Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-supervised learning and large-scale docking data accelerate RNA virtual screening

RNA-small molecule interactions play a critical role in biological processes and have emerged as attractive targets for drug discovery. Despite recent enhancements in RNA molecular docking for virtual screening, the computational cost of docking remains prohibitive. Previous work has shown that using a proper graph representation of RNA structures and machine learning enables us to quickly get enrichment factors in small molecule ligand prediction. However, the performance of this approach is limited by the paucity of available structural data including interaction with a small molecular compound. In this paper, we propose a competitive virtual screening pipeline to rapidly identify candidate ligands. We conduct large-scale docking experiments as a data augmentation and leverage unsupervised pre-training techniques for our RNA and ligand encoders to compensate for our reduced data. Our pipeline can also be used in combination with molecular docking, either by simply prioritizing compounds or by mixing the predicted score with the docking score. We show that our new model widely outperforms previous ones. We also introduce a virtual screening test case for RNA and evaluate the performance of our tool in a realistic drug discovery setting. Our affinity prediction model shows native ligand recovery rates competitive with full docking experiments with runtimes in seconds. When the affinity prediction model is used to prioritize docking candidates, we show a 1200% acceleration of native ligand recovery with no performance loss. Finally, we show that using our model in combination with docking enhances virtual screening results with almost no overhead. Our source code and data, as well as a Google Colab notebook for inference, are available on GitHub. 1
VirusImmu: a novel ensemble machine learning approach for viral immunogenicity prediction

Background The viruses threats provoke concerns regarding their sustained epidemic transmission, making the development of vaccines particularly important. In the prolonged and costly process of vaccine development, the most important initial step is to identify protective immunogens. Machine learning (ML) approaches are productive in analyzing big data such as microbial proteomes, and can remarkably reduce the cost of experimental work in developing novel vaccine candidates. Results We intensively evaluated the immunogenicity prediction power of eight commonly-used ML methods by random sampling cross validation on a large dataset consisting of known viral immunogens and non-immunogens we manually curated from the public domain. XGBoost, kNN and RF showed the strongest predictive power. We then proposed a novel soft-voting based ensemble approach (VirusImmu), which demonstrated a powerful and stable capability for viral immunogenicity prediction across the test set and external test set irrespective of protein sequence length. VirusImmu was successfully applied to facilitate identifying linear B cell epitopes against African Swine Fever Virus as confirmed by indirect ELISA in vitro. Conclusions VirusImmu exhibited tremendous potentials in predicting immunogenicity of viral protein segments. It is freely accessible at https://github.com/zhangjbig/VirusImmu.
Identification of conserved cross-species B-cell linear epitopes in human malaria: a subtractive proteomics and immuno-informatics approach targeting merozoite stage proteins

Human malaria, caused by five Plasmodium species (P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi), remains a significant global health burden. While most interventions target P. falciparum, the species associated with high mortality rates and severe clinical symptoms, non-falciparum species exhibit different transmission dynamics, remain hugely neglected, and pose a significant challenge to malaria elimination efforts. Recent studies have reported the presence of antigens associated with cross-protective immunity, which can potentially disrupt the transmission of various Plasmodium species. With the sequencing of the Plasmodium genome and the development of immunoinformatic tools, in this study, we sought to exploit the evolutionary history of Plasmodium species to identify conserved cross-species B-cell linear epitopes in merozoite proteins. We retrieved Plasmodium proteomes associated with human malaria and applied a subtractive proteomics approach focusing on merozoite stage proteins. Bepipred 2.0 and Epidope were used to predict B-cell linear epitopes using P. falciparum as the reference species. The predictions were further compared against human and non-falciparum databases and their antigenicity, toxicity, and allergenicity assessed. Subsequently, epitope conservation was carried out using locally sequenced isolates from a malaria-endemic region in western Kenya (n=27) and Kenyan isolates from MalariaGEN version 6 (n=131). Finally, physiochemical characteristics and tertiary structure of the B-cell linear epitopes were determined. The analysis revealed eight epitopes that showed high similarity (70-100%) between falciparum and non-falciparum species. These epitopes were highly conserved when assessed across local isolates and those from the MalariaGEN database and showed desirable physiochemical properties. Our results show the presence of conserved cross-species B-cell linear epitopes that could aid in targeting multiple Plasmodium species. Nevertheless, validating their efficacy in-vitro and in-vivo experimentally is essential.
Did the ECMWF seasonal forecast model outperform a statistical model over the last 15 years

Since 1997 the European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts with ensembles of a coupled ocean-atmosphere model (S1). In January 2002, a new version (S2) was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 years of hindcasts and forecasts are now available for verification. Seasonal predictability is to a large extent due to the El Ni ño — Southern Oscillation (ENSO) climate oscillations. ENSO predictions of the ECMWF models are compared with those of a few statistical models, mainly based on past sea surface temperature observations. The relative skill depends strongly on the season. The dynamical models are much better at forecasting the onset of El Ni ño or La Nĩna in boreal spring to summer. The statistical models are comparable at predicting the evolution of an event in boreal fall and winter. El Niño and La Nĩ na perturb the average weather in many regions and seasons throughout the world. A set of statistical models (STAT) is constructed based on persistence and a lagged regression with an ENSO index over 1901–1986 wherever the correlations are significant. As the number of verification data points is very low (15), the simplest measure of skill is used: the anomaly correlation coefficient of the ensemble mean. To further reduce the sampling uncertainties we restrict ourselves to areas and seasons of known ENSO teleconnections. The dynamical ECMWF models show better skill in 2-meter temperature forecasts over sea and the tropical land areas than STAT. The modeled ENSO teleconnection pattern to North America is shifted relative to observations, leading to little pointwise skill. Precipitation forecasts of the ECMWF models are very good, better than those of the statistical model, in South-East Asia, the equatorial Pacific and in the Americas in Dec–Feb but in Mar–May the skill is lower there. Overall, S1(S2) show better skill than STAT at lead time 2 months in 29(32) out of 40 regions and seasons of known ENSO teleconnections.
Ongoing monitoring of Tortugas Ecological Reserve : assessing the consequences of reserve designation

Over the past five years, a biogeographic characterization of Tortugas Ecological Reserve(TER) has been carried out to measure the post-implementation effects of TER as a refuge for exploited species. Our results demonstrate that there is substantial microalgal biomass at depths between 10 and 30 m in the soft sediments at the coral reef interface, and that this community may play an important role in the food web supporting reef organisms. In addition, preliminary stable isotope data, in conjunction with prior results from the west Florida shelf, suggest that the shallow water benthic habitats surrounding the coral reefs of TER will prove to be an important source of the primary production ultimately fueling fish production throughout TER. The majority of the fish analyzed so far have exhibited a C isotope signature consistent with a food web which relies heavily on benthic primary production. Fish counts indicate a marked increase in the abundance of large fish (>20 cm) within the Reserve relative to the Out and Park strata, across years. Faunal collections from open and protected soft bottom habitat near the northern boundary of Tortugas North strongly suggest that relaxation of trawling pressure has increased benthic biomass and diversity in this area of TER. These data, employing an integrated Before - After Control Impact (BACI) design at multiple spatial scales, will allow us to continue to document and quantify the post-implementation effects of TER. (PDF contains 58 pages)
Binary Classification under Local Label Differential Privacy Using Randomized Response Mechanisms

Label differential privacy is a popular branch of ϵ -differential privacy for protecting labels in training datasets with non-private features. In this paper, we study the generalization performance of a binary classifier trained on a dataset privatized under the label differential privacy achieved by the randomized response mechanism. Particularly, we establish minimax lower bounds for the excess risks of the deep neural network plug-in classifier, theoretically quantifying how privacy guarantee ϵ affects its generalization performance. Our theoretical result shows: (1) the randomized response mechanism slows down the convergence of excess risk by lessening the multiplicative constant term compared with the non-private case ( ϵ = ∞ ); (2) as ϵ decreases, the optimal structure of the neural network should be smaller for better generalization performance; (3) the convergence of its excess risk is guaranteed even if ϵ is adaptive to the size of training sample n at a rate slower than O ( n − 1 / 2 ). Our theoretical results are validated by extensive simulated examples and two real applications.
Generating Understandable Unit Tests through End-to-End Test Scenario Carving

Automatic unit test generators such as EvoSuite are able to automatically generate unit test suites with high coverage. This removes the burden of writing unit tests from developers, but the generated tests are often difficult to understand for them. In this paper, we introduce the MicroTestCarver approach that generates unit tests starting from manual or scripted end-to-end (E2E) tests. Using carved information from these E2E tests, we generate unit tests that have meaningful test scenarios and contain actual test data. When we apply our MicroTestCarver approach, we observe that 85% of the generated tests are executable. Through a user study involving 20 participants, we get indications that tests generated with MicroTestCarver are relatively easy to understand.
LONG‐TERM GROUNDWATER QUALITY AND SALINE INTRUSION ASSESSMENT IN AN IRRIGATED ENVIRONMENT: A CASE STUDY OF THE AQUIFER UNDER THE LBDC IRRIGATION SYSTEM

Pakistan's irrigation system is more than a century old. Due to the increasing population and consequent intensifying agriculture, dependence on groundwater is successively increasing. The underground reservoir that was recharged during the first half of the twentieth century by the newly built irrigation system with low cropping intensities is now being overexploited. Groundwater quality is variable, both vertically and horizontally, with the exception of areas adjacent to rivers, where it is fresh. This paper describes groundwater quality in the Lower Bari Doab Canal (LBDC) irrigation system based on 47 years of historic water quality data (TDS, SAR and RSC). Areas are delineated in the form of zones and depths. The tail end of the command is facing severe groundwater depletion, whereas in certain parts, groundwater quality deterioration with the passage of time has also been detected. Possible reasons and mechanisms of saline intrusion within the aquifer are described. Lateral saline intrusion is not a major issue due to very slow groundwater movement. But in areas with saline groundwater lying below the upper layer of irrigation leaked fresh water, mobilization of deeper saline water is taking place as a result of pumping by farmers. In addition, results of the solute transport model and MODPATH runs were evaluated in support of water quality analysis and groundwater flow rates. Copyright © 2013 John Wiley & Sons, Ltd.
Seasonality of the Paciﬁc Decadal Oscillation

The seasonality of the Paciﬁc decadal oscillation (PDO) is examined using North Paciﬁc sea surface temperature (SST) in observations and in a 480-yr simulation with the National Centers for Environmental Prediction(NCEP)ClimateForecastSystem(CFS)coupledmodel.The PDO,bothinobservations andinthe CFS, shows similar seasonality, with increasing SST variance during spring and a maximum in late spring and early summer. The vertical structure of the ocean temperature anomaly associated with the PDO in the CFS displays a signiﬁcant transition from a deep to a shallow structure during late spring, consistent with the seasonal variation of the mean ocean mixed layer depth (MLD). An analysis of atmospheric surface wind and SST anomalies from the CFS simulation indicates that there is a 1-month delay in the PDO-related SST response to the atmospheric wind forcing. The results based on the CFS simulation are generally consistent with observations, including both atmospheric data from the NCEP/Department of Energy (DOE) Global Reanalysis 2 (GR-2) and ocean data from the NCEP Global Ocean Data Assimilation System (GODAS). The 1-month delay together with the seasonal variation of the mean MLD tends to amplify the PDO-related SSTresponsetotheatmosphericsurfacewindin latespringto earlysummer,and thecombination leadsto the maximum variability of the PDO, which is a 3-month delay from the peak phase of the surface wind in February and March.
On the current density reduction ahead of dipolarization fronts

During their earthward propagation, dipolarization fronts (DFs) interact with the ambient plasma sheet on kinetic scales. The interaction region is important to the front's structure, propagation, and regional closure of the current system. However, the physics in this region, especially of its current system, is poorly understood. We present Time History of Events and Macroscale Interactions during Substorms (THEMIS) observations of the interaction region between DFs and the ambient plasma sheet at x ~ − 12 RE downtail; these observations show that the current density ahead of the DFs is significantly reduced near the neutral plane. We use a two‐dimensional particle‐in‐cell model to simulate the current density reduction ahead of DFs and investigate the physical mechanism that causes it: Ion reflection and acceleration at the front cause positive charge density to build up. The resultant electrostatic field, Ez, is directed away from the neutral plane. The positive cross‐tail Ez × Bx drift of electrons (which remain magnetized) does not affect demagnetized ions. This electron‐ion decoupling results in a dawnward cross‐field current carried by electrons that reduces the cross‐tail current ahead of the approaching front.
EVALUATING THE ML MODELS FOR MINDBIGDATA (IMAGENET) OF THE BRAIN SIGNALS

: Even though humans performance is excellent still humans can’t reach machines mechanism in visualizing the objects and images. Most of the new rediscovery of Neural Networks which has led to a notable performance and advancements in automated visual classification and their generalization capabilities are not up to human expectations, since machines learn biased feature space, which majorly turn on the working of the training datasets despite of its general principles. This project mainly inspects about the define classification of image-based processing of electroencephalogram [EEG] signals caused by the observer’s brain. In our project the main chore was to focus on the particular task of processing the EEG signals resulted in brain when an image is spotted, so the kind of object appeared in the image will be well defined. Proposed methodology focuses at interpreting an input multichannel temporal EEG sequence into a low proportions feature vector summing up the relevant content for an input sequence by directly connecting the time sequences from innumerable channels into a unique feature vector, neglecting temporal .
Grounded (Lifted) Linearizer at the IPC 2023: Solving Partial Order HTN Problems by Linearizing Them

In this paper, we would like to present Grounded (Lifted) Lin-earizer, a hierarchical task network (HTN) planning system which won the Partial Order (PO) Agile and Satisficing tracks of the International Planning Competition 2023 on Hierarchical Task Network (HTN) Planning. This system consists of two parts. The first part is a preprocessor developed in house which transforms a POHTN problem into a total order (TO) one and which is the main contribution of this paper. The second part is an existing HTN planner. The outstanding performance of our assembled planning system thus serves as an evidence for how our preprocessor can enhance the efficiency of other existing planners.
Guidelines on diabetes, pre-diabetes, and cardiovascular diseases: executive summary The Task Force on Diabetes and Cardiovascular Diseases of the European Society of Cardiology (ESC) and of the European Association for the Study of Diabetes (EASD)

Guidelines and Expert Consensus documents aim to present management and recommendations based on all of the relevant evidence on a particular subject in order to help physicians to select the best possible management strategies for the individual patient, suffering from a specific condition, taking into account not only the impact on outcome, but also the risk benefit ratio of a particular diagnostic or therapeutic procedure. The ESC recommendations for guidelines production can be found on the ESC website†. In brief, the ESC appoints experts in the field to carry out a comprehensive and critical evaluation of the use of diagnostic and therapeutic procedures and to assess the risk–benefit ratio of the therapies recommended for management and/or prevention of a given condition. The strength of evidence for or against particular procedures or treatments is weighed according to predefined scales for grading recommendations and levels of evidence, as outlined below. Once the document has been finalized and approved by all the experts involved in the Task Force, it is submitted to outside specialists for review. If necessary, the document is revised once more to be finally approved by the Committee for Practice Guidelines and selected members of the Board of the ESC. The ESC Committee for Practice Guidelines ( CPG ) supervises and coordinates the preparation of new Guidelines and Expert Consensus Documents produced by Task Forces, expert groups, or consensus panels. The chosen experts in these writing panels are asked to provide disclosure statements of all relationships they may have, which might be perceived as real or potential conflicts of interest. These disclosure forms are kept on file at the European Heart House, headquarters of the ESC. The Committee is also responsible for the endorsement of these Guidelines and Expert Consensus Documents or statements. | Classes of recommendations | |:-------------------------- | ------------------------------------------------------------------------------------------------------------------------ | | Class I | Evidence and/or general agreement that a given diagnostic procedure/treatment is beneficial, useful, and effective | | Class II | Conflicting evidence and/or a divergence of opinion about the usefulness/efficacy of the treatment or procedure | | Class IIa | Weight of evidence/opinion is in favour of usefulness/efficacy | | Class IIb | Usefulness/efficacy is less well established by evidence/opinion | | Class III | Evidence or general agreement that the treatment or procedure is not useful/effective and, in some cases, may be harmful | Diabetes and cardiovascular diseases (CVD) often appear …
Challenges of constructing and selecting the “perfect” initial and boundary conditions for the LES model PALM

We present the process and difficulties of acquiring the proper initial and boundary conditions (IBC) for the state-of-the-art LES based model PALM (Parallelized Large-Eddy Simulation Model). We use the mesoscale model WRF (Weather Research and Forecasting model) as a source of inputs for the PALM preprocessor, and investigate the influence of the mesoscale model on the performance of the PALM model. Sixteen different WRF configurations were used as a proxy for a multi-model ensemble. We developed a technique for selecting the suitable sets of IBC, performed PALM model simulations driven by 5 them, and investigated the consequences of selecting a sub-optimal WRF configuration. The procedure was tested for four episodes during different seasons of the year 2019, evaluating WRF and PALM outputs against the atmospheric radio sounding observations. We show that the PALM model outputs are heavily dependent on the imposed IBC, and have different responses for different times of the day, and different seasons. We demonstrate that the main driver of errors is the mesoscale model, and that the PALM model is capable of attenuating, but not fully correcting them. The PALM model attenuates the impact of 10 errors in IBC in wind speed, while for the air temperature, PALM shows variable behavior with respect to driving conditions. This study stresses the importance of high-quality driving IBC, and the complexity of the process of their construction and selection.
The Application of Nanomaterials in Agricultural Field

Nanomaterials are considered as materials having external dimension of 1-100 nanometers. Nanomaterials can be formed naturally or as by-products of combustion reactions or directly designed for a particular purpose. These materials can differ in their physical and chemical characteristics from their counterpart. Nanomaterials of various sizes and shapes have been created as a result of recent technological advancements. These breakthroughs will serve as the foundation for future engineering to produce new properties tailored to particular applications. Nanomaterials have been used successfully and safely in a variety of areas in the past, including food production, environmental science and medicine. Nanomaterials have the capacity to improve pathogen identification, plant safety, seed growth and germination, as well as detection of the herbicide residue or pesticide, according to the preliminary research. This paper summarizes nanomaterials' agricultural applications and their potential role in future agricultural development.
UvA-DARE (Digital Academic Repository) Lured into listening Engaging games as an alternative to reward-based crowdsourcing in music research

: This brief statement revisits some earlier observations on what makes web-based experiments, and especially citizen science using engaging games, an attractive alternative to laboratory-based setups. It suggests web-based experimenting to be a full-grown alternative to traditional laboratory-based experiments, especially in the field of music cognition, where sampling bias is a common problem and large amounts of empirical data are needed to characterize individual variability.