Miracle-dz/UniFAR-FTUs huggingface dataset:
Organic solvent-free matrix solid phase dispersion (MSPD) for determination of synthetic colorants in chilli powder by high-performance liquid chromatography (HPLC-UV)

A green and simple organic solvent-free matrix solid phase dispersion (MSPD) was developed and applied to the extraction of four synthetic colorants (amaranth, sunset yellow, allura red and erythrosine) in chilli samples. High-performance liquid chromatography (HPLC) coupled with ultraviolet (UV) detection was used for the separation and determination of the analytes. The chromatographic separation was performed on a Phenomenex Luna C18 (2) 100A column in a gradient using methanol/water as the mobile phase at a flow rate of 0.8 mL min−1 and 35 °C of temperature. Factors such as the type of dispersant, the ratio of sample to dispersant, and the type, volume, pH value and ionic strength of elution solvent were investigated. Under the optimum experimental conditions, the linearities for determining the analytes were in the range of 30–2000 ng g−1 for amaranth and erythrosine, 20–2000 ng g−1 for sunset yellow and allura erythrosine. Limits of detection ranged between 8.6 and 13.5 ng g−1. No organic solvents were used in the extraction procedure, and 4 mL water directly eluted the analytes. Thus, the method avoided environmental pollution of organic solvents and removed multi-step procedures in the sample pretreatment and has potential to be applied using a simple instrument presented in most analytical laboratories.
The CARDE-B Scoring System Predicts 30-Day Mortality After Revision Total Joint Arthroplasty.

BACKGROUND There exists a substantial risk of having a perioperative complication after revision total joint arthroplasty (TJA). The complex shared decision-making between surgeon and patient would benefit from a high-fidelity tool to identify patients at risk for mortality after revision TJA. Therefore, we developed the CARDE-B score. CARDE-B is an acronym for congestive heart failure, albumin or malnutrition (<3.5 mg/dL), renal failure on dialysis, dependence for daily living, elderly (>65 years of age), and body mass index <25 kg/m2. We developed and validated the CARDE-B score to determine the risk of death within 30 days of a revision TJA. METHODS A total of 13,118 revision TJAs (40% hip and 60% knee) from the National Surgical Quality Improvement Program (NSQIP) database were analyzed. A simple 1-point scoring system, CARDE-B, was created for predicting 30-day mortality after revision TJA, based on a logistic regression model. The CARDE-B scoring system assigns 1 point to each criterion in the acronym: congestive heart failure, albumin (<3.5 mg/dL), renal failure on dialysis, dependence for daily living, elderly (>65 years of age), and body mass index of <25 kg/m2. The CARDE-B scoring system was compared with 2 commonly utilized scores: American Society of Anesthesiologists (ASA) physical status classification and the 5-factor modified frailty index (mFI-5). The area under the curve (AUC) was used to assess the accuracy of each model. The Hosmer-Lemeshow test was used to assess goodness of fit. Finally, the Nationwide Inpatient Sample (NIS) was used for external validation of the CARDE-B score in 19,153 patients who underwent revision TJA in 2017. RESULTS Eighty-eight patients (0.7%) did not survive 30 days after revision TJA. The AUC for the logistic regression model was 0.88 in both the derivation and internal validation samples using NSQIP. The predicted probability of 30-day mortality after revision TJA increased stepwise from <0.01% for a CARDE-B score of 0 points to 39% for a CARDE-B score of 5 points. The AUC for the CARDE-B score predicting 30-day mortality after revision TJA was 0.85. This was more accurate (p < 0.001) than the ASA physical status classification (AUC, 0.77) and the mFI-5 (AUC, 0.67). The AUC for the CARDE-B score in the NIS external validation set was 0.75. The Hosmer-Lemeshow p value for goodness of fit was 0.34, indicating goodness of fit in the external validation sample. CONCLUSIONS The CARDE-B score is a simple system that predicts the risk of death within 30 days of a revision TJA, offering surgeons and patients a valuable and validated risk-stratification tool. LEVEL OF EVIDENCE Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
Interactions with Commensal and Pathogenic Bacteria Induce HIV-1 Latency in Macrophages through Altered Transcription Factor Recruitment to the Long Terminal Repeat

The major barrier toward the eradication of HIV-1 infection is the presence of a small reservoir of latently infected cells, which include CD4+ T cells and macrophages that escape immune-mediated clearance and the effects of antiretroviral therapy. There remain crucial gaps in our understanding of the molecular mechanisms that lead to transcriptionally silent or latent HIV-1 infection of macrophages. ABSTRACT Macrophages are infected by human immunodeficiency virus type 1 (HIV-1) in vivo and contribute to both viral spread and pathogenesis. Recent human and animal studies suggest that HIV-1-infected macrophages serve as a reservoir that contributes to HIV-1 persistence during antiretroviral therapy. The ability of macrophages to serve as persistent viral reservoirs is likely influenced by the local tissue microenvironment, including interactions with pathogenic and commensal microbes. Here, we show that the sexually transmitted pathogen Neisseria gonorrhoeae (gonococcus [GC]) and the gut-associated microbe Escherichia coli, which encode ligands for both Toll-like receptor 2 (TLR2) and TLR4, repressed HIV-1 replication in macrophages and thereby induced a state reminiscent of viral latency. This repression was mediated by signaling through TLR4 and the adaptor protein Toll/interleukin 1 (IL-1) receptor domain-containing adapter-inducing beta interferon (IFN) (TRIF) and was associated with increased production of type I interferons. Inhibiting TLR4 signaling, blocking type 1 interferon, or knocking down TRIF reversed lipopolysaccharide (LPS)- and gonococcus-mediated repression of HIV-1. Finally, the repression of HIV-1 in macrophages was associated with the recruitment of interferon regulatory factor 8 (IRF8) to the interferon-stimulated response element (ISRE) downstream of the HIV-1 5′ long terminal repeat (LTR). Our data indicate that IRF8 is responsible for repression of HIV-1 replication in macrophages in response to TRIF-dependent signaling during N. gonorrhoeae and E. coli coinfection. These findings highlight the potential role of macrophages as HIV-1 reservoirs, as well as the roles of the tissue microenvironment and coinfections as modulators of HIV-1 persistence. IMPORTANCE The major barrier toward the eradication of HIV-1 infection is the presence of a small reservoir of latently infected cells, which include CD4+ T cells and macrophages that escape immune-mediated clearance and the effects of antiretroviral therapy. There remain crucial gaps in our understanding of the molecular mechanisms that lead to transcriptionally silent or latent HIV-1 infection of macrophages. The significance of our research is in identifying microenvironmental factors, such as commensal and pathogenic microbes, that can contribute to the establishment and maintenance of latent HIV-1 infection in macrophages. It is hoped that identifying key processes contributing to HIV-1 persistence in macrophages may ultimately lead to novel therapeutics to eliminate latent HIV-1 reservoirs in vivo.
Deep SAR Imaging and Motion Compensation

Compressive sensing (CS) and matrix sensing (MS) techniques have been applied to the synthetic aperture radar (SAR) imaging problem to reduce the sampling amount of SAR echo using the sparse or low-rank prior information. To further exploit the redundancy and improve sampling efficiency, we take a different approach, wherein a deep SAR imaging algorithm is proposed. The main idea is to exploit the redundancy of the backscattering coefficient using an auto-encoder structure, wherein the hidden latent layer in auto-encoder has lower dimension and less parameters than the backscattering coefficient layer. Based on the auto-encoder model, the parameters of the auto-encoder structure and the backscattering coefficient are estimated simultaneously by optimizing the reconstruction loss associated with the down-sampled SAR echo. In addition, in order to meet the practical application requirements, a deep SAR motion compensation algorithm is proposed to eliminate the effect of motion errors on imaging results. The effectiveness of the proposed algorithms is verified on both simulated and real SAR data.
Association between serum zinc concentrations and metabolic risk factors among Chinese children and adolescents

Abstract The aim of the present study was to examine whether serum Zn concentrations were associated with metabolic risk factors in Chinese children and adolescents. This was a cross-sectional study including 3241 participants, aged 6 to 17 years, from Jiangsu, China. Metabolic risk factors included fasting glucose (FG), total cholesterol (TC), TAG, HDL-cholesterol, LDL-cholesterol, systolic blood pressure and diastolic blood pressure. Data were analysed using multi-variable linear regression and generalised additive models, which were adjusted for age, sex, high-sensitive C-reactive protein, estimated glomerular filtration rate, BMI and region of residence, to assess the associations of serum Zn concentrations with metabolic risk factors. We observed a negative association between serum Zn concentrations and FG (coefficient = −0·532; 95 % CI −0·569, −0·495; P < 0·001). Moreover, TC (coefficient = 0·175; 95 % CI 0·127, 0·222; P < 0·001), HDL-cholesterol (coefficient = 0·137; 95 % CI 0·082, 0·193; P < 0·001) and LDL-cholesterol (coefficient = 0·195; 95 % CI 0·128, 0·263; P < 0·001) were found to be positively associated with Zn levels. A generalised additive model showed that the negative association between serum Zn and FG was weak at lower serum Zn concentrations and was stronger with the increase in serum Zn concentrations. Additionally, a U-shaped association between serum Zn and TAG was observed. Serum Zn concentrations were associated with FG, TC, TAG, HDL-cholesterol and LDL-cholesterol levels in Chinese children and adolescents. Lower levels of serum Zn were more likely related to a poor metabolic status.
Genetic Evaluation of Nosocomial Candida auris Transmission

Whole-genome sequences of Candida auris isolates from nosocomial and nonnosocomial infections were compared. The average numbers of single nucleotide variations were different between the two groups. The small amount of genetic variability between intra- or interhost isolates suggests recovery of all colonizing or infecting genomes for comparison is required for outbreaks. ABSTRACT Whole-genome sequences of Candida auris isolates from nosocomial and nonnosocomial infections were compared. The average numbers of single nucleotide variations were different between the two groups. The small amount of genetic variability between intra- or interhost isolates suggests recovery of all colonizing or infecting genomes for comparison is required for outbreaks.
Fisheries management as a Stackelberg Evolutionary Game: Finding an evolutionarily enlightened strategy

Fish populations subject to heavy exploitation are expected to evolve over time smaller average body sizes. We introduce Stackelberg evolutionary game theory to show how fisheries management should be adjusted to mitigate the potential negative effects of such evolutionary changes. We present the game of a fisheries manager versus a fish population, where the former adjusts the harvesting rate and the net size to maximize profit, while the latter responds by evolving the size at maturation to maximize the fitness. We analyze three strategies: i) ecologically enlightened (leading to a Nash equilibrium in game-theoretic terms); ii) evolutionarily enlightened (leading to a Stackelberg equilibrium) and iii) domestication (leading to team optimum) and the corresponding outcomes for both the fisheries manager and the fish. Domestication results in the largest size for the fish and the highest profit for the manager. With the Nash approach the manager tends to adopt a high harvesting rate and a small net size that eventually leads to smaller fish. With the Stackelberg approach the manager selects a bigger net size and scales back the harvesting rate, which lead to a bigger fish size and a higher profit. Overall, our results encourage managers to take the fish evolutionary dynamics into account. Moreover, we advocate for the use of Stackelberg evolutionary game theory as a tool for providing insights into the eco-evolutionary consequences of exploiting evolving resources.
Creating and Evaluating Chatbots as Eligibility Assistants for Clinical Trials

Clinical trials are important tools to improve knowledge about the effectiveness of new treatments for all diseases, including cancers. However, studies show that fewer than 5% of cancer patients are enrolled in any type of research study or clinical trial. Although there is a wide variety of reasons for the low participation rate, we address this issue by designing a chatbot to help users determine their eligibility via interactive, two-way communication. The chatbot is supported by a user-centered classifier that uses an active deep learning approach to separate complex eligibility criteria into questions that can be easily answered by users and information that requires verification by their doctors. We collected all the available clinical trial eligibility criteria from the National Cancer Institute's website to evaluate the chatbot and the classifier. Experimental results show that the active deep learning classifier outperforms the baseline k-nearest neighbor method. In addition, an in-person experiment was conducted to evaluate the effectiveness of the chatbot. The results indicate that the participants who used the chatbot achieved better understanding about eligibility than those who used only the website. Furthermore, interfaces with chatbots were rated significantly better in terms of perceived usability, interactivity, and dialogue.
Efficient Auto-Tuning of Parallel Programs with Interdependent Tuning Parameters via Auto-Tuning Framework (ATF)

Auto-tuning is a popular approach to program optimization: it automatically finds good configurations of a program’s so-called tuning parameters whose values are crucial for achieving high performance for a particular parallel architecture and characteristics of input/output data. We present three new contributions of the Auto-Tuning Framework (ATF), which enable a key advantage in general-purpose auto-tuning: efficiently optimizing programs whose tuning parameters have interdependencies among them. We make the following contributions to the three main phases of general-purpose auto-tuning: (1) ATF generates the search space of interdependent tuning parameters with high performance by efficiently exploiting parameter constraints; (2) ATF stores such search spaces efficiently in memory, based on a novel chain-of-trees search space structure; (3) ATF explores these search spaces faster, by employing a multi-dimensional search strategy on its chain-of-trees search space representation. Our experiments demonstrate that, compared to the state-of-the-art, general-purpose auto-tuning frameworks, ATF substantially improves generating, storing, and exploring the search space of interdependent tuning parameters, thereby enabling an efficient overall auto-tuning process for important applications from popular domains, including stencil computations, linear algebra routines, quantum chemistry computations, and data mining algorithms.
Transfer Urban Human Mobility via POI Embedding over Multiple Cities

Rapidly developing location acquisition technologies provide a powerful tool for understanding and predicting human mobility in cities, which is very significant for urban planning, traffic regulation, and emergency management. However, with the existing methodologies, it is still difficult to accurately predict millions of peoples’ mobility in a large urban area such as Tokyo, Shanghai, and Hong Kong, especially when collected data used for model training are often limited to a small portion of the total population. Obviously, human activities in city are closely linked with point-of-interest (POI) information, which can reflect the semantic meaning of human mobility. This motivates us to fuse human mobility data and city POI data to improve the prediction performance with limited training data, but current fusion technologies can hardly handle these two heterogeneous data. Therefore, we propose a unique POI-embedding mechanism, that aggregates the regional POIs by categories to generate an artificial POI-image for each urban grid and enriches each trajectory snippet to a four-dimensional tensor in an analogous manner to a short video. Then, we design a deep learning architecture combining CNN with LSTM to simultaneously capture both the spatiotemporal and geographical information from the enriched trajectories. Furthermore, transfer learning is employed to transfer mobility knowledge from one city to another, so that we can fully utilize other cities’ data to train a stronger model for the target city with only limited data available. Finally, we achieve satisfactory performance of human mobility prediction at the citywide level using a limited amount of trajectories as training data, which has been validated over five urban areas of different types and scales.
An Empirical Study of Developer Discussions in the Gitter Platform

Developer chatrooms (e.g., the Gitter platform) are gaining popularity as a communication channel among developers. In developer chatrooms, a developer (asker) posts questions and other developers (respondents) respond to the posted questions. The interaction between askers and respondents results in a discussion thread. Recent studies show that developers use chatrooms to inquire about issues, discuss development ideas, and help each other. However, prior work focuses mainly on analyzing individual messages of a chatroom without analyzing the discussion thread in a chatroom. Developer chatroom discussions are context-sensitive, entangled, and include multiple participants that make it hard to accurately identify threads. Therefore, prior work has limited capability to show the interactions among developers within a chatroom by analyzing only individual messages. In this article, we perform an in-depth analysis of the Gitter platform (i.e., developer chatrooms) by analyzing 6,605,248 messages of 709 chatrooms. To analyze the characteristics of the posted questions and the impact on the response behavior (e.g., whether the posted questions get responses), we propose an approach that identifies discussion threads in chatrooms with high precision (i.e., 0.81 F-score). Our results show that inactive members responded more often and unique questions take longer discussion time than simple questions. We also find that clear and concise questions are more likely to be responded to than poorly written questions. We further manually analyze a randomly selected sample of 384 threads to examine how respondents resolve the raised questions. We observe that more than 80% of the studied threads are resolved. Advanced-level/beginner-level questions along with the edited questions are the mostly resolved questions. Our results can help the project maintainers understand the nature of the discussion threads (e.g., the topic trends). Project maintainers can also benefit from our thread identification approach to spot the common repeated threads and use these threads as frequently asked questions (FAQs) to improve the documentation of their projects.
Performance-Energy Trade-off in Modern CMPs

Chip multiprocessors (CMPs) are ubiquitous in all computing systems ranging from high-end servers to mobile devices. In these systems, energy consumption is a critical design constraint as it constitutes the most significant operating cost for computing clouds. Analogous to this, longer battery life continues to be an essential user concern in mobile devices. To optimize on power consumption, modern processors are designed with Dynamic Voltage and Frequency Scaling (DVFS) support at the individual core as well as the uncore level. This allows fine-grained control of performance and energy. For an n core processor with m core and uncore frequency choices, the total DVFS configuration space is now m(n+1) (with the uncore accounting for the + 1). In addition to that, in CMPs, the performance-energy trade-off due to core/uncore frequency scaling concerning a single application cannot be determined independently as cores share critical resources like the last level cache (LLC) and the memory. Thus, unlike the uni-processor environment, the energy consumption of an application running on a CMP depends not only on its characteristics but also on those of its co-runners (applications running on other cores). The key objective of our work is to select a suitable core and uncore frequency that minimizes power consumption while limiting application performance degradation within certain pre-defined limits (can be termed as QoS requirements). The key contribution of our work is a learning-based model that is able to capture the interference due to shared cache, bus bandwidth, and memory bandwidth between applications running on multiple cores and predict near-optimal frequencies for core and uncore.
Draft Genome Sequence of Bacillus subtilis subsp. subtilis MD 32, Isolated from the Korean Fermented Food Kimchi

Bacillus subtilis subsp. subtilis MD 32 was isolated from kimchi. The strain was sequenced using an Illumina MiSeq platform, and the genome size was 4,238,856 bp with a GC content of 43.41 mol%. The genome encoded 4,396 proteins, with 45 tRNAs, 6 rRNAs, and 5 noncoding RNAs. ABSTRACT Bacillus subtilis subsp. subtilis MD 32 was isolated from kimchi. The strain was sequenced using an Illumina MiSeq platform, and the genome size was 4,238,856 bp with a GC content of 43.41 mol%. The genome encoded 4,396 proteins, with 45 tRNAs, 6 rRNAs, and 5 noncoding RNAs.