Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Genome Editing Strategies by Crisper/Cas 9 System

: Clustered regularly interspaced short palindromic repeats (CRISPR) and associated Proteins Cas9 are part of bacterial defense mechanism against foreign genetics elements such as those present in the plasmids and phages . CRISPR-cas9 allows researchers for knock out or knock in of specific DNA sequences in animals models and human cells lines. Hence CRISPR-cas9 system could assist in treatment of cancer, Genome editing in genetic disorder like DMD, β thalasemia, Hemophilia and cystic fibrosis, Genetic engineering in primary human B cells and treatment of hematological disorder etc. Here the latest applications of CRISPR-cas9 editing system in various fields of biomedical researchers are reviewed and discussed
Large sulfur isotope fractionation by bacterial sulfide oxidation

The microbial oxidation of sulfide can produce large enrichments in the stable isotopes of sulfur. A sulfide-oxidizing microorganism, Desulfurivibrio alkaliphilus (DA), generates a consistent enrichment of sulfur-34 (34S) in the produced sulfate of +12.5 per mil or greater. This observation challenges the general consensus that the microbial oxidation of sulfide does not result in large 34S enrichments and suggests that sedimentary sulfides and sulfates may be influenced by metabolic activity associated with sulfide oxidation. Since the DA-type sulfide oxidation pathway is ubiquitous in sediments, in the modern environment, and throughout Earth history, the enrichments and depletions in 34S in sediments may be the combined result of three microbial metabolisms: microbial sulfate reduction, the disproportionation of external sulfur intermediates, and microbial sulfide oxidation.
Bioinspired mechanically active adhesive dressings to accelerate wound closure

Bioinspired adhesive dressings have been shown to shrink at skin temperature, contract the wound, and promote wound healing. Inspired by embryonic wound closure, we present mechanically active dressings to accelerate wound healing. Conventional dressings passively aid healing by maintaining moisture at wound sites. Recent developments have focused on drug and cell delivery to drive a healing process, but these methods are often complicated by drug side effects, sophisticated fabrication, and high cost. Here, we present novel active adhesive dressings consisting of thermoresponsive tough adhesive hydrogels that combine high stretchability, toughness, tissue adhesion, and antimicrobial function. They adhere strongly to the skin and actively contract wounds, in response to exposure to the skin temperature. In vitro and in vivo studies demonstrate their efficacy in accelerating and supporting skin wound healing. Finite element models validate and refine the wound contraction process enabled by these active adhesive dressings. This mechanobiological approach opens new avenues for wound management and may find broad utility in applications ranging from regenerative medicine to soft robotics.
Price forecasting & anomaly detection for agricultural commodities in India

Fluctuations in food prices can cause distress among both consumers and producers, and are often exacerbated by trading networks especially in developing economies where marketplaces may not be operating under conditions of perfect competition for various contextual reasons. We look at onion and potato trading in India and present the evaluation of a price forecasting model, and an anomaly detection and classification system to identify incidents of hoarding of stock by the traders. Our dataset is composed of time series of wholesale prices and arrival volumes of the agricultural commodities at several village-level marketplaces, and retail prices of the commodities at the city centers. We also provide an in-depth qualitative analysis of the effect on these time series of events such as hoarding, weather disturbances, and external shocks. Our results are encouraging and point towards the possibility of building pricing models for agricultural commodities which can be used to reduce information asymmetries and to detect anomalies that can help regulate agricultural markets to operate more fairly.
Social User Interest Mining: Methods and Applications

he abundance of user generated content on social networks pro-vides the opportunity to build models that are able to accurately and effectively extract, mine and predict users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users' interests and preferences. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) we introduce the information sources that are used for extracting user interests, (2) various types of user interest profiles that have been proposed in the literature, (3) techniques that have been adopted or proposed for mining user interests, (4) the scalability and re-source requirements of the state of the art methods, and finally (5)the evaluation methodologies that are adopted in the literature for validating the appropriateness of the mined user interest profiles.We also introduce existing challenges, open research question and exciting opportunities for further work.
Making the most of human memory: Studies on personalized fact-learning and visual working memory

Rote learning of facts is boring. Here, we present a way to make it more efficient so facts are learned faster. Learners usually need to repeat facts multiple times to learn them well. The optimal timing of these repetitions depends on the facts that are studied and the characteristics of the learner. Learning software that takes such differences into account is called adaptive. Here, we show that the adaptive system developed in our lab outperforms a flashcard system often utilized by students: on average, students learn more and make fewer errors during learning if they use the adaptive system. This improvement is possible because the system simulates a very simplified version of each learner’s memory and can predict when individual facts are forgotten. We show that parameters estimated during learning can predict test performance but are not related to intelligence or working memory capacity (in the tested sample). We also demonstrate that the parameters estimated for one learner are stable over time but differ slightly over materials. Finally, future developments and improvements of the system are discussed. For example, the adaptive system’s capabilities should be extended to multi-session learning. Gaining access to large amounts of data collected in realistic (educational) settings will be the key to these developments. Collaborations with Noordhoff and HoeGekIsNL? and a RUG-funded project called Rugged Learning will provide such data and will enable us to verify and test additional assumptions that should further improve the system.
Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks

There have long been connections between statistical mechanics and neural networks, but in recent decades these connections have withered. However, in light of recent failings of statistical learning theory and stochastic optimization theory to describe, even qualitatively, many properties of production-quality neural network models, researchers have revisited ideas from the statistical mechanics of neural networks. This tutorial will provide an overview of the area; it will go into detail on how connections with random matrix theory and heavy-tailed random matrix theory can lead to a practical phenomenological theory for large-scale deep neural networks; and it will describe future directions.
Design and Implementation of RDMA-based Database Operators

In this thesis, we present a novel, extensible RDMA database operator interface — where RDMA is not simply the acronym for Remote Direct Memory Accesses – in addition it also stands for Reusable, Distributed MAin-memory database operator interface. The interface is designed and implemented for distributed query pipelines scaling up algorithms to many thousand cores. It provides the usability of SQL, combined with the expressiveness and extensibility of Spark and will eventually achieve the performance of hand-tuned algorithms written in C++. We implement a distributed radix hash join algorithm and query 1 of the TPC-H Benchmark with the building blocks provided by the operator abstraction. While the former is intended to compare the performance to a hand-tuned, highly optimized implementation, the latter shows the expressiveness of the interface. Although the radix hash join implemented on top of the operator interface exhibits less performance, it has a similar sub-linear scaling behaviour. In the discussion section, we show the differences in the implementations and that the operator interface simply needs more fine-tuning to match similar performance or even surpass it.
Elevated Maternal and Child Mortality among Women with Multiple DUIs Compared to Sociodemographically Matched Controls.

AIMS To assess whether having multiple convictions for driving while under the influence of alcohol (MDUI) in women is a risk factor for maternal, infant and child mortality. DESIGN Retrospective cohort design using record linkage, comparing women with MDUI convictions with propensity matched women without alcohol-related driving offences ascertained through state records, on rates of maternal, infant and child mortality. SETTING Missouri, United States. PARTICIPANTS MDUI women (N=1658) and women with no alcohol-related driving convictions (CONTROL, N=184,252) who gave birth from 2000-2004. MEASUREMENTS Data were obtained from state administrative records and US Census data. The outcomes were maternal, infant and child mortality. The input variable was presence or absence of MDUI convictions. Propensity matching variables were maternal (smoking during pregnancy, delayed prenatal care, previous child deaths, age at birth, mother Missouri-born, education, pre-pregnancy obesity, marital status) reproductive partner (un-named partner, race/ethnicity, education, DUI status), and census tract (socioeconomic advantage, urbanicity) characteristics. FINDINGS Women with MDUI convictions had higher odds of maternal, infant and child mortality than propensity matched controls (OR=2.65, 95% CI=2.07-3.40 and OR=1.75, 95% CI=1.17-2.61 respectively). CONCLUSIONS Having multiple convictions for driving under the influence of alcohol in women appears to be a risk factor for increased maternal, infant and child mortality.
Protein wild-type and mutant ensemble database

Protein structures have been determined and deposited into Protein Data Bank at an increasing rate. In this work, we organize all the protein structures in the PDB and form a wild type and mutant structure database. The database groups the wild type and mutant structures of the same protein together. One direct benefit of the database is thus the easy accessibility of the structure ensembles of all the proteins. Such ensembles are known to be highly useful for representing the native states of proteins and for understanding their functions. For each protein, mutants are sorted by the number of mutations and the location(s) of the mutations. What distinguishes our work from other mutation databases is that it is structure-based and includes all the existing structures of the PDB. Synchronization with the PDB database will be maintained. As an application, we carry out an experimental structure-based statistical analysis of the effects of mutations, on both protein structure and protein dynamics. A key question we address in this work is: is it valid to use mutant structures (or variants from different species) to represent a native state sample of a given protein? Our results indicate that mutations can cause significant structure changes and dynamics changes, more than commonly expected. This implies that cautions must be taken when mutation structures are considered to be included as representative samples of the conformation space of a given protein.
Tandem Mass Spectrometric Analysis of Novel Caffeine Scaffold-Based Bifunctional Compounds for Parkinson's disease.

RATIONALE Novel bifunctional compounds composed of a caffeine scaffold attached to nicotine (C8 -6-N), 1-aminoindan (C8 -6-I), or caffeine (C8 -6-C8 ) were designed as therapeutics or diagnostics for Parkinson's disease (PD). In order to probe their pharmacological and toxicological profile, an appropriate analytical method is required. The goal of this study is to establish a tandem mass spectrometric fingerprint for the development of quantitative and qualitative methods that will aid future assessment of the in vitro and in vivo absorption, distribution, metabolism, excretion (ADME) and pharmacokinetic properties of these lead bifunctional compounds for PD. METHODS Accurate mass measurement was performed using a hybrid quadrupole orthogonal time-of-flight mass spectrometer while multistage MS/MS and MS3 analysis were conducted using a triple quadrupole-linear ion trap mass spectrometer. Both instruments are equipped with an electrospray ionization source (ESI) and were operated in the positive ion mode. The source and compound parameters were optimized for all three tested bifunctional compounds. RESULTS The MS/MS analysis indicates that the fragmentation of C8 -6-N and C8 -6-I is driven by the dissociation of nicotine and I-aminoindan moieties, respectively but not caffeine. A significant observation in the MS/MS fragmentation of C8 -6-C8 suggests that a previously reported loss of acetaldehyde during caffeine dissociation is instead a loss of CO2 . CONCLUSION The CID-MS/MS analysis of these novel bifunctional compounds revealed compound-specific diagnostic product ions and neutral losses for all three tested bifunctional compounds. The established MS/MS fingerprint will be applied to the future development of qualitative and quantitative methods.
Genetic parameters of meat quality, carcass composition and growth traits in commercial swine.

Swine industry breeding goals are mostly directed towards meat quality and carcass traits due to their high economic value. Yet, studies on meat quality and carcass traits including both phenotypic and genotypic information remain limited, particularly in commercial crossbred swine. The objectives of this study were: to estimate the heritabilities for different carcass composition traits and meat quality traits; and to estimate the genetic and phenotypic correlations between meat quality, carcass composition and growth traits in two large commercial swine populations: The Maschhoffs LLC (TML) and Smithfield Premium Genetics (SPG), using genotypes and phenotypes data. The TML dataset consists of 1,254 crossbred pigs genotyped with 60K SNP chip and phenotyped for meat quality, carcass composition and growth traits. The SPG population included over 35,000 crossbred pigs phenotyped for meat quality, carcass composition and growth traits. For TML datasets, the model included fixed effects of dam line, contemporary group (CG), gender, as well as random additive genetic effect and pen nested within CG. For the SPG dataset, fixed effects included parity, gender and CG, as well as random additive genetic effect and harvest group. Analyses were conducted using BLUPF90 suite of programs. Univariate and bivariate analyses were implemented to estimate heritabilities and correlations among traits. Primal yield traits were uniquely created in this study. Heritabilities [high posterior density interval] of meat quality traits ranged from 0.08[0.03,0.16] for pH and 0.08[0.03,0.1] for Minolta b* to 0.27[0.22,0.32] for marbling score, except intramuscular fat with the highest estimate of 0.52[0.40, 0.62]. Heritabilities of primal yield traits were higher than that of primal weight traits and ranged from 0.17[0.13, 0.25] for butt yield to 0.45[0.36, 0.55] for ham yield. The genetic correlations of meat quality and carcass composition traits with growth traits ranged from moderate to high in both directions. High genetic correlations were observed for male and female for all traits except pH. The genetic parameter estimates of this study indicate that a multi-trait approach should be considered for selection programs aimed at meat quality and carcass composition in commercial swine populations.
Ontology-based privacy protection for smartphone: A firewall implementation

During the past few years, smartphones have tended to replace feature phones in the user's pocket. They allow users to access various applications and services that aim at making their life easier. For that purpose, some applications access private data such as the user's location and do not always ask for the user's consent. Data collection is a growing issue for the user's privacy and the current protection mechanisms have proved to be inefficient [1]. In a previous work [2], we have proposed a real-time firewall that aims at preventing malicious applications from accessing private data on an Android smartphone. The firewall relies on an ontology that represents privacy protection concepts and the digital identity of a smartphone's user. Our firewall is based on a set of privacy rules and applies description logic reasoning to decide whether access to private data by a given application may be authorized or not. However, the use of semantic technologies can be an issue when implemented on devices that offer low computation capabilities. The contribution of the present work is an optimized implementation of the firewall that offers good performances as well as good privacy protection. This paper is organized as follows: the first part is a description of our optimized implementation; the second one details the criteria we have chosen to evaluate the implementation and is followed by the results of our tests on this implementation. Finally, the conclusion of the paper highlights the interest of the approach and proposes future improvements.
Real-bogus classification for the Zwicky Transient Facility using deep learning

Efficient automated detection of flux-transient, re-occurring flux-variable, and moving objects is increasingly important for large-scale astronomical surveys. We present braai, a convolutional-neural-network, deep-learning real/bogus classifier designed to separate genuine astrophysical events and objects from false positive, or bogus, detections in the data of the Zwicky Transient Facility (ZTF), a new robotic time-domain survey currently in operation at the Palomar Observatory in California, USA. Braai demonstrates a state-of-the-art performance as quantified by its low false negative and false positive rates. We describe the open-source software tools used internally at Caltech to archive and access ZTF’s alerts and light curves (kowalski ), and to label the data (zwickyverse). We also report the initial results of the classifier deployment on the Edge Tensor Processing Units that show comparable performance in terms of accuracy, but in a much more (cost-) efficient manner, which has significant implications for current and future surveys.
Adventures in Abstraction: Reachability in Hierarchical Drawings

We present algorithms and experiments for the visualization of directed graphs that focus on displaying their reachability information. Our algorithms are based on the concepts of the path and channel decomposition as proposed in the framework presented in GD 2018 (pp. 579-592) and focus on showing the existence of paths clearly. In this paper we customize these concepts and present experimental results that clearly show the interplay between bends, crossings and clarity. Additionally, our algorithms have direct applications to the important problem of showing and storing transitivity information of very large graphs and databases. Only a subset of the edges is drawn, thus reducing the visual complexity of the resulting drawing, and the memory requirements for storing the transitivity information. Our algorithms require almost linear time, $O(kn+m)$, where $k$ is the number of paths/channels, $n$ and $m$ is the number of vertices and edges, respectively. They produce progressively more abstract drawings of the input graph. No dummy vertices are introduced and the vertices of each path/channel are vertically aligned.
Phase transition unbiased estimation in high~dimensional settings

An important challenge in statistical analysis concerns the control of the finite sample bias of estimators. This problem is magnified in high dimensional settings where the number of variables p diverge with the sample size n. However, it is difficult to establish whether an estimator θˆ of θ0 is unbiased and the asymptotic order of E[θˆ] − θ0 is commonly used instead. We introduce a new property to assess the bias, called phase transition unbiasedness, which is weaker than unbiasedness but stronger than asymptotic results. An estimator satisfying this property is such that E[θˆ] − θ0 2 = 0, for all n greater than a finite sample size n ∗ . We propose a phase transition unbiased estimator by matching an initial estimator computed on the sample and on simulated data. It is computed using an algorithm which is shown to converge exponentially fast. The initial estimator is not required to be consistent and thus may be conveniently chosen for computational efficiency or for other properties. We demonstrate the consistency and the limiting distribution of the estimator in high dimension. Finally, we develop new estimators for logistic regression models, with and without random effects, that enjoy additional properties such as robustness to data contamination and to the problem of separability.
Label switching problem in Bayesian analysis for gravitational wave astronomy

The label switching problem arises in the Bayesian analysis of models containing multiple indistinguishable parameters with arbitrary ordering. Any permutation of these parameters is equivalent, therefore models with many such parameters have extremely multi-modal posterior distributions. It is difficult to sample efficiently from such posteriors. This paper discusses a solution to this problem which involves carefully mapping the input parameter space to a high dimensional hypertriangle. It is demonstrated that this solution is efficient even for large numbers of parameters and can be easily applied alongside any stochastic sampling algorithm. This method is illustrated using two example problems from the field of gravitational wave astronomy.
On Laplace–Carleson embeddings, and $L^p$-mapping properties of the Fourier transform

We investigate so-called Laplace--Carleson embeddings for large exponents. In particular, we extend some results by Jacob, Partington, and Pott. We also discuss some related results for Sobolev- and Besov spaces, and mapping properties of the Fourier transform. These variants of the Hausdorff--Young theorem appear difficult to find in the literature. We conclude the paper with an example related to an open problem.
Learning high-dimensional additive models on the torus

In this paper we study the multivariate ANOVA decomposition for $1$-periodic functions on the torus. In particular we use the integral projection operator that leads to the classical ANOVA decomposition. Relationships between the Fourier coefficients of the function and its ANOVA terms lead to special frequency index sets and give an understanding of the decomposition working in the frequency domain. Moreover, we consider the truncated ANOVA decomposition and provide error bounds for approximation in $\mathrm{L}_\infty$ and $\mathrm{L}_2$. We present an approximation method based on the truncated decomposition with regard to a superposition dimension $d_s$.