Miracle-dz/UniFAR-FTUs huggingface dataset:
Ancient DNA genomics and the renaissance of herbaria

Herbaria are undergoing a renaissance as valuable sources of genomic data for exploring plant evolution, ecology, and diversity. Ancient DNA retrieved from herbarium specimens can provide unprecedented glimpses into past plant communities, their interactions with biotic and abiotic factors, and the genetic changes that have occurred over time. Here, we highlight recent advances in the field of herbarium genomics and discuss the challenges and opportunities of combining data from modern and time-stamped historical specimens. We also describe how integrating herbarium genomics data with other data types can yield substantial insights into the evolutionary and ecological processes that shape plant communities. Herbarium genomic analysis is a tool for understanding plant life and informing conservation efforts in the face of dire environmental challenges. Description
Deep Learning methods for Semantic Role Labeling in Indian Languages

We introduce a supervised deep learning model for Indian languages namely, Hindi and Urdu which uses minimal syntax and yet improves over the current base-line for these languages signiﬁcantly. We progress with three different models inspired from the recent advancements in this ﬁeld. In the ﬁrst model we make use of sequence modeling to generate dependency path embeddings and jointly learning the classiﬁcation process i
Challenges and Issues in Developing an Annotated Corpus and HMM POS Tagger for Khasi

An attempt has been made to annotate a Khasi corpus with Part-of-Speech (POS) tags, using the Bureau of Indian Standards (BIS) POS tagset prepared by the POS Tag Standardization Committee of the Department of Information Technology (DIT), New Delhi, India for annotating Indian language corpora. This is the first initiative taken for Khasi-an understudied and under-resourced language, in developing an annotated corpus and POS tagger essential for language technology. This article highlights the challenges and issues that surfaced during annotation, and the decisions that were taken when tagging features characteristic of Khasi that are absent from mainstream Indian languages. A Hidden Markov Model (HMM) POS tagger is then constructed, taking into consideration the information provided by the morphological features of the Khasi language. The results of training and testing the Khasi HMM POS tagger are compared with the results of a Khasi baseline tagger, and a Khasi tagger constructed using Natural Language Toolkit (NLTK).
A Media Repertoires Approach to Selective Exposure: News Consumption and Political Polarization in Eastern Europe

In recent years, links between selective news exposure and political polarisation have attracted considerable attention among communication scholars. However, while the existence of selective exposure has been documented in both offline and online environments, the evidence of its extent and its impact on political polarisation is far from unanimous. To address these questions, and also to bridge methodological and geographical gaps in existing research, this paper adopts a media repertoires approach to investigate selective news exposure and polarisation in four Eastern European countries – the Czech Republic, Hungary, Poland and Serbia. Using a combination of population surveys, expert surveys and qualitative interviews, the data for the study were collected between November 2019 and May 2020. We identify five types of news repertoires based on their relative openness to counter-attitudinal sources, and show that selective news repertoires are present in 29% of the entire sample. Our findings also reveal significant cross-country differences, with the more selective news repertoires more prominent in countries characterised by higher levels of polarisation. Furthermore, while the selection of news sources is in line with people's electoral (and to a lesser extent ideological) preferences, our findings show that exposure to counter-attitudinal sources can also be strongly correlated with political and ideological leanings. Our qualitative data suggest that this is because exposure to counter-attitudinal sources can reinforce attitudes, and potentially contribute to polarisation. Qualitative data also highlight the influence of environmental factors (e.g., family), and suggest that selective news consumption is associated with normatively different conceptions of media trust.
Effective Recommendation of Cross-Project Correlated Issues based on Issue Metrics

The calling relationship between projects becomes complicated as the number of open-source projects increases. Different issues across projects can also be related, referred to as cross-project correlated issues (CPCIs), and bring new challenges for developers to fix these issues. When solving these CPCIs, developers have to accurately locate the source code that causes it in the current project and also needs to know the related issues in other projects. However, few studies have proposed specific methods to help developers effectively address these CPCIs, i.e., find related issues for CPCIs. This paper proposes a novel issue recommendation model for CPCIs. When developers fix a CPCI, they can find its associated issues based on our model. We first extract 26 issue metrics on CPCIs from four aspects: text similarity, cooperative relationship between developers, developers’ familiarity with the project, and developers’ fixing experience. Then, we utilize three classifiers (SVM, Logistic Regression, and Random Forest) to build CPCI recommendation models. To evaluate the model’s performance, we construct three baseline models based on text features and build experiments in the Python scientific computing software ecosystem, which mainly includes seven open-source software libraries. Moreover, we employ three indicators to measure the experimental results, i.e., MAP, MRR, and Recall-rate@k. The CPCI recommendation models built based on issue features have significantly better experimental results than the baseline models in most cases, which indicates that these issue metrics help recommend CPCIs.
Modeling seismically induced deformation and fluid flow in the Nankai subduction zone

Fluid pressure changes induced by seismic strains in the Nankai subduction zone were investigated through numerical modeling. Seismic strains resulting from dislocations along fault surfaces were coupled to pore pressure generation, and subsequent transient fluid flow was simulated. This study is distinct from previous efforts that modeled homogeneous systems. Effects of variable mechanical and hydrologic properties were investigated by assigning different mechanical and hydrological properties to marine sediments, the decollement zone, and the upper oceanic crust. Model sensitivity studies suggest that for a reasonable range of parameter scenarios, transient pressure head signals caused by discrete dislocations of a few meters in the updip region of the seismogenic zone can be observed over large areas of the margin from within shallow depths of the sediment wedge to the oceanic crust below the decollement.
A Deep Dive into the Featured iOS Apps

Millions of apps in markets have made it difficult for mobile users to find fancy and high quality apps. Mobile app markets have deployed mechanisms to recommend apps to users. Apple usually features apps in the iOS App Store, and mobile users could see the featured apps as soon as they open the App Store. In general, getting apps featured is an achievement all developers strive towards and it is a common belief that getting app featured means that the app is becoming popular. However, the official app recommendation mechanism has not been characterized yet. To fill the void, we present a large-scale and longitudinal study of featured apps on iOS App Store. Specifically, we collaborate with our industry partner to monitor the iOS App Store and collect the daily featured apps in both the US and China, covering a span of over 1.5 years. Based on this comprehensive dataset, we characterize the featured apps from various dimensions and investigate the impact of app recommendation on app popularity. We have revealed a number of observations that are unknown to the community. Most importantly, we observe that although getting featured indeed has a positive effect for most apps, the duration of this effect is short-lived. In addition, there are times when the recommendations are ineffective, and we propose some potential reasons and tips for this. Our study can offer practical implications on app promotion to stakeholders in the mobile app ecosystem.
An Empirical Study of the Apache Voting Process on Open Source Community Governance

Open-source software (OSS) projects have become a cornerstone of the software ecosystem, offering numerous benefits to developers and end-users alike. However, ensuring the long-term sustainability and success of OSS projects is challenging, requiring effective community engagement and mentorship. Previous studies have demonstrated that OSS projects benefit from having a larger number of members and an active community, as measured by communication and code contributions. The sustainability of OSS projects must require an effective approach to community governance. In the projects of the Apache Software Foundation Incubator (ASFI), voting plays an important role in community governance, which makes the decision-making process transparent and democratic. Therefore, our study aims to investigate the association between the voting process during the incubation period and the final outcomes of projects (graduated or retired) within ASFI. In this paper, we conduct a comprehensive analysis of the voting process in the ASF projects based on a large-scale data set of the mailing list from 272 sustainability-labeled ASF Incubator projects. We compute various metrics related to voting and investigate whether there are significant differences between graduated and retired projects based on these metrics. We also employ an approach to detect episodic changes based on these voting metrics. Our objective is to examine whether episodic changes in the voting process have a significant impact on the sustainability and success of the project. Our findings reveal that the voting process in the ASF Incubator is closely related to project outcomes, with graduated projects generally exhibiting higher interest and more stability of voting. In practice, these results can help practitioners and project mentors better understand the impact of the voting process on OSS project outcomes and inform strategies to foster a more conducive environment for project success and sustainability within the ASF Incubator and beyond.
SupConFL: Fault Localization with Supervised Contrastive Learning

Recent years have seen a growing interest in deep learning-based approaches to localize faults in software. However, existing methods have not reached a satisfying level of accuracy. The main reason is that the feature extraction of faulty code elements is insufficient. Namely, these deep learning-based methods will learn some features that are not relevant to fault localization, and thus ignore the features related to fault localization. We propose SupConFL, a new framework for statement-level fault localization. Our framework combines the statement-level abstract syntax tree with the statement sequence, and adopt controllable attention-based LSTM to locate the faulty elements. The training is done through contrastive learning between the faulty code and its fixed version. By comparing the faulty code with the fixed code, the model can learn richer features of the faulty code elements. Our experiments on Defects4j-1.2. 0 dataset show that our method outperforms the current state-of-the-art. Specifically, SupConFL improves Top-1 score by 7.96% in comparison with the current state-of-the-art. In addition, our method has also achieved good results in cross-project experiments.
Patient preferences for physical therapy programs after a lower extremity fracture: a discrete choice experiment

Objective To quantify patients’ preferences for physical therapy programmes after a lower extremity fracture and determine patient factors associated with preference variation. Design Discrete choice experiment. Setting Level I trauma centre. Participants One hundred fifty-one adult (≥18 years old) patients with lower extremity fractures treated operatively. Intervention Patients were given hypothetical scenarios and asked to select their preferred therapy course when comparing cost, mobility, long-term pain, session duration, and treatment setting. Main outcome measures A multinomial logit model was used to determine the relative importance and willingness to pay for each attribute. Results Mobility was of greatest relative importance (45%, 95% CI: 40% to 49%), more than cost (23%, 95% CI: 19% to 27%), long-term pain (19%, 95% CI: 16% to 23%), therapy session duration (12%, 95% CI: 9% to 5%) or setting (1%, 95% CI: 0.2% to 2%). Patients were willing to pay US$142 more per session to return to their preinjury mobility level (95% CI: US$103 to US$182). Willingness to pay for improved mobility was higher for women, patients aged 70 years and older, those with bachelor’s degrees or higher and those living in less-deprived areas. Patients were willing to pay US$72 (95% CI: US$50 to US$93) more per session to reduce pain from severe to mild. Patients were indifferent between formal and independent home therapy (willingness to pay: −US$12, 95% CI: −US$33 to US$9). Conclusions Patients with lower extremity fractures highly value recovering mobility and are willing to pay more for postoperative physical therapy programmes that facilitate returning to their pre-injury mobility level. These patient preferences might be useful when prescribing and designing new techniques for postoperative therapy.
The relative importance of plate-driven and buoyancy-driven flow at mid-ocean ridges

The dynamical interaction between three-dimensional (3-D) buoyant flow and plate-driven mantle flow beneath a mid-ocean ridge is examined using a combination of laboratory and numerical experiments. In a unique laboratory setup a layer of strongly temperature-dependent viscous fluid is heated from below and cooled from above to drive thermal convection. Forced, plate-driven flow is modeled by dragging mylar sheeting in opposite directions across the fluid surface. Uniform viscosity 3-D numerical models have been designed to simulate the laboratory runs, to provide additional information on the flow, and to attempt to isolate the effects of variable viscosity. In one type of experiment we modeled buoyancy on the scale of the partial melting region with a linear heat source beneath the spreading axis. In a second set of experiments the entire base of the tank is heated in order to investigate the interaction between plate-driven flow and larger-scale (upper mantle) convection. The pattern of segment-scale (∼100–150 km) convection beneath a spreading center is found to be a strong function of the spreading rate and the Rayleigh number (Ra) of the buoyant flow. Purely two-dimensional (2-D) flow exists only in the case of low Ra (∼105) and faster spreading rates (>4–6 cm/yr half rate). Three dimensionality is strongly enhanced during transient pulses of upwelling. Convection on the scale of the upper mantle can contribute significant long-wavelength spatial (∼600–1000 km) and temporal (20–40 m.y.) variability in upwelling rates and temperatures at spreading centers and may provide an alternative model for plume-ridge interaction. For a given Ra the upwelling near the spreading axis can essentially be described by three regimes: weakly 3-D at the slow spreading rates, strongly 3-D at slow to intermediate rates, and 2-D at fast spreading rates. The regime boundaries shift toward higher plate velocities with increasing Ra. Comparison of the laboratory and numerical experiments indicates that temperature-dependent viscosity may strengthen the position of focused upwelling centers and cause a sharper transition between 2-D and 3-D upwelling patterns. Taken together, these results suggest that buoyancy-driven, dynamic flow is an important element in the geodynamics of mid-ocean ridges.
Geomorphologic Modeling of a Macro-tidal Embayment with Extensive Tidal Flats: Skagit Bay, WA

Abstract : Understand morphology changes of mud tidal flats and provide quantification of the sensitivity of these changes to tidal action, river discharge and shoreline development. Demonstrate the use of a community numerical model for estimating the tidal flat morphology. The ONR/Delft Community model is being evaluated as a physically-based numerical simulation tool for several kinds of investigations. The objective of the project is to further demonstrate it use for tidal flat/channel systems. Investigate the relative roles of tidal action, river discharge and shoreline development on morphology. From a model tuning perspective, this objective includes advancing the understanding of the sensitivity of the model to parameter value adjustments and inclusion or exclusion of specific sediment transport processes and characterization in tidal flat and channel systems. Integrate model analyses with observations and field planning efforts. The model fidelity will be improved by incorporation of observational data in terms of model configuration, the assignment of boundary and initial conditions and for calibration and validation efforts. There is also a benefit to using the model to help with the design of the field programs and the interpretation of observational datasets. This has been demonstrated in other studies (e.g. Hibler, et al, 2008). A three-phase approach will be used. All phases are model configuration and evaluation intensive.
Vps18 contributes to phagosome membrane integrity in Mycobacterium tuberculosis-infected macrophages

Mycobacterium tuberculosis (Mtb) has evolved to be exquisitely adapted to survive within host macrophages. The capacity to damage the phagosomal membrane has emerged as central to Mtb virulence. While Mtb factors driving membrane damage have been described, host factors that repair that damage to contain the pathogen remain largely unknown. We used a genome-wide CRISPR screen to identify novel host factors required to repair Mtb-damaged phagosomal membranes. Vacuolar protein sorting-associated protein 18 (Vps18), a member of the HOPS and CORVET trafficking complexes, was among the top hits. Vps18 colocalized with Mtb in macrophages beginning shortly after infection, and Vps18-knockout macrophages demonstrated increased damage of Mtb-containing phagosomes without impaired autophagy. Mtb grew more robustly in Vps18-knockout cells, and the first-line anti-tuberculosis antibiotic pyrazinamide was less effective. Our results identify Vps18 as required for phagosomal membrane integrity in Mtb-infected cells and suggest that modulating phagosome integrity may hold promise for improving the efficacy of antibiotic treatment for TB.
Alveolar differentiation drives resistance to KRAS inhibition in lung adenocarcinoma

Lung adenocarcinoma (LUAD), commonly driven by KRAS mutations, is responsible for 7% of all cancer mortality. The first allele-specific KRAS inhibitors were recently approved in LUAD, but clinical benefit is limited by intrinsic and acquired resistance. LUAD predominantly arises from alveolar type 2 (AT2) cells, which function as facultative alveolar stem cells by self-renewing and replacing alveolar type 1 (AT1) cells. Using genetically engineered mouse models, patient-derived xenografts, and patient samples we found inhibition of KRAS promotes transition to a quiescent AT1-like cancer cell state in LUAD tumors. Similarly, suppressing Kras induced AT1 differentiation of wild-type AT2 cells upon lung injury. The AT1-like LUAD cells exhibited high growth and differentiation potential upon treatment cessation, whereas ablation of the AT1-like cells robustly improved treatment response to KRAS inhibitors. Our results uncover an unexpected role for KRAS in promoting intra-tumoral heterogeneity and suggest targeting alveolar differentiation may augment KRAS-targeted therapies in LUAD. Significance Treatment resistance limits response to KRAS inhibitors in LUAD patients. We find LUAD residual disease following KRAS targeting is composed of AT1-like cancer cells with the capacity to reignite tumorigenesis. Targeting the AT1-like cells augments responses to KRAS inhibition, elucidating a therapeutic strategy to overcome resistance to KRAS-targeted therapy.
Eccentricity strongly modulates visual processing delays

The temporal dynamics of visual information processing varies with the stimulus being processed and with the retinal location that initiates the processing. Here, we present psychophysical data with sub-millisecond precision showing that increasing eccentricity decreases the delay with which stimuli are processed. We show that, even within the central +/-6º of the visual field, processing delays change by a factor of up to three times. A simple model, grounded in retinal physiology, provides a good account of the data. The relative delays are on the order of only milliseconds. But if later processing leaves the delays unresolved, they can cause dramatic misperceptions of motion and 3D layout. We discuss the implications for how the human visual system solves the temporal binding problem across eccentricity. The results highlight the severe computational challenge of obtaining accurate, temporally-unified percepts of the environment with spatiotemporally-staggered processing across the visual field.
Efectos y controversias de los ácidos grasos omega-3: effects and controversies

Thanks to the discovery of omega-3 fatty acids mechanism of action, these have regained importance due to its cardio-protective effect. The ingestion of omega-3 fatty acids diminishes the risk of thrombosis and cerebro-vascular accidents by lowering serum lipids and improving endothelial function. Nevertheless, some epidemiological studies have not found a direct relationship with them and there is controversy with regard to the doses needed in order to achieve this cardio-protective effect. There are studies reporting that the methyl-mercury content in fish affects omega-3 fatty acids disposal. In the Colombian Caribbean region, fish contamination with methyl-mercury has been found.
AbNatiV: VQ-VAE-based assessment of antibody and nanobody nativeness for hit selection, humanisation, and engineering

Monoclonal antibodies have emerged as key therapeutics, and nanobodies are rapidly gaining momentum following the approval of the first nanobody drug in 2019. Nonetheless, the development of these biologics as therapeutics remains a challenge. Despite the availability of established in vitro directed evolution technologies that are relatively fast and cheap to deploy, the gold standard for generating therapeutic antibodies remains discovery from animal immunization or patients. Immune-system derived antibodies tend to have favourable properties in vivo, including long half-life, low reactivity with self-antigens, and low toxicity. Here, we present AbNatiV, a deep-learning tool for assessing the nativeness of antibodies and nanobodies, i.e., their likelihood of belonging to the distribution of immune-system derived human antibodies or camelid nanobodies. AbNatiV is a multi-purpose tool that accurately predicts the nativeness of Fv sequences from any source, including synthetic libraries and computational design. It provides an interpretable score that predicts the likelihood of immunogenicity, and a residue-level profile that can guide the engineering of antibodies and nanobodies indistinguishable from immune-system-derived ones. We further introduce an automated humanisation pipeline, which we applied to two nanobodies. Wet-lab experiments show that AbNatiV-humanized nanobodies retain binding and stability at par or better than their wild type, unlike nanobodies humanised relying on conventional structural and residue-frequency analysis. We make AbNatiV available as downloadable software and as a webserver.