Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-Tuning the Expression of Duplicate Genes by Translational Regulation in Arabidopsis and Maize

Plant genomes are extensively shaped by various types of gene duplication. However, in this active area of investigation, the vast majority of studies focus on the sequence and transcription of duplicate genes, leaving open the question of how translational regulation impacts the expression and evolution of duplicate genes. We explored this issue by analyzing the ribo- and mRNA-seq data sets across six tissue types and stress conditions in Arabidopsis thaliana and maize (Zea mays). We dissected the relative contributions of transcriptional and translational regulation to the divergence in the abundance of ribosome footprint (RF) for different types of duplicate genes. We found that the divergence in RF abundance was largely programmed at the transcription level and that translational regulation plays more of a modulatory role. Intriguingly, translational regulation is characterized by its strong directionality, with the divergence in translational efficiency (TE) globally counteracting the divergence in mRNA abundance, indicating partial buffering of the transcriptional divergence between paralogs by translational regulation. Divergence in TE was associated with several sequence features. The faster-evolving copy in a duplicate pair was more likely to show lower RF abundance, which possibly results from relaxed purifying selection compared with its paralog. A considerable proportion of duplicates displayed differential TE across tissue types and stress conditions, most of which were enriched in photosynthesis, energy production, and translation-related processes. Additionally, we constructed a database TDPDG-DB (http://www.plantdupribo.tk), providing an online platform for data exploration. Overall, our study illustrates the roles of translational regulation in fine-tuning duplicate gene expression in plants.
Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction, and Restoration

To estimate time-varying MIMO channel at base station, traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to extremely high overhead in massive MIMO systems. To tackle this problem, we propose a novel scheme for DL channel tracking in this paper. First, by utilizing virtual channel representation (VCR), we develop a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise expectation maximization (EM) algorithm is adopted for capturing model parameters, including the spatial signatures, time-correlation factors, off-grid bias, channel power, and noise power. By exploiting the UL/DL angle reciprocity, the spatial signatures, time-correlation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL. However, channel power and noise power are closely related with the carrier frequency, which cannot be perfectly inferred from the UL. Instead of discovering these two parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with partial prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.
Hematopoietic-Specific Deletion of Foxo1 Promotes NK Cell Specification and Proliferation

We previously reported that deletion of Foxo1, via Ncr1-iCre mice from the expression of NKp46 onward, led to enhanced natural killer (NK) cell maturation and effector function. In this model, however, the role of Foxo1 in regulating NK cell specification and early development remains exclusive. Herein, we utilized a murine model of hematopoietic-specific deletion of Foxo1 before lymphoid specification, by crossing mice carrying floxed Foxo1 alleles (Foxo1fl/fl) with Vav1-iCre mice, to revisit the role of Foxo1 on NK cell specification and early development. The data showed that hematopoietic-specific deletion of Foxo1 resulted in increased proportion and numbers of common lymphoid progenitors (CLP) (Lin−CD127+c-Kit+Sca-1+), pre-pro NK b cells (Lin−Sca-1+c-Kit−CD135−CD127+), as well as committed Lin−CD122+ cells and CD3−CD19−NKp46+ NK cells in bone marrow. Hematopoietic-specific deletion of Foxo1 also promoted NK cells proliferation in a cell-intrinsic manner, indicated by increased Ki-67 expression and more expansion of NK cell after ex vivo stimulation with IL-15. The reason for Foxo1 suppressing NK cell proliferation might be its direct transcription of the cell-cycle inhibitory genes, such as p21cip1, p27kip1, p130, Gadd45a, and Ccng2 (cyclin G2) in NK cells, supported by the evidence of decreased mRNA expression of p21cip1, p27kip1, p130, Gadd45a, and Ccng2 in Foxo1-deficient NK cells and direct binding of Foxo1 on their promoter region. Furthermore, hematopoietic-specific deletion of Foxo1 resulted in increased ratio of mature NK subsets, such as CD11b+CD27− and CD43+KLRG1+ NK cells, but decreased ratio of immature NK subsets, such as CD27+CD11b− and CD27+CD11b+ NK cells, consistent with the findings in the murine model of Ncr1-iCre mediated Foxo1 deletion. Conclusively, Foxo1 not only acts as a negative checkpoint on NK cell maturation, but also represses NK cell specification and proliferation. The relative higher expression of Foxo1 in CLP and early NK precursors may also contribute to the later NK cell proliferation and responsiveness, which warranties another separate study in the future.
Analyses of a Panel of Transcripts Identified From a Small Sample Size and Construction of RNA Networks in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world. Dysregulation of mRNAs and non-coding RNAs (ncRNAs) plays critical roles in the progression of HCC. Here, we investigated HCC samples by RNA-seq and identified a series of dysregulated RNAs in HCC. Various bioinformatics analyses established long non-coding RNA (lncRNA)-mRNA co-expression and competing endogenous RNA (ceRNA) networks in circRNA-miRNA-mRNA axis, indicating the potential cis and/or trans regulatory roles of lncRNAs and circRNAs. Moreover, GO pathway analysis showed that these identified RNAs were associated with many biological processes that were related to tumorigenesis and tumor progression. In conclusion, we systematically established functional networks of lncRNA-mRNA, circRNA-miRNA-mRNA to further unveil the potential interactions and biological processes in HCC. These results provide further insights into gene expression network of HCC and may assist future diagnosis of HCC.
Burch ideals and Burch rings

We introduce the notion of Burch ideals and Burch rings. They are easy to define, and can be viewed as generalization of many well-known concepts, for example integrally closed ideals of finite colength and Cohen--Macaulay rings of minimal multiplicity. We give several characterizations of these objects. We show that they satisfy many interesting and desirable properties: ideal-theoretic, homological, categorical. We relate them to other classes of ideals and rings in the literature.
The Santa Barbara Cluster Comparison Project: A Comparison of Cosmological Hydrodynamics Solutions

We have simulated the formation of an X-ray cluster in a cold dark matter universe using 12 different codes. The codes span the range of numerical techniques and implementations currently in use, including smoothed particle hydrodynamics (SPH) and grid methods with fixed, deformable, or multilevel meshes. The goal of this comparison is to assess the reliability of cosmological gasdynamical simulations of clusters in the simplest astrophysically relevant case, that in which the gas is assumed to be nonradiative. We compare images of the cluster at different epochs, global properties such as mass, temperature and X-ray luminosity, and radial profiles of various dynamical and thermodynamical quantities. On the whole, the agreement among the various simulations is gratifying, although a number of discrepancies exist. Agreement is best for properties of the dark matter and worst for the total X-ray luminosity. Even in this case, simulations that adequately resolve the core radius of the gas distribution predict total X-ray luminosities that agree to within a factor of 2. Other quantities are reproduced to much higher accuracy. For example, the temperature and gas mass fraction within the virial radius agree to within about 10%, and the ratio of specific dark matter kinetic to gas thermal energies agree to within about 5%. Various factors, including differences in the internal timing of the simulations, contribute to the spread in calculated cluster properties. Based on the overall consistency of results, we discuss a number of general properties of the cluster we have modeled.
Accurate Tissue Interface Segmentation via Adversarial Pre-Segmentation of Anterior Segment OCT Images

Optical Coherence Tomography (OCT) is an imaging modality that has been widely adopted for visualizing corneal, retinal and limbal tissue structure with micron resolution. It can be used to diagnose pathological conditions of the eye, and for developing pre-operative surgical plans. In contrast to the posterior retina, imaging the anterior tissue structures, such as the limbus and cornea, results in B-scans that exhibit increased speckle noise patterns and imaging artifacts. These artifacts, such as shadowing and specularity, pose a challenge during the analysis of the acquired volumes as they substantially obfuscate the location of tissue interfaces. To deal with the artifacts and speckle noise patterns and accurately segment the shallowest tissue interface, we propose a cascaded neural network framework, which comprises of a conditional Generative Adversarial Network (cGAN) and a Tissue Interface Segmentation Network (TISN). The cGAN pre-segments OCT B-scans by removing undesired specular artifacts and speckle noise patterns just above the shallowest tissue interface, and the TISN combines the original OCT image with the pre-segmentation to segment the shallowest interface. We show the applicability of the cascaded framework to corneal datasets, demonstrate that it precisely segments the shallowest corneal interface, and also show its generalization capacity to limbal datasets. We also propose a hybrid framework, wherein the cGAN pre-segmentation is passed to a traditional image analysis-based segmentation algorithm, and describe the improved segmentation performance. To the best of our knowledge, this is the first approach to remove severe specular artifacts and speckle noise patterns (prior to the shallowest interface) that affects the interpretation of anterior segment OCT datasets, thereby resulting in the accurate segmentation of the shallowest tissue interface. To the best of our knowledge, this is the first work to show the potential of incorporating a cGAN into larger deep learning frameworks for improved corneal and limbal OCT image segmentation. Our cGAN design directly improves the visualization of corneal and limbal OCT images from OCT scanners, and improves the performance of current OCT segmentation algorithms.
PARI: A Probabilistic Approach to AS Relationships Inference

Over the last two decades, several algorithms have been proposed to infer the type of relationship between Autonomous Systems (ASes). While the recent works have achieved increasingly higher accuracy, there has not been a systematic study on the uncertainty of AS relationship inference. In this paper, we analyze the factors contributing to this uncertainty and introduce a new paradigm to explicitly model the uncertainty and reflect it in the inference result. We also present PARI, an exemplary algorithm implementing this paradigm, that leverages a novel technique to capture the interdependence of relationship inference across AS links.
Correlation between the ionizing continuum and the variable C iv broad absorption line in multi-epoch observations of SDSS J141007.74+541203.3

Correlations between the variations of quasar absorption lines and the ionizing continuum have been confirmed recently in systematic studies. However, no convincing individual case is reported. We present a statistical analysis of the variable C iv broad absorption line (BAL) in the quasar SDSS J141007.74+541203.3, which has been observed with 44 epochs by the Sloan Digital Sky Survey Data Release 14. Grier et al. have concluded that the most likely cause of the variability of the BAL in SDSS J141007.74+541203.3 is a rapid response to changes in the incident ionizing continuum. In this paper, we confirm the anticorrelation between the equivalent width of BALs and the flux of the continuum based on the spectra of this quasar, which show significant variations. This serves as further independent evidence for the conclusions of Grier et al.
FSMI: Fast computation of Shannon mutual information for information-theoretic mapping

Exploration tasks are embedded in many robotics applications, such as search and rescue and space exploration. Information-based exploration algorithms aim to find the most informative trajectories by maximizing an information-theoretic metric, such as the mutual information between the map and potential future measurements. Unfortunately, most existing information-based exploration algorithms are plagued by the computational difficulty of evaluating the Shannon mutual information metric. In this article, we consider the fundamental problem of evaluating Shannon mutual information between the map and a range measurement. First, we consider 2D environments. We propose a novel algorithm, called the fast Shannon mutual information (FSMI). The key insight behind the algorithm is that a certain integral can be computed analytically, leading to substantial computational savings. Second, we consider 3D environments, represented by efficient data structures, e.g., an OctoMap, such that the measurements are compressed by run-length encoding (RLE). We propose a novel algorithm, called FSMI-RLE, that efficiently evaluates the Shannon mutual information when the measurements are compressed using RLE. For both the FSMI and the FSMI-RLE, we also propose variants that make different assumptions on the sensor noise distribution for the purpose of further computational savings. We evaluate the proposed algorithms in extensive experiments. In particular, we show that the proposed algorithms outperform existing algorithms that compute Shannon mutual information as well as other algorithms that compute the Cauchy–Schwarz quadratic mutual information (CSQMI). In addition, we demonstrate the computation of Shannon mutual information on a 3D map for the first time.
Switching Outpatients With Schizophrenia and Related Disorders on Long-Acting Injectable Antipsychotics to Olanzapine: An Open-Label Naturalistic Pilot Study

Little is known about the feasibility of switching patients with schizophrenia from long-acting injectable antipsychotics to oral olanzapine. We completed an open-label 14-week study to assess such a transition. This study included 25 stable outpatients (DSM-IV diagnosis of schizophrenia, schizophreniform disorder, or schizoaffective disorder) who were receiving long-acting injectable antipsychotics. Following a screening visit, patients began treatment with olanzapine 10 mg/day, which was initiated the day of their scheduled injection. Clinical assessments included the Clinical Global Impression—Improvement Scale (CGI-I) and the Positive and Negative Syndrome Scale (PANSS). Patient self-reports of adverse events were monitored and the Extrapyramidal Symptoms Rating Scale completed at each visit. In those completing the trial (N = 18), results revealed that a switch from injectable antipsychotics to olanzapine was associated with significant improvements on the CGI-I, negative symptoms, PANSS total scores, and parkinsonism. In considering the whole sample (last observation carried forward, N = 25), significant improvements on the CGI-I, parkinsonism, and dyskinesia were observed. Finally, those who failed to complete the trial (N = 7) did not change significantly from visit 1 to endpoint on any of the efficacy or safety measures. These results should be considered preliminary and require replication using appropriate control groups.
How do API changes trigger stack overflow discussions? a study on the Android SDK

The growing number of questions related to mobile development in StackOverflow highlights an increasing interest of software developers in mobile programming. For the Android platform, 213,836 questions were tagged with Android-related labels in StackOverflow between July 2008 and August 2012. This paper aims at investigating how changes occurring to Android APIs trigger questions and activity in StackOverflow, and whether this is particularly true for certain kinds of changes. Our findings suggest that Android developers usually have more questions when the behavior of APIs is modified. In addition, deleting public methods from APIs is a trigger for questions that are (i) more discussed and of major interest for the community, and (ii) posted by more experienced developers. In general, results of this paper provide important insights about the use of social media to learn about changes in software ecosystems, and establish solid foundations for building new recommenders for notifying developers/managers about important changes and recommending them relevant crowdsourced solutions
Sample-adaptive product quantization: asymptotic analysis and examples

Vector quantization (VQ) is an efficient data compression technique for low bit rate applications. However the major disadvantage of VQ is that its encoding complexity increases dramatically with bit rate and vector dimension. Even though one can use a modified VQ, such as the tree-structured VQ, to reduce the encoding complexity, it is practically infeasible to implement such a VQ at a high bit rate or for large vector dimensions because of the huge memory requirement for its codebook and for the very large training sequence requirement. To overcome this difficulty, a structurally constrained VQ called the sample-adaptive product quantizer (SAPQ) has recently been proposed. We extensively study the SAPQ that is based on scalar quantizers in order to exploit the simplicity of scalar quantization. Through an asymptotic distortion result, we discuss the achievable performance and the relationship between distortion and encoding complexity. We illustrate that even when SAPQ is based on scalar quantizers, it can provide VQ-level performance. We also provide numerical results that show a 2-3 dB improvement over the Lloyd-Max (1982, 1960) quantizers for data rates above 4 b/point.
Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot

Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming (GP) of the fastest possible (sidewinding) locomotion of simulated limbless, wheelless snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all physical forces, resulting from the actuators, joints constrains, frictions, gravity, and collisions. Reduction of the search space of the GP is achieved by representation of Snakebot as a system comprising identical morphological segments and by automatic definition of code fragments, shared among (and expressing the correlation between) the evolved dynamics of the vertical and horizontal turning angles of the actuators of Snakebot. Empirically obtained results demonstrate the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments. Robustness of the sidewinding Snakebot, which is considered to be the ability to retain its velocity when situated in an unanticipated environment, is illustrated by the ease with which Snakebot overcomes various types of obstacles such as a pile of or burial under boxes, rugged terrain, and small walls. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is discussed. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted. Exploring the opportunity for automatic design and adaptation of a simulated artifact, this work could be considered as a step toward building real Snakebots, which are able to perform robustly in difficult environments.
On distance utility in the exploration task

Performance of exploration strategies strongly depends on the process of determination of a next robot goal. Current approaches define different utility functions how to evaluate and select possible next goal candidates. One of the mostly used evaluation criteria is the distance cost that prefers candidates close to the current robot position. If this is the only criterion, simply the nearest candidate is chosen as the next goal. Although this criterion is simple to implement and gives feasible results there are situations where the criterion leads to wrong decisions. This paper presents the distance cost that reflects traveling through all goal candidates. The cost is determined as a solution of the Traveling Salesman Problem using the Chained Lin-Kernighan heuristic. The cost can be used as a stand-alone criterion as well as it can be integrated into complex decision systems. Experimental results for open-space and office-like experiments show that the proposed approach outperforms the standard one in the length of the traversed trajectory during the exploration while the computational burden is not significantly increased.
A Hierarchy of Power: The Place of Patient and Public Involvement in Healthcare Service Development

Amidst statutory and non-statutory calls for effective patient and public involvement (PPI), questions continue to be raised about the impact of PPI in healthcare services. Stakeholders, policy makers, researchers, and members of the public ask in what ways and at what level PPI makes a difference. Patient experience is widely seen as an important and valuable resource to the development of healthcare services, yet there remain legitimacy issues concerning different forms of knowledge that members of the public and professionals bring to the table, and related power struggles. This paper draws on data from a qualitative study of PPI in a clinical commissioning group (CCG) in the UK. The study looked at some of the activities in which there was PPI; this involved researchers conducting observations of meetings, and interviews with staff and lay members who engaged in CCG PPI activities. This paper explores power imbalances when it comes to influencing the work of the CCG mainly between professionals and members of public, but also between different CCG staff members and between different groups of members of public. The authors conclude that a hierarchy of power exists, with some professionals and public and lay members afforded more scope for influencing healthcare service development than others—an approach which is reflected in the ways and extent to which different forms and holders of knowledge are viewed, managed, and utilized.