Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutrient copper signaling promotes protein turnover by allosteric activation of ubiquitin E2D conjugases

Nutrient copper supply is critical for cell growth and differentiation, and its disturbance is associated with major pathologies including cancer and neurodegeneration. Although increasing copper bioavailability in late Precambrian facilitated emergence of novel cuproproteins, their intricate regulation by this essential trace element remains largely cryptic. We found that subtle rises in cellular copper strikingly increase polyubiquitination and accelerate protein degradation within 30 minutes in numerous mammalian cell lines. We track this surprising observation to allostery induced in the UBE2D ubiquitin conjugase clade through a conserved CXXXC sub-femtomolar-affinity Cu+ binding motif. Thus, physiologic fluctuation in cytoplasmic Cu+ is coupled to the prompt degradation of UBE2D protein targets, including p53. In Drosophila harboring a larval-lethal knockdown of the nearly identical fly orthologue UbcD1, complementation with human UBE2D2 restored near-normal development, but mutation of its CXXXC Cu+ binding motif profoundly disrupted organogenesis. Nutrient Cu+ emerges as a trophic allosteric modulator of UBE2D activity through a structural motif whose evolution coincides with animal multicellularity. One Sentence Summary Modulation of nutrient copper impacts protein turnover and animal morphogenesis through conserved allostery of ubiquitin E2D conjugases. Hilights Nutrient copper supply is critical for cell growth and differentiation The E2D clade of ubiquitin conjugases contains a sub-femtomolar-affinity Cu+ binding motif Allosteric activation by Cu+ markedly accelerates protein polyubiquitination This sensor couples physiologic fluctuations in cytoplasmic Cu+ with the degradation rate of E2D targets, including p53 This metazoan signaling mechanism is critical for drosophila morphogenesis In Brief Conserved allostery of ubiquitin E2D conjugases links nutrient copper signaling to protein degradation and animal morphogenesis. Graphical Abstract
Rapid SARS-CoV-2 Adaptation to Available Cellular Proteases

Recently emerging SARS-CoV-2 variants B.1. 1.7 (alpha variant), B.1. 617.2 (delta variant), and B.1. 1.529 (omicron variant) harbor spike mutations and have been linked to increased virus pathogenesis. The emergence of these novel variants highlights coronavirus adaptation and evolution potential, despite the stable consensus genotype of clinical isolates. ABSTRACT Recent emergence of SARS-CoV-1 variants demonstrates the potential of this virus for targeted evolution, despite its overall genomic stability. Here we show the dynamics and the mechanisms behind the rapid adaptation of SARS-CoV-2 to growth in Vero E6 cells. The selective advantage for growth in Vero E6 cells is due to increased cleavage efficiency by cathepsins at the mutated S1/S2 site. S1/S2 site also constitutes a heparan sulfate (HS) binding motif that influenced virus growth in Vero E6 cells, but HS antagonist did not inhibit virus adaptation in these cells. The entry of Vero E6-adapted virus into human cells is defective because the mutated spike variants are poorly processed by furin or TMPRSS2. Minor subpopulation that lack the furin cleavage motif in the spike protein rapidly become dominant upon passaging through Vero E6 cells, but wild type sequences are maintained at low percentage in the virus swarm and mediate a rapid reverse adaptation if the virus is passaged again on TMPRSS2+ human cells. Our data show that the spike protein of SARS-CoV-2 can rapidly adapt itself to available proteases and argue for deep sequence surveillance to identify the emergence of novel variants. IMPORTANCE Recently emerging SARS-CoV-2 variants B.1. 1.7 (alpha variant), B.1. 617.2 (delta variant), and B.1. 1.529 (omicron variant) harbor spike mutations and have been linked to increased virus pathogenesis. The emergence of these novel variants highlights coronavirus adaptation and evolution potential, despite the stable consensus genotype of clinical isolates. We show that subdominant variants maintained in the virus population enable the virus to rapidly adapt to selection pressure. Although these adaptations lead to genotype change, the change is not absolute and genomes with original genotype are maintained in the virus swarm. Thus, our results imply that the relative stability of SARS-CoV-2 in numerous independent clinical isolates belies its potential for rapid adaptation to new conditions.
Multiple lineages of Streptomyces produce antimicrobials within passalid beetle galleries across eastern North America

Some insects form symbioses in which actinomycetes provide defense against pathogens by making antimicrobials. The range of chemical strategies employed across these associations, and how these strategies relate to insect lifestyle, remains underexplored. We assessed subsocial passalid beetles of the species Odontotaenius disjunctus, and their frass (fecal material) which is an important food resource within their galleries, as a model insect/actinomycete system. Through chemical and phylogenetic analyses, we found that O. disjunctus frass collected across eastern North America harbored multiple lineages of Streptomyces and diverse antimicrobials. Metabolites detected in frass displayed synergistic and antagonistic inhibition of a fungal entomopathogen, Metarhizium anisopliae, and multiple streptomycete isolates inhibited this pathogen when co-cultivated directly in frass. These findings support a model in which the lifestyle of O. disjunctus accommodates multiple Streptomyces lineages in their frass, resulting in a rich repertoire of antimicrobials that likely insulates their galleries against pathogenic invasion.
Near-Infrared Spectroscopy of Organic Substances

Abstract The near-infrared spectral region is generally defined as the wavelength range from 700 nm to about 2500 nm, although there is considerable variation in wavelength ranges of the different instrument types. The absorption bands in this region are due to overtones and combinations of the fundamental mid-IR molecular vibration bands. The energy transitions are between the ground state and the second or third excited vibrational states. Because higher energy transitions are successively less likely to occur, each overtone is successively weaker in intensity. Since the energy required to reach a second o r third level excited.
Investigation of heat treatment condition, ethylene glycol and deionized water effects on the phase structure of Ba0.6Sr0.4TiO3 nanoparticles produced by the sol-gel method

Ba0.6Sr0. 4TiO3 nanoparticles were synthesized by the sol-gel method. The effects of the heat treatment conditions, ethylene glycol (EG) and deionized water (DW) on their phase structure and formation of the undesirable barium carbonate (BaCO3) phase were investigated. It was found that increasing the temperature of the samples caused an increase in the peak intensities of perovskite structures, and a decrease in the intensities of the BaCO3 peaks. Moreover, omitting EG, the sol stabilizer, and water, the solvent, decreased the peak intensities of BaCO3. Decreasing the heating rate caused better burnout of the contained organic materials and, as a result reduced the peak intensity of BaCO3. But, it was impossible to remove the BaCO3 completely up to 850C. However, single-phase perovskite barium strontium titanate nanoparticles were obtained at 750C by using an acetic acid based precursor sol through decreasing the gel heating rate, and omitting EG and DW.
A Review of Literature on Handwritten Text Recognition

: Optical Character Recognition is used to recognize text in the documents and convert it into an editable machine-encoded format. An OCR system depends mainly on feature extraction, recognition and classification into appropriate labels. This paper describes segmentation, recognition and identification of handwritten documents using image processing and CRNN based architecture. Further, a decoding algorithm, Word Beam search (WBS) is presented that helps decode the features into label sequences with the highest degree of probability.
Nonfarm enterprises and ‘ rural ’ livelihoods : survival and growth WIDE Series III Discussion Brief

 The WIDE research shows that in the context of modernising agriculture, shrinking per capita agricultural land, urbanising rural areas, and broader economic transformation, farming is no longer the only or even the main source of subsistence and economic growth for many ‘rural’ households and individuals. Both necessity and opportunity have led many to diversify beyond farming into the nonfarm sector, calling for a reconsideration of prevailing assumptions.
Ideal Energy‐Level Alignment at the ZnO/P3HT Photovoltaic Interface

Hybrid semiconductor‐polymer nanostructured solar cells hold the promise of photovoltaic energy conversion based on abundant and nontoxic materials and scalable manufacturing processes. After a decade of intense research activity, hybrid solar cells still exhibit low short‐circuit currents and moderate open‐circuit voltages. These bottlenecks call for a detailed understanding of the physics underlying the device operation at the nanoscale. Using first‐principles calculations the ideal energy‐level alignment of hybrid solar cell interfaces based on the wide bandgap semiconductor ZnO and the polymer poly(3‐hexylthiophene) (P3HT) is investigated. The interfacial charge transfer is quantified and it is shown that this effect increases the ideal open‐circuit voltage with respect to the electron‐affinity rule by as much as 0.5 V. The results of this work suggests that there is significant room for optimizing this class of excitonic solar cells by tailoring the semiconductor/polymer interface at the nanoscale.
Revision for periprosthetic joint infection rate stratified by seasonality of operation in a national population of total and unicompartmental knee arthroplasty patients: a register-based analysis

Abstract Aim: The aim of this study was to investigate whether the revision rate for periprosthetic joint infection (PJI) depends on the season of the primary procedure using a national population of knee arthroplasty (KA) patients. Seasonal variation of some surgical procedures has been observed to impact subsequent infection risks, with a higher risk of revision for surgeries performed during summer, but an analysis of PJI rates based on a national arthroplasty register has yet to be completed. We hypothesized that an increased risk of revision due to PJI could be demonstrated in a national population when primary surgery was performed during the summer. Methods: The Danish Knee Arthroplasty Registry (DKR) was used to determine the risk of revision due to PJI within 2 years after primary surgery. All primary KA procedures between 1 January 1997 and 31 December 2014 and revisions until 31 December 2016 were identified. Smoothing spline regression was used to identify possible seasonal pattern effects of the primary procedure on revision risk, and logistic regression was used to calculate risk of infection differences between seasons. Results: A total number of 124 809 primary procedures was registered in the study period. After excluding duplicates and matching primary procedures with the first revisions within 2 years after the primary procedure, 3391 were identified. Of these, 348 cases were recorded with an indication of deep infection requiring revision. Spline regression analyses did not demonstrate any clear seasonal pattern of the primary procedure regarding the risk of revision for infection or any other cause. Logistic regression found a decreased risk of revision for infection when the primary procedure was performed during the summer in the years 1997 to 2005, no influence on the risk of revision for infection in 2005 to 2012, and an increased risk of revision for infection following summer procedures during the years 2013 to 2014. Conclusion: It was not possible to demonstrate a consistent seasonal variation of the risk of revision for PJI following primary KA. This is most likely because the underlying etiologies for PJI are not subject to seasonal variation.
Muscle strength and muscle mass as predictors of hospital length of stay in patients with moderate to severe COVID‐19: a prospective observational study

Strength and muscle mass are predictors of relevant clinical outcomes in critically ill patients, but in hospitalized patients with COVID‐19, it remains to be determined. In this prospective observational study, we investigated whether muscle strength or muscle mass are predictive of hospital length of stay (LOS) in patients with moderate to severe COVID‐19 patients.
Die Kristallstruktur yon RbGeCl3 / The Crystal Structure of RbGeCl3

RbGeCl3 is prepared from RbCl and Ge(II) in 6 N HCl or by reaction of RbCl with HGeCl3. The compound crystallizes monoclinic, space group P21/m, with a = 798.8, b = 694.1, c = 580.0 pm, β = 106.34° and Z = 2. The crystal structure has been determined by SHELX and refined till R = 0.067. RbGeCl3 is built up by Rb+ and pyramidal GeCl3−-units. The coordination of Ge and the anionic framework is discussed.
UV SPECTROPHTOMETRIC METHODS FOR SIMULTANEOUS ESTIMATION OF TELMISARTAN AND CILOSTAZOL IN SYNTHETIC MIXTURE

A simple, rapid, precise, accurate and economical Spectrophotometric methods has been developed for simultaneous estimation of Telmisartan and Cilostazol in the synthetic mixture. Method I is based on the simultaneous equations method in which th e absorbance of both the drugs were determined at 296 nm for Telmisartan and 257 nm for Cilostazol. Method II is based on Q-absorbance ratio method in which absorbance of both the drugs were determined at 257 nm (λmax of Cilostazol) and at 274.45 nm (Isoabsorptive point). The linearity was observed in the concentration range of 2-12 µg/ml for the Telmisartan and 5-30 µg/ml for the Cilostazol. The methods were validated and shown linear response for Telmisartan and Cilostazol. The precision (intra-day, inter-day) of methods were found within limits (RSD <2%). Accuracy was determined by recovery studies. Validation of proposed methods was carried out according to ICH guidelines. The proposed methods were found accurate and reproducible for routine analysis of both the drugs in synthetic mixture.
Scaling for rectification of bipolar nanopores as a function of a modified Dukhin number: the case of 1:1 electrolytes

ABSTRACT The scaling behaviour for the rectification of bipolar nanopores is studied using the Nernst-Planck equation coupled to the Local Equilibrium Monte Carlo method. The bipolar nanopore's wall carries σ and surface charge densities in its two half regions axially. Scaling means that the device function (rectification) depends on the system parameters (pore length, H, pore radius, R, concentration, c, voltage, U, and surface charge density, σ) via a single scaling parameter that is a smooth analytical function of the system parameters. Here, we suggest using a modified Dukhin number, , where , is the Bjerrum length, is the Debye length, and is a reference voltage. We show how scaling depends on H, U, and σ and through what mechanisms these parameters influence the pore's behaviour.
Orbital dichotomy of Fermi liquid properties in 
Sr2RuO4
 revealed by Raman spectroscopy

We report a polarization-resolved Raman spectroscopy study of the orbital dependence of the quasiparticles properties in the prototypical multi-band Fermi liquid Sr\textsubscript{2}RuO\textsubscript{4}. We show that the quasiparticle scattering rate displays $\omega^{2}$ dependence as expected for a Fermi liquid. Besides, we observe a clear polarization-dependence in the energy and temperature dependence of the quasiparticle scattering rate and mass, with the $d_{xz/yz}$ orbital derived quasiparticles showing significantly more robust Fermi liquid properties than the $d_{xy}$ orbital derived ones. The observed orbital dichotomy of the quasiparticles is consistent with the picture of Sr\textsubscript{2}RuO\textsubscript{4} as a Hund's metal. Our study establishes Raman scattering as a powerful probe of Fermi liquid properties in correlated metals.
From noise to information: The transfer function formalism for uncertainty quantification in reconstructing the nuclear density

Background: The neutron distribution of neutron-rich nuclei provides critical information on the structure of finite nuclei and neutron stars. Parity violating experiments — such as PREX and CREX — provide a clean and largely model-independent determination of neutron densities. Such experiments, however, are challenging and expensive which is why sound statistical arguments are required to maximize the information gained. Purpose: To introduce a new framework, “the transfer function formalism”, aimed at uncertainty quantification, model selection, and experimental design in the context of neutron densities. Methods: The transfer functions (TFs) are built analytically by expressing the linear response of the objective function (e.g., χ) to small perturbations of the data. Using the TF formalism, we are able to analyze the expected overall uncertainty — quantified in terms of bias and variance — of the mean square radius and interior density of Ca and Pb. Results: Using relativistic mean field models as a proxy for the weak-charge density — and assuming that a total of five measurements could be performed on the weak form factor of Ca and Pb — we identify the optimal models and experimental locations that minimize the uncertainty in the extraction of the radius and interior density. We also explore the use of the TF formalism to understand the influence of prior distributions for the model parameters, as well as the optimization of model hyperparameters not constrained by the data. Conclusions: We establish how the choice of experimental locations and the model that is used can have a significant impact on the final uncertainties of the extracted quantities of interest. For challenging experiments such as CREX and PREX, a proper quantification of such uncertainties is critical. We have demonstrated how the TF formalism provides several advantages for this type of analysis.
Efficient Exploration of Interesting Aggregates in RDF Graphs

As large Open Data are increasingly shared as RDF graphs today, there is a growing demand to help users discover the most interesting facets of a graph, which are often hard to grasp without automatic tools. We consider the problem of automatically identifying the k most interesting aggregate queries that can be evaluated on an RDF graph, given an integer k and a user-specified interestingness function. Our problem departs from analytics in relational data warehouses in that (i) in an RDF graph we are not given but we must identify the facts, dimensions, and measures of candidate aggregates; (ii) the classical approach to efficiently evaluating multiple aggregates breaks in the face of multi-valued dimensions in RDF data. In this work, we propose an extensible end-to-end framework that enables the identification and evaluation of interesting aggregates based on a new RDF-compatible one-pass algorithm for efficiently evaluating a lattice of aggregates and a novel early-stop technique (with probabilistic guarantees) that can prune uninteresting aggregates. Experiments using both real and synthetic graphs demonstrate the ability of our framework to find interesting aggregates in a large search space, the efficiency of our algorithms (with up to 2.9x speedup over a similar pipeline based on existing algorithms), and scalability as the data size and complexity grow.
Compressing 1D Time-Channel Separable Convolutions using Sparse Random Ternary Matrices

We demonstrate that 1x1-convolutions in 1D time-channel separable convolutions may be replaced by constant, sparse random ternary matrices with weights in $\{-1,0,+1\}$. Such layers do not perform any multiplications and do not require training. Moreover, the matrices may be generated on the chip during computation and therefore do not require any memory access. With the same parameter budget, we can afford deeper and more expressive models, improving the Pareto frontiers of existing models on several tasks. For command recognition on Google Speech Commands v1, we improve the state-of-the-art accuracy from $97.21\%$ to $97.41\%$ at the same network size. Alternatively, we can lower the cost of existing models. For speech recognition on Librispeech, we half the number of weights to be trained while only sacrificing about $1\%$ of the floating-point baseline's word error rate.
Bidirectional group random walk based network embedding for asymmetric proximity

Network embedding aims to represent a network into a low dimensional space where the network structural information and inherent properties are maximumly preserved. Random walk based network embedding methods such as DeepWalk and node2vec have shown outstanding performance in the aspect of preserving the network topological structure. However, these approaches either predict the distribution of a node's neighbors in both direction together, which makes them unable to capture any asymmetric relationship in a network; or preserve asymmetric relationship in only one direction and hence lose the one in another direction. To address these limitations, we propose bidirectional group random walk based network embedding method (BiGRW), which treats the distributions of a node's neighbors in the forward and backward direction in random walks as two different asymmetric network structural information. The basic idea of BiGRW is to learn a representation for each node that is useful to predict its distribution of neighbors in the forward and backward direction separately. Apart from that, a novel random walk sampling strategy is proposed with a parameter {\alpha} to flexibly control the trade-off between breadth-first sampling (BFS) and depth-first sampling (DFS). To learn representations from node attributes, we design an attributed version of BiGRW (BiGRW-AT). Experimental results on several benchmark datasets demonstrate that the proposed methods significantly outperform the state-of-the-art plain and attributed network embedding methods on tasks of node classification and clustering.
Diamond quantum thermometry: from foundations to applications

Diamond quantum thermometry exploits the optical and electrical spin properties of colour defect centres in diamonds and, acts as a quantum sensing method exhibiting ultrahigh precision and robustness. Compared to the existing luminescent nanothermometry techniques, a diamond quantum thermometer can be operated over a wide temperature range and a sensor spatial scale ranging from nanometres to micrometres. Further, diamond quantum thermometry is employed in several applications, including electronics and biology, to explore these fields with nanoscale temperature measurements. This review covers the operational principles of diamond quantum thermometry for spin-based and all-optical methods, material development of diamonds with a focus on thermometry, and examples of applications in electrical and biological systems with demand-based technological requirements.