Miracle-dz/UniFAR-FTUs huggingface dataset:
Tumor-Infiltrating B- and T-Cell Repertoire in Pancreatic Cancer Associated With Host and Tumor Features

Background Infiltrating B and T cells have been observed in several tumor tissues, including pancreatic ductal adenocarcinoma (PDAC). The majority known PDAC risk factors point to a chronic inflammatory process leading to different forms of immunological infiltration. Understanding pancreatic tumor infiltration may lead to improved knowledge of this devastating disease. Methods We extracted the immunoglobulins (IGs) and T cell receptors (TCRs) from RNA-sequencing of 144 PDAC from TCGA and 180 pancreatic normal tissue from GTEx. We used Shannon entropy to find differences in IG/TCR diversity. We performed a clonotype analysis considering the IG clone definition (same V and J segments, same CDR3 length, and 90% nucleotide identity between CDR3s) to study differences among the tumor samples. Finally, we performed an association analysis to find host and tumor factors associated with the IG/TCR. Results PDAC presented a richer and more diverse IG and TCR infiltration than normal pancreatic tissue. A higher IG infiltration was present in heavy smokers and females and it was associated with better overall survival. In addition, specific IG clonotypes classified samples with better prognosis explaining 24% of the prognosis phenotypic variance. On the other hand, a larger TCR infiltration was present in patients with previous history of diabetes and was associated with lower nonantigen load. Conclusions Our findings support PDAC subtyping according to its immune repertoire landscape with a potential impact on the understanding of the inflammatory basis of PDAC risk factors as well as the design of treatment options and prognosis monitoring.
TLR-Agonist Mediated Enhancement of Antibody-Dependent Effector Functions as Strategy For an HIV-1 Cure

Background The current treatment for HIV-1 is based on blocking various stages in the viral replication cycle using combination antiretroviral therapy (ART). Even though ART effectively controls the infection, it is not curative, and patients must therefore continue treatment life-long. Aim Here we review recent literature investigating the single or combined effect of toll-like receptor (TLR) agonists and broadly neutralizing antibodies (bNAbs) with the objective to evaluate the evidence for this combination as a means towards an HIV-1 cure. Results Multiple preclinical studies found significantly enhanced killing of HIV-1 infected cells by TLR agonist-induced innate immune activation or by Fc-mediated effector functions following bNAb administration. However, monotherapy with either agent did not lead to sustained HIV-1 remission in clinical trials among individuals on long-term ART. Notably, findings in non-human primates suggest that a combination of TLR agonists and bNAbs may be able to induce long-term remission after ART cessation and this approach is currently being further investigated in clinical trials. Conclusion Preclinical findings show beneficial effects of either TLR agonist or bNAb administration for enhancing the elimination of HIV-1 infected cells. Further, TLR agonist-mediated stimulation of innate effector functions in combination with bNAbs may enhance antibody-dependent cellular cytotoxicity and non-human primate studies have shown promising results for this combination strategy. Factors such as immune exhaustion, proviral bNAb sensitivity and time of intervention might impact the clinical success.
Regulatory T Cells in Chronic Heart Failure

Heart failure is a global problem with high hospitalization and mortality rates. Inflammation and immune dysfunction are involved in this disease. Owing to their unique function, regulatory T cells (Tregs) have reacquired attention recently. They participate in immunoregulation and tissue repair in the pathophysiology of heart failure. Tregs are beneficial in heart by suppressing excessive inflammatory responses and promoting stable scar formation in the early stage of heart injury. However, in chronic heart failure, the phenotypes and functions of Tregs changed. They transformed into an antiangiogenic and profibrotic cell type. In this review, we summarized the functions of Tregs in the development of chronic heart failure first. Then, we focused on the interactions between Tregs and their target cells. The target cells of Tregs include immune cells (such as monocytes/macrophages, dendritic cells, T cells, and B cells) and parenchymal cells (such as cardiomyocytes, fibroblasts, and endothelial cells). Next-generation sequencing and gene editing technology make immunotherapy of heart failure possible. So, prospective therapeutic approaches based on Tregs in chronic heart failure had also been evaluated.
A Different View for an Old Disease: NEDDylation and Other Ubiquitin-Like Post-Translational Modifications in Chronic Lymphocytic Leukemia

Despite the enormous amount of molecular data obtained over the years, the molecular etiology of chronic lymphocytic leukemia (CLL) is still largely unknown. All that information has enabled the development of new therapeutic approaches that have improved life expectancy of the patients but are still not curative. We must increase our knowledge of the molecular alterations responsible for the characteristics common to all CLL patients. One of such characteristics is the poor correlation between mRNA and protein expression, that suggests a role of post-translational mechanisms in CLL physiopathology. Drugs targeting these processes have indeed demonstrated an effect either alone or in combination with other aimed at specific pathways. A recent article unveiled an increment in ubiquitin-like modifications in CLL, with many protein members of relevant pathways affected. Interestingly, the inhibition of the NEDD8-activating protein NAE reverted a substantial number of those modifications. The present review gets the scarce data published about the role of NEDDylation in CLL together and establishes connections to what is known from other neoplasias, thus providing a new perspective to the underlying mechanisms in CLL.
Nonhuman and Nonhuman-Human Communication: Some Issues and Questions

Deciphering nonhuman communication – particularly nonhuman vocal communication – has been a longstanding human quest. We are, for example, fascinated by the songs of birds and whales, the grunts of apes, the barks of dogs, and the croaks of frogs; we wonder about their potential meaning and their relationship to human language. Do these utterances express little more than emotional states, or do they convey actual bits and bytes of concrete information? Humans’ numerous attempts to decipher nonhuman systems have, however, progressed slowly. We still wonder why only a small number of species are capable of vocal learning, a trait that, because it allows for innovation and adaptation, would seem to be a prerequisite for most language-like abilities. Humans have also attempted to teach nonhumans elements of our system, using both vocal and nonvocal systems. The rationale for such training is that the extent of success in instilling symbolic reference provides some evidence for, at the very least, the cognitive underpinnings of parallels between human and nonhuman communication systems. However, separating acquisition of reference from simple object-label association is not a simple matter, as reference begins with such associations, and the point at which true reference emerges is not always obvious. I begin by discussing these points and questions, predominantly from the viewpoint of someone studying avian abilities. I end by examining the question posed by Premack: do nonhumans that have achieved some level of symbolic reference then process information differently from those that have not? I suggest the answer is likely “yes,” giving examples from my research on Grey parrots (Psittacus erithacus).
Postpartum Depression Is Associated With Altered Neural Connectivity Between Affective and Mentalizing Regions During Mother-Infant Interactions

Although there has been growing interest in mood-related neural alterations in women in the initial weeks postpartum, recent work has demonstrated that postpartum depression often lingers for months or years following birth. However, research evaluating the impact of depression on maternal brain function during mother-infant interactions in the late postpartum period is lacking. The current study tested the hypothesis that depressive symptoms at 12-months postpartum are associated with neural alterations in affective and social neural regions, using near-infrared spectroscopy during in vivo mother-infant interactions. Participants were 23 birth mothers of 12-month-old infants (60% boys). While undergoing near-infrared spectroscopy, mothers engaged in an ecologically valid interactive task in which they looked at an age-appropriate book with their infants. Mothers also reported on their depressive symptoms in the past week and were rated on their observed levels of maternal sensitivity during mother-infant play. Greater depressive severity at 12-months postpartum was related to lower connectivity between the right temporoparietal junction and the lateral prefrontal cortex, but greater connectivity between the right temporoparietal junction and anterior medial prefrontal cortex during mother-infant interaction. Given the putative functions of these neural regions within the maternal brain network, our findings suggest that in the context of depression, postpartum mothers' mentalizing about her infants' thoughts and feelings may be related to lower ability to express and regulate her own emotions, but greater ability to engage in emotional bonding with her infant. Future work should explore how connectivity among these regions is associated with longitudinal changes in maternal behavior, especially in the context of changes in mothers' depressive symptoms (e.g., with treatment) over time.
Multiview Common Subspace Clustering via Coupled Low Rank Representation

Multi-view subspace clustering (MVSC) finds a shared structure in latent low-dimensional subspaces of multi-view data to enhance clustering performance. Nonetheless, we observe that most existing MVSC methods neglect the diversity in multi-view data by considering only the common knowledge to find a shared structure either directly or by merging different similarity matrices learned for each view. In the presence of noise, this predefined shared structure becomes a biased representation of the different views. Thus, in this article, we propose a MVSC method based on coupled low-rank representation to address the above limitation. Our method first obtains a low-rank representation for each view, constrained to be a linear combination of the view-specific representation and the shared representation by simultaneously encouraging the sparsity of view-specific one. Then, it uses the k-block diagonal regularizer to learn a manifold recovery matrix for each view through respective low-rank matrices to recover more manifold structures from them. In this way, the proposed method can find an ideal similarity matrix by approximating clustering projection matrices obtained from the recovery structures. Hence, this similarity matrix denotes our clustering structure with exactly k connected components by applying a rank constraint on the similarity matrix’s relaxed Laplacian matrix to avoid spectral post-processing of the low-dimensional embedding matrix. The core of our idea is such that we introduce dynamic approximation into the low-rank representation to allow the clustering structure and the shared representation to guide each other to learn cleaner low-rank matrices that would lead to a better clustering structure. Therefore, our approach is notably different from existing methods in which the local manifold structure of data is captured in advance. Extensive experiments on six benchmark datasets show that our method outperforms 10 similar state-of-the-art compared methods in six evaluation metrics.
TLDS: A Transfer-Learning-Based Delivery Station Location Selection Pipeline

Delivery stations play important roles in logistics systems. Well-designed delivery station planning can improve delivery efficiency significantly. However, existing delivery station locations are decided by experts, which requires much preliminary research and data collection work. It is not only time consuming but also expensive for logistics companies. Therefore, in this article, we propose a data-driven pipeline that can transfer expert knowledge among cities and automatically allocate delivery stations. Based on existing well-designed station location planning in the source city, we first train a model to learn the expert knowledge about delivery range selection for each station. Then we transfer the learned knowledge to a new city and design three strategies to select delivery stations for the new city. Due to the differences in characteristics among different cities, we adopt a transfer learning method to eliminate the domain difference so that the model can be adapted to a new city well. Finally, we conduct extensive experiments based on real-world datasets and find the proposed method can solve the problem well.
Towards a holistic framework for delivering quality GIS education within and across disciplines

The current GIS education framework has gaps, undervalued elements, and decentralized components that make it difficult to effectively relate, teach, learn, and assess learning key constructs within and across disciplines. Building upon critical synthesis, analysis, and extension of multidisciplinary literature, we develop a student‐centered framework that places the 2D learning GIS space within the broader 3D educational space defined by cognitive, affective, and psychomotor domains of learning. The modalities of GIS learning are discussed in the contexts of the “circle of GIS thinking,” “triangle of GIS practice,” and “triangle of affective GIS. ” The framework suggests that well‐rounded GIS individuals possess hard and soft (non‐)geospatial knowledge, skills, and abilities gained through learning by thinking, doing, and feeling. It is coherent, intuitive, extensible, and in sync with modern GIS perspectives and knowledge, and multimodal ways of mastering this subject. We demonstrate how the framework informs design and content considerations for hybrid minds‐on, hands‐on, and “body‐on” classroom assessments that mirror the nature of GIS. We conclude that the framework shows considerable potential to facilitate consistent and persistent delivery of quality GIS education.
DILSA+: Predicting Urban Dispersal Events through Deep Survival Analysis with Enhanced Urban Features

Urban dispersal events occur when an unexpectedly large number of people leave an area in a relatively short period of time. It is beneficial for the city authorities, such as law enforcement and city management, to have an advance knowledge of such events, as it can help them mitigate the safety risks and handle important challenges such as managing traffic, and so forth. Predicting dispersal events is also beneficial to Taxi drivers and/or ride-sharing services, as it will help them respond to an unexpected demand and gain competitive advantage. Large urban datasets such as detailed trip records and point of interest (POI) data make such predictions achievable. The related literature mainly focused on taxi demand prediction. The pattern of the demand was assumed to be repetitive and proposed methods aimed at capturing those patterns. However, dispersal events are, by definition, violations of those patterns and are, understandably, missed by the methods in the literature. We proposed a different approach in our prior work [32]. We showed that dispersal events can be predicted by learning the complex patterns of arrival and other features that precede them in time. We proposed a survival analysis formulation of this problem and proposed a two-stage framework (DILSA), where a deep learning model predicted the survival function at each point in time in the future. We used that prediction to determine the time of the dispersal event in the future, or its non-occurrence. However, DILSA is subject to a few limitations. First, based on evidence from the data, mobility patterns can vary through time at a given location. DILSA does not distinguish between different mobility patterns through time. Second, mobility patterns are also different for different locations. DILSA does not have the capability to directly distinguish between different locations based on their mobility patterns. In this article, we address these limitations by proposing a method to capture the interaction between POIs and mobility patterns and we create vector representations of locations based on their mobility patterns. We call our new method DILSA+. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 2014 to 2016. Results show that DILSA+ can predict events in the next 5 hours with an F1-score of 0.66. It is significantly better than DILSA and the state-of-the-art deep learning approaches for taxi demand prediction.
An Efficient Hybrid Optimization Algorithm for Image Compression

: In this work, a novel image compression approach is developed that is processed in several series of technologies. Here, the first process is the image segmentation and it is done using Adaptive ACM that partitions or segments the image into two regions such as ROI as well as non-ROI. Here, the adaptiveness of this ACM is determined with the idea of optimization algorithm. To handle the ROI regions, the JPEG-LS technique is exploited and to handle the non-ROI region the wavelet-based lossy compression technique is utilized. The outcome of both the JPEG-LS technique, as well as a wavelet-based compression approach is integrated with respect to the bit-stream amalgamation in order to produce the compressed image. Then, the compressed image is exploited to the image decompression that will be the overturn process of compression. It will comprise the bitstream separation that is subsequently individually process in both the wavelet-based decomposition and JPEG-LS decoding for obtaining the non-ROI regions and ROI. At last, the original image is obtained accurately. Moreover, the main objective of this paper falls in the adaptiveness under optimization. The maximum iteration and weighting factor in ACM are optimally chosen and for this a novel hybrid optimization technique is proposed, which hybridizes the concept of Differential Evolution method with Monarch Butterfly Optimization Algorithm. Here, the proposed method is compared with the conventional methods in order to shows its effectiveness for image compression.
CMAR_A_222620 10135..10143

Chao Jiang 1,2 Zhenghong Ma Guoan Zhang Xigui Yang Qin Du Weibo Wang 1Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, People’s Republic of China; 2Departments of Oncology, Affiliated Hospital of Shandong Academy of Medical Sciences, Shandong First Medical University, Jinan, Shandong 250031, People’s Republic of China; 3Forensic Science Center, Jining Medical University, Jining, Shandong 272067, People’s Republic of China; 4Cancer Pathology Institute, Jining Medical University, Jining, Shandong 272000, People’s Republic of China; 5Departments of Oncology, Affiliated Hospital of Jining Medical University, Jining, Shandong 272000, People’s Republic of China Objective: Casein kinase 2 a1 (CSNK2A1) has been shown to be involved in tumorigenesis by enhancing several oncogenic signaling pathways in various cancers. However, the function and mechanism of CSNK2A1 in gastric cancer remain unclear, and this study aimed to elucidate the role of CSNK2A1 in gastric cancer. Methods: CSNK2A1 expression was assessed by Western blot and qPCR in four gastric cancer (GC) cell lines and one normal gastric epithelial cell line. Stable cancer cell lines with CSNK2A1 gene overexpression or knockdown were established to investigate the function and mechanism of CSNK2A1 in GC cells. Results: CSNK2A1 expression was higher in GC cells than in normal gastric epithelial cells. Stable overexpression of CSNK2A1 in SNU216 cells significantly increased cellular proliferation, invasion, and migration. Silencing CSNK2A1 expression in SGC-790 cells effectively inhibited its oncogenic function. We further verified that epithelial-mesenchymal transition (EMT) was affected by CSNK2A1 and that CSNK2A1 promotes GC cell invasion through the PI3K-Akt-mTOR signaling pathway. Conclusion: Our findings suggested that CSNK2A1 plays important oncogenic roles in GC invasion via EMT and the PI3K-Akt-mTOR signaling pathway and that CSNK2A1 may serve as a novel prognostic and/or therapeutic target in GC.
Factors associated with depression in cancer patients during chemotherapy*

Objective: to analyze the factors associated with depression in cancer patients during chemotherapy. Methods: a descriptive cross-sectional study of 208 patients undergoing chemotherapy. A general characterization questionnaire and the Beck Depression Inventory were used. Results: most were women, between 40 and 79 years old, married, Catholic, retired/housewives, with elementary school and had no depression (71.2%), while 17.3% had depression and 11.5% dysphoria. In the associations, it was found statistical significance in the associations of the Beck Depression Inventory with surgery (p=0.002), time since surgery (p=0.014), time since chemotherapy onset (p=0.030), and side effects of chemotherapy (p=0.019). Conclusion: there was a low incidence of depression, but the frequency was higher in patients who did not undergo concomitant surgery, who had started chemotherapy for up to six months and who reported more side effects during treatment. Descriptors: Neoplasms; Depression; Drug Therapy; Oncology Nursing.
Chemoperception of Specific Amino Acids Controls Phytopathogenicity in <named-content content-type='genus-species'>Pseudomonas syringae</named-content> pv. tomato

Chemotaxis has been associated with the pathogenicity of bacteria in plants and was found to facilitate bacterial entry through stomata and wounds. However, knowledge regarding the plant signals involved in this process is scarce. We have addressed this issue using Pseudomonas syringae pv. tomato, which is a foliar pathogen that causes bacterial speck in tomato. We show that the chemoreceptor P. syringae pv. tomato PscA (PsPto-PscA) recognizes specifically and with high affinity L-Asp, L-Glu, and D-Asp. The mutation of the chemoreceptor gene largely reduced chemotaxis to these ligands but also altered cyclic di-GMP (c-di-GMP) levels, biofilm formation, and motility, pointing to cross talk between different chemosensory pathways. Furthermore, the PsPto-PscA mutant strain showed reduced virulence in tomato. Asp and Glu are the most abundant amino acids in plants and in particular in tomato apoplasts, and we hypothesize that this receptor may have evolved to specifically recognize these compounds to facilitate bacterial entry into the plant. Infection assays with the wild-type strain showed that the presence of saturating concentrations of D-Asp also reduced bacterial virulence. IMPORTANCE There is substantive evidence that chemotaxis is a key requisite for efficient pathogenesis in plant pathogens. However, information regarding particular bacterial chemoreceptors and the specific plant signal that they sense is scarce. Our work shows that the phytopathogenic bacterium Pseudomonas syringae pv. tomato mediates not only chemotaxis but also the control of pathogenicity through the perception of the plant abundant amino acids Asp and Glu. We describe the specificity of the perception of Land D-Asp and L-Glu by the PsPto-PscA chemoreceptor and the involvement of this perception in the regulation of pathogenicity-related traits. Moreover, a saturating concentration of D-Asp reduces bacterial virulence, and we therefore propose that ligand-mediated interference of key chemoreceptors may be an alternative strategy to control virulence.