Regulation of gene expression by manipulating transcriptional repressor activity using a novel CoSRI technology

Targeted gene manipulation is a central strategy for studying gene function and identifying related biological processes. However, a methodology for manipulating the regulatory motifs of transcription factors is lacking as these factors commonly possess multiple motifs (e.g. repression and activation motifs) which collaborate with each other to regulate multiple biological processes. We describe a novel approach designated conserved sequence-guided repressor inhibition (CoSRI) that can specifically reduce or abolish the repressive activities of transcription factors in vivo. The technology was evaluated using the chimeric MYB80-EAR transcription factor and subsequently the endogenous WUS transcription factor. The technology was employed to develop a reversible male sterility system applicable to hybrid seed production. In order to determine the capacity of the technology to regulate the activity of endogenous transcription factors, the WUS repressor was chosen. The WUS repression motif could be inhibited in vivo and the transformed plants exhibited the wus-1 phenotype. Consequently, the technology can be used to manipulate the activities of transcriptional repressor motifs regulating beneficial traits in crop plants and other eukaryotic organisms

Edge4FR: A Novel Device-Edge Collaborative Framework for Facial Recognition in Smart UAV Delivery Systems

In recent years, smart UAV (unmanned aerial vehicle) delivery has become a promising solution to solve the last-mile delivery problem in smart logistics. In a smart UAV delivery system, the accurate identification of the goods receiver is a critical task. At present, using smart lockers with quick response (QR) codes is one of the most widely used solutions. However, this solution is very expensive and limited by the space available to deploy smart lockers. In contrast, using facial recognition technology for identification is a promising solution which does not need any extra equipment besides the UAV itself. However, due to the instability and the unusual shooting angle of the UAV from the air, existing facial recognition technologies often suffer the issue of low accuracy in practice. Therefore, to improve the accuracy of UAV based facial recognition, we propose Edge4FR, a Device-Edge Collaborative Framework based on face frontalization and facial recognition. Specifically, first, the facial detection algorithm based on deep learning deployed in the UAV can detect facial images frame by frame, and extract detected faces and transmit them to the nearby edge server. Afterwards, the face frontalization model trained by the generative adversarial network (GAN) deployed in the edge server can frontalize facial images. Finally, the facial recognition algorithm based on deep learning deployed in the edge server can confirm the identity by checking if the frontal facial image matches the goods receiver's facial image registered in the delivery system. Experimental results in a real-world smart UAV delivery system demonstrate the effectiveness of the proposed framework

A three-dimensional electrode array probe designed for visualising complex and dynamically changing internal pipeline corrosion

This short communication describes a corrosion probe design concept based on a three-dimensional electrochemically integrated electrode array. A representative probe was made for probing complex localised corrosion inside a liquid-containing pipe. Results demonstrate that the probe has sufficient temporal and spatial resolutions required for in-situ monitoring of atmospheric corrosion, channelling corrosion and flow-affected corrosion occurring simultaneously inside the pipe, and for investigating the effects of corrosion influencing factors including differential aeration, liquid flowing, pH and inhibitors. This work suggests that the three-dimensional electrode array probe is a promising tool for probing multi-phase and multi-mechanism localised corrosion in complex industrial environment

Fuzzy multicriteria decision support for information systems project selection

This paper presents a fuzzy multicriteria group decision making approach for selecting information systems projects. The inherent subjectiveness and imprecision of the selection process is modeled by using linguistic terms characterized by triangular fuzzy numbers. To avoid the complex and unreliable process of comparing fuzzy numbers usually required in fuzzy multicriteria analysis, a new algorithm is developed based on the degree of dominance and the degree of optimality concepts. A multicriteria decision support system is proposed to facilitate the evaluation and selection process. An information systems project selection problem is presented to demonstrate the effectiveness of the approach

The audit market for listed Australian companies from 2012 to 2018: A state of play

Motivated by recent regulatory scrutiny of auditing in Australia, we provide an overview of the audit market for Australian listed companies from 2012 to 2018. Using descriptive analyses, we explore audit market competition, the provision of non-audit services (NAS), and audit firm tenure. We find that the Australian audit market is highly segmented. Big 4 firms increasingly dominate the larger client segment, while Non-Big 4 firms focus on medium and smaller clients. Auditor-provided NAS fees represent a relatively small fraction of audit fees for smaller clients, but a relatively high fraction for larger clients. We further observe that the share of total revenue from NAS of Big 4 firms increases over time. Finally, a relatively small percentage of clients has long audit firm tenure, and that long tenure is more common in the larger client segments. We discuss the implications of these findings and research opportunities that emerge. JEL Classification: D40, L11, M42, L8

The Association Between Caregiver Psychosocial Factors and Depressive Symptoms in People With Dementia: A Systematic Review and Meta-Analysis

ABSTRACTAimsTo identify and evaluate the magnitude of the association between caregiver psychosocial factors and depressive symptoms among people with dementia.DesignSystematic review and meta‐analysis.MethodsA systematic review with meta‐analysis used a random‐effects model to estimate the effect size.Data SourcesMedline, PsycINFO, CINAHL, Scopus and Embase databases were searched for peer‐reviewed studies from inception to 25 November 2023.ResultsThe review included 88 articles, with 61 selected for meta‐analysis. Seven caregiver psychosocial factors were determined for the meta‐analysis: caregiver quality of life, distress, positive aspects of caregiving, depression, burden, quality of the relationship and anxiety.ConclusionThis study suggested that depressive symptoms in people with dementia were associated with caregiver quality of life, distress, burden, depression and positive aspects of caregiving.Implications for the Profession and/or Patient CareRecognising the association between caregiver psychosocial factors and depressive symptoms in people with dementia has essential nursing implications. Adopting family‐centred care models and integrating respite care and psychological support for caregivers can help improve patient outcomes and overall dementia care.ImpactThis study highlights the association between caregiver psychosocial factors and depressive symptoms in people with dementia. Caregiver distress, burden and depression were linked to increased depressive symptoms in people with dementia, while caregiver quality of life and positive aspects of caregiving were associated with depressive symptoms in people with dementia. These findings underscore the need for tailored interventions to enhance dyadic health.Reporting MethodThis systematic review and meta‐analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines.Patient or Public InvolvementThere was no patient or public contribution.Protocol RegistrationThis review was registered in PROSPERO (2024 CRD42024511383)

Multicriteria group decision support for information systems project selection

This paper presents a fuzzy multicriteria group decision making approach for evaluating and selecting information systems projects. The inherent subjectiveness and imprecision of the evaluation process is modeled by using linguistic terms characterized by triangular fuzzy numbers. A new algorithm based on the concept of the degree of dominance is developed to avoid the complex and unreliable process of comparing fuzzy numbers usually required in fuzzy multicriteria decision making. A multicriteria decision support system is proposed to facilitate the evaluation and selection process. An information systems project selection problem is presented to demonstrate the effectiveness of the approach. © 2009 Springer Berlin Heidelberg

SPTNET: Span-based Prompt Tuning for Video Grounding

When a Pre-trained Language Model (PLM) is adopted in video grounding task, it usually acts as a text encoder without having its knowledge fully utilized. Also, there exists an inconsistency problem between the pre-training and downstream objectives. To solve the issues, we propose a new paradigm, named Span-based Prompt Tuning (SPTNet). It can convert the video grounding task into a cloze form. Specifically, a query is first changed into a form with mask token by a template, then the video and the query embeddings are integrated through a cross-modal transformer. The start and end points of the query matching time span are predicted with the embedding of the mask token. Experimental results on two public benchmarks ActivityNet Captions and Charades-STA show that our SPTNet achieves surpassing performance compared with state-of-the-art methods

Probing top-of-the-line corrosion using coupled multi-electrode array in conjunction with local electrochemical measurement

An experimental method has been developed for probing top-of-the-line corrosion (TLC) of pipeline steel based on the use of the wire beam electrode (WBE) in conjunction with local electrochemical measurements. Results show that the location of the droplet, the droplet retention time, the water condensation rate and the local TLC rate could be well determined through the macro-cell current mapping and local electrochemical measurements. The precipitation and the scaling tendency of the FeCO3 beneath the droplet were quantitatively estimated. The micro-cell corrosion was significantly influenced by the thickness of the condensed water film and the protectiveness of the FeCO3 layer. The discrepancy of the film formation inside and outside the droplets was the driving force of macro-cell corrosion. The in-situ measurement and visualization of the corrosion processes and kinetics using the modified WBE could be conveniently used to facilitate the understanding of the initiation and propagation of localized TLC

Probabilistic machine learning for predicting desiccation cracks in clayey soils

Probabilistic machine learning for predicting desiccation cracks in clayey soil