Miracle-dz/UniFAR-FTUs huggingface dataset:
Biocompatible nickel-prussian blue@silver nanocomposites show potent antibacterial activities

Aim: Silver nanoparticles have long been used as potent antibacterial agents. However, toxicity concerns of silver nanoparticles have limited their successful clinical applications. Hence, development of silver-based novel biocompatible nanomaterials for antibacterial applications is a challenging task. Materials & methods: Accordingly, in this work, we synthesized a biocompatible silver-based nanocomposite for antibacterial applications. The nanocompostie was characterized by several analytical techniques. The nanocomposite was further tested for its cytotoxicity in cells, chicken embryo and bacteria. Results & Conclusion: Herein, we report a simple and cost-effective method for the synthesis of nickel-prussian blue@silver nanocomposites. The nanocomposite is highly stable and shows biocompatibility observed by in vitro assay and by ex vivo chicken embryonic angiogenesis assay. The nanocomposite exhibits profound antibacterial activity toward Gram-negative (Escherichia coli) and Gram-positive (Bacillus subtillus) bacteria. The results altogether suggest the future potential applications of nickel-prussian blue@silver nanocomposite as an antibacterial agent.
On Matched Filtering for Statistical Change Point Detection

Non-parametric and distribution-free two-sample tests have been the foundation of many change point detection algorithms. However, randomness in the test statistic as a function of time makes them susceptible to false positives and localization ambiguity. We address these issues by deriving and applying filters matched to the expected temporal signatures of a change for various sliding window, two-sample tests under IID assumptions on the data. These filters are derived asymptotically with respect to the window size for the Wasserstein quantile test, the Wasserstein-1 distance test, Maximum Mean Discrepancy squared (MMD$^2$), and the Kolmogorov-Smirnov (KS) test. The matched filters are shown to have two important properties. First, they are distribution-free, and thus can be applied without prior knowledge of the underlying data distributions. Second, they are peak-preserving, which allows the filtered signal produced by our methods to maintain expected statistical significance. Through experiments on synthetic data as well as activity recognition benchmarks, we demonstrate the utility of this approach for mitigating false positives and improving the test precision. Our method allows for the localization of change points without the use of ad-hoc post-processing to remove redundant detections common to current methods. We further highlight the performance of statistical tests based on the Quantile-Quantile (Q-Q) function and show how the invariance property of the Q-Q function to order-preserving transformations allows these tests to detect change points of different scales with a single threshold within the same dataset.
Induction of N-Hydroxycinnamoyltyramine Synthesis and Tyramine N-Hydroxycinnamoyltransferase (THT) Activity by Wounding in Maize Leaves

Both N-p-coumaroyl- and N-feruloyltyramine accumulated in response to wounding in leaf segments of maize. The amount of N-hydroxycinnamoyltyramines started to increase 3-6 h after wounding and peaked at 12 h. Thereafter, the amount of N-p-coumaroyltyramine decreased rapidly, while the N-feruloyltyramine content remained at a high level. The accumulation of N-hydroxycinnamoyltyramines was accompanied by an increase in the tyramine N-hydroxycinnamoyltransferase (THT) activity. This increase was initially detected 3 h after wounding and reached a maximum at 36 h, the level of activity being 40 and 11 times that in the leaves before wounding and in the control leaves, respectively. Partial purification of THT from wounded leaves by (NH4)2SO4 precipitation and subsequent two steps of anion-exchange chromatography resulted in a 12.5-fold increase in specific activity. Kinetic studies with this partially purified enzyme revealed that the best substrates were tyramine and feruloyl-CoA, although tryptamine and sinapoyl-CoA also efficiently served as substrates. The apparent native molecular weight of the enzyme was determined by gel filtration as 40 kDa.
Simultaneous optimization for wind derivatives based on prediction errors

Wind power energy has been paid much attention recently for various reasons, and the production of electricity with wind energy has been increasing rapidly for a few decades. In this work, we will propose a new type of weather derivatives based on the prediction errors for wind speeds, and estimate their hedge effect on wind power energy businesses. At first, we will investigate the correlation of prediction errors between the power output and the wind speed in a Japanese wind farm. Then we will develop a methodology that will optimally construct a wind derivative based on the prediction errors using nonparametric regressions. A simultaneous optimization technique of the loss and payoff functions for wind derivatives is demonstrated based on the empirical data.
Melasma treatment with pulsed-dye laser and triple combination cream: a prospective, randomized, single-blind, split-face study.

R ecent data show that melasma lesions have, in addition to increased pigmentation, more elastosis and vascularization than perilesional skin. The stabilized formulation of Kligman preparation has shown significant improvements in the treatment of melasma. However, most of the treatments only target the pigmentation, and none of them has been demonstrated so far to prevent the frequent relapses. Pulsed-dye laser treatment (PDL) is considered the gold standard therapy for vascular lesions. By targeting not only melanin but also vascularization and at least in part elastosis, PDL might provide, in combination with blanching cream, an effective and complete therapeutic approach for melasma. The objective of this pilot study was to evaluate the dual treatment of fixed triple combination cream (TCC) and PDL in the treatment of melasma.
Integrating biomarkers: The new frontier?

Abstract Risk stratification for cardiovascular diseases (CVD) remains suboptimal even after the introduction of global risk assessment by various scores. This has prompted the search for additional biomarkers which might help to improve risk stratification. Basically, there are blood biomarkers representing various pathophysiological pathways of atherosclerosis, markers of subclinical disease, and potentially genetic markers. Since inflammatory processes accompany all stages of atherosclerosis, measurement of plasma/serum concentrations of circulating inflammatory biomarkers has received great attention. Such biomarkers can be measured systemically by sensitive assays and elevated concentrations in the circulation have been shown to be associated with future CVD events. Thus, they might add to the predictive value of the atherogenic lipoprotein phenotype to further improve CVD risk assessment. In addition, several non-invasive imaging techniques are available for which also a predictive value for CVD could be established, in particular measurement of the intima-media thickness of the carotid artery using high resolution ultrasound and measurements of coronary calcium by coronary computed tomography. However, for most of these biomarkers the clinical utility has not yet been firmly established. This applies even more to an integrated approach combining blood biomarkers and markers of subclinical disease. Thus, more data, preferably from serial measurements in large populations taking also into account new candidates from “omics” technology are needed to gain further insight in the potential clinical usefulness of an integrated approach.
Statistical testing of Web applications

The World Wide Web, initially intended as a way to publish static hypertexts on the Internet, is moving toward complex applications. Static Web sites are being gradually replaced by dynamic sites, where information is stored in databases and non-trivial computation is performed.In such a scenario, ensuring the quality of a Web application from the user's perspective is crucial. Techniques are being investigated for the analysis and testing of Web applications for such a purpose. However, a static analysis of the source code may be extremely difficult (and, in general, infeasible) because of the presence of dynamic generation of the HTML code that is part of the application under analysis.In this paper, a dynamic analysis technique is proposed for the extraction of a Web application model through its execution. Availability of statistical data about the accesses to the pages generated by the Web application is exploited for statistical testing, based on the recovered model. Test cases can be prioritized, so as to exercise the most frequently followed paths first. Moreover, statistical reproduction of the user's navigation paths allows for an estimation of the reliability of the application.
Assays for Determination of Protein Concentration

Biochemical analysis of proteins relies on accurate quantitation of protein concentration. This appendix describes how to perform commonly used protein assays, e.g., Lowry, Bradford, BCA, and UV spectroscopic protein assays. The primary focus of the appendix is assay selection, emphasizing sample and buffer compatibility. Protein assay standard curves and data processing fundamentals are discussed in detail. This appendix also details high‐throughput adaptations of the commonly used protein assays, and also contains a protocol for BCA assay of total protein in SDS‐PAGE sample buffer that is used for equal loading of SDS‐PAGE gels, which is reliable, inexpensive, and quick. Curr. Protoc. Pharmacol. 38:A.3A. 1‐A.3A. 29. © 2007 by John Wiley & Sons, Inc.
Network Size Estimation in VANETs

Vehicular Ad-Hoc Networks (VANETs) is a branch of Ad-Hoc Networks that covers wide area and nodes are typically moving vehicles. The power level available in the vehicles is relatively higher than the power level available in other types of ad hoc networks. The reliability is an important issue in VANETs that is determined by specifying a mobility model and routing protocol. A key concern in designing reliable routing protocols is the network size assumption. Most protocols assume prior knowledge of the network size; however, this assumption requires a careful consideration and needs to be further investigated. Moreover, the neighborhood relationship between nodes is not well defined and estimated. The moving pattern for users is a critical factor in defining this relation. In this Paper, we investigate the influence of mobility models on the network size and neighborhood relationship, and then propose heuristic algorithm to define the nodes’ neighbors and to estimate the network size. The performance of the proposed algorithm is evaluated using simulation.
Dental characteristics of a large military population useful for identification.

The number and complexity of dental restorations has decreased for younger Americans. Since the presence and extent of restorations are important data for forensic science identification purposes, the Computer-Assisted Postmortem Identification (CAPMI) system was used to assess the practical effect of the decreased selectivity expected as a result of improved dental health. Dental examination data from 7030 soldiers were recorded on optical mark read forms and entered into a database. The data were reorganized and analyzed to generate summary statistics about the incidence of each type of restoration (divided into 16 categories) for both anterior, posterior, upper, lower, and combined segments. Patients' ages ranged from 17 to 49 with a mean of 24 years 5 months. Sixty percent were from 18 to 25 years old. A characteristic is defined as any situation other than a virgin tooth; for example, extracted, missing, unerupted, or restored. The average subject had 7 dental characteristics with approximately 75% having 4 or more. Within the entire population, 9% had 32 unrestored teeth. 3.6% had only one characteristic. To test the selectivity and uniqueness of various combinations of dental characteristics, 363 simulations using the CAPMI system were made against the entire 7030 subject database. Sample records (33 per group) with 2, 3, 4, 5, 6, 7, 8, 9-11, 12-14, 15-18, and 19+ characteristics were chosen at random from the database and searches were made. (ABSTRACT TRUNCATED AT 250 WORDS)
Phytoestrogens and risk of prostate cancer: an updated meta-analysis of epidemiologic studies

Abstract This updated meta-analysis was performed to clarify the relationship between phytoestrogens and prostate cancer risk. Twenty one case–control and two cohort studies were finally selected for this meta-analysis, totaling 11,346 cases and 140,177 controls. Analytical results showed that daidzein (OR = 0.85; 95% CI: 0.75–0.96), genistein (OR = 0.87; 95% CI: 0.78–0.98), and glycitein (OR = 0.89; 95% CI: 0.81–0.98) were associated with a reduction of prostate cancer risk, but total isoflavones (OR = 0.93; 95% CI: 0.84–1.04), equol (OR = 0.86; 95% CI: 0.66–1.14), total lignans (OROgna.05; 95% CI: 0.54–2.04), secoisolariciresinol (OR = 1.02; 95% CI: 0.83–1.24), matairesinol (OR = 0.91; 95% CI: 0.75–1.11), enterolactone (OR = 0.94; 95% CI: 0.73–1.20), and coumestrol (OR = 0.89; 95% CI: 0.76–1.06) were not. Sensitivity and publication bias analyses demonstrated that the pooled estimates were stable and reliable. The results support the notion that some phytoestrogens may have a role in decreasing the risk of prostate cancer. Additional large and well-designed cohort studies are needed to confirm these relationships.
Fair Clustering for Diverse and Experienced Groups

The ability for machine learning to exacerbate bias has led to many algorithms centered on fairness. For example, fair clustering algorithms typically focus on balanced representation of protected attributes within clusters. Here, we develop a fair clustering variant where the input data is a hypergraph with multiple edge types, representing information about past experiences of groups of individuals. Our method is based on diversity of experience, instead of protected attributes, with a goal of forming groups that have both experience and diversity with respect to participation in edge types. We model this goal with a regularized edge-based clustering objective, design an efficient 2-approximation algorithm for optimizing the NP-hard objective, and provide bounds on hyperparameters to avoid trivial solutions. We demonstrate a potential application of this framework in online review platforms, where the goal is to curate sets of user reviews for a product type. In this context, "experience" corresponds to users familiar with the type of product, and "diversity" to users that have reviewed related products.
Moderation of the Relation of County-Level Cost of Living to Nutrition by the Supplemental Nutrition Assistance Program.

OBJECTIVES To examine the association of county-level cost of living with nutrition among low-income Americans. METHODS We used the National Household Food Acquisition and Purchase Survey (2012-2013; n = 14 313; including 5414 persons in households participating in the Supplemental Nutrition Assistance Program [SNAP]) to examine associations between county-level cost-of-living metrics and both food acquisitions and the Healthy Eating Index, with control for individual-, household-, and county-level covariates and accounting for unmeasured confounders influencing both area of living and food acquisition. RESULTS Living in a higher-cost county-particularly one with high rent costs-was associated with significantly lower volume of acquired vegetables, fruits, and whole grains; greater volume of acquired refined grains, fats and oils, and added sugars; and an 11% lower Healthy Eating Index score. Participation in SNAP was associated with nutritional improvements among persons living in higher-cost counties. CONCLUSIONS Living in a higher-cost county (particularly with high rent costs) is associated with poorer nutrition among low-income Americans, and SNAP may mitigate the negative nutritional impact of high cost of living.
Unified Spectrum of Tropical Rainfall and Waves in a Simple Stochastic Model

In the tropics, rainfall is coupled with atmospheric dynamics in ways that are not fully understood, and often different mechanisms are proposed to underlie different modes of variability. Here it is shown that a unified model with a simple form can produce many different modes of variability. In particular, this includes the Madden‐Julian Oscillation and convectively coupled equatorial waves. The model predicts the length scales, time scales, structures, and spatiotemporal variability of these modes reasonably well for a simple model. Furthermore, the model produces a background spectrum of rainfall that resembles spatiotemporal red noise and is only weakly coupled with wave dynamics. The full spectrum is also shown to be shaped by antiresonance, whereby rainfall oscillations are prevented from occurring at the oscillation frequencies of dry waves. To produce all of these aspects simultaneously, a key factor is differing roles of lower and middle tropospheric water vapor.
0 61 27 07 v 1 2 2 D ec 2 00 6 Object detection in multi-epoch data

In astronomy multiple images are frequently obtained at the same position of the sky for followup co-addition as it helps one go deeper and look for fainter objects. With large scale panchromatic synoptic surveys becoming more common, image co-addition has become even more necessary as new observations start to get compared with co-added fiducial sky in real time. The standard co-addition techniques have included straight averages, variance weighted averages, medians etc. A more sophisticated nonlinear response chi-square method is also used when it is known that the data are background noise limited and the point spread function is homogenized in all channels. A more robust object detection technique capable of detecting faint sources, even those not seen at all epochs which will normally be smoothed out in traditional methods, is described. The analysis at each pixel level is based on a formula similar to Mahalanobis distance. The method does not depend on the point spread function.
Predicting and Analyzing Law-Making in Kenya

Modelling and analyzing parliamentary legislation, roll-call votes and order of proceedings in developed countries has received significant attention in recent years. In this paper, we focused on understanding the bills introduced in a developing democracy, the Kenyan bicameral parliament. We developed and trained machine learning models on a combination of features extracted from the bills to predict the outcome - if a bill will be enacted or not. We observed that the texts in a bill are not as relevant as the year and month the bill was introduced and the category the bill belongs to.
BE VISION, A Package of IBM 7090 FORTRAN Programs to Draw Orthographic Views of Combinations of Plane and Quadric Surfaces

BE VISION is a package of FORTRAN programs for drawing orthographic views of combinations of plane and quadric surfaces. As input, the package takes rectangular coordinate equations specifying the surfaces plus a three-angle specification of the viewing direction. Output is a drawing on the Stromberg Carlson 4020 Microfilm Recorder. Many views of one scene may be obtained simply by changing the viewpoint. The various subroutines of the package and their functions are described in this paper. It also gives numerous examples of pictures that were produced by BE VISION. The package has been in use since April 1964.
Distributionally Robust Batch Contextual Bandits

Policy learning using historical observational data are an important problem that has widespread applications. Examples include selecting offers, prices, or advertisements for consumers; choosing bids in contextual first-price auctions; and selecting medication based on patients’ characteristics. However, existing literature rests on the crucial assumption that the future environment where the learned policy will be deployed is the same as the past environment that has generated the data: an assumption that is often false or too coarse an approximation. In this paper, we lift this assumption and aim to learn a distributionally robust policy with incomplete observational data. We first present a policy evaluation procedure that allows us to assess how well the policy does under worst-case environment shift. We then establish a central limit theorem type guarantee for this proposed policy evaluation scheme. Leveraging this evaluation scheme, we further propose a novel learning algorithm that is able to learn a policy that is robust to adversarial perturbations and unknown covariate shifts with a performance guarantee based on the theory of uniform convergence. Finally, we empirically test the effectiveness of our proposed algorithm in synthetic datasets and demonstrate that it provides the robustness that is missing using standard policy learning algorithms. We conclude the paper by providing a comprehensive application of our methods in the context of a real-world voting data set. This paper was accepted by Hamid Nazerzadeh, data science. Funding: This work was supported by the National Science Foundation [Grant CCF-2106508] and the Air Force Office of Scientific Research [Award FA9550-20-1-0397]. Z. Zhou also gratefully acknowledges the JP Morgan AI Research Grant and the New York University’s Center for Global Economy and Business faculty research grant for support on this work. Additional support is gratefully acknowledged from the National Science Foundation [Grants 1915967 and 2118199]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023. 4678 .
The nucleotide sequence of a 2·1 kb fragment from chromosome VI of Saccharomyces cerevisiae identifies a tRNAGly gene, part of a delta element and a palindromic sequence

The nucleotide sequence was determined of a 2·1 kb DNA fragment located at approximately 35 kb to the right of the centromere of chromosome VI from Saccharomyces cerevisiae. Analysis revealed the presence of a tRNAGLy gene, part of a delta element and a remarkable palindromic sequence. The longest open reading frame found encodes a putative protein of 195 amino acids. Although the fragment was isolated by hybridization to a human diacylglycerol kinase cDNA, no evidence was obtained for the presence of a gene encoding diacylglycerol kinase.
Performance Evaluation of OpenID Connect for an IoT Information Marketplace

The continuous global growth of Internet of Things devices and deployments constantly produce an equally growing volume of streaming data. This volume puts high pressure on the cloud infrastructure that is expected to manage the storage and processing of such streams. Special purpose IoT data management platforms such as the IoT Framework which is the system under study in this paper, transform raw streaming data into actual information products thus providing value added services to interested users. The purpose of this paper is to evaluate and analyse the impact of the OpenID Connect protocol on the performance of the IoT Framework. The OpenID protocol was introduced in order to enable the IoT framework to be transformed into an IoT Information marketplace. The obtained results show that the introduction of an AA mechanism has a considerable impact on the performance of the IoT Framework. The reason is that the AA server is remote with respect to the IoT platform and therefore the network delay impacts the experienced end to end delay by the user. A possible means to alleviate this problem is to create a local AA server closer to the IoT Platform which either maintains local user AA credentials or federates with other public AA servers.