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Cancer stem-like cells (CSCs) are considered promising targets for anti-cancer therapy owing to their role in tumor progression. Extensive research is, therefore, being carried out on CSCs to identify potential targets for anti-cancer therapy. However, this requires the availability of patient-derived CSCs ex vivo, which remains restricted due to the low availability and diversity of CSCs. To address this limitation, a functional polymer thin-film (PTF) platform was invented to induce the transformation of cancer cells into tumorigenic spheroids. In this study, we demonstrated the functionality of a new PTF, polymer X, using a streamlined production process. Polymer X induced the formation of tumor spheroids with properties of CSCs, as revealed through the upregulated expression of CSC-related genes. Signal transducer and activator of transcription 3 (STAT3) phosphorylation in the cancer cells cultured on polymer X was upregulated by the fibronectin-integrin α5-Janus kinase 2 (JAK2) axis and maintained by the cytosolic LMO2/LBD1 complex. In addition, STAT3 signaling was critical in spheroid formation on polymer X. Our PTF platform allows the efficient generation of tumor spheroids from cancer cells, thereby overcoming the existing limitations of cancer research.

Lymphoma is a severe condition characterized by the proliferation of neoplastic lymphoid cells. A 4-year-old female mongrel dog presented with solitary lymph node enlargement. Significant right prescapular lymphadenopathy and abdominal enlargement were observed during physical examination. A complete blood count revealed lymphocytosis, and a peripheral blood smear revealed lymphoblastosis and Mott cells. Fine needle aspiration cytology (FNAC) of the right prescapular lymph node revealed a predominant population of lymphoblasts and Mott cells. Based on the FNAC and blood smear results, the patient was diagnosed with leukemic state multicentric B-cell lymphoma with Mott cell differentiation. Subsequent PCR for antigen receptor rearrangement and flow cytometry revealed that the patient exhibited cross-lineage rearrangement (CLRA) and lineage infidelity (LI), respectively. CHOP-based chemotherapy was initiated, however, the patient’s disease was progressive. The patient died three months after the initial presentation. Mott cell differentiation in canine B-cell lymphoma (MCL) has rarely been reported in the veterinary literature and seems to show an unusual clinical course. To the best of our knowledge, no reports of MCL with CLRA and LI exist. We report the clinical features, diagnosis, and treatment of MCL with CLRA and LI.

Autonomous vehicles (AVs) enable drivers to devote their primary attention to non-driving-related tasks (NDRTs). Consequently, AVs must provide intelligibility services appropriate to drivers’ in-situ states and in-car activities to ensure driver safety, and accounting for the type of NDRT being performed can result in higher intelligibility. We discovered that sleeping is drivers’ most preferred NDRT, and this could also result in a critical scenario when a take-over request (TOR) occurs. In this study, we designed TOR situations where drivers are woken from sleep in a high-fidelity AV simulator with motion systems, aiming to examine how drivers react to a TOR provided with our experimental conditions. We investigated how driving performance, perceived task workload, AV acceptance, and physiological responses in a TOR vary according to two factors: (1) feedforward timings and (2) presentation modalities. The results showed that when awakened by a TOR alert delivered >10 s prior to an event, drivers were more focused on the driving context and were unlikely to be influenced by TOR modality, whereas TOR alerts delivered

Fine control of morphology and exposed crystal facets of porous γ-Al2O3 is of significant importance in many application areas such as functional nanomaterials and heterogeneous catalysts. Herein, a morphology controlled in situ synthesis of Al@Al2O3 core–shell architecture consisting of an Al metal core and a porous γ-Al2O3 shell is explored based on interfacial hydrothermal reactions of an Al metal substrate in aqueous solutions of inorganic anions. It was found that the morphology and structure of boehmite (γ-AlOOH) nano-crystallites grown at the Al-metal/solution interface exhibit significant dependence on temperature, type of inorganic anions (Cl−, NO3−, and SO42−), and acid–base environment of the synthesis solution. Different extents of the electrostatic interactions between the protonated hydroxyl groups on (010) and (001) facets of γ-AlOOH and the inorganic anions (Cl−, NO3−, SO42−) appear to result in the preferential growth of γ-AlOOH toward specific crystallographic directions due to the selective capping of the facets by adsorption of the anions. It is hypothesized that the unique Al@Al2O3 core–shell architecture with controlled morphology and exposed crystal-facets of the γ-Al2O3 shell can provide significant intrinsic catalytic properties with enhanced heat and mass transport to heterogeneous catalysts for applications in many thermochemical reaction processes. The direct fabrication of γ-Al2O3 nano-crystallites from Al metal substrate with in-situ modulation of their morphologies and structures into 1D, 2D, and 3D nano-architectures explored in this work is unique and can offer significant opportunities over the conventional methods.

Stem cells derived from dental tissues—dental stem cells—are favored due to their easy acquisition. Among them, dental pulp stem cells (DPSCs) extracted from the dental pulp have many advantages, such as high proliferation and a highly purified population. Although their ability for neurogenic differentiation has been highlighted and neurogenic differentiation using electrospun nanofibers (NFs) has been performed, graphene-incorporated NFs have never been applied for DPSC neurogenic differentiation. Here, reduced graphene oxide (RGO)-polycaprolactone (PCL) hybrid electrospun NFs were developed and applied for enhanced neurogenesis of DPSCs. First, RGO-PCL NFs were fabricated by electrospinning with incorporation of RGO and alignments, and their chemical and morphological characteristics were evaluated. Furthermore, in vitro NF properties, such as influence on the cellular alignments and cell viability of DPSCs, were also analyzed. The influences of NFs on DPSCs neurogenesis were also analyzed. The results confirmed that an appropriate concentration of RGO promoted better DPSC neurogenesis. Furthermore, the use of random NFs facilitated contiguous junctions of differentiated cells, whereas the use of aligned NFs facilitated an aligned junction of differentiated cells along the direction of NF alignments. Our findings showed that RGO-PCL NFs can be a useful tool for DPSC neurogenesis, which will help regeneration in neurodegenerative and neurodefective diseases.

Data-driven fault diagnosis has received significant attention in the era of big data. Most data-driven methods have been developed under the assumption that both training and test data come from identical data distributions. However, in real-world industrial scenarios, data distribution often changes due to varying operating conditions, leading to a degradation of diagnostic performance. Although several domain adaptation methods have shown their feasibility, existing methods have overlooked metadata from the manufacturing process and treated all domains uniformly. To address these limitations, this article proposes a weighted domain adaptation method using a graph-structured dataset representation. Our framework involves encoding a collection of datasets into the proposed graph structure, which captures relations between datasets based on metadata and raw data simultaneously. Then, transferability scores of candidate source datasets for a target are estimated using the constructed graph and a graph embedding model. Finally, the fault diagnosis model is established with a voting ensemble of the base classifiers trained on candidate source datasets and their estimated transferability scores. For validation, two case studies on rotor machinery, specifically tool wear and bearing fault detection, were conducted. The experimental results demonstrate the effectiveness and superiority of the proposed method over other existing domain adaptation methods.

After the development of the Versatile Video Coding (VVC) standard, research on neural network-based video coding technologies continues as a potential approach for future video coding standards. Particularly, neural network-based intra prediction is receiving attention as a solution to mitigate the limitations of traditional intra prediction performance in intricate images with limited spatial redundancy. This study presents an intra prediction method based on coarse-to-fine networks that employ both convolutional neural networks and fully connected layers to enhance VVC intra prediction performance. The coarse networks are designed to adjust the influence on prediction performance depending on the positions and conditions of reference samples. Moreover, the fine networks generate refined prediction samples by considering continuity with adjacent reference samples and facilitate prediction through upscaling at a block size unsupported by the coarse networks. The proposed networks are integrated into the VVC test model (VTM) as an additional intra prediction mode to evaluate the coding performance. The experimental results show that our coarse-to-fine network architecture provides an average gain of 1.31% Bjøntegaard delta-rate (BD-rate) saving for the luma component compared with VTM 11.0 and an average of 0.47% BD-rate saving compared with the previous related work.

Understanding the molecular mechanisms underlying early seed development is important in improving the grain yield and quality of crop plants. We performed a comparative label-free quantitative proteomic analysis of developing rice seeds for the WT and osctps1-2 mutant, encoding a cytidine triphosphate synthase previously reported as the endospermless 2 (enl2) mutant in rice, harvested at 0 and 1 d after pollination (DAP) to understand the molecular mechanism of early seed development. In total, 5231 proteins were identified, of which 902 changed in abundance between 0 and 1 DAP seeds. Proteins that preferentially accumulated at 1 DAP were involved in DNA replication and pyrimidine biosynthetic pathways. Notably, an increased abundance of OsCTPS1 was observed at 1 DAP; however, no such changes were observed at the transcriptional level. We further observed that the inhibition of phosphorylation increased the stability of this protein. Furthermore, in osctps1-2, minichromosome maintenance (MCM) proteins were significantly reduced compared with those in the WT at 1 DAP, and mutations in OsMCM5 caused defects in seed development. These results highlight the molecular mechanisms underlying early seed development in rice at the post-transcriptional level.

The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.