Miracle-dz/UniFAR-FTUs huggingface dataset:
The metabolism of 11β-hydroxyandrostenedione by steroidogenic enzymes yields metabolites contributing to the androgen pool in prostate cancer

The maintenance of steroid homeostasis in the prostate is critical, with perturbation of steroidogenesis contributing to the modulation of active ligands in the androgen pool. In this scenario, enzymes catalysing the biosynthesis, inactivation and conjugation of steroids are the key players, regulating active ligand levels and in so doing, the activation of the androgen receptor (AR). The glucuronidation of potent ligands renders them unable to bind the AR, allowing the secretion of conjugated steroids. Uridine diphosphate glucuronosyltransferase 2B type 28 (UGT2B28), one of the UGT enzymes catalyzing the glucuronidation of androgens, has recently been given a prominent role in the regulation of prostate steroidogenesis—one which stands in contrast to the accepted dogma that lower androgen levels resulting from increased conjugation are associated with decreased prostate cancer (PCa) risk and disease progression. Increased DHT and its precursors, T and androstenediol, were reported to be associated with increased UGT2B28 tumor expression levels, linked to lower PSA levels but higher Gleason scores and increased PCa risk. In addition, the complete deletion of UGT2B28, was associated with decreased T, DHT and glucuronide derivatives when compared to patients carrying both alleles. UGT2B28 is encoded by a single gene giving rise to UGT2B28 type I which catalyses androgen glucuronidation and, due to alternative splicing, also produces two distinct transcripts, UGT2B28 type II and III. Type II with its premature stop codon, is devoid of the cofactor binding domain while type III is devoid of the substrate binding domain, both catalytically inactive, truncated proteins. Increased UGT2B28 mRNA expression was reported in primary tumours, and while variable nuclear and strong cytoplasmic staining were distinctive of tumour cells, the expression levels and compartmentalization of the specific protein isoforms remain unknown. While increased expression of type I would contribute towards lowering androgen levels, increased expression of types II and III would not. The abundance of type III transcripts in multiple tissues may provide insight into a regulatory role with truncated isoforms possibly affecting androgen levels by regulating substrate and/or co-factor availability, dimerization or the formation of protein complexes with other UGTs, while proteinprotein interaction may also impact cascade signaling pathways in PCa development and disease progression.
Bayesian Nonresponse Models for the Analysis of Data from Small Areas: An Application to BMD and Age in NHANES III

We analyze data on bone mineral density (BMD) and age for white females age 20+ in the third National Health and Nutrition Examination Survey. For the sample the age of each individual is known, but some individuals did not have their BMD measured, mainly because they did not show up in the mobile examination centers. We have data from 35 counties, the small areas. We use two types of models to analyze the data. In the ignorable nonresponse model, BMD does not depend on whether an individual responds or not. In the nonignorable nonresponse model, BMD is related to whether he/she responds. We incorporate this relationship in our model by using a Bayesian approach. We further divide these two types of models into continuous and categorical data models. Our nonignorable nonresponse models have one important feature: They are “close” to the ignorable nonresponse model thereby reducing the effects of the untestable assumptions so common in nonresponse models. In the continuous data models, because the age of all nonrespondents are known and there is a relation between BMD and age, age is used as a covariate. In the categorical data models BMD has three levels (normal, osteopenia, osteoporosis) and age has two levels (younger than 50 years, at least 50 years). Thus, age is a supplemental margin for the 2 × 3 categorical table. Our research on the categorical models is much deeper than on the continuous models. Our models are hierarchical, a feature that allows a “borrowing of strength” across the counties. Individual inference for most of the counties is unreliable because there is large variation. This “borrowing of strength” is therefore necessary because it permits a substantial reduction in variation. The joint posterior density of the parameters for each model is complex. Thus, we fit each model using Markov chain Monte Carlo methods to obtain samples from the posterior density. These samples are used to make inference about BMD and age, and the relation between BMD and age. For the continuous data models, we show that there is an important relation between BMD and age by using a deviance measure, and we show that the nonignorable nonresponse models are to be preferred. For the categorical data models, we are able to estimate the proportion of individuals in each BMD and age cell of the categorical table, and we can assess the relation between BMD and age using the Bayes factor. A sensitivity analysis shows that there are differences, typically small, in inference that permits different levels of association between BMD and age. A simulation study shows that there is not much difference in inference between the ignorable nonresponse models and the nonignorable nonresponse models. As expected, BMD depends on age and this inference can be obtained for some small counties. For the data we use, there are virtually no young individuals with osteoporosis. The nonignorable nonresponse models generalize the ignorable nonresponse models, and therefore, allow broader inference.
Local Skeleton Discovery for Incremental Bayesian Network Structure Learning

Nowadays there are a huge number of applications produce the immense amount of data in the form of a data stream, which needs real time analysis. Sensor networks, real-time surveillance and telecommunication systems are the examples of such applications. The real time analysis of the data stream leads to a number of computational and mining challenges. In this scenario new data arrives continuously and an efficient learning algorithm must be able to improve its learning accuracy by incorporating the time and memory constraints. This paper addresses the problem of incremental Bayesian network structure learning for high dimensional domains. The local skeleton discovery methods for Bayesian network structure learning are outperforming to deal with such domains. Here we transformed the local discovery algorithm Max-Min Parents and Childrens (MMPC) into an incremental fashion. We learned a set of candidate-parent-children for each variable by using incremental hill-climbing. The reduced search space saves a lot of computations and reduces the complexity. Our algorithm is then illustrated with a toy example.
Fine-Mapping the HOXB Region Detects Common Variants Tagging a Rare Coding Allele: Evidence for Synthetic Association in Prostate Cancer

The HOXB13 gene has been implicated in prostate cancer (PrCa) susceptibility. We performed a high resolution fine-mapping analysis to comprehensively evaluate the association between common genetic variation across the HOXB genetic locus at 17q21 and PrCa risk. This involved genotyping 700 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of 3195 SNPs in 20,440 PrCa cases and 21,469 controls in The PRACTICAL consortium. We identified a cluster of highly correlated common variants situated within or closely upstream of HOXB13 that were significantly associated with PrCa risk, described by rs117576373 (OR 1.30, P = 2.62×10−14). Additional genotyping, conditional regression and haplotype analyses indicated that the newly identified common variants tag a rare, partially correlated coding variant in the HOXB13 gene (G84E, rs138213197), which has been identified recently as a moderate penetrance PrCa susceptibility allele. The potential for GWAS associations detected through common SNPs to be driven by rare causal variants with higher relative risks has long been proposed; however, to our knowledge this is the first experimental evidence for this phenomenon of synthetic association contributing to cancer susceptibility.
Stability Improvement of Feedback Active Noise Control System Using Linear Prediction

In this paper, we propose a feedback active noise control (ANC) system which can surpress disturbance. The conventional feedback ANC system has a possibility of divergence because the disturbance of broadband noise corrupts the input signal for the system. To surpress the disturbance, we incorporate a linear prediction filter into the feedback ANC system. Simulation results show that the proposed feedback ANC system is superior to the conventional feedback ANC system on the stability.
Establishment of a Box-Jenkins Multivariate Time-Series Model to Simulate Ground-Level Peak Daily One-Hour Ozone Concentrations at Ta-Liao in Taiwan

Abstract Box-Jenkins univariate autoregressive integrated moving average (ARIMA) and regression with time-series error (RTSE) models were established to simulate historical peak daily 1-hr ozone concentrations at Ta-Liao, Taiwan, 1997– 2001. During 1995–2003, the 600 days of Pollution Standard Index (PSI) more than 100 (peak daily 1-hr ozone concentrations detected by greater than 120 ppm) at Tao-Liao showed the highest ozone exceedances among the six monitoring stations in Kaohsiung County. To improve the predictability of extremely high ozone, two different principal components, PC1 and PC(1 + 2), were introduced in the RTSE model. Four typical predictors (particular matter with an aerodynamic diameter less than or equal to 10 μm, temperature, wind speed, and wind direction) plus a PC trigger remained signiﬁcant in the RTSE model. The model performance statistics concluded that the RTSE model with PC1 was optimal, compared with the univariate ARIMA, the RTSE model without PC, and RTSE model with PC(1 + 2). The contingency table shows that the successful predictions of the univariate model were only 12.9% of that of the RTSE model with PC1. Also, the POD value was improved approximately 5-fold when the univariate model was replaced by the RTSE model, and almost 8-fold when it was replaced by the RTSE model with PC1. Moreover, introducing the PC trigger indeed enhanced the ozone predictability. After the PC trigger was introduced in the RTSE model, the POD was increased 69.9%, and the FAR was reduced 8.3%. The overall correlation between the observed and simulated ozone was improved 9.6%. Also, the ﬁrst principal component was more useful than the ﬁrst two components in playing the “trigger” role, though it counted only for58.62% of the environmental variance during the high ozone days.
The Salmonella virulence plasmid spv genes are required for cytopathology in human monocyte‐derived macrophages

The pathogenesis of serious systemic Salmonella infections is characterized by survival and proliferation of bacteria inside macrophages. Infection of human monocyte‐derived macrophages in vitro with S. typhimurium or S. dublin produces cytopathology characterized by detachment of cells that contain large numbers of proliferating bacteria. This cytopathology is dependent on the expression of the bacterial spv genes, a virulence locus previously shown to markedly enhance the ability of Salmonella to produce systemic disease. After 24 h of infection, macrophage cultures contain two populations of bacteria: (i) proliferating organisms present in a detached cell fraction; and (ii) a static bacterial population in macrophages remaining attached to the culture well. Mutations in either the essential transcriptional activator SpvR or the key SpvB protein markedly reduce the cytopathic effect of Salmonella infection. The spv‐dependent cytopathology in macrophages exhibits characteristics of apoptosis, with release of nucleosomes into the cytoplasm, nuclear condensation and DNA fragmentation. The current findings suggest that the mechanism of the spv effect is through induction of increased cytopathology in host macrophages.
Acetyl-L-carnitine (ALC) in attention-deficit/hyperactivity disorder: a multi-site, placebo-controlled pilot trial.

OBJECTIVE To determine whether acetyl-L-carnitine (ALC), a metabolite necessary for energy metabolism and essential fatty acid anabolism, might help attention-deficit/hyperactivity disorder (ADHD). Trials in Down's syndrome, migraine, and Alzheimer's disease showed benefit for attention. A preliminary trial in ADHD using L-carnitine reported significant benefit. METHOD A multi-site 16-week pilot study randomized 112 children (83 boys, 29 girls) age 5-12 with systematically diagnosed ADHD to placebo or ALC in weight-based doses from 500 to 1500 mg b.i. d. The 2001 revisions of the Conners' parent and teacher scales (including DSM-IV ADHD symptoms) were administered at baseline, 8, 12, and 16 weeks. Analyses were ANOVA of change from baseline to 16 weeks with treatment, center, and treatment-by-center interaction as independent variables. RESULTS The primary intent-to-treat analysis, of 9 DSM-IV teacher-rated inattentive symptoms, was not significant. However, secondary analyses were interesting. There was significant (p = 0.02) moderation by subtype: superiority of ALC over placebo in the inattentive type, with an opposite tendency in combined type. There was also a geographic effect (p = 0.047). Side effects were negligible; electrocardiograms, lab work, and physical exam unremarkable. CONCLUSION ALC appears safe, but with no effect on the overall ADHD population (especially combined type). It deserves further exploration for possible benefit specifically in the inattentive type.
Performance of HerpeSelect and Kalon Assays in Detection of Antibodies to Herpes Simplex Virus Type 2

ABSTRACT The performances of commercial enzyme-linked immunosorbent assays (ELISAs) in detecting herpes simplex virus type 2 (HSV-2) antibodies have been inconsistent for African and human immunodeficiency virus (HIV)-positive populations. We compared the performances of the HerpeSelect and Kalon glycoprotein G2 ELISAs for patients with genital ulcer disease in Ghana and the Central African Republic. Sera from 434 women were tested with the HerpeSelect assay, and a subsample (n = 199) was tested by the Kalon assay. Ulcer swabs and cervicovaginal lavage samples were tested for HSV-2 DNA by PCR. HSV-2-seronegative women with detectable genital HSV-2 DNA were retested for HSV-2 antibodies 14 and 28 days later by the two ELISAs. A total of 346 (80%) women were positive by HerpeSelect at baseline, and 225 (54%) had detectable genital (lesional or cervicovaginal) HSV-2 DNA. Sixty-six (19%) HerpeSelect-positive samples had low-positive index values (1.1 to 3.5), and 58% of these samples had detectable genital HSV-2 DNA. Global agreement between the two serological assays was 86%. Concordance was high (99%) for sera that were negative by HerpeSelect or had high index values (>3.5). Defining infection detected by HSV-2 DNA PCR and/or Kalon assay as true infection, 71% of sera with low-positive index values were associated with true HSV-2 infection. Twenty-five women were identified as having nonprimary first-episode genital HSV-2 infection. Rates of HSV-2 seroconversion at day 14 were 77% (10/13 patients) by HerpeSelect assay and 23% (3/13 patients) by Kalon assay, with four additional seroconversions detected by Kalon assay at day 28. HIV serostatus did not influence assay performance. Low index values obtained with the HerpeSelect assay may correspond to true HSV-2 infection, in particular to nonprimary first episodes of genital HSV-2 infection, and need to be interpreted in the context of clinical history.
Inpatient obesity intervention with postdischarge telephone follow-up: A randomized trial.

BACKGROUND Obesity-related comorbidities frequently contribute to acute illness. Obesity interventions during hospitalization are not often utilized but may be effective. OBJECTIVE To examine whether inpatient weight loss intervention with postdischarge follow-up results in weight loss at 6 months when compared to control. DESIGN Prospective, randomized controlled trial. SETTING Academic medical center in Chicago, Illinois. PATIENTS Obese adult inpatients. INTERVENTION Intervention subjects viewed a weight education video, underwent personalized counseling, and set specific weight loss, dietary, and fitness goals prior to discharge. All participants were followed by phone over the subsequent 6 months. The trial was unblinded to participants, physicians, and investigators. MEASUREMENTS Primary outcome was weight change between groups at 6 months. Weight change from baseline and waist-to-hip ratios (WHR) were also assessed. RESULTS For 176 participants in the intention-to-treat analysis, mean baseline weight for the intervention group was 107.7 kg (standard deviation [SD]=16.7) and 105.1 kg (SD=17.4) for controls. Mean weight loss at 6 months was 1.08 kg (SD=4.33) for intervention subjects and 1.35 kg (SD=3.65) among controls. There was no significant difference in weight loss between groups at 6 months (P=0.26). As-treated analysis yielded similar results. There were no differences in WHRs between the intervention and control at 6 months (0.04 vs 0.04, P=0.59). CONCLUSIONS We found no difference in weight loss between the intervention and control groups at 6 months.
Minimum Total Power Spectrum Aware Routing Scheme

In the recent past, routing in Cognitive Radio Networks (CRN) has attracted several researchers. Various metrics for route formation have been proposed such as Spectrum, End to End Delay, Network lifetime etc. Another important parameter that must be taken into consideration is Transmission Power (TP) since battery power depends on Transmission Power, which is further dependent on square or fourth power of the distance between the nodes. Therefore, a path must be chosen such that it minimizes the total Transmission Power or the distance so as to increase the battery life of a node. This paper is an effort for the same. The routing strategy is implemented in MATLAB-7.01. The performance metrics such as Energy, Packet Delivery Ratio, Delay, Success rate, Hop Count are taken into consideration for measuring the efficiency of SPR(Shortest path routing) and MTPR(minimum transmission power routing) protocols. The results show that Minimum Transmission Power protocol outperforms the Shortest Path Routing Protocol in terms of Energy.
Navigating Extracted Data with Schema Discovery

Open Information Extraction (OIE) is a recently-introduced type of information extraction that extracts small individual pieces of data from input text without any domainspecific guidance such as special training data or extraction rules. For example, an OIE system might discover the triple Frenzy, year, 1972 from a set of documents about movies. Because OIE is domain-independent, it promises to help users when they have a corpus of structured data, but that structure is unknown, such as when browsing a novel domain or formulating a query. We can describe the structure to the user by displaying a relational schema that fits the extracted data. Unfortunately, the extractions do not carry full schema information: we have extracted values, but not the correct relations, their rows, or their columns. In response we propose TGen, an algorithm for schema discovery, which automatically derives a high-quality relational schema for the extracted data. Different applications have different schema-design requirements, which can be encoded as input to TGen. We show that our data-mining approach runs in minutes on millions of documents while still resulting in schemas that are useful for exploring unfamiliar data or for composing queries over extracted data.
A Policy-switching learning approach for adaptive spoken dialogue agents

The reinforcement learning paradigm has been adopted for inferring optimized and adaptive spoken dialogue agents. Such agents are typically learnt and tested without combining competing agents that may yield better performance at some points in the conversation. This paper presents an approach that learns dialogue behaviour from competing agents—switching from one policy to another competing one—on a previously proposed hierarchical learning framework. This policy-switching approach was investigated using a simulated flight booking dialogue system based on different types of information request. Experimental results reported that the induced agent using the proposed policy-switching approach yielded 8.2% fewer system actions than three baselines with a fixed type of information request. This result suggests that the proposed approach is useful for learning adaptive and scalable spoken dialogue agents. Index Terms: spoken dialogue systems, adaptive behaviours, hierarchical reinforcement learning, policy-switching.
Write-Behind Logging

The design of the logging and recovery components of database management systems (DBMSs) has always been influenced by the difference in the performance characteristics of volatile (DRAM) and non-volatile storage devices (HDD/SSDs). The key assumption has been that non-volatile storage is much slower than DRAM and only supports block-oriented read/writes. But the arrival of new non-volatile memory (NVM) storage that is almost as fast as DRAM with fine-grained read/writes invalidates these previous design choices. This paper explores the changes that are required in a DBMS to leverage the unique properties of NVM in systems that still include volatile DRAM. We make the case for a new logging and recovery protocol, called write-behind logging, that enables a DBMS to recover nearly instantaneously from system failures. The key idea is that the DBMS logs what parts of the database have changed rather than how it was changed. Using this method, the DBMS flushes the changes to the database before recording them in the log. Our evaluation shows that this protocol improves a DBMS's transactional throughput by 1.3×, reduces the recovery time by more than two orders of magnitude, and shrinks the storage footprint of the DBMS on NVM by 1.5×. We also demonstrate that our logging protocol is compatible with standard replication schemes.
Persistent monitoring with a team of autonomous gliders using static soaring

Exploiting wind currents in the environment allows autonomous gliders to gain altitude and energy and consequently extend flight duration. This paper considers the problem of a persistent monitoring mission using multiple autonomous gliders and exploiting thermal soaring. Communications constraints and non-homogeneous teams of gliders are considered. A distributed method based on coordination variables is proposed to monitor the area in a cooperative manner by following a partitioning patrolling strategy. A distributed one-to-one coordination technique is used to manage the gliders' access to thermals according to their states and known thermal locations. Gliders perform a model-based estimation about their energy losses between thermals to estimate the optimal time to remain in a thermal to maintain persistent surveillance with minimum refresh time. Simulated test results are provided to evaluate how the proposed approach is able to extend the mission while maintaining near-optimal performance.