Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Sphingolipid Metabolites That Induce Obesity via Misregulation of Appetite, Caloric Intake and Fat Storage in Drosophila

Obesity is defined by excessive lipid accumulation. However, the active mechanistic roles that lipids play in its progression are not understood. Accumulation of ceramide, the metabolic hub of sphingolipid metabolism, has been associated with metabolic syndrome and obesity in humans and model systems. Here, we use Drosophila genetic manipulations to cause accumulation or depletion of ceramide and sphingosine-1-phosphate (S1P) intermediates. Sphingolipidomic profiles were characterized across mutants for various sphingolipid metabolic genes using liquid chromatography electrospray ionization tandem mass spectroscopy. Biochemical assays and microscopy were used to assess classic hallmarks of obesity including elevated fat stores, increased body weight, resistance to starvation induced death, increased adiposity, and fat cell hypertrophy. Multiple behavioral assays were used to assess appetite, caloric intake, meal size and meal frequency. Additionally, we utilized DNA microarrays to profile differential gene expression between these flies, which mapped to changes in lipid metabolic pathways. Our results show that accumulation of ceramides is sufficient to induce obesity phenotypes by two distinct mechanisms: 1) Dihydroceramide (C14:0) and ceramide diene (C14:2) accumulation lowered fat store mobilization by reducing adipokinetic hormone- producing cell functionality and 2) Modulating the S1P: ceramide (C14:1) ratio suppressed postprandial satiety via the hindgut-specific neuropeptide like receptor dNepYr, resulting in caloric intake-dependent obesity.
The Class III Cyclobutane Pyrimidine Dimer Photolyase Structure Reveals a New Antenna Chromophore Binding Site and Alternative Photoreduction Pathways*

Background: Photolyases use light energy to repair UV-damaged DNA. Results: The crystal structure of a “class III CPD” photolyase reveals a new binding pocket for an “5,10-methenyltetrahydrofolate” antenna chromophore. Conclusion: Related plant cryptochromes might use the same antenna chromophore binding pocket. Significance: The photolyase crystal structure allows a better understanding of the evolutionary transition of photolyases to plant cryptochromes. Photolyases are proteins with an FAD chromophore that repair UV-induced pyrimidine dimers on the DNA in a light-dependent manner. The cyclobutane pyrimidine dimer class III photolyases are structurally unknown but closely related to plant cryptochromes, which serve as blue-light photoreceptors. Here we present the crystal structure of a class III photolyase termed photolyase-related protein A (PhrA) of Agrobacterium tumefaciens at 1.67-Å resolution. PhrA contains 5,10-methenyltetrahydrofolate (MTHF) as an antenna chromophore with a unique binding site and mode. Two Trp residues play pivotal roles for stabilizing MTHF by a double π-stacking sandwich. Plant cryptochrome I forms a pocket at the same site that could accommodate MTHF or a similar molecule. The PhrA structure and mutant studies showed that electrons flow during FAD photoreduction proceeds via two Trp triads. The structural studies on PhrA give a clearer picture on the evolutionary transition from photolyase to photoreceptor.
CADE: Cumulative Acknowledgement Based Detection of Selective Forwarding Attacks in Wireless Sensor Networks

Wireless sensor networks are networks consisting of nodes with sensors to monitor physical or environmental conditions. Since nodes in wireless sensor networks have limited transmission capabilities, packets are generally transmitted to the destination node through several immediate nodes. However, if a malicious node is present on a route through which packets are forwarded, attackers can deliver selective forwarding attacks by simply dropping packets. Selective forwarding attacks can be serious threats on many applications including networks for military surveillance and health-care monitoring. Previous countermeasures to selective forwarding attacks cannot identify malicious nodes or require time synchronization. In this paper, we present a detecting scheme which identifies malicious nodes delivering selective forwarding attacks without the need for time synchronization. Our scheme also provides security against sinkhole attacks where attackers try to attract more traffic to malicious nodes.
A Real-Time Distributed Query Solution Based on the OPC

The distributed query is one of the research focus in the Big Data. Nowadays, many companies and institutions provide technology and products to realize function or improve efficiency in the all kinds of database. In the scene of electricity, using these techniques, the real-time requirement (<;10s) cannot be met. This paper provides a real-time distributed query solution with Objectification Parallel Computing (OPC) to solve the above challenges. The data split from Big Data, is distributed stored in memory of cluster in the OPC. In the solution, making use of the thought of divide and rule and tree merging, there are two stages. The first stage is local data query. The intermediate query result can be obtained. The second stage is multistage summarizing. The final result can be returned to user. The solution has been applied to the power production management system (PMS) of State Grid of China. The results show that solution is efficiently reliable and meets real-time.
On connectivity and delay in MANET

The performance of the mobile ad hoc network (MANET) is sensitive to nodes mobility which results in connectivity dynamics. In this paper, we focus on two issues: (i) How the mobility impacts on connectivity dynamics? We find a proportional relation formula to explain it. (ii) How connectivity dynamics results in the change of network delay? We proposed a model to reveal how the connectivity dynamics and node number impact on delay.
Towards bidirectional power line communication with Digital Load-Side Transmission

The unidirectional power line communication technology `Digital Load-Side Transmission' (DLT), which is typically used for controlling light sources in homes or small offices, has evolved into an official IEC standard [1]. However, the DLT methodology lacks a return channel enabling bidirectional communication. This paper presents a method to overcome the described limitation, proposing a system concept of receiver and transmitter for backward modulation in DLT systems that complies with the existing standard for DLT forward transmission [1]. The developed architectures of receiver and transmitter have been implemented and tested in a hardware realization using the results of [2]-[4]. Measurement results prove the feasibility of the derived bidirectional DLT system concept while the additional power consumed for backward transmission is kept within reasonable limits. Robustness of the proposed system is verified by recording the averaged `telegram error rate' (TER) for both, forward and backward transmission, when the system is connected to 230 VRMS, 50 Hz mains.
Enhancing everyday paper interactions with paper circuits

Our interactions with paper are so habitual as to be subconscious. Paper is an inextricable component of our daily lives. In this paper we present the crafting of, and the reflections on, four prototypes; these prototypes explore how adding new functionality through paper circuits can enrich interactions with paper. We define paper circuits as circuits that have been made through the process of printing or applying conductive ink onto standard paper in order to form electronic or electric circuits. We will provide reflections on not just the benefits of paper circuitry, but also how the newly added affordances gained from paper circuitry effect the experience of paper interactions. This paper will illustrate how this new evolution of paper can be used to produce cheap lightweight ubiquitous electronic products, new art forms, and most importantly enhance the user experience of paper without losing the existing well-loved affordances that paper currently possesses.
Model-based test case prioritization using cluster analysis: a soft-computing approach

Model-based testing is related to the particular relevant features of the software under test (SUT) and its environment. Real-life systems often require a large number of tests, which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance as the tester perceives it, usually given by several attributes of relevant events entailed. Based on event-oriented graph models, this paper proposes an approach to ranking test cases in accordance with their preference degrees. For forming preference groups, events are clustered using an unsupervised neural network and fuzzy c-means clustering algorithm. The suggested approach is model-based, so it does not necessitate the availability of the source code of the SUT. It diers from existing approaches also in that it needs no prior information about the tests carried out before. Thus, it can be used to reect the tester's preferences not only for regression testing as is common in the literature but also for ranking test cases in any stage of software development. For the purpose of experimental evaluation, we compare the suggested prioritization approach with six well-known prioritization methods.
A simple model for asset price bubble formation and collapse

We consider a simple stochastic differential equation for modeling bubbles in social context. A prime example is bubbles in asset pricing, but similar mechanisms may control a range of social phenomena driven by psychological factors (for example, popularity of rock groups, or a number of students pursuing a given major). Our goal is to study the simplest possible model in which every term has a clear meaning and which demonstrates several key behaviors. The main factors that enter are tendency of mean reversion to a stable value, speculative social response triggered by trend following and random fluctuations. The interplay of these three forces may lead to bubble formation and collapse. Numerical simulations show that the equation has distinct regimes depending on the values of the parameters. We perform rigorous analysis of the weakly random regime, and study the role of change in fundamentals in igniting the bubble.
Fast Unsupervised Bayesian Image Segmentation With Adaptive Spatial Regularisation

This paper presents a new Bayesian estimation technique for hidden Potts–Markov random fields with unknown regularisation parameters, with application to fast unsupervised $K$ -class image segmentation. The technique is derived by first removing the regularisation parameter from the Bayesian model by marginalisation, followed by a small-variance-asymptotic (SVA) analysis in which the spatial regularisation and the integer-constrained terms of the Potts model are decoupled. The evaluation of this SVA Bayesian estimator is then relaxed into a problem that can be computed efficiently by iteratively solving a convex total-variation denoising problem and a least-squares clustering ( $K$ -means) problem, both of which can be solved straightforwardly, even in high-dimensions, and with parallel computing techniques. This leads to a fast fully unsupervised Bayesian image segmentation methodology in which the strength of the spatial regularisation is adapted automatically to the observed image during the inference procedure, and that can be easily applied in large 2D and 3D scenarios or in applications requiring low computing times. Experimental results on synthetic and real images, as well as extensive comparisons with state-of-the-art algorithms, confirm that the proposed methodology offer extremely fast convergence and produces accurate segmentation results, with the important additional advantage of self-adjusting regularisation parameters.
Sleep and Serotonin Modulate Paracapsular Nitric Oxide Synthase Expressing Neurons of the Amygdala

Abstract Unraveling the roles of distinct neuron types is a fundamental challenge to understanding brain function in health and disease. In the amygdala, a brain structure regulating emotional behavior, the diversity of GABAergic neurons has been only partially explored. We report a novel population of GABAergic amygdala neurons expressing high levels of neuronal nitric oxide synthase (nNOS). These cells are predominantly localized along basolateral amygdala (BLA) boundaries. Performing ex vivo patch-clamp recordings from nNOS+ neurons in Nos1-CreER;Ai9 mice, we observed that nNOS+ neurons located along the external capsule display distinctive electrophysiological properties, axonal and dendritic arborization, and connectivity. Examining their c-Fos expression, we found that paracapsular nNOS+ neurons are activated during a period of undisturbed sleep following sleep deprivation, but not during sleep deprivation. Consistently, we found that dorsal raphe serotonin [5-hydroxytryptamine (5-HT)] neurons, which are involved in sleep–wake regulation, innervate nNOS+ neurons. Bath application of 5-HT hyperpolarizes nNOS+ neurons via 5-HT1A receptors. This hyperpolarization produces a reduction in firing rate and, occasionally, a switch from tonic to burst firing mode, thereby contrasting with the classic depolarizing effect of 5-HT on BLA GABAergic cells reported so far. Thus, nNOS+ cells are a distinct cell type of the amygdala that controls the activity of downstream neurons in both amygdaloid and extra-amygdaloid regions in a vigilance state-dependent fashion. Given the strong links among mood, sleep deprivation, and 5-HT, the recruitment of paracapsular nNOS+ neurons following high sleep pressure may represent an important mechanism in emotional regulation.
New dual-band open-loop resonator bandpass filters with controllable bandwidths and transmission zeros

This paper proposes a dual-mode dual-band open-loop bandpass microstrip filters. The open-loop is composed of two half-wave stepped-impedance resonators (SIR) with high-impedance short-circuited ends, which is equivalent to an inductor for providing coupling between the resonating modes and hence bandwidth control. Moreover, with the aid of source-load coupling which is employed in the second circuit, extra transmission zeros can be created to close the passband edges, greatly improving the performance in transition bands. The measured results show good agreement with the simulation.
Exploring Academic Knowledge Creation Models for Graduate Researches

Knowledge management theories and tools have been developed rapidly in the field of business administration. However, since their application in the academic field is still very limited, this paper focuses on knowledge creation in the academic field. It is usually difficult for young graduate students to imagine their research process, which proceeds from a small idea to a research paper. Wierzbicki and Nakamori [1] introduced three knowledge creation models for three different academic fields, which are normative or hypothetical models to be verified in applications. This paper modified these models by interviewing graduate students and their supervisors, who are doing research in four fields in knowledge science. This study made clear the knowledge gaps between graduate students and their supervisors.
LSD and Religious Experience

psychotomimeti c , i.e., psychosis-mimicking, was generally used; but this rather negatively loaded term was seen by some as being too narrow and limiting to accommodate the wide range of experiences being reported and observed. In 1957 Dr. Humphry Osmond (Osmond, 1957) proposed the term psychedelic, i.e., mind-opening or mind-manifesting, to encompass the full range of positive and negative experiences that he had seen in his work with such drugs. Psychedelic will be the term used in this paper to refer to the unique class of LSD-type drugs. In this discussion a survey will first be made of the various kinds of psychedelic experiences with an especial examination of the mystical type, and possible reasons for this variety will be mentioned. Then we shall look at some of the evidence that psychedelic mystical experiences occur at all and discuss whether or not they can be considered "religious. " The LSD churches that have grown out of the evangelistic enthusiasm associated with LSD will also be examined along with their possible impact on individuals and society. Finally, the future prospects for the investigation and use of psychedelic drugs will be discussed. Psychedelic experiences can be divided into five types: psychotic, psychodynamic, cognitive, aesthetic and psychedelic peak or mystical. These have been described elsewhere (Pahnke and Richards, 1966), and a brief description of each will suffice. The psychotic experience is described as very intense, negative, dysphoric and hellish by those who have been through it. Characteristic elements include fear to the point of panic, paranoid distrust, delusions of suspicion or grandeur, toxic confusion, impairment of abstract reasoning, remorse, depression, isolation and/ or somatic discomfort, all of which can be of monumental proportions. These words can perhaps be better illuminated by the following description written by a well-prepared subject who took LSD under medical supervision: Time itself seemed to have frozen. I was sick way down inside. I had lost trust in the doctor and the judgment of part of my own mind. The terrible thing was that I was going insane and the normal part of my mind knew this was taking place. I was two people in the same body. The one with the insane mind was pulling the one with the sane mind over on his side. I think I was 95%
A Note of Caution Regarding Applying Basic Latent Class Analysis

We investigate latent class analysis as a market segmentation method. In particular, we explore the ramifications of the often assumed, perhaps necessarily, local independence condition. Using survey data for a major soft drink brand, we identify brand characteristics which are perceived very differently across simply defined demographic segments. We next apply latent class analysis, assuming local independence, to these same characteristics to derive a segmentation structure. These derived segments, driven by the local independence assumption, are completely different from the initial demographic segments. Strong justification for this assumption, which dictates the solution and cannot be explored in advance, clearly should be present.
Weighted Logics for Nested Words and Algebraic Formal Power Series

Nested words, a model for recursive programs proposed by Alur and Madhusudan, have recently gained much interest. In this paper we introduce quantitative extensions and study nested word series which assign to nested words elements of a semiring. We show that regular nested word series coincide with series definable in weighted logics as introduced by Droste and Gastin. For this, we establish a connection between nested words and series-parallel-biposets. Applying our result, we obtain a characterization of algebraic formal power series in terms of weighted logics. This generalizes a result of Lautemann, Schwentick and Therien on context-free languages.
Peer selection for Load Balancing in 3G cellular networks

Due to the architecture and characteristics of 3G cellular networks, peer selection algorithms with the idea of traffic localization are not suitable for mobile P2P systems in 3G cellular networks, and load balance on the cells should be taken into account. In this paper, we propose a peer selection algorithm named CFLB for mobile P2P systems in 3G cellular networks. The CFLB algorithm always chooses a peer with the highest available uplink bandwidth from a cell with the lowest traffic load until the number of peers is reached. Simulation results indicate that the CFLB algorithm can achieve excellent load balance on the cells in 3G cellular networks while ensuring favorable peer performance.
Mining Patterns Using Relaxations of User Defined Constraints

The main drawbacks of sequential pattern mining have been its lack of focus on user expectations and the high number of discovered patterns. However, the solution commonly accepted – the use of constraints – approximates the mining process to a hypothesis-testing task. In this paper, we propose a new methodology to mine sequential patterns, keeping the focus on user expectations, without compromising the discovery of unknown patterns. Our methodology is based on the use of constraint relaxations, and it consists on using them to filter accepted patterns during the mining process. We propose a hierarchy of relaxations, applied to constraints expressed as context-free languages.
Phage-based platforms for the clinical detection of human bacterial pathogens

Bacteriophages (phages) have been utilized for decades as a means for uniquely identifying their target bacteria. Due to their inherent natural specificity, ease of use, and straightforward production, phage possess a number of desirable attributes which makes them particularly suited as bacterial detectors. As a result, extensive research has been conducted into the development of phage, or phage-derived products to expedite the detection of human pathogens. However, very few phage-based diagnostics have transitioned from the research lab into a clinical diagnostic tool. Herein we review the phage-based platforms that are currently used for the detection of Mycobacterium tuberculosis, Yersinia pestis, Bacillus anthracis and Staphylococcus aureus in the clinical field. We briefly describe the disease, the current diagnostic options, and the role phage diagnostics play in identifying the cause of infection, and determining antibiotic susceptibility.
An effective methodology to improve the performance of the up*/down* routing algorithm

Networks of workstations (NOWs) are being considered as a cost-effective alternative to parallel computers. Most NOWs are arranged as a switch-based network and provide mechanisms for discovering the network topology. Hence, they provide support for both regular and irregular topologies, which makes routing and deadlock avoidance quite complicated. Current proposals use the up*/down* routing algorithm to remove cyclic dependencies between channels and avoid deadlock. However, routing is considerably restricted and most messages must follow nonminimal paths, increasing latency and wasting resources. We propose and evaluate a simple and effective methodology to compute up*/down* routing tables. The new methodology is based on computing a depth-first search (DPS) spanning tree on the network graph that decreases the number of routing restrictions with respect to the breadth-first search (BFS) spanning tree used by the traditional methodology. Additionally, we propose different heuristic rules for computing the spanning trees to improve the efficiency of up*/down* routing. Evaluation results for several different topologies show that computing the up*/down* routing tables by using the new methodology increases throughput by a factor of up to 2.48 in large networks with respect to the traditional methodology, and also reduces latency significantly.