Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrocardiographically study of protective effect of Eruca Sativa M. versus doxorubicin induced cardiotoxicity in rabbits

Cardiotoxicity is defined as drug-induced damage to the heart muscle, which can result in cardiac failure or cardiac arrest. Cardiotoxicity is a common side effect of traditional anticancer drugs such as doxorubicin, taxanes, 5-fluorouracil, and cyclophosphamide, as well as newer agents such as biological antibodies such as Nivo. Cardiotoxicity is also caused by the long-term use of neurologic/psychiatric medications. This study was designed to determine the effect of eruca sativa mill (Gergeer) leaves extract on cardiotoxicity induced by doxorubicin in rabbits. The thirty-five rabbits used in this study were obtained from a local rabbit breeder. Animals ranged between 1-1.3 years old, and their weight was approximately 1-2kg without specifying gender. experiment: Thirty-five adult rabbits (n=35) will be randomly divided into four groups (7 rabbits/each group) first group: seven rabbits will receive distal water (D.W) orally (-ve control), Second group: fourteen rabbits divided into two subgroup equally administrated with doxorubicin 4 mg/kg B.W (I.P) between day to day for 14 days to induce cardiotoxicity (the first subgroup leave two weeks then killed, while the second subgroup leaves to 28 days without treatment),
Automated ArcticDEM iceberg detection tool: insights into area and volume distributions, and their potential application to satellite imagery and modelling of glacier–iceberg–ocean systems

Abstract. Iceberg calving accounts for up to half of mass loss from the Greenland Ice Sheet (GrIS), with their size distributions providing insights into glacier calving dynamics and impacting fjord environments through their melting and subsequent freshwater release. Iceberg area and volume data for the GrIS are currently limited to a handful of fjord locations, while existing approaches to iceberg detection are often time-consuming and are not always suited for long time series analysis over large spatial scales. This study presents a highly automated workflow that detects icebergs and appends their associated metadata within Google Earth Engine using high spatial resolution timestamped ArcticDEM (Arctic Digital Elevation Model) strip data. This is applied to three glaciers that exhibit a range of different iceberg concentrations and size distributions: Sermeq Kujalleq (Jakobshavn Isbræ), Umiammakku Isbræ and Kangiata Nunaata Sermia. A total of 39 ArcticDEM scenes are analysed, detecting a total of 163 738 icebergs with execution times of 6 min to 2 h for each glacier depending on the number of DEMs available and total area analysed, comparing well with the mapping of manually digitised outlines. Results reveal two distinct iceberg distributions at Sermeq Kujalleq and Kangiata Nunaata Sermia where iceberg density is high, and one distribution at Umiammakku Isbræ where iceberg density is low. Small icebergs (< 1000 m2) are found to account for over 80 % of each glacier's icebergs; however, they only contribute to 10 %–37 % of total iceberg volume suggesting that large icebergs are proportionally more important for glacier mass loss and as fjord freshwater reservoirs. The overall dataset is used to construct new area-to-volume conversions (with associated uncertainties) that can be applied elsewhere to two-dimensional iceberg outlines derived from optical or synthetic aperture radar imagery. When data are expressed in terms of total iceberg count and volume, insight is provided into iceberg distributions that have potential applicability to observations and modelling of iceberg calving behaviour and fjord freshwater fluxes. Due to the speed and automated nature of our approach, this workflow offers the potential to interrogate iceberg data on a pan-Arctic scale where ArcticDEM strip data coverage allows.
WORKING PAPER SERIES LOOKING BACK AT 50 YEARS OF THE CLEAN AIR ACT

We synthesize and review retrospective analyses of federal air quality regulations to examine the contributions of the Clean Air Act to the vast air quality improvements seen since 1970. Geographic heterogeneity in stringency affects emissions, public health, compliance costs, and employment. Cap-and-trade has delivered greater emission reductions at lower cost than conventional mandates, yet has fallen short of textbook ideals. Market power also influenced the CAA’s benefits and costs. New benefit categories have been identified ex post, but specific technology requirements have not yet been rigorously evaluated. Comparisons of aggregate benefits and costs of the CAA are beyond present capabilities. Joseph E. Aldy Harvard Kennedy School Ofer 403, Mailbox 114 79 JFK Street Cambridge, MA 02138 and NBER joseph_aldy@hks.harvard. edu Maximilian Auffhammer Agricultural and Resource Economics Department University of California, Berkeley 207 Giannini Hall Berkeley, CA 94720-3310 and NBER auffhammer@berkeley.edu Maureen Cropper 6241 Clearwood Rd. Bethesda, MD 20817 and NBER mcropper@umd.edu Arthur Fraas 1616 P St NW, Suite 600 Washington, DC 20036 fraas@rff.org Richard Morgenstern Resources for the Future 1616 P Street, NW Washington, DC 20036 morgenstern@rff.org
Discovering significant evolutionary trajectories in cancer phylogenies.

MOTIVATION Tumors are the result of a somatic evolutionary process leading to substantial intra-tumor heterogeneity. Single-cell and multi-region sequencing enable the detailed characterization of the clonal architecture of tumors and have highlighted its extensive diversity across tumors. While several computational methods have been developed to characterize the clonal composition and the evolutionary history of tumors, the identification of significantly conserved evolutionary trajectories across tumors is still a major challenge. RESULTS We present a new algorithm, MAximal tumor treeS TRajectOries (MASTRO), to discover significantly conserved evolutionary trajectories in cancer. MASTRO discovers all conserved trajectories in a collection of phylogenetic trees describing the evolution of a cohort of tumors, allowing the discovery of conserved complex relations between alterations. MASTRO assesses the significance of the trajectories using a conditional statistical test that captures the coherence in the order in which alterations are observed in different tumors. We apply MASTRO to data from nonsmall-cell lung cancer bulk sequencing and to acute myeloid leukemia data from single-cell panel sequencing, and find significant evolutionary trajectories recapitulating and extending the results reported in the original studies. AVAILABILITY AND IMPLEMENTATION MASTRO is available at https://github.com/VandinLab/MASTRO. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Probing Tree Physiology Using the Dual-Isotope Approach

The environmental and physiological interpretation of stable isotope variation in organic matter is affected by many different and interacting factors. This is especially true when considering isotope variation in tree rings, which are inﬂuenced not only by leaf-level photosynthetic gas exchange processes but also by post-photosynthetic fractionation. It has been proposed that measuring multiple isotopes on the same sample may constrain such interpretations if one isotope provides independent information about important fractionation events that cause variation in another isotope. Here we describe one such “dual-isotope approach” where oxygen isotopevariation( δ 18 O)isusedtoprobetheeffectsofstomatalconductanceoncarbon isotope ( δ 13 C) variation for the same sample. This chapter describes the development of this conceptual model, constraints on model applicability, particularly with respect to tree rings, and how it has been utilized to explore aspects of tree physiology.
Distributed Asynchronous Word2Vec Training

The Word2Vec skip-gram approach has gained a lot of popularity since its initial release in 2013. Although modern tweaks and improvements have led to a outstanding model performance, established implementation still have difficulties processing huge text corpora. Various researchers proposed different approaches for distributing training of word embeddings, often limited by the need of model synchronization. We present a leaderless implementation based on the Akka toolkit, theoretically capable of coping with corpora of any size. Employing the actor programming concept allows us to encapsulate model partitions, thus promoting synchronization-less model training through message-only communication.
The Algebraic Structure of Attributed Type Signatures

A game including a game board, a propellable playing piece and hand held bats for striking the playing piece wherein the game board is generally octagonal with four inclined playing surfaces sloping downward from the center portion of the game board to the marginal edges thereof. A frame is affixed to the game board to contain the playing piece and apertures are provided in the frame to capture the playing piece. A pair of cavities in the inclined playing surfaces capture and hold the playing piece while a "windmill" rotor and pegs establish obstacles in the path of the playing piece to the associated cavity or aperture.
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data

Automatic de-identification is a cost-effective and straightforward way of removing large amounts of personally identifiable information from large and sensitive corpora. However, these systems also introduce errors into datasets due to their imperfect precision. These corruptions of the data may negatively impact the utility of the de-identified dataset. This paper de-identifies a very large clinical corpus in Swedish either by removing entire sentences containing sensitive data or by replacing sensitive words with realistic surrogates. These two datasets are used to perform domain adaptation of a general Swedish BERT model. The impact of the de-identification techniques is assessed by training and evaluating the models using six clinical downstream tasks. The results are then compared to a similar BERT model domain-adapted using an unaltered version of the clinical corpus. The results show that using an automatically de-identified corpus for domain adaptation does not negatively impact downstream performance. We argue that automatic de-identification is an efficient way of reducing the privacy risks of domain-adapted models and that the models created in this paper should be safe to distribute to other academic researchers.
The importance of the population age-structure: insights from structured by since and

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Learning to rank by maximizing the AUC with linear programming for problems with binary output

Ranking is a popular machine learning problem that has been studied extensively for more then a decade. Typical machine learning algorithms are generally built to optimize predictive performance (usually measured in accuracy) by minimizing classification error. However, there are many real world problems where correct ordering of instances is of equal or greater importance than correct classification. Learning algorithms that are built to minimize classification error are often not effective when ordering within or among classes. This gap in research created a necessity to alter the objective of such algorithms to focus on correct ranking rather then classification. Area Under the ROC Curve (AUC), which is equivalent to the Wilcoxon-MannWhitney (WMW) statistic, is a widely accepted performance measure for evaluating ranking performance in binary classification problems. In this work we present a linear programming approach (LPR), similar to 1-norm Support Vector Machines (SVM), for ranking instances with binary outputs by maximizing an approximation to the WMW statistic. Our formulation handles non-linear problems by making use of kernel functions. Results on several well-known benchmark datasets show that our approach ranks better than 2-norm SVM and faster than the support vector ranker (SVR). The number of constraints in the linear programming formulation increases quadratically with the number of data points considered for the training of the algorithm. We tackle this problem by implementing a number of exact and approximate speed-up approaches inspired by well-known methods such as chunking, clustering and subgradient methods. The subgradient method is the most promising because of its solution quality and its fast convergence to the optimal solution. We adopted the LPR formulation to survival analysis. With this approach it is possible to order subjects by risk for experiencing an event. Such an ordering enables determination of high-risk and low-risk groups among the subjects that can be helpful not only in
The Analytic Hierarchy Process for New Product Screening Decisions

The selection of the most promising new product development projects is one of the most important decisions in a company. This decision affects the cost of failed projects, company’s profitability, and its survival. The selection of new product development projects with the greatest potential requires the evaluation criteria that reflect the adjustment of a new product to customer requirements, company’s strategy, manufacturing, and knowledge management issues. There are specified sub-criteria to identify the impact of a new product on a single area of the company’s activity, for example, on the knowledge management area. The criteria and sub-criteria are evaluated by managers, engineers (including knowledge engineers), and IT specialists, who work in departments such as research and development, sales and marketing, manufacturing, IT, and top management. Company professionals have the most updated information of the ongoing processes related to new product development. This paper is concerned with using the analytic hierarchy process (AHP) methodology to a new product screening problem, paying attention to the knowledge management perspective. So far, this perspective is neglected in the context of the decision problem of new product screening. This research develops the field of knowledge acquisition from experts towards selecting and evaluating criteria related to the potential of a new product. Knowledge acquisition refers to issues related to a new product, customer requirements, and uncertainties of project performance. Using criteria related to various areas of the company’s activity, the decision maker can identify factors significantly impacting performance of new product projects, and compare these projects with each other. Moreover, the AHP approach prioritizes criteria and sub-criteria, and as a result, it can identify areas of the company’s activity that could be improved.
A model-based analysis of extinction ratio effects on phase-OTDR distributed acoustic sensing system performance

Extinction ratio is an inherent limiting factor that has a direct effect on the detection performance of phase-OTDR based distributed acoustics sensing systems. In this work we present a model based analysis of Rayleigh scattering to simulate the effects of extinction ratio on the received signal under varying signal acquisition scenarios and system parameters. These signal acquisition scenarios are constructed to represent typically observed cases such as multiple vibration sources cluttered around the target vibration source to be detected, continuous wave light sources with center frequency drift, varying fiber optic cable lengths and varying ADC bit resolutions. Results show that an insufficient ER can result in high optical noise floor and effectively hide the effects of elaborate system improvement efforts.
The GBT350 survey of the Northern Galactic Plane for radio pulsars and transients

Using the Green Bank Telescope (GBT) and Pulsar Spigot at 350 MHz, we have surveyed the Northern Galactic Plane for pulsars and radio transients. This survey covers roughly 1000 square degrees of sky within 75°<l<165° and |b|<5.5°, a region of the Galactic Plane inaccessible to both the Parkes and Arecibo multibeam surveys. The large gain of the GBT along with the high time and frequency resolution provided by the Spigot make this survey more sensitive by factors of about 4 to slow pulsars and more than 10 to millisecond pulsars (MSPs), compared with previous surveys of this area. In a preliminary, reduced‐resolution search of all the survey data, we have discovered 33 new pulsars, almost doubling the number of known pulsars in this part of the Galaxy. While most of these sources were discovered by normal periodicity searches, 5 of these sources were first identified through single, dispersed bursts. We discuss the interesting properties of some of these new sources. Data processing using the data's full‐.
Modelling the Recommender Alignment Problem

Recommender systems (RS) mediate human experience online. Most RS act to optimize metrics that are imperfectly aligned with the best-interest of users but are easy to measure, like ad-clicks and user engagement. This has resulted in a host of hard-to-measure side-effects: political polarization, addiction, fake news. RS design faces a recommender alignment problem: that of aligning recommendations with the goals of users, system designers, and society as a whole. But how do we test and compare potential solutions to align RS? Their massive scale makes them costly and risky to test in deployment. We synthesized a simple abstract modelling framework to guide future work. To illustrate it, we construct a toy experiment where we ask:"How can we evaluate the consequences of using user retention as a reward function? "To answer the question, we learn recommender policies that optimize reward functions by controlling graph dynamics on a toy environment. Based on the effects that trained recommenders have on their environment, we conclude that engagement maximizers generally lead to worse outcomes than aligned recommenders but not always. After learning, we examine competition between RS as a potential solution to RS alignment. We find that it generally makes our toy-society better-off than it would be under the absence of recommendation or engagement maximizers. In this work, we aimed for a broad scope, touching superficially on many different points to shed light on how an end-to-end study of reward functions for recommender systems might be done. Recommender alignment is a pressing and important problem. Attempted solutions are sure to have far-reaching impacts. Here, we take a first step in developing methods to evaluating and comparing solutions with respect to their impacts on society.
Solvent Distribution Effects on Quantum Chemical Calculations with Quantum Computers.

We present a combination of three-dimensional reference interaction site model self-consistent field (3D-RISM-SCF) theory and the variational quantum eigensolver (VQE) to consider the solvent distribution effects within the framework of quantum-classical hybrid computing. The present method, 3D-RISM-VQE, does not include any statistical errors from the solvent configuration sampling owing to the analytical treatment of the statistical solvent distribution. We apply 3D-RISM-VQE to compute the spatial distribution functions of solvent water around a water molecule, the potential and Helmholtz energy curves of NaCl, and to analyze the Helmholtz energy component and related properties of H2O and NH4+. Moreover, we utilize 3D-RISM-VQE to analyze the extent to which solvent effects alter the efficiency of quantum calculations compared with calculations in the gas phase using the L1-norms of molecular electronic Hamiltonians. Our results demonstrate that the efficiency of quantum chemical calculations on a quantum computer in solution is virtually the same as that in the gas phase.
Virtual flux direct power-backstepping control of 5-level T-type multiterminal VSC-HVDC system

Summary In this study, a nonlinear control of a 5-level T-type converter topology-based multiterminal voltage source converter high-voltage direct current system is proposed. The idea of the proposed control is to combine the backstepping control and direct power control with virtual flux concept into one controller able to improve the performance of the transmission system. In the other side, the use of a 5-level space vector modulation with a balancing strategy based on effective use of the redundant switching states of the converters voltage vectors guarantees the objective of maintaining balanced voltages in DC-capacitors. Finally, simulations of the 5-level T-type multiterminal voltage source converter high-voltage direct current system validate the effectiveness of the proposed control law. The obtained results are compared with those performed by a conventional Proportional Integrator (PI) controller. These outcomes allow to exhibit excellent transient response during a range of operating conditions.
Identifying glitches near gravitational-wave events using the Q-transform

We present a computational method to identify glitches in gravitational-wave data that occur nearby gravitational-wave signals. We ﬂag any excess in the data surrounding a signal and compute the probability of such an excess occurring in Gaussian noise. We validate that the probabilities reported by this tool are self-consistent in colored Gaussian noise as well as data that contains a gravitational-wave event after subtracting the signal using the best-ﬁt template. Furthermore, we compare our glitch identiﬁcation results for events from LIGO-Virgo’s third observing run against the list of events that required glitch mitigation. Finally, we discuss how the precise, automated information about the data quality surrounding gravitational-wave events this tool provides can be used to improve astrophysical analyses of these events.
LinCQA: Faster Consistent Query Answering with Linear Time Guarantees

Most data analytical pipelines often encounter the problem of querying inconsistent data that violate pre-determined integrity constraints. Data cleaning is an extensively studied paradigm that singles out a consistent repair of the inconsistent data. Consistent query answering (CQA) is an alternative approach to data cleaning that asks for all tuples guaranteed to be returned by a given query on all (in most cases, exponentially many) repairs of the inconsistent data. In this paper, we identify a class of acyclic select-project-join (SPJ) queries for which CQA can be solved via SQL rewriting with a linear time guarantee. Our rewriting method can be viewed as a generalization of Yannakakis' algorithm for acyclic joins to the inconsistent setting. We present LinCQA, a system that takes as input any query in our class and outputs rewritings in both SQL and non-recursive Datalog with negation. We show that LinCQA often outperforms the existing CQA systems on both synthetic and real-world workloads, and in some cases, by orders of magnitude.