Miracle-dz/UniFAR-FTUs huggingface dataset:
“Omics”-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives

The pharmaceutical industry faces unsustainable program failure despite significant increases in investment. Dwindling discovery pipelines, rapidly expanding R&D budgets and increasing regulatory control, predict significant gaps in the future drug markets. The cumulative duration of discovery from concept to commercialisation is unacceptably lengthy, and adds to the deepening crisis. Existing animal models predicting clinical translations are simplistic, highly reductionist and, therefore, not fit for purpose. The catastrophic consequences of ever-increasing attrition rates are most likely to be felt in the developing world, where resistance acquisition by killer diseases like malaria, tuberculosis and HIV have paced far ahead of new drug discovery. The coming of age of Omics-based applications makes available a formidable technological resource to further expand our knowledge of the complexities of human disease. The standardisation, analysis and comprehensive collation of the “data-heavy” outputs of these sciences are indeed challenging. A renewed focus on increasing reproducibility by understanding inherent biological, methodological, technical and analytical variables is crucial if reliable and useful inferences with potential for translation are to be achieved. The individual Omics sciences—genomics, transcriptomics, proteomics and metabolomics—have the singular advantage of being complimentary for cross validation, and together could potentially enable a much-needed systems biology perspective of the perturbations underlying disease processes. If current adverse trends are to be reversed, it is imperative that a shift in the R&D focus from speed to quality is achieved. In this review, we discuss the potential implications of recent Omics-based advances for the drug development process.
A reliable distributed algorithm for group size estimation with minimal communication requirements

This study proposes a method that lets individual robots in a group estimate the size of the group in a distributed manner. The process is loosely based on the signaling behavior of fireflies and crickets. Holland et al. first devised a method based on local robot signaling to estimate the group's size. Each robot emits a signal and can perceive the signals of neighboring robots in close proximity. Following the approach of Holland et al., our robots count the number of emitted signals over a suitably defined period of time. Experiments show that the estimates calculated with Holland et al.'s method display a great deal of noise. We modify their method so as to sensibly stabilize the output. We assess the quality of our method through extensive simulation-based experiments.
Reducing the Cost of Regression Testing by Identifying Irreplaceable Test Cases

Test suite reduction techniques decrease the cost of software testing by removing the redundant test cases from the test suite while still producing a reduced set of tests that yields the same level of code coverage as the original suite. Most of the existing approaches to reduction aim to decrease the size of the test suite. Yet, the difference in the execution cost of the tests is often significant and it may be costly to use a test suite consisting of a few long-running test cases. Thus, this paper proposes an algorithm, based on the concept of test irreplaceability, which creates a reduced test suite with a decreased execution cost. Leveraging widely used benchmark programs, the empirical study shows that, in comparison to existing techniques, the presented algorithm is the most effective at reducing the cost of running a test suite.
Design and Construction of a Whole Cell Bacterial 4-Hydroxyphenylacetic Acid and 2-Phenylacetic Acid Bioassay

Introduction Auxins are hormones that regulate plant growth and development. To accurately quantify the low levels of auxins present in plant and soil samples, sensitive detection methods are needed. In this study, the design and construction of two different whole cell auxin bioassays is illustrated. Both use the auxin responsive element HpaA as an input module but differ in output module. The first bioassay incorporates the gfp gene to produce a fluorescent bioassay. Whereas the second one utilizes the genes phzM and phzS to produce a pyocyanin producing bioassay whose product can be measured electrochemically. Results The fluorescent bioassay is able to detect 4-hydroxyphenylacetic acid (4-HPA) and 2-phenylacetic acid (PAA) concentrations from 60 μM to 3 mM in a dose-responsive manner. The pyocyanin producing bioassay can detect 4-HPA concentrations from 1.9 to 15.625 μM and PAA concentrations from 15.625 to 125 μM, both in a dose-responsive manner. Conclusion A fluorescent whole cell auxin bioassay and an electrochemical whole cell auxin bioassay were constructed and tested. Both are able to detect 4-HPA and PAA at concentrations that are environmentally relevant to plant growth.
Overweight, ethnicity, and the prevalence of hypertension in school-aged children.

OBJECTIVES To describe the current prevalence of pediatric hypertension and the relationships between gender, ethnicity, overweight, and blood pressure. METHODS School-based screening was performed in 5102 children (13.5 +/- 1.7 years) from May through November 2002. Age, gender, ethnicity, weight, and height were ascertained, and body mass index (BMI) was calculated as weight (kg)/height (m2). Overweight was defined as BMI > or =95th percentile. Students with blood pressure >95th percentile on the first screening underwent a second screening 1 to 2 weeks later, and then a third screening if blood pressure was >95th percentile at the second screening. RESULTS Ethnicity distribution was 44% white, 25% Hispanic, 22% African American, and 7% Asian. Overall, overweight prevalence was 20%, which varied significantly by ethnicity (31% Hispanic, 20% African American, 15% white, and 11% Asian). The prevalence of elevated blood pressure after first, second, and third screenings was 19.4%, 9.5%, and 4.5%, respectively. Elevated blood pressure on first screening was highest among Hispanics (25%) and lowest among Asians (14%). Ethnic differences in the prevalence of hypertension (elevated blood pressure on 3 screenings) were not significant after controlling for overweight. The prevalence of hypertension increased progressively as the BMI percentile increased from < or =5th percentile (2%) to > or =95th percentile (11%). After adjustment for gender, ethnicity, overweight, and age, the relative risk of hypertension was significant for gender (relative risk: 1.50; confidence interval: 1.15, 1.95) and overweight (relative risk: 3.26; confidence interval: 2.50, 4.24). CONCLUSIONS These results confirm an evolving epidemic of cardiovascular risk in youth, as evidenced by an increase in the prevalence of overweight and hypertension, notably among ethnic minority children.
Prevalence of Atherosclerosis-Related Risk Factors and Diseases in the Philippines

Background We conducted a survey in 2008 to measure the prevalence of lifestyle-related diseases and risk factors in Philippine adults. Methods Stratified multistage sampling was used to cover the entire Philippine population of adults aged 20 years or older. Using health questionnaires, anthropometric measurements, and blood examinations, the prevalences of atherosclerosis-related risk factors and diseases were determined. Survey results were compared with those obtained in 2003. Results Out of 7700 eligible subjects, 64% to 93.7% responded to different survey items. Age-adjusted hypertension prevalence was 24.6% at a single visit and 20.6% when corrected for true prevalence. The prevalence of diabetes was 3.9% on the basis of fasting blood glucose (FBG), 5.2% by FBG and history, and 6.0% when 2-hour post-load plasma glucose level was determined. The prevalence of dyslipidemia was 72.0% and the prevalence of smoking was 31%. The prevalence of obesity was 4.9% by body mass index (BMI), and 10.2% and 65.6% by waist-hip ratio (WHR) in men and women, respectively. The prevalences of coronary, cerebrovascular, and peripheral arterial diseases were 1.1%, 0.9%, and 1.0%, respectively. Conclusions The prevalences of risk factors for atherosclerosis were higher in 2008 than in 2003, although the increase in diabetes was not significant and smoking decreased. These findings indicate a need for active collaborative intervention by all government agencies and medical societies in the Philippines.
β-Cell Function Preservation After 3.5 Years of Intensive Diabetes Therapy

OBJECTIVE To assess β-cell function preservation after 3.5 years of intensive therapy with insulin plus metformin (INS group) versus triple oral therapy (TOT group) with metformin, glyburide, and pioglitazone. RESEARCH DESIGN AND METHODS This was a randomized trial of 58 patients with treatment-naïve newly diagnosed type 2 diabetes. All patients were treated with insulin and metformin for a 3-month lead-in period followed by random assignment to the INS or TOT group. β-Cell function was assessed using a mixed-meal challenge test at randomization and 6, 12, 18, 30, and 42 months. Analyses were intention to treat and performed with repeated-measures models. RESULTS Completion rates at 3.5 years were 83% in the insulin group and 72% in the TOT group, with good compliance in both groups (87 ± 20% in the INS group vs. 90 ± 15% in the TOT group). β-Cell function was preserved at 3.5 years after diagnosis, with no significant change from baseline or difference between the two groups as measured by area under the curve (AUC) of C-peptide (P = 0.14) or the ratio of C-peptide to glucose AUC (P = 0.7). Excellent glycemic control was maintained in both groups (end-of-study HbA1c 6.35 ± 0.84% in the INS group vs. 6.59 ± 1.94% in the TOT group). Weight increased in both groups over time (from 102.2 ± 24.9 kg to 106.2 ± 31.7 kg in the INS group and from 100.9 ± 23.0 kg to 110.5 ± 31.8 kg in the TOT group), with no significant difference between groups (P = 0.35). Hypoglycemic events decreased significantly over time (P = 0.01) but did not differ between groups (P = 0.83). CONCLUSIONS β-Cell function can be preserved for at least 3.5 years with early and intensive therapy for type 2 diabetes with either insulin plus metformin or triple oral therapy after an initial 3-month insulin-based treatment period.
Phase noise and sub-carrier spacing effects on the performance of an OFDM communication system

This letter analyzes the phase noise effects on an orthogonal frequency division multiplexing (OFDM) signal and its dependence with the sub-carrier spacing. Pilot-based channel estimation, which has been suggested as a means of combating the channel effects, can also correct the phase noise effects under some circumstances, which are investigated.
3-Carboxyquinolin-1-ium-2-carboxylate monohydrate

The title compound, C11H7NO4·H2O, contains a 3-carboxyquinolin-1-ium-2-carboxylate (qda) zwitterion and one water molecule. In the crystal, pairs of N—H⋯O hydrogen bonds link the molecules into inversion dimers, and these dimers are further connected by O—H⋯O hydrogen bonds into a three-dimensional supramolecular architecture. In addition, π–π interactions occur between pyridine and benzene rings from different qda ligands [centroid–centroid distance = 3.749 (1) Å] and the dihedral angles of the –CO2H and –CO2 groups to the quinoline system are 8.47 (3) and 88.16 (6)°, respectively.
Dynamic Interpretations of Covariation Data

various types of evidence or cues (e.g., Einhorn & Hogarth, 1986). Virtually all models of causal learning (e.g., Cheng, 1997; Rescorla & Wagner, 1972) have focused on how causal relations are learned based on covariation information—namely, information about whether the presence or absence of one event (C or ~C)\edq1\ co-occurs with the presence or absence of another event (E or ~E). Thus, in all of these models, relevant input data are classified as CE, ~CE, C~E, or ~C ~E, as summarized in Figure 17-1. Existing models of causal learning have stipulated different ways in which these four types of covariation evidence would or should be combined to evaluate the causal relationship among events. Yet, these models in their current forms share an underlying assumption that all events of a given type (e.g., CE) play an identical role in assessing causal strength. One model of causal induction combines covariation information into a contingency measure called P\edq2\ (e.g., Jenkins & Ward, 1965). The value of P is calculated as follow:
Efficacy and safety of vortioxetine (Lu AA21004), 15 and 20 mg/day: a randomized, double-blind, placebo-controlled, duloxetine-referenced study in the acute treatment of adult patients with major depressive disorder

This study assessed the efficacy, tolerability and safety of vortioxetine versus placebo in adults with recurrent major depressive disorder. This double-blind, randomized, placebo-controlled study included 608 patients [Montgomery–Åsberg Depression Rating Scale (MADRS) total score≥26 and Clinical Global Impression – Severity score≥4]. Patients were randomly assigned (1 : 1 : 1 : 1) to vortioxetine 15 mg/day, vortioxetine 20 mg/day, duloxetine 60 mg/day or placebo. The primary efficacy endpoint was change from baseline in MADRS total score at week 8 (mixed model for repeated measurements). Key secondary endpoints were: MADRS responders; Clinical Global Impression – Improvement scale score; MADRS total score in patients with baseline Hamilton Anxiety Rating Scale ≥20; remission (MADRS⩽10); and Sheehan Disability Scale total score at week 8. On the primary efficacy endpoint, both vortioxetine doses were statistically significantly superior to placebo, with a mean difference to placebo (n=158) of −5.5 (vortioxetine 15 mg, P<0.0001, n=149) and −7.1 MADRS points (vortioxetine 20 mg, P<0.0001, n=151). Duloxetine (n=146) separated from placebo, thus validating the study. In all key secondary analyses, both vortioxetine doses were statistically significantly superior to placebo. Vortioxetine treatment was well tolerated; common adverse events (incidence≥5%) were nausea, headache, diarrhea, dry mouth and dizziness. No clinically relevant changes were seen in clinical safety laboratory values, weight, ECG or vital signs parameters. Vortioxetine was efficacious and well tolerated in the treatment of patients with major depressive disorder.
Unifying Class-Based Representation Formalisms

The notion of class is ubiquitous in computer science and is central in many formalisms for the representation of structured knowledge used both in knowledge representation and in databases. In this paper we study the basic issues underlying such representation formalisms and single out both their common characteristics and their distinguishing features. Such investigation leads us to propose a unifying framework in which we are able to capture the fundamental aspects of several representation languages used in different contexts. The proposed formalism is expressed in the style of description logics, which have been introduced in knowledge representation as a means to provide a semantically well-founded basis for the structural aspects of knowledge representation systems. The description logic considered in this paper is a subset of first order logic with nice computational characteristics. It is quite expressive and features a novel combination of constructs that has not been studied before. The distinguishing constructs are number restrictions, which generalize existence and functional dependencies, inverse roles, which allow one to refer to the inverse of a relationship, and possibly cyclic assertions, which are necessary for capturing real world domains. We are able to show that it is precisely such combination of constructs that makes our logic powerful enough to model the essential set of features for defining class structures that are common to frame systems, object-oriented database languages, and semantic data models. As a consequence of the established correspondences, several significant extensions of each of the above formalisms become available. The high expressiveness of the logic we propose and the need for capturing the reasoning in different contexts forces us to distinguish between unrestricted and finite model reasoning. A notable feature of our proposal is that reasoning in both cases is decidable. We argue that, by virtue of the high expressive power and of the associated reasoning capabilities on both unrestricted and finite models, our logic provides a common core for class-based representation formalisms.
ISI’s 2005 Statistical Machine Translation Entries

ISI entered two statistical machine translation systems in the IWSLT evaluation this year: one was phrase-based and the other syntax-based. The syntax-based system represents the results of a current research effort, while the phrase-based system is representative of the current techniques in state-ofthe-art machine translation. This paper primarily describes the syntax-based system and its comparison to the phrasebased system. We will give a brief overview of the components of the systems and discuss the performance on the IWSLT development data, the evaluation results, and some post-evaluation results.
Evaluating power of agents from dependence relations in boolean games

In this paper we propose a new methodology for evaluating the relative power of agents in a strategic situation formally represented by a boolean game. The methodology consists in extracting a power ranking from the dependence relation induced by a certain boolean game. Our approach is axiomatic. We provide a number of desirable postulates that a notion of dependence is expected to satisfy and we compare competing notions of dependence, included a notion based on the concept of veto player, with respect to them. Similarly, we provide a set of postulates for power functions (i.e., the family of functions mapping dependence graphs to power rankings) and evaluate some new methods as well as existing ones (e.g., Pagerank) with respect to this set of postulates.
Beyond pairwise classification and clustering using hypergraphs

In many applications, relationships among objects of interest are more complex than pairwise. Simply approximating complex relationships as pairwise ones can lead to loss of information. An alternative for these applications is to analyze complex relationships among data directly, without the need to first represent the complex relationships into pairwise ones. A natural way to describe complex relationships is to use hypergraphs. A hypergraph is a graph in which edges can connect more than two vertices. Thus we consider learning from a hypergraph, and develop a general framework which is applicable to classification and clustering for complex relational data. We have applied our framework to real-world web classification problems and obtained encouraging results.
ERPP: An experience-based randomized path planner

This paper presents a motion planning algorithm capable of exploiting the experience gained in previous path computations in the same static workspace. The algorithm takes advantage of a parallel approach to speed up computation and compile a graph retaining useful knowledge about the environment. Experimental results assess the performance improvement of the experience-based planner over the parallel implementation of a well-known probabilistic motion planning algorithm.
FINAL PROGRAM AND PROCEEDINGS Nineteenth Color and Imaging Conference Color Science and Engineering Systems

Logvinenko’s color atlas theory provides a structure in which a complete set of color-equivalent material and illumination pairs can be generated to match any given input RGB color. In chromaticity space, the set of such pairs forms a 2-dimensional manifold embedded in a 4-dimensional space. For single-illuminant scenes, the illumination for different input RGB values must be contained in all the corresponding manifolds. The proposed illumination-estimation method estimates the scene illumination based on calculating the intersection of the illuminant components of the respective manifolds through a Hough-like voting process. Overall, the performance on the two datasets for which camera sensitivity functions are available is comparable to existing methods. The advantage of the formulating the illumination-estimation in terms of manifold intersection is that it expresses the constraints provided by each available RGB measurement within a sound theoretical foundation.
Weakly supervised object detection with correlation and part suppression

In weakly supervised object detection, conventional methods treat object location in each image as a latent variable and use non-convex optimization to solve the latent variable. However, as the optimization objective is image-level instead of sample-level, the learning procedure tends to choose object parts as false positive samples. Furthermore, when multiple classes of objects appear in the same images, the models could invite class-correlations and lose discriminative capability. In this paper, we propose a simple but effective suppression strategy that mines hard negative samples in the learning procedure to ease the above problems. We propose using a spatial-voting strategy to help finding negative samples to suppress the impact of object parts. We also use regions from class-correlated images as negative samples to suppress the impact of class-correlations. Experiments show that our approach significantly improves the baseline by 6% and achieves state-of-the-art performance.
Pushing Analytics to the Edge

Edge or Fog computing is emerging as a new computing paradigm where the data processing, networking, storage and analytics are performed closer to the devices (IoT) and applications. The edge of a network plays an important role in the IoT system. It is an optimal site for off-loading bandwidth hungry IoT data. In order to generate business value out of the large volume of data on the edge, we need decentralized machine learning (ML) algorithms. These algorithms must enable edge devices to communicate autonomously and deliver information seamlessly to the decision makers. In this paper, we present EdgeSGD a decentralized stochastic gradient descent method to solve a large linear regression problem on the edge network. The solution is applied to the seismic imaging use-case and evaluated using an edge computing testbed. The proposed algorithm avoids sending raw data to the cloud, and offers faster and balanced computation. We compare the algorithm with the existing methods such as MapReduce, DGD and EXTRA. The results show that the EdgeSGD converges faster to the optimal value and is robust towards node/link failure. Finally, we test the algorithm using real-world seismic data trace from Parkfield, CA and show that our proposed algorithm can illuminate the underlying fault region of San-Andreas.