Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximizing Biocatalytic Cyclohexane Hydroxylation by Modulating Cytochrome P450 Monooxygenase Expression in P. taiwanensis VLB120

Cytochrome P450 monooxygenases (Cyps) effectively catalyze the regiospecific oxyfunctionalization of inert C–H bonds under mild conditions. Due to their cofactor dependency and instability in isolated form, oxygenases are preferably applied in living microbial cells with Pseudomonas strains constituting potent host organisms for Cyps. This study presents a holistic genetic engineering approach, considering gene dosage, transcriptional, and translational levels, to engineer an effective Cyp-based whole-cell biocatalyst, building on recombinant Pseudomonas taiwanensis VLB120 for cyclohexane hydroxylation. A lac-based regulation system turned out to be favorable in terms of orthogonality to the host regulatory network and enabled a remarkable specific whole-cell activity of 34 U gCDW–1. The evaluation of different ribosomal binding sites (RBSs) revealed that a moderate translation rate was favorable in terms of the specific activity. An increase in gene dosage did only slightly elevate the hydroxylation activity, but severely impaired growth and resulted in a large fraction of inactive Cyp. Finally, the introduction of a terminator reduced leakiness. The optimized strain P. taiwanensis VLB120 pSEVA_Cyp allowed for a hydroxylation activity of 55 U gCDW–1. Applying 5 mM cyclohexane, molar conversion and biomass-specific yields of 82.5% and 2.46 mmolcyclohexanol gbiomass–1 were achieved, respectively. The strain now serves as a platform to design in vivo cascades and bioprocesses for the production of polymer building blocks such as ε-caprolactone.
Highlights on the Management of Oligometastatic Disease

Purpose: The understanding of oligometastatic disease (OMD) is rapidly evolving and with this comes the ability to utilize a number of modalities that excel in the localized control of disease. It has been identified that there are no clear guidelines based on high-level evidence to standardized approaches toward the management of OMD. These highlights have been developed to provide a road map for all health-care professionals who are involved in the management of OMD to support standardized patient care. Methods: The Saudi Lung Cancer Guidelines Committee is a part of the Saudi Lung Cancer Association which, in turn, is part of the Saudi Thoracic Society. Considering that lung cancer constitutes a major proportion of OMD prevalence, the committee took the initiative to develop national highlights to support the management of OMD within Saudi Arabia. The committee members are national clinical leaders who collaborated with international expertise to establish these highlights to serve as a general clinical pathway in the management of OMD. Results: Standardization of the indications to diagnose oligometastases and patient selection criteria including ineligibility criteria for treatment are the basis of the highlights. Treatment approaches including surgical and the variety of radiotherapeutical options are discussed in relation to specific oligometastatic sites. Acceptable measurements for response to treatment and the future for the treatment of OMD conclude the development of the highlights. Conclusion: These are the first national highlights addressing this important disease in oncology. The implementation of these highlights as guidelines requires a robust multidisciplinary team and access to specific technology and expertise. These highlights are based on the most recent findings within the literature but will require repeated review and updating due to this rapidly evolving field in disease management.
Efficacy of Chinese Herbal Formula Sini Zuojin Decoction in Treating Gastroesophageal Reflux Disease: Clinical Evidence and Potential Mechanisms

Background Based on 122 cases reported in China, data mining indicated that Sini Powder (SNP) and the Zuojin Pill (ZJP) are both widely used as the basic recipe for treating Gastroesophageal Reflux Disease (GERD). Objectives To evaluate the intervention effects of Sini Zuojin Decoction (SNZJD) in patients with GERD. Methods A comprehensive collection of randomized controlled trials (RCTs) using SNZJD in patients with GERD that were published in domestic and foreign journals was made by computer retrieval. RevMan 5.3 software was used for meta-analysis and bias risk assessment, Stata 14.0 software was used for sensitivity analysis, GRADE profiler 3.6 was used to evaluate the level of evidence, and trial sequential analysis (TSA), employed to control for random errors, was performed to assess the main outcomes. Network pharmacology analysis was applied to preliminarily study the mechanisms of action of SNZJD on GERD. Results Thirteen articles were eventually included, covering a total of 966 patients. Meta-analysis indicated that: ① the SNZJD plus traditional stomach medicines (SPTSM) group was more effective than the traditional stomach medicines (TSM) group (RR = 1.16, 95% CI [1.04, 1.29], P = 0.009); ② the experimental group with SNZJD was significantly better than TSM controls in improving heartburn, substernal chest pain, acid regurgitation, and food regurgitation symptoms (P < 0.0001); ③ SPTSM could significantly decrease total symptom scores with substantial effectiveness (P < 0.00001). The recurrence rate and adverse effects of SNZJD treatment were significantly reduced (P < 0.05). TSA showed that the effective rate of meta-analysis might be reliable, but the recurrence and safety results were still uncertain. According to the evaluation by the GRADE method, the quality of evidence was low. Besides, SNZJD might treat GERD by acting on related targets and pathways such as inflammation, hormone regulation, and so on. Conclusions SNZJD might be useful in the treatment of GERD, but its long-term effects and specific clinical mechanisms are unclear. Due to the poor quality of the evidence, more samples and high-quality clinical studies should be tested and verified in the future.
Resistance Mechanisms to Anti-angiogenic Therapies in Cancer

Tumor growth and metastasis rely on tumor vascular network for the adequate supply of oxygen and nutrients. Tumor angiogenesis relies on a highly complex program of growth factor signaling, endothelial cell (EC) proliferation, extracellular matrix (ECM) remodeling, and stromal cell interactions. Numerous pro-angiogenic drivers have been identified, the most important of which is the vascular endothelial growth factor (VEGF). The importance of pro-angiogenic inducers in tumor growth, invasion and extravasation make them an excellent therapeutic target in several types of cancers. Hence, the number of anti-angiogenic agents developed for cancer treatment has risen over the past decade, with at least eighty drugs being investigated in preclinical studies and phase I-III clinical trials. To date, the most common approaches to the inhibition of the VEGF axis include the blockade of VEGF receptors (VEGFRs) or ligands by neutralizing antibodies, as well as the inhibition of receptor tyrosine kinase (RTK) enzymes. Despite promising preclinical results, anti-angiogenic monotherapies led only to mild clinical benefits. The minimal benefits could be secondary to primary or acquired resistance, through the activation of alternative mechanisms that sustain tumor vascularization and growth. Mechanisms of resistance are categorized into VEGF-dependent alterations, non-VEGF pathways and stromal cell interactions. Thus, complementary approaches such as the combination of these inhibitors with agents targeting alternative mechanisms of blood vessel formation are urgently needed. This review provides an updated overview on the pathophysiology of angiogenesis during tumor growth. It also sheds light on the different pro-angiogenic and anti-angiogenic agents that have been developed to date. Finally, it highlights the preclinical evidence for mechanisms of angiogenic resistance and suggests novel therapeutic approaches that might be exploited with the ultimate aim of overcoming resistance and improving clinical outcomes for patients with cancer.
Diagnosis of Nitrogen Nutrition in Rice Leaves Influenced by Potassium Levels

Evaluation of nitrogen (N) status by leaf color is a kind of classic nutritional diagnostic method. However, the color of leaves is influenced not only by N, but also by other nutrients such as potassium (K). Two-year field trials with a factorial combination of N and K were conducted to investigate the effects of different N and K rates on soil plant analysis development (SPAD) readings and leaf N, K, magnesium (Mg), and chlorophyll concentrations. Visual inspections in leaf greenness revealed darker green leaves with increasing N rates, while paler green leaves with increasing K rates. Data showed that SPAD readings, chlorophyll, N and Mg concentrations, and the chloroplast area increased significantly with raising N rates, while declined sharply with the increase in K rates due to the antagonistic relationships between K+ and NH4 + as well as Mg2+. It was also probable that the increase in K promoted the growth of leaves and diluted their N and Mg concentrations. The paler leaf appearance resulting from the application of K may overestimate the actual demand for N in the diagnosis of rice N status. The strong antagonistic relationships between K+, NH4 +, and Mg2+ should be considered in rice production and fertilization.
Bi-Directional Communication Between Neurons and Astrocytes Modulates Spinal Motor Circuits

Evidence suggests that astrocytes are not merely supportive cells in the nervous system but may actively participate in the control of neural circuits underlying cognition and behavior. In this study, we examined the role of astrocytes within the motor circuitry of the mammalian spinal cord. Pharmacogenetic manipulation of astrocytic activity in isolated spinal cord preparations obtained from neonatal mice revealed astrocyte-derived, adenosinergic modulation of the frequency of rhythmic output generated by the locomotor central pattern generator (CPG) network. Live Ca2+ imaging demonstrated increased activity in astrocytes during locomotor-related output and in response to the direct stimulation of spinal neurons. Finally, astrocytes were found to respond to neuronally-derived glutamate in a metabotropic glutamate receptor 5 (mGluR5) dependent manner, which in turn drives astrocytic modulation of the locomotor network. Our work identifies bi-directional signaling mechanisms between neurons and astrocytes underlying modulatory feedback control of motor circuits, which may act to constrain network output within optimal ranges for movement.
A Meta-Analysis of Observational Studies on the Association of Chronic Urticaria With Symptoms of Depression and Anxiety

Background: Chronic urticaria (CU) is a frequently occurring skin condition associated with many psychological factors, but the effect size of associations varied in literature. Objectives: To perform a systematic review and meta-analysis on the associations of CU with the symptoms of depression and anxiety. Methods: According to a pre-specified protocol, we systematically searched articles published in PubMed, Web of Science, CNKI, and CQVIP databases between January 2000 and January 2019. Pooled estimates in terms of odds ratios (ORs) or standardized mean differences (SMDs) were calculated according to outcome measures. Subgroup analysis by disease subtypes and tool of measurement, and sensitivity analysis were performed. Risk of bias and quality of studies were evaluated. Results: Twelve studies were selected for the systematic review. The ORs were 3.99 [95% confidence interval (CI): 3.24–4.91, P < 0.001] for anxiety and 2.94 (95% CI: 2.42–3.58, P < 0.001) for depression. The SMDs of severity were 0.98 for anxiety (95% CI: 0.76–1.200, P < 0.001) and 0.84 for depression (95% CI: 0.59–1.10, P < 0.001). Subgroup analysis by disease subtypes and tool of measurement showed variations in effect size, where chronic spontaneous urticaria showed greater effects on anxiety (OR = 6.62, 95% CI: 3.67–11.95, P < 0.001) and depression (OR = 6.13, 95% CI: 2.31–16.31, P < 0.001). Sensitivity analysis demonstrated consistent results. Conclusion: CU is associated with higher risks of anxiety and depression.
Genome-Wide Association Analysis of Fusarium Head Blight Resistance in Chinese Elite Wheat Lines

Fusarium head blight (FHB) is a devastating wheat disease worldwide. To decipher the genetic architecture of FHB resistance in Chinese germplasm, a Wheat Association Panel for Scab Research (WAPS) consisting of 240 leading Chinese wheat cultivars and elite lines was genotyped using the 90K single nucleotide polymorphism (SNP) arrays. The FHB response was evaluated in the field nurseries in Wuhan in Hubei Province over four consecutive years from 2014 to 2017. Five quantitative trait loci (QTL) were consistently identified on chromosome arms 1AS, 2DL, 5AS, 5AL, and 7DS using a mixed linear model (MLM), explaining 5.6, 10.3, 5.7, 5.4, and 5.6% of phenotypic variation, respectively. The QTL on 5AS, 5AL, and 7DS QTL are probably novel. The allelic frequency analysis indicated that cultivars from the Middle and Lower Yangtze River Valleys harbored more favorable alleles and were therefore more resistant than those from other regions. To facilitate in-house germplasm screening and marker-assisted selection (MAS), SNP-derived PCR markers were developed for the QTL regions on 1AS, 5AS, and 5AL QTL. In addition to the above five QTL, the WAPS population had a very low frequency of Fhb1, confirming that the gene is not widely used in Chinese wheat breeding programs. The resistant lines and molecular markers developed in this study are resources and information for enhancing FHB resistance in breeding populations by marker-assisted recurrent selection and gene stacking.
Regulation of Thermogenic Adipocyte Differentiation and Adaptive Thermogenesis Through Histone Acetylation

Over the past decade, the increasing prevalence of obesity and its associated metabolic disorders constitutes one of the most concerning healthcare issues for countries worldwide. In an effort to curb the increased mortality and morbidity derived from the obesity epidemic, various therapeutic strategies have been developed by researchers. In the recent years, advances in the field of adipocyte biology have revealed that the thermogenic adipose tissue holds great potential in ameliorating metabolic disorders. Additionally, epigenetic research has shed light on the effects of histone acetylation on adipogenesis and thermogenesis, thereby establishing the essential roles which histone acetyltransferases (HATs) and histone deacetylases (HDACs) play in metabolism and systemic energy homeostasis. In regard to the therapeutic potential of thermogenic adipocytes for the treatment of metabolic diseases, herein, we describe the current state of knowledge of the regulation of thermogenic adipocyte differentiation and adaptive thermogenesis through histone acetylation. Furthermore, we highlight how different HATs and HDACs maintain the epigenetic transcriptional network to mediate the pathogenesis of various metabolic comorbidities. Finally, we provide insights into recent advances of the potential therapeutic applications and development of HAT and HDAC inhibitors to alleviate these pathological conditions.
Estimation Method of L2 Learners' Second Language Ability by using Features in Conversation

We are conducting a research to train second language(L2) learners's second language ability by utilizing chat system. The main problem of existing chat systems is that it is not possible to chat with learners to adapt their second language level. In this research, in order to add a function to an existing chat system we need to measure the learner's second language level. So, to extract learners' second language capability, we propose a method to predict the language examination score of learners from chat context. This research investigates, first whether the number of utterances, number of sentences, word tokens and word types per utterance of chat context are correlated with second language examination score. Second, we build a predicting model to see the relationship between the chat context and second language examination score. As feature values of regression model for predicting the language examination score, we use variables chat time, sentence time, word token and word type. Also the unnatural sentence structure as a variable. For evaluation we use the root mean square error to check the results of prediction model, we use this model with Japanese and English chat and compare the results. We show how this chat context data is affecting the second language examination score and discuss strategies for future enhancements.
An Efficient Clustering based Data Collection using Mobile Sink in Wireless Sensor Networks

Data collection using mobile sink is a popular method that mitigates the sink hole problem in wireless sensor networks. In this regard, rendezvous point based data collection is a well-researched topic as it overcomes the problems of latency in data delivery and the scalability of the network. However, finding the optimal number of rendezvous points and their locations for the construction of the mobile sink tour is a challenging issue. An optimal path reduces delay in data collection and balances the energy consumption of the network. In this paper, we propose an algorithm that builds an efficient path for the mobile sink. The algorithm is based on the affinity propagation clustering which obtains an optimal set of rendezvous points. The extensive simulation results of the proposed algorithm show better performance over the existing algorithms in terms of number of the rendezvous points and path length.
Data Source Selection in Big Data Context

Big Data presents promising technological and economical opportunities. In fact, it has become the raw material of production for many organizations. Data is available in large quantities, and it continues generating abundantly. However, not all the data will have valuable knowledge. Unreliable sources provide misleading and biased information, and even reliable sources could suffer from low data quality. In this paper, we propose a novel methodology for the selectability of data sources, by both considering the presence and the absence of users' preferences. The proposed model integrates multiple factors that affect the reliability of data sources, including their quality, gain, cost and coverage. Experimental results on real world data-sets, show its capability to find the subset of relevant and reliable sources with the lowest cost.
PolarTag: Invisible Data with Light Polarization

Visual tags (e.g., barcodes, QR codes) are ubiquitous in modern day life, though they rely on obtrusive geometric patterns to encode data, degrading the overall user experience. We propose a new paradigm of passive visual tags which utilizes light polarization to imperceptibly encode data using cheap, widely-available components. The tag and its data can be extracted from background scenery using off-the-shelf cameras with inexpensive LCD shutters attached atop camera lenses. We examine the feasibility of this design with real-world experiments. Initial results show zero bit errors at distances up to 3.0~m, an angular-detection range of \ang110, and robustness to manifold ambient light and occlusion scenarios.
A Study of Keystroke Data in Two Contexts: Written Language and Programming Language Influence Predictability of Learning Outcomes

We study programming process data from two introductory programming courses. Between the course contexts, the programming languages differ, the teaching approaches differ, and the spoken languages differ. In both courses, students' keystroke data -- timestamps and the pressed keys -- are recorded as students work on programming assignments. We study how the keystroke data differs between the contexts, and whether research on predicting course outcomes using keystroke latencies generalizes to other contexts. Our results show that there are differences between the contexts in terms of frequently used keys, which can be partially explained by the differences between the spoken languages and the programming languages. Further, our results suggest that programming process data that can be collected non-intrusive in-situ can be used for predicting course outcomes in multiple contexts. The predictive power, however, varies between contexts possibly because the frequently used keys differ between programming languages and spoken languages. Thus, context-specific fine-tuning of predictive models may be needed.
Collaborative е-Rulemaking, Democratic Bots, and the Future of Digital Democracy

This article focuses on “deliberative e-rulemaking”: digital consultation processes that seek to facilitate public deliberation over policy or regulatory proposals [1, 2]. The main challenge of е-rulemaking platforms is to support an “intelligent” deliberative process that enables decision makers to identify a wide range of options, weigh the relevant considerations, and develop epistemically responsible solutions. This article discusses and critiques two approaches to this challenge: The Cornell RegulationRoom project and model of computationally assisted regulatory participation by Livermore et al. It then proceeds to explore two alternative approaches to e-rulemaking: One is based on the implementation of collaborative, wiki-styled tools. This article discusses the findings of an experiment, which was conducted at Bar-Ilan University and explored various aspects of a wiki-based collaborative е-rulemaking system. The second approach follows a more futuristic approach, focusing on the potential development of autonomous, artificial democratic agents. This article critically discusses this alternative, also in view of the recent debate regarding the idea of “augmented democracy. ”
Boyi: A Systematic Framework for Automatically Deciding the Right Execution Model of OpenCL Applications on FPGAs

FPGA vendors provide OpenCL software development kits for easier programmability, with the goal of replacing the time-consuming and error-prone register-transfer level (RTL) programming. Many studies explore optimization methods (e.g., loop unrolling, local memory) to accelerate OpenCL programs running on FPGAs. These programs typically follow the default OpenCL execution model, where a kernel deploys multiple work-items arranged into work-groups. However, the default execution model is not always a good fit for an application mapped to the FPGA architecture, which is very different from the multithreaded architecture of GPUs, for which OpenCL was originally designed. In this work, we identify three other execution models that can better utilize the FPGA resources for the OpenCL applications that do not fit well into the default execution model. These three execution models are based on two OpenCL features devised for FPGA programming (namely, single work-item kernel and OpenCL channel). We observe that the selection of the right execution model determines the performance upper bound of a particular application, which can vary by two orders magnitude between the most suitable execution model and the most unsuitable one. However, there is no way to select the most suitable execution model other than empiricall exploring the optimization space for the four of them, which can be prohibitive. To help FPGA programmers identify the right execution model, we propose Boyi, a systematic framework that makes automatic decisions by analyzing OpenCL programming patterns in an application. After finding the right execution model with the help of Boyi, programmers can apply other conventional optimizations to reach the performance upper bound. Our experimental evaluation shows that Boyi can 1) accurately determine the right execution model, and 2) greatly reduce the exploration space of conventional optimization methods.