Miracle-dz/UniFAR-FTUs huggingface dataset:
FigureQA: An Annotated Figure Dataset for Visual Reasoning

We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection. To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure. To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives. In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements. We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as a strong baseline. Preliminary results indicate that the task poses a significant machine learning challenge. We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.
Endometriosis risk alleles at 1p36.12 act through inverse regulation of CDC42 and LINC00339.

Genome-wide association studies (GWAS) have identified markers within the WNT4 region on chromosome 1p36.12 showing consistent and strong association with increasing endometriosis risk. Fine mapping using sequence and imputed genotype data has revealed strong candidates for the causal SNPs within these critical regions; however, the molecular pathogenesis of these SNPs is currently unknown. We used gene expression data collected from whole blood from 862 individuals and endometrial tissue from 136 individuals from independent populations of European descent to examine the mechanism underlying endometriosis susceptibility. Association mapping results from 7,090 individuals (2,594 cases and 4,496 controls) supported rs3820282 as the SNP with the strongest association for endometriosis risk (P = 1.84 × 10−5, OR = 1.244 (1.126-1.375)). SNP rs3820282 is a significant eQTL in whole blood decreasing expression of LINC00339 (also known as HSPC157) and increasing expression of CDC42 (P = 2.0 ×10−54 and 4.5x10−4 respectively). The largest effects were for two LINC00339 probes (P = 2.0 ×10−54; 1.0 × 10−34). The eQTL for LINC00339 was also observed in endometrial tissue (P = 2.4 ×10−8) with the same direction of effect for both whole blood and endometrial tissue. There was no evidence for eQTL effects for WNT4. Chromatin conformation capture provides evidence for risk SNPs interacting with the promoters of both LINC00339 and CDC4 and luciferase reporter assays suggest the risk SNP rs12038474 is located in a transcriptional silencer for CDC42 and the risk allele increases expression of CDC42. However, no effect of rs3820282 was observed in the LINC00339 expression in Ishikawa cells. Taken together, our results suggest that SNPs increasing endometriosis risk in this region act through CDC42, but further functional studies are required to rule out inverse regulation of both LINC00339 and CDC42.
Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Images

Prediction of treatment responses from available data is key to optimizing personalized treatment. Retinal diseases are treated over long periods and patients’ response patterns differ substantially, ranging from a complete response to a recurrence of the disease and need for re-treatment at different intervals. Linking observable variables in high-dimensional observations to outcome is challenging. In this paper, we present and evaluate two different data-driven machine learning approaches operating in a high-dimensional feature space: sparse logistic regression and random forests-based extra trees (ET). Both identify spatio-temporal signatures based on retinal thickness features measured in longitudinal spectral-domain optical coherence tomography (OCT) imaging data and predict individual patient outcome using these quantitative characteristics. We demonstrate on a data set of monthly SD-OCT scans of 155 patients with central retinal vein occlusion (CRVO) and 92 patients with branch retinal vein occlusion (BRVO) followed over one year that we can predict from initial three observations if the treated disease will recur within the covered interval. ET predicts the outcome on fivefold cross-validation with an area under the receiver operating characteristic curve (AuC) of 0.83 for BRVO and 0.76 for CRVO. Logistic regression achieved an AuC of 0.78 and 0.79, respectively. At the same time, the methods identified stable predictive signatures in the longitudinal imaging data that are the basis for accurate prediction. Furthermore, our results show that taking spatio-temporal features into account improves accuracy compared with features extracted at a single time-point. Our results demonstrate the feasibility of mining longitudinal data for predictive signatures, and building predictive models based on observed data.
Comparison of Two Versions of the Beers Criteria and Adverse Outcomes in Older Hospitalized Patients.

OBJECTIVE To compare the performance of the 2003 and 2012 Beers criteria (BC) to predict negative clinical outcomes associated with potentially inappropriate medications in hospitalized older adults. DESIGN Retrospective cohort study. SETTING Acute Care of Elders (ACE) unit in a community-based teaching hospital. PARTICIPANTS All patients admitted to an ACE unit who were older than 65 years of age and prescribed at least one medication upon hospital admission. MAIN OUTCOME MEASURE(S) The primary outcome was hospital length of stay (LOS). Secondary outcomes included likelihood of experiencing adverse drug events (ADEs) and in-hospital mortality. RESULTS A total of 340 patients were included in this study. Inpatients prescribed a BC drug at any time had a longer hospital LOS than those not prescribed a BC drug (2003 BC: adjusted geometric mean, 5.93 vs. 5.50 days, P = 0.003; 2012 BC: adjusted geometric mean, 5.87 vs. 4.21 days, P < 0.001). Patients prescribed a 2003 BC drug had an increased risk of experiencing an ADE compared with those not prescribed a BC drug (odds ratio [OR] = 1.86, 95% confidence interval [CI] 1.11-3.11); however, this outcome was not statistically significant after adjusting for confounders (OR = 1.51, 95% CI 0.870-2.63). There was no statistically significant difference in ADEs when using the 2012 BC (adjusted OR = 1.27, 95% CI 0.689-2.33). There was no difference in hospital mortality regardless of the BC version used. CONCLUSION Prescription of BC drugs in an acute care setting is associated with an increased hospital LOS; however, there is no difference in the risk of ADEs or in-hospital mortality.
Use and Selection of Bridges as Day Roosts by Rafinesque's Big-Eared Bats

ABSTRACT Rafinesque's big-eared bats (Corynorhinus rafinesquii) use bridges as day roosts in parts of their range, but information on bridge use across their range is lacking. From May to Aug. 2002 we surveyed 1129 bridges (12.5%) within all 46 counties of South Carolina to determine use and selection of bridges as day roosts by big-eared bats and to document their distribution across the state. During summer 2003, we visited 235 bridges in previously occupied areas of the state to evaluate short-term fidelity to bridge roosts. We found colonies and solitary big-eared bats beneath 38 bridges in 2002 and 54 bridges in 2003. Construction type and size of bridges strongly influenced use in both years; bats selected large, concrete girder bridges and avoided flat-bottomed slab bridges. The majority of occupied bridges (94.7%) were in the Upper and Lower Coastal Plains, but a few bridges (5.3%) were located in the Piedmont. Rafinesque's big-eared bats were absent beneath bridges in the Blue Ridge Mountains. We established new records of occurrence for 10 counties. In the Coastal Plains, big-eared bats exhibited a high degree of short-term fidelity to roosts in highway bridges. For bridges that were occupied at least once, mean frequency of use was 65.9%. Probability of finding bats under a bridge ranged from 0.46 to 0.73 depending on whether the bridge was occupied in the previous year. Thus, bridges should be inspected three to five times in a given year to determine whether they are being used. Regional bridge roost surveys may be a good method for determining the distribution of C. rafinesquii, particularly in the Coastal Plains, and protection of suitable bridges may be a viable conservation strategy where natural roost sites are limited.
Human cytomegalovirus encoded chemokine receptor US28 activates the HIF-1α/PKM2 axis in glioblastoma cells

The human cytomegalovirus (HCMV) encoded chemokine receptor US28 promotes tumorigenesis through activation of various proliferative and angiogenic signaling pathways. Upon infection, US28 displays constitutive activity and signals in a G protein-dependent manner, hijacking the host's cellular machinery. In tumor cells, the hypoxia inducible factor-1α/pyruvate kinase M2 (HIF-1α/PKM2) axis plays an important role by supporting proliferation, angiogenesis and reprogramming of energy metabolism. In this study we show that US28 signaling results in activation of the HIF-1α/PKM2 feedforward loop in fibroblasts and glioblastoma cells. The constitutive activity of US28 increases HIF-1 protein stability through a Gαq-, CaMKII- and Akt/mTOR-dependent mechanism. Furthermore, we found that VEGF and lactate secretion are increased and HIF-1 target genes, glucose transporter type 1 (GLUT1) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), involved in glucose metabolism, are upregulated in US28 expressing cells. In addition, PKM2 is phosphorylated and found to be in a tumor-associated dimeric state upon US28 expression. Also in HCMV-infected cells HIF-1 activity is enhanced, which in part is US28-dependent. Finally, increased proliferation of cells expressing US28 is abolished upon inhibition of the HIF-1α/PKM2 cascade. These data highlight the importance of HIF-1α and PKM2 in US28-induced proliferation, angiogenesis and metabolic reprogramming.
Recent opportunities in matrix metalloproteinase inhibitor drug design for cancer

ABSTRACT Introduction: The overexpression of matrix metalloproteinase (MMP) plays an important role in the context of tumor invasion and metastasis, and MMP-2 has been characterized as the most validated target for cancer. Therefore, it is necessary to design matrix metalloproteinase inhibitors (MMPIs) that would be active and selective against MMP-2 but non-selective toward other MMPs. Areas covered: This article clearly describes the structural character of MMP-2 followed by a review of the recent development of selective MMP-2 inhibitors based on their basic structures. Expert opinion: Over the past 30 years, MMPs have been considered to be attractive cancer targets, and several different types of synthetic inhibitors have been identified as anticancer agents, but only a small number of small MMPIs have been examined in clinical trials, and none of these molecules has been established as anticancer drugs due to their adverse effects. One major possibility is that the MMPIs used in clinical trials were broad-spectrum drugs that also inhibited the anti-tumor effects and influenced the mediation of the normal physiological processes of MMPs. MMP-2 has recently been characterized as the most validated target for cancer. Therefore, the design and synthesis of selective MMP-2 inhibitors would be helpful for the treatment of cancer.
Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records

Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge. Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions.
Energy-Efficient Communication in Wireless Networks

This chapter describes the evolution of, and state of the art in, energy‐efficient techniques for wirelessly communicating networks of embedded computers, such as those found in wireless sensor network (WSN), Internet of Things (IoT) and cyberphysical systems (CPS) applications. Specifically, emphasis is placed on energy efficiency as critical to ensuring the feasibility of long lifetime, low‐maintenance and increasingly autonomous monitoring and control scenarios. A comprehensive summary of link layer and routing protocols for a variety of traffic patterns is discussed, in addition to their combination and evaluation as full protocol stacks.
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

Gradient-based optimization is the foundation of deep learning and reinforcement learning. Even when the mechanism being optimized is unknown or not differentiable, optimization using high-variance or biased gradient estimates is still often the best strategy. We introduce a general framework for learning low-variance, unbiased gradient estimators for black-box functions of random variables. Our method uses gradients of a neural network trained jointly with model parameters or policies, and is applicable in both discrete and continuous settings. We demonstrate this framework for training discrete latent-variable models. We also give an unbiased, action-conditional extension of the advantage actor-critic reinforcement learning algorithm.
Preparation and characterization of bovine serum albumin alginate/chitosan microspheres for oral administration

Purpose: To establish a method for preparing protein microspheres for oral administration. Methods: Using bovine serum albumin (BSA) as a model protein and alginate and chitosan as carrier materials, BSA loaded alginate/chitosan microspheres were prepared by a modified emulsifying-gelatinization method. The influence of the preparation conditions on the encapsulation efficiency, drug loading and yield of the microspheres was investigated by an orthogonal design method and the optimal process parameters were obtained. The in vitro release of BSA from the alginate/chitosan microspheres was investigated in 0.1 M HCl solution (pH 1.2) and PBS (pH 7.4) as the release media. Results: The optimal process parameters for the preparation of BSA loaded alginate/chitosan microspheres were obtained. The concentrations of alginate solution, CaCl2 solution and chitosan solution were 1.5% (w/v), 6% (w/v) and 2% (w/v), respectively, and the cross-linking time was 10 min. The mean particle size of the microspheres was 3 μm, the encapsulation efficiency was 81.4 ± 1.5%, and the drug loading was 6.4% ± 0.1%. The in vitro release of BSA from the alginate/chitosan microspheres prepared with the optimal process showed that the initial burst release was marked both in 0.1 M HCl solution (pH 1.2) and PBS (pH 7.4). The release in PBS (pH 7.4) was faster than that in 0.1 M HCl solution (pH 1.2). Conclusion: The modified emulsifying-gelatinization method is suitable for the preparation of protein-loaded microspheres for oral administration.
Output Feedback Adaptive Control with Low-Frequency Learning and Fast Adaptation

Although adaptive control has been used in numerous applications to achieve system performance without excessive reliance on system models, the necessity of high-gain learning rates for achieving fast adaptation can be a serious limitation of adaptive controllers. Specifically, in safety-critical systems involving large system uncertainties and abrupt changes in system dynamics, fast adaptation is required to achieve stringent tracking performance specifications. However, fast adaptation using high-gain learning rates can cause high-frequency oscillations in the control response, resulting in system instability. This paper develops an output feedback adaptive control framework for continuous-time minimum-phase multivariable dynamical systems for output stabilization and command following to address the problem of achieving fast adaptation using high-gain learning rates for systems with partial state information. The proposed framework uses a controller architecture involving a modification term in the upd.
Pseudoexons provide a mechanism for allele-specific expression of APC in familial adenomatous polyposis

Allele-specific expression (ASE) of the Adenomatous Polyposis Coli (APC) gene occurs in up to one-third of families with adenomatous polyposis (FAP) that have screened mutation-negative by conventional techniques. To advance our understanding of the genomic basis of this phenomenon, 54 APC mutation-negative families (21 with classical FAP and 33 with attenuated FAP, AFAP) were investigated. We focused on four families with validated ASE and scrutinized these families by sequencing of the blood transcriptomes (RNA-seq) and genomes (WGS). Three families, two with classical FAP and one with AFAP, revealed deep intronic mutations associated with pseudoexons. In all three families, intronic mutations (c.646-1806T>G in intron 6, c.1408+729A>G in intron 11, and c.1408+731C>T in intron 11) created new splice donor sites resulting in the insertion of intronic sequences (of 127 bp, 83 bp, and 83 bp, respectively) in the APC transcript. The respective intronic mutations were absent in the remaining polyposis families and the general population. Premature stop of translation as the predicted consequence as well as co-segregation with polyposis supported the pathogenicity of the pseudoexons. We conclude that next generation sequencing on RNA and genomic DNA is an effective strategy to reveal and validate pseudoexons that are regularly missed by traditional screening methods and is worth considering in apparent mutation-negative polyposis families.
Efficient top-k aggregation of ranked inputs

A top-k query combines different rankings of the same set of objects and returns the k objects with the highest combined score according to an aggregate function. We bring to light some key observations, which impose two phases that any top-k algorithm, based on sorted accesses, should go through. Based on them, we propose a new algorithm, which is designed to minimize the number of object accesses, the computational cost, and the memory requirements of top-k search with monotone aggregate functions. We provide an analysis for its cost and show that it is always no worse than the baseline “no random accesses” algorithm in terms of computations, accesses, and memory required. As a side contribution, we perform a space analysis, which indicates the memory requirements of top-k algorithms that only perform sorted accesses. For the case, where the required space exceeds the available memory, we propose disk-based variants of our algorithm. We propose and optimize a multiway top-k join operator, with certain advantages over evaluation trees of binary top-k join operators. Finally, we define and study the computation of top-k cubes and the implementation of roll-up and drill-down operations in such cubes. Extensive experiments with synthetic and real data show that, compared to previous techniques, our method accesses fewer objects, while being orders of magnitude faster.
An estimate of the science-wise false discovery rate and application to the top medical literature.

The accuracy of published medical research is critical for scientists, physicians and patients who rely on these results. However, the fundamental belief in the medical literature was called into serious question by a paper suggesting that most published medical research is false. Here we adapt estimation methods from the genomics community to the problem of estimating the rate of false discoveries in the medical literature using reported $P$-values as the data. We then collect $P$-values from the abstracts of all 77 430 papers published in The Lancet, The Journal of the American Medical Association, The New England Journal of Medicine, The British Medical Journal, and The American Journal of Epidemiology between 2000 and 2010. Among these papers, we found 5322 reported $P$-values. We estimate that the overall rate of false discoveries among reported results is 14% (s.d. 1%), contrary to previous claims. We also found that there is no a significant increase in the estimated rate of reported false discovery results over time (0.5% more false positives (FP) per year, $P = 0.18$) or with respect to journal submissions (0.5% more FP per 100 submissions, $P = 0.12$). Statistical analysis must allow for false discoveries in order to make claims on the basis of noisy data. But our analysis suggests that the medical literature remains a reliable record of scientific progress.