Miracle-dz/UniFAR-FTUs huggingface dataset:
Cancer Therapy : Preclinical Therapeutic Implications for the Induced Levels of Chk 1 in Myc-Expressing Cancer Cells

Purpose: The transcription factor c-Myc (or "Myc") is a master regulator of pathways driving cell growth and proliferation. MYC is deregulated in many human cancers, making its downstream target genes attractive candidates for drug development. We report the unexpected finding that B-cell lymphomas from mice and patients exhibit a striking correlation between high levels ofMyc and checkpoint kinase 1 (Chk1). Experimental Design: By in vitro cell biology studies as well as preclinical studies using a genetically engineered mouse model, we evaluated the role of Chk1 in Myc-overexpressing cells. Results:We show that Myc indirectly induces Chek1 transcript and protein expression, independently of DNA damage response proteins such as ATM and p53. Importantly, we show that inhibition of Chk1, by either RNA interference or a novel highly selective small molecule inhibitor, results in caspasedependent apoptosis that affects Myc-overexpressing cells in both in vitro and in vivo mouse models of
Biasing Restricted Boltzmann Machines to Manipulate Latent Selectivity and Sparsity

This paper proposes a modification to the restricted Boltzmann machine (RBM) learning algorithm to incorporate inductive biases. These latent activation biases are ideal solutions of the latent activity and may be designed either by modeling neural phenomenon or inductive principles of the task. In this paper, we design activation biases for sparseness and selectivity based on the activation distributions of biological neurons. With this model, one can manipulate the selectivity of individual hidden units and the sparsity of population codes. The biased RBM yields a filter bank of Gabor-like filters when trained on natural images, while modeling handwritten digits results in filters with stroke-like features. We quantitatively verify that the latent representations assume the properties of the activation biases. We further demonstrate that RBMs biased with selectivity and sparsity can significantly outperform standard RBMs for discriminative tasks.
Discovering Communities of Similar R&D Projects

Datasets about research projects contain knowledge that is valuable for several types of subjects working in the R&D field â including innovative companies, research institutes and universities even individual researchers or research teams, as well as funding providers. The main goal of this paper is to introduce a software tool based on a reusable methodology that allows us to deal with similarity of projects in order to group them and provide a deeper insight into a structure of considered set of projects in a visual way. In our approach we use several concepts developed in social network analysis.
Cloning of the Gene Cluster Responsible for Biosynthesis of KS-505a (Longestin), a Unique Tetraterpenoid

KS-505a (longestin), produced by Streptomyces argenteolus, has a unique structure that consists of a tetraterpene (C40) skeleton, to which a 2-O-methylglucuronic acid and an o-succinyl benzoate moiety are attached. It is a novel inhibitor of calmodulin-dependent cyclic-nucleotide phosphodiesterase, which is representative of a potent anti-amnesia drug. As a first step to understanding the biosynthetic machinery of this unique and pharmaceutically useful compound, we cloned a KS505a biosynthetic gene cluster. First we searched for a gene encoding octaprenyl diphosphates, which yielded a C40 precursor by PCR, and four candidate genes were obtained. Among these, one was confirmed to have the expected enzyme activity by recombinant enzyme assay. On the basis of an analysis of the flanking regions of the gene, a putative KS-505a biosynthetic gene cluster consisting of 24 ORFs was judged perhaps to be present on a 28-kb DNA fragment. A gene disruption experiment was also employed to confirm that the cluster indeed participated in KS-505a biosynthesis. This is believed to be the first report detailing the gene cluster of a cyclized tetraterpenoid.
Reconstitution of Physiological Microtubule Dynamics Using Purified Components

Microtubules are dynamically unstable polymers that interconvert stochastically between polymerization and depolymerization. Compared with microtubules assembled from purified tubulin, microtubules in a physiological environment polymerize faster and transit more frequently between polymerization and depolymerization. These dynamic properties are essential for the functions of the microtubule cytoskeleton during diverse cellular processes. Here, we have reconstituted the essential features of physiological microtubule dynamics by mixing three purified components: tubulin; a microtubule-stabilizing protein, XMAP215; and a microtubule-destabilizing kinesin, XKCM1. This represents an essential first step in the reconstitution of complex microtubule dynamics–dependent processes, such as chromosome segregation, from purified components.
On solving inverse problems for electric fish like robots

This paper relates preliminary results concerning the solution of inverse problems arising in electric sense based navigation. This sense is used by electric fishes to move in dark waters using the electric current measurements perceived by the epidermal sensors as these are affected by the presence of obstacles. The latter change the resulting induced measures by instantaneously disturbing the fish self-produced electric field. The approach lies on a recently proposed graphical signature based classification methodology to overcome the computational burden associated to an explicit inversion of the mathematical equations. A preliminary validation of the proposed solution is obtained using a dedicated experimental setting.
Network anomaly detection based on TCM-KNN algorithm

Intrusion detection is a critical component of secure information systems. Network anomaly detection has been an active and difficult research topic in the field of Intrusion Detection for many years. However, it still has some problems unresolved. They include high false alarm rate, difficulties in obtaining exactly clean data for the modeling of normal patterns and the deterioration of detection rate because of some "noisy" data in the training set. In this paper, we propose a novel network anomaly detection method based on improved TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) machine learning algorithm. A series of experimental results on the well-known KDD Cup 1999 dataset demonstrate it can effectively detect anomalies with high true positive rate, low false positive rate and high confidence than the state-of-the-art anomaly detection methods. In addition, even interfered by "noisy" data (unclean data), the proposed method is robust and effective. Moreover, it still retains good detection performance after employing feature selection aiming at avoiding the "curse of dimensionality".
A Reference Architecture for Natural Language Generation Systems

We present the RAGS (Reference Architecture for Generation Systems) framework: a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.
Projection Design for Statistical Compressive Sensing: A Tight Frame Based Approach

In this paper, we develop a framework to design sensing matrices for compressive sensing applications that lead to good mean squared error (MSE) performance subject to sensing cost constraints. By capitalizing on the MSE of the oracle estimator, whose performance has been shown to act as a benchmark to the performance of standard sparse recovery algorithms, we use the fact that a Parseval tight frame is the closest design - in the Frobenius norm sense - to the solution of a convex relaxation of the optimization problem that relates to the minimization of the MSE of the oracleestimator with respect to the equivalent sensing matrix, subject to sensing energy constraints. Based on this result, we then propose two sensing matrix designs that exhibit two key properties: the designs are closed form rather than iterative; the designs exhibit superior performance in relation to other designs in the literature, which is revealed by our numerical investigation in various scenarios with different sparse recovery algorithms including basis pursuit de-noise (BPDN), the Dantzig selector and orthogonal matching pursuit (OMP).
Similarity and categorization

Introduction: similarity and categorization The role of similarity in natural categorization Induction and inherent similarity Categorization by simplicity: a minimum description length approach to unsupervised clustering Categorization versus similarity: the case of container names Dissociation between categorization and similarity judgement: differential effect of causal status on feature weights Structural alignment, similarity, and the internal structure of category representations Issues in case-based reasoning Background knowledge and models of categorization Dynamic similarity: a processing perspective on similarity The time course of perceptual categorization Interactions between taxonomic knowledge, categorization, and perception Conclusion: mere similarity?
Exploring the function of protein kinases in schistosomes: perspectives from the laboratory and from comparative genomics

Eukaryotic protein kinases are well conserved through evolution. The genome of Schistosoma mansoni, which causes intestinal schistosomiasis, encodes over 250 putative protein kinases with all of the main eukaryotic groups represented. However, unraveling functional roles for these kinases is a considerable endeavor, particularly as protein kinases regulate multiple and sometimes overlapping cell and tissue functions in organisms. In this article, elucidating protein kinase signal transduction and function in schistosomes is considered from the perspective of the state-of-the-art methodologies used and comparative organismal biology, with a focus on current advances and future directions. Using the free-living nematode Caenorhabditis elegans as a comparator we predict roles for various schistosome protein kinases in processes vital for host invasion and successful parasitism such as sensory behavior, growth and development. It is anticipated that the characterization of schistosome protein kinases in the context of parasite function will catalyze cutting edge research into host-parasite interactions and will reveal new targets for developing drug interventions against human schistosomiasis.
PENGEMBANGAN MODUL PEMBELAJARAN KEWIRAUSAHAAN MODEL STUDENT COMPANY DI SMK NEGERI 1 GODEAN

Penelitian ini bertujuan untuk mengembangkan modul kewirausahaan model student company yang layak digunakan di SMK. Penelitian ini merupakan penelitian dan pengembangan. Hasil penelitian adalah sebagai berikut. (1) Modul pembelajaran kewirausahaan model student company layak digunakan. (2) Menurut siswa, modul yang dikembangkan berkualitas sangat baik, serta jika dilihat dari nilai skewnes distribusi data adalah normal. (3) Penerapan modul dalam proses pembelajaran secara umun dapat terlaksana. (4) Peningkatan pemahaman siswa dengan menggunakan modul yang dikembangkan berkategori sedang dengan rerata Uji-t sebesar 80,03 dan selisih nilai (effect size) 13,97 dengan persentase ketuntasan siswa sebesar 95%. Pembelajaran menggunakan modul juga mampu melatih kemandirian siswa dalam belajar dan mampu menumbuhkan kemampuan dalam bekerjasama. THE DEVELOPMENT OF A STUDENT COMPANY MODEL OF ENTREPRENEURIAL LEARNING MODULE AT SMK NEGERI 1 GODEAN Abstract This study aimed to develop a student company model of entrepreneurial learning module which was appropriate for SMK. This study was research and development. The results of the study were as follows. (1) The student company model of entrepreneurship learning module was appropriate for use. (2) According to the students, the developed module was excellent and the skewness of the data distribution was normal. (3) Generally, the implementation of the module in the learning process was succesful. (4) The increase of the students‟ understanding by using the developed module was in the fair category with an average t-test score of 80,3 and the difference in value (effect size) of 13,97 with students‟ mastery percentage of 95%. The learning using the module was also able to train the students' autonomy in learning and able to improve the ability to cooperate.
Interferometric array assessment: Investigating UV coverage and imaging

Image quality assessment technology is introduced to assess interferometric array. Including existing assessments of Interferometric array, four categories of array assessment methods are concluded: assessing UV coverage directly, assessing UV coverage related to observing objects, assessing imaging quality related to observing objects and assessing imaging quality directly. A specific method in each category is proposed. All methods are used to design or optimize interferometric arrays, especially the early-stage study of Solar Low Frequency Array.
Use of Probiotics to Control Aflatoxin Production in Peanut Grains

Probiotic microorganisms (Saccharomyces cerevisiae var. boulardii, S. cerevisiae UFMG 905, and Lactobacillus delbrueckii UFV H2b20) were evaluated as biological control agents to reduce aflatoxin and spore production by Aspergillus parasiticus IMI 242695 in peanut. Suspensions containing the probiotics alone or in combinations were tested by sprinkling on the grains followed by incubation for seven days at 25°C. All probiotic microorganisms, in live and inactivated forms, significantly reduced A. parasiticus sporulation, but the best results were obtained with live cells. The presence of probiotics also altered the color of A. parasiticus colonies but not the spore morphology. Reduction in aflatoxin production of 72.8 and 65.8% was observed for S. boulardii and S. cerevisiae, respectively, when inoculated alone. When inoculated in pairs, all probiotic combinations reduced significantly aflatoxin production, and the best reduction was obtained with S. boulardii plus L. delbrueckii (96.1%) followed by S. boulardii plus S. cerevisiae and L. delbrueckii plus S. cerevisiae (71.1 and 66.7%, resp.). All probiotics remained viable in high numbers on the grains even after 300 days. The results of the present study suggest a different use of probiotics as an alternative treatment to prevent aflatoxin production in peanut grains.
Purification and characterization of the Sin Nombre virus nucleocapsid protein expressed in Escherichia coli.

Sin Nombre virus is a member of the Hantavirus genus, family Bunyaviridae, and is an etiologic agent of hantavirus pulmonary syndrome. The hantavirus nucleocapsid (N) protein plays an important role in the encapsidation and assembly of the viral negative-sense genomic RNA. The Sin Nombre N protein was expressed as a C-terminal hexahistidine fusion in Escherichia coli and initially purified by nickel-affinity chromatography. We developed methods to extract the soluble fraction and to solubilize the remainder of the N protein using denaturants. Maximal expression of protein from native purification was observed after a 1.5-h induction with IPTG (2.4 mg/L). The zwitterionic detergent Chaps did not enhance the yield of native purifications, but increased the yield of protein obtained from insoluble purifications. Both soluble and insoluble materials, purified by nickel-affinity chromatography, were also subjected to Hi Trap SP Sepharose fast-flow (FF) chromatography. Both soluble and insoluble proteins had a similar A(280) profile on the Sepharose FF column, and both suggested the presence of a nucleic acid contaminant. The apparent dissociation constant of the N protein, purified by nickel-affinity and SP Sepharose FF chromatography, and the 5' end of the viral S-segment genome were measured using a filter binding assay. The N protein-vRNA complex had an apparent dissociation constant of 140 nM.
BiLoRA: A Bi-level Optimization Framework for Overfitting-Resilient Low-Rank Adaptation of Large Pre-trained Models

Low-rank adaptation (LoRA) is a popular method for fine-tuning large-scale pre-trained models in downstream tasks by learning low-rank incremental matrices. Though LoRA and its variants effectively reduce the number of trainable parameters compared to full fine-tuning methods, they often overfit training data, resulting in sub-optimal generalization on test data. To address this problem, we introduce BiLoRA, an overfitting-alleviating fine-tuning approach based on bi-level optimization (BLO). BiLoRA employs pseudo singular value decomposition to parameterize low-rank incremental matrices and splits the training of pseudo singular vectors and values across two different subsets of training data. This division, embedded within separate levels of the BLO framework, mitigates the risk of overfitting to a single dataset. Tested on ten datasets covering natural language understanding and generation tasks and applied to various well-known large pre-trained models, BiLoRA significantly outperforms LoRA methods and other fine-tuning approaches, with similar amounts of trainable parameters.
Trusted Computing vs. Advanced Persistent Threats: Can a Defender Win This Game?

As both the number and the complexity of cyber attacks continuously increase, it is becoming evident that traditional security mechanisms have limited success in detecting sophisticated threats. Stuxnet, Duqu, Flame, Red October and, more recently, Miniduke, have troubled the security community due to their severe complexity and their ability to evade detection in some cases for several years, while exfiltrating gigabytes of data or sabotaging critical infrastructures. The significant technical and financial resources needed for orchestrating such complex attacks are a clear indication that perpetrators are well organized and, likely, working under a state umbrella. In this paper we perform a technical analysis of these advanced persistent threats, highlighting particular characteristics and identifying common patterns and techniques. We also focus on the issues that enabled the malware to evade detection from a wide range of security solutions and propose technical countermeasures for strengthening our defenses against similar threats.
OSVAuto: semi-automatic verifier for functional specifications of operating systems

We present the design and implementation of a tool for semi-automatic verification of functional specifications of operating system modules. Such verification tasks are traditionally done in interactive theorem provers, where the functionalities of the module are specified at abstract and concrete levels using data such as structures, algebraic datatypes, arrays, maps and so on. In this work, we provide encodings to SMT for these commonly occurring data types. This allows verification conditions to be reduced into a form suitable for SMT solvers. The use of SMT solvers combined with a tactic language allows semi-automatic verification of the specification. We apply the tool to verify functional specification for key parts of the uC-OS/II operating system, based on earlier work giving full verification of the system in Coq. We demonstrate a large reduction in the amount of human effort due to increased level of automation.
Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMs

Instruction-finetuned Large Language Models inherit clear political leanings that have been shown to influence downstream task performance. We expand this line of research beyond the two-party system in the US and audit Llama Chat in the context of EU politics in various settings to analyze the model's political knowledge and its ability to reason in context. We adapt, i.e., further fine-tune, Llama Chat on speeches of individual euro-parties from debates in the European Parliament to reevaluate its political leaning based on the EUandI questionnaire. Llama Chat shows considerable knowledge of national parties' positions and is capable of reasoning in context. The adapted, party-specific, models are substantially re-aligned towards respective positions which we see as a starting point for using chat-based LLMs as data-driven conversational engines to assist research in political science.
Modeling and Analysis of Stator Interturn Fault Location Effects on Induction Machines

Locating the stator interturn (SIT) fault on the motor winding structure adds an important feature in the fault diagnosis. This motivates to study the effects of SIT fault location on the induction machine. In this paper, a simple yet accurate stationary reference frame q-d-0 model of SIT-faulted induction machine, including the fault location parameter, is developed. The fundamental components of winding functions (WFs) are used to calculate the machine inductances for the proposed model. These inductances in stationary reference frame q-d-0 variables are rotor-position-independent expressions and functions of fault severity and fault location. The proposed model is as accurate as the multiple-coupled-circuit model for fault location study. It does not require the recalculation of machine inductances in each integration step to solve the model. The experimental validation of the model is presented. The steady-state analysis based on the proposed model indicates that the inclination of the current vector locus and the phase angle of the negative-sequence current phasor are the strong indicators of the SIT fault location.
A Transactional Model of Social Support, Self-Efficacy, and Physical Activity of Children in the Child and Adolescent Trial for Cardiovascular Health

Abstract Only a small percentage of the U.S. population engages in regular physical activity. Well-known barriers to physical activity exist, and behavioral models and technologies are needed to overcome them. In the Child and Adolescent Trial for Cardiovascular Health, social cognitive theory (SCT) was used to understand children's physical activity levels. Using the SCT model of reciprocal determinism, it was proposed that positive social support and/or self-efficacy for physical activity in third grade would predict moderate to vigorous physical activity (MVPA) in fifth grade, and that MVPA in fifth grade, in turn, would predict levels of positive social support and self-efficacy for physical activity in seventh grade. Linear regression analyses supported this model when used with a population of 2,698 students of three races/ethnicities (Caucasian, African-American, and Hispanic) in four diverse geographic sites in the United States. These results contribute support for attitudes influencing behavior, as well as behavior influencing attitudes, providing a wider range of options for interventions aimed at increasing the physical activity levels of children.