Miracle-dz/UniFAR-FTUs huggingface dataset:
Networks' characteristics are important for systems biology

Abstract A fundamental goal of systems biology is to create models that describe relationships between biological components. Networks are an increasingly popular approach to this problem. However, a scientist interested in modeling biological (e.g., gene expression) data as a network is quickly confounded by the fundamental problem: how to construct the network? It is fairly easy to construct a network, but is it the network for the problem being considered? This is an important problem with three fundamental issues: How to weight edges in the network in order to capture actual biological interactions? What is the effect of the type of biological experiment used to collect the data from which the network is constructed? How to prune the weighted edges (or what cut-off to apply)? Differences in the construction of networks could lead to different biological interpretations. Indeed, we find that there are statistically significant dissimilarities in the functional content and topology between gene co-expression networks constructed using different edge weighting methods, data types, and edge cut-offs. We show that different types of known interactions, such as those found through Affinity Capture-Luminescence or Synthetic Lethality experiments, appear in significantly varying amounts in networks constructed in different ways. Hence, we demonstrate that different biological questions may be answered by the different networks. Consequently, we posit that the approach taken to build a network can be matched to biological questions to get targeted answers. More study is required to understand the implications of different network inference approaches and to draw reliable conclusions from networks used in the field of systems biology.
Privacy-preserving Maximum Likelihood Estimation for Distributed Data

Recent technological advances enable the collection of huge amounts of data. Commonly, these data are generated, stored, and owned by multiple enti- ties that are unwilling to cede control of their data. This distributed environment requires statistical tools that can produce correct results while preserving data pri- vacy. Privacy-preserving protocols have been proposed to solve specic statistical analysis such as linear regression, clustering, and classication. In this paper, we present methods and protocols for privacy-preserving maximum likelihood estima- tion in general settings. We discuss both horizontally and vertically partitioned data, and propose procedures that allow participating parties to withdraw from the joint computation. Logistic regression is used to demonstrate our method.
Increased Risk of Exacerbation in Asthma Predominant Asthma–Chronic Obstructive Pulmonary Disease Overlap Syndrome

Background Obstructive airway disease patients with increased variability of airflow and incompletely reversible airflow obstruction are often categorized as having asthma–chronic obstructive pulmonary disease (COPD) overlap syndrome (ACOS). ACOS is heterogeneous with two sub-phenotypes: asthma-ACOS and COPD-ACOS. The objective of this study was to determine the difference in risk of exacerbation between the two sub-phenotypes of ACOS. Methods A total of 223 patients exhibiting incompletely reversible airflow obstruction with increased variability (spirometrically defined ACOS) were enrolled. These patients were divided into asthma-ACOS and COPD-ACOS according to their physician's diagnosis and smoking history of 10 pack-years. Within-group comparisons were made for asthma-ACOS versus COPD-ACOS and light smokers versus heavy smokers. Results Compared to patients with COPD-ACOS, patients with asthma-ACOS experienced exacerbation more often despite their younger age, history of light smoking, and better lung function. While the light-smoking group showed better lung function, they made unscheduled outpatient clinic visits more frequently. On multivariate analysis, asthma-ACOS and poor inhaler compliance were significantly associated with more than two unscheduled clinic visits during the previous year. Conclusion Spirometrically defined ACOS includes heterogeneous subgroups with different clinical features. Phenotyping of ACOS by physician's diagnosis could be significant in predicting future risk of exacerbation.
Two-pole narrow-band filter utilizing λ/2 resonators and source-load coupling

In this paper a simple and compact narrow-band filter with symmetric frequency response is proposed. A pair of transmission zeros (TZs) is obtained from a 2nd-order (N=2) filter by utilizing the source-load (S/L) coupling. The design is based on the ‘N+2’ transversal network coupling matrix (CM) and the number of transmission zeros obtained is ‘N’. The proposed two-pole bandpass filter (BPF) employs two magnetically coupled half-wavelength (λ/2) resonators to achieve good skirt-selectivity. A prototype is designed and fabricated for a fractional bandwidth (FBW) of 2.54% at 9.825 GHz. Two TZs are obtained, one on either side of the passband at 9.16 GHz and 10.65 GHz, respectively. Minimum insertion loss (IL) of 1.69 dB and return loss (RL) greater than 12dB is obtained in the passband. The proposed filter also possesses worthy characteristics like fairly flat response (0.7 dB ripple) and low group delay (less than 0.6 ns) in the operational frequency band. A good agreement between the measured and simulated results validates the design method.
Reinforcement Learning Using Negative Relevance Feedback

In the task of information filtering, the profile of the user's interest and preference is the key to the performance of the system. In traditional method, the profile usually represented as a set of features in the vector space model, but this kind of profile could not be satisfied by the user for lack of the negative information. This paper proposes on approach to construct the user's profile based on negative relevance feedback and Reinforcement Learning(RL) This approach uses not only positive feedback&nbsp;&nbsp;but also negative feedback, and suggests relevance feedback method with reinforcement learning. The proposed method improves the performance of filtering system by eliminating the documents that users don't prefer. We carried filtering about four topics so as to compare the proposed method to Rocchio and Widrow-Hoff(WH), representative relevance feedback methods. The experimental result shows that the performance of the case, where only positive relevance feedback is used, is 3 to 6% better than that of Rocchio and 2 to 3% better than that of Widrow-Hoff. And the performance of the case, where both positive and negative relevance feedback are used, is 6 to 10% better than that of Rocchio and 2 to 8% better than that of Widrow-Hoff.
Overall Downregulation of mRNAs and Enrichment of H3K4me3 Change Near Genome-Wide Association Study Signals in Systemic Lupus Erythematosus: Cell-Specific Effects

This study was designed to define gene expression and H3K4me3 histone modifications in T cells, B cells, and monocytes in systemic lupus erythematosus (SLE). Array studies of total peripheral blood mononuclear cells have demonstrated gene expression signatures related to neutrophils, interferon, and other inflammatory pathways. It is not clear how consistent these effects are across different cell types. In this study, RNA-seq and chromatin immunoprecipitation-seq were utilized to identify gene expression patterns and H3K4me3 histone modifications related to promoter activation in SLE. Across the three cell types, there was 55% concordance for gene expression changes related to SLE. Key conserved pathways were ribosome biogenesis among upregulated genes and heat shock response among downregulated genes. ETS family transcription factors (TFs) and STAT1 were revealed as common regulators by position weight matrices. When epigenetic changes were leveraged with gene expression, the pivotal TFs ATF3 and FOS were defined with ATF3 also cross-referencing with gene expression-identified TFs. Genome-wide association study (GWAS) single nucleotide polymorphisms associated with SLE were cross-referenced with both mRNA and H3K4me3 changes in SLE. Baseline mRNA expression and H3K4me3 peak height was higher at sites that cross-referenced with GWAS signals, however, all three cell types exhibited an overall decrease in expression of GWAS-associated RNAs differentially expressed in SLE. H3K4me3 changes in SLE were also enriched in GWAS-associated sites. In summary, the SLE disease process is associated with both shared and cell-specific changes in gene expression and epigenetics. Surprisingly, GWAS-associated RNAs were overall markedly decreased across all three cell types. TF analysis identified ATF3, FOS, STAT1, and ETS family members as critical, all pathways with a recognized relationship to the SLE disease process. GWAS signals clearly mark both cell-type specific changes in SLE as well as concordant changes across all three cell types. Interpretation of single nucleotide polymorphism effects in SLE will require tissue-specific mechanistic studies and therapeutics will require mechanistic studies in multiple cell types.
A Hybrid Approach Based Sentiment Extraction from Medical Context

In the domain of Bio medical Natural Language Processing (Bio-NLP), the information extraction and context sentiment identification are treated as emerging tasks. Several linguistic features like negation , uni-gram, bi-gram, Part-of-Speech (POS) have been used to extract the medical concepts and their sense-based context level information. Thus, in the present attempt, a hybrid approach which is the combination of both linguistic and machine learning approaches has been introduced to extract the contextual sense-based information from a medical corpus. The extraction of sentiment oriented keywords is the crucial part towards identifying the senses of medical contexts. In our previous work, we have developed a medical sense-based lexicon known as WordNet of Medical Event (WME). Several sentiment lexicons like Senti-WordNet, SenticNet etc. were used to represent WME. In contrast, one of our primary motivations here is to build a sentiment extraction model based on medical contexts to leverage the knowledge of WME using a hybrid approach. The developed model is based on two phases, namely pre-processing phase and learning phase. The prepro-cessing phase is responsible for extracting and preparing structural data from the raw contexts whereas the learning phase helps to identify the sentiment patterns and evaluate the sentiment extraction process. The two phased hybrid model provides us 81% accuracy for extracting the sentiment based medical contexts as positive and negative by employing NaïveBayes and Sequential minimal optimization (SMO) supervised classifiers.
Genetic Knock-Down of HDAC7 Does Not Ameliorate Disease Pathogenesis in the R6/2 Mouse Model of Huntington's Disease

Huntington's disease (HD) is an inherited, progressive neurological disorder caused by a CAG/polyglutamine repeat expansion, for which there is no effective disease modifying therapy. In recent years, transcriptional dysregulation has emerged as a pathogenic process that appears early in disease progression. Administration of histone deacetylase (HDAC) inhibitors such as suberoylanilide hydroxamic acid (SAHA) have consistently shown therapeutic potential in models of HD, at least partly through increasing the association of acetylated histones with down-regulated genes and by correcting mRNA abnormalities. The HDAC enzyme through which SAHA mediates its beneficial effects in the R6/2 mouse model of HD is not known. Therefore, we have embarked on a series of genetic studies to uncover the HDAC target that is relevant to therapeutic development for HD. HDAC7 is of interest in this context because SAHA has been shown to decrease HDAC7 expression in cell culture systems in addition to inhibiting enzyme activity. After confirming that expression levels of Hdac7 are decreased in the brains of wild type and R6/2 mice after SAHA administration, we performed a genetic cross to determine whether genetic reduction of Hdac7 would alleviate phenotypes in the R6/2 mice. We found no improvement in a number of physiological or behavioral phenotypes. Similarly, the dysregulated expression levels of a number of genes of interest were not improved suggesting that reduction in Hdac7 does not alleviate the R6/2 HD-related transcriptional dysregulation. Therefore, we conclude that the beneficial effects of HDAC inhibitors are not predominantly mediated through the inhibition of HDAC7.
Marker-less respiratory motion modeling using the Microsoft Kinect for Windows

Patient respiratory motion is a major problem during external beam radiotherapy of the thoracic and abdominal regions due to the associated organ and target motion. In addition, such motion introduces uncertainty in both radiotherapy planning and delivery and may potentially vary between the planning and delivery sessions. The aim of this work is to examine subject-specific external respiratory motion and its associated drift from an assumed average cycle which is the basis for many respiratory motion compensated applications including radiotherapy treatment planning and delivery. External respiratory motion data were acquired from a group of 20 volunteers using a marker-less 3D depth camera, Kinect for Windows. The anterior surface encompassing thoracic and abdominal regions were subject to principal component analysis (PCA) to investigate dominant variations. The first principal component typically describes more than 70% of the motion data variance in the thoracic and abdominal surfaces. Across all of the subjects used in this study, 58% of subjects demonstrate largely abdominal breathing and 33% exhibited largely thoracic dominated breathing. In most cases there is observable drift in respiratory motion during the 300s capture period, which is visually demonstrated using Kernel Density Estimation. This study demonstrates that for this cohort of apparently healthy volunteers, there is significant respiratory motion drift in most cases, in terms of amplitude and relative displacement between the thoracic and abdominal respiratory components. This has implications for the development of effective motion compensation methodology.
Comparing the Prognostic Accuracy for All-Cause Mortality of Frailty Instruments: A Multicentre 1-Year Follow-Up in Hospitalized Older Patients

Background Frailty is a dynamic age-related condition of increased vulnerability characterized by declines across multiple physiologic systems and associated with an increased risk of death. We compared the predictive accuracy for one-month and one-year all-cause mortality of four frailty instruments in a large population of hospitalized older patients in a prospective multicentre cohort study. Methods and Findings On 2033 hospitalized patients aged ≥65 years from twenty Italian geriatric units, we calculated the frailty indexes derived from the Study of Osteoporotic Fractures (FI-SOF), based on the cumulative deficits model (FI-CD), based on a comprehensive geriatric assessment (FI-CGA), and the Multidimensional Prognostic Index (MPI). The overall mortality rates were 8.6% after one-month and 24.9% after one-year follow-up. All frailty instruments were significantly associated with one-month and one-year all-cause mortality. The areas under the receiver operating characteristic (ROC) curves estimated from age- and sex-adjusted logistic regression models, accounting for clustering due to centre effect, showed that the MPI had a significant higher discriminatory accuracy than FI-SOF, FI-CD, and FI-CGA after one month (areas under the ROC curves: FI-SOF = 0.685 vs. FI-CD = 0.738 vs. FI-CGA = 0.724 vs. MPI = 0.765, p<0.0001) and one year of follow-up (areas under the ROC curves: FI-SOF = 0.694 vs. FI-CD = 0.729 vs. FI-CGA = 0.727 vs. MPI = 0.750, p<0.0001). The MPI showed a significant higher discriminatory power for predicting one-year mortality also in hospitalized older patients without functional limitations, without cognitive impairment, malnourished, with increased comorbidity, and with a high number of drugs. Conclusions All frailty instruments were significantly associated with short- and long-term all-cause mortality, but MPI demonstrated a significant higher predictive power than other frailty instruments in hospitalized older patients.
IcoRating: A Deep-Learning System for Scam ICO Identification

Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP, NEO) have been rapidly gaining ground in use, value, and understanding among the public, bringing astonishing profits to investors. Unlike other money and banking systems, most digital tokens do not require central authorities. Being decentralized poses significant challenges for credit rating. Most ICOs are currently not subject to government regulations, which makes a reliable credit rating system for ICO projects necessary and urgent. In this paper, we introduce IcoRating, the first learning--based cryptocurrency rating system. We exploit natural-language processing techniques to analyze various aspects of 2,251 digital currencies to date, such as white paper content, founding teams, Github repositories, websites, etc. Supervised learning models are used to correlate the life span and the price change of cryptocurrencies with these features. For the best setting, the proposed system is able to identify scam ICO projects with 0.83 precision. We hope this work will help investors identify scam ICOs and attract more efforts in automatically evaluating and analyzing ICO projects.
Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection

In this work, we investigate a potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection. The elucidation of the subcategorization of a pulmonary nodule type in CT images is an important preliminary step towards developing the nodule managements that are specific to each patient. We categorize lung cancers by analyzing volumetric distributions of CT values within lung cancers via a topic model such as latent Dirichlet allocation. Through applying our scheme to 3D CT images of nonsmall- cell lung cancer (maximum lesion size of 3 cm) , we demonstrate the potential usefulness of the topic model-based categorization of lung cancers as quantitative CT biomarkers.
Is Natural Language a Perigraphic Process? The Theorem about Facts and Words Revisited

As we discuss, a stationary stochastic process is nonergodic when a random persistent topic can be detected in the infinite random text sampled from the process, whereas we call the process strongly nonergodic when an infinite sequence of independent random bits, called probabilistic facts, is needed to describe this topic completely. Replacing probabilistic facts with an algorithmically random sequence of bits, called algorithmic facts, we adapt this property back to ergodic processes. Subsequently, we call a process perigraphic if the number of algorithmic facts which can be inferred from a finite text sampled from the process grows like a power of the text length. We present a simple example of such a process. Moreover, we demonstrate an assertion which we call the theorem about facts and words. This proposition states that the number of probabilistic or algorithmic facts which can be inferred from a text drawn from a process must be roughly smaller than the number of distinct word-like strings detected in this text by means of the Prediction by Partial Matching (PPM) compression algorithm. We also observe that the number of the word-like strings for a sample of plays by Shakespeare follows an empirical stepwise power law, in a stark contrast to Markov processes. Hence, we suppose that natural language considered as a process is not only non-Markov but also perigraphic.
Skeletal Muscle Interleukin-6 Regulates Hepatic Cytochrome P450 Expression: Effects of 16-Week High-Fat Diet and Exercise

High-fat diet (HFD) induces several changes to the pathways regulating energy homeostasis and changes the expression of the hepatic cytochrome p450 (Cyp) enzyme-system. Despite these pervious findings, it is still unclear how the effects of HFD and especially HFD in combination with treadmill running affect hepatic Cyp expression. In this study, we investigated the mRNA and protein expression of selected Cyp's in mice subjected to 16 weeks of HFD and treadmill running. To understand the regulatory mechanisms behind the exercise-induced reversion of the HFD-induced changes in Cyp expression, we used a model in which the exercise-induced myokine and known regulator of hepatic Cyp's, interleukin-6 (IL-6), were knocked out specifically in skeletal muscle. We found that HFD increased the mRNA expression of Cyp1a1 and Cyp4a10, and decreased the expression of Cyp2a4, Cyp2b10, Cyp2e1, and Cyp3a11. HFD in combination with treadmill running reversed the HFD increase in Cyp4a10 mRNA expression. In addition, we observed increased Cyp1a and Cyp3a protein expression as an effect of exercise, whereas Cyp2b expression was lowered as an effect of HFD. IL-6 effected the response in Cyp3a11 and Cyp1a expression. We observed no changes in the content of the aryl hydrocarbon receptor, constitutive androstane receptor, pregnane X receptor, or peroxisome proliferation activator receptor alpha. In conclusion, we show that both HFD and exercise in HFD-fed animals can regulate hepatic Cyp expression and that changes in Cyp3a in response to HFD and exercise are dependent on skeletal muscular IL-6.
Mining Semantic Correlation of Heterogeneous Multimedia Data for Cross-Media Retrieval

Although multimedia objects such as images, audios and texts are of different modalities, there are a great amount of semantic correlations among them. In this paper, we propose a method of transductive learning to mine the semantic correlations among media objects of different modalities so that to achieve the cross-media retrieval. Cross-media retrieval is a new kind of searching technology by which the query examples and the returned results can be of different modalities, e.g., to query images by an example of audio. First, according to the media objects features and their co-existence information, we construct a uniform cross-media correlation graph, in which media objects of different modalities are represented uniformly. To perform the cross-media retrieval, a positive score is assigned to the query example; the score spreads along the graph and media objects of target modality or MMDs with the highest scores are returned. To boost the retrieval performance, we also propose different approaches of long-term and short-term relevance feedback to mine the information contained in the positive and negative examples.
The comet assay applied to cells of the eye

The human eye is relatively unexplored as a source of cells for investigating DNA damage. There have been some clinical studies, using cells from surgically removed tissues, and altered DNA bases as well as strand breaks have been measured using the comet assay. Tissues examined include corneal epithelium and endothelium, lens capsule, iris and retinal pigment epithelium. For the purpose of biomonitoring for exposure to potential mutagens in the environment, the eye-relatively unprotected as it is compared with the skin-would be a valuable object for study; non-invasive techniques exist to collect lachrymal duct cells from tears, or cells from the ocular surface by impression cytology, and these methods should be further developed and validated.