Miracle-dz/UniFAR-FTUs huggingface dataset:
Discordance of Species Trees with Their Most Likely Gene Trees: A Unifying Principle

A labeled gene tree topology that disagrees with a labeled species tree topology is said to be anomalous if it is more probable under a coalescent model for gene lineage evolution than the labeled gene tree topology that matches the species tree. It has previously been shown that as a consequence of short internal branches of the species tree, for every labeled species tree topology with five or more taxa, and for asymmetric four-taxon species tree topologies, an assignment of species tree branch lengths can be made which gives rise to anomalous gene trees (AGTs). Here, I offer an alternative characterization of this result—a labeled species tree topology produces AGTs if and only if it contains two consecutive internal branches in an ancestor–descendant relationship—and I provide a proof that follows from the change in perspective. The reformulation and alternative proof of the existence result for AGTs provide the insight that it is not merely short internal branches that generate AGTs, but instead, short internal branches that are arranged consecutively.
Modulation of the Lytic Activity of the Dedicated Autolysin for Flagellum Formation SltF by Flagellar Rod Proteins FlgB and FlgF

ABSTRACT SltF was identified previously as an autolysin required for the assembly of flagella in the alphaproteobacteria, but the nature of its peptidoglycan lytic activity remained unknown. Sequence alignment analyses suggest that it could function as either a muramidase, lytic transglycosylase, or β-N-acetylglucosaminidase. Recombinant SltF from Rhodobacter sphaeroides was purified to apparent homogeneity, and it was demonstrated to function as a lytic transglycosylase based on enzymatic assays involving mass spectrometric analyses. Circular dichroism (CD) analysis determined that it is composed of 83.4% α-structure and 1.48% β-structure and thus is similar to family 1A lytic transglycosylases. However, alignment of apparent SltF homologs identified in the genome database defined a new subfamily of the family 1 lytic transglycosylases. SltF was demonstrated to be endo-acting, cleaving within chains of peptidoglycan, with optimal activity at pH 7.0. Its activity is modulated by two flagellar rod proteins, FlgB and FlgF: FlgB both stabilizes and stimulates SltF activity, while FlgF inhibits it. Invariant Glu57 was confirmed as the sole catalytic acid/base residue of SltF. IMPORTANCE The bacterial flagellum is comprised of a basal body, hook, and helical filament, which are connected by a rod structure. With a diameter of approximately 4 nm, the rod is larger than the estimated pore size within the peptidoglycan sacculus, and hence its insertion requires the localized and controlled lysis of this essential cell wall component. In many beta- and gammaproteobacteria, this lysis is catalyzed by the β-N-acetylglucosaminidase domain of FlgJ. However, FlgJ of the alphaproteobacteria lacks this activity and instead it recruits a separate enzyme, SltF, for this purpose. In this study, we demonstrate that SltF functions as a newly identified class of lytic transglycosylases and that its autolytic activity is uniquely modulated by two rod proteins, FlgB and FlgF.
Globigerina pachyderma in Pleistocene and Recent Arctic Ocean sediment

Four cores taken from ice-island T-3 from a small region of the Alpha Cordillera were studied for their fossils, particularly the planktonic foraminifer Globigerina pachyderma. The samples showed significant relationships in the number of small G. pachyderma and in the proportion of these small forms with dextral coiling. These two parameters are shown to vary in parallel fashion and to correlate among the four cores. A high proportion of small forms, and a high percentage of small forms showing dextral coiling, are assumed to indicate relatively warm water. Also, a benthonic/planktonic ratio change at the level of the last major magnetic reversal is suggested, but there is no evidence of species extinction such as found by workers in other areas. The data support the idea that at least during the last million years or so the Arctic Ocean has never been much warmer than today.
Label-free detection of protein-protein interactions using a calmodulin-modified nanowire transistor

In this study, we describe a highly sensitive and reusable silicon nanowire field-effect transistor for the detection of protein-protein interactions. This reusable device was made possible by the reversible association of glutathione S-transferase-tagged calmodulin with a glutathione modified transistor. The calmodulin-modified transistor exhibited selective electrical responses to Ca2+ (≥1 μM) and purified cardiac troponin I (∼7 nM); the change in conductivity displayed a linear dependence on the concentration of troponin I in a range from 10 nM to 1 μM. These results are consistent with the previously reported concentration range in which the dissociation constant for the troponin I-calmodulin complex was determined. The minimum concentration of Ca2+ required to activate calmodulin was determined to be 1 μM. We have also successfully demonstrated that the N-type Ca2+ channels, expressed by cultured 293T cells, can be recognized specifically by the calmodulin-modified nanowire transistor. This sensitive nanowire transistor can serve as a high-throughput biosensor and can also substitute for immunoprecipitation methods used in the identification of interacting proteins.
Question Selection for Crowd Entity Resolution

We study the problem of enhancing Entity Resolution (ER) with the help of crowdsourcing. ER is the problem of clustering records that refer to the same real-world entity and can be an extremely difficult process for computer algorithms alone. For example, figuring out which images refer to the same person can be a hard task for computers, but an easy one for humans. We study the problem of resolving records with crowdsourcing where we ask questions to humans in order to guide ER into producing accurate results. Since human work is costly, our goal is to ask as few questions as possible. We propose a probabilistic framework for ER that can be used to estimate how much ER accuracy we obtain by asking each question and select the best question with the highest expected accuracy. Computing the expected accuracy is #P-hard, so we propose approximation techniques for efficient computation. We evaluate our best question algorithms on real and synthetic datasets and demonstrate how we can obtain high ER accuracy while significantly reducing the number of questions asked to humans.
Adaptive patch-based sparsity estimation for image via MOEA/D

Due to the extensive and various information that natural images contain, it is very challenging to estimate the sparsity for an image. In this paper, we propose an adaptive sparsity estimation model for image patches, which consists of an offline training phase and online estimation phase. In offline training, for the training patch, MOEA/D is applied to obtain a group of Pareto solutions and determine a sparsity range. By processing a reduced number of representative training patches, all the sparsity ranges are stored in a look-up table (LUT) for reuse. In the online estimation phase, for a query patch, its sparsity range is set to that of the most similar training patch. And the corresponding sparse representation vector can be obtained by a sparsity-restricted greedy algorithm (SRGA) constrained by this range. Thus, the sparsity is adaptively determined by this sparse representation vector within this range. By comparing with the state-of-the-art greedy algorithms with fixed sparsity and one adaptive method, experimental studies on benchmark dataset demonstrate that our proposed approach is able to achieve better sparse representation quality in terms of PSNR and coding efficiency.
The Ayurvedic Medicine Salacia oblonga Attenuates Diabetic Renal Fibrosis in Rats: Suppression of Angiotensin II/AT1 Signaling

In human diabetic nephropathy, the extent of tubulointerstitial fibrosis is the leading cause of end-stage renal disease; fibrosis is closely correlated with renal dysfunction. Although a wide array of medicinal plants play a role in the prevention and treatment of diabetes, there are few reports of the application of herbal medicines in amelioration of renal fibrosis, or the underlying mechanisms by which such benefits are mediated. The efficacy of the Ayurvedic antidiabetic medicine Salacia oblonga (SO) root on rat renal fibrosis was investigated. An aqueous extract from SO (100 mg/kg, p.o., 6 weeks) diminished renal glomerulosclerosis and interstitial fibrosis in Zucker diabetic fatty (ZDF) rats, as revealed by van Giesen-staining. SO also reduced renal salt-soluble, acid-soluble and salt-insoluble collagen contents. These changes were accompanied by normalization of hypoalbuminemia and BUN. Gene profiling revealed that the increase in transcripts encoding the glomerulosclerotic mediators collagen I, collagen IV, fibronectin, angiotensin II type 1 receptor (AT1), transforming growth factor (TGF)-β1, plasminogen activator inhibitor (PAI)-1 observed in ZDF rat kidney was suppressed by SO. In rat-derived mesangial cells, similar to the effect of the AT1 antagonist telmisartan, SO and its major component mangiferin suppressed the stimulatory effect of angiotensin II on proliferation and increased mRNA expression and/or activities of collagen I, collagen IV, fibronectin, AT1, TGF-β1 and PAI-1. Considered together the present findings demonstrate that SO attenuates diabetic renal fibrosis, at least in part by suppressing anigiotensin II/AT1 signaling. Further, it now emerges that mangiferin is an effective antifibrogenic agent.
Catching Preference Drift with Initiators in Social Network

User's preference drift over time gets it difficult to make accurate recommendation. A recommender system ignoring the fact always recommends similar items which were loved previously by users while users' preference have changed and new items appear. It has been proven empirically that traditional algorithms handling the concept drift problem which simply takes time into account is not appropriate, so a model considering both the static and dynamic preference well is the key to catch users' preference drift. We put forward an original model which explores the behavior of influential people, the initiators who initiate trends in social network, to handle this problem. Compared to traditional collaborative filtering approaches and time weighted approaches, empirical study on lastfm dataset has shown that our model improves the accuracy of the recommendation.
A Mixed Finite Element Method to Solve the EEG Forward Problem

Finite element methods have been shown to achieve high accuracies in numerically solving the EEG forward problem and they enable the realistic modeling of complex geometries and important conductive features such as anisotropic conductivities. To date, most of the presented approaches rely on the same underlying formulation, the continuous Galerkin (CG)-FEM. In this article, a novel approach to solve the EEG forward problem based on a mixed finite element method (Mixed-FEM) is introduced. To obtain the Mixed-FEM formulation, the electric current is introduced as an additional unknown besides the electric potential. As a consequence of this derivation, the Mixed-FEM is, by construction, current preserving, in contrast to the CG-FEM. Consequently, a higher simulation accuracy can be achieved in certain scenarios, e.g., when the diameter of thin insulating structures, such as the skull, is in the range of the mesh resolution. A theoretical derivation of the Mixed-FEM approach for EEG forward simulations is presented, and the algorithms implemented for solving the resulting equation systems are described. Subsequently, first evaluations in both sphere and realistic head models are presented, and the results are compared to previously introduced CG-FEM approaches. Additional visualizations are shown to illustrate the current preserving property of the Mixed-FEM. Based on these results, it is concluded that the newly presented Mixed-FEM can at least complement and in some scenarios even outperform the established CG-FEM approaches, which motivates a further evaluation of the Mixed-FEM for applications in bioelectromagnetism.
Beyond Indicators and Reporting: Needs, Limitations and Applicability of Environmental Indicators and State of the Environment Reporting

This research examines the perceptions and use of environmental indicators and state of the environment reports by local government and Conservation Authority decision makers and practitioner’s within the Ontario portion of the Great Lakes and St. Lawrence basin. Participants describe their information needs and how indicators and SOER are used at the local level; and what limitations or challenges they face to bridge the gap between monitoring information and policy. A multi-method approach including a web-based survey and follow-up telephone interviews was the primary data collection method used. Indicator and SOER knowledge and information are further explored to determine information exchange amongst different levels of governance. To review the dissemination of indicator and SOER information from a higher spatial scale down to the local level, the State of the Great Lakes environmental indicators and SOER, developed by the governments of Canada and the United States served as a case study.
An Algorithm for Retrieving Land Surface Temperatures Using VIIRS Data in Combination with Multi-Sensors

A practical algorithm was proposed to retrieve land surface temperature (LST) from Visible Infrared Imager Radiometer Suite (VIIRS) data in mid-latitude regions. The key parameter transmittance is generally computed from water vapor content, while water vapor channel is absent in VIIRS data. In order to overcome this shortcoming, the water vapor content was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The analyses on the estimation errors of vapor content and emissivity indicate that when the water vapor errors are within the range of ±0.5 g/cm2, the mean retrieval error of the present algorithm is 0.634 K; while the land surface emissivity errors range from −0.005 to +0.005, the mean retrieval error is less than 1.0 K. Validation with the standard atmospheric simulation shows the average LST retrieval error for the twenty-three land types is 0.734 K, with a standard deviation value of 0.575 K. The comparison between the ground station LST data indicates the retrieval mean accuracy is −0.395 K, and the standard deviation value is 1.490 K in the regions with vegetation and water cover. Besides, the retrieval results of the test data have also been compared with the results measured by the National Oceanic and Atmospheric Administration (NOAA) VIIRS LST products, and the results indicate that 82.63% of the difference values are within the range of −1 to 1 K, and 17.37% of the difference values are within the range of ±2 to ±1 K. In a conclusion, with the advantages of multi-sensors taken fully exploited, more accurate results can be achieved in the retrieval of land surface temperature.
An off-line optimization approach for online energy storage managementin microgrid system

This paper investigates the real-time energy management in power system with distributed microgrids, which are independently operated and each is modeled to comprise of a renewable generation system, an energy storage system and an aggregated load. We jointly optimize the energy charged/discharged to/from the storage system and that drawn from the main grid over a finite horizon to minimize the total energy cost of conventional generation subject to given load and storage constraints. We assume that the renewable energy offset by the load over time, named net energy profile, is predictable but with finite errors. First, we consider the “off-line” optimization under an idealized assumption that the net energy profile is known ahead of time, and derive its optimal closed-form solution. Next, by applying the off-line solution combined with a sliding-window based sequential optimization, we propose a new “online” algorithm for real-time energy management under the practical setup with noisy predicted net energy profile subject to arbitrary errors. Finally, through simulations, we compare the performance of our proposed online algorithm against the conventional dynamic programming based solution as well as a heuristically designed myopic algorithm under a practical setup.
The thymus leukemia antigen binds human and mouse CD8

The thymus leukemia antigen (TLA) is a class Ib, or 'nonclassical' class I molecule, one of several encoded within the Tla locus of the mouse major histocompatibility complex (MHC). It structurally resembles the H-2K, D, and L class I transplantation antigens, which present processed peptides to cytotoxic T lymphocytes (CTLs). Although their function(s) are unknown, there has been recent speculation concerning the possibility that class Ib molecules may present antigens to T cells that express gamma delta T cell antigen receptors (TCRs). In this report, using both a cell-cell adhesion assay and adhesion of T lymphocyte clones to purified plate-bound TLA, we provide evidence that TLA can bind to both human and mouse CD8. We also show that a chimeric class I molecule containing the peptide antigen binding site of Ld and the alpha 3 domain, transmembrane, and cytoplasmic segments of TLA, can support a CD8-dependent immune response by CTLs. These results demonstrate for the first time binding of a class Ib molecule to CD8 with a functional outcome, as is observed for the class I transplantation antigens. The capacity to interact with CD8 has been conserved despite the extensive sequence divergence of TLA in the peptide antigen binding site, suggesting this interaction is highly significant. TLA is expressed by epithelial cells in the mouse small intestine. As these epithelial cells are in close contact with intestinal intraepithelial lymphocytes that are nearly all CD8+, and many of which express the gamma delta TCR, the data are consistent with the hypothesis that TLA is involved in antigen presentation, perhaps to gamma delta-positive lymphocytes in this site.
Neuronal Size in the Spinal Nucleus of the Bulbocavernosus: Direct Modulation by Androgen in Rats with Mosaic Androgen Insensitivity

The motoneurons of the spinal nucleus of the bulbocavernosus (SNB) and its target muscles, the bulbocavernosus and levator ani, form a sexually dimorphic circuit that is developmentally dependent on androgen exposure and exhibits numerous structural and functional changes in response to androgen exposure in adulthood. Castration of male adult rats causes shrinkage of SNB somata, and testosterone replacement reverses this effect, but the site at which androgen is acting to cause this change is undetermined. We exploited the X-chromosome residency of the androgen receptor (AR) gene to generate androgenized female rats that were heterozygous for the testicular feminization mutant (tfm) AR mutation and that, as a consequence of ontogenetic random X-inactivation, expressed a blend of androgen-sensitive wild-type cells and tfm-affected androgen-insensitive cells in the SNB. Chronic testosterone treatment of adult mosaics increased soma sizes only in androgen-competent wild-type SNB cells. The size of tfm-affected SNB somata in the same animals did not differ from the size of either the wild-type or tfm-affected SNB neurons in control mosaics that did not receive androgen treatment in adulthood. Because the muscle targets of the SNB are known to be uniformly androgen-sensitive in tfm mosaics, this mosaic analysis provides unambiguous evidence that androgenic effects on motoneuron soma size are mediated locally in the SNB. It is possible that the neuronal AR plays a permissive role in coordinating the actions of androgen.
Predictors of clinical improvement, angiographic recanalization, and intracranial hemorrhage after intra-arterial thrombolysis for acute ischemic stroke.

BACKGROUND AND PURPOSE We sought to evaluate predictors of clinical outcome, angiographic success, and adverse effects after intra-arterial administration of urokinase for acute ischemic stroke. METHODS We designed a Brain Attack program at University Hospitals of Cleveland for diagnosis and treatment of patients presenting within 6 hours of onset of neurological deficit. Patients with ischemia referable to the carotid circulation were treated with intra-arterial urokinase. Angiographic recanalization was assessed at the end of medication infusion. Intracerebral hemorrhage was investigated immediately after and 24 hours after treatment. Stroke severity was determined, followed by long-term outcome. RESULTS Fifty-four patients were treated. There was improvement of >/=4 points on the National Institutes of Health Stroke Scale from presentation to 24 hours after onset in 43% of the treated patients, and this was related to the severity of the initial deficit. Forty-eight percent of patients had a Barthel Index score of 95 to 100 at 90 days, and total mortality was 24%. Cranial CT scans revealed intracerebral hemorrhage in 17% of patients in the first 24 hours, and these patients had more severe deficits at presentation. Eighty-seven percent of patients received intravenous heparin after thrombolysis, and 9% of them developed a hemorrhage into infarction. Angiographic recanalization was the rule in complete occlusions of the horizontal portion of the middle cerebral artery, but distal carotid occlusions responded less well to thrombolysis. CONCLUSIONS The intra-arterial route for thrombolysis allows for greater diagnostic precision and achievement of a higher concentration of the thrombolytic agent in the vicinity of the clot. Disadvantages of this therapy lie in the cost and delay. Severity of stroke and site of angiographic occlusion may be important predictors of successful treatment.
Detection performance in self-organized wireless sensor networks

In designing a wireless sensor network, we suggest that one of the primary performance metrics should be the ability of the sensor network to detect, in a distributed manner, events of interest. Since wireless sensor networks will often have many nodes and will be deployed in remote environments, it is important that they have the ability to self-organize, forming clusters of sensor nodes. Thus, we consider the interaction of the self-organization and the distributed detection algorithms, with the primary goal of determining and optimizing the parameters that are responsible for the detection performance. In this paper, the self-organization is done using the linked cluster algorithm (LCA). The algorithm is distributed, and it uses two TDMA frames to allow each node to independently determine its neighbors. Every node uses its local connectivity matrix and its identification number to classify itself as either: (1) a cluster head, (2) a gateway, or (3) an ordinary node.