Recent Studies Research Articles

Recent Research on the Identification, Assessment, and Response to Fraud Risks: Implications for Audit Practice and Topics for Future Research

SYNOPSIS The financial statement auditor’s identification of fraud risk factors, their assessment of fraud risk, and their fraud risk responses are key to the auditor’s consideration of fraud and fraud detection. Given that the last review of research related to the search for fraud during the audit occurred nearly a decade ago, we provide a summary of recent academic research to update and inform practitioners, researchers, standard setters, regulators, and other stakeholders in the financial reporting process. We categorize and summarize findings from recent academic studies that focus on the auditor’s identification, assessment, and responses to fraud risks. Implications for practice are presented for each of these areas, along with topics and questions for future research. JEL Classifications: M40; M41; M42; M48.

Transcriptomic profiling of Schlemm's canal cells reveals a lymphatic-biased identity and three major cell states.

Schlemm's canal (SC) is central in intraocular pressure regulation but requires much characterization. It has distinct inner and outer walls, each composed of Schlemm's canal endothelial cells (SECs) with different morphologies and functions. Recent transcriptomic studies of the anterior segment added important knowledge, but were limited in power by SEC numbers or did not focus on SC. To gain a more comprehensive understanding of SC biology, we performed bulk RNA sequencing on C57BL/6J SC, blood vessel, and lymphatic endothelial cells from limbal tissue (~4500 SECs). We also analyzed mouse limbal tissues by single-cell and single-nucleus RNA sequencing (C57BL/6J and 129/Sj strains), successfully sequencing 903 individual SECs. Together, these datasets confirm that SC has molecular characteristics of both blood and lymphatic endothelia with a lymphatic phenotype predominating. SECs are enriched in pathways that regulate cell-cell junction formation pointing to the importance of junctions in determining SC fluid permeability. Importantly, and for the first time, our analyses characterize 3 molecular classes of SECs, molecularly distinguishing inner wall from outer wall SECs and discovering two inner wall cell states that likely result from local environmental differences. Further, and based on ligand and receptor expression patterns, we document key interactions between SECs and cells of the adjacent trabecular meshwork (TM) drainage tissue. Also, we present cell type expression for a collection of human glaucoma genes. These data provide a new molecular foundation that will enable the functional dissection of key homeostatic processes mediated by SECs as well as the development of new glaucoma therapeutics.

Synthesis of N1-caffeoyl-N10-dihydrocaffeoylspermidine (Scotanamine D)

Abstract: N1-caffeoyl-N10-dihydrocaffeoylspermidine (Scotanamine D), a spermidine alkaloid isolated from various plants, is a medicinally valuable natural product. Recent studies have pointed out several health benefits of this compound. However, its synthetic procedures are still not described in the literature. We report the synthesis of this compound following two different schemes comprising multiple steps with excellent overall yields, which are 57% and 81%, respectively. These two synthetic schemes, which use commercially available and cheaper starting materials, can facilitate the large-scale manufacturing of Scotanamine D.

Bayesian Dictionary Learning on Robust Tubal Transformed Tensor Factorization.

The recent study on tensor singular value decomposition (t-SVD) that performs the Fourier transform on the tubes of a third-order tensor has gained promising performance on multidimensional data recovery problems. However, such a fixed transformation, e.g., discrete Fourier transform and discrete cosine transform, lacks being self-adapted to the change of different datasets, and thus, it is not flexible enough to exploit the low-rank and sparse property of the variety of multidimensional datasets. In this article, we consider a tube as an atom of a third-order tensor and construct a data-driven learning dictionary from the observed noisy data along the tubes of the given tensor. Then, a Bayesian dictionary learning (DL) model with tensor tubal transformed factorization, aiming to identify the underlying low-tubal-rank structure of the tensor effectively via the data-adaptive dictionary, is developed to solve the tensor robust principal component analysis (TRPCA) problem. With the defined pagewise tensor operators, a variational Bayesian DL algorithm is established and updates the posterior distributions instantaneously along the third dimension to solve the TPRCA. Extensive experiments on real-world applications, such as color image and hyperspectral image denoising and background/foreground separation problems, demonstrate both effectiveness and efficiency of the proposed approach in terms of various standard metrics.

The Therapeutic Potential and Molecular Mechanisms Underlying the Neuroprotective Effects of Sativex® - A Cannabis-derived Spray.

Sativex is a cannabis-based medicine that comes in the form of an oromucosal spray. It contains equal amounts of Δ9-tetrahydrocannabinol and cannabidiol, two compounds derived from cannabis plants. Sativex has been shown to have positive effects on symptoms of amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and sleep disorders. It also has analgesic, antiinflammatory, antitumoral, and neuroprotective properties, which make it a potential treatment option for other neurological disorders. The article reviews the results of recent preclinical and clinical studies that support the therapeutic potential of Sativex and the molecular mechanisms behind its neuroprotective benefits in various neurological disorders. The article also discusses the possible advantages and disadvantages of using Sativex as a neurotherapeutic agent, such as its safety, efficacy, availability, and legal status.

The role of HSP40 in cancer: Recent advances.

Heat shock proteins (HSPs) are a family of proteins involved in protein folding and maturation that are expressed by cells in response to stressors including heat shock. Recent studies have demonstrated that HSPs play major roles in carcinogenesis by regulating angiogenesis, cell proliferation, migration, invasion, metastasis, apoptosis, as well as therapy resistance to certain anticancer drugs. Despite being the largest and most diverse subgroup of the HSP family, HSP40 (DNAJ) is an understudied family of co-chaperones. HSP40 family members are also known to be involved in various types of cancers. In this article, we review the involvement of human HSP40 family members in various aspects of cancer biology. In addition, we highlight the possible potential of HSP40 as a tumor biomarker or drug target for improving the prognosis and treatment of cancer patients in the future.

A Glove-Based Virtual Hand Rehabilitation System for Patients With Post-Traumatic Hand Injuries.

Recent studies have shown that virtual gamified therapy can be a potential adjunct to conventional orthopedic rehabilitation. However, the off-the-shelf gaming consoles used for virtual rehabilitation pose several practical challenges in deploying them in clinical settings. In this article, we present the design of a portable glove-based virtual hand rehabilitation system (RehabRelive Glove) that can be used at both clinics and homes for physiotherapy. We also evaluate the system's efficacy on patients with post-traumatic hand injuries. Thirty patients were randomly categorized into groups A (virtual rehabilitation) and B (conventional physiotherapy). Both groups received fifteen 25-minute sessions of respective therapy over three weeks. The wrist and finger joints' range of motion (ROM) and grip strength were measured every seven sessions to compare the efficacy. Group A showed about 1.5 times greater improvement in flexion/extension ROM of the wrist compared to Group B. While both groups improved finger ROM and grip strength with time, no significant difference was observed between the groups. The results suggest that the proposed virtual rehabilitation system effectively enables patients with hand injuries to recover ROM faster.

Casein kinase 2 affects epilepsy by regulating ion channels: a potential mechanism.

Epilepsy, characterized by recurrent seizures and abnormal brain discharges, is the third most common chronic disorder of the Central Nervous System (CNS). Although significant progress has been made in the research on antiepileptic drugs (AEDs), approximately one-third of patients with epilepsy are refractory to these drugs. Thus, research on the pathogenesis of epilepsy is ongoing to find more effective treatments. Many pathological mechanisms are involved in epilepsy, including neuronal apoptosis, mossy fiber sprouting, neuroinflammation, and dysfunction of neuronal ion channels, leading to abnormal neuronal excitatory networks in the brain. CK2 (Casein kinase 2), which plays a critical role in modulating neuronal excitability and synaptic transmission, has been shown to be associated with epilepsy. However, there is limited research on the mechanisms involved. Recent studies have suggested that CK2 is involved in regulating the function of neuronal ion channels by directly phosphorylating them or their binding partners. Therefore, in this review, we will summarize recent research advances regarding the potential role of CK2 regulating ion channels in epilepsy, aiming to provide more evidence for future studies.

SPD Manifold Deep Metric Learning for Image Set Classification.

By characterizing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, the approaches of visual content classification with image sets have made impressive progress. However, the key challenge of unhelpfully large intraclass variability and interclass similarity of representations remains open to date. Although, several recent studies have mitigated the two problems by jointly learning the embedding mapping and the similarity metric on the original SPD manifold, their inherent shallow and linear feature transformation mechanism are not powerful enough to capture useful geometric features, especially in complex scenarios. To this end, this article explores a novel approach, termed SPD manifold deep metric learning (SMDML), for image set classification. Specifically, SMDML first selects a prevailing SPD manifold neural network (SPDNet) as the backbone (encoder) to derive an SPD matrix nonlinear representation. To counteract the degradation of structural information during multistage feature embedding, we construct a Riemannian decoder at the end of the encoder, trained by a reconstruction error term (RT), to induce the generated low-dimensional feature manifold of the hidden layer to capture the pivotal information about the visual data describing the imaged scene. We demonstrate through theory and experiments that it is feasible to replace the Riemannian metric with Euclidean distance in RT. Then, the ReCov layer is introduced into the established Riemannian network to regularize the local statistical information within each input feature matrix, which enhances the effectiveness of the learning process. The theoretical analysis of the activation function used in the ReCov layer in terms of continuity and conditions for generating positive definite matrices is beneficial for network design. Inspired by the fact that the single cross-entropy loss used for training is unable to effectively parse the geometric distribution of the deep representations, we finally endow the suggested model with a novel metric learning regularization term. By explicitly incorporating the encoding and processing of the data variations into the network learning process, this term can not only derive a powerful Riemannian representation but also train an effective classifier. The experimental results show the superiority of the proposed approach on three typical visual classification tasks.

The Role of Mesenchymal Stem/Stromal Cells Secretome in Macrophage Polarization: Perspectives on Treating Inflammatory Diseases.

Mesenchymal stem/stromal cells (MSCs) have exhibited potential for treating multiple inflammation- related diseases (IRDs) due to their easy acquisition, unique immunomodulatory and tissue repair properties, and immune-privileged characteristics. It is worth mentioning that MSCs release a wide array of soluble bioactive components in the secretome that modulate host innate and adaptive immune responses and promote the resolution of inflammation. As the first line of defense, macrophages exist throughout the entire inflammation process. They continuously switch their molecular phenotypes accompanied by complementary functional regulation ranging from classically activated pro-inflammatory M1-type (M1) to alternatively activated anti-inflammatory M2-type macrophages (M2). Recent studies have shown that the active intercommunication between MSCs and macrophages is indispensable for the immunomodulatory and regenerative behavior of MSCs in pharmacological cell therapy products. In this review, we systematically summarized the emerging capacities and detailed the molecular mechanisms of the MSC-derived secretome (MSC-SE) in immunomodulating macrophage polarization and preventing excessive inflammation, providing novel insights into the clinical applications of MSC-based therapy in IRD management.

The Role of Hypoxia-inducible Factor-1 in Bladder Cancer.

Bladder cancer (BC) is one of the most common malignant tumors worldwide and poses a significant hazard to human health. During the development of BC, hypoxia plays a crucial role. Hypoxia-inducible factor (HIF) is a key transcription factor for hypoxic adaptation, which regulates the transcription of various genes, including inflammation, angiogenesis, and glycolytic metabolism. Recent studies have shown the precise role of HIF in various biological behaviors of BC. More importantly, a new antitumor medication targeting HIF-2 has been used to treat renal cancer. However, therapies targeting HIF-1 in BC have not yet been developed. In this review, we discussed how HIF-1 is expressed and affects the growth, metastasis, and angiogenesis of BC. At the same time, we investigated several HIF-1 inhibitors that provide new perspectives for targeting HIF-1.

Immunologic Crosstalk of Endoplasmic Reticulum Stress Signaling in Bladder Cancer.

Bladder cancer (BC) is a common malignant tumor of the urinary system. While current approaches involving adjuvant chemotherapy, radiotherapy, and immunotherapy have shown significant progress in BC treatment, challenges, such as recurrence and drug resistance, persist, especially in the case of muscle-invasive bladder cancer (MIBC). It is mainly due to the lack of pre-existing immune response cells in the tumor immune microenvironment. Micro-environmental changes (such as hypoxia and under-nutrition) can cause the aggregation of unfolded and misfolded proteins in the lumen, which induces endoplasmic reticulum (ER) stress. ER stress and its downstream signaling pathways are closely related to immunogenicity and tumor drug resistance. ER stress plays a pivotal role in a spectrum of processes within immune cells and the progression of BC cells, encompassing cell proliferation, autophagy, apoptosis, and resistance to therapies. Recent studies have increasingly recognized the potential of natural compounds to exhibit anti-BC properties through ER stress induction. Still, the efficacy of these natural compounds remains less than that of immune checkpoint inhibitors (ICIs). Currently, the ER stress-mediated immunogenic cell death (ICD) pathway is more encouraging, which can enhance ICI responses by mediating immune stemness. This article provides an overview of the recent developments in understanding how ER stress influences tumor immunity and its implications for BC. Targeting this pathway may soon emerge as a compelling therapeutic strategy for BC.

Sensory Reinforced Corticostriatal Plasticity.

Regional changes in corticostriatal transmission induced by phasic dopaminergic signals are an essential feature of the neural network responsible for instrumental reinforcement during discovery of an action. However, the timing of signals that are thought to contribute to the induction of corticostriatal plasticity is difficult to reconcile within the framework of behavioural reinforcement learning, because the reinforcer is normally delayed relative to the selection and execution of causally-related actions. While recent studies have started to address the relevance of delayed reinforcement signals and their impact on corticostriatal processing, our objective was to establish a model in which a sensory reinforcer triggers appropriately delayed reinforcement signals relayed to the striatum via intact neuronal pathways and to investigate the effects on corticostriatal plasticity. We measured corticostriatal plasticity with electrophysiological recordings using a light flash as a natural sensory reinforcer, and pharmacological manipulations were applied in an in vivo anesthetized rat model preparation. We demonstrate that the spiking of striatal neurons evoked by single-pulse stimulation of the motor cortex can be potentiated by a natural sensory reinforcer, operating through intact afferent pathways, with signal timing approximating that required for behavioural reinforcement. The pharmacological blockade of dopamine receptors attenuated the observed potentiation of corticostriatal neurotransmission. This novel in vivo model of corticostriatal plasticity offers a behaviourally relevant framework to address the physiological, anatomical, cellular, and molecular bases of instrumental reinforcement learning.

Potential Antidiabetic Activity of β-sitosterol from Zingiber roseum Rosc. via Modulation of Peroxisome Proliferator-activated Receptor Gamma (PPARγ).

To evaluate the antidiabetic potential of β-sitosterol from Zingiber roseum. Diabetes mellitus is a cluster of metabolic disorders, and 90% of diabetic patients are affected with Type II diabetes (DM2). For the treatment of DM2, thiazolidinedione drugs (TZDs) were proposed, but recent studies have shown that TZDs have several detrimental effects, such as weight gain, kidney enlargement (hypertrophy), fluid retention, increased risk of bone fractures, and potential harm to the liver (hepatotoxicity). That is why a new molecule is needed to treat DM2. The current research aimed to assess the efficacy of β-Sitosterol from methanolic extract of Zingiber roseum in managing diabetes via PPARγ modulation. Zingiber roseum was extracted using methanol, and GC-MS was employed to analyze the extract. Through homology modeling, PPARγ structure was predicted. Molecular docking, MD simulation, free binding energies, QSAR, ADMET, and bioactivity and toxicity scores were all used during the in-depth computer-based research. Clinically, agonists of synthetic thiazolidinedione (TZDs) have been used therapeutically to treat DM2, but these TZDs are associated with significant risks. Hence, GC-MS identified phytochemicals to search for a new PPAR-γ agonist. Based on the in-silico investigation, β-sitosterol was found to have a higher binding affinity (-8.9 kcal/mol) than standard drugs. MD simulations and MMGBSA analysis also demonstrated that β-sitosterol bound to the PPAR-γ active site stably. It can be concluded that β-sitosterol from Z. roseum attenuates Type-II diabetes by modulating PPARγ activity.

Targeting Ferroptosis: A Novel Strategy for the Treatment of Atherosclerosis.

Since ferroptosis was reported in 2012, its application prospects in various diseases have been widely considered, initially as a treatment direction for tumors. Recent studies have shown that ferroptosis is closely related to the occurrence and development of atherosclerosis. The primary mechanism is to affect the occurrence and development of atherosclerosis through intracellular iron homeostasis, ROS and lipid peroxide production and metabolism, and a variety of intracellular signaling pathways. Inhibition of ferroptosis is effective in inhibiting the development of atherosclerosis, and it can bring a new direction for treating atherosclerosis. In this review, we discuss the mechanism of ferroptosis and focus on the relationship between ferroptosis and atherosclerosis, summarize the different types of ferroptosis inhibitors that have been widely studied, and discuss some issues worthy of attention in the treatment of atherosclerosis by targeting ferroptosis.

PMSGAN: Parallel Multistage GANs for Face Image Translation.

In this article, we address the face image translation task, which aims to translate a face image of a source domain to a target domain. Although significant progress has been made by recent studies, face image translation is still a challenging task because it has more strict requirements for texture details: even a few artifacts will greatly affect the impression of generated face images. Targeting to synthesize high-quality face images with admirable visual appearance, we revisit the coarse-to-fine strategy and propose a novel p arallel m ultistage architecture on the basis of g enerative a dversarial n etworks (PMSGAN). More specifically, PMSGAN progressively learns the translation function by disintegrating the general synthesis process into multiple parallel stages that take images with gradually decreasing spatial resolution as inputs. To prompt the information exchange between various stages, a cross-stage atrous spatial pyramid (CSASP) structure is specially designed to receive and fuse the contextual information from other stages. At the end of the parallel model, we introduce a novel attention-based module that leverages multistage decoded outputs as in situ supervised attention to refine the final activations and yield the target image. Extensive experiments on several face image translation benchmarks show that PMSGAN performs considerably better than state-of-the-art approaches.

Recent Patents on Thermal Characteristic Analysis and Modeling of Machine Tools

Abstract: Modern machining machines are becoming more sophisticated and automated, but the problems of thermal deformation caused by machining have also come to the fore. CNC machine tools are used in high-speed, high-feed rate machining conditions, so the impact of thermal deformation factors will lead to a more prominent loss of accuracy. In this context, the design and manufacture of future CNC machine tools need to take further account of the errors caused by thermal deformation and the accompanying loss of accuracy. The principle of thermal characterisation and thermal error modeling of machine tools is to measure and reasonably analyse the machine tool at different temperature points and make corrections to the analysis data in order to get a value as close as possible to the real value and improve machining accuracy. Several representative patents and studies on thermal characterisation and thermal error modeling of machine tools at home and abroad are reviewed. This study presents a selection and summary of a large number of recent patents and studies on machine tool thermal characterisation and thermal error modeling, focusing on the selection of the machine tool thermal error measurement point area, thermal error modeling methods, spindle thermal displacement compensation, and other aspects, and discusses the future trends in the mainstream methods for reducing the thermal error of machine tools. The analysis and modeling of thermal errors in machine tools have an integral influence on further improvements in machining accuracy and efficiency in the future.

Haptic Guidance and Haptic Error Amplification in a Virtual Surgical Robotic Training Environment.

Teleoperated robotic systems have introduced more intuitive control for minimally invasive surgery, but the optimal method for training remains unknown. Recent motor learning studies have demonstrated that exaggeration of errors helps trainees learn to perform tasks with greater speed and accuracy. We hypothesized that training in a force field that pushes the user away from a desired path would improve their performance on a virtual reality ring-on-wire task. Thirty-eight surgical novices trained under a no-force, guidance, or error-amplifying force field over five days. Completion time, translational and rotational path error, and combined error-time were evaluated under no force field on the final day. The groups significantly differed in combined error-time, with the guidance group performing the worst. Error-amplifying field participants did not plateau in their performance during training, suggesting that learning was still ongoing. Guidance field participants had the worst performance on the final day, confirming the guidance hypothesis. Observed trends also suggested that participants who had high initial path error benefited more from guidance. Error-amplifying and error-reducing haptic training for robot-assisted telesurgery benefits trainees of different abilities differently, with our results indicating that participants with high initial combined error-time benefited more from guidance and error-amplifying force field training.

Determinants of Income Composition Inequality

A rich literature examines the determinants of income inequality. Less is known about the distribution of income composition, or the extent to which different socio-economic groups hold different sources of income. This article explores the drivers of income composition inequality (ICI). Contrary to recent studies, which show that the composition of government has ceased to shape redistribution and income inequality dynamics, this article posits that left-wing parties are associated with lower income composition inequality. We test this expectation with data from thirty European countries between 2003 and 2017. Our results suggest that the polarization between capital and labor income holders declines under left-wing governments. We establish that this is mainly because left-wing parties seek to broaden access to capital income.

Synthesis and Anticancer Activity of Novel Indole Derivatives as Dual EGFR/SRC Kinase Inhibitors.

Recent studies showed that the cooperation between c-SRC and EGFR is responsible for more aggressive phenotype in diverse tumors, including glioblastomas and carcinomas of the colon, breast, and lung. Studies show that combination of SRC and EGFR inhibitors can induce apoptosis and delay the acquired resistance to chemotherapy. Therefore, such combination may lead to a new therapeutic strategy for the treatment of EGFR-mutant lung cancer. Osimertinib was developed as a third-generation EGFR-TKI to combat the toxicity of EGFR mutant inhibitors. Due to the resistance and adverse reaction of osimertinib and other kinase inhibitors, 12 novel compounds structurally similar to osimertinib were designed and synthesized. Compounds were synthesized by developing novel original synthesis methods and receptor interactions were evaluated through a molecular docking study. To evaluate their inhibitory activities against EGFR and SRC kinase, in vitro enzyme assays were used. Anticancer potencies were determined using lung, breast, prostate (A549, MCF6, PC3) cancer cell lines. Compounds were also tested against normal (HEK293) cell line to evaluate their cyctotoxic effects. Although, none of compounds showed stronger inhibition compared to osimertinib in the EGFR enzyme inhibition studies, compound 16 showed the highest efficacy with an IC50 of 1.026 μM. It also presented potent activity against SRC kinase with an IC50 of 0.002 μM. Among the tested compounds, the urea containing derivatives 6-11 exhibited a strong inhibition profile (80.12-89.68%) against SRC kinase in comparison to the reference compound dasatinib (93.26%). Most of the compounds caused more than 50% of cell death in breast, lung and prostate cancer cell lines and weak toxicity for normal cells in comparison to reference compounds osimertinib, dasatinib and cisplatin. Compound 16 showed strong cytotoxicity on lung and prostate cancer cells. Treatment of prostate cancer cell lines with the most active compound, 16, significantly increased the caspase-3 (8-fold), caspase-8 (6-fold) and Bax (5.7-fold) levels and decreased the Bcl-2 level (2.3-fold) compared to the control group. These findings revealed that the compound 16 strongly induces apoptosis in the prostate cancer cell lines. Overall kinase inhibition, cytotoxicity and apoptosis assays presented that compound 16 has dual inhibitory activity against SRC and EGFR kinases while maintaining low toxicity against normal cells. Other compounds also showed considerable activity profiles in kinase and cell culture assays.