Miracle-dz/UniFAR-FTUs huggingface dataset:
Multifaceted regulatory function of tomato SlTAF1 in the response to salinity stress.

Salinity stress limits plant growth and has a major impact on agricultural productivity. Here, we identify NAC transcription factor SlTAF1 as a regulator of salt tolerance in cultivated tomato (Solanum lycopersicum). While overexpressing SlTAF1 improves salinity tolerance compared to wild type, lowering SlTAF1 expression causes stronger salinity-induced damage. Under salt stress, shoots of SlTAF1 knockdown plants accumulate more toxic Na+ ions, while SlTAF1 overexpressors accumulate less, in accordance with an altered expression of the Na+ transporter genes SlHKT1;1 and SlHKT1;2. Furthermore, stomatal conductance and pore area are increased in SlTAF1 knockdown plants during salinity stress, but decreased in SlTAF1 overexpressors. We identified stress-related transcription factor, ABA metabolism, and defense-related genes as potential direct targets of SlTAF1, correlating it with reactive oxygen species (ROS) scavenging capacity and changes in hormonal response. Salinity-induced changes in tricarboxylic acid cycle intermediates and amino acids are more pronounced in SlTAF1 knockdown than wild-type plants, but less so in SlTAF1 overexpressors. The osmoprotectant proline accumulates more in SlTAF1 overexpressors than knockdown plants. In summary, SlTAF1 controls tomato's response to salinity stress by combating both, osmotic and ion toxicity, highlighting it as a promising candidate for the future breeding of stress-tolerant crops.
A Software Framework for Image Retrieval and Visual Understanding in Dynamic and Sensor Rich Environments

Performing search and retrieval operations with massive amounts of visual and environmental sensor information is problematic in time sensitive and mission critical situations, such as emergency management and disaster response. Distinct sensor readings can be fused to create a compact multimodal representation of a location. Efficient search and retrieval would allow a system to scale to process larger amounts of information. Content Based Image Retrieval (CBIR) systems inherently rely on the search and retrieve operations to support timely and accurate responses. However, there is currently no adequate software framework for multimodal CBIR to support situational awareness in dynamic and sensor rich environments. In this thesis, an extensible framework for CBIR is proposed to support an understanding of a sensor rich environment through the automated search and retrieval of relevant images and the context of their capture. This constitutes assisted CBIR as embodied in the proposed framework for multi-sensor assisted CBIR system (MSACS). The framework of MSACS is proposed with a software design document and partially prototyped to implement the core CBIR system using the state of the art Bag of Visual Words paradigm. The system is evaluated using functional analysis against existing CBIR systems, prototype system, and a notional localization application. The extensibility of the framework allows a standalone application to interface with it. A localization application is created as a proofof-concept to provide a result in latitude and longitude in relation to known sensors. Assisted CBIR could lead to increased confidence in autonomous systems and support vision-based understanding of an environment to execute tractable search and retrieve operations in a timely manner.
Short‐Term Changes in Cardiorespiratory Fitness in Response to Exercise Training and the Association with Long‐Term Cardiorespiratory Fitness Decline: The STRRIDE Reunion Study

Background Substantial heterogeneity exists in the cardiorespiratory fitness (CRF) change in response to exercise training, and its long‐term prognostic implication is not well understood. We evaluated the association between the short‐term supervised training‐related changes in CRF and CRF levels 10 years later. Methods and Results STRRIDE (Studies of a Targeted Risk Reduction Intervention Through Defined Exercise) trial participants who were originally randomized to exercise training for 8 months and participated in the 10‐year follow‐up visit were included. CRF levels were measured at baseline, after training (8 months), and at 10‐year follow‐up as peak oxygen uptake (vo 2, mL/kg per min) using the maximal treadmill test. Participants were stratified into low, moderate, and high CRF response groups according to the training regimen–specific tertiles of CRF change. The study included 80 participants (age: 52 years; 35% female). At 10‐year follow‐up, the high‐response CRF group had the least decline in CRF compared with the moderate‐ and low‐response CRF groups (−0.35 versus −2.20 and −4.25 mL/kg per minute, respectively; P=0.02). This result was largely related to the differential age‐related changes in peak oxygen pulse across the 3 groups (0.58, −0.23, and −0.86 mL/beat, respectively; P=0.03) with no difference in the peak heart rate change. In adjusted linear regression analysis, high response was significantly associated with greater CRF at follow‐up independent of other baseline characteristics (high versus low [reference] CRF response: standard β=0.25; P=0.004). Conclusions Greater CRF improvement in response to short‐term training is associated with higher CRF levels 10 years later. Lack of CRF improvements in response to short‐term training may identify individuals at risk for exaggerated CRF decline with aging.
Comparison of the chemical constitution and bioactivity among different parts of Lonicera japonica Thunb.

BACKGROUND Lonicera japonica Thunb is a common herb in East Asia. The flower buds are usually regarded as the traditional medicinal part, while leaves and stems are considered less valuable and receive little attention. This study compared the chemical constituents and anti-inflammatory effects of the different tissues of Lonicera japonica Thunb for the first time. RESULTS Thirty compounds were identified by UPLC-PDA-Q/TOF-MS/MS analysis. Hydroxycinnamic acids, flavonoids and iridoids were identified as the major components. The flower buds (FLJ), leaves (LLJ) and stems (SLJ) of Lonicera japonica Thunb showed great similarities in chemical components. Therein, LLJ contained higher level of hydroxycinnamic acids and flavonoids than FLJ and SLJ. Furthermore, FLJ, LLJ and SLJ exhibited potent anti-inflammatory activities in croton oil induced ear edema and carrageenan-induced paw edema essay in mice. Moreover, FLJ, LLJ and SLJ showed cytoprotective effect on LPS stimulated RAW 264.7 macrophages. LPS-induced increases of nitric oxide (NO), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6) were suppressed by treatments of FLJ, LLJ and SLJ, respectively. LLJ possessed stronger anti-inflammatory effect than FLJ. CONCLUSION Leaves and stems of Lonicera japonica Thunb have chemical components and anti-inflammatory properties similar to flower buds, and may become alternative or supplementary sources of flower buds. This article is protected by copyright. All rights reserved.
Transient and sustained effects of dopamine and serotonin signaling in motivation‐related behavior

Pharmacological studies of antidepressants and atypical antipsychotics have suggested a role of dopamine and serotonin signaling in depression. However, depressive symptoms and treatment effects are difficult to explain based simply on brain‐wide decrease or increase in the concentrations of these molecules. Recent animal studies using advanced neuronal manipulation and observation techniques have revealed detailed dopamine and serotonin dynamics that regulate diverse aspects of motivation‐related behavior. Dopamine and serotonin transiently modulate moment‐to‐moment behavior at timescales ranging from sub‐second to minutes and also produce persistent effects, such as reward‐related learning and stress responses that last longer than several days. Transient and sustained effects often exhibit specific roles depending on the projection sites, where distinct synaptic and cellular mechanisms are required to process the neurotransmitters for each transient and sustained timescale. Therefore, it appears that specific aspects of motivation‐related behavior are regulated by distinct synaptic and cellular mechanisms in specific brain regions that underlie the transient and sustained effects of dopamine and serotonin signaling. Recent clinical studies have implied that subjects with depressive symptoms show impaired transient and sustained signaling functions; moreover, they exhibit heterogeneity in depressive symptoms and neuronal dysfunction. Depressive symptoms may be explained by the dysfunction of each transient and sustained signaling mechanism, and distinct patterns of impairment in the relevant mechanisms may explain the heterogeneity of symptoms. Thus, detailed understanding of dopamine and serotonin signaling may provide new insight into depressive symptoms.
Validation of the StepWatch activity monitor in individuals with vestibular disorders

ABSTRACT Purpose: The purpose of this study is to investigate the accuracy of the StepWatch Activity Monitor (SAM in measuring step count for individuals with vestibular disorders. Materials and Methods: Subjects were individuals with a vestibular disorder. The StepWatch Activity Monitor (SAM) was programed, placed on the patient’s right ankle, and calibrated. Subjects completed a 2 Minute Walk Test (2 MWT) wearing the SAM. All steps were counted live and by video using a tally counter (TC). Results: Twenty-Six (N = 26) subjects, 9 males and 17 females, with an average age of 61.5 ± 12.4 years (36–80) completed the study. The average number of steps counted by the TC during the 2 MWT was 219.1 ± 25.9 (168–251) compared to 217.4 ± 27.3 (153–252) counted by SAM, an average difference of 1.6 ± 4.6 steps. Regression analysis demonstrated that the mean difference in step count was not significantly different from 0 (t = −1.588, sig. = 0.125). A Bland-Altman plot was created using the 95% upper and lower confidence interval (−0.29–3.44). Conclusion: The results indicate that the SAM accurately measures step count in individuals with vestibular disorders. Researchers and vestibular therapists can use the SAM to collect step count data in individuals with vestibular disorders.
Robust High-Order Manifold Constrained Sparse Principal Component Analysis for Image Representation

In order to efficiently utilize the information in the data and eliminate the negative effects of outliers in the principal component analysis (PCA) method, in this paper, we propose a novel robust sparse PCA method based on maximum correntropy criterion (MCC) with high-order manifold constraints called the RHSPCA. Compared with the traditional PCA methods, the proposed RHSPCA has the following benefits: 1) the MCC regression term is more robust to outliers than the MSE-based regression term; 2) thanks to the high-order manifold constraints, the low-dimensional representations can preserve the local relations of the data and greatly improve the clustering and classification performance for image processing tasks; and 3) in order to further counteract the adverse effects of outliers, the MCC-based samples’ mean is proposed to better centralize the data. We also propose a new solver based on the half-quadratic technique and accelerated block coordinate update strategy to solve the RHSPCA model. Extensive experimental results show that the proposed method can outperform the state-of-the-art robust PCA methods on a variety of image processing tasks, including reconstruction, clustering, and classification, on outliers contaminated datasets.
Users’ Satisfaction with ICT-Based Resources and Services in University Libraries: A study of Igbinedion University Library, Okada, Edo State, Nigeria

Information and Communication Technology (ICT) based resources and services are fast replacing the traditional means of rendering library services in university libraries. No library can effectively satisfy the information needs of its numerous users without the application of ICT in its day to day services. It is on this ground that this study investigated the level of users’ satisfaction with ICT-based resources and services in Igbinedion University Library, Okada, Edo State, Nigeria. The research design adopted for the study was descriptive survey research design, while structured questionnaire designed after extensive review of related literature was the instrument used for data collection. The population which was 2,885 comprised all the undergraduate and postgraduate students that registered with the University Library for 2016/17 academic session. A sample size of 250 library users (200 undergraduate students and 50 postgraduate students) determined using Taro Yamane sampling formula was used for the study. Percentages and statistical mean were used to analyze data collected. The findings revealed that students in Igbinedion University were satisfied with the use of ICT-based resources and services, especially the use of online database resources, in meeting their information needs in the university library. It was therefore recommended that more effort should be made by the University Library Management to improve on the existing ICT-based resources and services available for use in the library.
Electrolyte Effects on the Electrochemical Reduction of CO2

Abstract The electrochemical reduction of CO2 to fuels or commodity chemicals is a reaction of high interest for closing the anthropogenic carbon cycle. The role of the electrolyte is of particular interest, as the interplay between the electrocatalytic surface and the electrolyte plays an important role in determining the outcome of the CO2 reduction reaction. Therefore, insights on electrolyte effects on the electrochemical reduction of CO2 are pivotal in designing electrochemical devices that are able to efficiently and selectively convert CO2 into valuable products. Here, we provide an overview of recently obtained insights on electrolyte effects and we discuss how these insights can be used as design parameters for the construction of new electrocatalytic systems.
Widening Siamese Neural Networks for Stereo Matching in Colonoscopy

Colorectal cancer is the third most common cancer worldwide [1] and, although the fourth deadliest form of cancer, early diagnosis can increase the survival rate significantly. As the quality of colonoscopy diagnosis highly depends on the proficiency of the operator, computer-aided systems have been researched aiming to improve the detection rate of polyps during the procedure. While segmentation algorithms have improved the accuracy of polyp detection on observed surfaces, 3D reconstruction of the colon has the potential to unveil unscanned areas and to provide instructions to the operator guiding the colonoscopy in a manner that guarantees the observation of the entire inner surface of the colon. 3D reconstruction from stereo images has been actively researched for many decades. Recent work has focused on improving stereo matching cost functions which describe the correspondence between two pixels algebraically. However, the performance of stereo algorithms depends on the choice of the cost-function, which might not be ideal for different environment conditions. Despite the progress in the field, accurately finding stereo correspondences in colonoscopy/surgery images is still very challenging because of occlusions, reflective surfaces, repetitive patterns, and textureless or low detail regions . Recent research has aimed to discard hand-crafted costfunctions and hand-selected parameters entirely. First approaches proposed to train Convolutional Neural Nets (CNNs) to compare two input images and output a similarity measure [2] or to directly output a depth map [3]. We apply similar Deep Learning methods but with small considerations for colonoscopy. We aim to improve the 3D reconstruction of the colon so we can provide better diagnostic information in clinical settings.
Machine learning approach for homolog chromosome classification

Automated analysis of human chromosomes is a necessary procedure to attain karyotyping and it is highly effective in cytology analysis to detect birth defects in metaspread chromosomes. In this, chromosomes are partitioned into “abnormal” and “normal” categories. However, the success of most traditional classification methods relies on the presence of accurate chromosome segmentation. Despite many years of research in this field, accurate segmentation and classification remains a challenge in the presence of cell clusters and pathologies. Many classification methods focused on hand crafted features, such as length, centromere positions. In this manuscript, proposed method focused on chromosome classification based on deep features using convolutional neural network. It is subsequently trained on various chromosome datasets consisting of adaptively resampled image patches. In the testing phase, average the prediction scores of a similar set of image patches is performed. The proposed method is evaluated on different overlapped, nonoverlapped chromosomes and normal, abnormal datasets. Proposed method better performs than previous algorithms in classification accuracy with 98.7%, area under the curve AUC is 0.97 values, and abnormality detection accuracy is 98.4%.
International trends in hepatocellular carcinoma incidence, 1978–2012

Primary liver cancer, the major histology of which is hepatocellular carcinoma (HCC), is the second leading cause of cancer death worldwide. We comprehensively examined recent international trends of primary liver cancer and HCC incidence using population‐based cancer registry data. Incidence for all primary liver cancer and for HCC by calendar time and birth cohort was examined for selected countries between 1978 and 2012. For each successive 5‐year period, age‐standardized incidence rates were calculated from Volumes V to XI of the Cancer Incidence in Five Continents (CI5) series using the online electronic databases, CI5plus. Large variations persist in liver cancer incidence globally. Rates of liver cancer remain highest in Asian countries, specifically in the East and South‐East, and Italy. However, rates in these high‐risk countries have been decreasing in recent years. Rates in India and in most countries of Europe, the Americas and Oceania are rising. As the population seroprevalence of hepatitis B virus (HBV) continues to decline, we anticipate rates of HCC in many high‐risk countries will continue to decrease. Treatment of hepatitis C virus (HCV) is likely to bring down rates further in some high‐rate, as well as low‐rate, countries with access to effective therapies. However, such gains in the control of liver cancer are at risk of being reversed by the growing obesity and diabetes epidemics, suggesting diabetes treatment and primary prevention of obesity will be key in reducing liver cancer in the longer‐term.
TCRM_A_215373 1141..1152

1Department of Surgery, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim 68167, Germany; 2Department of General, Visceral, Vascular, and Thoracic Surgery, Klinikum Frankfurt Höchst, Frankfurt 65929, Germany; 3Department of General and Visceral Surgery, GRN-Klinik Weinheim, Weinheim 69469, Germany; 4Department of Surgery, Alfried Krupp Hospital, Essen 45131, Germany; 5Department of Visceral, Vascular and Endocrine Surgery, University Hospital Halle, Halle, Germany Purpose: Pancreatic surgery demands complex multidisciplinary management, which is
Predicting blood glucose for type 2 diabetes patients

It is important for diabetics to keep their blood glucose in a healthy range. The Delicate project aims to use data collected on blood glucose, food intake, physical activity and health records to provide type 2 diabetics with personalized coaching and with blood glucose predictions. In this research, we predict future blood glucose values based on a patient's characteristics and behavior. We also determine how such as prediction model can be deployed and how the different input features influence the prediction. We were able to significantly (p = 60 minutes) using a multitask long short-term memory network (LSTM). The multitask LSTM predicts blood glucose for multiple timesteps into the future at the same time. This not only improves performance but also makes it more convenient to apply in a real-world application. The trained multitask LSTM uses input features such as food intake in a consistent manner, this makes it useful in showing patients how their actions affect their predicted blood glucose. We recommend visualizing the expected error of the predicted blood glucose in such a way that patients are aware of the limitations of the model, while still benefiting from the insight it provides.
Isolation, Identification and Synthesis of a New Prenylated Cinnamic Acid Derivative from Brazilian Green Propolis and Simultaneous Quantification of Bioactive Components by LC-MS/MS.

A new cinnamic acid derivative, (E)-3-[4-hydroxy-3-((E)-3-formyl-2-butenyl)phenyl]-2- propenoic acid (20) has been isolated from the ethanol extract of Brazilian green propolis along with three known cinnamic acid derivatives, 3,4-dihydroxy-5-prenyl-(E)-cinnamic acid (4), capillartemisin A (6) and 2,2-dimethylchromene-6-(E)-propenoic acid (8), and a flavonoid, dihydrokaempferide (16) by liquid-liquid participation, a series of column chromatography and preparative HPLC. Their structures have been determined by spectroscopic analyses and chemical synthesis of compound 20. The simultaneous quantification of 20 constituents, including 10 cinnamic acid derivatives, 7 flavonoids and 3 caffeoylquinic acid derivatives, has also been developed and validated using LC-MS/MS. The new compound 20 was shown to activate PPAR α but not PPAR β or γ.
Genomic Insights into the Fungal Lignocellulolytic Machinery of Flammulina rossica

Next-generation sequencing (NGS) of the Flammulina rossica (wood-rotting basidiomycete) genome was performed to identify its carbohydrate-active enzymes (CAZymes). De novo genome assembly (31 kmer) revealed a total length of 35,646,506 bp (49.79% GC content). In total, 12,588 gene models of F. rossica were predicted using an ab initio gene prediction tool (AUGUSTUS). Orthologous analysis with other fungal species revealed that 7433 groups contained at least one F. rossica gene. Additionally, 12,033 (95.6%) of 12,588 genes for F. rossica proteins had orthologs among the Dikarya, and F. rossica contained 12 species-specific genes. CAZyme annotation in the F. rossica genome revealed 511 genes predicted to encode CAZymes including 102 auxiliary activities, 236 glycoside hydrolases, 94 glycosyltransferases, 19 polysaccharide lyases, 56 carbohydrate esterases, and 21 carbohydrate binding-modules. Among the 511 genes, several genes were predicted to simultaneously encode two different CAZymes such as glycoside hydrolases (GH) as well as carbohydrate-binding module (CBM). The genome information of F. rossica offers opportunities to understand the wood-degrading machinery of this fungus and will be useful for biotechnological and industrial applications.