cssi/SciSciGPT-SciSciNet huggingface dataset:
The landscape of myeloid and astrocyte phenotypes in acute multiple sclerosis lesions

Activated myeloid cells and astrocytes are the predominant cell types in active multiple sclerosis (MS) lesions. Both cell types can adopt diverse functional states that play critical roles in lesion formation and resolution. In order to identify phenotypic subsets of myeloid cells and astrocytes, we profiled two active MS lesions with thirteen glial activation markers using imaging mass cytometry (IMC), a method for multiplexed labeling of histological sections. In the acutely demyelinating lesion, we found multiple distinct myeloid and astrocyte phenotypes that populated separate lesion zones. In the post-demyelinating lesion, phenotypes were less distinct and more uniformly distributed. In both lesions cell-to-cell interactions were not random, but occurred between specific glial subpopulations and lymphocytes. Finally, we demonstrated that myeloid, but not astrocyte phenotypes were activated along a lesion rim-to-center gradient, and that marker expression in glial cells at the lesion rim was driven more by cell-extrinsic factors than in cells at the center. This proof-of-concept study demonstrates that highly multiplexed tissue imaging, combined with the appropriate computational tools, is a powerful approach to study heterogeneity, spatial distribution and cellular interactions in the context of MS lesions. Identifying glial phenotypes and their interactions at different lesion stages may provide novel therapeutic targets for inhibiting acute demyelination and low-grade, chronic inflammation.
Towards a Zero-One Law for Column Subset Selection

There are a number of approximation algorithms for NP-hard versions of low rank approximation, such as finding a rank-$k$ matrix $B$ minimizing the sum of absolute values of differences to a given $n$-by-$n$ matrix $A$, $\min_{\textrm{rank-}k~B}\|A-B\|_1$, or more generally finding a rank-$k$ matrix $B$ which minimizes the sum of $p$-th powers of absolute values of differences, $\min_{\textrm{rank-}k~B}\|A-B\|_p^p$. Many of these algorithms are linear time columns subset selection algorithms, returning a subset of $\poly(k \log n)$ columns whose cost is no more than a $\poly(k)$ factor larger than the cost of the best rank-$k$ matrix. The above error measures are special cases of the following general entrywise low rank approximation problem: given an arbitrary function $g:\mathbb{R} \rightarrow \mathbb{R}_{\geq 0}$, find a rank-$k$ matrix $B$ which minimizes $\|A-B\|_g = \sum_{i,j}g(A_{i,j}-B_{i,j})$. A natural question is which functions $g$ admit efficient approximation algorithms? Indeed, this is a central question of recent work studying generalized low rank models. In this work we give approximation algorithms for {\it every} function $g$ which is approximately monotone and satisfies an approximate triangle inequality, and we show both of these conditions are necessary. Further, our algorithm is efficient if the function $g$ admits an efficient approximate regression algorithm. Our approximation algorithms handle functions which are not even scale-invariant, such as the Huber loss function, which we show have very different structural properties than $\ell_p$-norms, e.g., one can show the lack of scale-invariance causes any column subset selection algorithm to provably require a $\sqrt{\log n}$ factor larger number of columns than $\ell_p$-norms; nevertheless we design the first efficient column subset selection algorithms for such error measures.
The monoid structure on homotopy obstructions

Abstract Let A be a commutative noetherian ring, containing a field k, with 1 / 2 ∈ k , dim ⁡ A = d , and let P be a projective A-module, with r a n k ( P ) = n . Let L O ( P ) denote the set of all pairs ( I , ω ) , where I is an ideal of A and ω : P ↠ I / I 2 is a surjective map. The homotopy relations on L O ( P ) , induced by L O ( P [ T ] ) , leads to a set π 0 ( L O ( P ) ) of equivalence classes in L O ( P ) . There are two distinguished elements e 0 , e 1 ∈ π 0 ( L O ( P ) ) , respectively, the images of ( 0 , 0 ) and ( A , 0 ) . Define the obstruction class e ( P ) = e 0 ∈ π 0 ( L O ( P ) ) , to be called the (Nori) homotopy class of P. The following results are under suitable smoothness or regularity hypotheses. We prove, if 2 n ≥ d + 2 , then π 0 ( L O ( P ) ) has a natural structure of a monoid, which is a group if P ≅ Q ⊕ A . When 2 n ≥ d + 3 , we prove P ≅ Q ⊕ A ⟺ e ( P ) = e 1 (“the additive zero”) . Further, we give a definition of a Euler class group E ( P ) . Under suitable smoothness hypotheses, we prove, if P ≅ Q ⊕ A and 2 n ≥ d + 3 , then there is natural isomorphism E ( P ) ⟶ ∼ π 0 ( L O ( P ) ) of groups.
Recoverable Mutual Exclusion with Abortability

In light of recent advances in non-volatile main memory technology, there has been a flurry of research in designing algorithms that are resilient to process crashes. As a result of main memory non-volatility, a process is allowed to crash any time during the execution, without affecting the state of the data stored in the main memory. With the assumption that a process eventually restarts after a crash, prior works have focused on designing mutual exclusion algorithms that use the non-volatile main memory to recover from such crashes. Such mutual exclusion algorithms that provide multiple processes with a mutually exclusive access to a shared resource in the presence of process crashes are called Recoverable Mutual Exclusion (RME) algorithms. We present the first RME algorithm where a process has the ability to abort executing the algorithm, if it decides to give up its request for a shared resource before being granted access to that resource. With n being the maximum number of processes for which the algorithm is designed, in the absence of a crash our algorithm guarantees a worst-case remote memory references (RMR) complexity of \(O(\log n)\) per passage on the Distributed Shared Memory (DSM) machines, and a complexity of \(O(\log n)\) or O(n) on Cache Coherent (CC) machines, depending on how caches are managed.
Device-to-Device Secure Coded Caching

This paper studies device to device (D2D) coded-caching with information theoretic security guarantees. A broadcast network consisting of a server, which has a library of files, and end users equipped with cache memories, is considered. Information theoretic security guarantees for confidentiality are imposed upon the files. The server populates the end user caches, after which D2D communications enable the delivery of the requested files. Accordingly, we require that a user must not have access to files it did not request, i.e., secure caching. First, a centralized coded caching scheme is provided by jointly optimizing the cache placement and delivery policies. Next, a decentralized coded caching scheme is developed that does not require the knowledge of the number of active users during the caching phase. Both schemes utilize non-perfect secret sharing and one-time pad keying, to guarantee secure caching. Furthermore, the proposed schemes provide secure delivery as a side benefit, i.e., any external entity which overhears the transmitted signals during the delivery phase cannot obtain any information about the database files. The proposed schemes provide the achievable upper bound on the minimum delivery sum rate. Lower bounds on the required transmission sum rate are also derived using cut-set arguments indicating the multiplicative gap between the lower and upper bounds. Numerical results indicate that the gap vanishes with increasing memory size. Overall, the work demonstrates the effectiveness of D2D communications in cache-aided systems even when confidentiality constraints are imposed at the participating nodes and against external eavesdroppers.
Legacy and Athlete Preferences at Harvard

We use public documents from the Students for Fair Admissions v. Harvard University lawsuit to examine admissions preferences for recruited athletes, legacies, those on the dean’s interest list, an...
Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles

In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach areas. This system utilizes autonomous unmanned aerial vehicles (UAVs) with the main advantage of providing on-demand monitoring service faster than the current approaches of using satellite images, manned aircraft and remotely controlled drones. Furthermore, using autonomous drones facilitates minimizing human intervention in risky wildfire zones. In particular, to develop a fully autonomous system, we propose a distributed leader-follower coalition formation model to cluster a set of drones into multiple coalitions that collectively cover the designated monitoring field. The coalition leader is a drone that employs observer drones potentially with different sensing and imaging capabilities to hover in circular paths and collect imagery information from the impacted areas. The objectives of the proposed system include: i) to cover the entire fire zone with a minimum number of drones, and ii) to minimize the energy consumption and latency of the available drones to fly to the fire zone. Simulation results confirm that the performance of the proposed system- without the need for inter-coalition communications- approaches that of a centrally-optimized system.
A Socioeconomic Disaggregation of the World Bank Human Capital Index

This paper documents inequality in health and education outcomes by constructing an index of human capital disaggregated by quintiles of socioeconomic status (SES) for a sample 51 mostly low- and middle-income countries. The index measures the expected future human capital of children born today, following the methodology of the World Bank Human Capital Index that was launched in October 2018. Within-country disparities in human capital outcomes across SES quintiles are large, accounting for roughly one-third of the total variation. On average, human capital outcomes increase with income at roughly the same rate across socio-economic groups within countries as they do across countries.
Hamiltonian Nature of Monopole Dynamics

Abstract Classical electromagnetism with magnetic monopoles is not a Hamiltonian field theory because the Jacobi identity for the Poisson bracket fails. The Jacobi identity is recovered only if all of the species have the same ratio of electric to magnetic charge or if an electron and a monopole can never collide. Without the Jacobi identity, there are no local canonical coordinates or Lagrangian action principle. To build a quantum field of magnetic monopoles, we either must explain why the positions of electrons and monopoles can never coincide or we must resort to new quantization techniques.
The development and validation of the Planet Formation Concept Inventory

The discovery and characterisation of planets orbiting distant stars has shed light on the origin of our own Solar System. It is important that college-level introductory astronomy students have a ...
Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression.

Purpose Arterial Spin Labeling (ASL) is a quantitative, non-invasive alternative for perfusion imaging that does not use contrast agents. The magnetic resonance fingerprinting (MRF) framework can be adapted to ASL to estimate multiple physiological parameters simultaneously. In this work, we introduce an optimization scheme to increase the sensitivity of the ASL fingerprint. We also propose a regression based estimation framework for MRF-ASL. Methods To improve the sensitivity of MRF-ASL signals to underlying parameters, we optimized ASL labeling durations using the Cramer-Rao Lower Bound (CRLB). This paper also proposes a neural network regression based estimation framework trained using noisy synthetic signals generated from our ASL signal model. We tested our methods in silico and in vivo, and compared with multiple post labeling delay (multi-PLD) ASL and unoptimized MRF-ASL. We present comparisons of estimated maps for the six parameters of our signal model. Results The scan design process facilitated precise estimates of multiple hemodynamic parameters and tissue properties from a single scan, in regions of normal gray and white matter, as well as regions with anomalous perfusion activity in the brain. In particular, there was a 86.7% correlation of perfusion estimates with the ground truth in silico, using our proposed techniques. In vivo, there was roughly a 7 fold improvement in the Coefficient of Variation (CoV) for white matter perfusion, and 2 fold improvement in gray matter perfusion CoV in comparison to a reference Multi PLD method. The regression based estimation approach provided perfusion estimates rapidly, with estimation times of around 1s per map. Conclusions Scan design optimization, coupled with regression-based estimation is a powerful tool for improving precision in MRF-ASL.
Wide Field / Planetary Camera-II for the Hubble Space Telescope

The Wide Field/Planetary Camera-II will be available to replace the WF/PC at the first opportunity for in-orbit Shuttle maintenance and refurbishment of the Hubble Space Telescope. It is fundamentally an engineering “clone” of the original radial camera, which nevertheless incorporates a number of science and operational enhancements based on improved technologies. Generic WF/PC performance issues identified and understood early in the WF/PC-II program provide the opportunity for carefully considered, cost effective hardware refinements. The result is improved science performance and operational efficiency for WF/PC-II which will be highly visible to the GO astronomer, and will provide savings in data reduction and calibration overhead.
Patent Intensity and Concentration: The Effect of Institutional Quality on MSA Patent Activity

Patented innovation is a predominately local phenomenon. It is also an indicator of economic growth. Using data on U.S. metropolitan statistical areas, we examine the long‐run effect of economic freedom, and each of its three components, on local patent activity. Using an instrumental variables approach for identification, we find that increased government spending is associated with reduced individual patent activity, though this effect is modest in magnitude. Our strongest results suggest that increased economic freedom significantly reduces patent concentration in both ownership and product types. This implies that economic freedom creates an environment conducive to diverse and diffused innovation and may provide a viable alternative to place‐based economic development strategies. Our results are robust to a quantile regression instrumental variable analysis. La innovacion patentada es un fenomeno predominantemente local. Tambien es un indicador de crecimiento economico. Mediante el uso de datos de las areas estadisticas metropolitanas de los Estados Unidos, se examina el efecto a largo plazo de la libertad economica, y cada uno de sus tres componentes, en la actividad de las patentes locales. Se empleo un enfoque de variables instrumentales para la identificacion, y se encontro que el aumento del gasto publico esta asociado con la reduccion de la actividad en cuanto a patentes individuales, aunque la magnitud de este efecto es modesta. Nuestros resultados mas robustos sugieren que el aumento de la libertad economica reduce significativamente la concentracion de patentes, tanto respecto a la propiedad como a los tipos de productos. Esto implica que la libertad economica crea un entorno propicio para una innovacion diversa y difusa, y que puede ofrecer una alternativa viable a las estrategias de desarrollo economico basadas en el lugar. Los resultados son robustos respecto a un analisis de variables instrumentales de regresion cuantilica. イノベーションの特許取得は、主に地域的な現象である。また、経済成長の指標でもある。米国の大都市統計地域のデータを用いて、経済的自由と、その3つの構成要素それぞれが地域の特許取得活動に及ぼす長期的な影響を検討する。操作変数法によるアプローチを用いて識別したところ、影響の規模は小さいものの、政府の支出増加が個人の特許取得活動の減少に関連していることが分かった。今回の非常に強力な結果から、経済的自由度の上昇は、所有権と製品タイプの両方において特許出願の集中を著しく減少させることが示唆された。これは、経済的自由が、多様で拡散したイノベーションを促進する環境を生み出し、立地に基づいた経済開発戦略に代わる実行可能な選択肢を提供する可能性があることを意味している。本結果は、分位点回帰操作変数分析に対してロバストであった。
Stimulating the adoption of health information technology.

Perhaps the most profound effect on doctors and patients of the recently enacted stimulus bill will result from its unprecedented $19 billion program to promote the adoption and use of health information technology and electronic health records. Dr. David Blumenthal discusses the federal effort to modernize the information systems of a troubled health care system.
Benchmark of long non-coding RNA quantification for RNA sequencing of cancer samples.

Background Long non-coding RNAs (lncRNAs) are emerging as important regulators of various biological processes. While many studies have exploited public resources such as RNA sequencing (RNA-Seq) data in The Cancer Genome Atlas to study lncRNAs in cancer, it is crucial to choose the optimal method for accurate expression quantification. Results In this study, we compared the performance of pseudoalignment methods Kallisto and Salmon, alignment-based transcript quantification method RSEM, and alignment-based gene quantification methods HTSeq and featureCounts, in combination with read aligners STAR, Subread, and HISAT2, in lncRNA quantification, by applying them to both un-stranded and stranded RNA-Seq datasets. Full transcriptome annotation, including protein-coding and non-coding RNAs, greatly improves the specificity of lncRNA expression quantification. Pseudoalignment methods and RSEM outperform HTSeq and featureCounts for lncRNA quantification at both sample- and gene-level comparison, regardless of RNA-Seq protocol type, choice of aligners, and transcriptome annotation. Pseudoalignment methods and RSEM detect more lncRNAs and correlate highly with simulated ground truth. On the contrary, HTSeq and featureCounts often underestimate lncRNA expression. Antisense lncRNAs are poorly quantified by alignment-based gene quantification methods, which can be improved using stranded protocols and pseudoalignment methods. Conclusions Considering the consistency with ground truth and computational resources, pseudoalignment methods Kallisto or Salmon in combination with full transcriptome annotation is our recommended strategy for RNA-Seq analysis for lncRNAs.
Trading strategies generated pathwise by functions of market weights

Twenty years ago, E.R. Fernholz introduced the notion of “functional generation” to construct a variety of portfolios solely in terms of the individual companies’ market weights. I. Karatzas and J. Ruf recently developed another approach to the functional construction of portfolios which leads to very simple conditions for strong relative arbitrage with respect to the market. Here, both of these notions are generalized in a pathwise, probability-free setting; portfolio-generating functions, possibly less smooth than twice differentiable, involve the current market weights as well as additional bounded-variation functionals of past and present market weights. This leads to a wider class of functionally generated portfolios than was heretofore possible to analyze, to novel methods for dealing with the “size” and “momentum” effects, and to improved conditions for outperforming the market portfolio over suitable time horizons.
Good identification, meet good data

Abstract Causal inference lies at the heart of social science, and the 2019 Nobel Prize in Economics highlights the value of randomized variation for identifying causal effects and mechanisms. But causal inference cannot rely on randomized variation alone; it also requires good data. Yet the data-generating process has received less consideration from economists. We provide a simple framework to clarify how research inputs affect data quality and discuss several such inputs, including interviewer selection and training, survey design, and investments in linking across multiple data sources. More investment in research on the data quality production function would considerably improve casual inference generally, and poverty alleviation specifically.
Deception through Half-Truths

Deception is a fundamental issue across a diverse array of settings, from cybersecurity, where decoys (e.g., honeypots) are an important tool, to politics that can feature politically motivated “leaks” and fake news about candidates. Typical considerations of deception view it as providing false information. However, just as important but less frequently studied is a more tacit form where information is strategically hidden or leaked. We consider the problem of how much an adversary can affect a principal's decision by “half-truths”, that is, by masking or hiding bits of information, when the principal is oblivious to the presence of the adversary. The principal's problem can be modeled as one of predicting future states of variables in a dynamic Bayes network, and we show that, while theoretically the principal's decisions can be made arbitrarily bad, the optimal attack is NP-hard to approximate, even under strong assumptions favoring the attacker. However, we also describe an important special case where the dependency of future states on past states is additive, in which we can efficiently compute an approximately optimal attack. Moreover, in networks with a linear transition function we can solve the problem optimally in polynomial time.
A Cross-Repository Model for Predicting Popularity in GitHub

Social coding platforms, such as GitHub, can serve as natural laboratories for studying the diffusion of innovation through tracking the pattern of code adoption by programmers. This paper focuses on the problem of predicting the popularity of software repositories over time; our aim is to forecast the time series of popularity-related events (code forks and watches). In particular, we are interested in cross-repository patterns—how do events on one repository affect other repositories? Our proposed LSTM (Long Short-Term Memory) recurrent neural network integrates events across multiple active repositories, outperforming a standard ARIMA (Auto Regressive Integrated Moving Average) time series prediction based on the single repository. The ability of the LSTM to leverage cross-repository information gives it a significant edge over standard time series forecasting.
Auditory cortex shapes sound responses in the inferior colliculus

The extensive feedback from the auditory cortex (AC) to the inferior colliculus (IC) supports critical aspects of auditory behavior but has not been extensively characterized. Previous studies demonstrated that activity in IC is altered by focal electrical stimulation and pharmacological inactivation of AC, but these methods lack the ability to selectively manipulate projection neurons. We measured the effects of selective optogenetic modulation of cortico-collicular feedback projections on IC sound responses in mice. Activation of feedback increased spontaneous activity and decreased stimulus selectivity in IC, whereas suppression had no effect. To further understand how microcircuits in AC may control collicular activity, we optogenetically modulated the activity of different cortical neuronal subtypes, specifically parvalbumin-positive (PV) and somatostatin-positive (SST) inhibitory interneurons. We found that modulating the activity of either type of interneuron did not affect IC sound-evoked activity. Combined, our results identify that activation of excitatory projections, but not inhibition-driven changes in cortical activity, affects collicular sound responses.
A neural network approach to predicting and computing knot invariants

In this paper, we use artificial neural networks to predict and help compute the values of certain knot invariants. In particular, we show that neural networks are able to predict when a knot is qu...
A trajectory map for the pressureless Euler equations

We consider the dynamics of a collection of particles that interact pairwise and are restricted to move along the real line. Moreover, we focus on the situation in which particles undergo perfectly inelastic collisions when they collide. The equations of motion are a pair of partial differential equations for the particles' mass distribution and local velocity. We show that solutions of this system exist for given initial conditions by rephrasing these equations in Lagrangian coordinates and then by solving for the associated trajectory map.
What is the Lagrangian for Nonlinear Filtering

Duality between estimation and optimal control is a problem of rich historical significance. The first duality principle appears in the seminal paper of Kalman-Bucy, where the problem of minimum variance estimation is shown to be dual to a linear quadratic (LQ) optimal control problem. Duality offers a constructive proof technique to derive the Kalman filter equation from the optimal control solution.This paper generalizes the classical duality result of Kalman-Bucy to the nonlinear filter: The state evolves as a continuous-time Markov process and the observation is a nonlinear function of state corrupted by an additive Gaussian noise. A dual process is introduced as a backward stochastic differential equation (BSDE). The process is used to transform the problem of minimum variance estimation into an optimal control problem. Its solution is obtained from an application of the maximum principle, and subsequently used to derive the equation of the nonlinear filter. The classical duality result of Kalman-Bucy is shown to be a special case.
Distinguishing cell phenotype using cell epigenotype.

The relationship between microscopic observations and macroscopic behavior is a fundamental open question in biophysical systems. Here, we develop a unified approach that—in contrast with existing methods—predicts cell type from macromolecular data even when accounting for the scale of human tissue diversity and limitations in the available data. We achieve these benefits by applying a k-nearest-neighbors algorithm after projecting our data onto the eigenvectors of the correlation matrix inferred from many observations of gene expression or chromatin conformation. Our approach identifies variations in epigenotype that affect cell type, thereby supporting the cell-type attractor hypothesis and representing the first step toward model-independent control strategies in biological systems.
MitoPlex: A targeted multiple reaction monitoring assay for quantification of a curated set of mitochondrial proteins

Abstract Mitochondria are the major source of cellular energy (ATP), as well as critical mediators of widespread functions such as cellular redox balance, apoptosis, and metabolic flux. The organelles play an especially important role in the maintenance of cardiac homeostasis; their inability to generate ATP following impairment due to ischemic damage has been directly linked to organ failure. Methods to quantify mitochondrial content are limited to low throughput immunoassays, measurement of mitochondrial DNA, or relative quantification by untargeted mass spectrometry. Here, we present a high throughput, reproducible and quantitative mass spectrometry multiple reaction monitoring based assay of 37 proteins critical to central carbon chain metabolism and overall mitochondrial function termed ‘MitoPlex’. We coupled this protein multiplex with a parallel analysis of the central carbon chain metabolites (219 metabolite assay) extracted in tandem from the same sample, be it cells or tissue. In tests of its biological applicability in cells and tissues, “MitoPlex plus metabolites” indicated profound effects of HMG-CoA Reductase inhibition (e.g., statin treatment) on mitochondria of i) differentiating C2C12 skeletal myoblasts, as well as a clear opposite trend of statins to promote mitochondrial protein expression and metabolism in heart and liver, while suppressing mitochondrial protein and ii) aspects of metabolism in the skeletal muscle obtained from C57Bl6 mice. Our results not only reveal new insights into the metabolic effect of statins in skeletal muscle, but present a new high throughput, reliable MS-based tool to study mitochondrial dynamics in both cell culture and in vivo models.
Laplacian algebras, manifold submetries and the Inverse Invariant Theory Problem

Manifold submetries of the round sphere are a class of partitions of the round sphere that generalizes both singular Riemannian foliations, and the orbit decompositions by the orthogonal representations of compact groups. We exhibit a one-to-one correspondence between such manifold submetries and maximal Laplacian algebras, thus solving the Inverse Invariant Theory problem for this class of partitions. Moreover, a solution to the analogous problem is provided for two smaller classes, namely orthogonal representations of finite groups, and transnormal systems with closed leaves.
Functional Imaging of Microbial Interactions With Tree Roots Using a Microfluidics Setup.

Coupling microfludics with microscopy has emerged as a powerful approach to study at cellular resolution the dynamics in plant physiology and root-microbe interactions. Most devices have been designed to study the model plant Arabidopsis thaliana at higher throughput than conventional methods. However, there is a need for microfluidic devices which enable in vivo studies of root development and root-microbe interactions in woody plants. Here, we developed the RMI-chip, a simple microfluidic setup in which Populus tremuloides (aspen tree) seedlings can grow for over a month, allowing continuous microscopic observation of interactions between live roots and rhizobacteria. We find that the colonization of growing aspen roots by Pseudomonas fluorescens in the RMI-chip involves dynamic biofilm formation and dispersal, in keeping with previous observations in a different experimental set-up. Also, we find that whole-cell biosensors based on the rhizobacterium Bacillus subtilis can be used to monitor compositional changes in the rhizosphere but that the application of these biosensors is limited by their efficiency at colonizing aspen roots and persisting. These results indicate that functional imaging of dynamic root-bacteria interactions in the RMI-chip requires careful matching between the host plant and the bacterial root colonizer.
β-catenin-mediated Wnt signal transduction proceeds through an endocytosis-independent mechanism

The Wnt pathway is a key intercellular signaling cascade that regulates development, tissue homeostasis, and regeneration. However, gaps remain in our understanding of the molecular events that take place between ligand-receptor binding and target gene transcription. We used a novel tool for quantitative, real-time assessment of endogenous pathway activation, measured in single cells, to answer an unresolved question in the field-whether receptor endocytosis is required for Wnt signal transduction. We combined knockdown or knockout of essential components of clathrin-mediated endocytosis with quantitative assessment of Wnt signal transduction in mouse embryonic stem cells (mESCs). Disruption of clathrin-mediated endocytosis did not affect accumulation and nuclear translocation of β-catenin, as measured by single-cell live imaging of endogenous β-catenin, and subsequent target gene transcription. Disruption of another receptor endocytosis pathway, caveolin-mediated endocytosis, did not affect Wnt pathway activation in mESCs. Additional results in multiple cell lines support that endocytosis is not a requirement for Wnt signal transduction. We show that off-target effects of a drug used to inhibit endocytosis may be one source of the discrepancy among reports on the role of endocytosis in Wnt signaling.
Polynomial-Time Statistical Estimation of Species Trees Under Gene Duplication and Loss

Phylogenomics—the estimation of species trees from multi-locus datasets—is a common step in many biological studies. However, this estimation is challenged by the fact that genes can evolve under processes, including incomplete lineage sorting (ILS) and gene duplication and loss (GDL), that make their trees different from the species tree. In this paper, we address the challenge of estimating the species tree under GDL. We show that species trees are identifiable under a standard stochastic model for GDL, and that the polynomial-time algorithm ASTRAL-multi, a recent development in the ASTRAL suite of methods, is statistically consistent under this GDL model. We also provide a simulation study evaluating ASTRAL-multi for species tree estimation under GDL. All scripts and datasets used in this study are available on the Illinois Data Bank: https://doi.org/10.13012/B2IDB-2626814_V1.
BlendSM-DDM: BLockchain-ENabled Secure Microservices for Decentralized Data Marketplaces

To promote the benefits of the Internet of Things (IoT) in smart communities and smart cities, a real-time data marketplace middleware platform, called the Intelligent IoT Integrator (I3), has been recently proposed. While facilitating the easy exchanges of real-time IoT data streams between device owners and third-party applications through the marketplace, I3 is presently a monolithic, centralized platform for a single community. Although the service oriented architecture (SOA) has been widely adopted in the IoT and cyber-physical systems (CPS), it is difficult for a monolithic architecture to provide scalable, inter-operable and extensible services for large numbers of distributed IoT devices and different application vendors. Traditional security solutions rely on a centralized authority, which can be a performance bottleneck or susceptible to a single point of failure. Inspired by containerized microservices and blockchain technology, this paper proposed a BLockchain-ENabled Secure Microservices for Decentralized Data Marketplaces (BlendSM-DDM). Within a permissioned blockchain network, a microservices based security mechanism is introduced to secure data exchange and payment among participants in the marketplace. BlendSM-DDM is able to offer a decentralized, scalable and auditable data exchanges for the data marketplace.
A Content-Evaluating Mode of Computer-Aided Instruction

In the literature describing the languages and systems available for writing teaching programs, two main lines of development have been apparent [1]. At one pole is the system using the linguistic mode. Here, the student interacts "verbally" with the computer, in the sense that messages are sent to the computer by him and are then interpreted "verbally" by the machine. By this we mean that the steps the computer takes assume that the words received need not be decoded further; that is, the words themselves make up the target language.
Blockchain Disruption and Smart Contracts

Blockchain technology provides decentralized consensus and potentially enlarges the contracting space using smart contracts with tamper-proofness and algorithmic executions. Meanwhile, generating decentralized consensus entails distributing information which necessarily alters the informational environment. We analyze how decentralization affects consensus effectiveness, and how the quintessential features of blockchain reshape industrial organization and the landscape of competition. Smart contracts can mitigate informational asymmetry and improve welfare and consumer surplus through enhanced entry and competition, yet the irreducible distribution of information during consensus generation may encourage greater collusion. In general, blockchains can sustain market equilibria with a wider range of economic outcomes. We further discuss anti-trust policy implications targeted to blockchain applications, such as separating consensus record-keepers from users.
27 – The future

Publisher Summary This chapter discusses the future prospects of energy. The future of energy in the world is not clear, since both optimistic and pessimistic predictions have been made. The chapter identifies some of the factors that need to be considered in planning for the future of energy. The population growth of the world remains excessive, with growth rates in underdeveloped countries being highest. Nuclear power may play an important role in achieving sustainable development, avoid global warming, and in easing international energy tensions. Converter reactors may give way to breeder reactors and in turn to fusion reactors, giving full opportunity for the appropriate development of solar energy systems. Research and development are seen as key ingredients in the quest for energy adequate for the future.
A note on Schwartz functions and modular forms

We generalize the recent work of Viazovska by constructing infinite families of Schwartz functions, suitable for Cohn–Elkies style linear programming bounds, using quasi-modular and modular forms. In particular, for dimensions $$d \equiv 0 \pmod {8}$$, we give new constructions for obtaining sphere packing upper bounds via modular forms. In dimension 8 and 24, these exactly match the functions constructed by Viazovska and Cohn, Kumar, Miller, Radchenko, and Viazovska which resolved the sphere packing problem in those dimensions.
Multi-Agent Safety Verification using Symmetry Transformations

We show that symmetry transformations and caching can enable scalable, and possibly unbounded, verification of multi-agent systems. Symmetry transformations map any solution of the system to another solution. We show that this property can be used to transform cached reachsets to compute new reachsets, for hybrid and multi-agent models. We develop a notion of a virtual system which defines symmetry transformations for a broad class of agent models that visit waypoint sequences. Using this notion of a virtual system, we present a prototype tool CacheReach that builds a cache of reachsets, in a way that is agnostic of the representation of the reachsets and the reachability analysis method used. Our experimental evaluation of CacheReach shows up to 64% savings in safety verification computation time on multi-agent systems with 3-dimensional linear and 4-dimensional nonlinear fixed-wing aircraft models following sequences of waypoints. These savings and our theoretical results illustrate the potential benefits of using symmetry-based caching in the safety verification of multi-agent systems.
Epistemology: Who Needs It?

This reflection on the real-world relevance of epistemological ideas begins with the thought that all of us — when we wonder what to make of newspaper reports of supposed medical breakthroughs, of failures of military intelligence, etc., etc. — call, implicitly or explicitly, on epistemology; and shows how an understanding of, e.g., the differences between genuine inquiry and advocacy research, the nature of wishful and fearful thinking, and the material character of the relevance and its bearing on what relevant evidence we may be missing, can illuminate the ways in which inquiry can go wrong and evidence can mislead us.
Challenges in Water Stress Quantification Using Small Unmanned Aerial System (sUAS): Lessons from a Growing Season of Almond

We conducted a study in a large almond farm in Merced County, California, to monitor water status by using high-resolution multi-spectral imagery accquired by a Small Unmanned Aerial System (sUAS). More specifically, we would like to predict Stem Water Potential (SWP) via canopy Normalized Difference Vegetation Index (NDVI). During 2014, an aircraft equipped with multi-spectral cameras flew over the orchard weekly throughout the growing season. At the same time, SWP was measured for the sample trees under five different water treatment levels. Instead of averaging pixels in an orchard level, a block level or a canopy level, pixels were analyzed in the sub-canopy level to obtain canopy NDVI. An improved correlation between SWP and canopy NDVI was obtained by applying lower NDVI threshold. The relationship between SWP and canopy NDVI was also discussed at different growing stages–fruit development and post-harvest. However, tests of equality of distribution indicated that canopy NDVI distributions from different flights within a day were significantly different. Therefore, further calibration regarding the effects of solar motion on canopy NDVI is necessary.
Optimal Deployments of UAVs With Directional Antennas for a Power-Efficient Coverage

To provide a reliable wireless uplink for users in a given ground area, one can deploy Unmanned Aerial Vehicles (UAVs) as base stations (BSs). In another application, one can use UAVs to collect data from sensors on the ground. For a power-efficient and scalable deployment of such flying BSs, directional antennas can be utilized to efficiently cover arbitrary 2-D ground areas. We consider a large-scale wireless path-loss model with a realistic angle-dependent radiation pattern for the directional antennas. Based on such a model, we determine the optimal 3-D deployment of $N$ UAVs to minimize the average transmit-power consumption of the users in a given target area. The users are assumed to have identical transmitters with ideal omnidirectional antennas and the UAVs have identical directional antennas with given half-power beamwidth (HPBW) and symmetric radiation pattern along the vertical axis. For uniformly distributed ground users, we show that the UAVs have to share a common flight height in an optimal power-efficient deployment, by simulations. We also derive in closed-form the asymptotic optimal common flight height of $N$ UAVs in terms of the area size, data-rate, bandwidth, HPBW, and path-loss exponent.
Prevention of Chromatin Destabilization by FACT Is Crucial for Malignant Transformation.

Summary Histone chaperone FACT is commonly expressed and essential for the viability of transformed but not normal cells, and its expression levels correlate with poor prognosis in patients with cancer. FACT binds several components of nucleosomes and has been viewed as a factor destabilizing nucleosomes to facilitate RNA polymerase passage. To connect FACT's role in transcription with the viability of tumor cells, we analyzed genome-wide FACT binding to chromatin in conjunction with transcription in mouse and human cells with different degrees of FACT dependence. Genomic distribution and density of FACT correlated with the intensity of transcription. However, FACT knockout or knockdown was unexpectedly accompanied by the elevation, rather than suppression, of transcription and with the destabilization of chromatin in transformed, but not normal cells. These data suggest that FACT stabilizes and reassembles nucleosomes disturbed by transcription. This function is vital for tumor cells because malignant transformation is accompanied by chromatin destabilization.
Investors’ appetite for money-like assets: The MMF industry after the 2014 regulatory reform

Abstract This paper uses a quasi-natural experiment to estimate the premium for money-likeness. The 2014 Securities and Exchange Commission (SEC) reform of the money market fund (MMF) industry reduced the money-likeness of prime MMFs by increasing their information sensitivity, while leaving government MMFs unaffected. Investors fled from prime to government MMFs, with total outflows exceeding one trillion dollars. Using a difference-in-differences design, we estimate the premium for money-likeness to be between 20 and 30 basis points (bps). These premiums are not due to changes in investors’ risk tolerance or funds’ risk taking. Our results support recent developments in monetary theory identifying information insensitivity as a key feature of money.
Review of the Making Marvels Exhibit at the Metropolitan Museum of Art

The Making Marvels: Science & Splendor at the Courts of Europe exhibit at the Metropolitan Museum of Art in New York showcased almost 200 luxury highlights. Between 1550 and 1750, royal dynasties i...
Discovery of protein modifications using high resolution differential mass spectrometry proteomics

Recent studies have revealed diverse amino acid, post-translational and non-canonical modifications of proteins in diverse organisms and tissues. However, their unbiased detection and analysis remain hindered by technical limitations. Here, we present a spectral alignment method for the identification of protein modifications using high-resolution mass spectrometry proteomics. Termed SAMPEI for Spectral Alignment-based Modified PEptide Identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. Using synthetic standards and controlled chemical labeling experiments, we demonstrate its high specificity and sensitivity for the discovery of sub-stoichiometric protein modifications in complex cellular extracts. SAMPEI mapping of mouse macrophage differentiation revealed diverse post-translational protein modifications, including distinct forms of cysteine itaconatylation. SAMPEI′s robust parameterization and versatility are expected to facilitate the discovery of biological modifications of diverse macromolecules. SAMPEI is implemented as a Python package, and is available open-source from BioConda and GitHub (https://github.com/FenyoLab/SAMPEI).
Impacts of Modifiable Factors on Ambient Air Pollution: A Case Study of COVID-19 Shutdowns

COVID-19-related closures offered a novel opportunity to observe and quantify the impact of activity levels of modifiable factors on ambient air pollution in real time. We use data from a network o...
Hopper: a mathematically optimal algorithm for sketching biological data

Motivation Single-cell RNA-sequencing has grown massively in scale since its inception, presenting substantial analytic and computational challenges. Even simple downstream analyses, such as dimensionality reduction and clustering, require days of runtime and hundreds of gigabytes of memory for today's largest datasets. In addition, current methods often favor common cell types, and miss salient biological features captured by small cell populations. Results Here we present Hopper, a single-cell toolkit that both speeds up the analysis of single-cell datasets and highlights their transcriptional diversity by intelligent subsampling, or sketching. Hopper realizes the optimal polynomial-time approximation of the Hausdorff distance between the full and downsampled dataset, ensuring that each cell is well-represented by some cell in the sample. Unlike prior sketching methods, Hopper adds points iteratively and allows for additional sampling from regions of interest, enabling fast and targeted multi-resolution analyses. In a dataset of over 1.3 million mouse brain cells, Hopper detects a cluster of just 64 macrophages expressing inflammatory genes (0.004% of the full dataset) from a Hopper sketch containing just 5000 cells, and several other small but biologically interesting immune cell populations invisible to analysis of the full data. On an even larger dataset consisting of ∼2 million developing mouse organ cells, we show Hopper's even representation of important cell types in small sketches, in contrast with prior sketching methods. We also introduce Treehopper, which uses spatial partitioning to speed up Hopper by orders of magnitude with minimal loss in performance. By condensing transcriptional information encoded in large datasets, Hopper and Treehopper grant the individual user with a laptop the analytic capabilities of a large consortium. Availability and implementation The code for Hopper is available at https://github.com/bendemeo/hopper. In addition, we have provided sketches of many of the largest single-cell datasets, available at http://hopper.csail.mit.edu.