Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of triclosan on bacterial community composition and Vibrio populations in natural seawater microcosms

Pharmaceuticals and personal care products, including antimicrobials, can be found at trace levels in treated wastewater effluent. Impacts of chemical contaminants on coastal aquatic microbial community structure and pathogen abundance are unknown despite the potential for selection through antimicrobial resistance. In particular, Vibrio, a marine bacterial genus that includes several human pathogens, displays resistance to the ubiquitous antimicrobial compound triclosan. Here we demonstrated through use of natural seawater microcosms that triclosan (at a concentration of ~5 ppm) can induce a significant Vibrio growth response (68–1,700 fold increases) in comparison with no treatment controls for three distinct coastal ecosystems: Looe Key Reef (Florida Keys National Marine Sanctuary), Doctors Arm Canal (Big Pine Key, FL), and Clam Bank Landing (North Inlet Estuary, Georgetown, SC). Additionally, microbial community analysis by 16 S rRNA gene sequencing for Looe Key Reef showed distinct changes in microbial community structure with exposure to 5 ppm triclosan, with increases observed in the relative abundance of Vibrionaceae (17-fold), Pseudoalteromonadaceae (65-fold), Alteromonadaceae (108-fold), Colwelliaceae (430-fold), and Oceanospirillaceae (1,494-fold). While the triclosan doses tested were above concentrations typically observed in coastal surface waters, results identify bacterial families that are potentially resistant to triclosan and/or adapted to use triclosan as a carbon source. The results further suggest the potential for selection of Vibrio in coastal environments, especially sediments, where triclosan may accumulate at high levels.
Constant and shifting photoperiods as seasonal cues during larval development of the univoltine damselfly Lestes sponsa (Odonata: Lestidae)

Larvae were reared at 21.5°C from eggs from southernmost Sweden, and fed ad libitum to emergence in four different photoperiodic treatments, intended to represent increasing levels of time stress: constant LD 16:8, corresponding to late April (or August) conditions, a shift after about two weeks from LD 16:8 to 19.5:4.5, coarsely simulating late spring, constant LD 19.5:4.5, corresponding to the summer solstice, and a shift from LD 19.5:4.5 to 16:8, coarsely simulating late summer. Mean larval development time significantly decreased in this series: 47.5, 45.2, 43.0 and 39 days (n = 11–13 larvae), respectively. This suggests an ecologically relevant integration of absolute photoperiods and changes in photoperiod, allowing larvae to distinguish if LD 16:8 represented spring or late summer, depending on earlier experience. Thus, rapid development, a long day response during spring conditions, is further speeded up by shorter days during late summer. In early stadia, moulting intervals were uniform, but long days may to some extent have programmed young larvae to develop with fewer moults, thereby increasing development rate. In the last four stadia the principal effect was variation in moulting intervals. Adult size was little affected. Homogeneous conditions and low genetic diversity produced a remarkably synchronous development within treatments, with an emergence span of 5–10 days. Due to low numbers of larvae, derived from a single female, and problems with a switch, the generality of these results would need confirmation.
Few-Class Learning For Image-Classification-Aware Denoising

The accuracy of image classification networks decreases substantially in noisy situations such as low light environments. A simple solution to this problem is to integrate an image denoising network as a preprocessing operation before classification and then to retrain the classifier to improve the classification accuracy. However, this straightforward approach suffers from excessive training time because the denoising network requires the entire dataset to conduct end-to-end training. In this paper, instead of using classification accuracy as a loss function, we propose using the difference of the outputs of the hidden layer of the image classification network. This loss function, known as feature loss or perceptual loss, allows us to train the denoising network using only limited images containing extremely few classes from the dataset. The experimental results show that the proposed method dramatically improves the classification accuracy, when we use only a few classes (from 2.5% to 10% of the original dataset) for training. This approach is effective on previously unseen classes even when the image classifier network has been changed by fine-tuning.
Compression of Vehicle Trajectories with a Variational Autoencoder

The perception and prediction of the surrounding vehicles’ trajectories play a significant role in designing safe and optimal control strategies for connected and automated vehicles. The compression of trajectory data and the drivers’ strategic behavior’s classification is essential to communicate in vehicular ad-hoc networks (VANETs). This paper presents a Variational Autoencoder (VAE) solution to solve the compression problem, and as an added benefit, it also provides classification information. The input is the time series of vehicle positions along actual real-world trajectories obtained from a dataset containing highway measurements, which also serves as the target. During training, the autoencoder learns to compress and decompress this data and produces a small, few element context vector that can represent vehicle behavior in a probabilistic manner. The experiments show how the size of this context vector affects the performance of the method. The method is compared to other approaches, namely, Bidirectional LSTM Autoencoder and Sparse Convolutional Autoencoder. According to the results, the Sparse Autoencoder fails to converge to the target for the specific tasks. The Bidirectional LSTM Autoencoder could provide the same performance as the VAE, though only with double context vector length, proving that the compression capability of the VAE is better. The Support Vector Machine method is used to prove that the context vector can be used for maneuver classification for lane changing behavior. The utilization of this method, considering neighboring vehicles, can be extended for maneuver prediction using a wider, more complex network structure.
Jump Linear Quadratic Control for Microgrids with Commercial Loads

Due to the aging power-grid infrastructure and increased usage of renewable energies, microgrids (μGrids) have emerged as a promising paradigm. It is reasonable to expect that they will become one of the fundamental building blocks of a smart grid, since effective energy transfer and coordination of μGrids could help maintain the stability and reliability of the regional large-scale power-grid. From the control perspective, one of the key objectives of μGrids is load management using local generation and storage for optimized performance. Accomplishing this task can be challenging, however, particularly in situations where local generation is unpredictable both in quality and in availability. This paper proposes to address that problem by developing a new optimal energy management scheme, which meets the requirements of supply and demand. The method that will be described in the following models μGrids as a stochastic hybrid dynamic system. Jump linear theory is used to maximize storage and renewable energy usage, and Markov chain theory is applied to model the intermittent generation of renewable energy based on real data. Although the model itself is quite general, we will focus exclusively on solar energy, and will define the performance measure accordingly. We will demonstrate that the optimal solution in this case is a state feedback law with a piecewise constant gain. Simulation results are provided to illustrate the effectiveness of such an approach.
NAP: Natural App Processing for Predictive User Contexts in Mobile Smartphones

The resource management of an application is an essential task in smartphones. Optimizing the application launch process results in a faster and more efficient system, directly impacting the user experience. Predicting the next application that will be used can orient the smartphone to address the system resources to the correct application, making the system more intelligent and efficient. Neural networks have been presenting outstanding results in the state-of-the-art for mapping large sequences of data, outperforming all previous classification and prediction models. A recurrent neural network (RNN) is an artificial neural network associated with sequence models, and it can recognize patterns in sequences. One of the areas that use RNN is language modeling (LM). Given an arrangement of words, LM can learn how the words are organized in sentences, making it possible to predict the next word given a group of previous words. We propose building a predictive model inspired by LM. However, instead of using words, we will use previous applications to predict the next application. Moreover, some context features, such as timestamp and energy record, will be included in the prediction model to evaluate the impact of the features on the performance. We will provide the following application prediction result and extend it to the top-k possible candidates for the next application.
Harnessing “A Billion Years of Experimentation”: The Ongoing Exploration and Exploitation of CRISPR–Cas Immune Systems

Abstract The famed physicist-turned-biologist, Max Delbrück, once remarked that, for physicists, “the field of bacterial viruses is a fine playground for serious children who ask ambitious questions. ” Early discoveries in that playground helped establish molecular genetics, and half a century later, biologists delving into the same field have ushered in the era of precision genome engineering. The focus has of course shifted—from bacterial viruses and their mechanisms of infection to the bacterial hosts and their mechanisms of immunity—but it is the very same evolutionary arms race that continues to awe and inspire researchers worldwide. In this review, we explore the remarkable diversity of CRISPR–Cas adaptive immune systems, describe the molecular components that mediate nucleic acid targeting, and outline the use of these RNA-guided machines for biotechnology applications. CRISPR–Cas research has yielded far more than just Cas9-based genome-editing tools, and the wide-reaching, innovative impacts of this fascinating biological playground are sure to be felt for years to come.
Acute kidney disease and long-term outcomes in critically ill acute kidney injury patients with sepsis: a cohort analysis

Abstract Background Acute kidney injury (AKI) is frequent during hospitalization and may contribute to adverse short- and long-term consequences. Acute kidney disease (AKD) reflects the continuing pathological processes and adverse events developing after AKI. We aimed to evaluate the association of AKD, long-term adverse renal function and mortality in a cohort of patients with sepsis. Methods We performed a retrospective analysis of adult patients with septic AKI admitted to the Division of Intensive Medicine of the Centro Hospitalar Lisboa Norte (Lisbon, Portugal) between January 2008 and December 2014. Patients were categorized according to the development of AKI using the Kidney Disease: Improving Global Outcomes (KDIGO) classification. AKI was defined as an increase in absolute serum creatinine (SCr) ≥0.3 mg/dL or by a percentage increase in SCr ≥50% and/or by a decrease in urine output to <0.5 mL/kg/h for >6 h. AKD was defined as presenting at least KDIGO Stage 1 criteria for >7 days after an AKI initiating event. Adverse renal outcomes (need for long-term dialysis and/or a 25% decrease in estimated glomerular filtration rate after hospital discharge) and mortality after discharge were evaluated. Results From 256 selected patients with septic AKI, 53.9% developed AKD. The 30-day mortality rate was 24.5% (n = 55). The mean long-term follow-up was 45.9 ± 43.3 months. The majority of patients experience an adverse renal outcome [n = 158 (61.7%)] and 44.1% (n = 113) of patients died during follow-up. Adverse renal outcomes, 30-day mortality and long-term mortality after hospital discharge were more frequent among AKD patients [77.5 versus 43.2% (P < 0.001), 34.1 versus 6.8% (P < 0.001) and 64.8 versus 49.1% (P = 0.025), respectively]. The 5-year cumulative probability of survival was 23.2% for AKD patients, while it was 47.5% for patients with no AKD (log-rank test, P < 0.0001). In multivariate analysis, AKD was independently associated with adverse renal outcomes {adjusted hazard ratio [HR] 2.87 [95% confidence interval (CI) 2.0–4.1]; P < 0.001} and long-term mortality [adjusted HR 1.51 (95% CI 1.0–2.2); P = 0.040]. Conclusions AKD after septic AKI was independently associated with the risk of long-term need for dialysis and/or renal function decline and with the risk of death after hospital discharge.
Optimizing Machine Learning Parameters for Classifying the Sweetness of Pineapple Aroma Using Electronic Nose

: Electronic nose (e-nose) has been widely used to distinguish various scents in food. The output of e-nose is a signal that can be identified, compared, and analyzed. However, many researchers use e-nose without using standardization tools, therefore e-nose is still often questioned for its validity. This paper proposes an electronic nose (e-nose) to classify the sweetness of pineapples. The standard sweetness levels are measured by using a Brix meter as a standardization tool. The e-nose consists of a series of gas sensors MQ Series which are connected to the Arduino micro-controller. The sweetness levels measured by the Brix meter are then ordered into low, medium, high sweetness groups. These sweetness groups are used as label ground-truth for the e-nose. Signal processing PCA and mother wavelet is employed to reduce noise from the e-nose signals. The signal processing methods obtain optimal parameters to find the characteristics of each signal. Machine learning methods were successfully carried out with optimized parameters for the classification of three levels of sweetness of pineapple. The best accuracy is 82% using KNN with 3 neighbors.
The utility of LAI-2000 canopy analyzer studying the sunlight distribution characteristics in different plant colonies

【Objective】 To seek a fast and accurate method for determining the light transmission rate and extinction coefficient. 【Method】The LAI, foliar obliquity, canopy openness and light transmission rate in different plant colonies were measured using canopy analyzer LAI-2000 and the solar radiation instruments including the data logger LI-1400, solar radiation sensor LI-190 and LI-191. 【Result】 (1) the canopy openness measured by using LAI-2000 had a very close relationship with the sunlight transmission rate of the colonies. The correlation coefficient was 0.9308, the intercept of the regression line was 0.015, which was almost equal to 0, and the slope was 1.0668, which was almost equal to 1. So the canopy openness could be used to estimate the light transmission rate, and the cloudy weather was more suitable. (2) Using the measured LAI and colony light transmission rate, the extinction coefficient of different canopies were calculated accurately. Under the corn canopy, the extinction coefficient changed clearly during the daytime. It had two maximum values and one minimum value. The minimum value happened in the noon. 【Conclusion】LAI-2000 canopy analyzer can provide the sky openness of different zenith angles, so it can describe the colony structure in detail; The canopy openness under the canopy is almost equal to the light transmission rate, therefore the measurement of light transmission rate no longer needs the light measurements of over and under the canopy. The LAI of agricultural and forestry canopy measured by LAI-2000 without damaging the colony can be used to calculate the extinction coefficient, and the extinction coefficient calculated in this paper is close to those reported in the references.
Ice algae resource utilization by benthic macro- and megafaunal communities on the Pacific Arctic shelf determined through lipid biomarker analysis

We studied ice algae utilization by benthic fauna from the northern Bering and Chukchi Seas using highly branched isoprenoid (HBI) biomarkers. We assessed whether various food acquisition strategies influence the observed HBI signatures. The proportion of phytoplankton to ice algae-sourced HBIs was determined through the H-Print approach that is presumed to reflect the percentage of sea ice organic carbon (iPOC) incorporated into tissues, relative to phytoplankton organic carbon. Cluster analysis separated 3 groups based on location and feeding strategy that were significantly influenced by annual sea ice persistence. Ice algae utilization was most significant in the northeast Chukchi Sea, where seasonal sea ice was present the longest. General feeding strategy was determined to be a significant factor in the degree of ice algae utilization. Predominant deposit feeders (both surface and subsurface) used more ice algae relative to suspension feeders. Organic carbon incorporated by predominant suspension feeders was primarily phytoplankton-based. The vast majority of all organisms sampled (~90%) incorporated a measurable quantity of iPOC. Sipunculids and brittle stars had the highest relative dependence on ice algae, while other taxa displayed plastic dietary responses, including the suspension/surface deposit feeder Macoma calcarea. This study indicates that ice algae are widely utilized in Pacific Arctic benthic food webs, but most benthic organisms displayed flexibility in consuming the available food sources. The elevated utilization of ice algae by deposit feeders may prove to be a disadvantage for these organisms if they cannot adapt to the ongoing decline of iPOC as seasonal sea ice declines.
A Siamese Network Utilizing Image Structural Differences For Cross-Category Defect Detection

A machine learning based defect detection system generally requires a new training procedure upon a new product category. In industry applications where there are variant product categories, a re-training upon category changing could be time expensive and unacceptable. In this work, a two-layer neural networks are proposed for cross-category defect detection without re-training. Different from traditional neural networks, the proposed method learns differences from image-pairs containing certain structural similarity rather than from a single image. With the assumption that different categorical objects could share certain structural similarity indicated by these learned image pairwise differences, a pairwise Siamese neural network is used in the proposed neural networks for defect detection. The cross-category capability of the proposed method is evidenced via experiments based on real-world factory datasets.
Evaluation of clinical samples by various serological, biochemical and Polymerase Chain Reaction techniques for brucellosis

The present study included, the samples such as 24 whole blood, 24 sera, 24 milk and 24 vaginal samples from cattle withvarious clinical problems, were collected and used to detect etiological agent, antibody and genome for Brucellosis. All fourdifferent types of samples collected from each animal in this study consisted together a total of 96 samples. All samples weresubjected to isolation, biochemical, serological and polymerase chain reaction diagnostic tests. From 19 cattle with differentreproductive problems; uterine prolapse, retained placenta, abortion and combined with other problems such as reduction in milkyield, anorexia, diarrhoea, pregnancy and ketosis etc., tested showed none of the vaginal samples positive by culture methodand all biochemical tests. Of 24 milk samples tested by Milk Ring Test, 3 samples (12.5%) were found positive for antibody toBrucellosis. Among 24 serum samples screened , one sample (4.16%) and 2 samples (8.33%) were found positive by Rapid PlateAgglutination Test and Standard Tube Agglutination Test respectively. Out of 24 blood DNA tested for the detection of IS711gene and B4/B5 gene by polymerase Chain Reaction for Brucellosis, 15 DNA samples (62.5%) were detected as positive byboth gene primners. A total of 24 milk DNA samples screened by polymerase Chain Reaction 2 samples showed positivity forIS711 gene and B4/B5 gene for Brucellosis. Polymerase chain reaction showed higher positivity compared to other test used inthis study. Between the serological tests, STAT showed little higher response than RPT. Though culture method is gold standardtest, in this study not even a single vaginal samples were found positive by culture technique for BrucellosisKeywords: Cattle, Diagnostic Screening, Brucellosis, PCR.
Rapid or long-term employment? A Scandinavian comparative study of refugee integration policies and employment outcomes

ABSTRACT Although many studies compare national integration policies, analyses connecting these policies to integration outcomes are rare. This study combines longitudinal analysis of employment outcomes for Scandinavian refugees, with analyses of integration measures and policies to explain these differences. Can different usages of integration measures explain cross-national differences in employment outcomes between genders and with increased residence time? Moreover, can the countries’ integration policies explain such differences? Our analyses show substantial cross-national differences. Danish male refugees are employed faster; however, Sweden catches up, and Norway surpasses Danish employment levels with increased residence time. Additionally, Norway has a substantially smaller gender gap in employment than Sweden and particularly Denmark. We demonstrate that different usages of integration measures may explain differences in outcomes. We conjecture that different policies regulating (1) financial benefits and (2) employment and self-sufficiency requirements for obtaining a secure legal status may reinforce differences in programme participation and employment patterns.
Cumulative (and self-reinforcing) spatial inequalities: Interactions between accessibility and segregation in four Brazilian metropolises

The inequalities that mark global society have been deepening worldwide. They materialize in cities, putting pressure on public transport systems for spatial and temporal supply, at the same time as mobility itself generates multifaceted inequalities. From empirical evidence of four socially and spatially distinct Brazilian cities — São Paulo, Rio de Janeiro, Curitiba, and Fortaleza — we explore how differences in scale, geography, class, and race are related to spatial segregation, leading to different levels of access to jobs by public transport in the global peripheral context. These juxtaposed and combined inequalities create highly unfair and strongly cumulative effects on some social groups, contributing to the reproduction of inequality. Based on public and open data and combining methodologies of spatial analysis to enhance comparability and reproducibility, we explore different areal units, time thresholds, and metrics in order to examine transport inequalities in different urban contexts and refine our results. Upper classes have higher accessibility than lower classes, whites have higher accessibility than blacks, and large cities are more unequal than smaller ones. However, racial inequalities combine and overlap with class and city inequalities, changing these dichotomic notions when multiple dimensions are considered. The groups that polarize social hierarchy also polarize the urban space, since the white upper class and the black lower class are more segregated, but the way segregation interacts with accessibility is not straightforward and varies according to the socio-spatial structure.