Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of Differences in Transport Function in Slc11a Family Members*

Slc11a1 (formerly Nramp1) is a proton/divalent cation transporter that regulates cation homeostasis in macrophages. Slc11a2 mediates divalent cation uptake via the gut and delivery into cells. The mode of action of the two transporters remains controversial. Heterologous expression in frog oocytes shows Slc11a2 is a symporter, whereas Slc11a1 is an antiporter fluxing divalent cations against the proton gradient. This explains why Slc11a2, but not Slc11a1, can complement EGTA sensitivity in smf1Δ/smf2Δ/smf3Δ yeast. However, some studies of transport in mammalian cells suggest Slc11a1 is a symporter. We now demonstrate that Slc11a1, but not Slc11a2, complements a divalent cation stress phenotype in bsd2Δ/rer1Δ yeast. This is the first description of a yeast complementation assay for Slc11a1 function. Given the prior demonstration in frog oocytes that Slc11a1 acts as an antiporter, the most plausible interpretation of the data is that Slc11a1 is rescuing bsd2Δ/rer1Δ yeast by exporting divalent cations. Chimaeras define the N terminus, and a segment of the protein core preceding transmembrane domain 9 through transmembrane domain 12, as important in rescuing the divalent cation stress phenotype. EGTA sensitivity and divalent cation stress phenotypes in yeast expressing Slc11a orthologues show that symport activity is ancestral. Molecular changes that mediate rescue of the divalent cation stress phenotype post-date frogs and co-evolved with Slc11a1 orthologues that regulate divalent cation homeostasis in macrophages and resistance to infection in chickens and mammals.
Building book catalog using image processing

In this article we describe an automatic initialization-free method for building and managing book catalogs. At the heart of the system there is an image processing algorithm which can analyze the contents of an image taken of a bookshelf, separate the book spines into subsequent images and use an OCR algorithm on it to find the title and author of the book. The title and author information is searched for in public book databases to find all the details of the given book which can then be stored. The article describes the methods used along with accuracy measurements for the different modules of our system.
Construction CHR-based Text Mining and Classification of Google Search Results

The paper describes a tool which summarizes and classifies Google search results using Constraint Handling Rules (CHR). The tool extracts data from the Google API and combines data extraction, linguistic analysis and web development techniques. Linguistic analysis was implemented using CHR and includes counts of important words, non sequential and sequential patterns, known as sequences. Moreover, PHP and JavaScript were used for applying web development techniques. The tool features clustering, ranking results and viewing the sequences and important words’ counts as charts. The tool simplifies finding specific query, especially if the query has several synonyms.
Distinct Determinants of Sparse Activation during Granule Cell Maturation

Adult neurogenesis continually produces a small population of immature granule cells (GCs) within the dentate gyrus. The physiological properties of immature GCs distinguish them from the more numerous mature GCs and potentially enables distinct network functions. To test how the changing properties of developing GCs affect spiking behavior, we examined synaptic responses of mature and immature GCs in hippocampal slices from adult mice. Whereas synaptic inhibition restricted GC spiking at most stages of maturation, the relative influence of inhibition, excitatory synaptic drive, and intrinsic excitability shifted over the course of maturation. Mature GCs received profuse afferent innervation such that spiking was suppressed primarily by inhibition, whereas immature GC spiking was also limited by the strength of excitatory drive. Although the input resistance was a reliable indicator of maturation, it did not determine spiking probability at immature stages. Our results confirm the existence of a transient period during GC maturation when perforant path stimulation can generate a high probability of spiking, but also reveal that immature GC excitability is tempered by functional synaptic inhibition and reduced excitatory innervation, likely maintaining the sparse population activity observed in vivo.
A Method for Dimensioning Micro-Scale Solar Energy Harvesting Systems Based on Energy Level Simulations

Solar energy harvesting gains more and more attention in the field of wireless sensor networks. In situations, where these sensor systems are deployed outdoors, powering sensor nodes by solar energy becomes a suitable alternative to the traditional way of battery power supplies. Since solar energy, opposed to batteries, can be considered as an inexhaustible energy source, scavenging this source allows longer system lifetimes and brings wireless sensor networks closer to be an autonomous system with perpetual lifetime. Despite the possibility of designing and constructing these harvesting system, dimensioning becomes a crucial task to fit implemented components to application and load system demands. In this paper we present a way of dimensioning solar harvesting systems based on simulation. Method and implementation of component and system models are described on the basis of an example architecture that has been used in prior work. Furthermore we evaluate the model in comparison to deployment of the same architecture and show the suitability of using the simulation as a support to optimize choices for system parameters.
A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data

There has been much interest recently in the problem of rank aggregation from pairwise data. A natural question that arises is: under what sorts of statistical assumptions do various rank aggregation algorithms converge to an 'optimal' ranking? In this paper, we consider this question in a natural setting where pairwise comparisons are drawn randomly and independently from some underlying probability distribution. We first show that, under a 'time-reversibility' or Bradley-Terry-Luce (BTL) condition on the distribution, the rank centrality (PageRank) and least squares (HodgeRank) algorithms both converge to an optimal ranking. Next, we show that a matrix version of the Borda count algorithm, and more surprisingly, an algorithm which performs maximum likelihood estimation under a BTL assumption, both converge to an optimal ranking under a 'low-noise' condition that is strictly more general than BTL. Finally, we propose a new SVM-based algorithm for rank aggregation from pairwise data, and show that this converges to an optimal ranking under an even more general condition that we term 'generalized low-noise'. In all cases, we provide explicit sample complexity bounds for exact recovery of an optimal ranking. Our experiments confirm our theoretical findings and help to shed light on the statistical behavior of various rank aggregation algorithms.
Marker genes for activation of the RNA interference (RNAi) pathway in the free-living nematode Caenorhabditis elegans and RNAi development in the ovine nematode Teladorsagia circumcincta

Abstract The nematode Teladorsagiacircumcincta is a major cause of parasitic gastroenteritis in sheep in temperate regions. The development of resistance to the major anthelmintic classes used for its control is a threat to small ruminant farming sustainability. Vaccination is a potential alternative control method for this nematode. Gene datasets can be exploited to identify potential vaccine candidates and these validated further by methods such as RNA interference (RNAi) prior to vaccine trials. Previous reports indicate that RNAi in parasitic nematodes is inconsistent and, to date, there are no internal controls that indicate activation of the RNAi pathway in response to double-stranded RNA (dsRNA). The present aims were to determine whether or not the transcription levels of potential marker genes in the RNAi pathway could indicate activation of the pathway in Caenorhabditis elegans and to develop an RNAi platform in T. circumcincta. In C. elegans, transcript levels of three candidate marker genes, Ce-dcr-1 (Dicer), Ce-ego-1 (Enhancer of Glp-One family member) and Ce-rsd-3 (RNAi Spreading Defective), were analysed and results indicated that activation of the pathway had no effect on transcript levels of these genes. In T. circumcincta, two vaccine candidate genes from the Activation-associated Secreted Protein (ASP) family were targets for knockdown. RNAi experiments showed successful silencing of both targets, although inconsistencies in efficacy were observed. After testing a number of parameters that might affect variability, it was found that the length of the storage period of the larvae plays an important role in the consistency of the RNAi results.
A Lifecycle Based Approach for Malware Analysis

Most of the detection approaches like Signature based, Anomaly based and Specification based are not able to analyze and detect all types of malware. Signature-based approach for malware detection has one major drawback that it cannot detect zero-day attacks. The fundamental limitation of anomaly based approach is its high false alarm rate. And specification-based detection often has difficulty to specify completely and accurately the entire set of valid behaviors a malware should exhibit. Modern malware developers try to avoid detection by using several techniques such as polymorphic, metamorphic and also some of the hiding techniques. In order to overcome these issues, we propose a new approach for malware analysis and detection that consist of the following twelve stages Inbound Scan, Inbound Attack, Spontaneous Attack, Client-Side Exploit, Egg Download, Device Infection, Local Reconnaissance, Network Surveillance, & Communications, Peer Coordination, Attack Preparation, and Malicious Outbound Propagation. These all stages will integrate together as interrelated process in our proposed approach. This approach had solved the limitations of all the three approaches by monitoring the behavioral activity of malware at each any every stage of life cycle and then finally it will give a report of the maliciousness of the files or software's.
FIRST RESULTS FROM COPSS: THE CO POWER SPECTRUM SURVEY

We present constraints on the abundance of carbon monoxide in the early universe from the CO Power Spectrum Survey. We utilize a data set collected between 2005 and 2008 using the Sunyaev–Zel’dovich Array (SZA), which was previously used to measure arcminute-scale fluctuations of the cosmic microwave background. This data set features observations of 44 fields, covering an effective area of 1.7 square degrees, over a frequency range of 27–35 GHz. Using the technique of intensity mapping, we are able to probe the CO(1–0) transition, with sensitivity to spatial modes between k = 0.5–2 h Mpc−1 over a range in redshift of z = 2.3–3.3, spanning a comoving volume of 3.6 × 106 h−3 Mpc3. We demonstrate our ability to mitigate foregrounds, and present estimates of the impact of continuum sources on our measurement. We constrain the CO power spectrum to PCO < 2.6 × 104 μK2 (h−1 Mpc)3, or Δ2CO(k = 1 h Mpc−1) < 1.3 × 103 μK2, at 95% confidence. This limit resides near optimistic predictions for the CO power spectrum. Under the assumption that CO emission is proportional to halo mass during bursts of active star formation, this corresponds to a limit on the ratio of CO(1–0) luminosity to host halo mass of ACO < 1.2 × 10−5 L⊙ M⊙−1. Further assuming a Milky Way-like conversion factor between CO luminosity and molecular gas mass (αCO = 4.3 M⊙ (K km s−1 pc−2)−1), we constrain the global density of molecular gas to &rgr; z ∼ 3 ( M H 2 ) ≤ 2.8 × 10 8 M ⊙ Mpc − 3 ? > .
Post-stroke Depression and Rehabilitation of Neurological Function

Objective:To detect depression in stroke patients and observe its influence on rehabilitation of neurological function and the effect of fluxetine therapy Methods:132 acute stroke patients (78 with infarction, 54 with hemorrhage) were included in the study After inclusion, we did five times of assessments at the end of 2 weeks, 1 month, 3 months, 6 months and 1 year respectively using HAMD assessment to determine whether there had depression or not, as well as SSS (modified Edingburgh-Scandinavian Scoring Scale) for neurological function, ADL (activity of daily living), and self rate depression and anxiety (Zung's self rate depression scale and anxiety scale) Of those with depression, 25 received fluxetine 20mg/d, other 32 with only regular treatment of stroke as control Results:According to results of HAMD, 59 of the 132 had depression (44 7%) The rate of depression had no significant difference between male and female, between those with infarction and with hemorrhage Depression had negative influence on neurological function and activity of daily living Depression had no relation to location, size and unilateral or bilateral of the loci Fluxetine improved depression, neurological function and activity of daily living from 3 months after stroke; its influence on neurological impairment was most significant at 1 year after stroke Conclusion: Post stroke depression is common after cerebral infarction or hemorrhage, it has negative influence on rehabilitation of neurological function and activity of daily living Fluxetine improves both neurological impairments and activity of daily function, as the same time of its improvement of depression
Complementary Contributions of Prefrontal Neuron Classes in Abstract Numerical Categorization

The primate prefrontal cortex (PFC) plays a cardinal role in forming abstract categories and concepts. However, it remains elusive how this is accomplished and to what extent the interaction of functionally distinct neuron classes underlies this representation. Here, we inferred the major cortical cell types, putative pyramidal cells, and interneurons by characterizing the waveforms of action potentials recorded in monkeys performing a cognitively demanding numerosity categorization task. Putative interneurons responded much faster than cells classified as pyramidal neurons and exhibited a higher reliability of category discrimination, whereas putative pyramidal cells showed a higher degree of category selectivity. An analysis of the numerosity tuning profiles and the temporal interactions of adjacent neurons indicated that inhibitory input by putative interneurons shapes the tuning to numerical categories of putative PFC pyramidal cells. These findings favor feedforward mechanisms subserving cognitive categorization and help to clarify cellular interactions in PFC microcircuits.
Extending Battery Life of a Multi-buffered, Single-Threaded Processor in a Mobile Computing Device

We introduce an online speed-scaling algorithm that is used to determine the optimum processing rate of executing a set of N jobs by a single processor of a mobile computing device under the single-threading (multi-buffered) computing architecture. We consider heterogeneous tasks that could differ in computation volume, memory and processing requirements. By using speed-scaling, where the processor's speed is able to dynamically change within hardware and software processing constraints, the algorithm explicitly determines the optimum processing rate of executing each task. This optimum processing rate was found to be a function of the number of 'alive' tasks (N), the remaining battery energy percentage, the processor's energy inefficiency coefficient, the unit price of response time and lastly, the unit price of energy. The algorithm allows the user or OS to specify the unit cost of energy and response time for executing all tasks. The algorithm has an operation mode where all tasks' unit cost of energy is also heuristically affected by the device' remaining battery energy percentage in accordance with the micro-economic laws of demand and supply. We synthesize the algorithm by analytically minimizing the total cost of both response time and energy consumption of tasks. We also consider other conventional performance metrics to evaluate the algorithm. Using numerical simulations, we show that when the remaining battery energy percentage is factored, the algorithm performs slightly slower (mildly more slower when the battery is almost drained out), but consumes far less energy, can complete significantly more jobs and ultimately allows the mobile computing device to last longer on the go.
Segmentation of heart sound recordings from an electronic stethoscope by a duration dependent Hidden-Markov Model

Digital stethoscopes offer new opportunities for computerized analysis of heart sounds. Segmentation of hearts sounds is a fundamental step in the analyzing process. However segmentation of heart sounds recorded with handheld stethoscopes in clinical environments is often complicated by recording and background noise. A duration-dependent hidden Markov model (DHMM) is proposed for robust segmentation of heart sounds. The DHMM model was developed and tested with heart sounds recorded at bedside with a commercially available handheld stethoscope. In a population of 60 patients, the DHMM identified 739 S1 and S2 sounds out of 744 which corresponded to a 99.3% sensitivity. There were seven incorrectly classified sounds which corresponded to a 99.1% positive predictive value. Our results suggest that DHMM could be a suitable method for segmentation of clinically recorded heart sounds.
Campylobacter jejuni Lipooligosaccharides Modulate Dendritic Cell-Mediated T Cell Polarization in a Sialic Acid Linkage-Dependent Manner

ABSTRACT Carbohydrate mimicry between Campylobacter jejuni lipooligosaccharides (LOS) and host neural gangliosides plays a crucial role in the pathogenesis of Guillain-Barré syndrome (GBS). Campylobacter jejuni LOS may mimic various gangliosides, which affects the immunogenicity and the type of neurological deficits in GBS patients. Previous studies have shown the interaction of LOS with sialic acid-specific siglec receptors, although the functional consequences remain unknown. Cells that express high levels of siglecs include dendritic cells (DCs), which are crucial for initiation and differentiation of immune responses. We confirm that α2,3-sialylated GD1a/GM1a mimic and α2,8-sialylated GD1c mimic LOS structures interact with recombinant Sn and siglec-7, respectively. Although the linkage of the terminal sialic acid of LOS did not regulate expression of DC maturation markers, it displayed clear opposite expression levels of interleukin-12 (IL-12) and OX40L, molecules involved in DC-mediated Th cell differentiation. Accordingly, targeting DC-expressed siglec-7 with α2,8-linked sialylated LOS resulted in Th1 responses, whereas Th2 responses were induced by targeting with LOS containing α2,3-linked sialic acid. Thus, our data demonstrate for the first time that depending on the sialylated composition of Campylobacter jejuni LOS, specific Th differentiation programs are initiated, possibly through targeting of distinct DC-expressed siglecs.
Betrayed By My Shadow: Learning Data Identity via Trail Matching

The term “re-identification” refers to the correct relation of seemingly anonymous data to explicitly identifying information, such as the name or address, of people who are the subjects of the data. Historically, methods for re-identification have been based on data released from a single data holder. This paper extends the concept to trail re-identification in which a person is related to seemingly anonymous data left behind at multiple locations, thus the data’s trail. The main premise behind these methods is that some locations capture, in addition to seemingly anonymous data, an individual’s explicitly identifying information and, subsequently, provide separate data releases of the unidentified and identified data. A single location’s releases appear unrelated; however, when multiple locations make such releases of information, common patterns in the data trails of two types of data can be used to discover relationships between them. The algorithms presented herein differ in the amount of completeness and multiplicity assumed in the data. We report experiments and successful re-identifications of IP addresses to online users and households. This work provides a foundation for several new research directions, including the development of methods for learning identity and additional information across disparate datasets, as well as a foundation for methods that enable data holders to share information with guarantees of anonymity.
MACHINE LEARNING IN HYBRID HIERARCHICAL AND PARTIAL-ORDER PLANNERS FOR MANUFACTURING DOMAINS

ABSTRACT The application of AI planning techniques to manufacturing systems is being widely deployed for all the tasks involved in the process, from product design to production planning and control. One of these problems is the automatic generation of control sequences for the entire manufacturing system in such a way that final plans can be directly used as the sequential control programs which drive the operation of manufacturing systems. HYBIS is a hierarchical and nonlinear planner whose goal is to obtain partially ordered plans at such a level of detail that they can be used as sequential control programs for manufacturing systems. Currently, those sequential control programs are being generated by hand using modeling tools. This document describes a work aimed to improve the efficiency of solving problems with HYBIS by using machine learning techniques. It implements a deductive learning method that is able to automatically acquire control knowledge (heuristics) by generating bounded explanations of the problem-solving episodes. The learning approach builds on HAMLET, a system that learns control knowledge in the form of control rules.
Trimming Approach of Robust Clustering for Smartphone Behavioral Analysis

Nowadays, smart phones get increasingly popular which also attracted hackers. With the increasing capabilities of such phones, more and more malicious softwares targeting these devices have been developed. Malwares can seriously damage an infected device within seconds. In this paper, we propose to use the trimming approaches for automatic clustering (trimmed k-means, Tclust) of smartphone's applications. They aim to identify homogenous groups of applications exhibiting similar behavior and allow to handle a proportion of contaminating data to guarantee the robustness of clustering. Then, a clustering-based detection technique is applied to compute an anomaly score for each application, leading to discover the most dangerous among them. Initial experiments results prove the efficiency and the accuracy of the used clustering methods in detecting abnormal smartphone's applications and that with a low false alerts rate.
Commentary: Semi-Metric Topology of the Human Connectome: Sensitivity and Specificity to Autism and Major Depressive Disorder

This study was funded by the UK Medial Research Council (grants: G0802226 and G0701919), the National Institute for Health Research (NIHR) (grant: 06/05/01) and the Behavioural and Clinical Neuroscience Institute (BCNI), University of Cambridge. The BCNI is jointly funded by the Medical Research Council and the Wellcome Trust. Additional support was received from the NIHR Cambridge Biomedical Research Centre. CCH is supported by a Parke Davis Fellowship from the University of Cambridge and resides at Columbia University.