Miracle-dz/UniFAR-FTUs huggingface dataset:
Order Fulfillment Cycle Time Estimation for On-Demand Food Delivery

By providing customers with conveniences such as easy access to an extensive variety of restaurants, effortless food ordering and fast delivery, on-demand food delivery (OFD) platforms have achieved explosive growth in recent years. A crucial machine learning task performed at OFD platforms is prediction of the Order Fulfillment Cycle Time (OFCT), which refers to the amount of time elapsed between a customer places an order and he/she receives the meal. The accuracy of predicted OFCT is important for customer satisfaction, as it needs to be communicated to a customer before he/she places the order, and is considered as a service promise that should be fulfilled as well as possible. As a result, the estimated OFCT also heavily influences planning decisions such as dispatching and routing. In this paper, we present the OFCT prediction model that is currently deployed at Ele.me, which is one of the world's largest OFD platforms and delivers over 10 million meals in more than 200 Chinese cities every day. By dissecting the order fulfillment cycle of a meal order, we identify key factors behind OFCT, and capture them with numerous features constructed using a wide range of data sources. These features are fed into a deep neural network (DNN), which further incorporates representations of couriers, restaurants and delivery destinations to enhance prediction efficacy. Finally, a novel post-processing layer is introduced to improve convergence speed by better accounting for the distributional mismatch between the true OFCT values and those predicted by the model at initialization. Extensive offline and online experiments demonstrate the effectiveness of our approach.
Urban Driving Based on Condition Imitation Learning and Multi-Period Information Fusion

In recent years, autonomous driving has become a hot topic, especially in the complex urban road environment. The visual algorithm is the most used scheme for autonomous driving. The traditional condition imitation learning adopts the end-to-end deep learning network. But it lacks interpretability, and the ability of feature extraction and expression of network is limited. There are still some problems in the local planning and detail implementation. To solve these problems, we propose to use the deep residual network architecture and add the dual attention module to learn driving skills, which are closer to human beings. To further improve the detailed feature extraction ability of the network, the deeper residual network architecture is used. To adaptively integrate the global context long-range dependence of the image in the spatial and feature dimensions, the dual attention module is adopted to improve the ability of network expression. At the same time, in order to make full use of the multi-period attribute information of the camera image itself, we redesign the network architecture, extract, integrate the three-way temporal information features and the high-level semantics, and increase the interpretability of the temporal information of the model. This method is tested on the CARLA simulator. The experimental results show that compared with the benchmark algorithm, it achieves better driving effect. Deeper feature extraction and multi-period information fusion can effectively improve the driving ability and driving completion of the agent.
Post-weaning shifts in microbiome composition and metabolism revealed by over 25,000 pig gut metagenome assembled genomes

Using a previously described metagenomics dataset of 27 billion reads, we reconstructed over 50,000 metagenome-assembled genomes (MAGs) of organisms resident in the porcine gut, 46.5% of which were classified as >70% complete with a <10% contamination rate, and 24.4% were nearly complete genomes. Here we describe the generation and analysis of those MAGs using time-series samples. The gut microbial communities of piglets appear to follow a highly structured developmental program in the weeks following weaning, and this development is robust to treatments including an intramuscular antibiotic treatment and two probiotic treatments. The high resolution we obtained allowed us to identify specific taxonomic “signatures” that characterize the microbiome development immediately after weaning. Additionally, we characterized the carbohydrate repertoire of the organisms resident in the porcine gut, identifying 294 carbohydrate active enzymes. We tracked the shifts in abundance of these enzymes across time, and identified the species and higher-level taxonomic groups carrying each of these enzymes in their MAGs, raising the possibility of modifying the piglet microbiome through the tailored provision of carbohydrate substrates.
The Performance of Improved Infotaxis in 3D Turbulence

Emergency response to harmful gases in the environment is an important research field in environmental monitoring. In recent years, more autonomous search algorithms for harmful gas emission sources in uncertain environments have been proposed, which can avoid close contact with harmful gas by emergency personnel. In this paper, Infotaxis is extended to three-dimensional scene for application, and the source guidance in the reward function of the traditional Infotaxis algorithm is too small and the seven alternative direction movement methods in three-dimensional scenes tend to search by layers is optimized. At the same time, two multi-directional movement strategies based on the source guidance algorithm are proposed. The performance of the improved Infotaixs algorithm is analyzed under three internal release source conditions and two external environmental conditions, and the relative optimal mobile strategy is obtained. Many simulation experiments show that compared with the traditional Infotaxis algorithm, the Infotaxis algorithm based on source guidance with 14 alternative directions reduces the mean search path by 25.97% and improves the success rate by 0.2% in three-dimensional scene.
From the Andes to the desert: First overview of the bacterial community in the Rimac river, the main source of water for Lima, Peru

Background The Rimac river is the main source of water for Lima, Peru’s capital megacity. The river is constantly affected by different types of contamination including mine tailings in the Andes and urban sewage in the metropolitan area. We aim to produce the first characterization of bacterial communities in the Rimac river using a 16S rRNA amplicon sequencing approach which would be useful to identify bacterial diversity and potential understudied pathogens. Results We report a higher diversity in bacterial communities from the Upper and, especially, Middle Rimac compared to the Lower Rimac (Metropolitan zone). Samples were generally grouped according to their geographical location. Bacterial classes Alphaproteobacteria, Bacteroidia, Campylobacteria, Fusobacteriia, and Gammaproteobacteria were the most frequent along the river. Arcobacter cryaerophilus (Campylobacteria) was the most frequent species in the Lower Rimac while Flavobacterium succinicans (Bacteroidia) and Hypnocyclicus (Fusobacteriia) were the most predominant in the Upper Rimac. Predicted metabolic functions in the microbiota include bacterial motility, quorum sensing and xenobiotics metabolism. Additional metabolomic analyses showed the presence natural flavonoids and antibiotics in the Upper Rimac, and herbicides in the Lower Rimac. Conclusions The dominance in the Metropolitan area of Arcobacter cryaerophilus, an emergent pathogen associated with fecal contamination and antibiotic multiresistance, but that is not usually reported in traditional microbiological quality assessments, highlights the necessity to apply next-generation sequencing tools to improve pathogen surveillance. We believe that our study will encourage the integration of omics sciences in Peru and its application on current environmental and public health issues.
MultiGWAS: An integrative tool for Genome Wide Association Studies (GWAS) in tetraploid organisms

Summary The Genome-Wide Association Studies (GWAS) are essential to determine the genetic bases of either ecological or economic phenotypic variation across individuals within populations of model and non-model organisms. For this research question, current practice is the replication of the GWAS testing different parameters and models to validate the reproducibility of results. However, straightforward methodologies that manage both replication and tetraploid data are still missing. To solve this problem, we designed the MultiGWAS, a tool that does GWAS for diploid and tetraploid organisms by executing in parallel four software, two for polyploid data (GWASpoly and SHEsis) and two for diploids data (PLINK and TASSEL). MultiGWAS has several advantages. It runs either in the command line or in an interface. It manages different genotype formats, including VCF. It executes both the full and naïve models using several quality filters. Besides, it calculates a score to choose the best gene action model across GWASPoly and TASSEL. Finally, it generates several reports that facilitate the identification of false associations from both the significant and the best-ranked association SNP among the four software. We tested MultiGWAS with tetraploid potato data. The execution demonstrated that the Venn diagram and the other companion reports (i.e., Manhattan and QQ plots, heatmaps for associated SNP profiles, and chord diagrams to trace associated SNP by chromosomes) were useful to identify associated SNP shared among different models and parameters. Therefore, we confirmed that MultiGWAS is a suitable wrapping tool that successfully handles GWAS replication in both diploid and tetraploid organisms. Contact phreyes@agrosavia.co
Inhibition of CO2 Assimilation by Supraoptimal CO2: Effect of Light and Temperature

In cotton the rate of CO2 assimilation, at O2 partial pressures up to 200 mbar, increased to a maximum and then declined as the intercellular partial pressure of CO2 was increased. The specific intercellular partial pressure of CO2 at which rate of assimilation began to decline depended on the environmental conditions. At 19 mbar partial pressure of O2 the decline occurred at CO2 partial pressure >390 µbar. At 200 mbar partial pressure of O2 it occurred at CO2 partial pressure > 534 µbar. O2 increased the CO2 partial pressure required for inhibition but it did not appear to affect the steepness of the decline of rate of assimilation with further increase in partial pressure of CO2 once the decline became apparent. The decline was more readily observed at low temperature and low O2 partial pressure, and in plants grown at low light and NO3- levels. It was also observed in cowpea and sunflower. Changes in quantum efficiency in cotton at high and low CO2 concentrations were observed. At ambient CO2 concentration (300 µbar), the quantum yields measured at 19 and 200 mbar partial pressure of O2 were 0.072 ± 0.0003 and 0.053 ± 0.0060 mol CO2 per mol absorbed quanta, respectively. In contrast, at 900 µbar CO2 partial pressure the respective values were 0.050 ± 0.0023 and 0.070 ± 0.0006 mol CO2 per mol absorbed quanta. The nature of the inhibition of CO2 assimilation by high partial pressure of CO2 is discussed.
FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state and is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin, and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing against industry FLIM analysis standards.
Reboot: a straightforward approach to identify genes and splicing isoforms associated with cancer patient prognosis

Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to carry out these analyses. Here, we present Reboot, an original and efficient algorithm to find genes and splicing isoforms associated with cancer patient survival, disease progression, or other clinical endpoints. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests for supporting the findings, which are automatically plotted. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients’ prognosis, even in conjunction with other well-established clinical parameters. Similarly, Reboot was able to find a seven-splicing isoforms signature (CENPF-201; MLKL-202; NUP54-201; MCF2L-201; TFDP1-207; BBS1-206; HTT-202) related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive, and straightforward way for finding genes or splicing isoforms (transcripts) signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated sets of cancer-related genes. Reboot effectively runs on either servers or personal computers and it is freely available at github.com/galantelab/reboot.
Postoperative Functional Management Contributes to Anal Functional Recovery in Patients With Low Rectal Cancer After Robotic Total Intersphincteric Resection

Purpose: To evaluate the effectiveness of the comprehensive post-operative management including low-frequency endo-anal electrical stimulation and daily suppository usage on post-operative anal functional recovery for low rectal cancer patients who underwent robotic total intersphincteric resection (ISR). Methods: A retrospective analysis was performed on 42 low rectal cancer patients who underwent robotic total ISR, of which 23 patients received comprehensive post-operative management, including biofeedback low-frequency endo-anal electrical stimulation and daily suppository usage (management group). Wexner score and anorectal manometric values, including resting pressure (RP), maximum squeeze pressure (MSP), initial perceived volume (IPV), and maximum tolerated volume (MTV), were assessed and compared. Results: A total of 42 low rectal cancer patients were included in our study. The RP at 6 months after ISR (40.95 ± 6.95 mmHg vs. 33.29 ± 5.40 mmHg, p = 0.002) and MSP at 3 and 6 months after ISR (72.05 ± 10.16 mmHg vs. 69.05 ± 8.67 mmHg, p = 0.031; 91.57 ± 15.47 mmHg vs. 84.05 ± 12.94 mmHg, p = 0.039, respectively) were significantly higher in the management group. The median IPV at 1 and 3 months after ISR (17.81 ± 3.61 ml vs. 15.43 ± 5.08 ml, p = 0.038; 20.19 ± 4.35 ml vs. 17.67 ± 5.16 ml, p = 0.044, respectively) and MTV at 3 months after ISR (83.71 ± 5.44 ml vs. 76.10 ± 8.42 ml, p = 0.012) were significantly higher in the management group. Wexner scores at 1 and 3 months after closure of stoma (COS) in the management group were significantly lower (11.3 ± 2.9 vs. 13.4 ± 3.0, p = 0.041; 8.9 ± 2.0 vs. 10.6 ± 2.4, p = 0.036, respectively). Conclusions: Comprehensive post-operative management could accelerate the recovery of sphincteric function and anal sensitivity after robotic total ISR and could also contribute to treatment of fecal incontinence followed by COS.
Assessment of Inter-Laboratory Variation in the Characterization and Analysis of the Mucosal Microbiota in Crohn’s Disease and Ulcerative Colitis

Background In studies evaluating the microbiome, numerous factors can contribute to technical variability. These factors include DNA extraction methodology, sequencing protocols, and data analysis strategies. We sought to evaluate the impact these factors have on the results obtained when the sequence data are independently generated and analyzed by different laboratories. Methods To evaluate the effect of technical variability, we used human intestinal biopsy samples resected from individuals diagnosed with an inflammatory bowel disease (IBD), including Crohn’s disease (n = 12) and ulcerative colitis (n = 10), and those without IBD (n = 10). Matched samples from each participant were sent to three laboratories and studied using independent protocols for DNA extraction, library preparation, targeted-amplicon sequencing of a 16S rRNA gene hypervariable region, and processing of sequence data. We looked at two measures of interest – Bray–Curtis PERMANOVA R2 values and log2 fold-change estimates of the 25 most-abundant taxa – to assess variation in the results produced by each laboratory, as well the relative contribution to variation from the different extraction, sequencing, and analysis steps used to generate these measures. Results The R2 values and estimated differential abundance associated with diagnosis were consistent across datasets that used different DNA extraction and sequencing protocols, and within datasets that pooled samples from multiple protocols; however, variability in bioinformatic processing of sequence data led to changes in R2 values and inconsistencies in taxonomic assignment and abundance estimates. Conclusion Although the contribution of DNA extraction and sequencing methods to variability were observable, we find that results can be robust to the various extraction and sequencing approaches used in our study. Differences in data processing methods have a larger impact on results, making comparison among studies less reliable and the combined analysis of bioinformatically processed samples nearly impossible. Our results highlight the importance of making raw sequence data available to facilitate combined and comparative analyses of published studies using common data processing protocols. Study methodologies should provide detailed data processing methods for validation, interpretability, reproducibility, and comparability.
The Melanocortin System in Atlantic Salmon (Salmo salar L.) and Its Role in Appetite Control

The melanocortin system is a key neuroendocrine network involved in the control of food intake and energy homeostasis in vertebrates. Within the hypothalamus, the system comprises two main distinct neuronal cell populations that express the neuropeptides proopiomelanocortin (POMC; anorexigenic) or agouti-related protein (AGRP; orexigenic). Both bind to the melanocortin-4 receptor (MC4R) in higher order neurons that control both food intake and energy expenditure. This system is relatively well-conserved among vertebrates. However, in Atlantic salmon (Salmo salar L.), the salmonid-specific fourth round whole-genome duplication led to the presence of several paralog genes which might result in divergent functions of the duplicated genes. In the current study, we report the first comprehensive comparative identification and characterization of Mc4r and extend the knowledge of Pomc and Agrp in appetite control in Atlantic salmon. In silico analysis revealed multiple paralogs for mc4r (a1, a2, b1, and b2) in the Atlantic salmon genome and confirmed the paralogs previously described for pomc (a1, a2, and b) and agrp (1 and 2). All Mc4r paralogs are relatively well-conserved with the human homolog, sharing at least 63% amino acid sequence identity. We analyzed the mRNA expression of mc4r, pomc, and agrp genes in eight brain regions of Atlantic salmon post-smolt under two feeding states: normally fed and fasted for 4 days. The mc4ra2 and b1 mRNAs were predominantly and equally abundant in the hypothalamus and telencephalon, the mc4rb2 in the hypothalamus, and a1 in the telencephalon. All pomc genes were highly expressed in the pituitary, followed by the hypothalamus and saccus vasculosus. The agrp genes showed a completely different expression pattern from each other, with prevalent expression of the agrp1 in the hypothalamus and agrp2 in the telencephalon. Fasting did not induce any significant changes in the mRNA level of mc4r, agrp, or pomc paralogs in the hypothalamus or in other highly expressed regions between fed and fasted states. The identification and wide distribution of multiple paralogs of mc4r, pomc, and agrp in Atlantic salmon brain provide new insights and give rise to new questions of the melanocortin system in the appetite regulation in Atlantic salmon.
The Effect of Task on Object Processing revealed by EEG decoding

Recent studies showed that task demand affects object representations in higher-level visual areas and beyond, but not so much in earlier areas. There are, however, limitations in those studies including the relatively weak manipulation of task due to the use of familiar real-life objects, the low temporal resolution in fMRI, and the emphasis on the amount and not the source of information carried by brain activations. In the current study, observers categorized images of artificial objects in one of two orthogonal dimensions, shape and texture, while their brain activity was recorded with electroencephalogram (EEG). Results showed that object processing along the texture dimension was affected by task demand starting from a relatively late time (320-370ms time window) after image onset. The findings are consistent with the view that task exerts an effect on the later phases of object processing.
PIE-1 promotes SUMOylation and activation of HDAC1 during the C. elegans oogenesis

Germlines shape and balance heredity, integrating and regulating information from both parental and foreign sources. Insights into how the germline handles information have come from the identification of factors that specify or maintain the germline fate. In early C. elegans embryos, the CCCH zinc-finger protein PIE-1 localizes to the germline where it prevents somatic differentiation programs. Here we show that PIE-1 also functions in the meiotic ovary where it becomes SUMOylated and engages the SUMO-conjugating machinery. Using whole-proteome mass spectrometry to detect SUMO-conjugated proteins, we identify HDAC SUMOylation as a target of PIE-1. Our findings suggest that SUMOylation activates HDAC, lowering histone acetylation and enhancing Argonaute-mediated surveillance in the germline.