Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of cytoplasmic RNA stability: Lessons from Drosophila

The process of RNA degradation is a critical level of regulation contributing to the control of gene expression. In the last two decades a number of studies have shown the specific and targeted nature of RNA decay and its importance in maintaining homeostasis. The key players within the pathways of RNA decay are well conserved with their mutation or disruption resulting in distinct phenotypes as well as human disease. Model organisms including Drosophila melanogaster have played a substantial role in elucidating the mechanisms conferring control over RNA stability. A particular advantage of this model organism is that the functions of ribonucleases can be assessed in the context of natural cells within tissues in addition to individual immortalized cells in culture. Drosophila RNA stability research has demonstrated how the cytoplasmic decay machines, such as the exosome, Dis3L2 and Xrn1, are responsible for regulating specific processes including apoptosis, proliferation, wound healing and fertility. The work discussed here has begun to identify specific mRNA transcripts that appear sensitive to specific decay pathways representing mechanisms through which the ribonucleases control mRNA stability. Drosophila research has also contributed to our knowledge of how specific RNAs are targeted to the ribonucleases including AU rich elements, miRNA targeting and 3′ tailing. Increased understanding of these mechanisms is critical to elucidating the control elicited by the cytoplasmic ribonucleases which is relevant to human disease.
A Drosophila model of combined D-2- and L-2-hydroxyglutaric aciduria reveals a mechanism linking mitochondrial citrate export with oncometabolite accumulation

ABSTRACT The enantiomers of 2-hydroxyglutarate (2HG) are potent regulators of metabolism, chromatin modifications and cell fate decisions. Although these compounds are associated with tumor metabolism and commonly referred to as oncometabolites, both D- and L-2HG are also synthesized by healthy cells and likely serve endogenous functions. The metabolic mechanisms that control 2HG metabolism in vivo are poorly understood. One clue towards how cells regulate 2HG levels has emerged from an inborn error of metabolism known as combined D- and L-2HG aciduria (D-/L-2HGA), which results in elevated D- and L-2HG accumulation. Because this disorder is caused by mutations in the mitochondrial citrate transporter (CIC), citrate must somehow govern 2HG metabolism in healthy cells. The mechanism linking citrate and 2HG, however, remains unknown. Here, we use the fruit fly Drosophila melanogaster to elucidate a metabolic link between citrate transport and L-2HG accumulation. Our study reveals that the Drosophila gene scheggia (sea), which encodes the fly CIC homolog, dampens glycolytic flux and restricts L-2HG accumulation. Moreover, we find that sea mutants accumulate excess L-2HG owing to elevated lactate production, which inhibits L-2HG degradation by interfering with L-2HG dehydrogenase activity. This unexpected result demonstrates that citrate indirectly regulates L-2HG stability and reveals a feedback mechanism that coordinates L-2HG metabolism with glycolysis and the tricarboxylic acid cycle. Finally, our study also suggests a potential strategy for preventing L-2HG accumulation in human patients with CIC deficiency. This article has an associated First Person interview with the first author of the paper. Summary: This study reveals a mechanism that links export of mitochondrial citrate to accumulation of the oncometabolite L-2-hydroxyglutarate, suggesting a potential treatment for individuals with combined D-2- and L-2-hydroxyglutaric aciduria, a rare inborn error of metabolism.
Assessing Image Retrieval Quality at the First Glance

Image retrieval has achieved remarkable improvements with the rapid progress on visual representation and indexing techniques. Given a query image, search engines are expected to retrieve relevant results in which the top-ranked short list is of most value to users. However, it is challenging to measure the retrieval quality on-the-fly without direct user feedbacks. In this paper, we aim at evaluating the quality of retrieval results at the first glance (i.e., with the top-ranked images). For each retrieval result, we compute a correlation-based feature matrix that comprises of contextual information from the retrieval list, and then feed it into a convolutional neural network regression model for retrieval quality evaluation. In this proposed framework, multiple visual features are integrated together for robust representations. We optimize the output of this simple-yet-effective evaluation method to be consistent with discounted cumulative gain, the intuitive measure for the quality of the top-ranked results. We evaluate our method in terms of prediction accuracy and consistency with the ground truth, and demonstrate its practicability in applications, such as rank list selection and database image abundance analyses.
Fog Radio Access Networks: Ginibre Point Process Modeling and Analysis

In this paper, we consider fog radio access networks (F-RANs) consisting of cache-enabled device-to-device (D2D) transmitters and fog access points (F-APs), which deliver data by exploiting cached contents or leveraging cloud processing. We consider three types of modes at a typical user, namely, D2D, F-AP, and cooperative modes. In the D2D and the F-AP modes, when the user requests content, the user receives the content from a D2D transmitter and an F-AP caching the content, respectively. In the cooperative mode, F-APs located near the user send data aided by a centralized cloud processing unit. We also examine a mode selection algorithm in which the user adaptively selects one of the three modes. In practical scenarios, to mitigate interference, the transmitters may not be placed close to each other, and thus, there may exist a form of repulsion among the transmitters’ locations. In this context, we model the spatial distributions of the D2D transmitters and the F-APs as $\beta $ -Ginibre point processes, which reflect the repulsive behavior and contain the Poisson point process as a special case. Then, we provide analytical expressions for the coverage probabilities in the F-RANs. Our results are corroborated by Monte Carlo simulations.
Genetically confirmed familial hypercholesterolemia in outpatients with hypercholesterolemia

Background Familial hypercholesterolemia (FH) is an autosomal dominant disorder of lipoprotein metabolism which can lead to premature coronary heart disease (pCHD). There are about 3.8 million potential FH patients in China, whereas the clinical and genetic data of FH are limited. Methods Dutch Lipid Clinic Network (DLCN) criteria was used to diagnose FH in outpatients with hypercholesterolemia. Resequencing chip analysis combined with Sanger sequencing validation were used to identify mutations in the definite FH patients according to DLCN criteria. In silico analysis was conducted in mutations with previously unknown pathogenicity. Then, the novel mutant receptors were transfected into human embryo kidney 293 (HEK-293) cells. The binding and the internalization activities of the mutant receptors were analyzed by flow cytometry. Results The prevalence of definite FH in outpatients with hypercholesterolemia in this study is 3.2%. Using genetic testing, one homozygous FH (HoFH), one heterozygous FH (HeFH) and three compound heterozygous FH patients were confirmed. Eight mutations in low-density lipoprotein receptor (LDLR) gene were identified, in which c.357delG was a novel mutation and co-segregated with the FH phenotype. Bioinformatic analysis confirmed that c.357delG was a pathogenic mutation. Furthermore, when compared with the wild-type LDLRs by flow cytometry analysis, the binding and internalization activities of c.357delG mutant LDLRs were reduced by 35% and 49%, respectively. Conclusions This study identified eight LDLR gene mutations in five patients with definite FH, in which c.357delG is a novel pathogenic mutation. These findings increase our understanding of the genetic spectrum of FH in China.
Multitarget AOA Estimation Using Wideband LFMCW Signal and Two Receiver Antennas

Linear frequency modulated continuous wave (LFMCW) signals have been widely used in target detection and localization, thanks to their good detection sensitivity and high range resolution. However, estimation of the angle of arrival (AOA) of the LFMCW signal reflected from each target in a multitarget environment is still quite limited at present. In this paper, we propose a novel AOA estimation framework in which one transmitter antenna and two receiver antennas are employed to transmit a wideband LFMCW signal and receive the signals reflected from different targets, respectively. The signals received at two receiver antennas are processed in three steps. First, each of them is demodulated by mixing with the transmitted signal to transform the wideband nonstationary signal into superposition of a series of single-tone signals. Second, signals received at each receiver antenna from different targets are separated using bandpass filtering. Third, the corresponding AOA of each target is estimated using the phase difference between two receiver antennas. Different from the traditional subspace-based methods, the number of targets is not limited by the number of receiver antennas. The theoretical analysis and simulation results show that the proposed algorithm can achieve accurate AOA estimation for multiple targets even at a low signal to noise ratio.
Pathologies of Neural Models Make Interpretations Difficult

One way to interpret neural model predictions is to highlight the most important input features—for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word’s importance is determined by either input perturbation—measuring the decrease in model confidence when that word is removed—or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction, without accuracy loss on regular examples.
Acinetobacter baumannii K20 and K21 capsular polysaccharide structures establish roles for UDP-glucose dehydrogenase Ugd2, pyruvyl transferase Ptr2 and two glycosyltransferases

Infections caused by Acinetobacter baumannii isolates from the major global clones, GC1 and GC2, are difficult to treat with antibiotics, and phage therapy, which requires extensive knowledge of the variation in the surface polysaccharides, is an option under consideration. The gene clusters directing the synthesis of capsular polysaccharide (CPS) in A. baumannii GC1 isolate A388 and GC2 isolate G21 differ by a single glycosyltransferase (gtr) gene. They include genes encoding a novel UDP-glucose dehydrogenase (Ugd2) and a putative pyruvyl transferase (Ptr2). The composition and structures of the linear K20 and K21 tetrasaccharide repeats (K units) of the CPSs isolated from A338 and G21, respectively, were established by sugar analyses and Smith degradation along with 1D and 2D 1H and 13C NMR spectroscopy. The K20 and K21 CPSs are the first known to include GlcpA produced by Ugd2 and d-galactose with an (R)-configured 4,6-pyruvic acid acetal added by Prt2. The first sugar in the tetrasaccharide K units is 2-acetamido-4-amino-2,4,6-trideoxy-d-glucose (d-QuipNAc4N) that carries a 4-N-[(S)-3-hydroxybutanoyl] group in some K units and a 4-N-acetyl group in the others. Accordingly, K unit polymerases WzyK20 and WzyK21 form a β-d-QuipNAc4NR-(1→2)-d-Galp bond. The K20 and K21 units differ only in the configuration of the glycosidic linkages of d-GlcpNAc allowing the unique inverting glycosyltransferases Gtr43 and the retaining glycosyltransferase Gtr45 to be assigned to the formation of the β-d-GlcpNAc-(1→4)-d-GlcpA and α-d-GlcpNAc-(1→4)-d-GlcpA linkages, respectively.
Accurate WiFi Localization by Fusing a Group of Fingerprints via a Global Fusion Profile

The existing indoor localization approaches based on single fingerprints, such as received signal strength (RSS) and channel impulse response, are rather susceptible to the changing environment, multipath, and nonline-of-sight. It is well known that indoor localization can obtain higher positioning accuracy than the single-fingerprint-based methods by fusing multiple information sources (fingerprints/fingerprint functions). However, the existing fusion methods cannot fully exploit the intrinsic complementarity among multiple information sources and thus show lower accuracy. In this paper, we propose an accurate WiFi localization approach by Fusing A Group Of fingerprinTs (WiFi-FAGOT) via a global fusion profile (GFP). WiFi-FAGOT first constructs a WiFi-based GrOup Of Fingerprints (GOOF) in the offline phase, which consists of RSS, signal strength difference, and hyperbolic location fingerprint. Then, instead of direct localization by using the WiFi-based GOOF, we design multiple classifiers by training each fingerprint in the WiFi-based GOOF, namely GOOF classifiers. To fully leverage the intrinsic complementarity among different kinds of fingerprints, we propose a GFP construction algorithm by minimizing the average positioning error over the space of all GOOF classifiers. Finally, in the online phase, we derive a grid-dependent matching algorithm, namely, optimal classifier selection, to intelligently choose a fusion profile in the GFP for more accurate localization. Experimental results demonstrate that WiFi-FAGOT performs better than other systems in real complex indoor environments.
Framework for Traffic Proportional Energy Efficiency in Software Defined Networks

Software Defined Networking (SDN) achieves programmability of network elements by separating the control and the forwarding planes, and provides efficiency through optimized routing and flexibility in network management. As the energy costs contribute largely to the overall costs in networks, energy efficiency is a significant design requirement for modern networking mechanisms. However, designing energy efficient solutions is complicated since there is a trade-off between energy efficiency and network performance. In this paper, we propose traffic proportional energy efficient framework for SDN and heuristics algorithm that maintains the tradeoff between efficiency and performance. We also present IP formulation for traffic proportional energy efficiency problem. Comprehensive experiments conducted on Mininet emulator and POX controller using Abilene, Atlanta, and Nobel-Germany real-world topologies and traffic traces show that our approach saves up to 50% energy while achieving a performance closer to the algorithms prioritizing performance.
Digital trade coin: towards a more stable digital currency

We study the evolution of ideas related to creation of asset-backed currencies over the last 200 years and argue that recent developments related to distributed ledger technologies and blockchains give asset-backed currencies a new lease of life. We propose a practical mechanism combining novel technological breakthroughs with well-established hedging techniques for building an asset-backed transactional oriented cryptocurrency, which we call the digital trade coin (DTC). We show that in its mature state, the DTC can serve as a much-needed counterpoint to fiat reserve currencies of today.
Towards Secure Smart Home IoT: Manufacturer and User Network Access Control Framework

Insecure smart home IoT network is growing in number and size, and enforcing standard security solutions in IoT is a challenge due to its limited resources. The vulnerable smart home IoT poses huge security threats. It puts smart home network security at risk as it can be used as an entry point into the network, also it exposes users' privacy due to the amount of personal data it collects. Meanwhile, as IoT increases in popularity, it has a significant impact on the security of the rest of the Internet community (e.g. forming botnets). Previous research delegates IoT security to a third party (e.g. ISP) and ignores social and contextual factor. In this paper, we propose an SDN-based framework for enforcing network static and dynamic access control, where manufacturers, security providers, and users can cooperate to enhance the smart home IoT security. Proposed approach has three features: a) it allows the manufacturers to enforce the least privileged policy for IoT, and hence reduce the risk associated with exposing IoT to the Internet; b) it enables to enforce access policy as a feedback from security services; c) it enables users to customize IoT access based on social and contextual needs (e.g. only permits LAN access to the IoT through his/her mobile), which reduce the attack surface within the network. We also proposed IPv4 ARP server as an NFV security service to mitigate ARP spoofing attack by replying to ARP requests in the network. We implement a prototype to demonstrate the functionality of the framework against common attack scenarios (i.e. network scanning, ARP spoofing).
Modelling Spatial Correlations by Using Deep CNN and LSTM for Texture Image Classification

Texture is an important visual clue in images, which is ubiquitous and hard to describe. Classical methods use different kinds of hand-crafted features and classifiers to tackle the texture detection and classification problem. With the development of deep learning, the end-to-end modeling capacity of deep neural network can achieve better performance than classical methods. In this paper, we propose a new method that utilizes Long Short-Term Memory (LSTM) units together with deep convolutional network to modelling the spatial correlations in texture image. To this end, two bilateral LSTM units are attached to the feature maps outputted by the convolutional layer of base network. Extensive experiments on two benchmark datasets DTD and FMD were conducted to compare the proposed method with state-of-the-art methods. Results convince that the proposed model has stronger representation capacity for texture images and achieves better performance. Meanwhile, it is computationally efficient with the use of GPU parallel computing. Codes will be made publicly available [29].
Towards Immortal Wireless Sensor Networks by Optimal Energy Beamforming and Data Routing

The lifetime of a wireless sensor network (WSN) determines how long the network can be used to monitor the area of interest. Hence, it is one of the most important performance metrics for WSN. The approaches used to prolong the lifetime can be briefly divided into two categories: reducing the energy consumption, such as designing an efficient routing, and providing extra energy, such as using wireless energy transfer (WET) to charge the nodes. Contrary to the previous line of work where only one of those two aspects is considered, we investigate these two together. In particular, we consider a scenario where dedicated wireless chargers transfer energy wirelessly to sensors. The overall goal is to maximize the minimum sampling rate of the nodes while keeping the energy consumption of each node smaller than the energy it receives. This is done by properly designing the routing of the sensors and the WET strategy of the chargers. Although such a joint routing and energy beamforming problem is non-convex, we show that it can be transformed into a semi-definite optimization problem (SDP). We then prove that the strong duality of the SDP problem holds, and hence, the optimal solution of the SDP problem is attained. Accordingly, the optimal solution for the original problem is achieved by a simple transformation. We also propose a low-complexity approach based on pre-determined beamforming directions. Moreover, based on the alternating direction method of multipliers, the distributed implementations of the proposed approaches are studied. The simulation results illustrate the significant performance improvement achieved by the proposed methods. In particular, the proposed energy beamforming scheme significantly outperforms the schemes where one does not use energy beamforming, or one does not use optimized routing. A thorough investigation of the effect of system parameters, including the number of antennas, the number of nodes, and the number of chargers, on the system performance is provided. The promising convergence behavior of the proposed distributed approaches is illustrated.
Deep Learning Based Natural Language Processing for End to End Speech Translation

Deep Learning methods employ multiple processing layers to learn hierarchial representations of data. They have already been deployed in a humongous number of applications and have produced state-of-the-art results. Recently with the growth in processing power of computers to be able to do high dimensional tensor calculations, Natural Language Processing (NLP) applications have been given a significant boost in terms of efficiency as well as accuracy. In this paper, we will take a look at various signal processing techniques and then application of them to produce a speech-to-text system using Deep Recurrent Neural Networks.
Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans’ gait. However, decoding cortical processes associated with different walking conditions using EEG signals for gait-pattern classification is a less-explored research area. In this paper, we design an EEG-based experiment with four walking conditions (i.e., free walking, and exoskeleton-assisted walking at zero, low, and high assistive forces by the use of a unilateral exoskeleton to right lower limb). We proposed spatio-spectral representation learning (SSRL), a deep neural network topology with shared weights to learn the spatial and spectral representations of multi-channel EEG signals during walking. Adoption of weight sharing reduces the number of free parameters, while learning spatial and spectral equivariant features. SSRL outperformed state-of-the-art methods in decoding gait patterns, achieving a classification accuracy of 77.8%. Moreover, the features extracted in the intermediate layer of SSRL were observed to be more discriminative than the hand-crafted features. When analyzing the weights of the proposed model, we found an intriguing spatial distribution that is consistent with the distribution found in well-known motor-activated cortical regions. Our results show that SSRL advances the ability to decode human locomotion and it could have important implications for exoskeleton design, rehabilitation processes, and clinical diagnosis.