Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of NanoLuc Luciferase and Alkaline Phosphatase Luminescence Reporter Systems for Phage-Based Detection of Bacteria

Reporter phage assays are a promising alternative to culture-based assays for rapidly detecting viable bacteria. The reporter systems used in phage-based detection are typically enzymes and their corresponding substrates that provide a signal following infection and expression. While several reporter systems have been developed, comparing reporter systems based on reported bacteria detection limits from literature can be challenging due to factors other than the reporter system that influence detection capabilities. To advance the development of phage-based assays, a systematic comparison and understanding of the components are necessary. The objective of this study was to directly compare two common enzyme-mediated luminescence reporter systems, NanoLuc/Nano-Glo and alkaline phosphatase (ALP*)/DynaLight, for phage-based detection of bacteria. The detection limits of the purified enzymes were determined, as well as the expression levels and bacteria detection capabilities following engineering of the coding genes into T7 phage and infection of E. coli BL21. When comparing the sensitivity of the purified enzymes, NLuc/Nano-Glo enzyme/substrate system demonstrated a lower detection limit than ALP*/DynaLight. In addition, the expression of the NLuc reporter following phage infection of E. coli was greater than ALP*. The lower detection limit combined with the higher expression resulted in a greater than 100-fold increase in sensitivity for the NLuc/Nano-Glo® reporter system compared to ALP*/DynaLight when used for the detection of E. coli in a model system. These findings provide a comparative analysis of two common reporter systems used for phage-based detection of bacteria and a foundational understanding of these systems for engineering future reporter phage assays.
Coarse TRVO: A Robust Visual Odometry with Detector-Free Local Feature

The visual SLAM system requires precise localization. To obtain consistent feature matching results, visual features acquired by neural networks are being increasingly used to replace traditional manual features in situations with weak texture, motion blur, or repeated patterns. However, to improve the level of accuracy, most deep learning enhanced SLAM systems, which have a decreased efficiency. In this paper, we propose Coarse TRVO, a visual odometry system that uses deep learning for feature matching. The deep learning network uses a CNN and transformer structures to provide dense high-quality end-to-end matches for a pair of images, even under indistinctive settings with low-texture regions or repeating patterns occupying the majority of the field of view. Meanwhile, we made the proposed model compatible with NVIDIA TensorRT runtime to boost the performance of the algorithm. After obtaining the matching point pairs, the camera pose is solved in an optimized way by minimizing the re-projection error of the feature points. Experiments based on multiple data sets and real environments show that Coarse TRVO achieves a higher robustness and relative positioning accuracy in comparison with the current mainstream visual SLAM system.
The socially poor get richer, the rich get poorer: The effect of online self-disclosure on social connectedness and well-being is conditional on social anxiety and audience size

Self-disclosure taking place in computer-mediated communication (CMC) is generally associated with enhanced well-being because it evokes a greater sense of connectedness. It has been established that the magnitude of the benefits reaped from online self-disclosure is conditional on social anxiety (under the lens of the poor-get-richer vs. rich-get-richer hypotheses) or audience size. What remains to be understood is whether those with low (compared to high) social anxiety experience greater social connectedness and subjective well-being in dyadic and/or masspersonal CMC. A sample of 411 Canadian undergraduate students (aged 17–21 years old) self-reported their anxiety in social situations, online self-disclosure in dyadic and masspersonal communication, current feelings of social connectedness, and subjective well-being. Model 7 of the PROCESS macro for SPSS was used to test the indirect effect of online self-disclosure on subjective well-being through feelings of social connectedness, conditioned on values of social anxiety. The model was run separately for dyadic and masspersonal CMC. Online self-disclosure was associated with positive outcomes only for those with high social anxiety. In both contexts, online self-disclosure was associated with enhanced social connectedness and in turn more positive subjective well-being. In contrast, for those with low social anxiety, increases in self-disclosure in masspersonal CMC was associated with decreases in social connectedness and poorer well-being. The indirect effect was not significant for dyadic CMC. Overall, the findings contribute to a more informed understanding of online self-disclosure as a double-edged sword. Theoretical implications for the poor-get-richer and rich-get-richer perspectives are discussed.
The Influence of Graphene Content on the Antibacterial Properties of Polycaprolactone

This work contains an analysis of the impact of modifying a bioresorbable polymer—polycaprolactone (PCL)—with various additives on its antibacterial properties. To this end, samples of PCL filament containing various content levels of graphene (GNP), 0.5%, 5%, 10%, were obtained using injection molding. Polymer samples without additives were used for comparison. The next step was to assess the antimicrobial impact of the preparations under study against the following microorganisms: Staphylococcus aureus ATCC 25293, Escherichia coli ATCC 25922, Candida albicans ATCC 10231. Effective bactericidal activity of PCL with small amount of GNP, especially against C. albicans and S. aureus was confirmed. A decrease in this property or even multiplication of microorganisms was observed in direct proportion to the graphene content in the samples.
Observation of Anderson phase in a topological photonic circuit

Disordered systems play a central role in condensed matter physics, quantum transport, and topological photonics. It is commonly believed that a topological nontrivial phase would turn into a trivial phase where the transport vanishes under the effect of Anderson localization. Recent studies predict a counterintuitive result, that adding disorder to the trivial band structure triggers the emergence of protected edge states, the so-called topological Anderson phase. Here, we experimentally observe such a topological Anderson phase in a CMOS-compatible nanophotonic circuit, which implements the Su-Schrieffer-Heeger (SSH) model with incommensurate disorder in the intercell coupling amplitudes. The existence of the Anderson phase is veriﬁed by the spectral method, based on the continuous detection of the nanoscale light dynamics at the edge. Our results demonstrate the inverse transition between distinct topological phases in the presence of disorder, as well as offering a single-shot measurement technique to study the light dynamics in nanophotonic systems. DOI: 10.1103/PhysRevResearch.4. 033222
Ensemble Kalman Filtering for State and Parameter Estimation on a Reservoir Model

In reservoir management it is important with reservoir models that have good predictive abilities. Since the models initially are based on measurements with high uncertainties it is important to utilize new available data. Ensemble Kalman Filter (EnKF) is a new method for history matching that has received a lot of attention the last couple of years. This method is sequential and continuously update the reservoir model states (saturations, pressures etc.) and parameters (permeabilities, porosities etc) as data become available. The EnKF algorithm is derived and presented with a different notation, similar to that of the Kalman Filter (KF) used in control engineering. This algorithm is also verified on a simple linear example to illustrate that the covariance of the EnKF approaches that of the linear KF in case of an infinite ensemble size. In control theory this method falls under the category of parameter and state estimation of nonlinear large scale systems. Interesting aspects as observability and constraint handling arises, and these are linked to the EnKF and the reservoir case. To determine if the total problem is observable is a nearly impossible task, but one can learn a lot from introducing this concept. The EnKF algorithm was implemented on a simple shoe box reservoir model and four different problem initializations were tested. Although decent results were achieved from some of the simulations other failed completely. Some strange development in the ensemble when little information is available in the measurements was experienced and discussed. An outline was presented for a reservoir management scheme where EnKF is combined with Model Predictive Control (MPC). Some challenges was pointed out and these involve computation time, predictive ability, closed-loop behavior etc.
Mass-Resolved Momentum Imaging of Three Dichloroethylene Isomers by Femtosecond Laser-Induced Coulomb Explosion

Coulomb explosion experiments using linearly polarized intense 60 fs laser pulses were conducted for structural characterization of three dichloroethylene (DCE) isomers, 1,1-DCE, cis-1,2-DCE, and trans-1,2-DCE. Under relatively low laser intensity at 1.8 × 1014 W/cm2, mass-resolved momentum imaging (MRMI) for selected fragment ions of 35Cl+ and C2+ revealed different patterns for the three isomers. The C2+ ion fragmented from multiply charged trans-1,2-DCE was forced to leave perpendicularly to the direction of the laser polarization, due to recoil forces from adjacent cations. In contrast, the fast ions of C2+ from cis-1,2-DCE exhibited an isotropic distribution, whereas the fast ions of C2+ from 1,1-DCE recoiled along the laser polarization together with the slow C2+ ions, and thereby distinction of the three isomers was demonstrated. Coulomb explosion occurs predominantly at specific orientation, which is useful for potential applications of MRMI analysis to molecular structure assays.
Relational aspects of writer’s workshop in kindergarten: learning from Black boys who used their creativity and strength to speak back to the scripted curriculum

ABSTRACT Writer’s workshop is a popular curriculum opportunity in most US schools. This case study of two Black boys engaging in a Kindergarten writer’s workshop richly contextualizes their writing identities and practices. A critical childhood studies framework was used to show how Black boys and their White teachers improvised writing-related relationships. The research questions for the study were: (1) How did Black boys use creativity and strength to push back against the semi-scripted curriculum in Kindergarten writer’s workshop? (2) What can teachers and teacher educators learn from their examples? In the findings Black boys strove to think, understand, and practice writing freely and to use their personal and cultural resources outside the constraints of teaching scripts. However, when teachers did not navigate the semi-scripted curriculum, all the children were robbed of their writing identities but Black boys were most deeply affected. Reflection questions and suggestions for teacher educators and professional developers are designed to support the use of improvisational practices and African diaspora literacy in early childhood and elementary school writing contexts.
Impact of Elevated LDH on Cystatin C-Based Glomerular Filtration Rate Estimates in Patients with Cancer

Background: The determination of renal function is crucial for the clinical management of patients with cancer. The glomerular filtration rate (GFR) serves as a key parameter, estimated by creatinine clearance determination in 24-h collected urine (CrCl) as well as equation-based approaches (eGFR) relying on serum creatinine (eGFR CKD EPIcrea) or serum cystatin C (eGFR cystatin C). Serum creatinine and serum cystatin C levels differentially depend on muscle and tumor mass, respectively. Although muscle and tumor mass may thus represent confounding factors, comparative studies for eGFR estimate approaches in cancer patients are lacking. Methods: The present study retrospectively analyzed GFR estimates based on equations of creatinine (eGFRcr), cystatin C (eGFRcys) and combined creatinine-cystatin C levels (eGFRcr-cys) in a subset of patients. The associations of LDH with cystatin C or LDH with eGFRcr, eGFRcys and GFRcr-cys were explored. Results: The laboratory values of 123 consecutive patients were included. The median age was 59 (24–87) and 47.2% were female. There was a statistically significant difference in the mean of CKD EPIcrea (85.17 ± 21.63 mL/min/1.73 m2), CKD EPIcys (61.16 ± 26.03 mL/min/1.73 m2) and CKD EPIcrea-cys (70.42 ± 23.89 mL/min/1.73 m2) (p < 0.0001). Spearman’s correlation analysis revealed a significant correlation of elevated plasma LDH >1.5 UNV and cystatin C values (r = 0.270, p < 0.01, n = 123). LDH values >1.5 UNV were associated with significantly lower CKD EPIcys (r = 0.184, p < 0.01) or CKD EPIcrea-cys (r = 0.226, p < 0.05) estimates compared to CKD EPIcrea. Conclusions: The inclusion of cystatin C as a biomarker led to a lower eGFR estimates compared to creatinine alone or in a combination of both cystatin C and creatinine. The level of cystatin C correlated with the level of LDH, suggesting that the use of cystatin C-based calculations of GFR in cancer patients with elevated LDH should be used with caution.
Ultra-Wideband Power Amplifier Design Strategy for 5G Sub-6-GHz Applications

This paper presents a strategy to design ultrawideband power amplifiers with a fractional bandwidth of approximately 200%. It exploits a simple output matching network, which consists of a series transmission line together with a shunt stub, to compensate the output parasitic network of the device. Following this, a multisection transformer is implemented to obtain the optimal load at the intrinsic drain plane. As design examples, several output matching networks were designed for two different size GaN HEMT devices. One of these examples was implemented and characterized, and a drain efficiency from 52% to 70% and an output power between 40 dBm and 42.5 dBm were obtained, over 67% of the 5G sub-6-GHz band (i.e., 0.1 to 4 GHz). The aforementioned results, to the best of the authors’ knowledge, represent the state of the art in broadband power amplifiers.
Root Colonization by Fungal Entomopathogen Systemically Primes Belowground Plant Defense against Cabbage Root Fly

Entomopathogenic fungi infect insects via spores but also live inside plant tissues as endophytes. Frequently, colonization by entomopathogens provides plants with increased resistance against insects, but the mechanisms are little understood. This study investigated direct, local, and systemic root-mediated interactions between isolates of the fungus Metarhizium brunneum and larvae of the cabbage root fly (CRF) Delia radicum attacking Brassica napus plants. All fungal isolates infected CRF when conidia were present in the soil, leading to 43–93% mortality. Locally, root-associated M. brunneum isolates reduced herbivore damage by 10–20% and in three out of five isolates caused significant insect mortality due to plant-mediated and/or direct effects. A split-root experiment with isolate Gd12 also demonstrated systemic plant resistance with significantly reduced root collar damage by CRF. LC-MS analyses showed that fungal root colonization did not induce changes in phytohormones, while herbivory increased jasmonic acid (JA) and glucosinolate concentrations. Proteinase inhibitor gene expression was also increased. Fungal colonization, however, primed herbivore-induced JA and the expression of the JA-responsive plant defensin 1.2 (PDF1.2) gene. We conclude that root-associated M. brunneum benefits plant health through multiple mechanisms, such as the direct infection of insects, as well as the local and systemic priming of the JA pathway.
A Three-dimensional Attention-connected Convolutional Network for Acute Bilirubin Encephalopathy Classification

Neonatal jaundice is a relatively common clinical disorder. The overproduction of bilirubin by erythrocytes could lead to acute bilirubin encephalopathy, which may cause irreversible brain tissue damage(kernicterus) if not diagnosed and intervened early. Recently, deep learning algorithms, especially convolutional neural networks, have rapidly become the choice for medical imaging analysis. In this paper, a 3D convolutional neural network with an attention module is designed to assist in the analysis and diagnosis of acute bilirubin encephalopathy. By introducing an attention module between different dimensions through a connection strategy, the networks make full use of the implicit relationship between different dimensions and combine the characteristics of convolutional neural network and Magnetic Resonance Imaging to classify acute bilirubin encephalopathy. The experimental results demonstrate the availability of the proposal.
PARMEST: Stata module to create new data set with one observation per parameter of most recent model

The parmest package has 4 modules: parmest, parmby, parmcip and metaparm. parmest creates an output dataset, with 1 observation per parameter of the most recent estimation results, and variables corresponding to parameter names, estimates, standard errors, z- or t-test statistics, P-values, confidence limits and other parameter attributes. parmby is a quasi-byable extension to parmest, which calls an estimation command, and creates a new dataset, with 1 observation per parameter if the by() option is unspecified, or 1 observation per parameter per by-group if the by() option is specified. parmcip inputs variables containing estimates, standard errors and (optionally) degrees of freedom, and computes new variables containing confidence intervals and P-values. metaparm inputs a parmest-type dataset with 1 observation for each of a set of independently-estimated parameters, and outputs a dataset with 1 observation for each of a set of linear combinations of these parameters, with confidence intervals and P-values, as for a meta-analysis. The output datasets created by parmest, parmby or metaparm may be listed to the Stata log and/or saved to a file and/or retained in memory (overwriting any pre-existing dataset). The confidence intervals, P-values and other parameter attributes in the dataset may be listed and/or plotted and/or tabulated.
Comparison of the Rock Core Image Segmentation Algorithm

Rock core is an important basic geological data in oil field exploration and development. The scanned rock core image plays an important role in sediment environment inference, reservoir property and oily content analysis. Edge detection of rock core image is the primary content of rock core image digital research. This paper introduces several different image semantic segmentation algorithms, compares the actual segmentation effects of several algorithms, and draws the conclusion that K-means has the best segmentation effect among the four image segmentation algorithms, with the highest sense of detail level and the strongest definition.
Toward the nature of automatic associations: item-level computational semantic similarity and IAT-based alcohol-valence associations

Abstract Automatic associations involving alcohol have been proposed to play a role in drinking behavior. Such associations are often assessed using implicit measures such as the Implicit Association Test (IAT). Neural network language models provide computational measures of semantic relationships between words. These model-based measures could be related to behavioral alcohol-related associations as observed using the IAT. If so, this could provide a step toward better understanding of the nature of automatic associations and their relationship to behavior. The current study therefore aimed to test whether there is a systematic covariation over items between model-based and behavior-based associations. Analyses were performed for two single-target IATs from a previously published study. One task involved alcohol versus nonalcohol drinks and positive associates, and the other alcohol versus nonalcohol drinks and negative associates. The GenSim library and a pretrained word2vec model were used to calculate a relative computational association between specific items from the positive and negative categories, respectively, and the alcohol versus nonalcohol word sets. In both tasks, a significant covariance between items’ computational and behavioral measures of association was found over participants. The results thus add to the information on the relationship between neural network language models and psychological associations. They may provide methodological strategies for task design and data analysis. Models of semantic associations connect computational linguistics and social-cognitive psychology and may provide a theoretical link between measures of alcohol-related associations using verbal stimuli and alcohol-related cognition and behaviors.
Stylized Pairing for Robust Adversarial Defense

Recent studies show that deep neural networks (DNNs)-based object recognition algorithms overly rely on object textures rather than global object shapes, and DNNs are also vulnerable to human-less perceptible adversarial perturbations. Based on these two phenomenons, we conjecture that the preference of DNNs on exploiting object textures for decisions is one of the most important reasons for the existence of adversarial examples. At present, most adversarial defense methods are directly related to adversarial perturbations. In this paper, we propose an adversarial defense method independent of adversarial perturbations, which utilizes a stylized pairing technique to encourage logits for a pair of images and the corresponding stylized image to be similar. With stylized pairing training, DNNs can better learn shape-biased representation. We have empirically evaluated the performance of our method through extensive experiments on CIFAR10, CIFAR100, and ImageNet datasets. Results show that the models with stylized pairing training can significantly improve their performance against adversarial examples. The code is available at https://github.com/lingKok/robust-optimization-with-stylized-pairing.
Uncertainty Inclusive Runway Balancing Using Convolutional Neural Network

and arrival aircraft delay. The delay prediction for runway balancing optimization is obtained by a neural network, onlywithoutanyadditionalsimulations.Developinganaccuratesimulationmodelunderanuncertainenvironmentis difficult, but the proposed neural network model can estimate the average delay without modeling uncertainty explicitly. In this paper, the effectiveness of the proposed method is validated through numerical simulations. First, simulationsareusedtogeneratethedata,whicharethenusedtotraintheneuralnetwork.Next,therunwaybalancing problem is solved via simulated annealing using the delay predicted by the neural network. The simulation result shows that the proposed approach outperforms the simulation-based method under an uncertainty environment. Therefore, the neural network is shown to accurately estimate the delay under the uncertainty environment, which makes the proposed neural-network-based method applicable to objective function calculations for optimization.