Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart water grid management using LPWAN IoT technology

As the world population increases at a steady rate, providing clean water to the masses in an efficient manner is one of the biggest challenges faced in the modern era. Controlling the flow of water so that it doesn't go to waste is another challenge. Water flow is generally managed through locks and manually controlling these gates often turn out to be a long-winding process. Due to recent technological progress and increased connectivity in rural areas, a system of IoT devices proved to be alternative for managing the water grid. A new Low Power Wide Area Network (LPWAN) technology called LoRa is explored in our study for the communication of these IoT devices. The LoRa devices can communicate within a range of 2–4 KMs while running on batteries that last for years. As a pilot project, we implemented a smart water grid management system in Mori, a village in the eastern Godavari district in Andhra Pradesh situated near to Bay of Bengal. The water grid management system proposed in this paper involves different sensors deployed at various strategically chosen locations to measure the quality of water by generating real time data. The system also provides an alert mechanism which notifies the different level of authorities through email and SMS in case of any issues. Furthermore, it provides a solution for handling the locks that have been employed in and around the village to control the flow of water in a timely manner. The sensors attached with a micro controller in the LoRa module will communicate to the cloud environment through the LoRa gateway. A web page provides the interface to the residents and to the authorities to gauge the water quality after analyzing the data using the prediction algorithm.
Epigenetic regulation of ANKRD18B in lung cancer

The identification of the key genetic and epigenetic changes underlying lung carcinogenesis would aid effective early diagnosis and targeted therapies for lung cancer. In this study, we screened a novel hypermethylated gene ankyrin repeat domain 18B (ANKRD18B), to determine whether it is regulated by DNA methylation and clarify its biological and clinical implications in lung cancer. Methylation status and expression level were analyzed by methylation‐specific PCR, bisulfite genomic sequencing, and quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR). We detected ANKRD18B hypermethylation in 52 of 98 (53.1%) primary lung cancer tissues and in nine of 10 (90%) cell lines, whereas no methylation was seen in 10 normal lung tissue samples. ANKRD18B methylation was more frequently observed in patients with poor differentiation (P < 0.05). Notably, 62 pairs of samples from patients whose tumor tissue showed hypermethylation of ANKRD18B exhibited the same aberrant methylation in 72.7% and 69.7% of their corresponding plasma and sputum samples, respectively; whereas no hypermethylation of ANKRD18B was detected in the sputum and plasma from patients whose tumor sample lacked this alteration. In addition, ANKRD18B mRNA expression was significantly decreased or silenced in lung cancer tissues and cell lines associated with hypermethylation of the ANKRD18B region. Demethylation agent 5‐aza‐2′‐deoxycytidine significantly increased ANKRD18B mRNA expression in lung cancer cell lines. Furthermore, overexpression of ANKRD18B suppressed lung cancer cell growth. These results suggest that the expression of ANKRD18B is regulated by CpG island hypermethylation in lung cancer. Our findings confirm the importance of the identification of new markers of epigenetic dysregulation in cancer. © 2013 Wiley Periodicals, Inc.
Pyridine nucleotide regulation of the KATP channel Kir6.2/SUR1 expressed in Xenopus oocytes

The pancreatic β‐cell type of ATP‐sensitive potassium (KATP) channel (Kir6.2/SUR1) is inhibited by intracellular ATP and ADP, which bind to the Kir6.2 subunit, and is activated by Mg‐nucleotide interaction with the regulatory sulphonylurea receptor subunits (SUR1). The nicotinamide adenine dinucleotides NAD and NADP consist of an ADP molecule with a ribose group and a nicotinamide moiety attached to the terminal phosphate. Both these molecules block native KATP channels in pancreatic β‐cells at concentrations above 500 μM, and activate them at lower concentrations. We therefore investigated whether NAD and NADP interact with both Kir6.2 and SUR1 subunits of the KATP channel by comparing the potency of these agents on recombinant Kir6.2ΔC and Kir6.2/SUR1 channels expressed in Xenopus oocytes. Our results show that, at physiological concentrations, NAD and NADP interact with the nucleotide inhibitory site of Kir6.2 to inhibit Kir6.2/SUR1 currents. They may therefore contribute to the resting level of channel inhibition in the intact cell. Importantly, our data also reveal that this interaction is dependent on the presence of SUR1, which may act by increasing the width of the nucleotide‐binding pocket of Kir6.2.
Evidence Supporting a Role for Programmed Cell Death in Focal Cerebral Ischemia in Rats

Background and Purpose Cells die by one of two mechanisms, necrosis or programmed cell death. Necrosis has been implicated in stroke and occurs when the cytoplasmic membrane is compromised. Programmed cell death requires protein synthesis and often involves endonucleolytic cleavage of the cellular DNA. We assessed the potential contribution of programmed cell death to ischemia-induced neuronal death. Methods Cycloheximide (protein synthesis inhibitor; 1 mg/kg per 24 hours) or vehicle (1 mL/kg per 24 hours) was continuously infused into the right cerebral ventricle of spontaneously hypertensive rats. Neocortical focal ischemia was produced by tandem occlusion of the right common carotid artery and the ipsilateral middle cerebral artery. After 24 hours the brain was stained with 2% 2,3,5-triphenyltetrazolium and the ischemic zone quantitated. Protein synthesis was determined by [3H] methionine incorporation into acid-precipitated protein. DNA integrity was determined in isolated DNA by gel electrophoresis and in whole cells by flow cytometry. Results Continuous cycloheximide infusion caused approximately 70% reduction in cortical protein synthesis. Cycloheximide also reduced the size of the infarction produced by focal cerebral ischemia when compared with controls (ischemic brain volume, 147.5±25.9 and 188.7±16.8 mm3 for cycloheximide and saline, respectively; P<.01), suggesting that protein synthesis may contribute to cell death. Purified DNA from the ischemic zone showed evidence of endonucleolytic degradation when fractionated by gel electrophoresis. Flow cytometric analysis demonstrated increased propidium iodide fluorescence in intact cells isolated from ischemic cortex, indicating an increased accessibility of degraded DNA to the intercalating dye. Conclusions New protein synthesis appears to contribute to ischemic cell death in which endonucleolytic DNA degradation is apparent. These observations implicate programmed cell death in ischemic injury and may open unique therapeutic approaches for the preservation of neurons in stroke.
Dark Clouds on the Horizon: Using Cloud Storage as Attack Vector and Online Slack Space

During the past few years, a vast number of online file storage services have been introduced. While several of these services provide basic functionality such as uploading and retrieving files by a specific user, more advanced services offer features such as shared folders, real-time collaboration, minimization of data transfers or unlimited storage space. Within this paper we give an overview of existing file storage services and examine Dropbox, an advanced file storage solution, in depth. We analyze the Dropbox client software as well as its transmission protocol, show weaknesses and outline possible attack vectors against users. Based on our results we show that Dropbox is used to store copyright-protected files from a popular filesharing network. Furthermore Dropbox can be exploited to hide files in the cloud with unlimited storage capacity. We define this as online slack space. We conclude by discussing security improvements for modern online storage services in general, and Dropbox in particular. To prevent our attacks cloud storage operators should employ data possession proofs on clients, a technique which has been recently discussed only in the context of assessing trust in cloud storage operators.
Joint modeling of medical cost and survival in complex sample surveys

Medical cost data are typically highly skewed to the right with a large proportion of zero costs. It is also common for these data to be censored because of incomplete follow‐up and death. In the case of censoring due to death, it is important to consider the potential dependence between cost and survival. This association can occur because patients who incur a greater amount of medical cost tend to be frailer and hence are more likely to die. To handle this informative censoring issue, joint modeling of cost and survival with shared random effects has been proposed. In this paper, we extend this joint modeling approach to handle a final feature of many medical cost data sets, i.e., Specifically, the fact that data were obtained via a complex survey design. Specifically, we extend the joint model by incorporating the sample weights when estimating the parameters and using the Taylor series linearization approach when calculating the standard errors. We use a simulation study to compare the joint modeling approach with and without these adjustments. The simulation study shows that parameter estimates can be seriously biased when information about the complex survey design is ignored. It also shows that standard errors based on the Taylor series linearization approach provide satisfactory confidence interval coverage. The proposed joint model is applied to monthly hospital costs obtained from the 2004 National Long Term Care Survey. Copyright © 2012 John Wiley & Sons, Ltd.
Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models

A time-delay neural network (TDNN) for phoneme recognition is discussed. By the use of two hidden layers in addition to an input and output layer it is capable of representing complex nonlinear decision surfaces. Three important properties of the TDNNs have been observed. First, it was able to invent without human interference meaningful linguistic abstractions in time and frequency such as formant tracking and segmentation. Second, it has learned to form alternate representations linking different acoustic events with the same higher level concept. In this fashion it can implement trading relations between lower level acoustic events leading to robust recognition performance despite considerable variability in the input speech. Third, the network is translation-invariant and does not rely on precise alignment or segmentation of the input. The TDNNs performance is compared with the best of hidden Markov models (HMMs) on a speaker-dependent phoneme-recognition task. The TDNN achieved a recognition of 98.5% compared to 93.7% for the HMM, i.e., a fourfold reduction in error. <<ETX>>
Cloning and Molecular Analysis of the Plasma Membrane Ca2+ ‐ATPase Gene in Paramecium tetraurelia

ABSTRACT. We have determined the DNA sequence of the gene encoding the protein of the plasma membrane Ca2+‐ATPase in Paramecium tetraurelia. The predicted amino acid sequence of the plasma membrane Ca2+‐ATPase shows homology to conserved regions of known plasma membrane Ca2+‐ATPases and contains the known binding sites for ATP (FITC), acylphosphate formation, and calmodulin, as well as the “hinge” region: all characteristics common to plasma membrane Ca2+‐ATPases. The deduced molecular weight for this sequence is 131 kDa. The elucidation of this gene will assist in the studies of the mechanisms by which this excitable cell removes calcium entering through voltage gated calcium channels and the pump functions in chemosensory signal transduction.
Combining Named Entity Recognition Methods for Concept Extraction in Microposts

NER in microposts is a key and challenging task of mining semantics from social media. Our evaluation of a number of popular NE recognizers over a micropost dataset has shown a significant drop-o in results quality. Current state-of-theart NER methods perform much better on formal text than on microposts. However, the experiment provided us with an interesting observation ‐ although individual NER tools did not perform very well on micropost data, we have received recall over 90% when we merged all the results of the examined tools. This means that if we would be able to combine dierent NE recognizers in a meaningful way, we might be able to get NER in microposts of an acceptable quality. In this paper, we propose a method for NER in microposts, which is designed to combine annotations yielded by existing NER tools in order to produce more precise results than input tools alone. We combine NE recognizers utilizing ML techniques, namely decision tree and random forest using the C4.5 algorithm. The main advantage of the proposed method lies in the possibility of combining arbitrary NER methods and in its application on short, informal texts. The evaluation on a standard dataset shows that the proposed approach outperforms underlying NER methods as well as a baseline recognizer, which is a simple combination of the best underlying recognizers for each target NE class. To the best of our knowledge, up-to-date, the proposed approach achieves the highest F1 score on the #MSM2013 dataset.
Less pain, most of the gain: incrementally deployable ICN

Information-Centric Networking (ICN) has seen a significant resurgence in recent years. ICN promises benefits to users and service providers along several dimensions (e.g., performance, security, and mobility). These benefits, however, come at a non-trivial cost as many ICN proposals envision adding significant complexity to the network by having routers serve as content caches and support nearest-replica routing. This paper is driven by the simple question of whether this additional complexity is justified and if we can achieve these benefits in an incrementally deployable fashion. To this end, we use trace-driven simulations to analyze the quantitative benefits attributed to ICN (e.g., lower latency and congestion). Somewhat surprisingly, we find that pervasive caching and nearest-replica routing are not fundamentally necessary---most of the performance benefits can be achieved with simpler caching architectures. We also discuss how the qualitative benefits of ICN (e.g., security, mobility) can be achieved without any changes to the network. Building on these insights, we present a proof-of-concept design of an incrementally deployable ICN architecture.
Modeling Password Guessing with Neural Networks

—Passwords still dominate the authentication space, but they are vulnerable to many different attacks; in recent years, guessing attacks in particular have notably caused a few high-proﬁle information leaks. Password strength checkers, which attempt to guard against guessing attacks by enforcing heuristics like length and character variety, only model resistance to these brute-force attacks by proxy. In this paper, we reproduce the work of Melicher et. al [1], designing a strength checker that directly bases its ratings on resistance to guessing attacks. We provide a qualitative comparison of the ratings output by this system to those of a popular heuristic one, zxcvbn.
Mechanics and energetics in tool manufacture and use: a synthetic approach

Tool manufacture and use are observed not only in humans but also in other animals such as mammals, birds and insects. Manufactured tools are used for biomechanical functions such as effective control of fluids and small solid objects and extension of reaching. These tools are passive and used with gravity and the animal users' own energy. From the perspective of evolutionary biology, manufactured tools are extended phenotypes of the genes of the animal and exhibit phenotypic plasticity. This incurs energetic cost of manufacture as compared to the case with a fixed tool. This paper studies mechanics and energetics aspects of tool manufacture and use in non-human beings. Firstly, it investigates possible mechanical mechanisms of the use of passive manufactured tools. Secondly, it formulates the energetic cost of manufacture and analyses when phenotypic plasticity benefits an animal tool maker and user. We take a synthetic approach and use a controlled physical model, i.e. a robot arm. The robot is capable of additively manufacturing scoop and gripper structures from thermoplastic adhesives to pick and place fluid and solid objects, mimicking primates and birds manufacturing tools for a similar function. We evaluate the effectiveness of tool use in pick-and-place and explain the mechanism for gripper tools picking up solid objects with a solid-mechanics model. We propose a way to formulate the energetic cost of tool manufacture that includes modes of addition and reshaping, and use it to analyse the case of scoop tools. Experiment results show that with a single motor trajectory, the robot was able to effectively pick and place water, rice grains, a pebble and a plastic box with a scoop tool or gripper tools that were manufactured by itself. They also show that by changing the dimension of scoop tools, the energetic cost of tool manufacture and use could be reduced. The work should also be interesting for engineers to design adaptive machines.
Quantitative Analysis of Graph Algorithms: Models and Optimization Methods

With the prevalence of graph data in real-world applications and their ever-increasing size, many graph computing systems have been developed in recent years to scale the processing and analyzing of massive graphs. However, few study focuses on modeling the performance of graph algorithms and systems to identify the bottleneck of existing machines under the workload of large-scale graphs. In this paper, we propose an analytical performance model considering computation capacity, communication bandwidth and communication latency to quantify the performance of graph algorithms. Based on this model, we find the majority of graph processing runtime is spent on random access of neighbouring vertices, causing a high cache missrate, which makes the performance bounded by communication latency. We further present performance optimization methods from perspectives of both hardware and software to alleviate the bottlenecks. Experiment results from both synthesis graphs andreal graphs show that these optimization methods can produce up to 4X performance improvement in a variety of graph algorithms.
FSNC: Flexible and Scalable Network Coding in Wireless Networks

We are interested in throughput gains over wireless networks that employ network coding schemes (such as COPE [1] and BFLY [2]). However, very few studies have focused on the effect of information content, neighbor state learning model and buffer occupancy on these schemes. Sometimes they could consume coding gains we acquired laboriously. In this paper, we explain the phenomenon that pursuing high information content blindly will reduce network performance, and we prove the constant incorrect ratio in neighbor state learning model mathematically. Then, we analyze the relationship between traffic load and buffer occupancy of COPE through simulations. And finally, we develop FSNC, a novel network coding scheme, which can be used in complex environments and low-cost equipments.
Evidence for Positive Selection on a Number of MicroRNA Regulatory Interactions during Recent Human Evolution

MicroRNA (miRNA)–mediated gene regulation is of critical functional importance in animals and is thought to be largely constrained during evolution. However, little is known regarding evolutionary changes of the miRNA network and their role in human evolution. Here we show that a number of miRNA binding sites display high levels of population differentiation in humans and thus are likely targets of local adaptation. In a subset we demonstrate that allelic differences modulate miRNA regulation in mammalian cells, including an interaction between miR-155 and TYRP1, an important melanosomal enzyme associated with human pigmentary differences. We identify alternate alleles of TYRP1 that induce or disrupt miR-155 regulation and demonstrate that these alleles are selected with different modes among human populations, causing a strong negative correlation between the frequency of miR-155 regulation of TYRP1 in human populations and their latitude of residence. We propose that local adaptation of microRNA regulation acts as a rheostat to optimize TYRP1 expression in response to differential UV radiation. Our findings illustrate the evolutionary plasticity of the microRNA regulatory network in recent human evolution.
Work-related factors that impact social work practitioners’ subjective well-being: Well-being in the workplace

• Summary: This research is among the first to analyze social work practitioners’ workplace subjective well-being (SWB), the social scientific concept of happiness. From an initial survey of 646 social workers, 13 respondents with the highest SWB scores were interviewed: a cohort that can teach us much about creating and sustaining SWB. • Findings: The following reports on one aspect of those qualitative findings: the work related factors that impact overall SWB. Researchers found that the respondents’ overall SWB was impacted by characteristics of their work environment (i.e. physical, cultural, and systemic), interrelationships at work (i.e. with clients, colleagues, and supervisors), and specific aspects of the job (i.e. factors associated with both workload and type of work). • Applications: The findings are discussed in relation to social work administration, and future research. There are implications for direct social work practitioners, managers, and educators, and in particular with regard to workplace environments that support social worker SWB.