Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution and variation of the aerosol over the Qinghai-Tibet Plateau

Aerosol particles not only absorb and scatter solar radiation so that affect directly the energy budget in the earth-atmosphere system, but also affect the formation and development of clouds to affect indirectly the energy balance in the earth-atmosphere system. The HALOE satellite data was used to analyze the aerosol over Qinghai-Tibet Plateau and interview its distribution, vertical profile, seasonal changes, interannual changes. The results show that the distribution is different at different layer in the stratosphere. The concentration of aerosols first decreases and then increases with the latitude. There is a minimum at about 100hPa. Its concentration has obvious seasonal change at different layers. In the low stratosphere, it is highest in winter and lowest in summer. At the same time it is highest in summer and lowest in winter in upper stratosphere. It can be found that the annual change of aerosols has some differences at different altitude. The aerosol decrease is more distinct at 10hPa in the upper stratosphere. While the decrease trend of aerosol is relatively weaker at 100hPa in the lower stratosphere.
Venture Capital Conflicts of Interest: Evidence from Acquisitions of Venture-Backed Firms

Abstract We analyze the effects of venture capital (VC) backing on profitability of private firm acquisitions. We find that VC backing leads to significantly higher acquirer announcement returns, averaging 3%, even after controlling for deal characteristics and endogeneity of venture funding. This leads us to investigate whether some VCs have interests that conflict with those of other investors. We show that such conflicts arise from VCs having financial relationships with both acquirers and targets, corporate VCs having a dominant strategic focus, and VC funds nearing maturity experiencing pressure to liquidate. Our conclusions follow from examinations of target takeover premia and acquirer announcement returns.
Characterization of a New Epidemic Necrotic Pyoderma in Fur Animals and Its Association with Arcanobacterium phocae Infection

A new type of pyoderma was detected in Finnish fur animals in 2007. The disease continues to spread within and between farms, with severe and potentially fatal symptoms. It compromises animal welfare and causes considerable economic losses to farmers. A case-control study was performed in 2010–2011 to describe the entity and to identify the causative agent. Altogether 99 fur animals were necropsied followed by pathological and microbiological examination. The data indicated that the disease clinically manifests in mink (Neovison vison) by necrotic dermatitis of the feet and facial skin. In finnraccoons (Nyctereutes procyonoides), it causes painful abscesses in the paws. Foxes (Vulpes lagopus) are affected by severe conjunctivitis and the infection rapidly spreads to the eyelids and facial skin. A common finding at necropsy was necrotic pyoderma. Microbiological analysis revealed the presence of a number of potential causative agents, including a novel Streptococcus sp. The common finding from all diseased animals of all species was Arcanobacterium phocae. This bacterium has previously been isolated from marine mammals with skin lesions but this is the first report of A. phocae isolated in fur animals with pyoderma. The results obtained from this study implicate A. phocae as a potential causative pathogen of fur animal epidemic necrotic pyoderma (FENP) and support observations that the epidemic may have originated in a species -shift of the causative agent from marine mammals. The variable disease pattern and the presence of other infectious agents (in particular the novel Streptococcus sp.) suggest a multifactorial etiology for FENP, and further studies are needed to determine the environmental, immunological and infectious factors contributing to the disease.
How can gynaecologists cope with the silent killer – osteoporosis?

Osteoporosis is a very common disease among women. It is frequently called a silent epidemic and, due to its impact on osteoporotic fractures with high morbidity and mortality, also a silent killer. There are a number of significant risk factors for osteoporosis, some of them very strongly related to the functioning of the reproductive system. These include menstrual irregularities, premature ovarian failure, early natural or surgical menopause, a high number of pregnancies, and long-term breast-feeding. Hence, there is every reason to include gynaecologists in the multidisciplinary team striving to cope with this dreadful disease. Calculation of the 10-year fracture risk, done by means of the FRAX calculator, and classification of women according to the level of risk could prove to be an effective method of limiting the negative effects of osteoporosis.
Exploration on native actinomycetes strains and their potential against fungal plant pathogens

Actinomycetes have great potential in plant disease management. They are Gram +ve filamentous bacteria having high genomic (74%) G+C content. Actinomycetes are basically the degraders of organic matter and also very good producers of antibiotics and diverse medically and economically important secondary metabolites (Saugar et al., 2002; Basilio et al., 2003). They are also found to be a source of novel and very powerful agents which could be used in the control of a variety of pests and parasites (Wraight and Roberts, 1987). Approximately 23,000 bioactive microbial secondary metabolites have been reported of which over 10,000 are produced by actinomycetes, which is almost 45% of all bioactive microbial metabolites. The Streptomyces spp. is conspicuous among actinomycetes in terms of research studies and this alone is known to produce nearly 7,600 compounds (Berdy, 2005). They are also the important producers of enzymes like chitinase, cellulases, peptidase, protease, xylanase, liginase, amylases, sugar isomerase, pectinase, hemicellase and keratinase which have varied roles. Actinomycetes also produce secondary metabolites like Siderophores, hydrocyanic acid (HCN), Indole acetic acid (IAA) (Gopalakrishnan et al., 2011) and so on. ISSN: 2319-7706 Volume 3 Number 11 (2014) pp. 37-45 http://www.ijcmas. com
Systematic lossy error protection versus layered coding with unequal error protection

In this paper we compare two schemes for error-resilient video transmission, viz., systematic lossy error protection, and scalable coding with unequal error protection. In the first scheme, the systematic portion consists of the compressed video signal transmitted without channel coding. For error-resilience, an additional bitstream generated by Wyner-Ziv encoding of the video signal is transmitted. In the event of channel errors, the Wyner-Ziv description is decoded using the error-prone systematic description as side-information. In the second scheme, the video bitstream is partitioned into two or more layers, and each layer is assigned different amounts of parity information depending upon its relative significance. Since the base layer has heavy protection, a certain minimum video quality is guaranteed at the receiver. We compare experimentally, the performance of the competing schemes, for a particular application, i.e., Error-resilient digital video broadcasting. It is shown that systematic lossy error protection ensures graceful degradation of video quality without incurring the loss in rate-distortion performance characteristic of practical layered video coding schemes.
Determining the Minimum Degree of an S-box

S-boxes are important building blocks in block ciphers. For secure design one should not choose an S-box that has low degree. In this work we consider minimum degree of an S-box which is the minimum value of the degree of the nonzero component functions of the S-box. For an S-box F : F2 → F2 , there are 2m−1 nonzero component functions, we show that there is a better way to determine the minimum degree of an S-box which does not require to check all the 2m − 1 component functions. To the best of our knowledge, this is the best algorithm for determining the minimum degree of an S-box in the literature. keyword: Boolean function, S-box, degree, row echelon form, linear span.
A hybrid metric for digital video quality assessment

In this paper, we present a hybrid no-reference video quality metric. The proposed metric blindly estimates the quality of videos degraded by compression and transmission artifacts. The metric is composed by two no-reference artifact metrics that estimate the strength of blockiness and blurriness artifacts. A combination model is used to add the packet loss rate information to the quality estimate and eliminate the disturbance in the artifact metric values caused by the packet losses.
Inhibition of Calcineurin Abrogates While Inhibition of mTOR Promotes Regulatory T Cell Expansion and Graft-Versus-Host Disease Protection by IL-2 in Allogeneic Bone Marrow Transplantation

Regulatory T cells (Treg)s attenuate excessive immune responses, making their expansion beneficial in immune-mediated diseases including allogeneic bone marrow transplantation (BMT)-associated graft-versus-host disease (GVHD). We have recently reported that Treg expansion does not require phospholipase Cγ activation when IL-2 is provided. As such, the combination of IL-2 and a calcineurin inhibitor (Cyclosporine A; CsA) expands Tregs while inhibiting Tconv proliferation and protects against a mouse model of multiple sclerosis. However, CsA inhibits Treg proliferation in the presence of a TCR stimulus, suggesting that CsA may negatively impact Treg proliferation when they receive strong allogeneic MHC-mediated TCR signals. In this study, we show that CsA inhibits Treg proliferation and inducible Treg generation in allogeneic but not in syngeneic BMT when IL-2 is provided. In contrast to CsA, the mTOR inhibitor (Rapamycin) almost completely suppressed IL-2-mediated Treg proliferation. However, CsA and Rapamycin inhibited Treg proliferation to a similar extent when TCR stimulation was provided. Furthermore, Rapamycin promoted Treg expansion and inducible Treg generation in allogeneic BMT recipients treated with IL-2. Consistent with these observations, CsA abrogated while Rapamycin promoted the protective effect of IL-2 on allogeneic BMT-induced GVHD. These results suggest that while CsA permits IL-2-induced Treg proliferation in the syngeneic setting (absence of strong TCR signals), CsA in combination with IL-2 may be detrimental for Treg proliferation in an allogeneic setting. Thus, in allogeneic settings, an mTOR inhibitor such as Rapamycin is a better choice for adjunct therapy with IL-2 in expansion of Tregs and protection against allogeneic BMT-induced GVHD.
Using Qualitative Hypotheses to Identify Inaccurate Data Using Qualitative Hypotheses to Identify Inaccurate Data

Identifying inaccurate data has long been regarded as a di cult and signi cant problem in AI. In this paper, we present a new method to identify inaccurate data by considering qualitative connections among data. First, we put forward a new concept called support coe cient function(SCF). SCF can be used to extract, represent, and calculate qualitative e ects among data to enhance or depress the hypotheses that the inaccurate data are the expected ones, so that both fuzzy regions and membership degrees of inaccurate data can be dynamically determined on the basis of qualitative e ects among relating data, rather than on the basis of assumptions or prior probabilities supplied by human experts in advance. We have developed a practical system on infrared spectrum identi cation by using the method, and have run the system by hundreds of real spectra. The experimental results with the system are quite encouraging.
Activin in the Brain Modulates Anxiety-Related Behavior and Adult Neurogenesis

Activin, a member of the transforming growth factor-beta superfamily, is an endocrine hormone that regulates differentiation and proliferation of a wide variety of cells. In the brain, activin protects neurons from ischemic damage. In this study, we demonstrate that activin modulates anxiety-related behavior by analyzing ACM4 and FSM transgenic mice in which activin and follistatin (which antagonizes the activin signal), respectively, were overexpressed in a forebrain-specific manner under the control of the alphaCaMKII promoter. Behavioral analyses revealed that FSM mice exhibited enhanced anxiety compared to wild-type littermates, while ACM4 mice showed reduced anxiety. Importantly, survival of newly formed neurons in the subgranular zone of adult hippocampus was significantly decreased in FSM mice, which was partially rescued in ACM4/FSM double transgenic mice. Our findings demonstrate that the level of activin in the adult brain bi-directionally influences anxiety-related behavior. These results further suggest that decreases in postnatal neurogenesis caused by activin inhibition affect an anxiety-related behavior in adulthood. Activin and its signaling pathway may represent novel therapeutic targets for anxiety disorder as well as ischemic brain injury.
A genome-wide regulator-DNA interaction network in the human pathogen Mycobacterium tuberculosis H37Rv.

Transcription regulation translates static genome information to dynamic cell behaviors, making it central to understand how cells interact with and adapt to their environment. However, only a limited number of transcription regulators and their target genes have been identified in the pathogen Mycobacterium tuberculosis , which has greatly impeded our understanding of its pathogenesis and virulence. In this study, we constructed a genome-wide transcription regulatory network of M. tuberculosis H37Rv using a high-throughput bacterial one-hybrid technique. A transcription factor skeleton network was derived on the basis of the identification of more than 5400 protein-DNA interactions. Our findings further highlight the regulatory mechanism of the mammalian cell entry 1 (mce1) module, which includes mce1R and the mce1 operon. Mce1R was linked to global negative regulation of cell growth, but was found to be positively regulated by the dormancy response regulator DevR. Expression of the mce1 operon was shown to be negatively regulated by the virulence regulator PhoP. These findings provide important new insights into the molecular mechanisms of several mce1 module-related hypervirulence phenotypes of the pathogen. Furthermore, a model of mce1 module-centered signal circuit for dormancy regulation in M. tuberculosis is proposed and discussed.
Clinical research informatics: challenges, opportunities and definition for an emerging domain.

OBJECTIVES Clinical Research Informatics, an emerging sub-domain of Biomedical Informatics, is currently not well defined. A formal description of CRI including major challenges and opportunities is needed to direct progress in the field. DESIGN Given the early stage of CRI knowledge and activity, we engaged in a series of qualitative studies with key stakeholders and opinion leaders to determine the range of challenges and opportunities facing CRI. These phases employed complimentary methods to triangulate upon our findings. MEASUREMENTS Study phases included: 1) a group interview with key stakeholders, 2) an email follow-up survey with a larger group of self-identified CRI professionals, and 3) validation of our results via electronic peer-debriefing and member-checking with a group of CRI-related opinion leaders. Data were collected, transcribed, and organized for formal, independent content analyses by experienced qualitative investigators, followed by an iterative process to identify emergent categorizations and thematic descriptions of the data. RESULTS We identified a range of challenges and opportunities facing the CRI domain. These included 13 distinct themes spanning academic, practical, and organizational aspects of CRI. These findings also informed the development of a formal definition of CRI and supported further representations that illustrate areas of emphasis critical to advancing the domain. CONCLUSIONS CRI has emerged as a distinct discipline that faces multiple challenges and opportunities. The findings presented summarize those challenges and opportunities and provide a framework that should help inform next steps to advance this important new discipline.
Integration of behaviors and languages with a hierarchal structure self-organized in a neuro-dynamical model

This paper proposes an approach for robots to ac-quire language grounding in their robot's sensory-motor flow using neuro-dynamical models. We trained our neuro-dynamical model over a set of sentences represented as sequences of characters. For the integrated recognition, we introduced a cognitive hypothesis for integrated recognition where a human's brain separately processed the “structure” and “contents” of a sentence. Our model was trained with the spelling of words and their semantic role emerged in the first model. As a result of binding the model with sensory-motion patterns, we confirmed that it could associate proper word spellings with a sensory-motor flows and a semantic roles, even if an observed flow had not been learned.
A parallel spatial data analysis infrastructure for the cloud

Spatial data analysis applications are emerging from a wide range of domains such as building information management, environmental assessments and medical imaging. Time-consuming computational geometry algorithms make these applications slow, even for medium-sized datasets. At the same time, there is a rapid expansion in available processing cores, through multicore machines and Cloud computing. The confluence of these trends demands effective parallelization of spatial query processing. Unfortunately, traditional parallel spatial databases are ill-equipped to deal with the performance heterogeneity that is common in the Cloud. We introduce Niharika, a parallel spatial data analysis infrastructure that exploits all available cores in a heterogeneous cluster. Niharika first uses a declustering technique that creates balanced spatial partitions. Then, Niharika adapts to performance heterogeneity and processing skew in the spatial dataset using dynamic load-balancing. We evaluate Niharika with three load-balancing algorithms and two different spatial datasets (both from TIGER) using Amazon EC2 instances. Niharika adapts to the performance heterogeneity in the EC2 nodes, thereby achieving excellent speedups (e.g., 63.6X using 64 cores on 16 4-core EC2 nodes, in the best case) and outperforming an approach that does not adapt.
Machine learning paradigms for weed mapping via unmanned aerial vehicles

This paper presents a novel strategy for weed monitoring, using images taken with unmanned aerial vehicles (UAVs) and concepts of image analysis and machine learning. Weed control in precision agriculture designs site-specific treatments based on the coverage of weeds, where the key is to provide precise weed maps timely. Most traditional remote platforms, e.g. piloted planes or satellites, are, however, not suitable for early weed monitoring, given their low temporal and spatial resolutions, as opposed to he ultra-high spatial resolution of UAVs. The system here proposed makes use of UAV-imagery and is based on: 1) Divide the image, 2) compute and binarise the vegetation indexes, 3) detect crop rows, 4) optimise the parameters and 4) learn a classification model. Since crops are usually organised in rows, the use of a crop row detection algorithm helps to separate properly weed and crop pixels, which is a common handicap given the spectral similitude of both. Several artificial intelligence paradigms are compared in this paper to identify the most suitable strategy for this topic (i.e. unsupervised, supervised and semi-supervised approaches). Our experiments also study the effect of different parameteres: the flight altitude, the sensor and the use of previously trained models at a different height. Our results show that 1) very promising performance can be obtained, even when using very few labelled data and 2) the classification model can be learnt in a subplot of the experimental field at low altitude and then applied to the whole field at a higher height, which simplifies the whole process. These results motivate the use of this strategy to design weed monitoring strategies for early post-emergence weed control.
Heavy-Machinery Traffic Impacts Methane Emissions as Well as Methanogen Abundance and Community Structure in Oxic Forest Soils

ABSTRACT Temperate forest soils are usually efficient sinks for the greenhouse gas methane, at least in the absence of significant amounts of methanogens. We demonstrate here that trafficking with heavy harvesting machines caused a large reduction in CH4 consumption and even turned well-aerated forest soils into net methane sources. In addition to studying methane fluxes, we investigated the responses of methanogens after trafficking in two different forest sites. Trafficking generated wheel tracks with different impact (low, moderate, severe, and unaffected). We found that machine passes decreased the soils' macropore space and lowered hydraulic conductivities in wheel tracks. Severely compacted soils yielded high methanogenic abundance, as demonstrated by quantitative PCR analyses of methyl coenzyme M reductase (mcrA) genes, whereas these sequences were undetectable in unaffected soils. Even after a year after traffic compression, methanogen abundance in compacted soils did not decline, indicating a stability of methanogens here over time. Compacted wheel tracks exhibited a relatively constant community structure, since we found several persisting mcrA sequence types continuously present at all sampling times. Phylogenetic analysis revealed a rather large methanogen diversity in the compacted soil, and most mcrA gene sequences were mostly similar to known sequences from wetlands. The majority of mcrA gene sequences belonged either to the order Methanosarcinales or Methanomicrobiales, whereas both sites were dominated by members of the families Methanomicrobiaceae Fencluster, with similar sequences obtained from peatland environments. The results show that compacting wet forest soils by heavy machinery causes increases in methane production and release.
A Time-Series Analysis of U.S. Kidney Transplantation and the Waiting List: Donor Substitution Effects and "Dirty Altruism"

This paper provides an econometric analysis of the relationship between live and deceased (cadaveric) kidney donations for the United States for the period 1992:IV through 2006:II. Statistical analysis shows that increases in deceased donor transplants reduce future live donor grafts, controlling for both waiting list effects and exogenous trends. This result has important, and potentially dire, implications for efforts to reduce the organ shortage by increasing use of cadaver donors.
A comparison of audiovisual and auditory-only training on the perception of spectrally-distorted speech

Recent research suggests that using visual speech in auditory training can improve auditory-only speech perception. The long term aim of our work is to investigate this approach for hearing-impaired users, in particular cochlear-implant users. In the pilot study presented in this paper, we use spectrally-distorted speech to train two different groups of normal hearing subjects: native English and non-native, English-speaking Saudi listeners. Our pilot study suggests that both groups attain similar improvement in audio-only speech perception when visual speech is introduced into the training process. This may provide evidence that cochlear implant users would benefit from introducing visual speech in training, given that the reduced processing abilities of non-native listeners for native speech could be compared to the reduced processing abilities of cochlear implant users as a result of the inherent noise in the processing of sound by a cochlear implant.