Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Chemical Tools to Monitor Human Kallikrein 13 (KLK13) Activity

Kallikrein 13 (KLK13) was first identified as an enzyme that is downregulated in a subset of breast tumors. This serine protease has since been implicated in a number of pathological processes including ovarian, lung and gastric cancers. Here we report the design, synthesis and deconvolution of libraries of internally quenched fluorogenic peptide substrates to determine the specificity of substrate binding subsites of KLK13 in prime and non-prime regions (according to the Schechter and Berger convention). The substrate with the consensus sequential motive ABZ-Val-Arg-Phe-Arg-ANB-NH2 demonstrated selectivity towards KLK13 and was successfully converted into an activity-based probe by the incorporation of a chloromethylketone warhead and biotin bait. The compounds described may serve as suitable tools to detect KLK13 activity in diverse biological samples, as exemplified by overexpression experiments and targeted labeling of KLK13 in cell lysates and saliva. In addition, we describe the development of selective activity-based probes targeting KLK13, to our knowledge the first tool to analyze the presence of the active enzyme in biological samples.
Integrate-and-Fire模型输入的最优解码

研究了随机相关输入的Integrate-and-Fire（IF）神经元模型的最优解码问题．使用Fisher信息，在理论上解决了这样一个问题：当抑制和兴奋输入的比r取何值时，神经元能最精确地解码IF神经元的输入．指出相关输入整体上减小了解码的精确性．
The Role of Tyramine β-Hydroxylase in Density Dependent Immunityof Oriental Armyworm (Mythmina separata) Larva

High population density alters insect prophylactic immunity, with density-dependent prophylaxis (DDP) being reported in many polyphonic insects. However, the molecular mechanism for DDP remains unclear. In current study, the role of tyramine β-hydroxylase (Tβh) in the immune response of M. separata larvae that were subject to different rearing densities conditions was investigated. The tyramine β-hydroxylase activity of larvae from high density treatments (10 and 30 larvae per jar) was significantly higher than that of the larvae from low density treatments (one, two, and five larvae/jar). A tyramine β-hydroxylase (designated MsTβh) containing a 1779 bp open reading frame was identified. Multiple sequence alignment and phylogenetic analysis indicated that MsTβh was orthologous to the Tβh that was found in other lepidopterans. Elevated MsTβh expression was observed in larvae under high density (10 larvae per jar). Silencing MsTβh expression by the injection of dsRNA in larvae from the high density treatment produced a 25.1% reduction in octopamine levels, while at the same time, there was a significant decrease in phenoloxidase (PO) and lysozyme activity, total haemocyte counts, and survival against Beauveria infection 56.6%, 88.5%, 82.0%, and 55.8%, respectively, when compared to control larvae. Our findings provide the first insights into how MsTβh mediates the octopamine level, which in turn modulates the immune response of larvae under different population densities.
The components of colour vision

Colour perception is formed of many different components, such as colour discrimination, colour constancy, colour term naming, and the dimensions of colour (hue, chroma and lightness). It is a ‘toolbox’ of processes, not one cohesive function. Some of the components of colour vision develop into adult-like function over childhood, but they do not necessarily mature at the same speed. The studies in this thesis investigate adult, child and infant colour perception and cognition. Paper 1 finds a relationship between colour constancy and colour term naming in three- to four-year-old children. This relationship has wider implications for the co-development of language and perception. Paper 2 (Rogers, Knoblauch & Franklin, 2016) uses the technique of Maximum Likelihood Conjoint Measurement (MLCM) in adult participants to investigate the interaction between lightness and chroma in perception. Paper 3 combines MLCM analysis with preferential looking methods to compare interaction of lightness and chroma in infant and adult participants. This study paves the way for the use of MLCM and eye-tracking for studying other dimensions in development such as face perception, language, surface and shape. Paper 4 investigates why discrimination is poorest along the blue-yellow direction of cone opponent space (also known as the daylight locus). We tested the theory that this is adaptive for colour constancy by comparing illumination discrimination to surface discrimination in adult participants. We found equally poor discrimination for blue-yellow in both conditions, suggesting colour constancy is not the only explanatory factor. Together, these papers add to our understanding of the key components of colour vision over the life span and how perception of colour depends on various contextual and individual factors. Furthermore, this thesis develops novel applications of experimental techniques, and paves the way for these methods to be used to study other cognitive and developmental domains.
Making Compiling Query Engines Practical

Compiling queries to machine code is a very efficient way for executing queries. One often overlooked problem with compilation is the time it takes to generate machine code. Even with fast compilation frameworks like LLVM, generating machine code for complex queries often takes hundreds of milliseconds. Such durations can be a major disadvantage for workloads that execute many complex, but quick queries. To solve this problem, we propose an adaptive execution framework, which dynamically switches from interpretation to compilation. We also propose a fast bytecode interpreter for LLVM, which can execute queries without costly translation to machine code and dramatically reduces the query latency. Adaptive execution is fine-grained, and can execute code paths of the same query using different execution modes. Our evaluation shows that this approach achieves optimal performance in a wide variety of settings—low latency for small data sets and maximum throughput for large data sizes. Besides compilation time, we also focus on debugging, which is another important challenge of compilation-based query engines. To address this problem, we present a novel, database-specific debugger for compiling query engines.
Spatiotemporal information deep fusion network with frame attention mechanism for video action recognition

Abstract. In the deep learning-based video action recognition, the function of the neural network is to acquire spatial information, motion information, and the associated information of the above two kinds of information over an uneven time span. We propose a network for extracting semantic information of video sequences based on the deep fusion feature of local spatial–temporal information. Convolutional neural networks (CNNs) are used in the network to extract local spatial information and local motion information, respectively. The spatial information is in three-dimensional convolution with the motion information of the corresponding time to obtain local spatial–temporal information at a certain moment. The local spatial–temporal information is then input into the long- and short-time memory (LSTM) to obtain the context relationship of the local spatial–temporal information in the long-time dimension. We add the ability of the regional attention mechanism of video frames in the neural network mechanism for obtaining context. That is, the last layer of convolutional layer spatial information and the first layer of the fully connected layer are, respectively, input into different LSTM networks, and the outputs of the two LSTMs at each time are merged again. This enables a fully connected layer that is rich in categorical information to provide a frame attention mechanism for the spatial information layer. Through the experiments on the three action recognition common experimental datasets UCF101, UCF11, and UCFSports, the spatial–temporal information deep fusion network proposed has a high correct recognition rate in the task of action recognition.
Clinical profile and determinants of short-term outcome of acute kidney injury: A hospital-based prospective study from Northeastern India

CONTEXT: Acute kidney injury (AKI) is an outcome of multiple etiologies and is mostly reversible. Data on its incidence and outcome, particularly from India, are limited. AIMS: To study the etiology, clinical profile, and short-term prognosis in AKI. SETTINGS AND DESIGN: A hospital-based prospective observational study on AKI. SUBJECTS AND METHODS: Seventy-five AKI patients diagnosed by Acute Kidney Injury network criteria were selected. Patients with preexisting chronic kidney disease were excluded. STATISTICAL ANALYSIS USED: Data were compiled using the Statistical Package for the Social Sciences version 17. Regression analysis was done for determining the association of various variables for mortality. P < 0.05 was considered statistically significant. RESULTS: The mean age of patients was 41.09 ± 16.17 years with a male:female ratio of 1.42:1. Comorbidities were present in 37.3%, with diabetes mellitus (10.6%) and chronic liver disease (10.6%) being the most common. Fever was the most common (40%) presenting symptom, followed by oliguria (25.8%). Infection was the most common cause of AKI (56%), with sepsis in 26.7% followed by acute gastroenteritis in 17.3%. Pneumonia was the primary focus in 50% of cases with sepsis. Mean serum creatinine and urea at admission were 2.37 ± 0.90 and 92.44 ± 39.67 mg/dl, respectively. Serum creatinine rose progressively to 2.96 ± 1.18 and 3.26 ± 1.56 mg/dl at 24 and 48 h, respectively, since hospitalization. Majority of the cases (73.3%) were nonoliguric. Hemodialysis was necessary in 24% of cases. Mean hospital stay was 8.16 days. In-hospital mortality was 24%. Among survivors, 92.9% had complete renal recovery on discharge. Sepsis, need for hemodialysis, urea >100 mg/dl, and peak serum creatinine >3 mg/dl were contributors to mortality (P < 0.01). CONCLUSIONS: Infections, especially sepsis, were the most common cause of AKI. Hemodialysis was required in one-quarter of the patients. Sepsis, need for hemodialysis, and high creatinine were associated with a significantly higher mortality.
Archetypes of urban travelers: Clustering of mobile phone users in Singapore

An important task in transportation planning is to segment the travelling demand into groups of homogenous individuals for a better analysis. Traditionally travel demand is segmented through socio-demographic information. However, this information is often times not available in new streams of Big Data. We propose then a methodology to uncover the different archetypes of urban travellers based only on the mobility traces left by their mobile phones. We deﬁned 5 variables that explain traits of travel behaviour within these digital traces, followed by an evaluation of two clustering algorithms on a dimensional-reduced space. We ﬁnally present 16 archetypes of urban travellers for the case of Singapore. Results from this study constitute one of the ﬁrst steps towards the development of new Big Data travel demand models when privacy is a concern.
Robust Fitting of Geometric Primitives on LiDAR Data

This paper deals with robust surface fitting on spatial points measured by a LiDAR device. The point clouds contain hundreds of thousands data points. Therefore, the time demand of the algorithms is crucial for fast operation. We present two novel algorithms based on the RANSAC method: one for plane detection and one for other object detection. The execution time of the novel algorithms is significantly lower as only one random sampling is required because a deterministic teqnique selects the other data points. The accuracy of the novel methods are validated on synthesized data as well as real indoor and outdoor measurements.
Adversarial Media-fusion Approach to Strain Prediction for Bridges

This paper contributes to the wide acceptance of autonomous health monitoring for real bridges. Our approach involves dynamic simulation, whereby damage may be identifi d by detecting abnormal mechanical behavior in the bridge components in response to passing vehicles. Co nventionally, dynamic simulation requires expert knowledge of mechanics, components, materials, and struct ures, in addition to accurate modeling. Moreover, it requires detailed specification of the external forces ap plied, such as vehicle speeds, loci, and axle weights. This paper introduces a novel media-fusion framework to obt ain a bridge dynamic model in a fully data-driven fashion. The proposed generative model also successfully s imulated strain responses for a real road bridge by using a camera and strain sensors on the bridge. The generati ve ne work was trained by an adversarial learning algorithm customized for media-fusion analysis.
Database-Driven Iterative Learning for Building Temperature Control

Building interior temperatures are affected by the outdoor air temperature. Note that outdoor weather patterns are somewhat repetitive in nature and historical records of outdoor temperature are readily accessible; we present a data-driven iterative learning approach to improve the room temperature tracking performance over time. By comparing the short-term temperature forecast with the past data, chains of (nonconsecutive) days exhibiting similar outside temperature patterns can be identified. The corresponding building operation record (heat input and temperature output trajectories) may then be used in the iterative learning control (ILC) to update the input based on the past temperature tracking error. Multizone buildings are strictly passive from the heat input to temperature output in all zones. This property assures the convergence of the ILC iteration if the update gain is suitably bounded, without the need of an accurate model. This means that for each chain, the zone temperature deviation from the specified profile will converge to zero as the number of days in the chain grows (i.e., as more iterations of ILC are performed). Using a six-zone physical test bed with programmable ambient temperatures, we demonstrate the practicality of the proposed approach in multiple experimental trials. Additional longer-duration simulations are performed based on the actual temperature recorded in Orlando, FL, USA and New York, NY, USA over a two-year period. In all cases, ILC is shown to improve the tracking error in the presence of ambient temperature fluctuations. Note to Practitioners—This paper uses historic operating data and weather forecasting to improve the temperature tracking in multizone buildings. The proposed approach exploits the inherent passivity property of the building thermal model and, therefore, does not require the accurate model identification prior to implementation. As a result, this method may be conveniently retrofitted to any existing heating, ventilation, and air conditioning system, provided that the system is capable of adjusting the heat input (e.g., by controlling the supply air flow rate) and storing temperature sensor data. The algorithm uses weather prediction to match the upcoming ambient temperature variation with those experienced in the past. With the past operating data (control input and resulting temperature error) from the matched day, a feed-forward control is iteratively updated to improve the tracking performance. This paper includes the experimental results to demonstrate the efficacy of the approach in our test bed, and simulation examples to provide the insight into the real-world implementation. This approach may be extended to other external heat sources exhibiting repetitive patterns, e.g., occupancy, equipment, and solar input.
Actin cytoskeleton remodeling defines a distinct cellular function for adhesion G protein-coupled receptors ADGRL/latrophilins 1, 2 and 3

ABSTRACT Latrophilins represent a subgroup of the adhesion G protein-coupled receptor family, which bind to actin-associated scaffolding proteins. They are expressed in various tissues, suggesting that they might participate in biological processes that are ubiquitous. Here we focus on actin cytoskeleton dynamics to explore the role of latrophilins in mammalian cells. Individual overexpression of each latrophilin isoform comparably increased cell volume while modifying the net profile of F-actin-dependent cell extensions, as evaluated by confocal microscopy analysis. Latrophilin deletion mutants evidenced that direct coupling to the intracellular machinery was a requirement for modulating cell extensions. The association between latrophilins and the actin cytoskeleton was detected by co-immunoprecipitation assays and corroborated with immunocytochemistry analysis. Consistent with the destabilization of F-actin structures, latrophilin isoforms constitutively induced a prominent increase in the activity of actin-depolymerizing factor, cofilin. Intercellular adhesion events stabilized by heterophilic Teneurin-4 trans-interactions disrupted latrophilin colocalization with F-actin and led to an isoform-specific rescue of cell extensions. Thus, we find that the actin cytoskeleton machinery constitutes an important component of constitutive as well as ligand-induced signaling for latrophilins. This article has an associated First Person interview with the first author of the paper. Summary: Synapses involve the adhesion function of latrophilins within existing neuronal extensions. We show that latrophilins engage the actin cytoskeleton, both constitutively and upon ligand stimulation, to dictate cell extension patterns.
ArhGEF37 assists dynamin 2 during clathrin-mediated endocytosis

ABSTRACT Clathrin-mediated endocytosis (CME) engages over 30 proteins to secure efficient cargo and membrane uptake. While the function of most core CME components is well established, auxiliary mechanisms crucial for fine-tuning and adaptation remain largely elusive. In this study, we identify ArhGEF37, a currently uncharacterized protein, as a constituent of CME. Structure prediction together with quantitative cellular and biochemical studies present a unique BAR domain and PI(4,5)P2-dependent protein–membrane interactions. Functional characterization yields accumulation of ArhGEF37 at dynamin 2-rich late endocytic sites and increased endocytosis rates in the presence of ArhGEF37. Together, these results introduce ArhGEF37 as a regulatory protein involved in endocytosis. Summary: Accumulation of ArhGEF37 at dynamin 2-rich late endocytic sites yields increased rates of endocytosis.
eHAPAC: A Privacy-Supported Access Control Model for IP-Enabled Wireless Sensor Networks

The implementation of IP technology in wireless sensor networks has promoted the development of many smart scenarios. To enhance secure access in IP-enabled wireless sensor networks, access control to sensor nodes is a necessary process. However, access control currently faces two challenges, feasibility and preservation of user access privacy. In this paper, we propose eHAPAC, a novel privacy-preserving access control model for IP-enabled wireless sensor networks. The contributions of our paper include three parts. First, this paper integrates the Hidra access control protocol and APAC privacy-preserving model, addressing the issue of privacy-preserving access control in resource-constrained devices. Second, this paper proposes an enhanced Hidra protocol to implement the unlinkability of protocol message exchanges. Third, to solve the problem of third party credibility, this paper improves the group signature-based APAC model and utilizes blockchain technology to manage the storage and publication of public group signature keys. Security analysis and performance evaluation prove that our protocol is secure and effective.
Critical digital humanities

In a digital age the humanities need to communicate humanistic values and their own contribution to public culture more than ever. The humanities are particularly important for continuing to ask the question: what is a life worth living? Today we live within a horizon of interpretability determined in large part by the capture of data by algorithms which overtake our lives and thoughts. This is a world that relies upon automation by computation and the manipulation of data using sophisticated software. It is a data-intensive world built on the economic realisation of an increasingly data-intensive scientific milieu.
DNA methylation in genes of longevity-regulating pathways: association with obesity and metabolic complications

Aging is the main risk factor for most chronic diseases. Epigenetic mechanisms, such as DNA methylation (DNAm) plays a pivotal role in the regulation of physiological responses that can vary along lifespan. The aim of this research was to analyze the association between leukocyte DNAm in genes involved in longevity and the occurrence of obesity and related metabolic alterations in an adult population. Subjects from the MENA cohort (n=474) were categorized according to age (<45 vs 45>) and the presence of metabolic alterations: increased waist circumference, hypercholesterolemia, insulin resistance, and metabolic syndrome. The methylation levels of 58 CpG sites located at genes involved in longevity-regulating pathways were strongly correlated (FDR-adjusted< 0.0001) with BMI. Fifteen of them were differentially methylated (p<0.05) between younger and older subjects that exhibited at least one metabolic alteration. Six of these CpG sites, located at MTOR (cg08862778), ULK1 (cg07199894), ADCY6 (cg11658986), IGF1R (cg01284192), CREB5 (cg11301281), and RELA (cg08128650), were common to the metabolic traits, and CREB5, RELA, and ULK1 were statistically associated with age. In summary, leukocyte DNAm levels of several CpG sites located at genes involved in longevity-regulating pathways were associated with obesity and metabolic syndrome traits, suggesting a role of DNAm in aging-related metabolic alterations.
Minimum Volume Topic Modeling

We propose a new topic modeling procedure that takes advantage of the fact that the Latent Dirichlet Allocation (LDA) log likelihood function is asymptotically equivalent to the logarithm of the volume of the topic simplex. This allows topic modeling to be reformulated as finding the probability simplex that minimizes its volume and encloses the documents that are represented as distributions over words. A convex relaxation of the minimum volume topic model optimization is proposed, and it is shown that the relaxed problem has the same global minimum as the original problem under the separability assumption and the sufficiently scattered assumption introduced by Arora et al. (2013) and Huang et al. (2016). A locally convergent alternating direction method of multipliers (ADMM) approach is introduced for solving the relaxed minimum volume problem. Numerical experiments illustrate the benefits of our approach in terms of computation time and topic recovery performance.
Document Summarization using Word and Part-of-speech based on Attention Mechanism

Automatic text summarization is one of the research hotspots in natural language processing (NLP). Recently, there has been a considerable research interest in summarization. While most of the current researches about automatic text summarization are only based on word embedding features, which has the problem of too few text features and can’t effectively utilize some other features of the text. In this paper, we propose a novel deep learning model called Document Summarization using Word and Part-of-speech based on Attention Mechanism (WPABS). We employ the word embedding and part-of-speech embedding to make full use of the features of the text and evaluate our model on English datasets Gigaword and DUC-2004. The experimental results show that our model is better than most methods. Also, compared with WABS(Word Attention-Based Summarization), our proposed model performs better. It is proved that it is useful to combine the word and part-of-speech.