the absence of glycosylated Gag and the expression of a specific viral envelope glycoprotein. On the other hand, SERINC3 had no antiviral function. Our findings have implications for the development of therapeutics that target SERINC5 during retrovirus infection.
Morphisms between categorified spin networks

We introduce a graphical calculus for computing morphism spaces between the categorified spin networks of Cooper and Krushkal. The calculus, phrased in terms of planar compositions of categorified ...
Tree-projected gradient descent for estimating gradient-sparse parameters on graphs

We study estimation of a gradient-sparse parameter vector $\boldsymbol{\theta}^* \in \mathbb{R}^p$, having strong gradient-sparsity $s^*:=\|\nabla_G \boldsymbol{\theta}^*\|_0$ on an underlying graph $G$. Given observations $Z_1,\ldots,Z_n$ and a smooth, convex loss function $\mathcal{L}$ for which $\boldsymbol{\theta}^*$ minimizes the population risk $\mathbb{E}[\mathcal{L}(\boldsymbol{\theta};Z_1,\ldots,Z_n)]$, we propose to estimate $\boldsymbol{\theta}^*$ by a projected gradient descent algorithm that iteratively and approximately projects gradient steps onto spaces of vectors having small gradient-sparsity over low-degree spanning trees of $G$. We show that, under suitable restricted strong convexity and smoothness assumptions for the loss, the resulting estimator achieves the squared-error risk $\frac{s^*}{n} \log (1+\frac{p}{s^*})$ up to a multiplicative constant that is independent of $G$. In contrast, previous polynomial-time algorithms have only been shown to achieve this guarantee in more specialized settings, or under additional assumptions for $G$ and/or the sparsity pattern of $\nabla_G \boldsymbol{\theta}^*$. As applications of our general framework, we apply our results to the examples of linear models and generalized linear models with random design.
Co-phosphorylation networks reveal subtype-specific signaling modules in breast cancer

Motivation Protein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as downregulation of tumor suppressors and upregulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers. Results We develop a network-based algorithm, CoPPNet, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural and functional association of phosphosites, kinase-substrate associations and protein-protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific-weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry-based phosphorylation datasets for breast cancer (BC), and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive BC subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers. Availability and implementation CoPPNet is available at http://compbio.case.edu/coppnet/. Supplementary information Supplementary data are available at Bioinformatics online.
Racial and Ethnic Disparities in Population-Level Covid-19 Mortality.

Abstract Background Current reporting of Covid-19 mortality data by race and ethnicity across the United States could bias our understanding of population-mortality disparities. Moreover, stark differences in age distribution by race and ethnicity groups are seldom accounted for in analyses. Methods To address these gaps, we conducted a cross-sectional study using publicly-reported Covid-19 mortality data to assess the quality of race and ethnicity data (Black, Latinx, white), and estimated age-adjusted disparities using a random effects meta-analytic approach. Results We found only 28 states, and NYC, reported race and ethnicity-stratified Covid-19 mortality along with large variation in the percent of missing race and ethnicity data by state. Aggregated relative risk of death estimates for Black compared to the white population was 3.57 (95% CI: 2.84-4.48). Similarly, Latinx population displayed 1.88 (95% CI: 1.61-2.19) times higher risk of death than white patients. Discussion In states providing race and ethnicity data, we identified significant population-level Covid-19 mortality disparities. We demonstrated the importance of adjusting for age differences across population groups to prevent underestimating disparities in younger population groups. The availability of high-quality and comprehensive race and ethnicity data is necessary to address factors contributing to inequity in Covid-19 mortality.
Mapping the design space of photonic topological states via deep learning

Topological states in photonics offer novel prospects for guiding and manipulating photons and facilitate the development of modern optical components for a variety of applications. Over the past few years, photonic topology physics has evolved and unveiled various unconventional optical properties in these topological materials, such as silicon photonic crystals. However, the design of such topological states still poses a significant challenge. Conventional optimization schemes often fail to capture their complex high dimensional design space. In this manuscript, we develop a deep learning framework to map the design space of topological states in the photonic crystals. This framework overcomes the limitations of existing deep learning implementations. Specifically, it reconciles the dimension mismatch between the input (topological properties) and output (design parameters) vector spaces and the non-uniqueness that arises from one-to-many function mappings. We use a fully connected deep neural network (DNN) architecture for the forward model and a cyclic convolutional neural network (cCNN) for the inverse model. The inverse architecture contains the pre-trained forward model in tandem, thereby reducing the prediction error significantly.
Induced metal-free star formation around a massive black hole seed

The direct formation of a massive black hole is a potential seeding mechanism of the earliest observed supermassive black holes. We investigate how the existence of a massive black hole seed impacts the ionization and thermal state of its pre-galactic host halo and subsequent star formation. We show that its X-ray radiation ionizes and heats the medium, enhancing $\rm{H}_2$ formation in shielded regions, within the nuclear region in the span of a million years. The enhanced molecular cooling triggers the formation of a $\sim 10^4~{\rm M}_\odot$ metal-free stellar cluster at a star formation efficiency of $\sim 0.1\%$ in a single event. Star formation occurs near the edges of the H II region that is partially ionized by X-rays, thus the initial size depends on the black hole properties and surrounding environment. The simulated metal-free galaxy has an initial half-light radius of $\sim 10$ pc but expands to $\sim 50$ pc after 10 million years because of the outward velocities of their birth clouds. Supernova feedback then quenches any further star formation for tens of millions of years, allowing the massive black hole to dominate the spectrum once the massive metal-free stars die.
Temporal Trends in Opioid Prescribing Patterns Among Oncologists in the Medicare Population

BACKGROUND In the wake of the US opioid epidemic, there have been efforts to curb opioid prescribing. However, it is unknown whether these efforts have affected prescribing among oncologists, whose patients often require opioids for symptom management. We investigated temporal patterns in opioid prescribing for Medicare beneficiaries among oncologists and nononcologists. METHODS We queried the Centers for Medicare and Medicaid Services Part D prescriber dataset for all physicians between January 1, 2013, and December 31, 2017. We used population-averaged multivariable negative binomial regression to estimate the association between time and per-provider opioid and gabapentinoid prescribing rate, defined as the annual number of drug claims (original prescriptions and refills) per beneficiary, among oncologists and nononcologists on a national and state level. RESULTS From 2013 to 2017, the national opioid-prescribing rate declined by 20.7% (P < .001) among oncologists and 22.8% (P < .001) among non oncologists. During this time frame, prescribing of gabapentin increased by 5.9% (P < .001) and 23.1% (P < .001) among oncologists and nononcologists, respectively. Among palliative care providers, opioid prescribe increased by 15.3% (P < .001). During the 5-year period, 43 states experienced a decrease (P < .05) in opioid prescribing among oncologists, and in 5 states, opioid prescribing decreased more among oncologists than nononcologists (P < .05). CONCLUSIONS Between 2013 and 2017, the opioid-prescribing rate statistically significantly decreased nationwide among oncologists and nononcologists, respectively. Given similar declines in opioid prescribing among oncologists and nononcologists, there is concern that opioid-prescribing guidelines intended for the noncancer population are being applied inappropriately to patients with cancer and cancer survivors.
Explaining the nonlinear response of stock markets to oil price shocks

Abstract This paper is set to reconcile the existent conflicting empirical evidence on the effect of oil prices on stock prices. We estimate various nonlinear models where the response changes according to a first-order Markov switching process. More importantly, we model the transition probabilities between the high- and low-response regimes to depend on state variables to allow us to explain the forces behind the asymmetry in the response. The results show statistically significant asymmetries that can be explained by economic recessions and to a lower extent depend on the magnitude of the oil price shift and on whether the shift is positive or negative. In the high response regime, the effect is positive and lasts longer. We also find evidence of asymmetries in the response of stock prices to crude oil supply shocks, global aggregate demand shocks, and oil-specific demand shocks.
Tailoring a Pair of Pants: The Phase Tropical Version

We show that the phase tropical pair-of-pants ⊂ (ℂ*)n is (ambient) isotopic to the complex pair-of-pants P° ⊂ (ℂ*)n. The existence of an isotopy between the complex and ober-tropical pairs-of-pants was recently established by the author jointly with H. Ruddat. Thereby all the three (complex, phase tropical, and ober-tropical) versions are isotopic.
On the Spectral Property of Kernel-Based Sensor Fusion Algorithms of High Dimensional Data

We apply local laws of random matrices and free probability theory to study the spectral properties of two kernel-based sensor fusion algorithms, nonparametric canonical correlation analysis (NCCA) and alternating diffusion (AD), for two simultaneously recorded high dimensional datasets under the null hypothesis. The matrix of interest is the product of the kernel matrices associated with the databsets, which may not be diagonalizable in general. We prove that in the regime where dimensions of both random vectors are comparable to the sample size, if NCCA and AD are conducted using a smooth kernel function, then the first few nontrivial eigenvalues will converge to real deterministic values provided the datasets are independent Gaussian random vectors. Toward the claimed result, we also provide a convergence rate of eigenvalues of a kernel affinity matrix.