Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-quorum sensing evaluation of methyleugenol, the principal bioactive component, from the Melaleuca bracteata leaf oil

Quorum sensing (QS) is a cell-to-cell communication in bacteria that couples gene expression through the accumulation of signaling molecules, which finally induce the production of several virulence factors and modulate bacterial behaviors. Plants have evolved an array of quorum sensing inhibitors (QSIs) to inhibit the pathogens, of which aromatic compounds are widely recognized. The essential oil of Melaleuca bracteata was found to exhibit anti-quorum sensing activity, and its principal bioactive component, methyleugenol (ME), had been isolated in our previous study. Here, ME interfered effectively with the QS-regulated processes of toxin secretion in Chomobacterium violaceum ATCC31532, resulting in strong inhibition of QS genes, cviR, cviI, vioA-E, hmsHNR, lasA-B, pilE1-3, and hcnABC, leading to impaired virulence, including violacein production, biofilm biomass, and swarming motility. The accumulation of the signal molecule (N-hexanoyl-DL-homoserine lactone, C6-HSL) in C. violaceum declined upon treatment with ME, suggesting an inhibition effect on the C6-HSL production, and the ME was also capable of degrading the C6-HSL in vitro assay. Molecular docking technique and the consumption change of exogenous C6-HSL in C. violaceum CV026 revealed the anti-QS mechanism of ME consisted of inhibition of C6-HSL production, potentially via interaction with CviR and/or CviI protein. Collectively, the isolated ME, the principal active components of M. bracteata EO, exhibited a wide range of inhibition processes targeting C. violaceum QS system, which supports the potential anti-pathogenic use of M. bracteata EO and ME for treatment of pathogen contamination caused by bacterial pathogens.
Electroencephalogram microstates and functional connectivity of cybersickness

Virtual reality (VR) is a rapidly developing technology that simulates the real world. However, for some cybersickness-susceptible people, VR still has an unanswered problem—cybersickness—which becomes the main obstacle for users and content makers. Sensory conflict theory is a widely accepted theory for cybersickness. It proposes that conflict between afferent signals and internal models can cause cybersickness. This study analyzes the brain states that determine cybersickness occurrence and related uncomfortable feelings. Furthermore, we use the electroencephalogram (EEG) microstates and functional connectivity approach based on the sensory conflict theory. The microstate approach is a time–space analysis method that allows signals to be divided into several temporarily stable states, simultaneously allowing for the exploration of short- and long-range signals. These temporal dynamics can show the disturbances in mental processes associated with neurological and psychiatric conditions of cybersickness. Furthermore, the functional connectivity approach gives us in-depth insight and relationships between the sources related to cybersickness. We recruited 40 males (24.1 ± 2.3 years), and they watched a VR video on a curved computer monitor for 10 min to experience cybersickness. We recorded the 5-min resting state EEG (baseline condition) and 10-min EEG while watching the VR video (task condition). Then, we performed a microstate analysis, focusing on two temporal parameters: mean duration and global explained variance (GEV). Finally, we obtained the functional connectivity data using eLoreta and lagged phase synchronization (LPS). We discovered five sets of microstates (A–E), including four widely reported canonical microstates (A–D), during baseline and task conditions. The average duration increased in microstates A and B, which is related to the visual and auditory networks. The GEV and duration decreased in microstate C, whereas those in microstate D increased. Microstate C is related to the default mode network (DMN) and D to the attention network. The temporal dynamics of the microstate parameters are from cybersickness disturbing the sensory, DMN, and attention networks. In the functional connectivity part, the LPS between the left and right parietal operculum (OP) significantly decreased (p < 0.05) compared with the baseline condition. Furthermore, the connectivity between the right OP and V5 significantly decreased (p < 0.05). These results also support the disturbance of the sensory network because a conflict between the visual (V5) and vestibular system (OP) causes cybersickness. Changes in the microstates and functional connectivity support the sensory conflict theory. These results may provide additional information in understanding brain dynamics during cybersickness.
Post-traumatic knee osteoarthritis; the role of inflammation and hemarthrosis on disease progression

Knee injuries such as anterior cruciate ligament ruptures and meniscal injury are common and are most frequently sustained by young and active individuals. Knee injuries will lead to post-traumatic osteoarthritis (PTOA) in 25–50% of patients. Mechanical processes where historically believed to cause cartilage breakdown in PTOA patients. But there is increasing evidence suggesting a key role for inflammation in PTOA development. Inflammation in PTOA might be aggravated by hemarthrosis which frequently occurs in injured knees. Whereas mechanical symptoms (joint instability and locking of the knee) can be successfully treated by surgery, there still is an unmet need for anti-inflammatory therapies that prevent PTOA progression. In order to develop anti-inflammatory therapies for PTOA, more knowledge about the exact pathophysiological mechanisms and exact course of post-traumatic inflammation is needed to determine possible targets and timing of future therapies.
Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo

Quantification of histological information from excised human abdominal aortic aneurysm (AAA) specimens may provide essential information on the degree of infiltration of inflammatory cells in different regions of the AAA. Such information will support mechanistic insight in AAA pathology and can be linked to clinical measures for further development of AAA treatment regimens. We hypothesize that artificial intelligence can support high throughput analyses of histological sections of excised human AAA. We present an analysis framework based on supervised machine learning. We used TensorFlow and QuPath to determine the overall architecture of the AAA: thrombus, arterial wall, and adventitial loose connective tissue. Within the wall and adventitial zones, the content of collagen, elastin, and specific inflammatory cells was quantified. A deep neural network (DNN) was trained on manually annotated, Weigert stained, tissue sections (14 patients) and validated on images from two other patients. Finally, we applied the method on 95 new patient samples. The DNN was able to segment the sections according to the overall wall architecture with Jaccard coefficients after 65 epocs of 92% for the training and 88% for the validation data set, respectively. Precision and recall both reached 92%. The zone areas were highly variable between patients, as were the outputs on total cell count and elastin/collagen fiber content. The number of specific cells or stained area per zone was deterministically determined. However, combining the masks based on the Weigert stainings, with images of immunostained serial sections requires addition of landmark recognition to the analysis path. The combination of digital pathology, the DNN we developed, and landmark registration will provide a strong tool for future analyses of the histology of excised human AAA. In combination with biomechanical testing and microstructurally motivated mathematical models of AAA remodeling, the method has the potential to be a strong tool to provide mechanistic insight in the disease. In combination with each patients’ demographic and clinical profile, the method can be an interesting tool to in supportof a better treatment regime for the patients.
Feasibility of novel approaches to detect viable Mycobacterium tuberculosis within the spectrum of the tuberculosis disease

Tuberculosis (TB) is a global disease caused by Mycobacterium tuberculosis (Mtb) and is manifested as a continuum spectrum of infectious states. Both, the most common and clinically asymptomatic latent tuberculosis infection (LTBI), and the symptomatic disease, active tuberculosis (TB), are at opposite ends of the spectrum. Such binary classification is insufficient to describe the existing clinical heterogeneity, which includes incipient and subclinical TB. The absence of clinically TB-related symptoms and the extremely low bacterial burden are features shared by LTBI, incipient and subclinical TB states. In addition, diagnosis relies on cytokine release after antigenic T cell stimulation, yet several studies have shown that a high proportion of individuals with immunoreactivity never developed disease, suggesting that they were no longer infected. LTBI is estimated to affect to approximately one fourth of the human population and, according to WHO data, reactivation of LTBI is the main responsible of TB cases in developed countries. Assuming the drawbacks associated to the current diagnostic tests at this part of the disease spectrum, properly assessing individuals at real risk of developing TB is a major need. Further, it would help to efficiently design preventive treatment. This quest would be achievable if information about bacterial viability during human silent Mtb infection could be determined. Here, we have evaluated the feasibility of new approaches to detect viable bacilli across the full spectrum of TB disease. We focused on methods that specifically can measure host-independent parameters relying on the viability of Mtb either by its direct or indirect detection.
Social media usage and acceptance in higher education: A structural equation model

The adoption and use of social media as an educational technology in higher education has been exacerbated during the COVID-19 pandemic. As a result, this study applied the unified theory of usage and acceptance of technology theory and the technology acceptance model as predictors of behavioral intention to use social media and actual social media use. These, as posited by the model, affect the performance impact of social media usage. This study involved a quantitative survey with 312 undergraduate university students in Malaysia. Using structural equation modeling, this study identified that unified theory of usage and acceptance of technology theory and the technology acceptance model influence behavioral intentions to use and actual use of social media, resulting in an improved performance impact. That is, when students see the value in particular technologies, feel their performance (e.g., passing their studies) will be improved by using that technology, offers behavioral nudges toward adoption and use.
Comprehensive germline and somatic genomic profiles of Chinese patients with biliary tract cancer

Background Biliary tract cancer (BTC) is an uncommon but highly lethal malignancy with poor clinical outcomes. To promote the development of precision medicine for BTC, uncovering its genomic profile becomes particularly important. However, studies on the genomic feature of Chinese BTC patients remain insufficient. Methods A total of 382 Chinese patients with BTC were enrolled in this study, including 71 with intrahepatic cholangiocarcinoma (ICC), 194 with extrahepatic cholangiocarcinoma (ECC), and 117 with gallbladder carcinoma (GBC). Genetic testing was performed by utilizing the next-generation sequencing (NGS) of 499 cancer-related genes and the results were compared to those of Western BTC patients (MSKCC cohorts). Results The most prevalent genes were TP53 (51.6%), ARID1A (25.9%), KMT2C (24.6%), NCOR1 (17%), SMAD4 (15.2%), KRAS (14.9%), KMT2D (14.9%), ATM (14.1%), and APC (13.9%) in Chinese BTC patients. TP53, SMAD4, and APC were more prevalent in GBC, ECC, and ICC, respectively. In addition, 10.5% of Chinese BTC patients harbored pathogenic or likely pathogenic (P/LP) germline alterations in 41 genes, which were mainly related to DNA damage repair (DDR). Additionally, the genomic features of Chinese and Western BTC tumors were similar, with the exception of the notable difference in the prevalence of TP53, KRAS, IDH1, KMT2C, and SMAD4. Notably, Chinese BTC patients had high prevalence (57.1%) of actionable alterations, especially for those with ECC, and half (192/382) of them had somatic DDR alterations, with the prevalence of deleterious ones being significantly higher than their Western counterparts. Twenty-three percent of patients had a higher tumor mutational burden (TMB-H, over 10 mutations/MB), and TMB was significantly higher in those with deleterious DDR alterations and/or microsatellite instability-high. The most common mutational signature in BTC patients was Signature 1, and interestingly, Signatures 1, 4, and 26 were significantly associated with higher TMB level, but not with the survival of patients who had received immunotherapy in pan-cancer. Conclusion Our study elaborated the distinct germline and somatic genomic characteristics of Chinese BTC patients and identified clinically actionable alterations, highlighting the possibility for the development and application of precision medicine.
A single molecule investigation of i-motif stability, folding intermediates, and potential as in-situ pH sensor

We present a collection of single molecule work on the i-motif structure formed by the human telomeric sequence. Even though it was largely ignored in earlier years of its discovery due to its modest stability and requirement for low pH levels (pH < 6.5), the i-motif has been attracting more attention recently as both a physiologically relevant structure and as a potent pH sensor. In this manuscript, we establish single molecule Förster resonance energy transfer (smFRET) as a tool to study the i-motif over a broad pH and ionic conditions. We demonstrate pH and salt dependence of i-motif formation under steady state conditions and illustrate the intermediate states visited during i-motif folding in real time at the single molecule level. We also show the prominence of intermediate folding states and reversible folding/unfolding transitions. We present an example of using the i-motif as an in-situ pH sensor and use this sensor to establish the time scale for the pH drop in a commonly used oxygen scavenging system.
Retinal microvasculature and imaging markers of brain frailty in normal aging adults

Background The retina and brain share a similar embryologic origin, blood barriers, and microvasculature features. Thus, retinal imaging has been of interest in the aging population to help in the early detection of brain disorders. Imaging evaluation of brain frailty, including brain atrophy and markers of cerebral small vessel disease (CSVD), could reflect brain health in normal aging, but is costly and time-consuming. In this study, we aimed to evaluate the retinal microvasculature and its association with radiological indicators of brain frailty in normal aging adults. Methods Swept-source optical coherence tomography angiography (SS-OCTA) and 3T-MRI brain scanning were performed on normal aging adults (aged ≥ 50 years). Using a deep learning algorithm, microvascular tortuosity (VT) and fractal dimension parameter (Dbox) were used to evaluate the superficial vascular complex (SVC) and deep vascular complex (DVC) of the retina. MRI markers of brain frailty include brain volumetric measures and CSVD markers that were assessed. Results Of the 139 normal aging individuals included, the mean age was 59.43 ± 7.31 years, and 64.0% (n = 89) of the participants were females. After adjustment of age, sex, and vascular risk factors, Dbox in the DVC showed a significant association with the presence of lacunes (β = 0.58, p = 0.007), while VT in the SVC significantly correlated with the score of cerebral deep white matter hyperintensity (β = 0.31, p = 0.027). No correlations were found between brain volumes and retinal microvasculature changes (P > 0.05). Conclusion Our report suggests that imaging of the retinal microvasculature may give clues to brain frailty in the aging population.
Development and validation of a novel fibroblast scoring model for lung adenocarcinoma

The interaction between cancer-associated fibroblasts (CAFs) and the tumor microenvironment (TME) is a key factor for promoting tumor progression. In lung cancer, the crosstalk between CAFs and malignant and immune cells is expected to provide new directions for the development of immunotherapy. In this study, we have systematically analyzed a single-cell dataset and identified interacting genes between CAFs and other cells. Subsequently, a robust fibroblast-related score (FRS) was developed. Kaplan-Meier (KM) and ROC analyses showed its good predictive power for patient prognoses in the training set comprising of specimens from the cancer genome atlas (TCGA) and in three external validation sets from the Gene Expression Omnibus (GEO). Univariate and multivariate Cox regression analyses suggested that FRS was a significant prognostic factor independent of multiple clinical characteristics. Functional enrichment and ssGSEA analyses indicated that patients with a high FRS developed “cold” tumors with active tumor proliferation and immunosuppression capacities. In contrast, those with a low FRS developed “hot” tumors with active immune function and cell killing abilities. Genomic variation analysis showed that the patients with a high FRS possessed a higher somatic mutation burden and copy number alterations and were more sensitive to chemotherapy; patients with a low FRS were more sensitive to immunotherapy, particularly anti-PD1 therapy. Overall, these findings advance the understanding of CAFs in tumor progression and we generated a reliable FRS-based model to assess patient prognoses and guide clinical decision-making.
Making Big Data Small: Strategies to Expand Urban and Geographical Research Using Social Media

ABSTRACT While exciting, Big Data (particularly geotagged social media data) has proven difficult for many urbanists and social science researchers to use. As a partial solution, we propose a strategy that enables the fast extracting of only relevant data from large sets of geosocial data. While contrary to many Big Data approaches—in which analysis is done on the entire dataset—much productive social science work can use smaller datasets—around the same size as census or survey data—within standard methodological frameworks. The approach we outline in this paper—including the example of a fully operating system—offers a solution for urban researchers interested in these types of data but reluctant to personally build data science skills.
Microencapsulation and in situ incubation methodology for the cultivation of marine bacteria

Environmental microorganisms are important sources of biotechnology innovations; however, the discovery process is hampered by the inability to culture the overwhelming majority of microbes. To drive the discovery of new biotechnology products from previously unculturable microbes, several methods such as modification of media composition, incubation conditions, single-cell isolation, and in situ incubation, have been employed to improve microbial recovery from environmental samples. To improve microbial recovery, we examined the effect of microencapsulation followed by in situ incubation on the abundance, viability, and diversity of bacteria recovered from marine sediment. Bacteria from marine sediment samples were resuspended or encapsulated in agarose and half of each sample was directly plated on agar and the other half inserted into modified Slyde-A-Lyzer™ dialysis cassettes. The cassettes were incubated in their natural environment (in situ) for a week, after which they were retrieved, and the contents plated. Colony counts indicated that bacterial abundance increased during in situ incubation and that cell density was significantly higher in cassettes containing non-encapsulated sediment bacteria. Assessment of viability indicated that a higher proportion of cells in encapsulated samples were viable at the end of the incubation period, suggesting that agarose encapsulation promoted higher cell viability during in situ incubation. One hundred and 46 isolates were purified from the study (32–38 from each treatment) to assess the effect of the four treatments on cultivable bacterial diversity. In total, 58 operational taxonomic units (OTUs) were identified using a 99% 16S rRNA gene sequence identity threshold. The results indicated that encapsulation recovered greater bacterial diversity from the sediment than simple resuspension (41 vs. 31 OTUs, respectively). While the cultivable bacterial diversity decreased by 43%–48% after in situ incubation, difficult-to-culture (Verrucomicrobia) and obligate marine (Pseudoalteromonas) taxa were only recovered after in situ incubation. These results suggest that agarose encapsulation coupled with in situ incubation in commercially available, low-cost, diffusion chambers facilitates the cultivation and improved recovery of bacteria from marine sediments. This study provides another tool that microbiologists can use to access microbial dark matter for environmental, biotechnology bioprospecting.
The landscape of circular RNA in preterm birth

Background Preterm birth (PTB) is a multifactorial syndrome that seriously threatens the health of pregnant women and babies worldwide. Recently, circular RNAs (circRNAs) have been understood as important regulators of various physiological and pathological processes. However, the expression pattern and potential roles of circRNAs in PTB are largely unclear. Methods In this study, we extracted and analyzed the circRNA expression profiles in maternal and fetal samples of preterm and term pregnancies, including maternal plasma, maternal monocytes, myometrium, chorion, placenta, and cord blood. We identified the circRNAs which is associated with PTB in different tissues and explored their relationships from the perspective of the overall maternal-fetal system. Furthermore, co-expression analysis of circRNAs and mRNAs, target microRNAs (miRNAs), and RNA-binding proteins (RBPs), provided new clues about possible mechanisms of circRNA function in PTB. In the end, we investigated the potential special biofunctions of circRNAs in different tissues and their common features and communication in PTB. Results Significant differences in circRNA types and expression levels between preterm and term groups have been proved, as well as between tissues. Nevertheless, there were still some PTB-related differentially expressed circRNAs (DECs) shared by these tissues. The functional enrichment analysis showed that the DECs putatively have important tissue-specific biofunctions through their target miRNA and co-expressed mRNAs, which contribute to the signature pathologic changes of each tissue within the maternal-fetal system in PTB (e.g., the contraction of the myometrium). Moreover, DECs in different tissues might have some common biological activities, which are mainly the activation of immune-inflammatory processes (e.g., interleukin1/6/8/17, chemokine, TLRs, and complement). Conclusions In summary, our data provide a preliminary blueprint for the expression and possible roles of circRNAs in PTB, which lays the foundation for future research on the mechanisms of circRNAs in PTB.