Miracle-dz/UniFAR-FTUs huggingface dataset:
MicroRNA-1179 regulates proliferation and chemosensitivity of human ovarian cancer cells by targeting the PTEN-mediated PI3K/AKT signaling pathway

Introduction Owing to widespread roles of miRs, the dysregulation of their expression in human tissues has been linked with the development of several diseases such as cancer. The study was designed to investigate the role and therapeutic potential of miR-1179 in ovarian cancer. Material and methods Proliferation rate was monitored by MTT assay. Transfections were performed using Lipofectamine 2000 reagent. Cell cycle apoptosis was detected by AO/EB and annexin V/PI staining. Expressions analysis was carried out by qRT-PCR and western blotting. In vivo evaluation was carried out in xenografted mouse models. Results The results revealed that miR-1179 is considerably upregulated in ovarian cancer cell lines. Inhibition of miR-1179 triggers decrease in the viability via initiation of apoptotic cell death of ovarian PA-1 cancer cells. TargetScan analysis showed PTEN to be the main target of miR-1179 in PA-1 cells. Exploration of PTEN expression in ovarian cancer cell lines revealed up to 9-fold downregulation of PTEN. However, inhibition of miR-1179 in PA-1 cells resulted in upregulation of PTEN expression. In addition, overexpression of PTEN caused a reduction of PA-1 cell viability via induction of apoptotic cell death. However, silencing of miR-1179 could rescue the effects of miR-1179 inhibition on the proliferation of miR-1179. The miR-1179 suppression was accompanied by a significant decline in phosphorylation of PI3K and AKT expression in the PA-1 cells. The in vivo study showed that miR-1179 suppression inhibits the xenografted tumor growth. Conclusions The results of this study indicate that miR-1179 may prove to be an important therapeutic target for ovarian cancer.
Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment

The research field of systems biology has greatly advanced and, as a result, the concept of network pharmacology has been developed. This advancement, in turn, has shifted the paradigm from a “one-target, one-drug” mode to a “network-target, multiple-component-therapeutics” mode. Network pharmacology is more effective for establishing a “compound-protein/gene-disease” network and revealing the regulation principles of small molecules in a high-throughput manner. This approach makes it very powerful for the analysis of drug combinations, especially Traditional Chinese Medicine (TCM) preparations. In this work, we first summarized the databases and tools currently used for TCM research. Second, we focused on several representative applications of network pharmacology for TCM research, including studies on TCM compatibility, TCM target prediction, and TCM network toxicology research. Third, we compared the general statistics of several current TCM databases and evaluated and compared the search results of these databases based on 10 famous herbs. In summary, network pharmacology is a rational approach for TCM studies, and with the development of TCM research, powerful and comprehensive TCM databases have emerged but need further improvements. Additionally, given that several diseases could be treated by TCMs, with the mediation of gut microbiota, future studies should focus on both the microbiome and TCMs to better understand and treat microbiome-related diseases.
Trends in and Predictions of Colorectal Cancer Incidence and Mortality in China From 1990 to 2025

Colorectal cancer (CRC) has emerged as a major public health concern in China during the last decade. In this study, we investigated the disease burden posed by CRC and analyzed temporal trends in CRC incidence and mortality rates in this country. We collected CRC incidence data from the Cancer Incidence in Five Continents, Volume XI dataset and the age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) of CRC by sex and age, from the 2016 Global Burden of Diseases Study. We used the average annual percentage change (AAPC) to quantify temporal trends in CRC incidence and mortality from 1990 to 2016 and found the ASIR of CRC increased from 14.25 per 100,000 in 1990 to 25.27 per 100,000 in 2016 (AAPC = 2.34, 95% confidence interval [CI] 2.29, 2.39). Cancer cases increased from 104.3 thousand to 392.8 thousand during the same period. The ASIR increased by 2.76% (95% CI 2.66%, 2.85%) and 1.70% (95% CI 1.64%, 1.76%) per year in males and females, respectively. The highest AAPC was found in people aged 15–49 years (2.76, 95% CI 2.59, 2.94). Cancer deaths increased from 81.1 thousand in 1990 to 167.1 thousand in 2016, while the ASMR remained stable (−0.04, 95% CI −0.13, 0.05), A mild increase (AAPC = 0.42, 95% CI 0.34, 0.51) was found among males and a significant decrease (AAPC = −0.75, 95% CI −0.90, −0.60) was found among females. Between 2016 and 2025, cancer cases and deaths are expected to increase from 392.8 and 167.1 thousand in 2016 to 642.3 (95% CI 498.4, 732.1) and 221.1 thousand (95% CI 122.5, 314.8) in 2025, respectively. Our study showed a steady increase in the CRC incidence in China over the past three decades and predicted a further increase in the near future. To combat this health concern, the prevention and management of known risk factors should be promoted through national polices. Greater priority should be given to CRC prevention in younger adults, and CRC screening should be widely adopted for this population.
The technological performance of populism

This article investigates the Trump campaign’s strategic use of digital platforms and their affordances and norms that contribute to a technological performance of populism. To do so, I build on theories of populism as a performance, rather than a set of identifiable qualities, and make a theoretical intervention calling for the need to add a material and technological focus to how scholars approach the concept in our contemporary media environment. This article presents a model for understanding populist affordances as those that center “the people” to various degrees, and applies that model in a case study of how campaigns in the 2016 US presidential race engaged in a technological performance of populism across a variety of platforms, including email, Twitter, Instagram, Facebook, and campaign-created mobile apps. Central to this analysis are campaign strategies of controlled interactivity, amateurism, participatory/user-generated content, and data-driven campaigning.
Where Do Human Heuristics Come From?

Human decision-making deviates from the optimal solution, that maximizes cumulative rewards, in many situations. Here we approach this discrepancy from the perspective of bounded rationality and our goal is to provide a justification for such seemingly sub-optimal strategies. More specifically we investigate the hypothesis, that humans do not know optimal decision-making algorithms in advance, but instead employ a learned, resource-bounded approximation. The idea is formalized through combining a recently proposed meta-learning model based on Recurrent Neural Networks with a resource-bounded objective. The resulting approach is closely connected to variational inference and the Minimum Description Length principle. Empirical evidence is obtained from a two-armed bandit task. Here we observe patterns in our family of models that resemble differences between individual human participants.
Wheat Germ Agglutinin as a Potential Therapeutic Agent for Leukemia

Dietary lectins are carbohydrate-binding proteins found in food sources. We used a panel of seven dietary lectins to analyze cytotoxicity against hematological cancers. Wheat germ agglutinin (WGA), even at low doses, demonstrated maximum toxicity toward acute myeloid leukemia (AML) cells. Using AML cell lines, we show time- and dose-dependent killing by WGA. We also show that low doses of WGA kills primary patient AML cells, irrespective of subtype, with no significant toxicity to normal cells. WGA caused AML cell agglutination, but failed to agglutinate RBC's at this dose. WGA, primarily, binds to N-acetyl-D-glucosamine (GlcNAc) and is also reported to interact with sialic-acid-containing glycoconjugates and oligosaccharides. After neuraminidase pre-treatment, which catalyzes the hydrolysis of terminal sialic acid residues, AML cells were less sensitive to WGA-induced cell death. AML cells were also not sensitive to succinyl-WGA, which does not react with sialic acid. Incubation with LEL lectin, which recognizes GlcNAc or SNA, which binds preferentially to sialic acid attached to terminal galactose in α-2,6 and to a lesser degree α-2,3 linkage, did not alter AML cell viability. These data indicate that WGA-induced AML cell death is dependent on both GlcNAc binding and interaction with sialic acids. We did not observe any in vitro or in vivo toxicity of WGA toward normal cells at the concentrations tested. Finally, low doses of WGA injection demonstrated significant in vivo toxicity toward AML cells, using xenograft mouse model. Thus, WGA is a potential candidate for leukemia therapy.
The evolution of Lightning Network's Topology during its first year and the influence over its core values

It is now a whole year since Lightning Network (LN) has been launched on the Bitcoi's mainnet. LN has been claimed as the solution for several of Bitcoin's weaknesses such as its difficulty to scale in number of transactions per second and its expensiveness for relative small amount exchanged. LN is based upon a network of micro-channels opened and closed by issuing transactions on the Blockchain and capable to interact among themselves thanks to the Multi-hop framework. In this work I analysed the evolution of LN from its topological point of view and tried to understand how it impacts over some of Bitcoin's historical core foundamentals such as the resilience against attacks and failures and the user's anonymity. Another question I have tried to answer is whether the evolution of LN's topology will make its synchronization easier.
Association between intravenous acetaminophen and reduction in intraoperative opioid consumption during transsphenoidal surgery for pituitary tumors

Background and Aims: Pain during and after transsphenoidal surgeries originates from stimulation of branches of the trigeminal cranial nerve that supply the inner aspect of the nose cavity and dura mater. Thereby, patients undergoing transsphenoidal surgery may require moderate-to-large amounts of analgesics including opioids. Intravenous acetaminophen provides analgesia and reduces opioid consumption for a wide variety of surgeries. We hypothesized that the use of intravenous acetaminophen is associated with a reduction in intraoperative opioid consumption and provides significant analgesia during and after transsphenoidal surgery. Material and Methods: This retrospective study included 413 patients who underwent transsphenoidal surgery for pituitary adenomas. The primary outcome of this study was intraoperative opioid consumption. Secondary outcomes included pain intensity, Richmond Agitation Sedation Scale scores, and nausea and vomiting upon arrival to postoperative anesthesia care unit. Patients were divided into two groups based on the intraoperative acetaminophen use. A prospensity score matching analysis was used to balance for important variables between the two groups of treatment. Regression models were fitted after matching the covariates. A P < 0.05 was considered statistically significant. Results: After matching, 126 patients were included in each group of treatment. Patients in the acetaminophen group required significantly less amount (a decrease by 14.9%) of opioids during surgery than those in the non-acetaminophen group. Postoperative pain, postoperative nausea and vomiting, and sedation scores were not significantly different between patients who received intravenous acetaminophen and those who did not. Conclusion: Intravenous acetaminophen is associated with a reduction in intraoperative opioids during transsphenoidal pituitary surgery.
Robust Structured Group Local Sparse Tracker Using Deep Features

Sparse representation has recently been successfully applied in visual tracking. It utilizes a set of templates to represent target candidates and find the best one with the minimum reconstruction error as the tracking result. In this paper, we propose a robust deep features-based structured group local sparse tracker (DF-SGLST), which exploits the deep features of local patches inside target candidates and represents them by a set of templates in the particle filter framework. Unlike the conventional local sparse trackers, the proposed optimization model in DF-SGLST employs a group-sparsity regularization term to seamlessly adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we propose an efficient and fast numerical algorithm that consists of two subproblems with the closed-form solutions. Different evaluations in terms of success and precision on the benchmarks of challenging image sequences (e.g., OTB50 and OTB100) demonstrate the superior performance of the proposed tracker against several state-of-the-art trackers.
Prevalence of Dental Caries and Fissure Sealants in the First Permanent Molars among Male Children in Riyadh, Kingdom of Saudi Arabia

Aims To evaluate the prevalence of dental caries and the presence of fissure sealants on the first permanent molars (FPMs) among 6 to 9 years old primary school boys in Riyadh, Kingdom of Saudi Arabia. Materials and methods The carious status and the presence of fissure sealants on the FPMs were examined in 1844 schoolboys, aged 6-9 years (the first three grades), from 17 randomly selected primary schools in Riyadh city, Kingdom of Saudi Arabia.Chi-square test was used to assess the significance of differences in prevalence and proportions. Results A total of 5394 FPMs were assessed in the 1844 children. Eighty-three point five percent of children were caries free. Only 0.8% of the children had at least one fissure sealant applied. At tooth level, the decayed FPMs counted for 10.6%. There was obvious underuse of fissure sealants on the FPMs; 0.5% (n = 25). The caries prevalence in the mandibular FPMs (14.4%), was significantly higher than in the maxillary FPMs, 7.7% (p < 0.01). The proportion of carious FPMs increased with age of the children significantly (p < 0.01). There was no significant difference in the proportion of the presence of fissure sealants among the three different grade/age groups (p > 0.05). Conclusion Caries prevalence in the FPMs was moderate but serious amongst this cohort of young students. This was opposed with very low use of fissure sealants. Clinical significance Careful examination of the first permanent molars among children is mandatory because of their high caries susceptibility. It is important to increase public and dental professionals’ awareness of the effectiveness of fissure sealants and encouraging more placement. How to cite this article: Aldossary MS, Alamri AA, Alshiha SA, Hattan MA, Alfraih YK, Alwayli HM. Prevalence of Dental Caries and Fissure Sealants in the First Permanent Molars among Male Children in Riyadh, Kingdom Saudi Arabia. Int J Clin Pediatr Dent. , 2018;11(5):365-370.
Toward a Neurobiologically Plausible Model of Language-Related, Negative Event-Related Potentials

Language-related event-related potential (ERP) components such as the N400 have traditionally been associated with linguistic or cognitive functional interpretations. By contrast, it has been considerably more difficult to relate these components to neurobiologically grounded accounts of language. Here, we propose a theoretical framework based on a predictive coding architecture, within which negative language-related ERP components such as the N400 can be accounted for in a neurobiologically plausible manner. Specifically, we posit that the amplitude of negative language-related ERP components reflects precision-weighted prediction error signals, i.e., prediction errors weighted by the relevance of the information source leading to the error. From this perspective, precision has a direct link to cue validity in a particular language and, thereby, to relevance of individual linguistic features for internal model updating. We view components such as the N400 and LAN as members of a family with similar functional characteristics and suggest that latency and topography differences between these components reflect the locus of prediction errors and model updating within a hierarchically organized cortical predictive coding architecture. This account has the potential to unify findings from the full range of the N400 literature, including word-level, sentence-, and discourse-level results as well as cross-linguistic differences.
Endoscopic Ultrasound-Guided Tissue Acquisition Using Fork-Tip Needle Improves Histological Yield, Reduces Needle Passes, Without On-Site Cytopathological Evaluation

Abstract Background and Aim: Endoscopic ultrasound (EUS)-guided fine needle biopsy (FNB) and fine needle aspiration (FNA) are established methods in tissue acquisition. A new fork-tip FNB needle has been used to obtain core tissue samples. We compared the performance of the FNB using fork-tip needles with that of the FNA using conventional needles in patients who had solid neoplastic lesions within and around the upper gastrointestinal (GI) tract. Methods: In this retrospective single-center study, patients who underwent EUS examinations for solid neoplastic lesions between October 2013 and February 2017 were included. The procedures were performed in the absence of an on-site cytologist. The main objectives were to compare the diagnostic yield and average number of passes of FNB using fork-tip needles versus those of FNA using conventional needles. Results: EUS/FNA and EUS/FNB were performed on 181 solid neoplastic lesions primarily in the pancreas and GI tract walls. There was no significant difference in patient's age, gender, tumor location, or tumor size. The mean number of needle passes was significantly lower in the fork-tip needle group than in the conventional needle group (3.8 vs. 5.9; p < 0.0001). There was a trend toward higher sensitivity (89.9% vs. 81%) using the fork-tip needles than when using the conventional needles (p = 0.119). No significant difference in rates of adverse events between two groups was found. Conclusions: Our study demonstrates that, compared with FNA using conventional needles, FNB using fork-tip needles required significantly fewer needle passes while achieving a relatively higher diagnostic yield due to its superior capacity in tissue acquisition from solid neoplastic lesions in and around GI tract walls without on-site cytological assessment.
Evaluation of an Automated Proton Planning Solution

Purpose Intensity-modulated proton therapy (IMPT) treatments are increasing, however, treatment planning remains complex and prone to variability. RapidPlanTMPT (Varian Medical Systems, Palo Alto, California, USA) is a pre-clinical, proton-specific, automated knowledge-based planning solution which could reduce variability and increase efficiency. It uses a library of previous IMPT treatment plans to generate a model which can predict organ-at-risk (OAR) dose for new patients, and guide IMPT optimization. This study details and evaluates RapidPlanTMPT. Methods IMPT treatment plans for 50 head-and-neck cancer patients populated the model-library. The model was then used to create knowledge-based plans (KBPs) for 10 evaluation-patients. Model quality and accuracy were evaluated using model-provided OAR regression plots and examining the difference between predicted and achieved KBP mean dose. KBP quality was assessed through comparison with respective manual IMPT plans on the basis of boost/elective planning target volume (PTVB/PTVE) homogeneity and OAR sparing. The time to create KBPs was recorded. Results Model quality was good, with an average R2 of 0.85 between dosimetric and geometric features. The model showed high predictive accuracy with differences of <3 Gy between predicted and achieved OAR mean doses for 88/109 OARs. On average, KBPs were comparable to manual IMPT plans with differences of <0.6% in homogeneity. Only 2 of 109 OARs in KBPs had a mean dose >3 Gy more than the manual plan. On average, dose-volume histogram (DVH) predictions required 0.7 minutes while KBP optimization and dose calculation required 4.1 minutes (a ‘continue optimization’ phase, if required, took an additional 2.8 minutes, on average). Conclusions RapidPlanTMPT demonstrated efficiency and consistency and IMPT KBPs were comparable to manual plans. Because worse OAR sparing in a KBP was not always associated with geometric-outlier warnings, manual plan checks remain important. Such an automated planning solution could also assist in clinical trial quality assurance and overcome the learning curve associated with IMPT.
Murine genotype influences the in vitro production of γ (immune) interferon

Spleen cells from mice of 6 different strains were stimulated with pokeweed mitogen and the amount of γ interferon in the supernatants was measured. Three strains (BALB/c, A/J and C3H/He) were found to be low producers (less than 50 units) whereas three other strains (C57BL6, CBA and AKR) were high producers (50 to 500 units). F1 hybrids between a low producer strain (A/J) and a high producer strain (C57BL6) were found to produce low amounts of interferon. Nude mice did not produce detectable amounts of pokeweed mitogen‐induced γ interferon. The level of production of this T cell lymphokine is thus under genetic control.
DNNVM: End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-Based CNN Accelerators

The convolutional neural network (CNN) has become a state-of-the-art method for several artificial intelligence domains in recent years. The increasingly complex CNN models are both computation-bound and I/O-bound. Field-programmable gate array-based accelerators driven by custom instruction set architecture (ISA) achieve a balance between generality and efficiency, but there is much on them left to be optimized. We propose the full-stack compiler deep neural network virtual machine (DNNVM), which is an integration of optimizers for graphs, loops and data layouts, an assembler, a runtime supporter, and a validation environment. The DNNVM works in the context of deep learning frameworks and transforms CNN models into the directed acyclic graph: XGraph. Based on XGraph, we transform the optimization challenges for both data layout and pipeline into graph-level problems. DNNVM enumerates all potentially profitable fusion opportunities by a heuristic subgraph isomorphism algorithm to leverage pipeline and data layout optimizations, and searches for the best choice of execution strategies of the whole computing graph. On the Xilinx ZU2@330 MHz and ZU9@330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by naïve implementations without optimizations, and the throughput is further improved up to $1.26\times $ by leveraging heterogeneous optimizations in DNNVM. Finally, with ZU9@330 MHz, we achieve state-of-the-art performance for VGG and ResNet50. We achieve a throughput of 2.82 TOPs/s and an energy efficiency of 123.7 GOPs/s/W for VGG. Additionally, we achieve 1.38 TOPs/s for ResNet50 and 1.41 TOPs/s for GoogleNet.