Pachydermoperiostosis: Cosmetic implications in a multi-system genetic condition.

We present the case of a 17-year-old gentleman, initially referred to rheumatology with painful enlargement of his joints, along with prominence of facial features consistent with cutis verticis gyrata. Genetic testing confirmed a diagnosis of pachydermoperiostosis, a rare genetic condition characterised by digital clubbing, long bone periostosis and pachydermia. Our patient exhibited heterozygosity for the SLCO2A1 variant, which is known to confer more prominent skin and bone features than other known underlying mutations. His arthralgia caused significant functional impairment, but moreover there were concerns regarding the psychosocial impact of his cosmetic features, for which there appear to be few available medical therapies. As a multi-system genetic condition, pachydermoperiostosis is also known to be associated with other significant sequelae such as myelofibrosis and gastric ulceration, highlighting the importance of its early detection, which may be aided by the recognition of dermatological signs. In our patient's case, the concurrence of orthostatic hypotension and iron deficiency anaemia were concluded to be a function of restricted dietary intake due to his change in appearance, once again highlighting the cosmetic implications of this condition, and importance of further research into its medical management.

Phase Ib Study of Ulixertinib Plus Gemcitabine and Nab-Paclitaxel in Patients with Metastatic Pancreatic Adenocarcinoma.

BACKGROUND: Ulixertinib is a novel oral ERK inhibitor that has shown promising single-agent activity in a phase I clinical trial that included patients with RAS-mutant cancers. METHODS: We conducted a phase Ib trial combining ulixertinib with gemcitabine and nab-paclitaxel (GnP) for untreated metastatic pancreatic adenocarcinoma. The trial comprised a dose de-escalation part and a cohort expansion part at the recommended phase II dose (RP2D). Primary endpoint was to determine the RP2D of ulixertinib plus GnP and secondary endpoints were to assess toxicity and safety profile, biochemical and radiographic response, progression-free survival (PFS) and overall survival (OS). RESULTS: Eighteen patients were enrolled. Ulixertinib 600 mg PO twice daily (BID) with GnP was initially administered but was de-escalated to 450 mg BID as RP2D early during dose expansion due to poor tolerability, which ultimately led to premature termination of the study. Common treatment-related adverse events (TRAEs) were anemia, thrombocytopenia, rash and diarrhea. For 5 response evaluable patients, one patient achieved a partial response and 2 patients achieved stable disease. For 15 patients who received the triplet, median PFS and OS were 5.46 and 12.23 months, respectively. CONCLUSION: Ulixertinib plus GnP had similar frequency of grade ≥3 TRAEs and potentially efficacy as GnP, however was complicated by a high rate of all-grade TRAEs (ClinicalTrials.gov Identifier: NCT02608229).

IRAK4 Signaling Drives Resistance to Checkpoint Immunotherapy in Pancreatic Ductal Adenocarcinoma.

BACKGROUND & AIMS: Checkpoint immunotherapy is largely ineffective in pancreatic ductal adenocarcinoma (PDAC). The innate immune nuclear factor (NF)-κB pathway promotes PDAC cell survival and stromal fibrosis, and is driven by Interleukin-1 Receptor Associated Kinase-4 (IRAK4), but its impact on tumor immunity has not been directly investigated. METHODS: We interrogated The Cancer Genome Atlas data to identify the correlation between NF-κB and T cell signature, and a PDAC tissue microarray (TMA) to correlate IRAK4 activity with CD8+ T cell abundance. We performed RNA sequencing (RNA-seq) on IRAK4-deleted PDAC cells, and single-cell RNA-seq on autochthonous KPC (p48-Cre/TP53f/f/LSL-KRASG12D) mice treated with an IRAK4 inhibitor. We generated conditional IRAK4-deleted KPC mice and complementarily used IRAK4 inhibitors to determine the impact of IRAK4 on T cell immunity. RESULTS: We found positive correlation between NF-κB activity, IRAK4 and T cell exhaustion from The Cancer Genome Atlas. We observed inverse correlation between phosphorylated IRAK4 and CD8+ T cell abundance in a PDAC tissue microarray. Loss of IRAK4 abrogates NF-κB activity, several immunosuppressive factors, checkpoint ligands, and hyaluronan synthase 2, all of which drive T cell dysfunction. Accordingly, conditional deletion or pharmacologic inhibition of IRAK4 markedly decreased tumor desmoplasia and increased the abundance and activity of infiltrative CD4+ and CD8+ T cells in KPC tumors. Single-cell RNA-seq showed myeloid and fibroblast reprogramming toward acute inflammatory responses following IRAK4 inhibition. These changes set the stage for successful combination of IRAK4 inhibitors with checkpoint immunotherapy, resulting in excellent tumor control and markedly prolonged survival of KPC mice. CONCLUSION: IRAK4 drives T cell dysfunction in PDAC and is a novel, promising immunotherapeutic target.

Induction of cancer neoantigens facilitates development of clinically relevant models for the study of pancreatic cancer immunobiology.

Neoantigen burden and CD8 T cell infiltrate are associated with clinical outcome in pancreatic ductal adenocarcinoma (PDAC). A shortcoming of many genetic models of PDAC is the lack of neoantigen burden and limited T cell infiltrate. The goal of the present study was to develop clinically relevant models of PDAC by inducing cancer neoantigens in KP2, a cell line derived from the KPC model of PDAC. KP2 was treated with oxaliplatin and olaparib (OXPARPi), and a resistant cell line was subsequently cloned to generate multiple genetically distinct cell lines (KP2-OXPARPi clones). Clones A and E are sensitive to immune checkpoint inhibition (ICI), exhibit relatively high T cell infiltration, and have significant upregulation of genes involved in antigen presentation, T cell differentiation, and chemokine signaling pathways. Clone B is resistant to ICI and is similar to the parental KP2 cell line in terms of relatively low T cell infiltration and no upregulation of genes involved in the pathways noted above. Tumor/normal exome sequencing and in silico neoantigen prediction confirms successful generation of cancer neoantigens in the KP2-OXPARPi clones and the relative lack of cancer neoantigens in the parental KP2 cell line. Neoantigen vaccine experiments demonstrate that a subset of candidate neoantigens are immunogenic and neoantigen synthetic long peptide vaccines can restrain Clone E tumor growth. Compared to existing models, the KP2-OXPARPi clones better capture the diverse immunobiology of human PDAC and may serve as models for future investigations in cancer immunotherapies and strategies targeting cancer neoantigens in PDAC.

Convex Optimization for Nonequilibrium Steady States on a Hybrid Quantum Processor.

Finding the transient and steady state properties of open quantum systems is a central problem in various fields of quantum technologies. Here, we present a quantum-assisted algorithm to determine the steady states of open system dynamics. By reformulating the problem of finding the fixed point of Lindblad dynamics as a feasibility semidefinite program, we bypass several well-known issues with variational quantum approaches to solving for steady states. We demonstrate that our hybrid approach allows us to estimate the steady states of higher dimensional open quantum systems and discuss how our method can find multiple steady states for systems with symmetries.

Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer.

Gait recognition, the task of identifying an individual based on their unique walking style, can be difficult because walking styles can be influenced by external factors such as clothing, viewing angle, and carrying conditions. To address these challenges, this paper proposes a multi-model gait recognition system that integrates Convolutional Neural Networks (CNNs) and Vision Transformer. The first step in the process is to obtain a gait energy image, which is achieved by applying an averaging technique to a gait cycle. The gait energy image is then fed into three different models, DenseNet-201, VGG-16, and a Vision Transformer. These models are pre-trained and fine-tuned to encode the salient gait features that are specific to an individual's walking style. Each model provides prediction scores for the classes based on the encoded features, and these scores are then summed and averaged to produce the final class label. The performance of this multi-model gait recognition system was evaluated on three datasets, CASIA-B, OU-ISIR dataset D, and OU-ISIR Large Population dataset. The experimental results showed substantial improvement compared to existing methods on all three datasets. The integration of CNNs and ViT allows the system to learn both the pre-defined and distinct features, providing a robust solution for gait recognition even under the influence of covariates.

Fine-Tuned Temporal Dense Sampling with 1D Convolutional Neural Network for Human Action Recognition.

Human action recognition is a constantly evolving field that is driven by numerous applications. In recent years, significant progress has been made in this area due to the development of advanced representation learning techniques. Despite this progress, human action recognition still poses significant challenges, particularly due to the unpredictable variations in the visual appearance of an image sequence. To address these challenges, we propose the fine-tuned temporal dense sampling with 1D convolutional neural network (FTDS-1DConvNet). Our method involves the use of temporal segmentation and temporal dense sampling, which help to capture the most important features of a human action video. First, the human action video is partitioned into segments through temporal segmentation. Each segment is then processed through a fine-tuned Inception-ResNet-V2 model, where max pooling is performed along the temporal axis to encode the most significant features as a fixed-length representation. This representation is then fed into a 1DConvNet for further representation learning and classification. The experiments on UCF101 and HMDB51 demonstrate that the proposed FTDS-1DConvNet outperforms the state-of-the-art methods, with a classification accuracy of 88.43% on the UCF101 dataset and 56.23% on the HMDB51 dataset.

EnViTSA: Ensemble of Vision Transformer with SpecAugment for Acoustic Event Classification.

Recent successes in deep learning have inspired researchers to apply deep neural networks to Acoustic Event Classification (AEC). While deep learning methods can train effective AEC models, they are susceptible to overfitting due to the models' high complexity. In this paper, we introduce EnViTSA, an innovative approach that tackles key challenges in AEC. EnViTSA combines an ensemble of Vision Transformers with SpecAugment, a novel data augmentation technique, to significantly enhance AEC performance. Raw acoustic signals are transformed into Log Mel-spectrograms using Short-Time Fourier Transform, resulting in a fixed-size spectrogram representation. To address data scarcity and overfitting issues, we employ SpecAugment to generate additional training samples through time masking and frequency masking. The core of EnViTSA resides in its ensemble of pre-trained Vision Transformers, harnessing the unique strengths of the Vision Transformer architecture. This ensemble approach not only reduces inductive biases but also effectively mitigates overfitting. In this study, we evaluate the EnViTSA method on three benchmark datasets: ESC-10, ESC-50, and UrbanSound8K. The experimental results underscore the efficacy of our approach, achieving impressive accuracy scores of 93.50%, 85.85%, and 83.20% on ESC-10, ESC-50, and UrbanSound8K, respectively. EnViTSA represents a substantial advancement in AEC, demonstrating the potential of Vision Transformers and SpecAugment in the acoustic domain.

HGR-ViT: Hand Gesture Recognition with Vision Transformer.

Hand gesture recognition (HGR) is a crucial area of research that enhances communication by overcoming language barriers and facilitating human-computer interaction. Although previous works in HGR have employed deep neural networks, they fail to encode the orientation and position of the hand in the image. To address this issue, this paper proposes HGR-ViT, a Vision Transformer (ViT) model with an attention mechanism for hand gesture recognition. Given a hand gesture image, it is first split into fixed size patches. Positional embedding is added to these embeddings to form learnable vectors that capture the positional information of the hand patches. The resulting sequence of vectors are then served as the input to a standard Transformer encoder to obtain the hand gesture representation. A multilayer perceptron head is added to the output of the encoder to classify the hand gesture to the correct class. The proposed HGR-ViT obtains an accuracy of 99.98%, 99.36% and 99.85% for the American Sign Language (ASL) dataset, ASL with Digits dataset, and National University of Singapore (NUS) hand gesture dataset, respectively.

Recent Advances in Traffic Sign Recognition: Approaches and Datasets.

Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their ability to accurately recognize traffic signs. This makes traffic sign recognition a critical component of autonomous driving systems. To address this challenge, researchers have been exploring various approaches to traffic sign recognition, including machine learning and deep learning. Despite these efforts, the variability of traffic signs across different geographical regions, complex background scenes, and changes in illumination still poses significant challenges to the development of reliable traffic sign recognition systems. This paper provides a comprehensive overview of the latest advancements in the field of traffic sign recognition, covering various key areas, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. The paper also delves into the commonly used traffic sign recognition datasets and their associated challenges. Additionally, this paper sheds light on the limitations and future research prospects of traffic sign recognition.

Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning.

Identifying people's identity by using behavioral biometrics has attracted many researchers' attention in the biometrics industry. Gait is a behavioral trait, whereby an individual is identified based on their walking style. Over the years, gait recognition has been performed by using handcrafted approaches. However, due to several covariates' effects, the competence of the approach has been compromised. Deep learning is an emerging algorithm in the biometrics field, which has the capability to tackle the covariates and produce highly accurate results. In this paper, a comprehensive overview of the existing deep learning-based gait recognition approach is presented. In addition, a summary of the performance of the approach on different gait datasets is provided.

Gait-ViT: Gait Recognition with Vision Transformer.

Identifying an individual based on their physical/behavioral characteristics is known as biometric recognition. Gait is one of the most reliable biometrics due to its advantages, such as being perceivable at a long distance and difficult to replicate. The existing works mostly leverage Convolutional Neural Networks for gait recognition. The Convolutional Neural Networks perform well in image recognition tasks; however, they lack the attention mechanism to emphasize more on the significant regions of the image. The attention mechanism encodes information in the image patches, which facilitates the model to learn the substantial features in the specific regions. In light of this, this work employs the Vision Transformer (ViT) with an attention mechanism for gait recognition, referred to as Gait-ViT. In the proposed Gait-ViT, the gait energy image is first obtained by averaging the series of images over the gait cycle. The images are then split into patches and transformed into sequences by flattening and patch embedding. Position embedding, along with patch embedding, are applied on the sequence of patches to restore the positional information of the patches. Subsequently, the sequence of vectors is fed to the Transformer encoder to produce the final gait representation. As for the classification, the first element of the sequence is sent to the multi-layer perceptron to predict the class label. The proposed method obtained 99.93% on CASIA-B, 100% on OU-ISIR D and 99.51% on OU-LP, which exhibit the ability of the Vision Transformer model to outperform the state-of-the-art methods.

Assessment of in-cabin noise of wide-body aircrafts.

The aviation industry has seen dramatic growth over the decades till the recent disruption due to the COVID-19 pandemic. Moreover, long-haul routes with a distance of more than 4000 km are common for major airlines worldwide. Therefore, aircraft cabin noise assessment is essential, especially in long-haul flights, for passenger and flight crew health wellness. In this paper, the cabin noise of five wide-body aircraft, namely Airbus A330-300ER, A350-900, A380-800, and Boeing B777-200ER and B787-900, was recorded using a calibrated in-house developed smartphone application. The sound pressure levels of in-cabin noise have been measured on two different decibel scales, namely, A-weighted [dB(A)] and C-weighted scales [dB(C)]. The sound pressure levels of Airbus A380-800 were lowest among selected models, while the in-cabin pressure level values of Airbus A350-900 were maximum. However, the difference in decibel levels between the aircraft is minimal as it is within 3 dB.

A Case of a Pathological Complete Response to Neoadjuvant Nivolumab plus Ipilimumab in Periampullary Adenocarcinoma.

Herein, we report on a patient with known Lynch syndrome and periampullary adenocarcinoma that exhibited a pathological complete response to neoadjuvant nivolumab plus ipilimumab. Two MSH2 mutations, high microsatellite instability, high tumor mutational burden, and elevated PD-L1 expression were identified by next-generation sequencing and immunohistochemistry. Following FOLFIRINOX (Fluorouracil/Leucovorin/Irinotecan/Oxaliplatin) administration and disease progression, nivolumab (1 mg/kg) and ipilimumab (3 mg/kg) were administered every 3 weeks for four total cycles. The patient responded well with minimal adverse effects and significant improvement in epigastric pain, appetite, and body weight. She then underwent resection consisting of pancreaticoduodenectomy, which demonstrated pathological complete response. Complete genomic profiling of periampullary carcinomas is crucial for optimal treatment selection as true ampullary masses and pancreatic ductal adenocarcinoma have different genetic profiles. This case provides an example of a patient who may have further benefited from first-line nivolumab plus ipilimumab to avoid the reduced efficacy and significant side effects associated with chemotherapy. KEY POINTS: A patient with known Lynch syndrome and ampullary adenocarcinoma harboring two MSH2 mutations, high microsatellite instability (MSI-high), high tumor mutational burden (TMB), and elevated PD-L1 expression achieved pathological complete response with neoadjuvant nivolumab plus ipilimumab. The combination of nivolumab plus ipilimumab may be a better first-line option for patients with ampullary adenocarcinomas harboring deficient mismatch repair, MSI-high, and high TMB. Complete genomic profiling of periampullary adenocarcinomas is crucial for optimal treatment selection as true ampullary masses and pancreatic ductal adenocarcinoma have different genetic profiles. The presence of either MSI-high or high TMB could be an appropriate predictive biomarker for response to nivolumab plus ipilimumab in the context of Lynch syndrome.

Development of resistance to FAK inhibition in pancreatic cancer is linked to stromal depletion.

OBJECTIVE: We investigated how pancreatic cancer developed resistance to focal adhesion kinase (FAK) inhibition over time. DESIGN: Pancreatic ductal adenocarcinoma (PDAC) tumours from KPC mice (p48-CRE; LSL-KRasG12D/wt; p53flox/wt) treated with FAK inhibitor were analysed for the activation of a compensatory survival pathway in resistant tumours. We identified pathways involved in the regulation of signal transducer and activator of transcription 3 (STAT3) signalling on FAK inhibition by gene set enrichment analysis and verified these outcomes by RNA interference studies. We also tested combinatorial approaches targeting FAK and STAT3 in syngeneic transplantable mouse models of PDAC and KPC mice. RESULTS: In KPC mice, the expression levels of phosphorylated STAT3 (pSTAT3) were increased in PDAC cells as they progressed on FAK inhibitor therapy. This progression corresponded to decreased collagen density, lowered numbers of SMA+ fibroblasts and downregulation of the transforming growth factor beta (TGF-ß)/SMAD signalling pathway in FAK inhibitor-treated PDAC tumours. Furthermore, TGF-ß production by fibroblasts in vitro drives repression of STAT3 signalling and enhanced responsiveness to FAK inhibitor therapy. Knockdown of SMAD3 in pancreatic cancer cells abolished the inhibitory effects of TGF-ß on pSTAT3. We further found that tumour-intrinsic STAT3 regulates the durability of the antiproliferative activity of FAK inhibitor, and combinatorial targeting of FAK and Janus kinase/STAT3 act synergistically to suppress pancreatic cancer progression in mouse models. CONCLUSION: Stromal depletion by FAK inhibitor therapy leads to eventual treatment resistance through the activation of STAT3 signalling. These data suggest that, similar to tumour-targeted therapies, resistance mechanisms to therapies targeting stromal desmoplasia may be critical to treatment durability.

RNA structure replaces the need for U2AF2 in splicing.

RNA secondary structure plays an integral role in catalytic, ribosomal, small nuclear, micro, and transfer RNAs. Discovering a prevalent role for secondary structure in pre-mRNAs has proven more elusive. By utilizing a variety of computational and biochemical approaches, we present evidence for a class of nuclear introns that relies upon secondary structure for correct splicing. These introns are defined by simple repeat expansions of complementary AC and GT dimers that co-occur at opposite boundaries of an intron to form a bridging structure that enforces correct splice site pairing. Remarkably, this class of introns does not require U2AF2, a core component of the spliceosome, for its processing. Phylogenetic analysis suggests that this mechanism was present in the ancestral vertebrate lineage prior to the divergence of tetrapods from teleosts. While largely lost from land dwelling vertebrates, this class of introns is found in 10% of all zebrafish genes.

Folate Receptor α-Targeted 89Zr-M9346A Immuno-PET for Image-Guided Intervention with Mirvetuximab Soravtansine in Triple-Negative Breast Cancer.

Folate receptor α (FRα) is a well-studied tumor biomarker highly expressed in many epithelial tumors such as breast, ovarian, and lung cancers. Mirvetuximab soravtansine (IMGN853) is the antibody-drug conjugate of FRα-binding humanized monoclonal antibody M9346A and cytotoxic maytansinoid drug DM4. IMGN853 is currently being evaluated in multiple clinical trials, in which the immunohistochemical evaluation of an archival tumor or biopsy specimen is used for patient screening. However, limited tissue collection may lead to inaccurate diagnosis due to tumor heterogeneity. Herein, we developed a zirconium-89 (89Zr)-radiolabeled M9346A (89Zr-M9346A) as an immuno-positron emission tomography (immuno-PET) radiotracer to evaluate FRα expression in triple-negative breast cancer (TNBC) patients, providing a novel means to guide intervention with therapeutic IMGN853. In this study, we verified the binding specificity and immunoreactivity of 89Zr-M9346A by in vitro studies in FRαhigh cells (HeLa) and FRαlow cells (OVCAR-3). In vivo PET/computed tomography (PET/CT) imaging in HeLa xenografts and TNBC patient-derived xenograft (PDX) mouse models with various levels of FRα expression demonstrated its targeting specificity and sensitivity. Following PET imaging, the treatment efficiencies of IMGN853, pemetrexed, IMGN853 + pemetrexed, paclitaxel, and saline were assessed in FRαhigh and FRαlow TNBC PDX models. The correlation between 89Zr-M9346A tumor uptake and treatment response using IMGN853 in FRαhigh TNBC PDX model suggested the potential of 89Zr-M9346A PET as a noninvasive tool to prescreen patients based on the in vivo PET imaging for IMGN853-targeted treatment.