Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishment of knockout adult sea urchins by using a CRISPR‐Cas9 system

Sea urchins are used as a model organism for research on developmental biology and gene regulatory networks during early development. Gene knockdown by microinjection of morpholino antisense oligonucleotide (MASO) has been used to analyze gene function in early sea urchin embryos. However, as the effect of MASO is not long lasting, it is impossible to perturb genes expressed during late development by MASO. Recent advances in genome editing technologies have enabled gene modification in various organisms. We previously reported genome editing in the sea urchin Hemicentrotus pulcherrimus using zinc‐finger nuclease (ZFN) and transcription activator‐like effector nuclease (TALEN); however, the efficiencies of these technologies were not satisfactory. Here, we applied clustered regularly interspaced short palindromic repeat (CRISPR)‐CRISPR‐associated nuclease 9 (Cas9) technology to knock out the Pks1 gene in H. pulcherrimus. When sgRNAs targeting Pks1, which is required for the biosynthesis of larval pigment, were microinjected into fertilized eggs with SpCas9 mRNA, high‐efficiency mutagenesis was achieved within 24 hr post fertilization and SpCas9/sgRNA‐injected pluteus larvae had an albino phenotype. One of the sgRNAs yielded 100% mutagenesis efficiency, and no off‐target effect was detected. In addition, the albino phenotype was maintained in juvenile sea urchins after metamorphosis, and the knockout sea urchins survived for at least one year and grew to albino adult sea urchins. These findings suggest that knockout adult sea urchins were successfully established and the CRISPR‐Cas9 system is a feasible method for analyzing gene functions from late developmental to adult stage.
The Challenge of Modeling High Speed Flows

Current and expected developments in space transportation have led to growing interest in new space vehicles. These new vehicles require essential improvements over current vehicles in order to ensure economic viability and to fullfill mission and safety constraints. The size and complexity of this problem has led to growing importance of numerical methods for design and optimization involving all disciplines as well as the optimal use of all technical potentialities is necessary. The material presented here reviews the growth and advances achieved at DLR in the last years. The Status of the physical modeling, code development issues such as algorithms, grid generation and validation strategy is provided. Viscous, high speed unsteady flows are still restricted to simple problems due to a strong demand in Computer resources. The short coming to obtain adequate data for code validation in high speed flow problems is discussed. It tums out that little progress has been realized in numerical modeling since past major difficulties to obtain reliable experimental data in the high enthalpy flow environments still remain. On the other side, CFD based multidisciplinary analysis is emerging as a key discipline in aerospace design. Supported by a continuous, almost linear, grows in computer capacity and performance, this new procedure to conduct configuration analysis is paying off its way. A number of selected applications of multidisciplinary problems with complex physics are presented.
Speech and language therapy approaches to managing primary progressive aphasia

The term primary progressive aphasia (PPA) describes a group of neurodegenerative disorders with predominant speech and language dysfunction as their main feature. There are three main variants – the semantic variant, the nonfluent or agrammatic variant and the logopenic variant – each with specific linguistic deficits and different neuroanatomical involvement. There are currently no curative treatments or symptomatic pharmacological therapies. However, speech and language therapists have developed several impairment-based interventions and compensatory strategies for use in the clinic. Unfortunately, multiple barriers still need to be overcome to improve access to care for people with PPA, including increasing awareness among referring clinicians, improving training of speech and language therapists and developing evidence-based guidelines for therapeutic interventions. This review highlights this inequity and the reasons why neurologists should refer people with PPA to speech and language therapists.
Localized Electronic Structure of Nitrogenase FeMoco Revealed by Selenium K-Edge High Resolution X-ray Absorption Spectroscopy

The size and complexity of Mo-dependent nitrogenase, a multicomponent enzyme capable of reducing dinitrogen to ammonia, have made a detailed understanding of the FeMo cofactor (FeMoco) active site electronic structure an ongoing challenge. Selective substitution of sulfur by selenium in FeMoco affords a unique probe wherein local Fe–Se interactions can be directly interrogated via high-energy resolution fluorescence detected X-ray absorption spectroscopic (HERFD XAS) and extended X-ray absorption fine structure (EXAFS) studies. These studies reveal a significant asymmetry in the electronic distribution of the FeMoco, suggesting a more localized electronic structure picture than is typically assumed for iron–sulfur clusters. Supported by experimental small molecule model data in combination with time dependent density functional theory (TDDFT) calculations, the HERFD XAS data is consistent with an assignment of Fe2/Fe6 as an antiferromagnetically coupled diferric pair. HERFD XAS and EXAFS have also been applied to Se-substituted CO-inhibited MoFe protein, demonstrating the ability of these methods to reveal electronic and structural changes that occur upon substrate binding. These results emphasize the utility of Se HERFD XAS and EXAFS for selectively probing the local electronic and geometric structure of FeMoco.
The determinants of IPO initial returns for Chinese A-shares: an application of quantile regression

Abstract This paper investigates the determinants of initial public offering (IPO) initial returns for a sample of Chinese A-shares, the period from January 1993 to August 2006. Some features are highlighted in our paper as follows. First, in order to investigate the difference of determinants of IPO initial returns between the high and low initial returns, this paper applies the Quantile Regression (QR, hereafter) method. Second, this paper is the first of its kind to derive and explore the economic implications of the effect of market returns on the market-adjusted initial return. Third, we also take into consideration the important factors of “Macro Regulation and Control” and the “Reform of the Offering Price Model” in studying the IPO initial returns of A-shares. The results of the estimation are as follows. First, there are various differences between high and low initial returns, meaning that the empirical results of the quantile regression are more capable of describing the impact of the explanatory variables on the high and low IPO initial returns. Secondly, the macro controls and the reforms do in fact mitigate the high IPO initial returns. Third, the positive effect of the market returns on the high market-adjusted initial returns (IPO initial returns) indicates that these two returns move in the same direction, but that the volatility of the market-adjusted returns is higher than that of the market returns.
Enhanced Plasmon-Induced Resonance Energy Transfer (PIRET)-mediated Photothermal and Photodynamic Therapy Guided by Photoacoustic and Magnetic Resonance Imaging.

Phototherapy, containing photothermal and photodynamic therapy, has attracted extensive attention due to its noninvasive nature, low toxicity, and high anticancer efficiency. Charge-separation mechanism of plasmon-induced resonance energy transfer (PIRET), has been increasingly employed to design nanotheranotic agents. Herein, we developed a novel and smart PIRET-mediated nanoplatform for enhanced, imaging-guided phototherapy. Prussian blue (PB) was incorporated into Au@Cu2O nanostructure, which was then assembled with poly(allylamine) (PAH) modified black phosphorus quantum dots (Au@PB@Cu2O@BPQDs/PAH nanocomposites). The hybrid nanosystem exhibited great absorption in NIR region, as well as the ability to self-supply O2 by catalyzing hydrogen peroxide and convert O2 into singlet oxygen (1O2) under 650 nm laser light (0.5 W/cm2) irradiation. In vitro and in vivo assay showed that the generated heat and toxic 1O2 from Au@PB@Cu2O@BPQDs/PAH nanocomposites could effectively kill the cancer cells and suppress tumor growth. Moreover, the unique properties of PB modified nanosystem allowed for synergistic therapy with the aid of T1-weighed magnetic resonance imaging (T1-weighted MRI) and photoacoustic imaging (PAI). This study presented a suitable way to fabricate smart PIRET-based nanosystem with enhanced PTT/PDT eﬃcacy and dual-modal imaging functionality. The great biocompatibility and low toxicity ensured their high potential for use in cancer therapy.
The keystone flap in the reconstruction of the back of the hand

was commenced on a tapering dose of prednisolone. The systemic symptoms resolved, but he still had marked palmoplantar involvement which is currently being treated with methotrexate and acitretin. The pathophysiology of palmoplantar psoriasis, PPPP and PPP is thought to be different to chronic plaque-type psoriasis with different cytokines such as IL-36 and IL-17 playing a role. Ustekinumab, an IL-12/23 inhibitor, is effective in treating PPP, PPPP and paradoxical reactions secondary to TNF inhibitors and can be used as a first-line biologic agent for PPP. At least six cases have been published suggesting a temporal association of PPPP/PPP and ustekinumab (Table 1). Pustules tend to form within days to months of starting ustekinumab. Another two reports describe worsening of chronic plaque psoriasis or development of inverse psoriasis with ustekinumab. An increase in IFN-alpha due to blocking IL-23, TNFalpha and the Th-17 pathway may be responsible for paradoxical reactions. Though alternative explanations for clinical deterioration including infections (bacterial, viral), lack of response to biologic or natural disease fluctuation were considered in our three cases, temporality similar to the published case reports supports a paradoxical reaction. A preferred treatment option for paradoxical reactions cannot be extrapolated from the small number of case reports. Cases typically improved with switching biologic agent or adding a topical or oral corticosteroid. Further research is required to determine why this paradoxical reaction occurs, which subsets of patients are at risk, the reason for a predominant pustular presentation and appropriate therapy.
On the estimation of average treatment effects with right‐censored time to event outcome and competing risks

We are interested in the estimation of average treatment effects based on right‐censored data of an observational study. We focus on causal inference of differences between t‐year absolute event risks in a situation with competing risks. We derive doubly robust estimation equations and implement estimators for the nuisance parameters based on working regression models for the outcome, censoring, and treatment distribution conditional on auxiliary baseline covariates. We use the functional delta method to show that these estimators are regular asymptotically linear estimators and estimate their variances based on estimates of their influence functions. In empirical studies, we assess the robustness of the estimators and the coverage of confidence intervals. The methods are further illustrated using data from a Danish registry study.
Nonlinear causality of general first-order relativistic viscous hydrodynamics

Effective theory arguments are used to derive the most general energy-momentum tensor of a relativistic viscous fluid with an arbitrary equation of state (in the absence of other conserved currents) that is first-order in the derivatives of the energy density and flow velocity and does not include extended variables such as in Mueller-Israel-Stewart-like theories. This energy-momentum tensor leads to a causal theory, provided one abandons the usual conventions for the out-of-equilibrium hydrodynamic variables put forward by Landau-Lifshitz and Eckart. In particular, causality requires nonzero out-of-equilibrium energy density corrections and heat flow. Conditions are found to ensure linear stability around equilibrium in flat space-time. We also prove local existence and uniqueness of solutions to the equations of motion. Our causality, existence, and uniqueness results hold in the full nonlinear regime, without symmetry assumptions, in four space-time dimensions, with or without coupling to Einstein’s equations, and are mathematically rigorously established. Furthermore, a kinetic theory realization of this energy-momentum tensor is also provided
Covering up bias in CelebA-like datasets with Markov blankets: A post-hoc cure for attribute prior avoidance

Attribute prior avoidance entails subconscious or willful non-modeling of (meta)attributes that datasets are oft born with, such as the 40 semantic facial attributes associated with the CelebA and CelebA-HQ datasets. The consequences of this infirmity, we discover, are especially stark in state-of-the-art deep generative models learned on these datasets that just model the pixel-space measurements, resulting in an inter-attribute bias-laden latent space. This viscerally manifests itself when we perform face manipulation experiments based on latent vector interpolations. In this paper, we address this and propose a post-hoc solution that utilizes an Ising attribute prior learned in the attribute space and showcase its efficacy via qualitative experiments.
Trainability and Accuracy of Artificial Neural Networks: An Interacting Particle System Approach

Neural networks, a central tool in machine learning, have demonstrated remarkable, high fidelity performance on image recognition and classification tasks. These successes evince an ability to accurately represent high‐dimensional functions, but rigorous results about the approximation error of neural networks after training are few. Here we establish conditions for global convergence of the standard optimization algorithm used in machine learning applications, stochastic gradient descent (SGD), and quantifying the scaling of its error with the size of the network. This is done by reinterpreting SGD as the evolution of a particle system with interactions governed by a potential related to the objective or “loss” function used to train the network. We show that, when the number n of units is large, the empirical distribution of the particles descends on a convex landscape towards the global minimum at a rate independent of n, with a resulting approximation error that universally scales as O(n−1). These properties are established in the form of a law of large numbers and a central limit theorem for the empirical distribution. Our analysis also quantifies the scale and nature of the noise introduced by SGD and provides guidelines for the step size and batch size to use when training a neural network. We illustrate our findings on examples in which we train neural networks to learn the energy function of the continuous 3‐spin model on the sphere. The approximation error scales as our analysis predicts in as high a dimension as d = 25. © 2022 Courant Institute of Mathematics and Wiley Periodicals LLC.
A Practical Guide for the Effective Evaluation of Twitter User Geolocation

Geolocating Twitter users—the task of identifying their home locations—serves a wide range of community and business applications such as managing natural crises, journalism, and public health. Many approaches have been proposed for automatically geolocating users based on their tweets; at the same time, various evaluation metrics have been proposed to measure the effectiveness of these approaches, making it challenging to understand which of these metrics is the most suitable for this task. In this article, we propose a guide for a standardized evaluation of Twitter user geolocation by analyzing 15 models and two baselines in a controlled experimental setting. Models are evaluated using 10 metrics over four geographic granularities. We use rank correlations to assess the effectiveness of these metrics. Our results demonstrate that the choice of effectiveness metric can have a substantial impact on the conclusions drawn from a geolocation system experiment, potentially leading experimenters to contradictory results about relative effectiveness. We show that for general evaluations, a range of performance metrics should be reported, to ensure that a complete picture of system effectiveness is conveyed. Given the global geographic coverage of this task, we specifically recommend evaluation at micro vs. macro levels to measure the impact of the bias in distribution over locations. Although a lot of complex geolocation algorithms have been applied in recent years, a majority class baseline is still competitive at coarse geographic granularity. We propose a suite of statistical analysis tests, based on the employed metric, to ensure that the results are not coincidental.1
A Workload and Programming Ease Driven Perspective of Processing-in-Memory

Many modern and emerging applications must process increasingly large volumes of data. Unfortunately, prevalent computing paradigms are not designed to efficiently handle such large-scale data: the energy and performance costs to move this data between the memory subsystem and the CPU now dominate the total costs of computation. This forces system architects and designers to fundamentally rethink how to design computers. Processing-in-memory (PIM) is a computing paradigm that avoids most data movement costs by bringing computation to the data. New opportunities in modern memory systems are enabling architectures that can perform varying degrees of processing inside the memory subsystem. However, there are many practical system-level issues that must be tackled to construct PIM architectures, including enabling workloads and programmers to easily take advantage of PIM. This article examines three key domains of work towards the practical construction and widespread adoption of PIM architectures. First, we describe our work on systematically identifying opportunities for PIM in real applications, and quantify potential gains for popular emerging applications (e.g., machine learning, data analytics, genome analysis). Second, we aim to solve several key issues on programming these applications for PIM architectures. Third, we describe challenges that remain for the widespread adoption of PIM.
Improved Super-Resolution Convolution Neural Network for Large Images

Single image super-resolution (SISR) is a very popular topic nowadays, which has both research value and practical value. In daily life, we crop a large image into sub-images to do super-resolution and then merge them together. Although convolution neural network performs very well in the research field, if we use it to do super-resolution, we can easily observe cutting lines from merged pictures. To address these problems, in this paper, we propose a refined architecture of SRCNN with 'Symmetric padding', 'Random learning' and 'Residual learning'. Moreover, we have done a lot of experiments to prove our model performs best among a lot of the state-of-art methods.
Broadband Internet Access and the Digital Divide: Federal Assistance Programs

Some policymakers, believing that disparities in broadband access across American society could have adverse economic and social consequences on those left behind, assert that the federal government should play a more active role to avoid a “digital divide” in broadband access. One approach is for the federal government to provide financial assistance to support broadband deployment in underserved areas. Others, however, believe that federal assistance for broadband deployment is not appropriate. Some opponents question the reality of the “digital divide,” and argue that federal intervention in the broadband marketplace would be premature and, in some cases, counterproductive.
Smaller p-Values via Indirect Information

Abstract This article develops p-values for evaluating means of normal populations that make use of indirect or prior information. A p-value of this type is based on a biased frequentist hypothesis test that has optimal average power with respect to a probability distribution that encodes indirect information about the mean parameter, resulting in a smaller p-value if the indirect information is accurate. In a variety of multiparameter settings, we show how to adaptively estimate the indirect information for each mean parameter while still maintaining uniformity of the p-values under their null hypotheses. This is done using a linking model through which indirect information about the mean of one population may be obtained from the data of other populations. Importantly, the linking model does not need to be correct to maintain the uniformity of the p-values under their null hypotheses. This methodology is illustrated in several data analysis scenarios, including small area inference, spatially arranged populations, interactions in linear regression, and generalized linear models. Supplementary materials for this article are available online.
Comparing partitions through the Matching Error

With the aim to propose a non parametric hypothesis test, this paper carries out a study on the Matching Error (ME), a comparison index of two partitions obtained from the same data set, using for example two clustering methods. This index is related to the misclassifica-tion error in supervised learning. Some properties of the ME and, especially, its distribution function for the case of two independent partitions are analyzed. Extensive simulations show the efficiency of the ME and we propose a hypothesis test based on it.
Feedback Passivation of Linear Systems With Fixed-Structure Controllers

This letter addresses the problem of designing an optimal output feedback controller with a specified controller structure for linear time-invariant (LTI) systems to maximize the passivity level for the closed-loop system, in both continuous-time (CT) and discrete-time (DT). Specifically, the set of controllers under consideration is linearly parameterized with constrained parameters. Both input feedforward passivity (IFP) and output feedback passivity (OFP) indices are used to capture the level of passivity. Given a set of stabilizing controllers, a necessary and sufficient condition is proposed for the existence of such fixed-structure output feedback controllers that can passivate the closed-loop system. Moreover, it is shown that the condition can be used to obtain the controller that maximizes the IFP or the OFP index by solving a convex optimization problem.
Lattice Structural Analysis on Sniffing to Denial of Service Attacks

Sniffing is one of the most prominent causes for most of the attacks in the digitized computing environment. Through various packet analyzers or sniffers available free of cost, the network packets can be captured and analyzed. The sensitive information of the victim like user credentials, passwords, a PIN which is of more considerable interest to the assailants can be stolen through sniffers. This is the primary reason for most of the variations of DDoS attacks in the network from a variety of its catalog of attacks. An effective and trusted framework for detecting and preventing these sniffing has greater significance in today's computing. A counter hack method to avoid data theft is to encrypt sensitive information. This paper provides an analysis of the most prominent sniffing attacks. Moreover, this is one of the most important strides to guarantee system security. Also, a Lattice structure has been derived to prove that sniffing is the prominent activity for DoS or DDoS attacks.