Miracle-dz/UniFAR-FTUs huggingface dataset:
Running on goodwill and fairydust - midwives experiences of facilitating and delivering local breastfeeding support via Facebook groups: a qualitative descriptive study

Background The use of groups for providing and accessing breastfeeding support on Facebook (BSF) is now widespread, including groups aimed specifically at mothers in specific areas. Some of these groups are created and/or facilitated by midwives, but little is known about their motivations or experiences. Objective To examine how midwives involved in providing breastfeeding support via a local BSF group perceive the value of this provision and what are their experiences of creating these services and of engaging with mothers online. Methods Semi-structured interviews were conducted with midwives between July-September 2021, and thematically analysed. Results Three themes were identified in relation to the research question: (1) Imperatives and Value, (2) Goodwill & lack of resources, (3) Community of Practice. Theme 1 described that groups were seen as a necessary part of developing services to meet the needs and expectations of the current generation of mothers, and to improve dire breastfeeding rates. Theme 2 identified that BSF groups are reliant on both midwives and peer supporters working voluntarily, issues of sustainability and frustration at the lack of value placed on their time, skill and investment. Theme 3 highlighted the function of BSF groups as communities of practice, providing opportunities for social learning for all members and personal and professional benefits for midwives. Conclusions Findings have identified that midwives become involved in delivering online support motivated by a belief in the value of this provision, but feel unsupported by wider services in doing so. Midwives have concerns about the sustainability of this provision regardless of its value whilst it remains reliant on volunteers, and call for further support. Significant benefits for midwives derived from involvement were highlighted which should inform further research, guidance and investment.
Evaluation of "Q" in an Electrically Small Antenna in Prolate Spheroidal Coordinates

Abstract : This document describes the energy distribution within an electrically small antenna. Chu (1948) derived expressions for the "Q" of an omnidirectional antenna whose entire structure is enclosed within a sphere. Many authors have extended this technique. Because the sphere is not conformal to the structure of most linear polarized antennas, much energy is not accounted for. If the structure is surrounded by an ellipsoid of revolution, the fit is much better. Prolate spheroidal coordinates are expected to provide a much better description for a linearly polarized antenna. We derive expressions for the electromagnetic field and the Poynting vector for vertically and horizontally polarized antennas. From the expressions, we calculate the Q of an electrically small antenna. We can derive an analytic expression for the value of Q for the leading term. The leading term is a product of the inverse of the cube of the dimensionless wave number (product of the wave number and half the distance between foci of the ellipse) and a factor that depends only upon the shape. This result is reformulated so that as the shape changes, the volume and wave number remain constant.
EaT-PIM: Substituting Entities in Procedural Instructions Using Flow Graphs and Embeddings

When cooking, it can sometimes be desirable to substitute ingredients for purposes such as avoiding allergens, replacing a missing ingredient, or exploring new ﬂavors. More generally, the problem of substituting entities used in procedural instructions is challenging as it requires an understanding of how entities and actions in the instructions interact to produce the ﬁnal result. To support the task of automatically identifying viable substitutions, we introduce a methodology to (1) parse instructions, using NLP tools and domain-speciﬁc ontologies, to generate ﬂow graph representations, (2) train a novel embedding model which captures ﬂow and interaction of entities in each step of the instructions, and (3) utilize the embeddings to identify plausible substitutions. Our embedding strategy aggregates nodes and dynamically computes intermediate results within the ﬂow graphs, which requires learning embed-dings for fewer nodes than typical graph embedding models. Our rule-based ﬂow graph generation method shows comparable performance to machine learning-based work, while our embedding model outperforms baselines on a link-prediction task for ingredients in recipes.
Enabling Reproducibility in Group Recommender Systems

Reproducibility is a challenging aspect that considerably affects the quality of most scientiﬁc papers. To deal with this, many open frameworks allow to build, test, and benchmark recommender systems for single users. Group recommender systems involve additional tasks w.r. t. those for single users, such as the identiﬁcation of the groups, or their modeling. While this clearly ampliﬁes the possible reproducibility issues, to date, no framework to benchmark group recommender systems exists. In this work, we enable reproducibility in group recommender systems by extending the LibRec library, which stands out as one of the richest, with more than 70 different recommender algorithms, good performance and several evaluation metrics. Speciﬁcally, we include several approaches for all the stages of group recommender systems: group formation, group modeling strategies, and evaluation. To validate our framework, we consider a use-case that compares several group building, recommendation, and group modeling approaches.
Measurements of Charmless Hadronic b->s Penguin Decays in the pi+pi-K+pi- Final State and First Observation of B0 ->rho0K+pi-

S. -H. Kyeong, Y. -J. Kwon, I. Adachi, H. Aihara, A. M. Bakich, V. Balagura, E. Barberio, A. Bay, K. Belous, M. Bischofberger, A. Bozek, M. Bračko, T. E. Browder, M. -C. Chang, Y. Chao, A. Chen, B. G. Cheon, C. -C. Chiang, I. -S. Cho, Y. Choi, J. Dalseno, A. Das, A. Drutskoy, W. Dungel, S. Eidelman, 32 N. Gabyshev, 32 P. Goldenzweig, H. Ha, J. Haba, B. -Y. Han, T. Hara, Y. Hasegawa, K. Hayasaka, H. Hayashii, Y. Hoshi, H. J. Hyun, K. Inami, A. Ishikawa, R. Itoh, M. Iwasaki, N. J. Joshi, D. H. Kah, J. H. Kang, P. Kapusta, N. Katayama, T. Kawasaki, H. O. Kim, J. H. Kim, Y. I. Kim, Y. J. Kim, K. Kinoshita, B. R. Ko, S. Korpar, 15 P. Križan, R. Kumar, M. J. Lee, T. Lesiak, 3 J. Li, C. Liu, Y. Liu, R. Louvot, A. Matyja, S. McOnie, K. Miyabayashi, H. Miyata, Y. Miyazaki, R. Mizuk, Y. Nagasaka, E. Nakano, M. Nakao, Z. Natkaniec, S. Nishida, K. Nishimura, O. Nitoh, S. Ogawa, T. Ohshima, S. Okuno, H. Ozaki, P. Pakhlov, G. Pakhlova, C. W. Park, H. Park, H. K. Park, R. Pestotnik, L. E. Piilonen, H. Sahoo, K. Sakai, Y. Sakai, O. Schneider, J. Schümann, C. Schwanda, A. J. Schwartz, A. Sekiya, K. Senyo, M. E. Sevior, M. Shapkin, C. P. Shen, J.-G. Shiu, B. Shwartz, 32 J. B. Singh, R. Sinha, A. Sokolov, S. Stanič, M. Starič, J. Stypula, K. Sumisawa, T. Sumiyoshi, G. N. Taylor, Y. Teramoto, K. Trabelsi, S. Uehara, Y. Unno, P. Urquijo, G. Varner, K. E. Varvell, K. Vervink, A. Vinokurova, 32 C. H. Wang, M. -Z. Wang, P. Wang, Y. Watanabe, R. Wedd, E. Won, B. D. Yabsley, Y. Yamashita, Z. P. Zhang, V. Zhilich, 32 V. Zhulanov, 32 T. Zivko, A. Zupanc, and O. Zyukova 32
Tightness of Banach spaces and Baire category

We prove several dichotomies on linear embeddings between Banach spaces. Given an arbitrary Banach space X with a basis, we show that the relations of isomorphism and bi-embedding are meager or co-meager on the Polish set of block-subspaces of X. We relate this result with tightness and minimality of Banach spaces. Examples and open questions are provided.
2D hydrodynamical systems: invariant measures of Gaussian type

Gaussian measures $\mu^{\beta,\nu}$ are associated to some stochastic 2D hydrodynamical systems. They are of Gibbsian type and are constructed by means of some invariant quantities of the system depending on some parameter $\beta$ (related to the 2D nature of the fluid) and the viscosity $\nu$. We prove the existence and the uniqueness of the global flow for the stochastic viscous system; moreover the measure $\mu^{\beta,\nu}$ is invariant for this flow and is unique. Finally, we prove that the deterministic inviscid equation has a $\mu^{\beta,\nu}$-stationary solution (for any $\nu>0$).
CASCADE - A framework for modeling fluvial sediment connectivity and its application for drsigning low impact hydropower portfolios

Human activities, and especially dam constructions, have altered water and sediment fluxes in river basins with an unprecedented rate over the last decades. The resulting changes in river and delta processes pose a risk to river, floodplain, and coastal ecosystems and human livelihoods. While fluvial sediment transfers act on network-scales, impact assessments of dam sediment trapping (regarding downstream impacts and storage loss in reservoirs) and economic benefits focus commonly on single dam sites. Such local approaches omit the connected functioning of fluvial sediment transfers and the resulting network-scale trade-offs between dam economic benefits and cumulative sediment trapping of dams. Such analyses would require sediment data and models to evaluate future impacts of dams on networkscale sediment transport. However, neither sediment data nor numerical sediment transport models are commonly available on network scales. Within this context, the thesis sets out to answer the research questions of what would be optimal trade-offs between dam sediment trapping and economic hydropower benefits in large river systems? To answer this research question, the objectives of this thesis are 1) developing a network-scale sediment transport and connectivity model, 2) test its application for various, data scarce river basins with various initialization strategies and evaluate the robustness of results, and, 3) apply the model for quantifying cumulative dam sediment trapping and network-scale trade-offs between sediment trapping and hydroelectric production. First, we introduce a numerical modeling framework for connected sediment transfers on the scale of large river networks. The CASCADE (CAtchment Sediment Connectivity and DElivery) framework is a parsimonious statistical framework coupling network scale, grain-size specific sediment
TensorHE: a homomorphic encryption transformer for privacy-preserving deep learning

In recent years, deep learning (DL) techniques have grown rapidly and facilitated advancements in different applications. With the concerns about information security increasing, the privacy sensitivity of machine learning algorithms needs to be taken into consideration. Homomorphic encryption (HE) based solutions have been proposed to preserve data privacy while enjoying the benefits of deep learning technologies. However, the existing approaches require extensive expert knowledge and human attention, which hampers the opportunities for promoting technologies in different application domains. In this work, we developed a transformer, called TensorHE, to automatically convert a pre-trained convolutional neural network (CNN) into the HE-based version. A systematical method is proposed to facilitate automatic transformation, enabling to build and deploy domain-specific applications. Our experimental results demonstrate the applicability and scalability of TensorHE by converting the CNN models for handling the MNIST and CIFAR-10 datasets. The delivered accuracies of the transformed models are as close as (less than 3%) those of the input CNN models.
Objective Driven Portfolio Construction Using Reinforcement Learning

Recent advancement in reinforcement learning has enabled robust data-driven direct optimization on the investor’s objectives without estimating the stock movements as in the traditional two-step approach [8]. Given diverse investment styles, a single trading strategy cannot serve different investor objectives. We propose an objective function formulation to augment the direct optimization approach in AlphaPortfolio (Cong et al. [6]). In addition to simple baseline Sharpe ratio used in AlphaPortfolio, we add three investor’s objectives for (i) achieving excess alpha by maximizing the information ratio; (ii) mitigating downside risks through optimizing maximum drawdown-adjusted return; and (iii) reducing transaction costs via restricting the turnover rate. We also introduce four new features: momentum, short-term reversal, drawdown, and maximum drawdown to the framework. Our objective function formulation allows for controlling the trade-off between both maximum drawdown and turnover with respect to realized return, creating flexible trading strategies for various risk appetites. The maximum drawdown efficient frontier curve, derived using a range of values of hyper-parameter α, reflects the similar concave relationship as observed in the theoretical study by Chekhlov et al. [5]. To improve the interpretability of the deep neural network and drive insights into traditional factor investment, we further explore the drivers that contribute to the top and bottom performing firms by running regression analysis using Random Forest, which achieves R2 of approximately 0.8 in producing the same winner scores as our model. Finally, to uncover the balance between profits and diversification, we investigate the impact of the trading size on strategy behaviors.
Riparian ecosystems mapping at fine scale: a density approach based on multi‐temporal UAV photogrammetric point clouds

In recent years, numerous directives worldwide have addressed the conservation and restoration of riparian corridors, activities that rely on continuous vegetation mapping to understand its volumetric features and health status. Mapping riparian corridors requires not only fine‐scale resolution but also the coverage of relatively large areas. The use of Unmanned Aerial Vehicles (UAV) allows for meeting both conditions, although the cost‐effectiveness of their use is highly influenced by the type of sensor mounted on them. Few works have so far investigated the use of photogrammetric sensors for individual tree crown detection, despite being cheaper than the most common Light Detection and Ranging (LiDAR) ones. This work aims to improve the individual crown detection from UAV‐photogrammetric datasets in a twofold way. Firstly, the effectiveness of a new approach that has already achieved interesting results in LiDAR applications was tested for photogrammetric point clouds. The test was carried out by comparing the accuracy achieved by the new approach, which is based on the point density features of the analysed dataset, with those related to the more common local maxima and textural methods. The results indicated the potentiality of the density‐based method, which achieved accuracy values (0.76 F‐score) consistent with the traditional methods (0.49–0.80 F‐score range) but was less affected by under‐ and over‐fitting. Secondly, the potential improvement of working on intra‐annual multi‐temporal datasets was assessed by applying the density‐based approach to seven different scenarios, three of which were constituted by single‐epoch datasets and the remaining given by the joining of the others. The F‐score increased from 0.67 to 0.76 when passing from single‐ to multi‐epoch datasets, aligning with the accuracy achieved by the new method when applied to LiDAR data. The results demonstrate the potential of multi‐temporal acquisitions when performing individual crown detection from photogrammetric data.
Investigating the Unique Ability of Trichodesmium To Fix Carbon and Nitrogen Simultaneously Using MiMoSA

Trichodesmium erythraeum is a marine cyanobacterium responsible for approximately half of all biologically fixed nitrogen, making it an integral part of the global nitrogen cycle. Interestingly, unlike other nitrogen-fixing cyanobacteria, Trichodesmium does not use temporal or spatial separation to protect nitrogenase from oxygen poisoning; instead, it operates photosynthesis and nitrogen fixation reactions simultaneously during the day. ABSTRACT The open ocean is an extremely competitive environment, partially due to the dearth of nutrients. Trichodesmium erythraeum, a marine diazotrophic cyanobacterium, is a keystone species in the ocean due to its ability to fix nitrogen and leak 30 to 50% into the surrounding environment, providing a valuable source of a necessary macronutrient to other species. While there are other diazotrophic cyanobacteria that play an important role in the marine nitrogen cycle, Trichodesmium is unique in its ability to fix both carbon and nitrogen simultaneously during the day without the use of specialized cells called heterocysts to protect nitrogenase from oxygen. Here, we use the advanced modeling framework called multiscale multiobjective systems analysis (MiMoSA) to investigate how Trichodesmium erythraeum can reduce dimolecular nitrogen to ammonium in the presence of oxygen. Our simulations indicate that nitrogenase inhibition is best modeled as Michealis-Menten competitive inhibition and that cells along the filament maintain microaerobia using high flux through Mehler reactions in order to protect nitrogenase from oxygen. We also examined the effect of location on metabolic flux and found that cells at the end of filaments operate in distinctly different metabolic modes than internal cells despite both operating in a photoautotrophic mode. These results give us important insight into how this species is able to operate photosynthesis and nitrogen fixation simultaneously, giving it a distinct advantage over other diazotrophic cyanobacteria because they can harvest light directly to fuel the energy demand of nitrogen fixation. IMPORTANCE Trichodesmium erythraeum is a marine cyanobacterium responsible for approximately half of all biologically fixed nitrogen, making it an integral part of the global nitrogen cycle. Interestingly, unlike other nitrogen-fixing cyanobacteria, Trichodesmium does not use temporal or spatial separation to protect nitrogenase from oxygen poisoning; instead, it operates photosynthesis and nitrogen fixation reactions simultaneously during the day. Unfortunately, the exact mechanism the cells utilize to operate carbon and nitrogen fixation simultaneously is unknown. Here, we use an advanced metabolic modeling framework to investigate and identify the most likely mechanisms Trichodesmium uses to protect nitrogenase from oxygen. The model predicts that cells operate in a microaerobic mode, using both respiratory and Mehler reactions to dramatically reduce intracellular oxygen concentrations.
Functionally annotating cysteine disulfides and metal binding sites in the plant kingdom using AlphaFold2 predicted structures

Deep learning algorithms such as AlphaFold2 predict three-dimensional protein structure with high confidence. The recent release of more than 200 million structural models provides an unprecedented resource for functional protein annotation. Here, we used AlphaFold2 predicted structures of fifteen plant proteomes to functionally and evolutionary analyze cysteine residues in the plant kingdom. In addition to identification of metal ligands coordinated by cysteine residues, we systematically analyzed cysteine disulfides present in these structural predictions. Our analysis demonstrates most of these predicted disulfides are trustworthy due their high agreement (~96%) with those present in X-ray and NMR protein structures, their characteristic disulfide stereochemistry, the biased subcellular distribution of their proteins and a higher degree of oxidation of their respective cysteines as measured by proteomics. Adopting an evolutionary perspective, zinc binding sites are increasingly present at the expense of iron-sulfur clusters in plants. Interestingly, disulfide formation is increased in secreted proteins of land plants, likely promoting sequence evolution to adapt to changing environments encountered by plants. In summary, Alphafold2 predicted structural models are a rich source of information for studying the role of cysteines residues in proteins of interest and for protein redox biology in general.
The histone methyltransferase DOT1L regulates chromatin reorganization and gene 1 expression during the postmeiotic differentiation of male germ cells

39 40 Spermatozoa have a unique genome organization: their chromatin is almost completely devoid of 41 histones and is formed instead of protamines which confer a higher level of compaction than 42 nucleosomes and preserve paternal genome integrity until fertilization. Histone-to-protamine 43 transition takes place in postmeiotic male germ cells called spermatids, and is indispensable for the 44 production of functional spermatozoa and thus for male fertility. Here we show that the H3K79 45 (histone 3 lysine 79) methyltransferase DOT1L controls spermatid chromatin remodelling and 46 subsequent reorganization and compaction of spermatozoon genome. Using a mouse model in which 47 Dot1l is knocked-out in adult male germ cells, we found that the chromatin of Dot1l -knockout (KO) 48 spermatozoa is less compact and characterized by a higher level of retained histones and of immature 49 forms of protamine 2. Proteomic analyses performed on differentiating male germ cells reveal that 50 Dot1l KO extensively modifies the spermatid chromatin prior to histone removal. By transcriptomics, 51 we also show that Dot1l KO deregulates the expression of ~1500 genes, many of which are involved in 52 other essential aspects of spermatid differentiation such as flagellum formation. As a consequence of 53 all these chromatin and gene expression defects, Dot1l -KO spermatozoa have misshapen and less 54 compact heads, and are less motile, which results in impaired fertility. 55
DNA Methylation Signatures in Breast Cancer: A Systematic Review and Meta-Analysis

Epigenetic changes are involved in the onset and progression of cancer, and the detection of DNA methylation signatures may foster the improvement of diagnosis and prognosis. While the emergence of innovative technologies has fostered numerous studies in breast cancer, many lack statistical power due to the small sample sizes generally involved. In this study, we present a novel meta-analysis that identifies a common pattern of DNA methylation in all breast cancer subtypes. We obtained DNA methylation signatures at the gene and biological function level, identifying those significant groups of genes and functional pathways affected. To achieve this, we conducted a thorough systematic review following PRISMA statement guidelines for the selection of studies on DNA methylation in breast cancer. In total, we gathered four studies (GSE52865, GSE141338, GSE59901 and GSE101443) that were split into 13 comparisons comprising a set of 144 individuals. We discovered that most breast cancer subtypes share a significant deregulation in the immune system and alterations to the cell cycle. This integrative approach combines all available information from public data repositories and possesses greater statistical power than any individual study. Further evaluations of the identified differential biological processes and pathways may support the identification of novel biomarkers and therapeutic targets. Simple summary The identification of DNA methylation patterns in breast cancer represents a potentially valuable approach in defining more accurate diagnoses and treatment options. In this study, we applied a novel methodology that integrates the DNA methylation profiles of all studies available in public repositories via systematic review and meta-analysis. The results provide evidence of a common DNA methylation signature in distinct breast cancer subtypes, which reflects a significant deregulation of the immune system and alterations to the cell cycle. Overall, these results may support the selection of disease/treatment biomarkers and the identification of therapeutic targets.