Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of soil salinity on Bt protein concentration in square of transgenic Bt cotton

Insect-resistance of transgenic Bacillus thuringiensis (Bt) cotton varies among plants organs and with different environmental conditions. The objective of this study was to examine the influence of soil salinity on Bt protein concentration in cotton squares and to elucidate the potential mechanism of Bt efficacy reduction. Two cotton cultivars (NuCOTN 33B and CCRI 07, salt-sensitive and salt-tolerant) were subjected to salinity stress under four natural saline levels in field conditions in 2015 and 2016 and seven regimes of soil salinity ranged from 0.5 to 18.8 dS m-1 in greenhouse conditions in 2017. Results of field studies revealed that Bt protein content was not significantly changed at 7.13 dS m-1 salinity, but exhibited a significant drop at the 10.41 and 14.16 dS m-1 salinity. The greenhouse experiments further showed similar trends that significant declines of the insecticidal protein contents in squares were detected when soil salinity exceeded 9.1 dS m-1. Meanwhile, high salinity resulted in significant reduction in contents of soluble protein and total nitrogen, activities of nitrate reductase (NR), glutamine synthetase (GS) and glutamic-pyruvic transaminase (GPT), but increased amino acid content, activities of protease and peptidase in cotton squares. High salinity also decreased root vigor (RV), root total absorption area (RTA) and root active absorption area (RAA). The extent of decrease of Bt protein content was more pronounced in NuCOTN 33B than CCRI 07, and CCRI07 exhibited stronger enzymes activities involved in square protein synthesis and higher levels of RV, RTA and RAA. Therefore, the results of our present study indicated that insecticidal protein expression in cotton squares were significantly affected by higher salinity (equal to or higher than 9.1 dS m-1), reduced protein synthesis and increased protein degradation in squares and reduced metabolic activities in roots might lead to the decrease of Bt protein content in squares.
Towards Connecting Experiences during Collocated Events through Data Mining and Visualization

Themed collocated events, such as conferences, workshops, and seminars, invite people with related life experiences to connect with each other. In this era when people record lives through the Internet, individual experiences exist in different forms of digital contents. People share digital life records during collocated events, such as sharing blogs they wrote, Twitter posts they forwarded, and books they have read. However, connecting experiences during collocated events are challenging. Not only one is blind to the large contents of others, identifying related experiential items depends on how well experiences are retrieved. The collection of personal contents from all participants forms a valuable group repository, from which connections between experiences can be mined. Visualizing same or related experiences inspire conversations and support social exchange. Common topics in group content also help participants generate new perspectives about the collocated group. Advances in machine learning and data visualization provide automated approaches to process large data and enable interactions with data repositories. This position paper promotes the idea of event mining: how to utilize state-of-the-art data processing and visualization techniques to design event mining systems for connecting experiences during collocated activities. We discuss empirical and constructive problems in this design space, and our preliminary study of deploying a tabletop-based system, BlogCloud, which supports experience re-visitation and exchange with machine-learning and data visualization.
Design and Construction of an Intelligent Stacking Cone Upper Limb Rehabilitation System

Stroke patients, due to lack of exercise, usually suffer from muscle atrophy or functional deterioration. Therefore the patients need to do long-term rehabilitation therapy activities. The current clinical practice uses static rehabilitation tools such as stacking cones without immediate functional feedback. During the procedure, an occupational therapist (OT) fills out a Brunnstrom stage form to evaluate the status of upper limb. This approach has not been effective because patients may feel tired and boring with these tools. According to Brunnstrom stage, the palm grip ability level between three to six, and the arm strength level between four to six can use stacking cones for rehabilitation purposes. The research methods in this project are based on the patients' need to do rehabilitation with stacking cones, such as those with stroke, spinal cord injury, brain injury, Parkinson's disease, hand and visual perception problems. This study aims to establish a smart stacking cones device for upper limb rehabilitation with evaluation function. The goal is to raise the efficacy and add fun level during diagnostic and therapeutic procedures, therefore to achieve improved upper limb action, palm grip action, strength, endurance, coordination and other capabilities.
An Improved Local or Global Active Contour Driven by Legendre Polynomials

In the paper, an improved local or global active contour model driven by Legendre Polynomials(LGLP) is proposed. It implemented with a special method, which selectively penalizes the level set function and then uses a filter to regularize it. Firstly, utilizing Legendre Polynomials approximates region intensity. Secondly, an improved region-based signed pressure force (ISPF) function is proposed, which efficiently stop the contours at weak edges, especially for the segmented image with intensity inhomogeneity. Finally, an edge stopping function is added to robustly capture the boundaries of objects. Experimental results show that the improved method is faster and achieve higher accuracy than other models on real images with intensity inhomogeneity, noise and multiple objects.
Locata Challenge-Evaluation Tasks and Measures

Sound source localization and tracking algorithms provide estimates of the positional information about active sound sources in acoustic environments. Despite substantial advances and significant interest in the research community, a comprehensive benchmarking campaign of the various approaches using a common database of audio recordings has, to date, not been performed. The aim of the IEEE-AASP Challenge on sound source localization and tracking (LOCATA) is to objectively benchmark state-of-the-art localization and tracking algorithms using an open-access data corpus of recordings for scenarios typically encountered in audio and acoustic signal processing applications. The challenge tasks range from the localization of a single source with a static microphone array to tracking of multiple moving sources with a moving microphone array. This paper provides an overview of the challenge tasks, describes the performance measures used for evaluation of the LOCATA Challenge, and presents baseline results for the development dataset.
Photoplethysmography Detection of Overreaching

Introduction Detecting the onset of functional overreaching (FOR) or nonfunctional overreaching in endurance athletes is of prior importance to ensure reactive amendment of the scheduled training program. The objective of this study was to assess photoplethysmography (PPG) in overloaded athletes and test whether 1) it would be affected differently in functional overreached (FOR) or nonoverreached acutely fatigued (AF) athletes and 2) specific PPG characteristics would allow for timely distinction of FOR and AF. Methods Fifteen athletes performed 2-wk baseline training followed by 3-wk overload (+45%; OVL) and 2-wk recovery (−20%). Three-thousand-meter time-trial running was used to assess performance at the end of baseline, OVL, and recovery and distinguish FOR and AF. PPG was recorded overnight using a wearable sensor, every third night. Overnight means and variances of systolic, diastolic, and dicrotic amplitudes and times as well as systolic and diastolic slopes were used to discriminate FOR and AF athletes. Results Performance was decreased in FOR and improved in AF at the end of OVL. Diastolic time was greater in AF than FOR, whereas systolic slope was smaller in AF than in FOR during OVL. The variances of systolic, diastolic, dicrotic amplitudes, systolic, diastolic slopes, and pulse areas were smaller in AF compared with FOR in the last week of OVL. Conclusion PPG is an efficient tool for the detection of overreaching because it distinguished FOR and AF athletes during OVL (prior performance decrement). This fast-responding method would therefore allow for adjusting the daily training content to prevent nonfunctional overreaching.
DJ-1 promotes survival of human colon cancer cells under hypoxia by modulating HIF-1α expression through the PI3K-AKT pathway

Background Protein/nucleic acid deglycase (DJ-1) and hypoxia-inducible factor-1α (HIF-1α) play significant roles in the progression of various types of cancer and are associated with the phosphatidylinositol 3-kinase (PI3K) pathway. However, their functions in colorectal cancer (CRC) have not been identified. The aim of this study was to analyze the putative signaling pathway encompassing DJ-1, PI3K, and HIF-1α in a series of CRC tissues and cell lines. Purpose This study aimed at exploring the expression status of DJ-1 in colon cancer and its role in survival of cancer cell lines. Methods The expression and localization of DJ-1, PI3K-p110α, phosphorylated Akt (p-AKT), and HIF-1α were determined by immunohistochemistry in 73 resected CRC tissues. The effect of DJ-1 on cell activity was explored by in vitro knockdown and overexpression experiments in SW480 and HT-29 cells. The cells were treated with a PI3K inhibitor (LY294002 or wortmannin), and p-AKT and HIF-1α protein expression were then analyzed. Apoptosis was analyzed by flow cytometry. The expression levels of several HIF-1 target genes were assessed under hypoxic conditions by reverse transcription-PCR and Western blot. Xenograft tumor growth studies were conducted in DJ-1 knockdown or overexpression cells. Results High DJ-1 expression was found in 68.49% (50/73) of CRC tissues and associated with larger tumor size and advanced clinical stages. DJ-1 expression was positively associated with PI3K-p110α, p-AKT, and HIF-1α expression in CRC. HIF-1α and p-AKT protein levels were lower in SW480 and HT-29 cells with stable DJ-1 knockdown than in those with DJ-1 overexpression. PI3K inhibitors almost completely blocked DJ-1-induced AKT phosphorylation. However, the expression of HIF-1α was partially preserved after treatment with PI3K inhibitors. We also show that DJ-1 is necessary for the transcriptional ability of HIF-1α and CRC cell survival after hypoxic stress. Moreover, DJ-1 promoted the growth of established tumor xenografts in nude mice. Conclusion Our findings are the first to show that DJ-1 is overexpressed in CRC. We suggest a model in which DJ-1 mediates CRC cell survival by regulating the PI3K-AKT-HIF-1α pathway.
Peer specialists in suicide prevention: Possibilities and pitfalls.

The emergence of peer specialists with histories of suicidality in mental health care services is a recent but scarcely researched societal phenomenon. The current study aimed to explore how peer specialists who have experienced suicidality (either attempted suicide or suicidal ideation) use their experiences to contribute to suicide prevention in mental health care services. Qualitative interviews with 20 peer specialists who have personally dealt with suicidality in their past were conducted. Interviewees perceived their work to have unique value in terms of their approach to making contact with suicidal care consumers on an emotional level, which was perceived to lead to less reluctance on the part of suicidal care consumers to talk about suicidality, as well as affect feelings of being acknowledged and heard. However, the lack of professional distance was perceived to carry several risks, including burdening clients with the peer specialists' own suicidal experiences, perceived reluctance of coworkers to let peer specialists work with suicidal clients, and the burden of working with suicidal clients for the peer specialists. Specific conditions that were perceived to be needed in order to work with suicidal clients consisted of personal distance to own process of recovery and suicidality, establishing boundaries with the team or colleagues for the peer specialists' work concerning suicide risk assessment, safety, privacy, and sharing responsibility. Further discussion between mental health care clinicians and peer specialists regarding the role of the peer specialist in suicide prevention is needed to further clarify and optimize their role. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
dAIrector: Automatic Story Beat Generation through Knowledge Synthesis

dAIrector is an automated director which collaborates with humans storytellers for live improvisational performances and writing assistance. dAIrector can be used to create short narrative arcs through contextual plot generation. In this work, we present the system architecture, a quantitative evaluation of design choices, and a case-study usage of the system which provides qualitative feedback from a professional improvisational performer. We present relevant metrics for the understudied domain of human-machine creative generation, specifically long-form narrative creation. We include, alongside publication, open-source code so that others may test, evaluate, and run the dAIrector.
Influence of the Activation of NMDA Receptors on the Resting Membrane Potential of the Postsynaptic Cell at the Neuromuscular Junction

Impaired function or insufficient expression of glutamate N-methyl-D-aspartate (NMDA) receptors underlies a number of brain pathologies; these receptors are, therefore, regarded as a pharmacological target for many neuroactive drugs. It was shown that in the CNS, this type of glutamate receptors participate in the processes of neuronal excitation, synaptic plasticity [1, 2], and excitotoxicity in neurodegenerative diseases and are also involved in the pathogenesis of epilepsy and seizures. However, until recently, the presence and activity of NMDA receptors beyond the CNS had never been considered. This research shows that activation of NMDA receptors at the mammalian neuromuscular junction alters the resting membrane potential of the postsynaptic cell evoked by cation entry through the receptor-associated channel.
Energy-Efficient Medium Access Control Protocols for Cognitive Radio Sensor Networks: A Comparative Survey

The increase of application areas in wireless sensor networks demands novel solutions in terms of energy consumption and radio frequency management. Cognitive radio sensor networks (CRSNs) are key for ensuring efficient spectrum management, by making it possible to use the unused licensed frequency spectrum together with the unlicensed frequency spectrum. Sensor nodes powered by energy-constrained batteries necessarily require energy-efficient protocols at the routing and medium access control (MAC) layers. In CRSNs, energy efficiency is more important because the sensor nodes consume additional energy for spectrum sensing and management. To the best of authors’ knowledge, there is no survey on “energy-efficient” MAC protocols for CRSNs in the literature, even though a conceptual review on MAC protocols for CRSNs was presented at a conference recently. In this paper, energy-efficient MAC protocols for CRSNs are extensively surveyed and qualitatively compared. Open issues, and research challenges in the design of MAC protocols for CRSNs, are also discussed.
Analysis of the Influence of Training Data on Road User Detection

In this paper, we discuss the relevance of training data on modern object detectors used on onboard applications. Whereas modern deep learning techniques require large amounts of data, datasets with typical scenarios for autonomous vehicles are scarce and have a reduced number of samples. We conduct a comprehensive set of experiments to understand the effect of using a combination of two relatively small datasets to train an end-to-end object detector, based on the popular Faster R-CNN and enhanced with orientation estimation capabilities. We also test the adequacy of training models using partially available ground-truth labels, as a consequence of combining datasets aimed at different applications. Data augmentation is also introduced into the training pipeline. Results show a significant performance improvement in our exemplary case as a result of the higher variability of the training samples, thus opening a new way to improve the detection performance independently from the detector architecture.
On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results

We study in this paper the rate of convergence for learning distributions with the adversarial framework and Generative Adversarial Networks (GANs), which subsumes Wasserstein, Sobolev and MMD GANs as special cases. We study a wide range of parametric and nonparametric target distributions, under a collection of objective evaluation metrics. On the nonparametric end, we investigate the minimax optimal rates and fundamental difficulty of the density estimation under the adversarial framework. On the parametric end, we establish a theory for general neural network classes (including deep leaky ReLU as a special case), that characterizes the interplay on the choice of generator and discriminator. We investigate how to obtain a good statistical guarantee for GANs through the lens of regularization. We discover and isolate a new notion of regularization, called the \textit{generator/discriminator pair regularization}, that sheds light on the advantage of GANs compared to classical parametric and nonparametric approaches for density estimation. We develop novel oracle inequalities as the main tools for analyzing GANs, which is of independent theoretical interest.
Hardware-Efficient Structure of the Accelerating Module for Implementation of Convolutional Neural Network Basic Operation

This paper presents a structural design of the hardware-efficient module for implementation of convolution neural network (CNN) basic operation with reduced implementation complexity. For this purpose we utilize some modification of the Winograd minimal filtering method as well as computation vectorization principles. This module calculate inner products of two consecutive segments of the original data sequence, formed by a sliding window of length 3, with the elements of a filter impulse response. The fully parallel structure of the module for calculating these two inner products, based on the implementation of a naive method of calculation, requires 6 binary multipliers and 4 binary adders. The use of the Winograd minimal filtering method allows to construct a module structure that requires only 4 binary multipliers and 8 binary adders. Since a high-performance convolutional neural network can contain tens or even hundreds of such modules, such a reduction can have a significant effect.
Uncovering full-length transcript isoforms of sugarcane cultivar Khon Kaen 3 using single-molecule long-read sequencing

Background Sugarcane is an important global food crop and energy resource. To facilitate the sugarcane improvement program, genome and gene information are important for studying traits at the molecular level. Most currently available transcriptome data for sugarcane were generated using second-generation sequencing platforms, which provide short reads. The de novo assembled transcripts from these data are limited in length, and hence may be incomplete and inaccurate, especially for long RNAs. Methods We generated a transcriptome dataset of leaf tissue from a commercial Thai sugarcane cultivar Khon Kaen 3 (KK3) using PacBio RS II single-molecule long-read sequencing by the Iso-Seq method. Short-read RNA-Seq data were generated from the same RNA sample using the Ion Proton platform for reducing base calling errors. Results A total of 119,339 error-corrected transcripts were generated with the N50 length of 3,611 bp, which is on average longer than any previously reported sugarcane transcriptome dataset. 110,253 sequences (92.4%) contain an open reading frame (ORF) of at least 300 bp long with ORF N50 of 1,416 bp. The mean lengths of 5′ and 3′ untranslated regions in 73,795 sequences with complete ORFs are 1,249 and 1,187 bp, respectively. 4,774 transcripts are putatively novel full-length transcripts which do not match with a previous Iso-Seq study of sugarcane. We annotated the functions of 68,962 putative full-length transcripts with at least 90% coverage when compared with homologous protein coding sequences in other plants. Discussion The new catalog of transcripts will be useful for genome annotation, identification of splicing variants, SNP identification, and other research pertaining to the sugarcane improvement program. The putatively novel transcripts suggest unique features of KK3, although more data from different tissues and stages of development are needed to establish a reference transcriptome of this cultivar.
Emerging Role of Immune Checkpoint Blockade in Pancreatic Cancer

Immune checkpoint blockade (ICB) with programmed cell death protein-1(PD-1)/programmed death ligand -1(PD-L1) antibodies has revolutionized the management of several cancers, especially non-small cell lung cancer, melanoma, urothelial, and renal cancer. Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers associated with high morbidity and mortality. Based on available data, it’s obvious that ICB has limited success in PDACs, which can be explained by the low immunogenicity and immunosuppressive tumor microenvironment of these tumors. In this review article, we focus on PD-L1 expression and microsatellite instability (MSI) in PDAC, and their roles as prognostic and predictive markers. We also discuss data supporting combination therapies to augment cancer immunity cycle. Combining anti-PD-1/PD-L1 agents with other modalities such as vaccines, chemotherapy, and radiation could potentially overcome resistance patterns and increase immune responsiveness in PDAC.
Use of a Smartphone Application Can Improve Assessment of High-Fat Food Consumption in Overweight Individuals

Background: We evaluated the feasibility of an application for measuring the frequency of consumption of high-fat foods and compared this application with standard methods. Methods: Twenty-six females and thirty six males aged 20–40 were enrolled in Poland. Participants completed the Block Screening Questionnaire for Fat Intake (BSQF; Q1) and a second questionnaire (Q2) with additional high-fat foods. The participants were then monitored for ten days in a real-time manner using a smartphone application that employed the same lists of food as Q2. Results: Most subjects (84%) gave replies to at least three prompts on at least 5 days. The results from Q1 and the application were correlated (r = 0.42, p < 0.001). Energy intake and the frequency of consumption of high-fat foods were correlated in the overweight/obese group (r = 0.83, p < 0.001). The mean differences between Q2 and the app were similar in both groups but the agreement limits were wider in the overweight/obese group than in the normal weight group. Conclusions: An application for mobile devices is a feasible tool for capturing the frequency of high-fat food consumption and it seems to improve the measured variable, especially in overweight or obese people.