Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive microbiome causal mediation analysis using MiMed on user-friendly web interfaces

Abstract It is a central goal of human microbiome studies to see the roles of the microbiome as a mediator that transmits environmental, behavioral, or medical exposures to health or disease outcomes. Yet, mediation analysis is not used as much as it should be. One reason is because of the lack of carefully planned routines, compilers, and automated computing systems for microbiome mediation analysis (MiMed) to perform a series of data processing, diversity calculation, data normalization, downstream data analysis, and visualizations. Many researchers in various disciplines (e.g. clinicians, public health practitioners, and biologists) are not also familiar with related statistical methods and programming languages on command-line interfaces. Thus, in this article, we introduce a web cloud computing platform, named as MiMed, that enables comprehensive MiMed on user-friendly web interfaces. The main features of MiMed are as follows. First, MiMed can survey the microbiome in various spheres (i) as a whole microbial ecosystem using different ecological measures (e.g. alpha- and beta-diversity indices) or (ii) as individual microbial taxa (e.g. phyla, classes, orders, families, genera, and species) using different data normalization methods. Second, MiMed enables covariate-adjusted analysis to control for potential confounding factors (e.g. age and gender), which is essential to enhance the causality of the results, especially for observational studies. Third, MiMed enables a breadth of statistical inferences in both mediation effect estimation and significance testing. Fourth, MiMed provides flexible and easy-to-use data processing and analytic modules and creates nice graphical representations. Finally, MiMed employs ChatGPT to search for what has been known about the microbial taxa that are found significantly as mediators using artificial intelligence technologies. For demonstration purposes, we applied MiMed to the study on the mediating roles of oral microbiome in subgingival niches between e-cigarette smoking and gingival inflammation. MiMed is freely available on our web server (http://mimed.micloud.kr).
Structural properties of disk galaxies

Context. Knowledge of the intrinsic shapes of galaxy components provides crucial information when constraining phenomena driving their formation and evolution. Aims. We analize the structural parameters of a magnitude-limited sample of 148 unbarred S0–Sb galaxies to derive the intrinsic shape of their bulges. Methods. We developed a new method to derive the intrinsic shapes of bulges based on geometrical relationships between the apparent and intrinsic shapes of bulges and disks. Bulges were assumed to be triaxial ellipsoids sharing the same center and polar axis of their surrounding disks. Disks were assumed to be circular, infinitesimally thin, and to lie on the equatorial plane of bulges. The equatorial ellipticity and intrinsic flattening of bulges were obtained from the length of the apparent major and minor semi-axes of the bulge, the twist angle between the apparent major axis of the bulge and the galaxy line of nodes, and the galaxy inclination. Results. We find that the intrinsic shape is well constrained for a subsample of 115 bulges with favorable viewing angles. A large fraction of them are characterized by an elliptical section (B/A 50% and none has a probability >90% of being elongated along the polar axis. The distribution of triaxiality is strongly bimodal. This bimodality is driven by bulges with Sersic index n > 2, or equivalently, by the bulges of galaxies with a bulge-to-total ratio B/T > 0.3. Bulges with n ≤ 2 and with B/T ≤ 0.3 follow a similar distribution, which differs from that of bulges with n > 2 and B/T > 0.3. In particular, bulges with n ≤ 2 and B/T ≤ 0.3 exhibit a larger fraction of oblate axisymmetric (or nearly axisymmetric) bulges, a smaller fraction of triaxial bulges, and fewer prolate axisymmetric (or nearly axisymmetric) bulges with respect to bulges with n > 2 and with B/T > 0.3, respectively. No correlation is found between the intrinsic shape and either the luminosity or velocity dispersion of bulges. Conclusions. According to predictions of the numerical simulations of bulge formation, bulges with n ≤ 2, which show a high fraction of oblate axisymmetric (or nearly axisymmetric) shapes and have B/T ≤ 0.3, may be the result of dissipational minor mergers. Both major dissipational and dissipationless mergers seem to be required to explain the variety of shapes found for bulges with n > 2 and B/T > 0.3.
Rural farmers’ learning of weed management methods in Malaysia

for modern high-technology farms by 2030. To achieve this, a significant increase in intensive agricultural activities and weed management practices is required. To develop strategies and achieve the goals of sustainable agriculture, the present study used a semi-structured questionnaire survey to assess farmers’ knowledge and perception of weeds, their sources of information, and their reasons for willingness or unwillingness to adopt non-chemical weed control methods. The survey was conducted from June to October 2018 and included 62 members of the Malim Nawar Vegetable Farmers Association. Descriptive and chi-square statistics were used for the statistical analyses. Of the 62 participants, 50 (80.6%) were over 50 years of age, and 47 (75.7%) spoke the Hakka dialect. Pest infestation and crop diseases were the most important constraints in crop production, followed by weed infestation. Knowledge of weed species led to the anticipation of yield loss and exploration of potential control methods. Social networking and agriculture chemical companies were the main sources of information on weed control methods. Despite knowing the harmful effects of chemical herbicides, farmers’ willingness/resistance to adopt non-chemical weed control methods depended on many different factors. The survey results showed that the proactive-ness of farmers’ associations and relevant government agencies is a prerequisite for achieving agricultural development through education. Moreover, structure and systematic learning using innovative methods adjusted to local socioeconomic conditions could facilitate a paradigm shift from chemical control to environment-friendly weed control methods.
Multi-Graded Macaulay Dual Spaces

We describe an algorithm for computing Macaulay dual spaces for multi-graded ideals. For homogeneous ideals, the natural grading is inherited by the Macaulay dual space which has been leveraged to develop algorithms to compute the Macaulay dual space in each homogeneous degree. Our main theoretical result extends this idea to multi-graded Macaulay dual spaces inherited from multi-graded ideals. This natural duality allows ideal operations to be translated from homogeneous ideals to their corresponding operations on the multi-graded Macaulay dual spaces. In particular, we describe a linear operator with a right inverse for computing quotients by a multi-graded polynomial. By using a total ordering on the homogeneous components of the Macaulay dual space, we also describe how to recursively construct a basis for each component. Several examples are included to demonstrate this new approach.
The effect of parental thermal environment on maternal investment and offspring performance

As heat wave phenomena and thermal variance events increase in frequency and intensity, it is important to understand how the effect of exposure to constant elevated temperatures on fitness differs from the one of thermal fluctuations. This study aims to investigate how parental thermal environment affects maternal fitness measured as maternal investment and offspring performance. A wild population of the amphipod Gammarus chevreuxi was exposed to constant (15, 20 and 25 °C) or fluctuating (variable between 15 and 25 °C) thermal regimes and allowed to mate. Immediately after fertilization, mothers carrying eggs were transferred to common garden conditions (15 °C), to allow embryo development until hatching. Egg size and hatchling heart rate were measured as proxies for maternal investment and offspring performance respectively. Parental thermal environment did not affect maternal investment but did affect offspring performance. Mothers acclimated to constant, elevated thermal regimes, within or beyond their temperature range, produced offspring with lower heart rate than those acclimated to control conditions. Interestingly, the heart rate of offspring from mothers exposed to fluctuating environments was intermediate between that of offspring from mothers exposed to control and elevated, within range, constant temperatures. This study concludes that aquatic animals which experience thermal fluctuations in their natural environment may be able to adopt mechanisms in response to environmental thermal variance that minimise negative effects on their fitness. Furthermore, it is uncertain whether bradycardia in crustacea hatchlings is adaptive or maladaptive. If it was adaptive, these results would also suggest that G. chevreuxi hatchlings displays a higher performance after parental acclimation to constant thermal stress, in comparison to control conditions. If it was maladaptive the opposite would be suggested.
The Local Bow Shock Environment during Magnetosheath Jet Formation: Results from a Hybrid-Vlasov Simulation

Magnetosheath jets are plasma structures that are characterised by enhanced dynamics pressure and/or plasma velocity. A jet generation mechanism that has been widely discussed in observational and numerical studies is steepened Ultra Low Frequency (ULF) waves interacting with the bow shock. However, other formation mechanisms have also been proposed. In Suni et al. (2021), we studied jets in four 2D simulation runs of the global magnetospheric hybrid-Vlasov model Vlasiator and found 5 that 75% of the jets were associated with regions of enhanced magnetic field and dynamic pressure in the foreshock that we called foreshock compressive structures (FCS). In this study we focus on investigating the jets in the same simulation runs that are not associated with FCSs (non-FCS-jets), and then compare them to FCS-associated jets (FCS-jets). The data set consists of 791 jets in total, of which 562 (71%) are FCS-jets and 229 (29%) are non-FCS-jets. We find that the non-FCS-jets can be divided into two categories based on their direction of propagation, either predominantly antisunward or predominantly towards 10 the flanks of the magnetosphere. Using this new categorisation methodology, we compare the plasma and magnetic field properties of flankward-propagating jets, antisunward-propagating jets, and FCS-jets at and around their formation times and locations, using both case studies and statistical analysis. We find that 120 (52%) of the non-FCS-jets propagate antisunward, are associated with dynamic pressure and magnetic field enhancements in the foreshock, and are very reminiscent of FCS-jets in their properties. Thus 86% of the jets in the data set are associated with dynamic pressure and magnetic field enhancements in 15 the foreshock. The remaining 109 (48%) propagate toward the flanks and exhibit higher perpendicular than parallel temperature with respect to the magnetic field, suggesting that they could consist of quasi-perpendicular magnetosheath plasma. We propose that these jets could be associated with local turning of the shock geometry from quasi-parallel to quasi-perpendicular due to bow shock reformation at the oblique shock caused by foreshock ULF wave activity. As antisunward-propagating jets can potentially impact the magnetopause and have effects on the magnetosphere, understanding which foreshock and bow shock 20 phenomena are associated with them and which are not is important.
Exploring the Effectiveness of Object Detection Training in Virtual Environments

Large and diverse datasets are required to train accurate object detection models. To create these datasets, specialized annotation software is used to specify where objects are present in an image. Object locations are defined by bounding boxes, which are then exported in a format that can be used in model training. A lot of human labour is required to construct these datasets. Alternatively, synthetic datasets can be generated in 3D virtual environments. No human annotators are needed in this case, as computers can calculate ground truth bounding boxes for all objects present in the environment. In this thesis, we explore how training object detection models using synthetic datasets can contribute to the performance of an object detection model. In particular, we focus on object detection tasks relevant to the RoboCup Standard Platform League. Namely, detecting NAO robots and a small football. We do so by generating synthetic datasets from a 3D virtual RoboCup environment using the Unity Perception package. From our experiments, we found that synthetic data can be used to increase a models performance when used in combination with real-world images. However, using only synthetic images as training data will result in poor model performance compared to comparable amounts of real-world images. We conclude that synthetic datasets can be used to increase the accuracy of object detection and could therefore be a valuable addition in preparing NAO robot systems for the RoboCup Standard Platform League.
3D RECONSTRUCTION AIDED BY MULTIPLE SEGMENTATIONS

3D reconstruction in Computer Vision has always been a computationally intensive task. Applications based on this technique have grown substantially in the industry in recent years. Autonomous navigation for cars, drones and robots; augmented reality glasses and many others are examples of such kind of applications. Techniques using stereo cameras are widely solicited for being cheaper and more accessible than light sensors, for instance. This work‘s goal is to present a novel technique based on stereo cameras to improve any base disparity maps on a set of image segmentations. In conclusion, it presents experiments that can show how effective the method performed, with its advantages and disadvantages.
Estimation and evaluation of hourly MetOp satellites GPS DCBs with two different methods

Differential code bias (DCB) is one of Global Positioning System (GPS) errors, which affects the calculation of total electron content (TEC) and ionospheric modeling. In the past, DCB was normally estimated as a constant in one day, while DCB of low Earth orbit (LEO) satellite GPS receiver may have large variations within one day due to complex space environments and highly dynamic orbit conditions. In this study, daily and hourly DCBs of Meteorological Operational 10 (MetOp) satellites GPS receivers are calculated and evaluated using spherical harmonic function (SHF) and local spherical symmetry (LSS) assumption. The results demonstrated that both approaches could obtain accurate and consistent DCB values. The estimated daily DCB standard deviation (STD) is within 0.1 ns in accordance with the LSS assumption, and is numerically less than the standard deviation of the reference value provided by COSMIC Data Analysis and Archive Center (CDAAC). The average error's absolute value is within 0.2 ns with the respect to the provided DCB reference value. As for 15 the SHF method, the DCB's standard deviation is within 0.1ns, which is also less than the standard deviation of the CDAAC reference value. The average error of the absolute value is within 0.2 ns. The estimated hourly DCB with LSS assumptions suggested that calculated results of MetOpA, MetOpB, and MetOpC are, respectively, 0.5 ns to 3.1 ns, -1.1 ns to 1.5 ns, and -1.3 ns to 0.7 ns. The root mean square error (RMSE) is less than 1.2 ns, and the STD is under 0.6ns. According to the SHF method, the results of MetOpA, MetOpB, and MetOpC are 1 ns to 2.7 ns, - 1 ns to 1 ns, and - 1.3 ns to 0.6 ns, respectively. 20 The RMSE is under 1.3 ns and STD is less than 0.5 ns. The STD for solar active days is less than 0.43 ns, 0.49 ns, and 0.44 ns, respectively, with the LSS assumption, and the appropriate fluctuation ranges are 2.0 ns, 2.2 ns, and 2.2 ns. The variation ranges for the SHF method are 1.5 ns, 1.2 ns, and 1.2 ns, respectively, while the STD is under 0.28 ns, 0.35 ns, and 0.29 ns.
: Evidence from seven randomised

Microfinance has become a widely used tool to provide credit to areas and populations that traditionally lack access to conventional banking services. Originating as a poverty alleviation mechanism, microfinance has grown in scope and design as a larger means of improving financial inclusion. Despite the increase in access for new borrowers, much of the existing evidence has failed to find transformational effects on key outcomes such as profits and income. However, results are subject to significant variation across geographies, programme design and beneficiaries, and the heterogenous effects do lead to significant gains for certain populations. The incentives given to borrowers to encourage on-time repayment, the timing of repayments, and the flexibility of borrowers’ contracts all have an impact on both business outcomes and loan default rates. More recently, there is a growing body of literature looking at alternatives to loans, such as asset-based microfinance, that also show promise. Yet, as new innovations to microfinance are adapted around the world, further research is needed to explore which adaptations prove effective, in which contexts, and for whom. Abstract
Intestinal In ﬂ ammation Reversibly Alters the Microbiota to Drive Susceptibility to Clostridioides dif ﬁ cile Colonization in a Mouse Model of Colitis

Susceptibility to Clostridioides dif ﬁ cile infection (CDI) typically follows the administration of antibiotics. Patients with in ﬂ ammatory bowel disease (IBD) have increased incidence of CDI, even in the absence of antibiotic treatment. However, the mechanisms underlying this susceptibility are not well understood. To explore the intersection between CDI and IBD, we recently described a mouse model where colitis triggered by the murine gut bacterium, Helicobacter hepaticus , in IL-10 2 / 2 mice led to susceptibility to C. dif ﬁ cile colonization without antibiotic administration. The current work disentangles the relative contributions of in ﬂ ammation and gut microbiota in colonization resistance to C. dif ﬁ cile in this model. We show that in ﬂ ammation drives changes in microbiota composition, which leads to CDI susceptibility. Decreasing in ﬂ ammation with an anti-p40 monoclonal antibody promotes a shift of the microbiota back toward a colonization-resistant state. Transferring microbiota from susceptible and resistant mice to germfree animals transfers the susceptibility phenotype, supporting the primacy of the microbiota in colonization resistance. These ﬁ ndings shine light on the complex interactions between the host, microbiota, and C. dif ﬁ cile in the context of intestinal in ﬂ ammation, and may form a basis for the development of strategies to prevent or treat CDI in IBD patients. IMPORTANCE Patients with in ﬂ ammatory bowel disease (IBD) have an increased risk of developing C. dif ﬁ cile infection (CDI), even in the absence of antibiotic treatment. Yet, mechanisms regulating C. dif ﬁ cile colonization in IBD patients remain unclear. Here, we use an antibiotic-independent mouse model to demonstrate that intestinal in ﬂ ammation alters microbiota composition to permit C. dif ﬁ cile colonization in mice with colitis. Notably, treating in ﬂ ammation with an anti-p40 monoclonal antibody, a clinically relevant IBD therapeutic, restores microbiota-mediated colonization resistance to the pathogen. Through microbiota transfer experiments in germ-free mice, we con ﬁ rm that the microbiota shaped in the setting of IBD is the primary driver of susceptibility to C. dif ﬁ icile colonization. Collectively, our ﬁ ndings provide insight into CDI pathogenesis in the context of intestinal in ﬂ ammation, which may inform methods to manage infection in IBD patients. More broadly, this work advances our understanding of mechanisms by which the host-microbiota interface modulates colonization resistance to C. dif ﬁ cile .
Observation of Volkov-Pankratov states in topological HgTe heterojunctions using high-frequency compressibility

It is well established that topological insulators sustain Dirac fermion surface states as a consequence of band inversion in the bulk. These states have a helical spin polarization and a linear dispersion with large Fermi velocity. In this article we report on a set of experimental observations indicating the existence of massive surface states. These states are confined at the interface and dominate equilibrium and transport properties at high energy and/or high electric field. By monitoring the AC admittance of HgTe topological insulator field-effect capacitors, we access the compressibility and conductivity of surface states in a broad range of energy and electric fields. The Dirac surface states are characterized by a compressibility minimum, a linear energy dependence and a high mobility persisting up to energies much larger than the transport bandgap of the bulk. New features are revealed at high energies with signatures such as conductance peaks, compressibility bumps, a strong charge metastability and a Hall resistance anomaly. These features point to the existence of excited massive surface states, responsible for a strong intersubband scattering with the Dirac states and the nucleation of metastable bulk carriers. The spectrum of excited states agrees with predictions of a phenomenological model of the topological-trivial semiconductor interface. The model accounts for the finite interface depth and the effect of electric fields. The existence of excited topological states is essential for the understanding of topological phases and opens a route for engineering and exploiting topological resources in quantum technology.
Non-Autoregressive Unsupervised Summarization with Length-Control Algorithms

Text summarization aims to generate a short summary for an input text and has extensive real-world applications such as headline generation. State-of-the-art summarization models are mainly supervised; they require large labeled training corpora and thus cannot be applied to less popular areas, e.g., less spoken languages, where paired data are rare. In this thesis, I present a non-autoregressive unsupervised summarization model, which does not require parallel data for training. Our approach first performs editbased search towards a heuristically defined score and generates a summary as pseudogroundtruth. Then, we train an encoder-only non-autoregressive Transformer based on the search results. Further, we design two length-control algorithms for the model, which perform dynamic programming on the model output and are able to explicitly control the number of words and characters in the generated summary, respectively. Such length control is important for the summarization task, because the main evaluation metric for summarization systems, i.e., ROUGE score, is sensitive to the summary length, and because real-word applications generally involve length constraints. Experiments on two benchmark datasets show that our approach achieves state-ofthe-art performance for unsupervised summarization, yet largely improves inference efficiency. Further, our length-control algorithms are able to perform length-transfer generation, i.e., generating summaries of different lengths than the training target.