Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationships Between Colorectal Cancer Survivors’ Positive Psychology, Symptoms, and Quality of Life

This cross-sectional study examined colorectal cancer (CRC) survivors’ symptom and symptom cluster characteristics (occurrence, frequency, and severity), positive psychology (benefit-finding and post-traumatic growth), and quality of life (QoL), and determined whether positive psychology moderates symptoms and QoL relationship during acute cancer survivorship, time from diagnosis to treatment completion. A total of 117 CRC survivors completed demographics, symptoms, QoL, and positive psychology questionnaires. Descriptive statistics, multiple linear regression, and moderation analyses were performed. Participants reported high QoL (94%, M = 5.15) and moderate-high positive psychology (75%, M = 3.21). Nineteen symptoms and five symptom clusters were inversely related to QoL (p < .05). Positive psychology (M = ~≥3.24) moderated the relationship of QoL (p < .05) with symptoms occurrence (n = 10), symptom severity (n = 1), and with the generalized symptom cluster (weakness, fatigue, dizziness, drowsy, sleep disturbances, and pain). Positive psychology aids in symptom management and improves QoL. Nurses are poised to identify, prevent, promote, and advocate self-management skills to improve health-related outcomes.
Prognostic significance of HDL-C on long-term mortality in patients with COVID-19 pneumonia in the Turkish population: A potential mechanism for population differences

Coronavirus disease 2019 (COVID-19) is diagnosed by the evidence of the presence of multiple phenotypes, including thrombosis, inflammation, and alveolar and myocardial damage, which can cause severe illness and mortality. High-density lipoprotein cholesterol (HDL-C) has pleiotropic properties, including anti-inflammatory, anti-infectious, antithrombotic, and endothelial cell protective effects. The aim of this study was to investigate the HDL-C levels and one-year mortality after the first wave of patients with COVID-19 was hospitalized. Data from 101 patients with COVID-19 were collected for this single-center retrospective study. Lipid parameters were collected on the admission. The relationship between lipid parameters and long-term mortality was investigated. The mean age of the non-survivor group (n = 38) was 68.8 ± 14.1 years, and 55% were male. The HDL-C levels were significantly lower in the non-survivors group compared with the survivors (26.9 ± 9.5 vs 36.8 ± 12.8 mg/dl, respectively p < 0.001). Multivariate regression analysis determined that age, C-reactive protein, D-dimer, hypertension, and HDL-C as independent predictors for the development of COVID-19 mortality. HDL-C levels <30.5 mg/dl had 71% sensitivity and 68% specificity to predict one-year mortality after COVID-19. The findings of this study showed that HDL-C is a predictor of one-year mortality in Turkish patients with COVID-19. COVID-19 is associated with decreased lipid levels, and it is an indicator of the inflammatory burden and increased mortality rate. The consequences of long-term metabolic dysregulations in patients that have recovered from COVID-19 still need to be understood.
Boosting Photon-Efficient Image Reconstruction With A Unified Deep Neural Network

Photon-efficient imaging, which captures 3D images with single-photon sensors, has enabled a wide range of applications. However, two major challenges limit the reconstruction performance, i.e., the low photon counts accompanied by low signal-to-background ratio (SBR) and the multiple returns. In this paper, we propose a unified deep neural network that, for the first time, explicitly addresses these two challenges, and simultaneously recovers depth maps and intensity images from photon-efficient measurements. Starting from a general image formation model, our network is constituted of one encoder, where a non-local block is utilized to exploit the long-range correlations in both spatial and temporal dimensions of the raw measurement, and two decoders, which are designed to recover depth and intensity, respectively. Meanwhile, we investigate the statistics of the background noise photons and propose a noise prior block to further improve the reconstruction performance. The proposed network achieves decent reconstruction fidelity even under extremely low photon counts / SBR and heavy blur caused by the multiple-return effect, which significantly surpasses the existing methods. Moreover, our network trained on simulated data generalizes well to real-world imaging systems, which greatly extends the application scope of photon-efficient imaging in challenging scenarios with a strict limit on optical flux. Code is available at https://github.com/JiayongO-O/PENonLocal.
Computational Optics for Mobile Terminals in Mass Production

Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task. Due to the limitation on volume and the demand for mass production, existing mobile terminals cannot rectify optical degradation. In this work, we systematically construct the perturbed lens system model to illustrate the relationship between the deviated system parameters and the spatial frequency response (SFR) measured from photographs. To further address this issue, an optimization framework is proposed based on this model to build proxy cameras from the machining samples’ SFRs. Engaging with the proxy cameras, we synthetic data pairs, which encode the optical aberrations and the random manufacturing biases, for training the learning-based algorithms. In correcting aberration, although promising results have been shown recently with convolutional neural networks, they are hard to generalize to stochastic machining biases. Therefore, we propose a dilated Omni-dimensional dynamic convolution (DOConv) and implement it in post-processing to account for the manufacturing degradation. Extensive experiments which evaluate multiple samples of two representative devices demonstrate that the proposed optimization framework accurately constructs the proxy camera. And the dynamic processing model is well-adapted to manufacturing deviations of different cameras, realizing perfect computational photography. The evaluation shows that the proposed method bridges the gap between optical design, system machining, and post-processing pipeline, shedding light on the joint of image signal reception (lens and sensor) and image signal processing (ISP).
Beyond preferential attachment: falling of stars and survival of superstars

Numerous studies over the past decades established that real-world networks typically follow preferential attachment and detachment principles. Subsequently, this implies that degree fluctuations monotonically increase while rising up the ‘degree ladder’, causing high-degree nodes to be prone for attachment of new edges and for detachment of existing ones. Despite the extensive study of node degrees (absolute popularity), many domains consider node ranks (relative popularity) as of greater importance. This raises intriguing questions—what dynamics are expected to emerge when observing the ranking of network nodes over time? Does the ranking of nodes present similar monotonous patterns to the dynamics of their corresponding degrees? In this paper, we show that surprisingly the answer is not straightforward. By performing both theoretical and empirical analyses, we demonstrate that preferential principles do not apply to the temporal changes in node ranking. We show that the ranking dynamics follows a non-monotonous curve, suggesting an inherent partition of the nodes into qualitatively distinct stability categories. These findings provide plausible explanations to observed yet hitherto unexplained phenomena, such as how superstars fortify their ranks despite massive fluctuations in their degrees, and how stars are more prone to rank instability.
Modelling predicts differences in chimeric antigen receptor T-cell signalling due to biological variability

In recent decades, chimeric antigen receptors (CARs) have been successfully used to generate engineered T cells capable of recognizing and eliminating cancer cells. The structure of CARs typically includes costimulatory domains, which enhance the T-cell response upon antigen encounter. However, it is not fully known how those co-stimulatory domains influence cell activation in the presence of biological variability. In this work, we used mathematical modelling to elucidate how the inclusion of one such costimulatory molecule, CD28, impacts the response of a population of CAR T cells under different sources of variability. Particularly, we demonstrate that CD28-bearing CARs mediate a faster and more consistent population response under both target antigen variability and kinetic rate variability. Next, we identify kinetic parameters that have the most impact on cell response time. Finally, based on our findings, we propose that enhancing the catalytic activity of lymphocyte-specific protein tyrosine kinase can result in drastically reduced and more consistent response times among heterogeneous CAR T-cell populations.
Biochemical and Structural Study of RuvC and YqgF from Deinococcus radiodurans

Deinococcus radiodurans is one of the most radioresistant bacteria in the world due to its robust DNA damage response and repair abilities, which are contributed by its efficient homologous recombination repair system. However, the late steps of homologous recombination, especially the Holliday junction (HJ) resolution process, have not yet been well-studied in D. radiodurans. ABSTRACT Deinococcus radiodurans possesses robust DNA damage response and repair abilities, and this is mainly due to its efficient homologous recombination repair system, which incorporates an uncharacterized Holliday junction (HJ) resolution process. D. radiodurans encodes two putative HJ resolvase (HJR) homologs: RuvC (DrRuvC) and YqgF (DrYqgF). Here, both DrRuvC and DrYqgF were identified as essential proteins for the survival of D. radiodurans. The crystal structures and the biochemical properties of DrRuvC and DrYqgF were also studied. DrRuvC crystallized as a homodimer, while DrYqgF crystallized as a monomer. DrRuvC could preferentially cleave HJ at the consensus 5′-(G/C)TC↓(G/C)-3′ sequence and could prefer using Mn2+ for catalysis in vitro, which would be different from the preferences of the other previously characterized RuvCs. On the other hand, DrYqgF was identified as a Mn2+-dependent RNA 5′–3′ exo/endonuclease with a sequence preference for poly(A) and without any HJR activity. IMPORTANCE Deinococcus radiodurans is one of the most radioresistant bacteria in the world due to its robust DNA damage response and repair abilities, which are contributed by its efficient homologous recombination repair system. However, the late steps of homologous recombination, especially the Holliday junction (HJ) resolution process, have not yet been well-studied in D. radiodurans. We characterized the structural and biochemical features of the two putative HJ resolvases, DrRuvC and DrYqgF, in D. radiodurans. It was identified that DrRuvC and DrYqgF exhibit HJ resolvase (HJR) activity and RNA exo/endonuclease activity, respectively. Furthermore, both DrRuvC and DrYqgF digest substrates in a sequence-specific manner with a preferred sequence that is different from those of the other characterized RuvCs or YqgFs. Our findings provide new insights into the HJ resolution process and reveal a novel RNase involved in RNA metabolism in D. radiodurans.
Regulatory Interplay between RNase III and Antisense RNAs in E. coli: the Case of AsflhD and FlhD, Component of the Master Regulator of Motility

The role of antisense RNAs (asRNAs) in the regulation of gene expression remains largely unexplored in bacteria. Here, we confirm that asRNAs can be part of layered regulatory networks, since some are found opposite to genes encoding global regulators. ABSTRACT In order to respond to ever-changing environmental cues, bacteria display resilient regulatory mechanisms controlling gene expression. At the post-transcriptional level, this is achieved by a combination of RNA-binding proteins, such as ribonucleases (RNases), and regulatory RNAs, including antisense RNAs (asRNAs). Bound to their complementary mRNA, asRNAs are primary targets for the double-strand-specific endoribonuclease, RNase III. Taking advantage of our own and previously published transcriptomic data sets obtained in strains inactivated for RNase III, we selected several candidate asRNAs and confirmed the existence of RNase III-sensitive asRNAs for crp, ompR, phoP, and flhD genes, all encoding global regulators of gene expression in Escherichia coli. Using FlhD, a component of the master regulator of motility (FlhD4C2), as our model, we demonstrate that the asRNA AsflhD, transcribed from the coding sequence of flhD, is involved in the fine-tuning of flhD expression and thus participates in the control of motility. IMPORTANCE The role of antisense RNAs (asRNAs) in the regulation of gene expression remains largely unexplored in bacteria. Here, we confirm that asRNAs can be part of layered regulatory networks, since some are found opposite to genes encoding global regulators. In particular, we show how an antisense RNA (AsflhD) to the flhD gene, encoding the transcription factor serving as the primary regulator of bacterial swimming motility (FlhD4C2), controls flhD expression, which in turn affects the expression of other genes of the motility cascade. The role of AsflhD highlights the importance of fine-tuning mechanisms mediated by asRNAs in the control of complex regulatory networks.
Moisture Controls the Suppression of Panax notoginseng Root Rot Disease by Indigenous Bacterial Communities

Soilborne diseases pose a major problem in high-intensity agricultural systems due to the imbalance of microbial communities in soil, resulting in the buildup of soilborne pathogens. Harnessing indigenous soil microbial suppression is an emerging strategy for overcoming soilborne plant diseases. ABSTRACT Harnessing indigenous soil microbial suppression is an emerging strategy for managing soilborne plant diseases. Soil moisture is a vital factor in soil microbiomes, but its role in the regulation of microbial suppression is poorly understood. Here, we investigated the correlation of root rot disease of Panax notoginseng with rhizosphere microbial communities mediated by soil moisture gradients from 55% to 100% field capacity (FC); then, we captured the disease-suppressive and disease-inductive microbiomes and validated their functions by a culture experiment with synthetic microbiotas containing keystone species. We found that proper soil moisture at 75% to 95% FC could maintain a disease-suppressive microbiome to alleviate root rot disease. However, extremely low or high soil moistures (>95% FC or <75% FC) could aggravate root rot disease by depleting the disease-suppressive microbiome while enriching the disease-inductive microbiome. Both the low-soil-moisture-enriched pathogen Monographella cucumerina and the high-soil-moisture-enriched pathogen Ilyonectria destructans could synergize with different disease-inductive microbiomes to aggravate disease. Metagenomic data confirmed that low- and high-moisture conditions suppressed antibiotic biosynthesis genes but enriched pathogenicity-related genes, resulting in a change in the soil state from disease suppressive to inductive. This study highlights the importance of soil moisture when indigenous microbial suppression is harnessed for disease control. IMPORTANCE Soilborne diseases pose a major problem in high-intensity agricultural systems due to the imbalance of microbial communities in soil, resulting in the buildup of soilborne pathogens. Harnessing indigenous soil microbial suppression is an emerging strategy for overcoming soilborne plant diseases. In this study, we showed that soil moisture is a key factor in balancing microbiome effects on root rot disease. Proper soil moisture management represent an effective approach to maintain microbial disease resistance by enriching disease-suppressive microbiomes. Conversely, moisture stresses may enrich for a disease-inductive microbiome and aid accumulation of host-specific soilborne pathogens threatening crop production. This work could provide a new strategy for sustainable control of soilborne diseases by enriching the indigenous disease-suppressive microbiome through soil moisture management.
Surgical Strategies to Approaching the Splenic Artery in Robotic Distal Pancreatectomy

Background/Aim: Understanding different surgical approaches and anatomical landmarks adjacent to the splenic artery (SpA) is important for safe robotic distal pancreatectomy (RDP). Herein, we propose our standardized RDP techniques, focusing on these issues. Patients and Methods: Between April 2021 and April 2022, 19 patients who underwent RDP at our Institution were reviewed. Anatomical patterns of the SpA were classified into three types: Type 1, no pancreatic parenchyma on the root of the SpA; type 2, any pancreatic parenchyma on the root of the SpA; and type 3, dorsal pancreatic artery around the bifurcation of the common hepatic artery and SpA. Next, the surgical strategy for approaching the SPA was determined according to the location of the pancreatic transection line: On the superior mesenteric vein (SMV) or on the left side of the root of the SpA. Results: There were seven cases of type 1, nine cases of type 2, and three cases of type 3. When transecting the pancreas on the SMV, the SpA-first ligation technique was used for type 1 SpA anatomy, and the pancreas-first division technique was applied for types 2 and 3. In patients in whom the pancreas was transected at the left side of the root of the SpA, the SpA-first ligation technique was used. Conclusion: Our standardized surgical strategy based on anatomical landmarks and focusing on the approach to the SpA in RDP is demonstrated. Our strategy should help trainees approach the SpA and perform RDP safely.
Aptamer-cocktail Functionalized Nano-microfluidic Chip for Enhancing Isolation and Characterization of Circulating Cancer Cells

Background/Aim: Circulating tumor cells (CTCs) have been shown to have a correlation to metastasis and prognosis of patients with cancer. The enumeration and downstream gene analysis of CTCs have attracted efforts for personalized medicine. However, enumeration and phenotypic profiling in most capture devices are challenging due to the rarity and heterogeneity of CTCs. Here, we report an aptamer-cocktail strategy coupled in nano-microfluidic chip for enhancing isolation performance and characterizing the phenotypes of CTCs. Materials and Methods: Aptamer-cocktail recognizing EpCAM/Vimentin/EGFR/CD44 were bound to a nanopillar array on a nano-microfluidic chip. The recognition was validated with cancer cells by flow cytometry and the critical parameters were optimized with the nano-microfluidic chip. Finally, the system was applied to clinical samples. Results: The proposed aptamer-cocktail showed the predominant affinity with MDA-MB-231 and SK-BR-3. When utilized to capture artificial clinical samples, it showed 71% to 83% capture efficiency. CTC detection rate was 100% in five pre-treatment and five post-treatment breast cancer patients. The enumeration data ranged from 6-33 per 2 ml. The number of CTCs in breast cancer patients before therapy was 1.27-2 times higher than that after therapy. CTCs with epithelial and mesenchymal phenotype were both detected and identified; interestingly, the mean diameter of CTCpckpos acquired in these cases was much larger than that of CTCvimentinpos. Conclusion: The nano-microfluidic chip not only made it easier to phenotyping epithelial-like or mesenchymal-like CTCs, but can also be used to detect downstream genetic variation. The established platform can be applied in clinical research and facilitate auxiliary diagnosis with tumor recurrence and metastasis in advance.
Two-Phase Deep Learning Algorithm for Detection and Differentiation of Ewing Sarcoma and Acute Osteomyelitis in Paediatric Radiographs

Background/Aim: Ewing sarcoma is a highly malignant tumour predominantly found in children. The radiological signs of this malignancy can be mistaken for acute osteomyelitis. These entities require profoundly different treatments and result in completely different prognoses. The purpose of this study was to develop an artificial intelligence algorithm, which can determine imaging features in a common radiograph to distinguish osteomyelitis from Ewing sarcoma. Materials and Methods: A total of 182 radiographs from our Sarcoma Centre (118 healthy, 44 Ewing, 20 osteomyelitis) from 58 different paediatric (≤18 years) patients were collected. All localisations were taken into consideration. Cases of acute, acute on chronic osteomyelitis and intraosseous Ewing sarcoma were included. Chronic osteomyelitis, extra-skeletal Ewing sarcoma, malignant small cell tumour and soft tissue-based primitive neuroectodermal tumours were excluded. The algorithm development was split into two phases and two different classifiers were built and combined with a Transfer Learning approach to cope with the very limited amount of data. In phase 1, pathological findings were differentiated from healthy findings. In phase 2, osteomyelitis was distinguished from Ewing sarcoma. Data augmentation and median frequency balancing were implemented. A data split of 70%, 15%, 15% for training, validation and hold-out testing was applied, respectively. Results: The algorithm achieved an accuracy of 94.4% on validation and 90.6% on test data in phase 1. In phase 2, an accuracy of 90.3% on validation and 86.7% on test data was achieved. Grad-CAM results revealed regions, which were significant for the algorithms decision making. Conclusion: Our AI algorithm can become a valuable support for any physician involved in treating musculoskeletal lesions to support the diagnostic process of detection and differentiation of osteomyelitis from Ewing sarcoma. Through a Transfer Learning approach, the algorithm was able to cope with very limited data. However, a systematic and structured data acquisition is necessary to further develop the algorithm and increase results to clinical relevance.
Establishment of Cytotoxic and Non-cytotoxic NK-92 Cell Clones – Evaluation of Their Function as Target Cells

Background/Aim: Previous studies have demonstrated that NK cells present in PBMCs might explain why clinical trials conducted with NK-92 as well as CAR modified NK-92 cells have to a large extent failed. Two NK-92 clones with different NK target cell properties have been established and are described here. Materials and Methods: Two NK-92 cell clones, NK-92 clone 1 and clone 2, were established using the limiting dilution technique. A time-resolved fluorometric assay (TDA-labeled NK-92 clone 1, 2 or K562 as target cells) was used for measuring their sensitivities to NK cell-mediated cytolysis and their NKG2D expression was identified with immunoblotting. Results: A striking difference between the NK-92 clones in their cytotoxic capacity against K562 cells was observed. A clear correlation was noticed between these NK-92 clones when used as target cells and their ability to kill K562 cells. A 50:1 effector:target ratio (PBMCs:NK-92 clone 1) gave 6.50±5.44% lysis whereas the corresponding value was 39.9±10.0% with NK-92 clone 2 as target cells. Interestingly, incubating PBMCs in medium for longer times slightly potentiated their NK activity also against the NK-92 clone 1 (E:T ratio 50:1), from 2.5±0.88% lysis (24 h pre-incubation time) to 13.7±9.04% (48 h) and 13.8±6.89% (72 h). Immunoblotting with anti-NKG2D antibodies stained an approximately 34 kDa protein band in lysates prepared from NK-92 clone 1 cells, which corresponds to the NKG2D antigen. A very faint band of the same size was observed in lysates prepared from NK-92 clone 2 cells. Conclusion: The NK-92 clones 1 and 2, established and described here, might turn out to be very useful for finding possible solutions for using NK-92 and CAR NK-92 cells in future treatments of human malignant diseases.