Miracle-dz/UniFAR-FTUs huggingface dataset:
The Disorazole Z Family of Highly Potent Anticancer Natural Products from Sorangium cellulosum: Structure, Bioactivity, Biosynthesis, and Heterologous Expression

Microbial secondary metabolites are a prolific reservoir for the discovery of bioactive compounds, which prove to be privileged scaffolds for the development of new drugs such as antibacterial and small-molecule anticancer drugs. Consequently, the continuous discovery of novel bioactive natural products is of great importance for pharmaceutical research. ABSTRACT Myxobacteria serve as a treasure trove of secondary metabolites. During our ongoing search for bioactive natural products, a novel subclass of disorazoles termed disorazole Z was discovered. Ten disorazole Z family members were purified from a large-scale fermentation of the myxobacterium Sorangium cellulosum So ce1875 and characterized by electrospray ionization–high-resolution mass spectrometry (ESI-HRMS), X-ray, nuclear magnetic resonance (NMR), and Mosher ester analysis. Disorazole Z compounds are characterized by the lack of one polyketide extension cycle, resulting in a shortened monomer in comparison to disorazole A, which finally forms a dimer in the bis-lactone core structure. In addition, an unprecedented modification of a geminal dimethyl group takes place to form a carboxylic acid methyl ester. The main component disorazole Z1 shows comparable activity in effectively killing cancer cells to disorazole A1 via binding to tubulin, which we show induces microtubule depolymerization, endoplasmic reticulum delocalization, and eventually apoptosis. The disorazole Z biosynthetic gene cluster (BGC) was identified and characterized from the alternative producer S. cellulosum So ce427 and compared to the known disorazole A BGC, followed by heterologous expression in the host Myxococcus xanthus DK1622. Pathway engineering by promoter substitution and gene deletion paves the way for detailed biosynthesis studies and efficient heterologous production of disorazole Z congeners. IMPORTANCE Microbial secondary metabolites are a prolific reservoir for the discovery of bioactive compounds, which prove to be privileged scaffolds for the development of new drugs such as antibacterial and small-molecule anticancer drugs. Consequently, the continuous discovery of novel bioactive natural products is of great importance for pharmaceutical research. Myxobacteria, especially Sorangium spp. , which are known for their large genomes with yet-underexploited biosynthetic potential, are proficient producers of such secondary metabolites. From the fermentation broth of Sorangium cellulosum strain So ce1875, we isolated and characterized a family of natural products named disorazole Z, which showed potent anticancer activity. Further, we report on the biosynthesis and heterologous production of disorazole Z. These results can be stepping stones toward pharmaceutical development of the disorazole family of anticancer natural products for (pre)clinical studies.
The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks

Toxicological information as needed for risk assessments of chemical compounds is often sparse. Unfortunately, gathering new toxicological information experimentally often involves animal testing. Simulated alternatives, e.g., quantitative structure–activity relationship (QSAR) models, are preferred to infer the toxicity of new compounds. Aquatic toxicity data collections consist of many related tasks—each predicting the toxicity of new compounds on a given species. Since many of these tasks are inherently low-resource, i.e., involve few associated compounds, this is challenging. Meta-learning is a subfield of artificial intelligence that can lead to more accurate models by enabling the utilization of information across tasks. In our work, we benchmark various state-of-the-art meta-learning techniques for building QSAR models, focusing on knowledge sharing between species. Specifically, we employ and compare transformational machine learning, model-agnostic meta-learning, fine-tuning, and multi-task models. Our experiments show that established knowledge-sharing techniques outperform single-task approaches. We recommend the use of multi-task random forest models for aquatic toxicity modeling, which matched or exceeded the performance of other approaches and robustly produced good results in the low-resource settings we studied. This model functions on a species level, predicting toxicity for multiple species across various phyla, with flexible exposure duration and on a large chemical applicability domain.
Monitoring α-synuclein ubiquitination dynamics reveals key endosomal effectors mediating its trafficking and degradation

While defective α-synuclein homeostasis is central to Parkinson’s pathogenesis, fundamental questions about its degradation remain unresolved. We have developed a bimolecular fluorescence complementation assay in living cells to monitor de novo ubiquitination of α-synuclein and identified lysine residues 45, 58, and 60 as critical ubiquitination sites for its degradation. This is mediated by NBR1 binding and entry into endosomes in a process that involves ESCRT I-III for subsequent lysosomal degradation. Autophagy or the autophagic chaperone Hsc70 is dispensable for this pathway. Antibodies against diglycine-modified α-synuclein peptides confirmed that endogenous α-synuclein is similarly ubiquitinated in the brain and targeted to lysosomes in primary and iPSC-derived neurons. Ubiquitinated α-synuclein was detected in Lewy bodies and cellular models of aggregation, suggesting that it may be entrapped with endo/lysosomes in inclusions. Our data elucidate the intracellular trafficking of de novo ubiquitinated α-synuclein and provide tools for investigating the rapidly turned-over fraction of this disease-causing protein.
Discussion: Should a Working Model Actually Work?

Defining precisely the term working model is impossible. But here is a working definition: a working model is a model we adopt for particular purposes with the knowledge that it may be flawed in some other aspects. All models are working models then, following George Box’s mantra that all models are wrong, but some are useful. Unfortunately, the complement of ‘useful’ in this mantra is not merely ‘useless’, because all models are wrong, but some are harmful. It is of course impossible either to define precisely what Box meant by ‘useful’ or what amounts to as ‘harmful’ without contextual information; one person’s poison can easily be another person’s medicine. Furthermore, one could–and many do–question what constitutes a true model, when the very concept of reality has been intensely debated by physicists, let alone philosophers; see Peat (2002) for a fascinating account of this debate. But I surmise that most of us would agree that, as a practical guideline, a model is harmful if it routinely leads to misleading results for which the model is designed, especially when we have both empirical verification and theoretical understanding of the harm. This overdue paper by Yang, Wang and He (YWH) provides exactly that for the asymmetric Laplace (AL) likelihood for Bayesian quantile regression. The paper is overdue because much harm has already been done, judging from the list of literature cited in YWH. I am therefore pleased and puzzled as a discussant: pleased because finally the harm can be stopped (hopefully), but puzzled by the fact that this seriously flawed model has been on the Bayesian track for so long, when there were clear warning signs from the moment it was built (and there were no Volkswagen technicians involved, as far as I can tell), as I’ll discuss in Section 2. Tongue in cheek or not, a working model needs to have a reasonable chance to work, that is, to be a reasonable depiction of the data being analysed. This seemingly tautological requirement actually has some wisdom teeth in it (which can be taken out once the wisdom is retained). It implies that a working model should not be determined, solely or even largely, by particular analysis interests, mathematical simplicity or beauty, or estimation or computational procedures. Life would be too easy if nature would generate data according to our particular interests or our analysis or computational ability.
Three-dimensional fluid simulation of an AC-PDP cell

Using a three-dimensional fluid model, we present top-views of AC-PDP cells with three different electrode shapes-a conventional electrode cell, a T-shaped electrode cell, and an asymmetric electrode cell. Figures include the contours of consumed power and photon generation power that are time averaged over the half period and space averaged over the direction that is not shown.
RENEWAL PROCESS FOR THE PH/G/1 QUEUE

We consider a new embedded Markov chain for the PH/G/1 queue by recording the queue length, the phase of the arrival process and the number of services completed during the current busy period at the successive departure epochs. Algorithmically tractable matrix formulas are obtained which permit the analysis of the fluctuations of the queue length and waiting times during a typical busy cycle. These are useful in the computation of certain profile curves arising in the statistical analysis of queues. In addition, informative expressions for the mean waiting times in the stable GI/G/1 queue and a simple new algorithm to evaluate the waiting-time distributions for the
Minority Oversampling Technique for Imbalanced Data

Today, solving imbalanced problems is difficult task as it contains an unequal distribution of data samples among different classes and poses a challenge to any classifier becoming hard to learn the minority class samples. Unequal distribution of data samples among many classes confuses supervised learning based classifier as it makes challenging to learn minority class samples.Generating synthetic minority class samples tries to balance the sample distribution between minority and majority classes. To handle imbalanced learning problem, proposed work finds minority samples which are difficult to learn and computes Euclidean distance between nearest majority class samples. Using clustering approach and weighted minority class samples it generates synthetic samples for oversampling purpose. Proposed work will evaluate this approach on real & artificial datasets.
IIS-Mine: A new efficient method for mining frequent itemsets

A new approach to mine all frequent itemsets from a transaction database is proposed. The main features of this paper are as follows: (1) the proposed algorithm performs database scanning only once to construct a data structure called an inverted index structure (IIS); (2) the change in the minimum support threshold is not affected by this structure, and as a result, a rescan of the database is not required; and (3) the proposed mining algorithm, IIS-Mine, uses an efficient property of an extendable itemset, which reduces the recursiveness of mining steps without generating candidate itemsets, allowing frequent itemsets to be found quickly. We have provided definitions, examples, and a theorem, the completeness and correctness of which is shown by mathematical proof. We present experiments in which the run time, memory consumption and scalability are tested in comparison with a frequent-pattern (FP) growth algorithm when the minimum support threshold is varied. Both algorithms are evaluated by applying them to synthetics and real-world datasets. The experimental results demonstrate that IIS-Mine provides better performance than FP-growth in terms of run time and space consumption and is effective when used on dense datasets.
Effectiveness of exergaming in reducing cancer-related fatigue among children with acute lymphoblastic leukemia: a randomized controlled trial

Abstract Background Little is known about the effectiveness of the newly emerging technology of exergaming in reducing Cancer Related Fatigue (CRF). Objectives The study’s primary aim was to examine the effectiveness of exergaming in reducing CRF; the secondary aims were to improve functional capacity/endurance and promote physical activity (PA) among children with acute lymphoblastic leukemia (ALL). Methods In this Randomized Controlled Trial (RCT), 45 children aged 6–14 years were randomly assigned into group-I, n = 22, and group II, n = 23. Group-I played exergaming of moderate intensity for 60 min, twice a week for three weeks. Group II was given an instructional session regarding the benefits of PA with advice to practice PA for 60 min twice a week. CRF, functional capacity/endurance, and PA were measured using the pediatric quality of life multidimensional fatigue scale (Ped-QLMFS), six-minute walk test (6-MWT), and Godin-Shepard Leisure Time Physical Activity Questionnaire (QSLTPAQ) respectively. All measurements were taken thrice; in the first, third, and fifth weeks of intervention. Results Group-I demonstrated a significant reduction of CRF, and a significant increase of functional capacity/endurance compared to group-II over the five weeks study period. The effect of time × intervention interaction was significant. Based on Cohen’s guidelines, CRF and functional capacity/endurance had large effect sizes (η2 = 0.41, p = .00) and (η2 = 0.27, p = .00) respectively. Conclusion The protocol of exergaming used in this RCT effectively reduces CRF and promotes functional capacity/endurance and PA in children with ALL undergoing chemotherapy. It may provide an alternative treatment modality to decrease the healthcare load. Key messages Cancer-related fatigue (CRF) is described as physical exhaustion, sleep disturbance, emotional distress, and cognitive dysfunction. Exergaming reduces CRF and promotes functional capacity/endurance and physical activity in children with acute lymphoblastic leukemia undergoing chemotherapy. Exergaming may provide an alternative treatment modality to decrease the healthcare load.
A Study of the Planar Circular Restricted Three Body Problem and the Vanishing Twist

Introduction Since the 17th century, the N-body problem has held the attention of generations of astronomers and mathematicians. The problem is simple: given a collection of N celestial bodies (be they planets, asteroids, stars, black holes) interacting with each other through gravitational forces, what will their trajectories be? For N = 2, the problem has been solved for centuries; for N ≥ 3, the problem still has no solution in any meaningful sense. As the theory and vocabulary of dynamics have evolved, so too has the analysis of the problem, and indeed the study of the problem has often directly led to the development of new concepts and ideas in dynamics. In this thesis, we consider the planar circular restricted three body problem, a specific case of the N-body problem for N = 3. The primary goal is to develop a fast, user-friendly program which can quickly and reliably calculate trajectories from user input. The program will also calculate Poincaré maps, which will be used to analyse the system for various parameter values. We then hope to verify the existence of a particular bifurcation called the twistless bifurcation for orbits near the Lagrangian points. The twistless bifurcation was found for a general system by Dullin, Meiss and Sterling [12], and it is expected that the planar circular restricted three body problem will exhibit the same behaviour. We begin with a discussion of the history of the problem in Chapter 2, using Barrow-Green [5], Valtonen & Karttunen [10] and James [23] as our primary sources. This background serves a dual purpose, neatly introducing many of the theoretical concepts used to analyse the problem. We discuss several " particular solutions " which illustrate useful ideas and dynamics, and give a summary of the theory of Lagrangian and Hamiltonian mechanics. In Chapter 3, the solution to the two body problem is presented, and the dynamics for the three body problem are derived. Following Koon, Lo, Marsden & Ross [22], we take a Hamiltonian approach to the problem. Other physical considerations such as the Hill region and Lagrangian points are introduced. Also defined are the Poincaré map and extended phase space. Chapter 4 deals with the biggest obstacle in any attempt to integrate trajec-tories of the N-body problem, regularising collision orbits. Although an elegant split-step integrator can be found for the problem, regularising transforms are still required. The discussion of these transformations follows from …
ON NONPARAMETRIC INTERVAL ESTIMATION OF A REGRESSION FUNCTION BASED ON THE RESAMPLING

A nonparametric regression model E(Y) = m(x) is considered where Y is a dependent variable, x is d – a dimensional vector of independent variables (regressors) and m is an unknown function. A sequence of independent observations (Yi, xi,) i = 1, 2, . , n, is available. Our aim is to construct an upper confidence bound for m(x) that corresponds to probability γ. The resampling approach is used. The suggested method allows calculating true cover probability.
Free Particle to Complex KdV breathers through Isospectral Deformation

The free particle in quantum mechanics in real space is endowed with supersymmetry, which enables a natural extension to complex spectra with a built-in parity (P) and time reversal (T) symmetry. It also explains the origin of unbroken and broken phases of the PT-symmetry and their relationship with the real and complex eigenvalues respectively, the latter further displaying zero-width resonances. This is possible as the extension of the eigenvalue problem to the complex plane enables the incorporation of bound and decaying states in the enlarged Hilbert space. The inherent freedom of modification of the potential without changing the spectra in supersymmetry naturally explains the connection of complex breather solutions of KdV with PT-symmetry and the free particle on the complex plane. Further, non-trivial zero-width resonances in the broken PT phase mandate a generalization that is directly connected to the sl(2, R) potential algebra.
Characterizing Bugs in Python and R Data Analytics Programs

R and Python are among the most popular languages used in many critical data analytics tasks. However, we still do not fully understand the capabilities of these two languages w.r. t. bugs encountered in data analytics tasks. What type of bugs are common? What are the main root causes? What is the relation between bugs and root causes? How to mitigate these bugs? We present a comprehensive study of 5,068 Stack Overflow posts, 1,800 bug fix commits from GitHub repositories, and several GitHub issues of the most used libraries to understand bugs in R and Python. Our key findings include: while both R and Python have bugs due to inexperience with data analysis, Python see significantly larger data preprocessing bugs compared to R. Developers experience significantly more data flow bugs in R because intermediate results are often implicit. We also found changes and bugs in packages and libraries cause more bugs in R compared to Python while package or library misselection and conflicts cause more bugs in Python than R. While R has a slightly higher readability barrier for data analysts, the statistical power of R leads to a less number of bad performance bugs. In terms of data visualization, R packages have significantly more bugs than Python libraries. We also identified a strong correlation between comparable packages in R and Python despite their linguistic and methodological differences. Lastly, we contribute a large dataset of manually verified R and Python bugs.
Omnipotent dark energy: A phenomenological answer to the Hubble tension

This paper introduces the class of omnipotent dark energy (DE) models characterized by nonmonotonic energy densities that are capable of attaining negative values with corresponding equation of state parameters featuring phantom divide line (PDL) crossings and singularities. These nontrivial features are phenomenologically motivated by findings of previous studies that reconstruct cosmological functions from observations, and the success of extensions of $\Lambda$CDM, whose actual or effective DE density is omnipotent, in alleviating the observational discordance within $\Lambda$CDM. As an example, we focus on one embodiment of omnipotent DE, viz., the DE parametrization introduced in Di Valentino et al. [Dark energy with phantom crossing and the H0 tension, Entropy 23, 404 (2021)] (DMS20). By updating and extending the datasets used in the original paper where it was introduced, we confirm the effectiveness of DMS20 in alleviating the observational discrepancies. Additionally, we uncover that its negative DE density feature, importance of which was not previously investigated, plays a crucial role in alleviating the tensions, along with the PDL crossing feature that the parametrization presupposes. In particular, we find that there is a positive correlation between the $H_0$ parameter and the scale ($a_p$) at which DE density transitions from negative to positive, in agreement with previous studies that incorporate this transition feature. For our full dataset, the model yields $H_0=70.05 \pm 0.64$ (68% CL) relaxing the $H_0$ tension with a preference of crossing to negative DE densities ($a_p>0$ at 99% CL), along with the constraint $a_m=0.922^{+0.041}_{-0.035}$ on the scale of the presupposed PDL crossing.
Coherent scattering from coupled two level systems

We study the resonance fluorescence properties of an optically active spin 1/2 system, elucidating the effects of a magnetic field on the coherence of the scattered light. We derive a master equation model for this system that reproduces the results of a two level system (TLS) while also being applicable to a spin system with ground state coupling. This model is then solved analytically in the weak excitation regime. The inclusion of spin dynamics in our model alters the properties of the coherently scattered light at a fundamental level. For a TLS the coherence properties are known to be determined by the input laser. We show that spin scattered light inherits the coherence properties of the spin. This mapping allows us to measure spin dynamics and coherence time through direct measurement of the scattered fields. Furthermore, we show the ability to resolve sub-natural linewidth zeeman splittings. Along with representing an invaluable tool for spin spectroscopy understanding the coherence properties of the spin-scattered field will be vital for spin-photon based quantum technologies.
Bayesian Non-linear Latent Variable Modeling via Random Fourier Features

The Gaussian process latent variable model (GPLVM) is a popular probabilistic method used for nonlinear dimension reduction, matrix factorization, and state-space modeling. Inference for GPLVMs is computationally tractable only when the data likelihood is Gaussian. Moreover, inference for GPLVMs has typically been restricted to obtaining maximum a posteriori point estimates, which can lead to overfitting, or variational approximations, which mischaracterize the posterior uncertainty. Here, we present a method to perform Markov chain Monte Carlo (MCMC) inference for generalized Bayesian nonlinear latent variable modeling. The crucial insight necessary to generalize GPLVMs to arbitrary observation models is that we approximate the kernel function in the Gaussian process mappings with random Fourier features; this allows us to compute the gradient of the posterior in closed form with respect to the latent variables. We show that we can generalize GPLVMs to non-Gaussian observations, such as Poisson, negative binomial, and multinomial distributions, using our random feature latent variable model (RFLVM). Our generalized RFLVMs perform on par with state-of-the-art latent variable models on a wide range of applications, including motion capture, images, and text data for the purpose of estimating the latent structure and imputing the missing data of these complex data sets.