Miracle-dz/UniFAR-FTUs huggingface dataset:
Bet-hedging and variability in plant development: seed germination and beyond

When future conditions are unpredictable, bet-hedging strategies can be advantageous. This can involve isogenic individuals producing different phenotypes, under the same environmental conditions. Ecological studies provide evidence that variability in seed germination time has been selected for as a bet-hedging strategy. We demonstrate how variability in germination time found in Arabidopsis could function as a bet-hedging strategy in the face of unpredictable lethal stresses. Despite a body of knowledge on how the degree of seed dormancy versus germination is controlled, relatively little is known about how differences between isogenic seeds in a batch are generated. We review proposed mechanisms for generating variability in germination time and the current limitations and new possibilities for testing the model predictions. We then look beyond germination to the role of variability in seedling and adult plant growth and review new technologies for quantification of noisy gene expression dynamics. We discuss evidence for phenotypic variability in plant traits beyond germination being under genetic control and propose that variability in stress response gene expression could function as a bet-hedging strategy. We discuss open questions about how noisy gene expression could lead to between-plant heterogeneity in gene expression and phenotypes. This article is part of a discussion meeting issue ‘Causes and consequences of stochastic processes in development and disease’.
Pacific Reef Assessment and Monitoring Program. Data report : ecological monitoring 2016 : reef fishes and benthic habitats of the main Hawaiian Islands, Northwestern Hawaiian Islands, Pacific Remote Island Areas, and American Samoa

This report outlines some of the coral reef monitoring surveys conducted by the National Oceanic and Atmospheric Administration (NOAA) Pacific Islands Fisheries Science Center’s (PIFSC) Coral Reef Ecosystem Program (CREP) in 2016. This includes the following regions: Northwestern Hawaiian Islands, main Hawaiian Islands, Pacific Remote Island Areas, and American Samoa. data are annual means of total large fish ( ≥ 50 cm TL) density, as well as density per major family or other grouping --- Acanthuridae, Scaridae, Carangidae, Lutjanidae, Sphyraenidae, and ‘reef sharks’ (i.e. all Carcharhinidae, Ginglymostomatidae, and Sphyrnidae). Towed-diver surveys were only conducted in the Northwestern Hawaiian Islands (NWHI) and the main Hawaiian Islands (MHI). For each island within a region, maps illustrate the SPC site level data from the past and most recent surveys and a standard set of graphs show summary information on the fish and benthic community at the island scale. On each fish biomass graph, a reference line indicates the region wide mean estimate, provided as a relevant regional comparison for island-level estimates. Fish biomass is shown for each year surveyed of all fish, parrotfish in 2 size classes, and by consumer group. Mean fish size is also indicated, as well as average percent cover of several major benthic groups: hard coral, macroalgae, and encrusting algae.
Miocene transpression effects at the boundary of Central Carpathian Paleogene Basin and Pieniny Klippen Belt - examples from Polish-Slovakian borderland

A geological structural study has been conducted along the tectonic contact zone of the Central Carpathian Paleogene Basin (CCPB) and Pieniny Klippen Belt (PKB) in the eastern Podhale and western Spisska Magura areas. It concerned mostly the Central Carpathian Paleogene flysch strata and, to a lesser degree, the Upper Cretaceous rocks of the PKB. Tectonic deformation structures genetically related to the important tectonic boundary in question occur within a c. 1.5–2 km-wide strip of the Paleogene flysch rocks adjacent from the south to the PKB. Two parallel structural domains have been distinguished within this strip: the contact zone proper in the north and the peri-Pieniny monocline in the south. Most of the minor faults documented in the Paleogene flysch bear a record of dextral motion parallel to the contact zone. Some dextral-reverse oblique slip faults of NE-SW and W-E trends have also been recognized. Discrepancies in the orientation and sense of movement on strike-slip faults in the Paleogene flysch rocks and those in marlstones of the “klippen envelope” of the PKB were encountered. They probably reflect differences in the structural history of both the adjacent rock complexes, as the Upper Cretaceous deposits of the PKB must have experienced more deformation events and, in general, were affected by much more intense strain than those of the CCPB. Contractional structures, such as south-vergent reverse faults and recumbent folds which point to ca N-S tectonic shortening, have also been found in the Paleogene rocks. The entirety of the structural features found in the CCPB is characteristic of a transpressional regime. The regionally consistent coexistence of structures resulting from strike-slip movements and tectonic shortening, as well as features pointing directly to a transpressional regime, prove the transpressional dextral nature of the contact between the CCPB and PKB.
Salidroside inhibited the proliferation of gastric cancer cells through up-regulating tumor suppressor miR-1343-3p and down-regulating MAP3K6/MMP24 signal molecules

ABSTRACT Salidroside inhibited the proliferation of cancer cell. Nevertheless, the mechanism has not been completely clarified. The purpose of the study is to explore the mechanisms of salidroside against gastric cancer. To analyze the changes of microRNA (miRNA) in gastric cancer cells under the treatment of salidroside, the miRNA expression was analyzed by using RNA-seq in cancer cells for 24 h after salidroside treatment. The differentially expressed miRNAs were clustered and their target genes were analyzed. Selected miRNA and target mRNA genes were further verified by q-PCR. The expressions of target genes in cancer cells were detected by immunohistochemistry. Cancer cell apoptotic index was significantly increased after salidroside treatment. The proliferation of gastric cancer cells were blocked at S-phase cell cycle. The expression of 44 miRNAs changed differentially after salidroside treatment in cancer cells. Bioinformatic analysis showed that there were 1384 target mRNAs corresponding to the differentially expressed miRNAs. Surprisingly, salidroside significantly up-regulated the expression of tumor suppressor miR-1343-3p, and down-regulated the expression of MAP3K6, STAT3 and MMP24-related genes. Salidroside suppressed the growth of gastric cancer by inducing the cancer cell apoptosis, arresting the cancer cell cycle and down-regulating the related signal transduction pathways. miRNAs are expressed differentially in gastric cancer cells after salidroside treatment, playing important roles in regulating proliferation and metastasis. Salidroside may suppress the growth of gastric cancer by up-regulating the expression of the tumor suppressor miR-1343-3p and down-regulating the expression of MAP3K6 and MMP24 signal molecules.
Development of a droplet digital polymerase chain reaction assay for the sensitive detection of total and integrated HIV-1 DNA

Abstract Background: Total human immunodeficiency virus (HIV) DNA and integrated HIV DNA are widely used markers of HIV persistence. Droplet digital polymerase chain reaction (ddPCR) can be used for absolute quantification without needing a standard curve. Here, we developed duplex ddPCR assays to detect and quantify total HIV DNA and integrated HIV DNA. Methods: The limit of detection, dynamic ranges, sensitivity, and reproducibility were evaluated by plasmid constructs containing both the HIV long terminal repeat (LTR) and human CD3 gene (for total HIV DNA) and ACH-2 cells (for integrated HIV DNA). Forty-two cases on stable suppressive antiretroviral therapy (ART) were assayed in total HIV DNA and integrated HIV DNA. Correlation coefficient analysis was performed on the data related to DNA copies and cluster of differentiation 4 positive (CD4+) T-cell counts, CD8+ T-cell counts and CD4/CD8 T-cell ratio, respectively. The assay linear dynamic range and lower limit of detection (LLOD) were also assessed. Results: The assay could detect the presence of HIV-1 copies 100% at concentrations of 6.3 copies/reaction, and the estimated LLOD of the ddPCR assay was 4.4 HIV DNA copies/reaction (95% confidence intervals [CI]: 3.6–6.5 copies/reaction) with linearity over a 5-log10-unit range in total HIV DNA assay. For the integrated HIV DNA assay, the LLOD was 8.0 copies/reaction (95% CI: 5.8–16.6 copies/reaction) with linearity over a 3-log10-unit range. Total HIV DNA in CD4+ T cells was positively associated with integrated HIV DNA (r = 0.76, P <0.0001). Meanwhile, both total HIV DNA and integrated HIV DNA in CD4+ T cells were inversely correlated with the ratio of CD4/CD8 but positively correlated with the CD8+ T-cell counts. Conclusions: This ddPCR assay can quantify total HIV DNA and integrated HIV DNA efficiently with robustness and sensitivity. It can be readily adapted for measuring HIV DNA with non-B clades, and it could be beneficial for testing in clinical trials.
SSD TRENDS AND RELATED WORK 2 . 1 SSD Characteristics , Costs , and Trends

Solid state drives perform random reads more than 100x faster than traditional magnetic hard disks, while offering comparable sequential read and write bandwidth. Because of their potential to speed up applications, as well as their reduced power consumption, these new drives are expected to gradually replace hard disks as the primary permanent storage media in large data centers. However, although they may benefit applications that stress random reads immediately, they may not improve database applications, especially those running long data analysis queries. Database query processing engines have been designed around the speed mismatch between random and sequential I/O on hard disks and their algorithms currently emphasize sequential accesses for disk-resident data. In this paper, we investigate data structures and algorithms that leverage fast random reads to speed up selection, projection, and join operations in relational query processing. We first demonstrate how a column-based layout within each page reduces the amount of data read during selections and projections. We then introduce FlashJoin, a general pipelined join algorithm that minimizes accesses to base and intermediate relational data. FlashJoin’s binary join kernel accesses only the join attributes, producing partial results in the form of a join index. Subsequently, its fetch kernel retrieves the attributes for later nodes in the query plan as they are needed. FlashJoin significantly reduces memory and I/O requirements for each join in the query. We implemented these techniques inside Postgres and experimented with an enterprise SSD drive. Our techniques improved query runtimes by up to 6x for queries ranging from simple relational scans and joins to full TPC-H queries.
A protocol for the comparison of telephone and in-person interview modalities: duration, richness, and costs in the context of exploring determinants of equitable access to community health services in Meru, Kenya

Background: Our research team is conducting phenomenological interviews with people who have not been able to access health services in Meru County, Kenya, aiming to explore the barriers they face and their perceptions of how we could modify our community outreach services to improve accessibility. We plan to conduct an embedded study that compares in-person and telephone interview modalities in terms of the richness of the data and the resources required for each modality. Methods/Design: This is a qualitative mode comparison study, embedded within a broader project to understand and address the issues that lead to inequitable access to local outreach clinics in Kenya. We will recruit at least 40 people who have been referred to local services but who have not been able to attend. We will conduct in-person interviews with half of these people, and telephone interviews with the other half. We will use random numbers to determine the modality that is used for each participant. All interviews will be conducted in the same month by a team of six research assistants who will use the same topic guide and analytic matrix for each interview. For all interviews conducted in each mode we will record and compare the mean duration; mean number of themes reported by each participant; total number of themes reported; interviewer rating of perceived richness; interviewer rating of perceived ease of building rapport; number of days taken by the team to complete all interviews; and all costs associated with conducting the interviews. Discussion: The findings will help us to weigh up the relative strengths and weaknesses of each modality for our research context. Given that we are exploring a focused research question in a fairly homogenous population, we anticipate that there may not be a meaningful difference in the number of themes reported.
Enhancing COVID-19 Case Forecasting in the United States: A Comparative Analysis of ARIMA, SARIMA, and RNN Models with Grid Search Optimization

The COVID-19 pandemic has resulted in a substantial number of fatalities in the United States since its onset in January 2020. In an effort to mitigate the spread of this highly infectious disease, a range of measures, including social distancing, mask-wearing, lockdowns, and vaccination campaigns, have been implemented. However, despite these extensive efforts, the persistent transmission of the virus can be attributed to a combination of vaccine hesitancy among certain individuals and the emergence of new viral strains. To effectively manage the ongoing pandemic, healthcare providers and government officials rely on infectious disease modeling to anticipate and secure the necessary resources. Accurate short-term case number forecasting is of paramount importance for healthcare systems. Since the beginning of the pandemic, numerous models have been employed to forecast the number of confirmed cases. In this study, we undertake a comparative analysis of six time-series techniques: Simple Moving Average (SMA), Exponentially Weighted Moving Average (EWMA), Holt-Winters Double Exponential Smoothing Additive (HWDESA), Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Recurrent Neural Network (RNN), with regard to their modeling and forecasting capabilities. SMA, EWMA, and HWDESA were employed for predictive modeling, while the ARIMA, SARIMA, and RNN models were utilized for case number forecasting. A comprehensive grid search was carried out to determine the optimal parameter combinations for both the ARIMA and SARIMA models. Our research findings demonstrate that the Holt-Winters Double Exponential model outperforms both the Exponentially Weighted Moving Average and Simple Moving Average in predicting the number of cases. On the other hand, the RNN model surpasses conventional time-series models such as ARIMA and SARIMA in terms of its forecasting accuracy. The finding of this study emphasizes the importance of accurately predicting the number of COVID-19 cases, given the substantial loss of lives caused by both the virus itself and the societal responses to it. Equipping healthcare managers with precise tools like Recurrent Neural Networks (RNNs) can enable them to forecast future cases more accurately and enhance their preparedness for effective response.
Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate

Reinforcement learning provides a mathematical framework for learning-based control, whose success largely depends on the amount of data it can utilize. The efficient utilization of historical trajectories obtained from previous policies is essential for expediting policy optimization. Empirical evidence has shown that policy gradient methods based on importance sampling work well. However, existing literature often neglect the interdependence between trajectories from different iterations, and the good empirical performance lacks a rigorous theoretical justification. In this paper, we study a variant of the natural policy gradient method with reusing historical trajectories via importance sampling. We show that the bias of the proposed estimator of the gradient is asymptotically negligible, the resultant algorithm is convergent, and reusing past trajectories helps improve the convergence rate. We further apply the proposed estimator to popular policy optimization algorithms such as trust region policy optimization. Our theoretical results are verified on classical benchmarks.
An iterative method for the solution of Laplace-like equations in high and very high space dimensions

This paper deals with the equation $-\Delta u+\mu u=f$ on high-dimensional spaces $\mathbb{R}^m$, where the right-hand side $f(x)=F(Tx)$ is composed of a separable function $F$ with an integrable Fourier transform on a space of a dimension $n>m$ and a linear mapping given by a matrix $T$ of full rank and $\mu\geq 0$ is a constant. For example, the right-hand side can explicitly depend on differences $x_i-x_j$ of components of $x$. Following our publication [Numer. Math. (2020) 146:219--238], we show that the solution of this equation can be expanded into sums of functions of the same structure and develop in this framework an equally simple and fast iterative method for its computation. The method is based on the observation that in almost all cases and for large problem classes the expression $\|T^ty\|^2$ deviates on the unit sphere $\|y\|=1$ the less from its mean value the higher the dimension $m$ is, a concentration of measure effect. The higher the dimension $m$, the faster the iteration converges.
Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey

This survey paper presents a comprehensive analysis of crime prediction methodologies, exploring the various techniques and technologies utilized in this area. The paper covers the statistical methods, machine learning algorithms, and deep learning techniques employed to analyze crime data, while also examining their effectiveness and limitations. We propose a methodological taxonomy that classifies crime prediction algorithms into specific techniques. This taxonomy is structured into four tiers, including methodology category, methodology sub-category, methodology techniques, and methodology sub-techniques. Empirical and experimental evaluations are provided to rank the different techniques. The empirical evaluation assesses the crime prediction techniques based on four criteria, while the experimental evaluation ranks the algorithms that employ the same sub-technique, the different sub-techniques that employ the same technique, the different techniques that employ the same methodology sub-category, the different methodology sub-categories within the same category, and the different methodology categories. The combination of methodological taxonomy, empirical evaluations, and experimental comparisons allows for a nuanced and comprehensive understanding of crime prediction algorithms, aiding researchers in making informed decisions. Finally, the paper provides a glimpse into the future of crime prediction techniques, highlighting potential advancements and opportunities for further research in this field
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting

This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in various scenarios and facilitated the development of numerous downstream applications such as urban planning and transportation management. However, the efficacy of existing methods remains sub-optimal due to their tendency to model temporal and spatial relationships independently, thereby inadequately accounting for complex high-order interactions of both worlds. Moreover, the diversity of transitional patterns in traffic forecasting makes them challenging to capture for existing approaches, warranting a deeper exploration of their diversity. Toward this end, this paper proposes Conjoint Spatio-Temporal graph neural network (abbreviated as COOL), which models heterogeneous graphs from prior and posterior information to conjointly capture high-order spatio-temporal relationships. On the one hand, heterogeneous graphs connecting sequential observation are constructed to extract composite spatio-temporal relationships via prior message passing. On the other hand, we model dynamic relationships using constructed affinity and penalty graphs, which guide posterior message passing to incorporate complementary semantic information into node representations. Moreover, to capture diverse transitional properties to enhance traffic forecasting, we propose a conjoint self-attention decoder that models diverse temporal patterns from both multi-rank and multi-scale views. Experimental results on four popular benchmark datasets demonstrate that our proposed COOL provides state-of-the-art performance compared with the competitive baselines.
$\textit{L+M-24}$: Building a Dataset for Language + Molecules @ ACL 2024

Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released which are 1) small and scraped from existing databases, 2) large but noisy and constructed by performing entity linking on the scientific literature, and 3) built by converting property prediction datasets to natural language using templates. In this document, we detail the $\textit{L+M-24}$ dataset, which has been created for the Language + Molecules Workshop shared task at ACL 2024. In particular, $\textit{L+M-24}$ is designed to focus on three key benefits of natural language in molecule design: compositionality, functionality, and abstraction.
Multi-Task Learning Using Uncertainty to Weigh Losses for Heterogeneous Face Attribute Estimation

Face images contain a wide variety of attribute information. In this paper, we propose a generalized framework for joint estimation of ordinal and nominal attributes based on information sharing. We tackle the correlation problem between heterogeneous attributes using hard parameter sharing of shallow features, and trade-off multiple loss functions by considering homoskedastic uncertainty for each attribute estimation task. This leads to optimal estimation of multiple attributes of the face and reduces the training cost of multitask learning. Experimental results on benchmarks with multiple face attributes show that the proposed approach has superior performance compared to state of the art. Finally, we discuss the bias issues arising from the proposed approach in face attribute estimation and validate its feasibility on edge systems.