Miracle-dz/UniFAR-FTUs huggingface dataset:
The Importance of Food pH in Commercial Canning Operations

Oklahoma Cooperative Extension Service • Division of Agricultural Sciences and Natural Resources Introduction The pH value of a food is a direct function of the free hydrogen ions present in that food. Acids present in foods release these hydrogen ions, which give acid foods their distinct sour flavor. Thus, pH may be defined as a measure of free acidity. More precisely, pH is defined as the negative log of the hydrogen ion concentration. Therefore, if a food has a pH value of 3, then the concentration of hydrogen ions present in that food is equal to 10-3 (0.001) moles/liter. If the pH value is 6, then the concentration of hydrogen ions equals 10-6 (0.000001) moles/liter. These examples show the concentration of hydrogen ions decreases as the pH value of the food increases. This explains why a low-pH food is a high-acid food and vice versa. The range of pH is commonly considered to extend from zero to 14. A pH value of 7 is neutral, because pure water has a pH value of exactly 7. Values less than 7 are considered acidic, while those greater than 7 are considered basic or alkaline. Figure 1, below, shows the approximate pH values of several types of foods. A more detailed list is given in Appendix 1. A few foods, such as egg whites, sweet corn and some baked goods may be basic. Most foods are naturally acidic, with a pH value less than 7.0. Even so, the pH value of a particular food may have a dramatic effect on the type of processing needed to safely preserve it. The Importance of Food pH in Commercial Canning Operations
Interleukin-22 (IL-22) Binding Protein Constrains IL-22 Activity, Host Defense, and Oxidative Phosphorylation Genes during Pneumococcal Pneumonia

Streptococcus pneumoniae is the most common cause of community-acquired pneumonia worldwide, and interleukin-22 (IL-22) helps contain pneumococcal burden in lungs and extrapulmonary tissues. Administration of IL-22 increases hepatic complement 3 and complement deposition on bacteria and improves phagocytosis by neutrophils. The effects of IL-22 can be tempered by a secreted natural antagonist, known as IL-22 binding protein (IL-22BP), encoded by Il22ra2. ABSTRACT Streptococcus pneumoniae is the most common cause of community-acquired pneumonia worldwide, and interleukin-22 (IL-22) helps contain pneumococcal burden in lungs and extrapulmonary tissues. Administration of IL-22 increases hepatic complement 3 and complement deposition on bacteria and improves phagocytosis by neutrophils. The effects of IL-22 can be tempered by a secreted natural antagonist, known as IL-22 binding protein (IL-22BP), encoded by Il22ra2. To date, the degree to which IL-22BP controls IL-22 in pulmonary infection is not well defined. Here, we show that Il22ra2 inhibits IL-22 during S. pneumoniae lung infection and that Il22ra2 deficiency favors downregulation of oxidative phosphorylation (OXPHOS) genes in an IL-22-dependent manner. Il22ra2−/− mice are more resistant to S. pneumoniae infection, have increased IL-22 in lung tissues, and sustain longer survival upon infection than control mice. Transcriptome sequencing (RNA-seq) analysis of infected Il22ra2−/− mouse lungs revealed downregulation of genes involved in OXPHOS. Downregulation of this metabolic process is necessary for increased glycolysis, a crucial step for transitioning to a proinflammatory phenotype, in particular macrophages and dendritic cells (DCs). Accordingly, we saw that macrophages from Il22ra2−/− mice displayed reduced OXPHOS gene expression upon infection with S. pneumoniae, changes that were IL-22 dependent. Furthermore, we showed that macrophages express IL-22 receptor subunit alpha-1 (IL-22Ra1) during pneumococcal infection and that Il22ra2−/− macrophages rely more on the glycolytic pathway than wild-type (WT) controls. Together, these data indicate that IL-22BP deficiency enhances IL-22 signaling in the lung, thus contributing to resistance to pneumococcal pneumonia by downregulating OXPHOS genes and increasing glycolysis in macrophages.
Enhanced mTOR complex 1 signaling attenuates diabetic cardiac injury in OVE26 mice

The protein kinase mechanistic target of rapamycin (mTOR) performs diverse cellular functions through 2 distinct multiprotein complexes, mTOR complex (mTORC)1 and 2. Numerous studies using rapamycin, an mTORC1 inhibitor, have implicated a role for mTORC1 in several types of heart disease. People with diabetes are more susceptible to heart failure. mTORC1 activity is increased in the diabetic heart, but its functional significance remains controversial. To investigate the role of mTORC1 in the diabetic heart, we crossed OVE26 type 1 diabetic mice with transgenic mice expressing a constitutively active mTOR (mTORca) or kinase‐dead mTOR (mTORkd) in the heart. The expression of mTORca or mTORkd affected only mTORC1 but not mTORC2 activities, with corresponding changes in the activities of autophagy, a cellular degradation pathway negatively regulated by mTORC1. Diabetic cardiac damage in OVE26 mice was dramatically reduced by mTORca but exacerbated by mTORkd expression as assessed by changes in cardiac function, oxidative stress, and myocyte apoptosis. These findings demonstrated that the enhanced mTORC1 signaling in the OVE26 diabetic heart was an adaptive response that limited cardiac dysfunction, suggesting that manipulations that enhance mTORC1 activity may reduce diabetic cardiac injury, in sharp contrast to the results previously obtained with rapamycin. —Xu, X. , Kobayashi, S. , Timm, D. , Huang, Y. , Zhao, F. , Shou, W. , Liang, Q. Enhanced mTOR complex 1 signaling attenuates diabetic cardiac injury in OVE26 mice. FASEB J. 33, 12800–12811 (2019). www.fasebj. org
HYBRID APPROACH FOR ROOT CAUSE ANALYSIS OF ASPECTS USING ONLINE REVIEWS

What they may think” has always beenpredominant for most of us during the process of decision making. Today user opinion on products is increasing day by day on the web. But, in point of fact it is difficult for a customer to examine the reviews to decide to buy the product or not. Also the producer of the product needs to govern the opinion of the customer. Identifying the aspects corresponding to a target and its sentiment is a crucial part in opinion mining. This work proposes machine learning methods for identifying implicit and explicit aspects. The aspects are categorized as positive and negative using Support Vector Machine (SVM). Finally, the classified opinions are further grouped into aspect categories using Kmeans clustering, which allows the identification of positive and negative root causes for a product. Experiment conducted on benchmark review data set which includes reviews about five distinct products. The Performance achieved by the proposed hybrid approach is compared with CNN+ LP, POPSCU and TF-RBM state-of-the-art models. The results of the proposed method shows improved Precision, Recall and F1-score that outshine the state-of-the-art baselines.
Neurobiological correlates of distinct PTSD symptom profiles during threat anticipation in combat veterans

Background—Previous research in posttraumatic stress disorder (PTSD) has identified disrupted ventromedial prefrontal cortex (vmPFC) function in those with versus without PTSD. It is unclear whether this brain region is uniformly affected in all individuals with PTSD, or whether vmPFC dysfunction is related to individual differences in discrete features of this heterogeneous disorder. Methods—In a sample of 51 male veterans of Operation Enduring Freedom/Operation Iraqi Freedom, we collected functional magnetic resonance imaging data during a novel threat anticipation task with crossed factors of threat condition and temporal unpredictability. Voxelwise regression analyses related anticipatory brain activation to individual differences in overall PTSD symptom severity, as well as individual differences in discrete symptom subscales (reexperiencing, emotional numbing/avoidance, and hyperarousal). Results—The vmPFC showed greater anticipatory responses for safety relative to threat, driven primarily by deactivation during threat anticipation. During unpredictable threat anticipation, increased PTSD symptoms were associated with relatively greater activation for threat vs. safety. However, simultaneous regression on individual symptom subscales demonstrated that this effect was driven specifically by individual differences in hyperarousal symptoms. Furthermore, this analysis revealed an additional, anatomically distinct region of the vmPFC in which reexperiencing symptoms were associated with greater activation during threat anticipation. Contact information: Dan Grupe, PhD, Waisman Laboratory for Brain Imaging and Behavior, 1500 Highland Ave, Madison, WI 53705, grupe@wisc.edu, Phone: (608) 263-7572, Fax: (608) 262-9440. Supplementary information: Supplementary methods and results; 7 figures; 4 tables; 1 movie Portions of this work were previously presented at the 20th annual meeting of the Cognitive Neuroscience Society, San Francisco, CA, April 15, 2013; at the Anxiety and Depression Conference, Chicago, IL, March 29, 2014; and at the 44th annual meeting of the Society for Neuroscience, Washington, D.C., November 17, 2014. Dr. Grupe had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Financial Disclosures and Conflicts of Interest Dr. Davidson serves on the board of directors for the following non-profit organizations: The Mind and Life Institute and Healthy Minds Innovations. Dr. Grupe, Mr. Wielgosz, and Dr. Nitschke report no conflicts of interest or financial disclosures. HHS Public Access Author manuscript Psychol Med. Author manuscript; available in PMC 2016 July 01. Published in final edited form as: Psychol Med. 2016 July ; 46(9): 1885–1895. doi:10.1017/S0033291716000374. A uhor M anscript
A Neural Networks Approach for Intelligent Fault Prediction in HPC Environments

Reliability is a well-known issue in today’s HPC environments and is expected to become even more challenging in the next generation peta-scale systems. Because current fault tolerance approaches (e.g., checkpoint/restart mechanisms) are considered to be inefficient due to performance and scalability issues, improved fault tolerance approaches such as Proactive Fault Avoidance (PFA) are today under investigation. The PFA approach is based on fault prediction and migration in order to reduce both the impact of failures on applications and the recovery time. In this document, we explore the usage of Artificial Neural Networks (ANNs) techniques for fault prediction improvement in a PFA context. By initially training the feed-forward network with a supervised back propagation learning algorithm, this network is then fed with historical IPMI sensor data collected from our cluster. Results show a prediction performance improvement over the previous “thresholds trigger” approach.
CoCo 2015 Participant : CSÎ ho 0 . 1 ∗

Higher-order rewriting combines standard, first-order rewriting with notions and concepts from the λ-calculus, resulting in rewriting systems with higher-order functions and bound variables. CSÎ ho is a tool for automatically proving confluence of such higher-order systems, specifically pattern rewrite systems (PRSs) as introduced by Nipkow [2,3]. The restriction to pattern left-hand sides is essential for obtaining decidability of unification and thus makes it possible to compute critical pairs. To this end CSÎ ho implements a version of Nipkow’s algorithm for higher-order pattern unification [4]. CSÎ ho is built on top of CSI [8], a powerful confluence prover for first-order term rewrite systems, and is available from
Incentive Framework for Cellular Traffic Offloading In

From the last few years, the popularity of video, social media and Internet gaming across a range of new devices like smartphones and tablets has created a surge of data traffic over cellular networks. Device to device connectivity will give rise to a new universe of applications that will further create stress on network capacity [3]. In the next three years alone, it is accepted that data traffic will grow towards tenfold creating a tremendous capacity crunch for operators. While data revenues are expected to only double during this period, which will create a huge gap. As a result, different innovative solutions have emerged to manage data traffic. Some of the key technologies include Wi-Fi, LTE Small Cell and Relay, femtocells, DTN-based Network, and IP flow mobility. Therefore, telecom operators need to constantly review their implement traffic offloading mechanisms that will help them manage their network load and capacity more efficiently. This paper describes various data offload strategies and considers the challenges and benefits associated with each of them. This paper aims to provide a survey of mobile data offloading technologies including insights from the business perspective as well. Keywords— Mobile data offloading, Wi-Fi, LTE Small Cell, femtocells, disruption tolerant network
Versatile layered depth video coding based on distributed video coding

Video content constitutes today a large part of the data traf fic on the Internet. This is allowed by the capillary spreading o f video codec technologies: nowadays, every computer, table t and smart phone is equipped with video encoding and decoding technologies. As a matter of facts, the video content oft en exists in different formats, that, even though be incompati ble among each other, still have a significant mutual redundancy . The incompatibility prevents an efficient exploitation of t he scalability, that on the other hand is a very important chara cteristic when it comes to efficient network use. An interesting alternative to classical scalable video, is using distributed video coding (DVC) for the enhancement layers. In the envisaged scenario, clients have different d coders for the base layer, adapted to the characteristic of t heir device. However they can share the same enhancement layer, since DVC allows encoding frames independently from the reference that will be employed at the decoder. This approach has been considered in the past in order to improve temporal and spatial scalability. In this work we review the existing approach, we improve them using more recent DVC techniques and we perform a new analysis for the emerging multi-view applications.
How people make sense of communication behaviour in global virtual teams

Global virtual teams (GVT) continue to struggle with the challenges of communication. In order to understand and study GVT actors’ behaviour in GVTs, it is imperative to understand what they attribute their own and others’ communication behaviour to. We examined a multinational corporation, with its headquarters in Europe and conducted 11 interviews with GVT actors. With our qualitative research we investigated how GVT members explain their own as well as others’ communication behaviour in GVT. This study enriches the existing GVT literature by providing information on how actors make sense of their own social world in a different way than they make sense of their co-workers’ social world. We show that there is a mismatch in the types of explanations given for the interviewee’s own communication behaviour and others’ communication behaviour. One example of the mismatch is that GVT actors tended to explain their own constructive communication behaviour, but placed greater emphasis on explaining other persons’ defensive and negative communication behaviour. Another example is that GVT actors explain others’ communication behaviour with the other persons’ cultural background, but not their own communication behaviour. Our study contributes to research on communication behaviour in GVTs.
CNN-Based Accidental Detection in Dense Printed Piano Scores

The recognition of mid-18th to mid-20th century piano scores presents segmentation challenges caused by touching and broken symbols produced by imprinting techniques and time degradation. We present a new notehead accidental dataset containing 2955 images from dense and damaged piano scores. We address this detection problem with very small training samples using a simple Spatial Transformer (ST)-based Convolutional Neural Network detector improved through bootstrapping and contextual information, and more powerful deep learning detectors (Faster R-CNN, R-FCN, and SSD) with transfer-learning on the COCO dataset. We trained all our detectors using 5 fold cross-validation and obtain 98.73% mean Average Precision (mAP) for an Intersection over Union (IoU) threshold of 0.75 with our best detector. Our ST-based detector obtains a slightly lower mAP of 94.81%, but runs 40 times faster, and uses 18 times less memory.
Optical system for head mounted display

An optical system for a head mount display is provided to correct both a spherical aberration and a color aberration by using a magnification lens system having an aspherical plastic lens and a diffraction optical element. An optical system for a head mount display includes a micro display device(10), an aspherical lens(20), and a diffraction optical element(30). The micro display device provides an image display function. The aspherical lens is arranged in front of the micro display device and refracts a display beam from the micro display device to provide a magnified screen. The diffraction optical element is arranged between the micro display device and the aspherical lens and diffracts the display beam. The diffraction optical element includes plural groove patterns, which are repeatedly formed on a surface of the aspherical lens, which faces the micro display device.
Visualizing Music in its Entirety using Acoustic Features: Music Flowgram

In this paper, we present an automatic method for visualizing a music audio ﬁle from its beginning to end, especially for classical music. Our goal is developing an easy-to-use visualization method that is helpful for listeners and can be used for various kinds of classical music, even for complex orchestral music. To represent musical characteristics, the method uses audio features such as volume, onset density, and auditory roughness, which describe loudness, tempo, and dissonance, respectively. These features are visually mapped into static two-dimensional graph, so that users can see how the music changes by time at a look. We implemented the method with Web Audio API so that users can access to the visualization system on their web browser and make visualizations from their own music audio ﬁles. Two types of user tests were conducted to verify the effects and usefulness of the visualization for classical music listeners. The result shows that it helps listeners to memorize and understand a structure of music, and to easily ﬁnd a speciﬁc part of the music.
Bargaining Power of Nordic-Baltic coalition in EU Council Negotiations

Negotiations in the European Union Council of Ministers are not only taking place within formal decision-making structures. Member states strive to find allies and coordinate their positions prior to formal negotiation meetings. They either create ad hoc coalitions to pool voting power or cooperate within more durable institutionalised coalitions that traditionally exist on a geographical proximity basis. Institutionalised coalitions bestow their members with a bargaining advantage even if they cannot generate enough voting weight to reach voting thresholds. My argument is that cooperation within institutionalised coalitions enhances the bargaining power of its participants through three mechanisms: an exchange of information, which counterbalances the asymmetries in information distribution in the pre-negotiation stage(s); the pooling of expertise that allows the member states to share resources and provide common argumentation for their positions and the „rhetorical action‟ that strengthens normative justifications. This argument differs from existing research that suggests that durable coalitions can influence the preferences of participating member states. I test my argument based on a study of the Nordic-Baltic coalition‟s role in the negotiations on climate policy, the Stockholm programme and the Baltic Sea strategy. The evidence, largely gained through elite interviews, confirms the argument by showing that institutionalised coalitions provide their members with better information and expertise, and allow them to jointly frame arguments in favour of their positions, thus generating a bargaining advantage for them. The study also highlights the limitations of regional cooperation within the EU that stem from the heterogeneity of policy preferences.
Influence of Biochar Particle Size and Shape on Soil Hydraulic Properties

Different physical and chemical properties of biochar, which is made out of a variety of biomass materials, can impact water movement through amended soil. The objective of this research was to develop a decision support tool evaluating the impact of the shape and the size distribution of biochar on soil saturated hydraulic conductivity (KSAT). Plastic beads of different size and morphology were compared with biochar to assess impacts on soil KSAT. Bead and biochar at the rate of were 5% (v/w) were added to a coarse sand. The particle size of bead and biochar had an effect on the KSAT, with larger and smaller particle sizes than the original sand grains (0.5mm) decreasing the KSAT value. The equivalent size bead or biochar to the sand grains had no impact on KSAT. The amendment shape also influenced soil hydraulic properties, but only when the particle size was between 3-6mm. Intra-particle porosity had no significant influence on the KSAT due to its small pore size and increased tortuosity compared to the inter-particle spaces (macro-porosity). The results support the conclusion that both particle size and shape of the biochar amendment will impact the KSAT value.
MULTIPLE FEATURES FOR MULTIWORD EXTRACTION: A LEARNING-TO-RANK APPROACH

This paper describes the extraction of multiword expressions (MWEs) from corpora for inclusion in a large online lexical resource for Russian. The novelty of the proposed approach is twofold: 1) we use two corpora – the Russian National Corpus and Russian Wikipedia – in parallel and 2) employ an extended set of features based on both data sources. To combine syntactic and statistical features derived from two corpora, we experiment with several learning-to-rank (LETOR) methods that have been proven to be highly effective in information retrieval (IR) scenarios. We make use of bigrams from existing dictionaries for learning, which leads to very sparing manual annotation efforts. Evaluation shows that machine-learned rankings with rich features significantly outperform traditional corpus-based association measures and their combinations. Analysis of resulting lists supports the claim that multiple features and diverse data sources improve the quality of extracted MWEs. The proposed method is language-independent.