Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Single-Cell-Level Phenotypic Assay for Residual Drug Susceptibility and Reduced Replication Capacity of Drug-Resistant Human Immunodeficiency Virus Type 1

ABSTRACT Human immunodeficiency virus type 1 (HIV-1)-infected individuals who develop drug-resistant virus during antiretroviral therapy may derive benefit from continued treatment for two reasons. First, drug-resistant viruses can retain partial susceptibility to the drug combination. Second, therapy selects for drug-resistant viruses that may have reduced replication capacities relative to archived, drug-sensitive viruses. We developed a novel single-cell-level phenotypic assay that allows these two effects to be distinguished and compared quantitatively. Patient-derived gag-pol sequences were cloned into an HIV-1 reporter virus that expresses an endoplasmic reticulum-retained Env-green fluorescent protein fusion. Flow cytometric analysis of single-round infections allowed a quantitative analysis of viral replication over a 4-log dynamic range. The assay faithfully reproduced known in vivo drug interactions occurring at the level of target cells. Simultaneous analysis of single-round infections by wild-type and resistant viruses in the presence and absence of the relevant drug combination divided the benefit of continued nonsuppressive treatment into two additive components, residual virus susceptibility to the drug combination and selection for drug-resistant variants with diminished replication capacities. In some patients with drug resistance, the dominant circulating viruses retained significant susceptibility to the combination. However, in other cases, the dominant drug-resistant viruses showed no residual susceptibility to the combination but had a reduced replication capacity relative to the wild-type virus. In this case, simplification of the regimen might still allow adequate suppression of the wild-type virus. In a third pattern, the resistant viruses had no residual susceptibility to the relevant drug regimen but nevertheless had a replication capacity equivalent to that of wild-type virus. In such cases, there is no benefit to continued treatment. Thus, the ability to simultaneously analyze residual susceptibility and reduced replication capacity of drug-resistant viruses may provide a basis for rational therapeutic decisions in the setting of treatment failure.
Attitudes for Agents in Dynamic Worlds

In this paper, we propose that in multiagent dynamic worlds, agents which are autonomous, need to be guided by attitudes for effective problem solving. Often agents, existing in dynamic and hostile worlds, are required to exhibit the specified problem solving behaviors for prolonged periods of time either continually or intermittently. In order to be able to perform these types of behaviors, autonomous agents need meta-level controls known as attitudes to guide them towards selecting the proper goals and actions. In this paper, we investigate the role of attitudes in problem solving in dynamic worlds, and suggest several attitudes for the agents in a hostile dynamic world, the fire world. We then evaluate and compare the problem solving behaviors of the agents in a simulated fire world using different types of attitudes. In a dynamic environment, meta-level control is needed not only to improve the efficiency of reasoning, but also the accuracy and utility of the results of reasoning. - Martha Pollack in The uses of plans, AI 57(1992) 43-68.
A Note on Liquidity Risk Management

When a firm is unable to roll over its debt, it may have to seek more expensive sources of financing or even liquidate its assets. This paper provides a normative analysis of minimizing such rollover risk, through the optimal dynamic choice of the maturity structure of debt. The objective of a firm with long-term assets is to maximize the effective maturity of its liabilities across several refinancing cycles, rather than to maximize the maturity of the current bonds outstanding. An advantage of short-term financing is that a firm, while in good financial health, can readjust its maturity structure more quickly in response to changes in its asset value.
Disseminated growth of a human multiple myeloma cell line in mice with severe combined immunodeficiency disease.

We have successfully engrafted a human multiple myeloma cell line, ARH-77, into C.B. 17 SCID mice. When ARH-77 cells were injected s.c. , tumors grew only at the site of inoculation (five of five). When ARH-77 cells were injected i.v. tumors did not grow in any of the mice (zero of five). However, when mice were given gamma-irradiation with 150 rads and then inoculated i.v. with 10(7) ARH-77 cells, 100% (13 of 13) of the mice developed tumors. Hind leg paralysis was observed in 13 of 16 mice as a result of compression of the spinal cord by tumor. Histological analysis demonstrated that myeloma cells proliferated and formed osteolytic lesions (15 of 16) in the vertebrae and bones of the skull (14 of 16). Tumor cells also invaded the brain and meninges (14 of 16), lung (13 of 15), liver (seven of 15), and kidney (two of 15). Flow cytometric analysis demonstrated that the phenotype of 31% of the bone marrow cells in the vertebrae and 79% of s.c. tumor cells was similar to ARH-77 cells (CD38+, PCA-1+, HLA-Classes 1 and II+). Furthermore, DNA hybridization with a human AluI probe confirmed their human origin. ARH-77-derived human immunoglobulin was detected in the serum of SCID/ARH-77 mice by ELISA. These observations demonstrate systemic involvement of human multiple myeloma following i.v. injection of ARH-77 cells into irradiated mice. This in vivo model should be useful for evaluating new therapeutic modalities for myeloma.
Change-Aware Build Prediction Model for Stall Avoidance in Continuous Integration

Continuous Integration(CI) is a widely used development practice where developers integrate their work after submitting code changes at central repository. CI servers usually monitor central repository for code change submission and automatically build software with changed code, perform unit testing, integration testing and provide test summary report. If build or test fails developers fix those issues and submit the code changes. Continuous submission of code modification by developers and build latency time creates stalls at CI server build pipeline and hence developers have to wait long time to get build outcome. In this paper, we proposed build prediction model that uses TravisTorrent data set with build error log clustering and AST level code change modification data to predict whether a build will be successful or not without attempting actual build so that developer can get early build outcome result. With the proposed model we can predict build outcome with an average F-Measure over 87% on all three build systems (Ant, Maven, Gradle) under the cross-project prediction scenario.
Variable Selection through Statistical Sensibility Analysis: Application to Feedforward and Recurrent Neural Networks.

In this paper a method for the analysis of the influence input variables have on the output of a fitted model is presented. It is based on the statistical study of the derivatives of the output of the model with regards to its inputs (also called I/O sensibilities). The smaller these derivatives are, the more irrelevant the corresponding input is. We have tested the technique on different problems, with a special focus on time series prediction problems, although it is not the only possible application domain. We apply the method to connectionist models (Artificial Neural Networks), but the method can be applied to any non-linear model, as far as its I/O sensibilities can be computed or estimated. We therefore do not impose anything on the underlying process as far as it can be estimated as close as possible by a derivable model. This procedure has also been applied to recurrent models. After the introduction of a derivative scheme which leads to the recursive equation needed in the estimation process, we show that it is possible to capture the noise of the time series in order to modelize its Moving Average components. The primary aim of this analysis is the selection of an appropriate subset of input variables to estimate the best and more parsimonious model. It is also a useful tool for the explanation of the underlying process. 1Also with the National Institute of Telecommunication (INT, Evry-France). The work of T. Czernichow has been partially supported by Electricité de France, Direction des Etudes et Recherches (EDF-DER). 2The work of A. Muñoz has been supported by the "programa de Formacion del Personal Investigador" of the Spanish ministry of education and Science (PN91-11802926). A part of this job has alos been possible through the COMETT grants. SUBMITTED TO NEURAL COMPUTATION 33
Eumycetoma versus actinomycetoma: Diagnosis on cytology

Eumycetoma is a chronic cutaneous and subcutaneous infection caused by various genera of fungi producing specific colored granules known as grains. A 45-year-old farmer presented clinically with a left foot mass with multiple discharging sinuses existing for last 3 years. Clinical and radiological findings suggested a diagnosis of chronic osteomyelitis with suspicion of tuberculosis. Imprints plus fine needle aspiration cytology (FNAC) smears exhibited distinct brown-black colonies of a fungus having branching and septate hyphae embedded in matrix like material against a mixed inflammatory background. Periodic acid Schiff (PAS) stain gave positive staining and subsequent fungal culture confirmed the cytological diagnosis and aided in species identification as Madurella mycetomatis. Thus, eumycetoma can precisely be diagnosed and confidently differentiated from similar conditions such as actinomycetoma by simple and inexpensive cytological techniques such as FNAC and imprint smears, employing routine May-Grünwald-Giemsa, Papanicolaou and simple PAS stains on cytological specimen, thus leading to rapid diagnosis for institution of correct treatment.
olvent design for crystallization of carboxylic acids runprakash

Critical to crystallization chemical product design is the choice of an appropriate solvent. Traditional methods have focused on bench scale experiments using classes of solvents (e.g. polarity) with the different classes giving rise to different crystal morphologies. However, there are instances where some solvents belonging to a particular class give completely different morphology from other solvents in the same class. There has been some modeling effort aimed at predicting crystal morphology. A major drawback with some of these morphology prediction models is that they tend to be limited in application. It is clear that the solvent selection, with respect to crystal morphology cannot be carried out efficiently by
Design segmented slot waveguide for integrated waveguide modulator

In this article, segmented slot waveguide structures are designed and analysed numerically. We present a theoretical investigation of overlap factors and low half voltage for different segmented slot waveguide structures to determine optimised geometrical dimensions based on slot widths. Results shows that high overlap factor, and low half voltage, occurs for 200 and 300 nm periodicities at a 100 nm slot width, while for a 400 nm periodicity this width is 125 nm. Thus, achieving a very low Vπ compared to conventional slot waveguides demonstrates a high sensitivity of segmented slot waveguide and unprecedented sensing in a sensor application.
Studying the fix-time for bugs in large open source projects

Background: Bug fixing lies at the core of most software maintenance efforts. Most prior studies examine the effort needed to fix a bug (fix-effort). However, the effort needed to fix a bug may not correlate with the calendar time needed to fix it (fix-time). For example, the fix-time for bugs with low fix-effort may be long if they are considered to be of low priority. Aims: We study the fix-time for bugs in large open source projects. Method: We study the fix-time along three dimensions: (1) the location of the bug (e.g., which component), (2) the reporter of the bug, and (3) the description of the bug. Using these three dimensions and their associated attributes, we examine the fix-time for bugs in two large open source projects: Eclipse and Mozilla, using a random forest classifier. Results: We show that we can correctly classify ~65% of the time the fix-time for bugs in these projects. We perform a sensitivity analysis to identify the most important attributes in each dimension. We find that the time of the filing of a bug and its location are the most important attributes in the Mozilla project for determining the fix-time of a bug. On the other hand, the fix-time in the Eclipse project is highly dependant on the severity of the bug. Surprisingly, the priority of the bug is not an important attribute when determining the fix-time for a bug in both projects. Conclusion: Attributes affecting the fix-time vary between projects and vary over time within the same project.
Polyphasic characterization of Gluconacetobacter diazotrophicus isolates obtained from different sugarcane varieties

A polyphasic approach was applied to characterize 35 G. diazotrophicus isolates obtained from sugarcane varieties cultivated in Brazil. The isolates were analyzed by phenotypic (use of different carbon sources) and genotypic tests (ARDRA and RISA–RFLP techniques). Variability among the isolates was observed in relation to the carbon source use preference. Glucose and sucrose were used by all isolates in contrast to myo-inositol, galactose and ribose that were not metabolized. The results of the analysis showed the presence of two groups clustered at 68% of similarity. The genetic distance was higher when RISA-RFLP analysis was used. Analysis of 16S rDNA sequences from isolates showed that all of them belonged to the G. diazotrophicus species. Neither effect of the plant part nor sugarcane variety was observed during the cluster analysis. The observed metabolic and genetic variability will be helpful during the strain selection studies for sugarcane inoculation in association with sugarcane breeding programs.
Quasiconvex Programming

We define quasiconvex programming, a form of generalized linear programming in which one seeks the point minimizing the pointwise maximum of a collection of quasiconvex functions. We survey algorithms for solving quasiconvex programs either numerically or via generalizations of the dual simplex method from linear programming, and describe varied applications of this geometric optimization technique in meshing, scientific computation, information visualization, automated algorithm analysis, and robust statistics.
Practical private shortest path computation based on Oblivious Storage

As location-based services (LBSs) become popular, location-dependent queries have raised serious privacy concerns since they may disclose sensitive information in query processing. Among typical queries supported by LBSs, shortest path queries may reveal information about not only current locations of the clients, but also their potential destinations and travel plans. Unfortunately, existing methods for private shortest path computation suffer from issues of weak privacy property, low performance or poor scalability. In this paper, we aim at a strong privacy guarantee, where the adversary cannot infer almost any information about the queries, with better performance and scalability. To achieve this goal, we introduce a general system model based on the concept of Oblivious Storage (OS), which can deal with queries requiring strong privacy properties. Furthermore, we propose a new oblivious shuffle algorithm to optimize an existing OS scheme. By making trade-offs between query performance, scalability and privacy properties, we design different schemes for private shortest path computation. Eventually, we comprehensively evaluate our schemes upon real road networks in a practical environment and show their efficiency.
Improved Backtracking Algorithm for Efficient Sensor-Based Random Tree Exploration

Mobile robots need to explore novel environments to build useful maps for later navigation and motion planning. Sensor-based Random Tree, (SRT), technique had been used for exploration but it is problematic since the robot may visit the same place more than one time during backtracking process. In this paper, we propose a new heuristic algorithm to reduce this backtracking problem using the obtained map data. This algorithm is tested through computer simulations for several scenarios. The performance is evaluated in terms of exploration time, travelled distance and number of visited nodes. Since these classical evaluation metrics are correlated, we propose a new evaluation metric, that combines the total performance. The new algorithm is confirmed to reduce the exploration time of up to 30 %. The new evaluation metric is also shown to encapsulate the exploration performance and can be regarded as a much better representative of the performance that facilitate comparisons.
Perception of 3 D Spatial Relations for 3 D Displaysa

We test perception of 3D spatial relations in 3D images rendered by a 3D display (Perspecta from Actuality Systems) and compare it to that of a high-resolution flat panel display. 3D images provide the observer with such depth cues as motion parallax and binocular disparity. Our 3D display is a device that renders a 3D image by displaying, in rapid succession, radial slices through the scene on a rotating screen. The image is contained in a glass globe and can be viewed from virtually any direction. In the psychophysical experiment several families of 3D objects are used as stimuli: primitive shapes (cylinders and cuboids), and complex objects (multi-story buildings, cars, and pieces of furniture). Each object has at least one plane of symmetry. On each trial an object or its “distorted” version is shown at an arbitrary orientation. The distortion is produced by stretching an object in a random direction by 40%. This distortion must eliminate the symmetry of an object. The subject’s task is to decide whether or not the presented object is distorted under several viewing conditions (monocular/binocular, with/without motion parallax, and near/far). The subject’s performance is measured by the discriminability d’, which is a conventional dependent variable in signal detection experiments.
Structured Sparse Principal Component Analysis

We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes. This \emph{structured sparse PCA} is based on a structured regularization recently introduced by [1]. While classical sparse priors only deal with \textit{cardinality}, the regularization we use encodes higher-order information about the data. We propose an efficient and simple optimization procedure to solve this problem. Experiments with two practical tasks, face recognition and the study of the dynamics of a protein complex, demonstrate the benefits of the proposed structured approach over unstructured approaches.
Text classification by boosting weak learners based on terms and concepts

Document representations for text classification are typically based on the classical bag-of-words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results.
Weak pattern matching in colored graphs: Minimizing the number of connected components

In the context of metabolic network analysis, Lacroix et al.11 introduced the problem of finding occurrences of motifs in vertex-colored graphs, where a motif is a multiset of colors and an occurrence of a motif is a subset of connected vertices which are colored by all colors of the motif. We consider in this paper the above-mentioned problem in one of its natural optimization forms, referred hereafter as the Min-CC problem: Find an occurrence of a motif in a vertex-colored graph, called the target graph, that induces a minimum number of connected components. Our results can be summarized as follows. We prove the Min-CC problem to be APX–hard even in the extremal case where the motif is a set and the target graph is a path. We complement this result by giving a polynomial-time algorithm in case the motif is built upon a fixed number of colors and the target graph is a path. Also, extending recent research8 , we prove the Min- CC problem to be fixed-parameter tractable when parameterized by the size of the motif, and we give a faster algorithm in case the target graph is a tree. Furthermore, we prove the Min-CC problem for trees not to be approximable within ratio c log n for some constant c > 0, where n is the order of the target graph, and to be W[2]–hard when parameterized by the number of connected components in the occurrence of the motif. Finally, we give an exact efficient exponential-time algorithm for the Min-CC problem in case the target graph is a tree.
AntMonitor: A System for Monitoring from Mobile Devices

We propose AntMonitor - a system for passive monitoring, collection and analysis of fine-grained, large-scale packet measurements from Android devices. AntMonitor is the first system of its kind that combines the following properties: (i) it provides participating users with fine-grained control of which data to contribute; (ii) it does not require administrative privileges; (iii) it supports client-side analysis of traffic; and (iv) it supports collection of large-scale, fine-grained, and semantic-rich traffic. The first three properties benefit mobile users, by giving them control over their privacy while also enabling a number of services to incentivize their participation. The last property makes AntMonitor a powerful tool for network researchers who want to collect and analyze large-scale, yet fine-grained mobile measurements. As a proof-of-concept, we have developed and deployed a prototype of AntMonitor, and we have used it to monitor 9 users for several months. AntMonitor has high network throughput while incurring lower CPU and battery costs than existing mobile monitoring systems. Our preliminary experience with the prototype demonstrates its capabilities and its potential for enabling several research activities, including network measurement from the edge, classification of mobile traffic, and detection of privacy leakage and other malicious behaviors.