Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the current and future potential distribution of the bean bug Riptortus pedestris with increasingly serious damage to soybean.

BACKGROUND The bean bug Riptortus pedestris has received intense attention in recent years because of its involvement in increasing outbreaks of the staygreen syndrome in soybean (Glycine max (L.)) often causing almost 100% losses of soybean yield in China. However, for this pest of great economic importance, the potential current and future distribution patterns and their underlying driving factors remain unclear. RESULTS The Maxent modelling under climate, elevation and land-use (including the distribution information of Glycine max) variables showed the current potential distribution covered a vast geographic range, primarily including most parts of South, Southeast and East Asia. Under future environmental scenarios, the suitable habitat was markedly expanded. The areas to newly become highly suitable for R. pedestris were primarily located in Northeast China and West India. Five bioclimatic (BIO13, BIO08, BIO18, BIO02 and BIO07) and one land-use (C3 annual crops) predictors contributed approximately 95% to the modelling, and analyses of curve responses showed that R. pedestris preferred relatively high temperature and precipitation to a certain degree. Our results indicate that a high risk of R. pedestris outbreaks is present in parts of Asia, especially in the growing regions of soybean in China, and this risk will continue in the future. CONCLUSION The predicted distribution pattern and key regulating factors identified herein could provide a vital reference for developing policies in pest management and further alleviate the incidence of staygreen syndrome in soybean.
Small-For-Size Syndrome and Graft Inflow Modulation Techniques in Liver Transplantation

Background: Partial liver transplantation has recently been proposed to alleviate organ shortages. However, transplantation of a small-for-size graft is associated with an increased risk of posttransplant hepatic dysfunction, commonly referred to as small-for-size syndrome (SFSS). This review describes the etiology, pathological features, clinical manifestations, and diagnostic criteria of SFSS. Moreover, we summarize strategies to improve graft function, focusing on graft inflow modulation techniques. Finally, unmet needs and future perspectives are discussed. Summary: In fact, posttransplant SFSS can be attributed to various factors such as preoperative status of the recipients, surgical techniques, donor age, and graft quality, except for graft size. With targeted improvement measures, satisfactory clinical outcomes can be achieved in recipients at increased risk of SFSS. Given the critical role of relative portal hyperperfusion in the pathogenesis of SFSS, various pharmacological and surgical treatments have been established to reduce or partially divert excessive portal inflow, and recipients will benefit from individualized therapeutic regimens after careful evaluation of benefits against potential risks. However, there remain unmet needs for further research into different aspects of SFSS to better understand the correlation between portal hemodynamics and patient outcomes. Key Messages: Contemporary transplant surgeons should consider various donor and recipient factors and develop case-specific prevention and treatment strategies to improve graft and recipient survival rates.
Chemical cues of identity and reproductive status in Japanese macaques

Olfactory communication plays an important role in the regulation of socio‐sexual interactions in mammals. There is growing evidence that both human and nonhuman primates rely on odors to inform their mating decisions. Nevertheless, studies of primate chemical ecology remain scarce due to the difficulty of obtaining and analyzing samples. We analyzed 67 urine samples from five captive female Japanese macaques (Macaca fuscata) and 30 vaginal swabs from three of these females using gas chromatography–mass spectrometry and examined the relationship between odor (compounds identified, richness, intensity, and diversity) and female identity as well as cycle phase. We found a total of 36 urine compounds of which we identified 31, and 68 vaginal compounds of which we identified 37. Our results suggest that urine and vaginal odor varied more between individuals than within cycle phases. However, we found that within a female cycle, urine samples from similar phases may cluster more than samples from different phases. Our results suggest that female odor may encode information about identity (vaginal and urine odor) and reproductive status (urine odor). The question of how conspecifics use female urine and vaginal odor remains open and could be tested using bioassays. Our results and their interpretation are constrained by our limited sample size and our study design. Nonetheless, our study provides insight into the potential signaling role of female odor in sexual communication in Japanese macaques and contributes to our understanding of how odors may influence mating strategies in primates.
Handling fatigue in everyday activities at five years after stroke: A long and demanding process

Abstract Background Fatigue is common and can be challenging after stroke. Aim To explore how post-stroke fatigue (PSF) was experienced and handled among people with stroke in their performance of everyday activities and in participation in social activities five years after stroke. Methods Nine persons who perceived PSF one year after stroke onset were interviewed five years later. The interviews were analysed using qualitative content analysis. Results Most participants experienced PSF even five years after stroke and reported longstanding difficulties in everyday activities. Handling fatigue—a long slow process with invisible adjustments in everyday life emerged as the theme. By implementing new strategies in everyday life their PSF lessened over time. Understanding among significant others as to how PSF appears and providing information about PSF early after stroke was perceived important. Conclusion This study adds new knowledge regarding experiences of PSF and long-term support needs. Even if PSF still was reported the participants experienced improvements in everyday life through the application of new strategies. Information about PSF and strategies for managing everyday life should be provided during rehabilitation. Significance This study is one out of only a few focussing on long-term PSF and adjustment to its consequences in everyday life.
psupertime: supervised pseudotime analysis for time-series single-cell RNA-seq data

Abstract Motivation Improvements in single-cell RNA-seq technologies mean that studies measuring multiple experimental conditions, such as time series, have become more common. At present, few computational methods exist to infer time series-specific transcriptome changes, and such studies have therefore typically used unsupervised pseudotime methods. While these methods identify cell subpopulations and the transitions between them, they are not appropriate for identifying the genes that vary coherently along the time series. In addition, the orderings they estimate are based only on the major sources of variation in the data, which may not correspond to the processes related to the time labels. Results We introduce psupertime, a supervised pseudotime approach based on a regression model, which explicitly uses time-series labels as input. It identifies genes that vary coherently along a time series, in addition to pseudotime values for individual cells, and a classifier that can be used to estimate labels for new data with unknown or differing labels. We show that psupertime outperforms benchmark classifiers in terms of identifying time-varying genes and provides better individual cell orderings than popular unsupervised pseudotime techniques. psupertime is applicable to any single-cell RNA-seq dataset with sequential labels (e.g. principally time series but also drug dosage and disease progression), derived from either experimental design and provides a fast, interpretable tool for targeted identification of genes varying along with specific biological processes. Availability and implementation R package available at github.com/wmacnair/psupertime and code for results reproduction at github.com/wmacnair/psupplementary. Supplementary information Supplementary data are available at Bioinformatics online.
A nationwide study on characteristics and outcome of cancer patients with sepsis requiring intensive care

Abstract Background Sepsis is the leading cause of admission to the intensive care unit (ICU) for cancer patients and survival rates have historically been low. The aims of this nationwide cohort study were to describe the characteristics and outcomes of cancer patients admitted to the ICU with sepsis compared with other sepsis patients requiring ICU admission. Material and methods This was a retrospective, observational study. All adult admissions to Icelandic ICUs during years 2006, 2008, 2010, 2012, 2014 and 2016 were screened for severe sepsis or septic shock by ACCP/SCCM criteria. Clinical characteristics and outcomes of sepsis patients with cancer were compared to those without cancer. Results In the study period, 235 of 971 (24%) patients admitted to Icelandic ICUs because of sepsis had cancer, most often a solid tumour (100), followed by metastatic tumours (69) and haematological malignancies (66). Infections were more often hospital-acquired in cancer patients (52%) than other sepsis patients (18%, p < 0.001) and sites of infections differed, with abdominal infections being most common in patients with solid and metastatic tumours but lungs and bloodstream infections in haematological malignancies. The length of stay in the ICU was shorter for sepsis patients with metastatic disease than other sepsis patients (2 vs. 4 days, p < 0.001) and they were more likely to have treatment limitations (52 vs. 19%, p < 0.05). Median survival of patients with metastatic disease was 19 days from ICU admission. The 28-day mortality (25%) of solid tumour patients was comparable to that of sepsis patients without cancer (20%, p < 0.001). Conclusions Cancer is a common comorbidity in patients admitted to the ICU with sepsis. The clinical presentation and outcome differs between cancer types. Individuals with metastatic cancer were unlikely to receive prolonged invasive ICU care treatment. Comparable short-term outcome was found for patients with solid tumours and no cancer.
Blanco DECam Bulge Survey (BDBS). V. Cleaning the foreground populations from Galactic bulge colour-magnitude diagrams using Gaia EDR3

Aims. The Blanco DECam Bulge Survey (BDBS) has imaged more than 200 square degrees of the southern Galactic bulge, providing photometry in the u g rizy ﬁlters for ∼ 250 million unique stars. The presence of a strong foreground disk population, along with complex reddening and extreme image crowding, has made it di ﬃ cult to constrain the presence of young and intermediate age stars in the bulge population. Methods. We employed an accurate cross-match of BDBS with the latest data release (EDR3) from the Gaia mission, matching more than 140 million sources with BDBS photometry and Gaia EDR3 photometry and astrometry. We relied on Gaia EDR3 astrometry, without any photometric selection, to produce clean BDBS bulge colour-magnitude diagrams (CMDs). Gaia parallaxes were used to ﬁlter out bright foreground sources, and a Gaussian mixture model ﬁt to Galactic proper motions could identify stars kinematically consistent with bulge membership. We applied this method to 127 di ﬀ erent bulge ﬁelds of 1deg 2 each, with | (cid:96) | ≤ 9 . 5 ◦ and − 9 . 5 ◦ ≤ b ≤ − 2 . 5 ◦ . Results. The astrometric cleaning procedure removes the majority of blue stars in each ﬁeld, especially near the Galactic plane, where the ratio of blue to red stars is (cid:46) 10%, increasing to values ∼ 20% at higher Galactic latitudes. We rule out the presence of a widespread population of stars younger than 2Gyr. The vast majority of blue stars brighter than the turno ﬀ belong to the foreground population, according to their measured astrometry. We introduce the distance between the observed red giant branch bump and the red clump as a simple age proxy for the dominant population in the ﬁeld, and we conﬁrm the picture of a predominantly old bulge. Further work is needed to apply the method to estimate ages to ﬁelds at higher latitudes, and to model the complex morphology of the Galactic bulge. We also produce transverse kinematic maps, recovering expected patterns related to the presence of the bar and of the X-shaped nature of the bulge.
Szloca: towards a framework for full 3D tracking through a single camera in context of interactive arts

— Realtime virtual data of objects and human presence in a large area holds a valuable key in enabling many experiences and applications in various industries and with exponential rise in the technological development of artificial intelligence – computer vision has expanded the possibilities of tracking and classifying things through just video inputs, which is also surpassing the limitations of most popular and common hardware setups known traditionally to detect human pose and position, such as low field of view and limited tracking capacity. The benefits of using computer vision in application development is large as it augments traditional input sources (like video streams) and can be integrated in many environments and platforms. In the context of new media interactive arts, based on physical movements and expanding over large areas or gallaries, this research presents a novel way and a framework towards obtaining data and virtual representation of objects/people - such as three-dimensional positions, skeltons/pose and masks from a single rgb camera. Looking at the state of art through some recent developments and building on prior research in the field of computer vision, the paper also proposes an original method to obtain three dimensional position data from monocular images, the model does not rely on complex training of computer vision systems but combines prior computer vision research and adds a capacity to represent z depth, ie-to represent a world position in 3 axis from a 2d input source.
Transfer learning for ensembles: reducing computation time and keeping the diversity

Transferring a deep neural network trained on one problem to another requires only a small amount of data and little additional computation time. The same behaviour holds for ensembles of deep learning models typically superior to a single model. However, a transfer of deep neural networks ensemble demands relatively high computational expenses. The probability of overfitting also increases. Our approach for the transfer learning of ensembles consists of two steps: (a) shifting weights of encoders of all models in the ensemble by a single shift vector and (b) doing a tiny fine-tuning for each individual model afterwards. This strategy leads to a speed-up of the training process and gives an opportunity to add models to an ensemble with significantly reduced training time using the shift vector. We compare different strategies by computation time, the accuracy of an ensemble, uncertainty estimation and disagreement and conclude that our approach gives competitive results using the same computation complexity in comparison with the traditional approach. Also, our method keeps the ensemble’s models’ diversity higher.
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

Molecular optimization is a fundamental goal in the chemical sciences and is of central interest to drug and material design. In recent years, significant progress has been made in solving challenging problems across various aspects of computational molecular optimizations, emphasizing high validity, diversity, and, most recently, synthesizability. Despite this progress, many papers report results on trivial or self-designed tasks, bringing additional challenges to directly assessing the performance of new methods. Moreover, the sample efficiency of the optimization--the number of molecules evaluated by the oracle--is rarely discussed, despite being an essential consideration for realistic discovery applications. To fill this gap, we have created an open-source benchmark for practical molecular optimization, PMO, to facilitate the transparent and reproducible evaluation of algorithmic advances in molecular optimization. This paper thoroughly investigates the performance of 25 molecular design algorithms on 23 tasks with a particular focus on sample efficiency. Our results show that most"state-of-the-art"methods fail to outperform their predecessors under a limited oracle budget allowing 10K queries and that no existing algorithm can efficiently solve certain molecular optimization problems in this setting. We analyze the influence of the optimization algorithm choices, molecular assembly strategies, and oracle landscapes on the optimization performance to inform future algorithm development and benchmarking. PMO provides a standardized experimental setup to comprehensively evaluate and compare new molecule optimization methods with existing ones. All code can be found at https://github.com/wenhao-gao/mol_opt.
Resource-Centric Serverless Computing

Today’s serverless computing has several key limitations including per-function resource limits, ﬁxed CPU-to-memory ratio, and constant resource allocation throughout a function execution and across different invocations of it. The root cause of these limitations is the “function-centric” model: a function is a ﬁxed-size box that is allocated, executed, and terminated as an inseparable unit. This unit is pre-deﬁned by the cloud provider and cannot properly capture user needs. We propose a “resource-centric” model for serverless computing, which adapts the underlying provider serverless systems to follow applications’ natural resource needs. To achieve this vision, we built Scad based on two ideas: dis-aggregating a serverless application based on its resource features and aggregating disaggregated units for better performance. Our results show that Scad solves various resource-related issues of today’s serverless computing, while retaining or even improving its performance.
"Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media

To address the vaccine hesitancy which impairs the efforts of the COVID-19 vaccination campaign, it is imperative to understand public vaccination attitudes and timely grasp their changes. In spite of reliability and trustworthiness, conventional attitude collection based on surveys is time-consuming and expensive, and cannot follow the fast evolution of vaccination attitudes. We leverage the textual posts on social media to extract and track users' vaccination stances in near real time by proposing a deep learning framework. To address the impact of linguistic features such as sarcasm and irony commonly used in vaccine-related discourses, we integrate into the framework the recent posts of a user's social network neighbours to help detect the user's genuine attitude. Based on our annotated dataset from Twitter, the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to state-of-the-art text-only models. Using this framework, we successfully validate the feasibility of using social media to track the evolution of vaccination attitudes in real life. We further show one practical use of our framework by validating the possibility to forecast a user's vaccine hesitancy changes with information perceived from social media.
A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function

The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein-ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization.
Learning to Control Local Search for Combinatorial Optimization

Combinatorial optimization problems are encountered in many practical contexts such as logistics and production, but exact solutions are particularly difficult to find and usually NP-hard for considerable problem sizes. To compute approximate solutions, a zoo of generic as well as problem-specific variants of local search is commonly used. However, which variant to apply to which particular problem is difficult to decide even for experts. In this paper we identify three independent algorithmic aspects of such local search algorithms and formalize their sequential selection over an optimization process as Markov Decision Process (MDP). We design a deep graph neural network as policy model for this MDP, yielding a learned controller for local search called NeuroLS. Ample experimental evidence shows that NeuroLS is able to outperform both, well-known general purpose local search controllers from Operations Research as well as latest machine learning-based approaches.
Suppression of electrical breakdown phenomena in liquid TriMethyl Bismuth based ionization detectors

Organometallic liquids provide good properties for ionization detectors. TriMethyl Bismuth (TMBi) has been proposed as a detector medium with charge and Cherenkov photon readout for Positron Emission Tomography. In this work, we present studies for the handling of TMBi at different electric fields and under different environmental conditions to find applicable configurations for the suppression of electrical breakdowns in TMBi at room temperature. A simple glass cell with two electrodes filled with TMBi was constructed and tested under different operation conditions. Working at the vapour pressure of TMBi at room temperature of about 40 mbar and electric fields of up to 20 kV/cm in presence of a small oxygen contamination we found the formation of a discharge channel in the liquid and a steady increase in the current. Further reduction of pressure by pumping caused the TMBi to boil and a spontaneous combustion. Eliminating the oxygen contamination led the TMBi under the same condition to only decompose. When operating the setup under an argon atmosphere of 1 bar we did not observe breakdowns of the electrical potential up to field strengths of 20 kV/cm. Still, in presence of a small oxygen contamination fluctuating currents in the nA range were observed, but no decomposition or combustion. We conclude from our experiments that TMBi at room temperature in a pure argon atmosphere of 1 bar remains stable against electrical breakdown at least up to electric field strengths of 20 kV/cm, presumably because the formation of gaseous TMBi was prevented.