Miracle-dz/UniFAR-FTUs huggingface dataset:
Carotid atherosclerosis and lectin-like oxidized low density lipoprotein receptor-1 levels in hemodialysis patients

Objectives: Atherosclerotic cardiovascular disease risk is increased in hemodialysis patients. Oxidized low density lipoprotein has an important role in atherosclerotic process and it exerts this effect via lectin like oxidized low density lipoprotein-1 (LOX-1). Carotid artery intima-media thickness (CIMT) is accepted as a god indicator of subclinical atherosclerosis. In this study, we aimed to investigate LOX-1 and CIMT levels in hemodialysis patients. Methods: Twenty-eight patients treated with hemodialysis at least 6 months and 19 healthy subjects were enrolled in this study. Serum LOX-1 levels and simultaneously with CIMT were measured in hemodialysis patients and healthy control group. Results: CIMT value was found to be statistically significantly higher in the hemodialysis group compared to control group (0.9 mm in hemodialysis group vs. 0.7 mm in control group, p < 0.001). There was no statistically significant difference between groups in terms of LOX-1 levels. (0.172 ng/ml in hemodialysis group vs. 0.213 ng/ml in healthy control group, p > 0.05). Conclusions: Although cardiovascular risk markers like CIMT, CRP were higher in hemodialysis group as expected, increase in LOX-1 levels was not detected.
An in-silico analysis to identify structural, functional and regulatory role of SNPs in hMRE11

ABSTRACT Meiotic recombination 11 (MRE11) is a component of the tri-molecular MRE11-RAD50-NBS1 (MRN) complex, which functions as an exonuclease and endonuclease which is involved in identifying, signalling, protecting and repairing double-strand breaks in DNA (DSBs). Ataxia-telangiectasia-like disorder (ATLD) 1 and Nijmegen breakage syndrome (NBS)-like disorder are MRE11 associated diseases. In the present study, we used an integrated computational approach to identify the most deleterious SNPs and their structural and functional impact on human MRE11. Five of the 68 observed non-synonymous SNP (nsSNPs; I162T, S273C, W210C, D311Y and R364L) should be worked on due to their strong possible pathogenicity and the risk of changing protein properties. All the nsSNPs were highly conserved and decrease the protein stability located in the MRE11 nuclease and MRE11 DNA binding presumed domain. R364L and I162T were predicted to be involved in post-translational modification (PTM) sites. Furthermore, we also analysed the regulatory effect of noncoding SNPs on MRE11 gene regulation in which 6 SNPs were found to affect gene regulation. All six noncoding SNPs predicted chromatin interactive site whereas only one SNP was noted its association with miRNA binding site which disrupts 5 miRNA conserved site. These findings help future studies to get more insights into the role of these variants in the alteration of the MRE11 function. Communicated by Ramaswamy H. Sarma Graphical abstract
A Power-Efficient TVC-Based Fast Auto-Frequency Calibration for PLLs

This brief presents a low-power, time-to-voltage converter (TVC)-based auto-frequency calibration (AFC) technology for phase-locked loops (PLLs). Instead of using power-hungry counters and finite-state machine (FSM), the proposed AFC uses TVC and comparator circuits to get the frequency information. Relying on a simple digital operable divide-by-3 (O-Div3) circuit, sampling procedure of TVCs is easy to be realized, which avoids the accumulation of multiple reference periods. Meanwhile, reference frequency is reused, that reduces the cost of extra clock. Beyond that, a binary-like search algorithm is proposed without frequency error. The 4.7-6.8-GHz PLL is implemented in 65-nm CMOS process. The total AFC setting time is less than 750 ns under 20-MHz reference, and consumes below 0.1 mW. Simultaneously, the adopted PLL achieves −116.2-dBc/Hz@1MHz phase noise when generating 4.89-GHz output.
Moderate Treadmill Exercise Modulates Gut Microbiota and Improves Intestinal Barrier in High-Fat-Diet-Induced Obese Mice via the AMPK/CDX2 Signaling Pathway

Objective The aim of this study was to investigate the effects of moderate treadmill exercise on gut microbiota, expression of proteins associated with gut barrier and to elucidate the mechanisms underlying their role in high-fat-diet-induced obese mice. Methods Six-week-old male C57BL/6 mice were randomly divided into standard chow diet control group (SD + Sed, n=6), chow diet exercise group (SD + Exe, n=6), high-fat diet control group (HFD + Sed, n=6) and high-fat diet exercise group (HFD + Exe, n=6). Exercise groups were trained on a motorized treadmill for 45 min/d at running speeds of 12 m/min, 5 days/week, for 12 consecutive weeks. The body weight and fasting blood glucose of the mice were recorded before euthanasia. Thereafter, the mice were sacrificed and the alteration of adipose mass, colonic histopathology, gut microbiome and gut barrier-related molecules were tested. Results It was found that the moderate treadmill exercise prevented the development of adiposity and hyperglycemia and effectively improved the loss of diversity and the relative abundance of intestinal microflora induced by high-fat diet. Moreover, regular exercise reversed the intestinal pathology and elevated the number of goblet cells in obesity. Besides, compared with the sedentary obese mice, the protein expression levels of colonic ZO-1 and occludin were enhanced and AMPK/CDX2 signaling pathway was significantly upregulated in obese mice that underwent exercise. Conclusion Long-term moderate treadmill exercise can markedly reduce the degree of obesity, modulate the colonic gut microbiota, and effectively activating AMPK/CDX2 signaling pathway to improve intestinal barrier in obese mice induced by high-fat diet.
Querying and mining chemical databases for drug discovery

Drug discovery and development has exploded into a multi-billion dollar industry. Unfortunately, despite a steady increase in pharmaceutical research, the number of new drugs discovered has been, at best, flat. The low productivity of current approaches to drug discovery has been ascribed to a number of factors including limited focus to a single protein target and undesirable effects, such as toxicity, that are discovered too late in the discovery process. In this dissertation, I propose strategies to combat the low productivity of current drug-discovery techniques and show that by integrating the principles of statistical significance and diversity into the molecular analysis framework, we can accelerate the drug discovery rate. In the first part of my thesis, I explore the importance of mining statistically significant patterns from large collections of scientific data and demonstrate their utility in drug discovery. I show that over-represented subgraphs in molecular databases are correlated with biological activity and can be used to learn accurate classification models. Furthermore, statistically significant pharmacophoric patterns can be employed to predict the binding mechanisms between small molecules and protein targets. Finally, I show that mining discriminative subgraphs from protein-protein interaction networks allows us to learn the complex network-encoded logic functions that decide the clinical outcomes of diseases. In the second part of my thesis, I explore the importance of structural diversity in top-k queries, and develop index structures to answer such queries in a scalable manner. First, I explore the importance of modeling attractive and repulsive dimensions in molecular analysis and demonstrate their utility in going beyond traditional similarity or distance measures. Next, I show that diversity-aware top-k answer sets are informationally denser than traditional top-k answer sets. Overall, this thesis proposes core indexing and mining algorithms that extend the current state of the art in computer science research. Among the various applications of the developed algorithms, impact in the field of drug discovery acts as the unifying theme binding all of the chapters together. However, these methods are also applicable in other scientific domains such as software bug mining, analysis of communication graphs, social networks, sensor networks, and transportation networks.
Modeling effective tumor burden of primary lesion and metastatic lymph node in breast cancer patients from the SEER database.

Background Characterizing the role of the primary tumor and axillary lymph nodes (ALNs) in metastasis progression can provide a rational basis for effective local control and systemic treatments. Methods We employed data from the Surveillance, Epidemiology, and End Results (SEER) database to perform survival analysis and modeling to estimate breast cancer (BC) progression in both untreated and treated patients. Effective tumor burden was defined as the number of cancer cells that can lead to distant metastasis. Quantified analysis was conducted for primary lesion and metastatic nodes, respectively. A tumor growth model was built based on mathematical modeling: N ( t ) = m N T ( t ) + n N N ( t ) . In this model, the parameters "m" and "n" represent the contributions of the primary lesion and metastatic lymph nodes (LNs) to distant metastasis, respectively. Results We found that both the primary lesion and the metastatic LNs contribute to the effective tumor burden. Through fitting the built tumor growth model, "m" in treated groups was determined to be 1, and "n" was determined to be 1.5; while in the untreated group, "m" was determined to be 1, and "n" was 0.15, which was 10 times smaller than in treated groups. Conclusions Our study revealed that the prognostic value of the anatomic stage in BC progression varied in different historical periods, due to the development of oncological treatments. While tumor size was a significant prognostic factor in both untreated and treated patients, the prognostic value of nodal status was only significant in patients who received locoregional treatment and systemic treatment.
Assessment of the Usability of SAR and Optical Satellite Data for Monitoring Spatio-Temporal Changes in Surface Water: Bodrog River Case Study

Mapping watercourses and their surroundings through remote sensing methods is a fast, continuous, and effective method and is a crucial tool for capturing change and possibly predicting hazards. Thanks to Synthetic Aperture Radar (SAR) technology and the ability of its backscattered and emitted radiation to penetrate the atmosphere under any conditions, this type of mapping of water surfaces is of particular importance. This paper presents the possibility of using SAR technology for long-term observations of changes in the behaviour of rivers and river systems, combined with optical multispectral images Sentinel-2. Additionally, it aims to demonstrate the suitability of satellite SAR and multispectral data implementation for mapping changes in watercourses, caused not only by their natural development but especially by inundation processes in their catchment area. Appropriate Sentinel-1 image processing evaluation procedures demonstrate that the usage of vertical-vertical (VV) type polarisation configuration is a suitable methodology for documenting water bodies, and a Lee filter is an acceptable tool for radar noise filtering. The extraction process of water surfaces is based on the determination of threshold values using the “Otsu” principle. Subsequently, the comparison of the results is realised by the spectral indices of water—the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and supervised classification method Maximum Likelihood Classification (MLC). The results are numerical and graphical evaluated. In assessing the accuracy of SAR extraction, the highest values achieved in Overall Accuracy (OA) were a maximum of 98.6%. On average, the lower values were in User Accuracy (UA) with a maximum of 93.1%, where VV polarisation also dominates. However, vertical-horizontal (VH) polarisation dominates in Producer Accuracy (PA) with a maximum of 84.9%.
Steigerung der Energieeffizienz: ein Muss für die Energiewende, ein Wachstumsimpuls für die Wirtschaft

As part of the energy transition process, the German government has set far-reaching energy efficiency targets, including doubling the annual energy-efficient refurbishment rate for existing residential buildings from one to two percent. DIW Berlin has estimated the additional energy-related investment required to meet these targets and analyzed the impact this could have on the economy. In the long term, the savings on household energy bills far exceed the additional investment. This, combined with further measures to increase energy efficiency in other sectors, substantially reduces energy consumption and greenhouse gas emissions. Even allowing for some elements of uncertainty, these measures to improve energy efficiency have a positive impact on income and domestic demand. They could also result in significantly positive effects on employment, depending on the ratio of productivity gains and new jobs. Nevertheless the most recent savings are not nearly enough to achieve the German government's energy efficiency targets. Clear and reliable framework conditions are needed soon to increase the number of buildings being refurbished for energy efficiency. Given the present analyses, which indicate that forcing the pace of energy efficiency improvements has a positive impact on German economic growth and employment, the government's hesitation seems even less justified. Im Zuge der Energiewende hat die Bundesregierung weitreichende Energieeffizienzziele definiert. Fur die energetische Sanierung des Bestandes an Wohngebauden wird eine Verdopplung der jahrlichen Sanierungsrate von ein auf zwei Prozent angestrebt. Das DIW Berlin hat die hierfur notwendigen energiebedingten Mehrinvestitionen abgeschatzt und deren volkswirtschaftliche Wirkungen analysiert. Langerfristig ubersteigen die eingesparten Energieausgaben der privaten Haushalte die Mehrinvestitionen deutlich. Zusammen mit weiteren Masnahmen zur Steigerung der Energieeffizienz in anderen Bereichen konnen der Energieverbrauch und die Treibhausgasemissionen kraftig gesenkt werden. Die Masnahmen zur Beschleunigung der Energieeffizienz konnen sich auch unter Berucksichtigung verschiedener Unsicherheitsfaktoren positiv auf Einkommen und inlandische Nachfrage auswirken. Je nach dem Verhaltnis von Produktivitatssteigerungen und Neueinstellungen kann es auch zu erheblichen positiven Beschaftigungseffekten kommen. Die Einsparerfolge der jungeren Vergangenheit reichen jedoch bei weitem nicht aus, um die Energieeffizienzziele der Bundesregierung zu verwirklichen. Die Steigerung der energetischen Gebaudesanierung erfordert schnell klare und verlassliche Rahmenbedingungen. Das Zogern der Politik erscheint umso weniger gerechtfertigt, als die hier vorgestellten Analysen belegen, dass eine Forcierung der Energieeffizienz fur die deutsche Volkswirtschaft positive Wachstums- und Beschaftigungseffekte haben kann.
Data Collection and Evaluation of Higher Education Informatization based on Grey Convolution

The rapid de velopment of information technology also promotes the continuous progress of education, especially accelerates the reform of education. Facing the massive and multi-dimensional data of various application systems, it is a new problem for university management to use the data to effectively support the decision-making activities of teaching management. This paper uses the quantitative evaluation method of state characteristic curve to extract the most representative state characteristic curve in different training models. This method can effectively characterize the monotonicity and sensitivity of the state characteristic curve, and finally extract the state characteristic curve.
Algorithm of Fiber Fault Analysis Based on Wavelet Transform

Otdr can only measure the attenuation value of every sample, but it can’t judge event point automatically. In real optic fiber circuit, signal will be often mixed with noises. During fault analysis, it’s necessary to find event information from the noise, and accurately judge position of event points. After simply analyzing otdr test principle, the algorithm of fiber fault analysis based on wavelet transform is proposed. First, the threshold filter and the signal correlation enhancement will be carried out to the high frequency data of wavelet transformation, it can not only denoise commendably, but also strengthen the point efficiently. Then according to position and characteristic of mold maximum value, the strengthen signal will be detected to judge the event position and type. As an effective method, the simulation experiment result indicated that, this algorithm still had high precision and reliability in the strong noise jamming situation.
Early competitive effects on growth of loblolly pine grown in co-culture with switchgrass

This study: (1) examined competitive interactions between switchgrass and loblolly pine grown in co-culture, and (2) assessed early growth rates of loblolly pine as affected by differing switchgrass competition treatments. Co-cultures were established and monitored on two Upper Coastal Plain sites for 2 years. The Pontotoc site has a history of agricultural use with some likely residual fertility, while the Starr site has a history of forest use, although it has been maintained for several decades as a mowed field. Five treatments were employed at each site: (1) switchgrass only (SG); (2) pine only (Pine); (3) pine planted into switchgrass with a 1.2-m competition-free zone on either side of the row of trees (PS-4); (4) pine in switchgrass with a 0.6-m competition-free zone (PS-2); (5) and pine planted into switchgrass with no-competition free zone (PS-0). Switchgrass yields at Starr were roughly one quarter of those at Pontotoc, which led to differences in competitive intensities between species at the two sites. Loblolly pine heights at Pontotoc and Starr averaged 70 and 63 cm following year 1, and 161 and 176 cm following year 2, respectively. Planting pines directly into switchgrass significantly reduced tree heights, but seedlings in PS-4 treatments experienced growth ≥ Pine treatments. Switchgrass yields did not significantly differ by distance from tree row for any treatment. Ourresults suggest switchgrass dominates resource competition in early phases of this production system. However, these competitive pressures can be managed to effectively co-produce loblolly pine and switchgrass on southern lands.
Application of Machine-Learning Algorithms to the Stratigraphic Correlation of Archean Shale Units Based on Lithogeochemistry

Data-driven methods have increasingly been applied to solve geoscientific problems. Incorporation of data-driven methods with hypothesis testing can be effective to address some long-standing debates and reduce interpretation uncertainty by leveraging larger volumes of data and more objective data analytics, which leads to increased reproducibility. In this study, lithogeochemical data from regionally persistent Archean shale units were aggregated from literature, with special reference to the Kaapvaal Craton of South Africa—namely, shales from the Barberton, Witwatersrand, Pongola, and Transvaal Supergroups—and the Belingwe and Buhwa Greenstone Belts of the Zimbabwe Craton. We examine the feasibility of using machine-learning algorithms to produce a geochemical classification and demonstrate that machine learning is capable of accurately correlating stratigraphy at the formation, group, and supergroup levels. We demonstrate the ability to extract highly useful scientific findings through a data-driven approach, such as geological implications for the uniqueness of the sediment compositions of the Central Rand and West Rand Groups. We further demonstrate that when lithogeochemistry and machine-learning algorithms are used, only about 50 samples per geological unit are necessary to reach accuracy levels of around 80%–90% for our shale samples. Consequently, for many traditional tasks, such as rock identification and mapping, some expensive analyses and manual labor can be replaced by an abundance of cheaper data and machine learning. This approach could transform large-scale geological surveys by enabling more detailed mapping than currently possible, by vastly increasing the coverage rate and total coverage. In addition, the aggregation of historical data facilitates data reuse and open science. These results justify the need to bridge data- and hypothesis-driven techniques for the stratigraphic correlation and prediction of rock units, which can improve the accuracy of the inferred stratigraphic correlation and basin setting.
Improving Spanish translations of tobacco dependence measures using cognitive interviewing

ABSTRACT Background Cognitive interviewing is the practice of systematically collecting feedback about survey items from members of the priority population, with the goal of identifying and rectifying problems to increase the comprehensibility of the survey. Evidence is limited on the extent to which this method of pretesting improves survey items. Objective The current study examined the utility of incorporating cognitive interviewing to improve the Spanish translations of two measures of tobacco dependence. Methods Items from the Spanish versions of the Wisconsin Inventory of Smoking Dependence Motives (68 items) and Nicotine Dependence Syndrome Scale (19 items) were subjected to cognitive interviews with Spanish-speaking smokers. Problematic items were revised based on participant feedback and re-assessed in a second round of interviews (N = 23, 78.3% male; 21.7% female). Results Twenty-three of the 87 items demonstrated comprehension problems, and 67 items elicited at least one problem report. Number of problems were significantly fewer pre- vs. post-revision (t [90] = 6.55, p < .001). Conclusions In combination with standard translation procedures, cognitive interviewing with the priority population appears to be a useful method for ensuring comprehensible and relevant item content.
Utilizing Mobile Phones in Open Innovation Platforms: A New Business Model Especially Geared to The Mobile Phone Sector in Developing Countries

The objective of the paper was to propose a new business model for an open innovation platform especially geared to the mobile phone sector in developing countries. Open innovation has in many cases proven to be a superior means to innovate, but in developing countries open innovation has so far been utilized only scantly. The ways to use open innovation that have been successful in developed countries can not directly be used in developing countries. Thus, a new business model is proposed. Increased potential in innovation development and penetration of mobile phones in developing countries are discussed in the paper. The proposed platform will offer both indirect and direct financial benefits. Indirect benefits are attained as the service provider operating the platform is able to develop new and better services based on a better understanding of its customer needs. Direct financial benefits take the form of revenue from advertisements on the platform, subscriber fees, and commissions from trading done by using the platform. Initial investment costs of the platform as well as platform maintenance and promotion costs are considered in the paper. It seems very likely that the business model of the open innovation platform would be profitable. Challenges faced by open innovation platforms are pointed out.