Miracle-dz/UniFAR-FTUs huggingface dataset:
Organic Waste-Based Fertilizer in Hydroponics Increases Tomato Fruit Size but Reduces Fruit Quality

In regions with intensive agricultural production, large amounts of organic waste are produced by livestock animals. Liquid digestate from manure-based biogas production could potentially serve as fertilizer if integrated with closed horticultural irrigation systems. The aim of this experiment was to investigate how fertilizer based on liquid biogas by-products of pig manure digestion can affect the growth and production of tomato plants. Integration of a nitrification bioreactor presumes a significantly lower concentration of nutrient solutions and a higher level of oxygenation than classical mineral cultivation. Therefore, additional controls were included. We compared plant growth and fruit quality traits of tomato plants grown in a hydroponic solution with organic fertilizer with two levels of mineral fertilizer. The tomatoes grown with organic waste-based liquid fertilizer showed reduced growth rates but increased mean fruit size, resulting in no significant change in total yield compared with high-mineral cultivation. The growth rate was similarly reduced in plants cultivated with low-mineral fertilizer. Plants cultivated with organic waste-based fertilizer had high Cl− concentration in xylem sap, leaves, and, ultimately, fruits. The leaves of plants cultivated with organic waste-based fertilizer contained higher concentrations of starch and soluble carbohydrate and low concentrations of phosphorous (P) and sulfur (S). The plants grown with organic waste-based or low-mineral medium showed significantly poorer fruit quality than the plants cultivated with the high-mineral solution. The low-mineral treatment increased xylem sap contribution to fruit weight because of higher root power. The organic waste-based fertilization did not change the root power but increased fruit size. In conclusion, organic waste-based cultivation is a possible solution for sustainable plant production in greenhouses. However, additional adjustment of nutrient supply is required to improve fruit quality.
Cardiomyocyte Proliferative Capacity Is Restricted in Mice With Lmna Mutation

LMNA is one of the leading causative genes of genetically inherited dilated cardiomyopathy (DCM). Unlike most DCM-causative genes, which encode sarcomeric or sarcomere-related proteins, LMNA encodes nuclear envelope proteins, lamin A and C, and does not directly associate with contractile function. However, a mutation in this gene could lead to the development of DCM. The molecular mechanism of how LMNA mutation contributes to DCM development remains largely unclear and yet to be elucidated. The objective of this study was to clarify the mechanism of developing DCM caused by LMNA mutation. Methods and Results: We assessed cardiomyocyte phenotypes and characteristics focusing on cell cycle activity in mice with Lmna mutation. Both cell number and cell size were reduced, cardiomyocytes were immature, and cell cycle activity was retarded in Lmna mutant mice at both 5 weeks and 2 years of age. RNA-sequencing and pathway analysis revealed “proliferation of cells” had the most substantial impact on Lmna mutant mice. Cdkn1a, which encodes the cell cycle regulating protein p21, was strongly upregulated in Lmna mutants, and upregulation of p21 was confirmed by Western blot and immunostaining. DNA damage, which is known to upregulate Cdkn1a, was more abundantly detected in Lmna mutant mice. To assess the proliferative capacity of cardiomyocytes, the apex of the neonate mouse heart was resected, and recovery from the insult was observed. A restricted cardiomyocyte proliferating capacity after resecting the apex of the heart was observed in Lmna mutant mice. Conclusions: Our results strongly suggest that loss of lamin function contributes to impaired cell proliferation through cell cycle defects. The inadequate inborn or responsive cell proliferation capacity plays an essential role in developing DCM with LMNA mutation.
Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIRECT)

Background Sepsis contributes significantly to morbidity and mortality globally. In Australia, 20,000 develop sepsis every year, resulting in 5,000 deaths, and more than AUD$846 million in expenditure. Prompt, appropriate antibiotic therapy is effective in improving outcomes in sepsis. Conventional culture-based methods to identify appropriate therapy have limited yield and take days to complete. Recently, nanopore technology has enabled rapid sequencing with real-time analysis of pathogen DNA. We set out to demonstrate the feasibility and diagnostic accuracy of pathogen sequencing direct from clinical samples, and estimate the impact of this approach on time to effective therapy when integrated with personalised software-guided antimicrobial dosing in children and adults on ICU with sepsis. Methods The DIRECT study is a pilot prospective, non-randomized multicentre trial of an integrated diagnostic and therapeutic algorithm combining rapid direct pathogen sequencing and software-guided, personalised antibiotic dosing in children and adults with sepsis on ICU. Participants and interventions DIRECT will collect microbiological and pharmacokinetic samples from approximately 200 children and adults with sepsis admitted to one of four ICUs in Brisbane. In Phase 1, we will evaluate Oxford Nanopore Technologies MinION sequencing direct from blood in 50 blood culture-proven sepsis patients recruited from consecutive patients with suspected sepsis. In Phase 2, a further 50 consecutive patients with suspected sepsis will be recruited in whom MinION sequencing will be combined with Bayesian software-guided (ID-ODS) personalised antimicrobial dosing. Outcome measures The primary outcome is time to effective antimicrobial therapy, defined as trough drug concentrations above the MIC of the pathogen. Secondary outcomes are diagnostic accuracy of MinION sequencing from whole blood, time to pathogen identification and susceptibility testing using sequencing direct from whole blood and from positive blood culture broth. Discussion Rapid pathogen sequencing coupled with antimicrobial dosing software has great potential to overcome the limitations of conventional diagnostics which often result in prolonged inappropriate antimicrobial therapy. Reduced time to optimal antimicrobial therapy may reduce sepsis mortality and ICU length of stay. This pilot study will yield key feasibility data to inform further, urgently needed sepsis studies. Phase 2 of the trial protocol is registered with the ANZCTR (ACTRN12620001122943). Trial registration Registered with the Australia New Zealand Clinical Trials Registry Number ACTRN12620001122943
Organ-Specific Transcriptome Analysis Identifies Candidate Genes Involved in the Stem Specialization of Bermudagrass (Cynodon dactylon L.)

As an important warm-season turfgrass and forage grass species with wide applications, bermudagrass (Cynodon dactylon L.) simultaneously has shoot, stolon and rhizome, three types of stems with different physiological functions. To better understand how the three types of stems differentiate and specialize, we generated an organ-specific transcriptome dataset of bermudagrass encompassing 114,169 unigenes, among which 100,878 and 65,901 could be assigned to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Gene Ontology (GO) terms, respectively. Using the dataset, we comprehensively analyzed the gene expression of different organs, especially the shoot, stolon and rhizome. The results indicated that six organs of bermudagrass all contained more than 52,000 significantly expressed unigenes, however, only 3,028 unigenes were enrich-expressed in different organs. Paired comparison analyses further indicated that 11,762 unigenes were differentially expressed in the three types of stems. Gene enrichment analysis revealed that 39 KEGG pathways were enriched with the differentially expressed unigenes (DEGs). Specifically, 401 DEGs were involved in plant hormone signal transduction, whereas 1,978 DEGs were transcription factors involved in gene expression regulation. Furthermore, in agreement with the starch content and starch synthase assay results, DEGs encoding starch synthesis-related enzymes all showed the highest expression level in the rhizome. These results not only provided new insights into the specialization of stems in bermudagrass but also made solid foundation for future gene functional studies in this important grass species and other stoloniferous/rhizomatous plants.
Soluble Neuropilin-1 Response to Hypoglycemia in Type 2 Diabetes: Increased Risk or Protection in SARS-CoV-2 Infection?

Introduction Neuropilin-1(NRP1) is a cofactor that enhances SARS-CoV-2 coronavirus cell infectivity when co-expressed with angiotensin-converting enzyme 2(ACE2). The Renin-Angiotensin System (RAS) is activated in type 2 diabetes (T2D); therefore, the aim of this study was to determine if hypoglycaemia-induced stress in T2D would potentiate serum NRP1(sNRP1) levels, reflecting an increased risk for SARS-CoV-2 infection. Methods A case-control study of aged-matched T2D (n = 23) and control (n = 23) subjects who underwent a hyperinsulinemic clamp over 1-hour to hypoglycemia(<40mg/dl) with subsequent timecourse of 4-hours and 24-hours. Slow Off-rate Modified Aptamer (SOMA)-scan plasma protein measurement determined RAS-related proteins: renin (REN), angiotensinogen (AGT), ACE2, soluble NRP1(sNRP1), NRP1 ligands (Vascular endothelial growth factor, VEGF and Class 3 Semaphorins, SEM3A) and NRP1 proteolytic enzyme (A Disintegrin and Metalloproteinase 9, ADAM9). Results Baseline RAS overactivity was present with REN elevated and AGT decreased in T2D (p<0.05); ACE2 was unchanged. Baseline sNRP1, VEGF and ADAM9 did not differ between T2D and controls and remained unchanged in response to hypoglycaemia. However, 4-hours post-hypoglycemia, sNRP1, VEGF and ADAM9 were elevated in T2D(p<0.05). SEMA3A was not different at baseline; at hypoglycemia, SEMA3A decreased in controls only. Post-hypoglycemia, SEMA3A levels were higher in T2D versus controls. sNRP1 did not correlate with ACE2, REN or AGT. T2D subjects stratified according to ACE inhibitor (ACEi) therapies showed no difference in sNRP1 levels at either glucose normalization or hypoglycaemia. Conclusion Hypoglycemia potentiated both plasma sNRP1 level elevation and its ligands VEGF and SEMA3A, likely through an ADAM9-mediated mechanism that was not associated with RAS overactivity or ACEi therapy; however, whether this is protective or promotes increased risk for SARS-CoV-2 infection in T2D is unclear. Clinical Trial Registration https://clinicaltrials.gov, identifier NCT03102801.
Can Leaders Prevent Technology From Backfiring? Empowering Leadership as a Double-Edged Sword for Technostress in Care

Purpose: Recent studies have called for more contextual studies of technostress and the role leaders can have in this experience. While technostress is an increasingly prevalent and severe phenomenon in care professions, limited studies have addressed its potential negative consequences for employee well-being and quality of care delivered in this sector or, more importantly, examined how the adverse consequences of technostress could be mitigated. Therefore, the present study addresses this gap by investigating how technostress in childcare affects quality of care delivered via emotional exhaustion and what influence empowering leadership plays in this relationship. Design/methodology approach: Incorporating the views of 339 Dutch childcare workers, this study tests a model in which technostress influences quality of care delivered, mediated by emotional exhaustion and moderated by empowering leadership. Findings: Results confirm that techno-invasion and techno-overload predict higher emotional exhaustion and lower quality of care delivered among childcare workers. Empowering leadership reduced the influence of techno-invasion on emotional exhaustion but strengthened the influence of techno-overload. Originality/value: Our results provide childcare organizations with relevant information on the increasing use of ICT that influences both childcare workers' well-being and quality of care they deliver. Important implications are suggested for leadership geared at stimulating employees' responsibility and accountability for different dimensions of technostress.
Evaluation of Peroxidase in Herbal Medicines Based on an Electrochemical Sensor

Peroxidases are species-specific. Differences in peroxidase can objectively reflect the genetics among species. The use of peroxidase to assist in species identification is relatively simple and effective. In this work, we proposed a graphene-modified electrode. This electrode can amplify the signal of electrocatalytic reduction of hydrogen peroxide. Since peroxidase can catalyze the reduction of hydrogen peroxide, this signal can be used as an indicator to demonstrate the content of peroxidase in different plant tissues. Twelve herbal medicines were selected for our study. The results show that this electrochemical-based detection technique was comparable to colorimetric method in terms of accuracy.
Make your own: The Potential of Chatbot Customization for the Development of User Trust

Interacting with chatbots has become ubiquitous nowadays. Nevertheless, conversational agents often remain unable to reliably succeed in social contexts, which negatively influences users’ experience and prevents them from exploiting the technology's full potential. To improve user experience and subsequent trust-formation in chatbots only very little attention has been paid to the active involvement of the user and with that to customization options. Employing a preregistered experimental 1x2 between-subjects study design (N = 171) this study explores an alternative approach to the typical one-chatbot-fits-all solution and investigates the potential of active user-based chatbot customization for the development of trust in chatbots. While customization had no direct effect on trust, anthropomorphism was identified as a significant mediator. The chatbot's interpersonal communicational competence was not affected by customization, yet it did predict trust. Exploratory analyses of participants’ feedback point towards the importance of individual differences between users and generally show a positive impact of customization on the overall chatbot experience.
Development and Control of a Ball Riding Robot with a Pitch and Roll Controlled Inverted Pendulum

Ball riding robot is a robot with wheels which tries to balance on a ball by controlling the ball movement. Inverted pendulum is a device which uses an actuating cart to maintain the upright position. In this paper a ball riding robot is equipped with a pitch and roll planar inverted pendulum. Thus, the controller has to balance both ballbot body and the inverted pendulum by relying on the actuated movement of the ball driven by the Ballbot wheels. This paper explains the design and control of this novel dynamics system.
Action recognition for sports video analysis using part-attention spatio-temporal graph convolutional network

Abstract. Action recognition makes significant contributions to sports video analysis, especially for athletes’ training evaluations. For sports video analysis, the action information is mainly conveyed by human body parts’ temporal movement, and each of the parts has a unique importance to the action representation. Aiming to involve this point in action recognition, we propose a part-attention spatio-temporal graph convolutional network (PSGCN) to exploit the dynamic spatio-temporal information in a sports video; it learns the importance of different parts to emphasize the contribution on the task of action recognition. Specifically, PSGCN first divides the human body into six parts and extracts their convolutional neural network (CNN) features, as well as concatenating the global feature of the whole frame; it then utilizes a cross-part and cross-frame graph building module to formulate the graph correlation of the parts from different frames. Inspired by the larger temporal variation of the same part containing more action information, we further propose a part-attention (PA) learning module to estimate the importance of each part, which can strengthen the graph correlation to support a PA graph. Finally, PSGCN conducts a graph convolutional network on the learned PA spatio-temporal graph with the learned part CNN features, which can obtain the action representation for the given sports video. In addition, the whole network is optimized by two losses of PA and action classification. To perform the superiority of PSGCN, we carry out extensive experiments of our model compared with several state-of-the-art methods on widely used action recognition datasets, especially for sports action. The results reflect the advantages of the proposed PSGCN on sports video analysis.
Fast and robust interactive image segmentation in bilateral space with reliable color modeling and higher order potential

Abstract. We propose an optimization framework for interactive image segmentation (IIS) that operates in bilateral space to achieve robust object extraction and instant visual feedback. More specifically, we first resample an input image using a regular bilateral grid with a resolution that is typically coarser than the input image to reduce the complexity of subsequent IIS tasks. We then design a Markov random field energy on the vertices of the bilateral grid that can be solved efficiently using a standard graph cut label assignment. To achieve this, we introduce reliable color models to distinguish the foreground and background despite the presence of extremely difficult cases and a higher-order potential to encourage spatial consistency in segmentation. We conduct comprehensive experiments on three standard interactive segmentation datasets, MSRA 10K, IIS, and PASCAL VOC 2012 segmentation validation set. The results show that the proposed method achieves competitive performance compared with state-of-the-art methods while making the current system efficient in terms of speed.
Tensor dispersion-based multi-view feature embedding for dimension reduction

Abstract. With the development of feature extraction technique, one image object can be represented by multiple heterogeneous features from different views that locate in high-dimensional space. Multiple features can reflect various characteristics of the same object; they contain compatible and complementary information among each other, integrating them together used in the special image processing application that can obtain better performance. However, facing these multi-view features, most dimensionality reduction methods fail to completely achieve the desired effect. Therefore, how to construct an uniform low-dimensional embedding subspace, which exploits useful information from multi-view features is still an important and urgent issue to be solved. So, we propose an innovative fusion dimension reduction method named tensor dispersion-based multi-view feature embedding (TDMvFE). TDMvFE reconstructs a feature subspace of each object by utilizing its k nearest neighbors, which preserves the underlying neighborhood structure of the original manifold in the lower dimensional mapping space. The new method fully exploits the channel correlations and spatial complementarities from the multi-view features by tensor dispersion analysis model. Furthermore, the method constructs an optimization model and derives an iterative procedure to generate the unified low dimensional embedding. Various evaluations based on the applications of image classification and retrieval demonstrate the effectiveness of our proposed method on multi-view feature fusion dimension reduction.
Space-time super-resolution with motion-perceptive deformable alignment

Abstract. We proposed a motion-perceptive deformable alignment network that introduces a pre-computed optical flow to improve the motion perception of the deformable alignment process. The pre-computed flows share the burden of the learned offsets for motion estimation while the flexibility of the deformable convolutional network is maintained. In addition, a motion-adaptive pyramid structure in which the features are aligned in multi-scale levels and then merged based on the motion strength among input frames is proposed. With the above structures, an innovative space-time super-resolution (STSR) network is constructed with an improved motion compensation ability. STSR aims to restore a high-resolution and high-frame-rate sequence from its corresponding low-resolution and low-frame-rate version. The proposed STSR network is trained with a Vimeo-90K dataset, and tests are conducted on the Vimeo-90K, densely-annotated video segmentation (DAVIS), and realistic and diverse scenes (REDS) datasets. Performance is evaluated using the peak signal-to-noise ratio and structural similarity index of the entire restored frame on the Y channel. Extensive experiments demonstrate the superiority of the proposed network among both the one- and two-stage STSR methods, its improved alignment ability, and its significantly improved interpolated frame synthesis.
Super-resolution of compressed images using enhanced attention network

Abstract. In recent years, to save bandwidth and storage space, images are usually compressed to reduce data volume, which leads to the loss of image details and affects the super-resolution (SR) performance. SR of compressed images is a key technique for addressing this problem. We propose the compressed-image super-resolution using enhanced attention network (CISREAN). First, the task is divided into two subtasks: image decompression and SR. Each subprocess introduces an enhanced residual module (ERM) with an attention mechanism. The ERM consists of several wide-activation residual blocks (WARBs) and an attention unit called the cascading residual attention (CRA) block. A WARB achieves better reconstruction than traditional image processing with the same computational complexity, and the CRA extracts more useful information in feature mapping even with fewer channels. This makes the ERM light and effective. Next, initial features are selected for wider inception and less blocking by overlapping extractions from the compressed image during decompression by convolutional layers. After completing both subprocesses, an end-to-end network is trained; it reduces compression artifacts, performing SR simultaneously. Extensive experiments on JPEG images with different quality factors show that CISREAN provides state-of-the-art performance based on objective metrics and subjective visual quality.
Towards a Novel Benchmark for Automatic Generation of ClaimReview Markup

The spreading of disinformation throughout the web has become a critical problem for a democratic society. The dissemination of fake news has become a profitable business and a common practice among politicians and content producers. On the other hand, journalists and fact-checkers work unceasingly to debunk misinformation and prevent it from further spreading. In 2015, a new web markup called ClaimReview has been introduced to grant access to the fact-checking article’s meaning by search engines. It is an important initiative to fight fake news by promoting and highlighting fact-check articles among users. However, barely half of fact-checkers have adopted the ClaimReview markup so far, resulting in low findability of fact-check articles, especially in under-represented countries and languages. In this work, we investigate the viability of using Artificial Intelligence for generating ClaimReview automatically. Besides promoting fact-check articles, the automatic generating of ClaimReview is an important step towards the creation of updated multilingual knowledge base for fighting disinformation. Our experiments show noticeable results, which indicate a viable solution in a production environment. Furthermore, this work has created a benchmark that can be used in upcoming investigations in this domain.