Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of forest fire severity on terrestrial carbon emission and ecosystem production in the Himalayan region, India.

Remote sensing techniques have been used effectively for measuring the overall loss of terrestrial ecosystem production and biodiversity due to the forest fire. The current research focuses on assessing the impact of forest fire severity on terrestrial ecosystem productivity using different burn indices in Uttarakhand, India. Satellite-based land surface temperature (LST) was calculated for pre-fire (2014) and fire (2016) year using MODerate Resolution Imaging Spectroradiometer (MODIS) to identify the burn area hotspots across all eco-regions in Uttarakhand. In this study, spatial and temporal changes of different vegetation and burn area indices i.e Normalized Burn Ratio (NBR), Burnt Area Index (BAI), Normalized Multiband Drought Index (NMDI), Soil Adjusted Vegetation Index (SAVI), Global Environmental Monitoring Index (GEMI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI)were estimated for both pre-fire and fire years. Additionally, two Light Use Efficiency (LUE) models i.e Carnegie- Ames-Stanford-Approach (CASA) and Vegetation Photosynthesis Model (VPM) were selected to quantify the terrestrial Net Primary Productivity (NPP) in pre-fire and fire years across all biomes of the study area.The present approach appears to be promising and has a potential in quantifying the loss of ecosystem productivity due to forest fires. A detailed field observation data is required for further training, and testing of remotely sensed fire maps for future research.
A Topic Detection Method Based on KeyGraph and Community Partition

More and more media stream data is created on the Internet every day. It's more difficult for persons to obtain valuable information due to information overload. Topic detection is the method that extracts valuable hot topics from media stream data. It is the tool to help to solve the problem of overload information. The topic positive accuracy of cluster method is very low. In this paper, we proposed one topic detection method based on KeyGraph to improve the positive accuracy, and took experiments compared with baseline method on corpus marked by graduate students. In the result, the positive accuracy of KeyGraph method reaches 88.48% with great improvement. The result verified the effectiveness of our proposed method.
The effect analysis of critical subgroup based on K-core on public outbreak as big data

Now, information diffusion based on the most influential single node is a mainstream research perspective. However, very little attention has been given to the issue how the essential subgroups in the network are influenced the spread of information. Here we use k-shell decomposition method to measure critical subgroup in the Micro-blog, which examine its efficacy in realistic information spreading. With the increase of its degree of activity, the network information will burst in varying degrees. We have not only verified this method in three actual data sets, but also compared with the random strategy and the maximum value of the strategy. Our experimental results show that our algorithm not only finds a more influential group, but also has a higher level of influence than other strategies. This result has significance for the efficient transportation, epidemic control, and emergency evacuation.
Comparative gene expression analysis after exposure to 123I-iododeoxyuridine, γ- and α-radiation—potential biomarkers for the discrimination of radiation qualities

Abstract Gene expression analysis was carried out in Jurkat cells in order to identify candidate genes showing significant gene expression alterations allowing robust discrimination of the Auger emitter 123I, incorporated into the DNA as 123I-iododeoxyuridine (123IUdR), from α- and γ-radiation. The γ-H2AX foci assay was used to determine equi-effect doses or activity, and gene expression analysis was carried out at similar levels of foci induction. Comparative gene expression analysis was performed employing whole human genome DNA microarrays. Candidate genes had to show significant expression changes and no altered gene regulation or opposite regulation after exposure to the radiation quality to be compared. The gene expression of all candidate genes was validated by quantitative real-time PCR. The functional categorization of significantly deregulated genes revealed that chromatin organization and apoptosis were generally affected. After exposure to 123IUdR, α-particles and γ-rays, at equi-effect doses/activity, 155, 316 and 982 genes were exclusively regulated, respectively. Applying the stringent requirements for candidate genes, four (PPP1R14C, TNFAIP8L1, DNAJC1 and PRTFDC1), one (KLF10) and one (TNFAIP8L1) gene(s) were identified, respectively allowing reliable discrimination between γ- and 123IUdR exposure, γ- and α-radiation, and α- and 123IUdR exposure, respectively. The Auger emitter 123I induced specific gene expression patterns in Jurkat cells when compared with γ- and α-irradiation, suggesting a unique cellular response after 123IUdR exposure. Gene expression analysis might be an effective tool for identifying biomarkers for discriminating different radiation qualities and, furthermore, might help to explain the varying biological effectiveness at the mechanistic level.
Prediction of the potential global distribution for Biomphalaria straminea, an intermediate host for Schistosoma mansoni

Background Schistosomiasis is a snail-borne parasitic disease and is endemic in many tropical and subtropical countries. Biomphalaria straminea, an intermediate host for Schistosoma mansoni, is native to the southeastern part of South America and has established in other regions of South America, Central America and southern China during the last decades. S. mansoni is endemic in Africa, the Middle East, South America and the Caribbean. Knowledge of the potential global distribution of this snail is essential for risk assessment, monitoring, disease prevention and control. Methods and findings A comprehensive database of cross-continental occurrence for B. straminea was compiled to construct ecological models. We used several approaches to investigate the distribution of B. straminea, including direct comparison of climatic conditions, principal component analysis and niche overlap analyses to detect niche shifts. We also investigated the impacts of bioclimatic and human factors, and then used the bioclimatic and footprint layers to predict the potential distribution of B. straminea at global scale. We detected niche shifts accompanying the invasions of B. straminea in the Americas and China. The introduced populations had enlarged its habitats to subtropical regions where annual mean temperature is relatively low. Annual mean temperature, isothermality and temperature seasonality were identified as most important climatic features for the occurrence of B. straminea. Additionally, human factors improved the model prediction (P<0.001). Our model showed that under current climate conditions the snail should mostly be confined to the tropic and subtropic regions, including South America, Central America, Sub-Saharan Africa and Southeast Asia. Conclusions Our results confirmed that niche shifts took place in the invasions of B. straminea, in which bioclimatic and human factors played an important role. Our model predicted the global distribution of B. straminea based on habitat suitability, which would help for prioritizing monitoring and management efforts for B. straminea control in the context of ongoing climate change and human disturbances.
A Novel Approach to Identify Polytraumatized Patients in Extremis

Introduction Due to the fact that early objective identification of polytraumatized patients in extremis is crucial for carrying out immediate life-saving measures, our objectives were to provide and scrutinize a definition that results in a particularly high mortality rate and to identify predictors of mortality in this group. Materials and Methods A polytraumatized patient (ISS ≥ 16) was classified “in extremis” if five out of seven parameters (arterial paCO2 > 50 mmHg, hemoglobin < 9.5 g/dl, pH value < 7.2, lactate level > 4 mmol/l, base excess < −6 mmol/l, shock index > 1, and Horowitz index < 300) were met. By applying this definition, polytraumatized patients (age ≥ 18 years), admitted to our level I trauma center within a time period of three years, were retrospectively allocated to the “in extremis” group and to an age-, gender-, and ISS-matched “non-in extremis” group for comparison. Results Out of 64 polytraumatized patients (mean ISS, 43.6), who formed the “in extremis” group, 36 patients (56.3%) died, thus revealing a threefold higher mortality rate than in the matched group (18.9%). Within the “in extremis” group, age and ISS were identified as predictors of mortality. Conclusion Our definition might serve as a valuable early warning score or at least an impetus for defining polytraumatized patients in extremis in clinical practice.
A Highly Reliable Embedded Optical Torque Sensor Based on Flexure Spring

We propose a new highly reliable and lightweight embedded optical torque sensor for biomimetic robot arm enabling the torque measurement in joints, which can measure torque of the joint by detecting torsion of its elastic element (mechanical structure or flexure element). Flexure spring is introduced as the elastic element of the torque sensor in this paper. Because of its curve modeling, flexure spring is not inclined to be broken contrast to crossbeam structure, which is commonly used in torque sensor. Thanks to this structure, we can build a torque sensor as an extremely compact and highly reliable size. Six types of flexure spring are proposed to be used as the elastic element of the torque sensor in this paper, which have the potential for the requirements of measurement range and multidimensional detection. The optical electronic, less influenced by electromagnetic interferences, is selected to measure the torsion displacement of the flexure spring. The proposed design is analyzed, which can obtain the successful measurement of the torque with a load capacity of 1 Nm. One of the designed optical torque sensors is optimized by FEM. The calibration and experiment are conducted to ensure its feasibility and performance.
Transcriptional Regulation of Macrophages Polarization by MicroRNAs

Diversity and plasticity are the hallmarks of cells from the monocyte–macrophage lineage. Macrophages undergo classical M1 or alternative M2 activation in response to the microenvironment signals. Several transcription factors, such as peroxisome proliferator-activated receptors, signal transducers and activators of transcription, CCAAT-enhancer-binding proteins, interferon regulatory factors, Kruppel-like factors, GATA binding protein 3, nuclear transcription factor-κB, and c-MYC, were found to promote the expression of specific genes, which dictate the functional polarization of macrophages. Importantly, these transcription factors can be regulated by microRNAs (miRNAs), a group of small non-coding RNAs, which regulate gene expression through translation repression or mRNA degradation. Recent studies have also revealed that miRNAs control macrophage polarization by regulating transcription factors in response to the microenvironment signals. This review will summarize recent progress of miRNAs in the transcriptional regulation of macrophage polarization and provide the insights into the development of macrophage-centered diagnostic and therapeutic strategies.
A New Method of Assessment Based on Fuzzy Ranking and Aggregated Weights (AFRAW) for MCDM Problems Under Type-2 Fuzzy Environment

Fuzzy multi-criteria decision-making (MCDM) methods and problems have increasingly been considered in the past years. Type-1 fuzzy sets are usually used by decision-makers (DMs) to express their evaluations in the process of decision-making. Interval type-2 fuzzy sets (IT2FSs), which are extensions of type-1 fuzzy sets, have more degrees of flexibility in modeling of uncertainty. In this research, a new ranking method to calculate the ranking values of interval type-2 fuzzy sets is proposed. A comparison is performed to show the efficiency of this ranking method. Using the proposed ranking method and the arithmetic operations of IT2FSs, a new method of Assessment based on Fuzzy Ranking and Aggregated Weights (AFRAW)is developed for multi-criteria group decision-making. To obtain more realistic and practical weights for the criteria, the subjective weights expressed by DMs and objective weights calculated based on a deviation-based method are combined, and the aggregated weights are used in the proposed method. A numerical example related to assessment of suppliers in a supply chain and selecting the best one is used to illustrate the procedure of the proposed method. Moreover, a comparison and a sensitivity analysis are performed in this study. The results of these analyses show the validity and stability of the proposed method.
Hybrid key/Wyner-Ziv frames with flexible macroblock ordering for improved low delay distributed video coding

This paper proposes a concealment based approach to generating the side information and estimating the correlation noise for low-delay, pixel-based, distributed video coding. The proposed method employs a macroblock pattern similar to the one used in the dispersed type FMO of H.264 for grouping the macroblocks of each frame into intra coded (key) and Wyner-Ziv groups. Temporal concealment is then used at the decoder for "concealing" the missing macroblocks (estimating the side information-predicting the Wyner-Ziv macroblocks). The actual intra coded/decoded macroblocks are used for estimating the correlation noise. The results indicate significant performance improvements relative to existing motion extrapolation based approaches (up to 25% bit rate reduction).
Hardware Transactional Memory Meets Memory Persistency

Persistent Memory (PM) and Hardware Transactional Memory (HTM) are two recent architectural developments whose joint usage promises to drastically accelerate the performance of concurrent, data-intensive applications. Unfortunately, combining these two mechanisms using existing architectural supports is far from being trivial. This paper presents NV-HTM, a system that allows the execution of transactions over PM using unmodified commodity HTM implementations. NV-HTM relies on a hardware-software co-design technique, which is based on three key ideas: i) relying on software to persist transactional modifications after they have been committed via HTM; ii) postponing the externalization of commit events to applications until it is ensured, via software, that any data version produced and observed by committed transactions is first logged in PM; ii) pruning the commit logs via checkpointing schemes that not only bound the log space and recovery time, but also implement wear levelling techniques to enhance PM's endurance. By means of an extensive experimental evaluation, we show that NV-HTM can achieve up to 10× speed-ups and up to 11.6× reduced flush operations with respect to state of the art solutions, which, unlike NV-HTM, require custom modifications to existing HTM systems.
Modelling structured data with Probabilistic Graphical Models

Most clustering and classification methods are based on the assumption that the objects to be clustered are independent. However, in more and more modern applications, data are structured in a way that makes this assumption not realistic and potentially misleading. A typical example that can be viewed as a clustering task is image segmentation where the objects are the pixels on a regular grid and depend on neighbouring pixels on this grid. Also, when data are geographically located, it is of interest to cluster data with an underlying dependence structure accounting for some spatial localisation. These spatial interactions can be naturally encoded via a graph not necessarily regular as a grid. Data sets can then be modelled via Markov random fields and mixture models ( e.g. the so-called MRF and Hidden MRF). More generally, probabilistic graphical models are tools that can be used to represent and manipulate data in a structured way while modeling uncertainty. This chapter introduces the basic concepts. The two main classes of probabilistic graphical models are considered: Bayesian networks and Markov networks. The key concept of conditional independence and its link to Markov properties is presented. The main problems that can be solved with such tools are described. Some illustrations are given associated with some practical work.
Leveraging Multi-Perspective Apriori Knowledge in Predictive Business Process Monitoring

Predictive business process monitoring is an area dedicated to exploiting past process execution data in order to predict the future unfolding of a currently executed business process instance. Most of the research done in this domain focuses on exploiting the past process execution data only, leaving neglected additional a-priori knowledge that might become available at runtime. Recently, an approach was proposed, which allows to leverage a-priori knowledge on the control-flow in the form of LTL-rules. However, cases exist in which more granular a-priori knowledge becomes available about perspectives that go beyond the pure control-flow like data, time and resources (multi-perspective a-priori knowledge). In this thesis, we propose a technique that enables to leverage multi-perspective a-priori knowledge when making predictions of complex sequences, i.e., sequences of events with a subset of the data attributes attached to them. The results, obtained by applying the proposed technique to 20 synthetic logs and 1 real-life log, show that the proposed technique is able to overcome state-of-the-art approaches by successfully leveraging multi-perspective a-priori knowledge.
A Nonlinear Calibration Algorithm Based on Harmonic Decomposition for Two-Axis Fluxgate Sensors

Nonlinearity is a prominent limitation to the calibration performance for two-axis fluxgate sensors. In this paper, a novel nonlinear calibration algorithm taking into account the nonlinearity of errors is proposed. In order to establish the nonlinear calibration model, the combined effort of all time-invariant errors is analyzed in detail, and then harmonic decomposition method is utilized to estimate the compensation coefficients. Meanwhile, the proposed nonlinear calibration algorithm is validated and compared with a classical calibration algorithm by experiments. The experimental results show that, after the nonlinear calibration, the maximum deviation of magnetic field magnitude is decreased from 1302 nT to 30 nT, which is smaller than 81 nT after the classical calibration. Furthermore, for the two-axis fluxgate sensor used as magnetic compass, the maximum error of heading is corrected from 1.86° to 0.07°, which is approximately 11% in contrast with 0.62° after the classical calibration. The results suggest an effective way to improve the calibration performance of two-axis fluxgate sensors.
A Mild Traumatic Brain Injury in Mice Produces Lasting Deficits in Brain Metabolism

Abstract Metabolic uncoupling has been well-characterized during the first minutes-to-days after a traumatic brain injury (TBI), yet mitochondrial bioenergetics during the weeks-to-months after a brain injury is poorly defined, particularly after a mild TBI. We hypothesized that a closed head injury (CHI) would be associated with deficits in mitochondrial bioenergetics at one month after the injury. A significant decrease in state-III (adenosine triphosphate production) and state-V (complex-I) driven mitochondrial respiration was found at one month post-injury in adult C57Bl/6J mice. Isolation of synaptic mitochondria demonstrated that the deficit in state-III and state-V was primarily neuronal. Injured mice had a temporally consistent deficit in memory recall at one month post-injury. Using proton magnetic resonance spectroscopy (1H MRS) at 7-Tesla, we found significant decreases in phosphocreatine, N-Acetylaspartic acid, and total choline. We also found regional variations in cerebral blood flow, including both hypo- and hyperperfusion, as measured by a pseudocontinuous arterial spin labeling MR sequence. Our results highlight a chronic deficit in mitochondrial bioenergetics associated with a CHI that may lead toward a novel approach for neurorestoration after a mild TBI. MRS provides a potential biomarker for assessing the efficacy of candidate treatments targeted at improving mitochondrial bioenergetics.
Reproductive performance in dairy cows with cystic ovarian disease after single treatment with buserelin acetate or dinoprost

The treatment of cystic ovarian disease (COD) in dairy cows is still controversial, and some researchers recommend using gonadotropin-releasing hormone (GnRH) regardless of the type of cysts. The aim of this study was to comparatively evaluate the reproductive performance of cows diagnosed with follicular or luteal cystic structures, after treatment with either buserelin acetate (GnRH agonist) or dinoprost (prostaglandin F2-alpha or PGF2α). The diagnosis was established by ultrasonographic examinations performed twice a month starting 40–45 days after calving, until the cows were diagnosed pregnant after artificial insemination. Both types of cysts were treated either with 21 µg buserelin acetate or 25 mg dinoprost, resulting in four subject groups. After treatment, the estrus rate in cows with follicular cysts treated with dinoprost (55.1%) was significantly lower (P<0.05) than in cows treated with buserelin acetate (77.5%) or in cows diagnosed with luteal cysts (77.2% for dinoprost and 72.4% for buserelin acetate). The conception rate was higher (P<0.05) in cows with follicular cysts treated with buserelin acetate (67.7%) than in those treated with dinoprost (60%) or in those with luteal cysts (56.9% for dinoprost and 47.5% for buserelin acetate). These results show that it is preferable to differentiate between the two types of ovarian cysts before treatment. Luteal cysts can be treated with either PGF2α or GnRH analogues, with better results when PGF2α is used. Contrastingly, follicular cysts are better treated with GnRH analogues. When differentiation is not possible, GnRH analogues are recommended over PGF2α.
A recent history of nitroxyl chemistry, pharmacology and therapeutic potential

Due to the excitement surrounding the discovery of NO as an endogenously generated signalling molecule, a number of other nitrogen oxides were also investigated as possible physiological mediators. Among these was nitroxyl (HNO). Over the past 25 years or so, a significant amount of work by this laboratory and many others has disclosed that HNO possesses unique chemical properties and important pharmacological utility. Indeed, the pharmacological potential for HNO as a treatment for heart failure, among other uses, has garnered this curious molecule a considerable amount of recent attention. This review summarizes the events that led to this recent attention as well as poses important questions that are still to be answered with regards to understanding the chemistry and biology of HNO.
Recurrent Ischemic Strokes as the First Manifestation of A Pancreatic Cancer

C l i n M e d International Library Citation: Renna R (2015) Recurrent Ischemic Strokes as the First Manifestation of A Pancreatic Cancer. Clin Med Rev Case Rep 1:013 Received: December 10, 2014: Accepted: January 01, 2015: Published: January 03, 2015 Copyright: © 2015 Renna R. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Renna. Clin Med Rev Case Rep 2015, 2:1