Miracle-dz/UniFAR-FTUs huggingface dataset:
Timing of Population Fragmentation in a Vulnerable Minnow, the Umpqua Chub, and the Role of Nonnative Predators

Abstract We examined the distribution of Umpqua Chub Oregonichthys kalawatseti, an endemic, vulnerable minnow in western Oregon, and whether six ecological populations (based on distribution patterns) had sufficient genetic cohesion to be considered evolutionary populations. We also evaluated the influence of Holocene geological events and recent nonnative predator introductions on the timing of population formation or fragmentation. Based on data from 10 microsatellite loci, we found evidence for four evolutionary populations of Umpqua Chub. One population, in the Smith River, is isolated by the Umpqua estuary and is more than 100 river kilometers from the other three populations: Elk Creek, Calapooya Creek–Olalla Creek, and Cow Creek–South Umpqua River. Quantile regression was used to examine the timing of genetic divergence among evolutionary populations assuming a genetic isolation-by-distance model. The quantile regression suggested that the genetic differentiation index (F ST) should change by at le.
Combination therapy with androgen receptor N‐terminal domain antagonist EPI‐7170 and enzalutamide yields synergistic activity in AR‐V7‐positive prostate cancer

Resistance of castration‐resistant prostate cancer (CRPC) to enzalutamide and abiraterone involves the expression of constitutively active, truncated androgen receptor (AR) splice variants (AR‐Vs) that lack a C‐terminal ligand‐binding domain (LBD). Both full‐length AR and truncated AR‐Vs require a functional N‐terminal domain (NTD) for transcriptional activity thereby providing rationale for the development of ralaniten (EPI‐002) as a first‐in‐class antagonist of the AR‐NTD. Here, we evaluated the antitumor effect of a next‐generation analog of ralaniten (EPI‐7170) as a monotherapy or in combination with enzalutamide in prostate cancer cells that express AR‐V7 that were resistant to enzalutamide. EPI‐7170 had 8–9 times improved potency compared to ralaniten. Enzalutamide increased levels of AR‐V7 and expression of its target genes. Knockdown of AR‐V7 restored sensitivity to enzalutamide, indicating a role for AR‐V7 in the mechanism of resistance. EPI‐7170 inhibited expression of genes transcriptionally regulated by full‐length AR and AR‐V7. A combination of EPI‐7170 and enzalutamide resulted in synergistic inhibition of proliferation of enzalutamide‐resistant cells that was consistent with results from cell cycle and clonogenic assays. In addition, this drug enhanced the antitumor effect of enzalutamide in enzalutamide‐resistant CRPC preclinical models. Thus, a combination therapy targeting both the NTD and LBD of AR, and thereby blocking both full‐length AR and AR‐Vs, has potential for the treatment of enzalutamide‐resistant CRPC.
Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition

Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition Yuzong Liu Chair of the Supervisory Committee: Research Professor Katrin Kirchhoff Electrical Engineering Acoustic models require a large amount of training data. However, lots of labor is required to annotate the training data for automatic speech recognition. More importantly, the performance of the acoustic model could degenerate during test time, where the conditions of test data differ from the training data in speaker characteristics, channel and recording environment. To compensate for the deviation between training and test conditions, we investigate a graph-based semi-supervised learning approach to acoustic modeling in automatic speech recognition. Graph-based semi-supervised learning (SSL) is a widely used semi-supervised learning method in which the labeled data and unlabeled data are jointly represented as a weighted graph, and the information is propagated from the labeled data to the unlabeled data. The key assumption that graph-based SSL makes is that data samples lie on a low dimensional manifold, where samples that are close to each other are expected to have the same class label. More importantly, by exploiting the relationship between training and test samples, graph-based SSL implicitly adapts to the test data. In this thesis, we address several key challenges in applying graph-based SSL to acoustic modeling. We first investigate and compare several state-of-the-art graph-based SSL algorithms on a benchmark dataset. In addition, we propose novel graph construction methods that allow graph-based SSL to handle variable-length input features. We next investigate the efficacy of graph-based SSL in context of a fully-fledged DNN-based ASR system. We compare two different integration frameworks for graph-based learning. First, we propose a lattice-based late integration framework that combines graph-based SSL with the DNN-based acoustic modeling and evaluate the framework on continuous word recognition tasks. Second, we propose an early integration framework using neural graph embeddings and compare two different neural graph embedding features that capture the information of the manifold at different levels. The embedding features are used as input to a DNN system and are shown to outperform the conventional acoustic feature inputs on several medium-to-large vocabulary conversational speech recognition tasks.
REM sleep stabilizes hypothalamic representation of feeding behavior

Significance The lateral hypothalamus encompasses neural circuits that are highly active during feeding behavior and rapid eye movement sleep. Yet, it remains unclear whether these mutually exclusive behaviors share common neural populations. Here, we recorded and perturbed the activity of inhibitory neurons of the lateral hypothalamus (LH) across feeding behavior and sleep in freely behaving mice. We found that feeding is reliably encoded by specific patterns of neuron activity and that these patterns are reactivated during rapid eye movement sleep. Disrupting the activity of these inhibitory neurons specifically during rapid eye movement sleep decreased subsequent feeding behavior. These results suggest that rapid eye movement sleep stabilizes the hypothalamic representation of feeding behavior and modulates future food intake. During rapid eye movement (REM) sleep, behavioral unresponsiveness contrasts strongly with intense brain-wide neural network dynamics. Yet, the physiological functions of this cellular activation remain unclear. Using in vivo calcium imaging in freely behaving mice, we found that inhibitory neurons in the lateral hypothalamus (LHvgat) show unique activity patterns during feeding that are reactivated during REM, but not non-REM, sleep. REM sleep-specific optogenetic silencing of LHvgat cells induced a reorganization of these activity patterns during subsequent feeding behaviors accompanied by decreased food intake. Our findings provide evidence for a role for REM sleep in the maintenance of cellular representations of feeding behavior.
Assessing the Anthropometric Profile of Spanish Elite Reserve Soccer Players by Playing Position over a Decade

The aims of this study were to describe the evolution of the anthropometric profile of soccer players over a decade and to compare the anthropometric profiles of players promoted from an elite reserve team to high-level soccer with those players who were not promoted. We examined the body mass, height, body-mass index, and body fat of 98 players enrolled in the reserve team from 2008 to 2018. The players were classified in terms of (a) the highest competitive level they achieved up to the 2019/2020 season (i.e., Spanish 1st–2nd divisions or semi-professional); (b) the period in which they played their last season on the team; and (c) their playing position. Over time, the height of goalkeepers, lateral midfielders, and attackers has increased (effect size = 0.66 ± 1.13) but has decreased in central midfielders (effect size = 0.83). The body fat of defenders has also fallen (effect size = 0.55 ± 0.95). Spanish high-level goalkeepers, lateral midfielders, and attackers were taller than their semi-professional player counterparts (effect size = 1.20 ± 1.98). Body fat did not determine promotion from a reserve team to high-level soccer, but height may be an advantage for several playing positions. The assessment of the anthropometric profile and the application of interventions should be designed according to the playing position.
DCAF1 regulates Treg senescence via the ROS axis during immunological ageing.

As a hallmark of immunological ageing, the low-grade, chronic inflammation with accumulation of effector-memory T cells contributes to the increased susceptibility of many ageing-related diseases. While the proinflammatory state of aged T cells indicates a dysregulation of immune homeostasis, whether and how ageing drives regulatory T (Treg) cell ageing and alters their function is not fully understood due to a lack of specific ageing markers. Here, by a combination of cellular, molecular and bioinformatic approaches, we discover that Treg cells senesce more severely than conventional T (Tconv) cells during ageing. We found Treg cells from aged mice were less efficient than young Treg cells to suppress Tconv cell function in an inflammatory-bowel-disease model and to prevent Tconv cell ageing in the irradiation-induced ageing model. Furthermore, we revealed that DCAF1 (DDB1 and CUL4 associated factor 1) was downregulated in aged Treg cells and was critical to restrain Treg cell ageing via glutathione S-transferase P (GSTP1) regulated reactive-oxygen-species (ROS). Importantly, interfering with GSTP1 and ROS pathways reinvigorated the proliferation and function of aged Treg cells. Therefore, our studies uncover an important role of DCAF1-GSTP1-ROS axis in Treg cell senescence, which leads to uncontrolled inflammation and immunological ageing.
SDN/NFV-based DDoS Mitigation via Pushback

Distributed Denial of Service (DDoS) attacks aim at bringing down or decreasing the availability of services for their legitimate users, by exhausting network or server resources. It is difficult to differentiate attack traffic from legitimate traffic as the attack can come from distributed nodes that additionally might spoof their IP addresses. Traditional DoS mitigation solutions fail to defend all kinds of DoS attacks and huge DoS attacks might exceed the processing capacity of routers and firewalls easily. The advent of Software-defined Networking (SDN) and Network Function Virtualization (NFV) has brought a new perspective for network defense. Key features of such technologies like global network view and flexibly positionable security functionality can be used for mitigating DDoS attacks. In this paper, we propose a collaborative DDoS attack mitigation scheme that uses SDN and NFV. We adopt a machine learning algorithm from related work to derive accurate patterns describing DDoS attacks. Our experimental results indicate that our framework is able to differentiate attack and legitimate traffic with high accuracy and in near-realtime. Furthermore, the derived patterns can be used to create OpenFlow (OF) or Firewall rules that can be pushed back into the direction of the attack origin for more efficient and distributed filtering.
Global Trajectory Tracking for a Quadrotor through Event-Triggered Control: Synthesis, Simulations, and Experiments*

This paper presents an event-triggered controller that solves the problem of trajectory tracking for an aerial vehicle with a thrust actuation in a single body-fixed direction and full angular velocity actuation. Under the framework of hybrid dynamical systems, we first design a globally stabilizing hybrid control law and then derive an appropriate eventtriggering mechanism for sampling of actuation signals. We prove that bounded reference trajectories are rendered globally asymptotically stable for the closed-loop system. To enable practical implementation of the proposed event-triggered controller on digital platforms, we provide a modified event-triggering mechanism that achieves practical stability while avoiding Zeno solutions. The results are illustrated by numerical simulations and further verified by experiments.
Model Predictive Tracking Controller for Quadcopters with Setpoint Convergence Guarantees

This paper tackles the trajectory tracking problem for a quadrotor following a cascaded control design approach; an outer-loop model predictive controller (MPC) generates acceleration references to be tracked by an inner-loop nonlinear attitude controller. Leveraging the differential flatness property of the quadrotor’s dynamics, the proposed model predictive controller uses a linear third order model for prediction, enabling the attitude controller to track exactly the sufficiently smooth virtual acceleration while coping with state and input constraints. Asymptotic stability is established for the regulation problem for any initial position and velocity. In other words, the quadrotor provably tracks any setpoint reference. Simulations of the proposed cascade system are performed to validate and assess the convergence properties.
Transcriptome Analysis of Testes and Uterus: Reproductive Dysfunction Induced by Toxoplasma gondii in Mice

Toxoplasma gondii (T. gondii) infection in female mammals during pregnancy can result in poor pregnancy. Similarly, it can result in male reproductive disorders in male mammals. Although the testes and uterus have very different biological makeup, they are still both attacked by T. gondii resulting in reproductive dysfunctions. We hypothesized that there are significant common genes in the testes and uterus that interact with T. gondii. Finding out and studying these genes is vital to understand the infection mechanism of T. gondii and the induced disease pathogenesis. To achieve this goal, we built a mice model of acute infection with T. gondii and the testes and uterus of the mice were sequenced by RNA-Seq. A total of 291 and 679 significantly differently expressed genes (DEGs) were obtained from the testes and the uterus, respectively. In the Gene Ontology (GO) analysis, part of the DEGs in the testes and uterus were related to 35 GO functions. When compared with the KEGG database, seven pathways affecting both the testes and uterus during the course of T. gondii infection were identified. In addition, Toxoplasmosis can significantly affect the expression of Nlrp5 and Insc leading to negative outcomes in the host. On the other hand, the host regulates Gbp7, Gbp2b, and Ifit3 to defend against T. gondii infection.
Deep Learning Driven Wireless Real-time Human Activity Recognition

Human activity recognition based on wireless sensing is advantageous at various features such as privacy preservation, but also very challenging due to the instability of wireless signals. This paper proposes a deep learning driven wireless human activity recognition solution based on Multiple-Input-Multiple-Output (MIMO) radar sensing. User activities are first sensed by a low-power Frequency-Modulated Continuous Wave (FMCW) MIMO radar. Then a sequence of 3D images are generated out of the reflected signal strength. Next, deep neural networks (DNNs) are designed to analyze the correlation among the sequential 3D images to recognize various types of human activities. This work has developed: 1) a large dataset containing over 1,500 training videos of six different types of indoor activities, 2) a customized deep learning video data-loader to select proper training data in each training epoch, 3) a deep recurrent neural network (RNN) model to recognize human activities based on radar imaging results. This solution has been extensively evaluated in a research lab room. The results show that the solution is able to generate wireless imaging frame-by-frame, and it can achieve over 86.7% accuracy in recognizing six different types of human activities.
TensorFlow-Based Automatic Personality Recognition Used in Asynchronous Video Interviews

With the development of artificial intelligence (AI), the automatic analysis of video interviews to recognize individual personality traits has become an active area of research and has applications in personality computing, human–computer interaction, and psychological assessment. Advances in computer vision and pattern recognition based on deep learning (DL) techniques have led to the establishment of convolutional neural network models that can successfully recognize human nonverbal cues and attribute their personality traits with the use of a camera. In this paper, an end-to-end AI interviewing system was developed using asynchronous video interview (AVI) processing and a TensorFlow AI engine to perform automatic personality recognition (APR) based on the features extracted from the AVIs and the true personality scores from the facial expressions and self-reported questionnaires of 120 real job applicants. The experimental results show that our AI-based interview agent can successfully recognize the “big five” traits of an interviewee at an accuracy between 90.9% and 97.4%. Our experiment also indicates that although the machine learning was conducted without large-scale data, the semisupervised DL approach performed surprisingly well with regard to APR despite the lack of labor-intensive manual annotation and labeling. The AI-based interview agent can supplement or replace existing self-reported personality assessment methods that job applicants may distort to achieve socially desirable effects.
Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU With Attention Mechanism

Convolutional Neural Network(CNN) and Recurrent Neural Network(RNN) have been widely used in the field of text sentiment analysis and have achieved good results. However, there is an anteroposterior dependency between texts, although CNN can extract local information between consecutive words of a sentence, it ignores the contextual semantic information between words. Bidirectional GRU can make up for the shortcomings that CNN can’t extract contextual semantic information of long text, but it can’t extract the local features of the text as well as CNN. Therefore, we propose a multi-channel model that combines the CNN and the bidirectional gated recurrent unit network with attention mechanism (MC-AttCNN-AttBiGRU). The model can pay attention to the words that are important to the sentiment polarity classification in the sentence through the attention mechanism and combine the advantages of CNN to extract local features of text and bidirectional GRU to extract contextual semantic information of long text, which improves the text feature extraction ability of the model. The experimental results on the IMDB dataset and Yelp 2015 dataset show that the proposed model can extract more rich text features than other baseline models, and can achieve better results than other baseline models.
Generalized SVD reduced-order observers for Nonlinear systems*

A nonlinear observer design method is proposed for the reduced order observation of nonlinear systems in the presence of sensor and process noise. Supernumerary sensors to the measured states are assumed to be available. State variables unavailable for observation by measurement are estimated with the proposed observer structure that requires lower computation than full order observers. By modeling output measurements as a generalized linear combination of observable states and measurement noise, this method combines generalized singular value decomposition (GSVD) static estimation of noisy output measurement and reduced order observer theory for estimating unmeasured state variables in nonlinear systems. This relatively low computation alternative to full-order observation can be of economic advantage in model predictive control applications.
Biomass Equations for Six Commercial Tree Species in Quebec

Six commercial species were sampled throughout Quebec and prediction equations of ovendry masses for the total above-ground tree and its components white cedar (Thuya occidentalis L.) and eastern hemlock(Tsuga canadensis (L.) Carr. ). The deciduous species are: red maple (Acer rubrum L.), white ash (Fraxinus americana L.), black ash (Fraxinus nigra Marsh.), and beech (Fagus grandifolia Ehrh.). The components expressed in ovendry masses are: the total tree, the stem, the merchantable stem, the wood and bark of the merchantable stem, and the crown. A nonlinear model is used with the diameter at breast height and the total height as predictors. Key words: Biomass, prediction equations, eastern white cedar, eastern hemlock, red maple white ash, black ash, beech.
Sequence diversity and copy number variation of Mutator-like transposases in wheat

Partial transposase-coding sequences of Mutator-like elements (MULEs) were isolated from a wild einkorn wheat, Triticum urartu, by degenerate PCR. The isolated sequences were classified into a MuDR or Class I clade and divided into two distinct subclasses (subclass I and subclass II). The average pair-wise identity between members of both subclasses was 58.8% at the nucleotide sequence level. Sequence diversity of subclass I was larger than that of subclass II. DNA gel blot analysis showed that subclass I was present as low copy number elements in the genomes of all Triticum and Aegilops accessions surveyed, while subclass II was present as high copy number elements. These two subclasses seemed uncapable of recognizing each other for transposition. The number of copies of subclass II elements was much higher in Aegilops with the S, Sl and D genomes and polyploid Triticum species than in diploid Triticum with the A genome, indicating that active transposition occurred in S, Sl and D genomes before polyploidization. DNA gel blot analysis of six species selected from three subfamilies of Poaceae demonstrated that only the tribe Triticeae possessed both subclasses. These results suggest that the differentiation of these two subclasses occurred before or immediately after the establishment of the tribe Triticeae.
Delay Independent Output Feedback Stabilization of Discrete-time Linear Systems with Bounded Input Delay

Bounded input delay frequently arises in control systems. The popular predictor feedback design requires the exact knowledge of the delay. Such a requirement on the availability of the delay knowledge poses challenges in the design of robust control laws when only the knowledge of an upper bound of the delay is known. We propose such a design in this paper. Based on the delay independent truncated predictor state feedback design, an observer based output feedback law is constructed that does not contain the exact value of the delay. For a general, possibly exponentially unstable, discrete-time linear system, the proposed output feedback law stabilizes the system as long as the delay does not exceed a certain amount. For a system with all open loop poles at z = 1 or inside the unit circle, a low gain feedback design enables the feedback law to handle any bounded time-varying delay.