Miracle-dz/UniFAR-FTUs huggingface dataset:
Landscape of somatic mutated genes and inherited susceptibility genes in gynecological cancer

Traditionally, gynecological cancers have been classified based on histology. Since remarkable advancements in next‐generation sequencing technology have enabled the exploration of somatic mutations in various cancer types, comprehensive sequencing efforts have revealed the genomic landscapes of some common forms of human cancer. The genomic features of various gynecological malignancies have been reported by several studies of large‐scale genomic cohorts, including The Cancer Genome Atlas. Although recent comprehensive genomic profiling tests, which can detect hundreds of genetic mutations at a time from cancer tissues or blood samples, have been increasingly used as diagnostic clinical biomarkers and in therapeutic management decisions, germline pathogenic variants associated with hereditary cancers can also be detected using this test. Gynecological cancers are closely related to genetic factors, with approximately 5% of endometrial cancer cases and 20% of ovarian cancer cases being caused by germline pathogenic variants. Hereditary breast and ovarian cancer syndrome and Lynch syndrome are the two major cancer susceptibility syndromes among gynecological cancers. In addition, several other hereditary syndromes have been reported to be associated with gynecological cancers. In this review, we highlight the genes for somatic mutation and germline pathogenic variants commonly seen in gynecological cancers. We first describe the relationship between clinicopathological attributes and somatic mutated genes. Subsequently, we discuss the characteristics and clinical management of inherited cancer syndromes resulting from pathogenic germline variants in gynecological malignancies.
Modèle dynamique de l’effet couronne pour l’étude de la propagation de la surtension le long des lignes de transmission HT en utilisant l’EMTP

The majority of the sections of this thesis concerned with the study and analysis of corona phenomenon that appear as a result of overvoltages on electric power lines level, for reasons may be external, such as the appearance of lightning strike or internal, such as the faults, which is led us to divide this work into two major parts which can be summarized as follows: Part I: Lightning is the most important electrical risk at level of power transmission lines network where its peak value can reach up to 200 kA in a few seconds, leading to the occurrence of a surge on transmission lines and therefore a corona phenomenon occurs, where we made a dynamic mathematical model, which is consistent with all configurations of electrical conductors, as well as most applied surge waveforms, such as: exponential waves, multiple and oscillatory impulses. A modeling of this phenomenon in the electromagnetic transients program ATP / EMTP is the advantage of this part and an important contribution to the second part of this work. Part II: After the development and the representation of the corona phenomenon by a dynamic model in the ATP / EMTP program, we have entered it again into a nonlinear element of the MODELES language in the ATP / EMTP program for the integration into power transmission lines, and that was in order to analyze the propagation of surge waves at the level of these lines, where the study proved the effectiveness of the dynamic model of the approved corona effect and its important role in the attenuation and distortion of surge waves on the transmission lines and thus protect the rest of the network parts from the risk of these overvoltage’s.
Effects of fertilising systems on maize production in longterm monoculture.

Maize monoculture is still present in Serbia and usually increases weed and pest infestation and decreases yield. It can also deteriorate physico-chemical and biological properties of soil. The objective of the study was to evaluate effects of an applied fertiliser system in long-term maize monoculture on soil properties and yield. The experiment was set up at the Maize Research Institute (MRI), Zemun Polje, in 1972 and since then, the late maturing maize hybrid ZPSC 684 Ultra was continuously grown in the same field with the application of cattle manure each fifth year. The crop residues were ploughed down in the whole and half of the amount, and they were removed from the third variant. The mineral fertiliser application included three variants: without mineral fertilisers, the application of NPK fertiliser in autumn and of N in spring, and the application of only N fertiliser in spring. The content of N, P and K and organic matter in the soil was evaluated twice per maize growing season at the beginning and at the harvest time, during the last two seasons 2011 and 2012. The maize grain yield was evaluated and calculated at 14% moisture. Crop residues increased the N, P, K and organic matter contents in the soil especially in the variant without application of cattle manure. In both years, the highest average maize yield was achieved with the application of cattle manure, whole amount of crop residues, and N fertiliser distributed over the soil surface at the beginning of the growing period. Nevertheless, although the late maturing hybrid potential and fertiliser rates were high in the maize monoculture, the highest yield amounted to not more than 10.4 t/ha. This underlines the importance of all cropping practices (crop rotation, fertilization, and hybrid type), their interaction and integrated effects on maize production.
The enantiostylous floral polymorphism of Barberetta aurea (Haemodoraceae) facilitates wing pollination by syrphid flies

Abstract Background and Aims Sexual polymorphisms of flowers have traditionally been interpreted as devices that promote cross-pollination, but they may also represent adaptations for exploiting particular pollination niches in local environments. The cross-pollination function of enantiostyly, characterized by flowers having either left- or right-deflected styles, has been uncertain in some lineages, such as the Haemodoraceae, because the positioning of stamens and styles is not always completely reciprocal among morphs. Methods We examined the floral biology of populations of the poorly known species Barberetta aurea (Haemodoraceae) across its native range in South Africa to establish the general features of its enanatiostylous reproductive system and the agents and mechanism of pollen transfer. Results We confirmed that B. aurea has a system of dimorphic enantiostyly. Style morph ratios varied among populations sampled, but with an overall tendency to being equal. Crossing experiments demonstrated that B. aurea is fully self-compatible, that intra- and inter-morph crosses are equally fertile and that it is wholly dependent on pollinator visits for seed production. Pollination is mainly by syrphid flies that transfer the sticky pollen via their wings, which contact the anthers and stigma precisely as they hover during approach and feeding. The majority of syrphid fly visitors feed on a film of highly concentrated nectar situated at the base of ultraviolet-absorbent ‘nectar guides’. Because one of the three stamens is deflected in the same direction as the style, we predicted a high likelihood of intra-morph pollination, and this was corroborated by patterns of transfer of coloured dye particles in cage experiments involving syrphid flies. Conclusions Barbaretta aurea exhibits dimorphic enantiostyly and, in contrast to most enantiostylous species, which are pollinated by bees, its flowers are specialized for pollination by syrphid flies. The lack of complete reciprocity of the enantiostylous arrangement of sexual organs facilitates both inter- and intra-morph pollen transfer on the wings of these flies.
Diverse roles of glucocorticoids in the ruminant mammary gland: modulation of mammary growth, milk production, and mastitis

Abstract As endocrine hormones, glucocorticoids (GCs) play a pivotal role in numerous physiological processes, including mammary growth and lactation, circulatory metabolism, and responses to external stimuli. In the dairy industry, milk production from cows or goats is important for newborns and economic benefits. However, the milk yields from ruminant animals are always affected by the extent of mammary development, mammary disease, stress, or changes in metabolism. Thus, it is necessary to clarify how GCs changes in ruminants affect ruminant mammary gland function and mammary disease. This review summarizes the findings identifying that GCs modulate mammary gland development before lactation, but the stress-induced excessive release of GCs leads to milk production loss. In addition, the manner of GCs release may change under different concentrations of metabolites or during mastitis or inflammatory challenge. Nevertheless, exogenous GCs administration to animals may alleviate the clinical symptoms of mastitis. This review demonstrates that GCs offer a fascinating contribution to both physiologic and pathogenic conditions of the mammary gland in ruminant animals. Characterizing and understanding these changes or functions of endogenous and exogenous GCs in animals will be crucial for developing more endocrine regulators and therapies for improving milk production in ruminants.
TC-LIF: A Two-Compartment Spiking Neuron Model for Long-term Sequential Modelling

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues. To address this challenge, we propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF. The proposed model incorporates carefully designed somatic and dendritic compartments that are tailored to facilitate learning long-term temporal dependencies. Furthermore, the theoretical analysis is provided to validate the effectiveness of TC-LIF in propagating error gradients over an extended temporal duration. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, and high energy efficiency of the proposed TC-LIF model. Therefore, this work opens up a myriad of opportunities for solving challenging temporal processing tasks on emerging neuromorphic computing systems. Our code is publicly available at https://github.com/ZhangShimin1/TC-LIF.
Almost sure scattering for defocusing energy critical Hartree equation on $\R^5$

We consider the defocusing energy-critical Hartree equation $i\pa_tu+\Delta u=(|\cdot|^{-4}\ast|u|^2)u$ in spatial dimension $d=5$ and prove almost sure scattering with initial data $u_0\in H^s_x(\R^5)$ for any $s\in\R$. The proof relies on the modified interaction Morawetz estimate, the stability theories, the ``Narrowed'' Wiener randomization. We are inspired to consider this problem by the work of Shen-Soffer-Wu \cite{Shen-Soffer-Wu 1}, which treated the analogous problem for the energy-critical Schr\"{o}dinger equation. The new ingredient in this paper are that we take an alternative proof to give the interaction Morawetz estimate. And the nonlocal nonlinearity term will bring some difficulties.
Using Adamic-Adar Index Algorithm to Predict Volunteer Collaboration: Less is More

Social networks exhibit a complex graph-like structure due to the uncertainty surrounding potential collaborations among participants. Machine learning algorithms possess generic outstanding performance in multiple real-world prediction tasks. However, whether machine learning algorithms outperform specific algorithms designed for graph link prediction remains unknown to us. To address this issue, the Adamic-Adar Index (AAI), Jaccard Coefficient (JC) and common neighbour centrality (CNC) as representatives of graph-specific algorithms were applied to predict potential collaborations, utilizing data from volunteer activities during the Covid-19 pandemic in Shenzhen city, along with the classical machine learning algorithms such as random forest, support vector machine, and gradient boosting as single predictors and components of ensemble learning. This paper introduces that the AAI algorithm outperformed the traditional JC and CNC, and other machine learning algorithms in analyzing graph node attributes for this task.
Atmospheric structure prior to tornadoes as derived from proximity and precedent upper-air soundings, covering the period April 1977-June 1979

The uniqueness of the thermodynamic and dynamic structure of the atmosphere in the area of imminent tornado bearing storm development is analyzed by comparing 115 tornado proximity soundings with upper air soundings made at the same location 6 and 12 hours earlier (precedent soundings) and with soundings made simultaneously at neighboring upper air stations. The comparisons suggest that both the proximity station and the neighboring station upstream with respect to the mean flow in the low level moist air display very similar degrees of hydrostatic and potential-convective instability by late afternoon. The principal difference is in the wind profiles at the two locations. The tornado proximity station displays significantly stronger wind speeds above 1 km with the most striking difference being in the vertical shear of the wind in the layer from 1 to 3 km above ground level. In this layer the winds at the proximity station show an average increase of about 3 m sec/sup -1/ while the upstream, non-tornadic, station shows a slight decrease of wind speed with height.
Significant-attributed Community Search in Heterogeneous Information Networks

Community search is a personalized community discovery problem aimed at finding densely-connected subgraphs containing the query vertex. In particular, the search for communities with high-importance vertices has recently received a great deal of attention. However, existing works mainly focus on conventional homogeneous networks where vertices are of the same type, but are not applicable to heterogeneous information networks (HINs) composed of multi-typed vertices and different semantic relations, such as bibliographic networks. In this paper, we study the problem of high-importance community search in HINs. A novel community model is introduced, named heterogeneous significant community (HSC), to unravel the closely connected vertices of the same type with high attribute values through multiple semantic relationships. An HSC not only maximizes the exploration of indirect relationships across entities of the anchor-type but incorporates their significance. To search the HSCs, we first develop online algorithms by exploiting both segmented-based meta-path expansion and significance increment. Specially, a solution space reuse strategy based on structural nesting is designed to boost the efficiency. In addition, we further devise a two-level index to support searching HSCs in optimal time, based on which a space-efficient compact index is proposed. Extensive experiments on real-world large-scale HINs demonstrate that our solutions are effective and efficient for searching HSCs, and the index-based algorithms are 2-4 orders of magnitude faster than online algorithms.
$p$-group Galois covers of curves in characteristic $p$ II

Let $k$ be an algebraically closed field of characteristic $p>0$ and let $G$ be a finite $p$-group. The results of Harbater, Katz and Gabber associate a $G$-cover of the projective line ramified only over $\infty$ to every $k$-linear action of $G$ on $k[[t]]$. In this paper we relate the HKG-covers to the classical problem of determining the equivariant structure of cohomologies of a curve with an action of a $p$-group. To this end, we present a new way of computing cohomologies of HKG-covers. As an application of our results, we compute the equivariant structure of the de~Rham cohomology of Klein four covers in characteristic~$2$.
Giant orbit-to-charge conversion induced via the inverse orbital Hall effect

We investigate the orbit-to-charge conversion in YIG/Pt/nonmagnetic material (NM) trilayer heterostructures. With the additional Ru layer on the top of YIG/Pt stacks, the charge current signal increases nearly an order of magnitude in both longitudinal spin Seebeck effect (SSE) and spin pumping (SP) measurements. Through thickness dependence studies of the Ru metal layer and theoretical model, we quantitatively clarify different contributions of the increased SSE signal that mainly comes from the inverse orbital Hall effect (IOHE) of Ru, and partially comes from the orbital sink effect in the Ru layer. A similar enhancement of SSE(SP) signals is also observed when Ru is replaced by other materials (Ta, W, and Cu), implying the universality of the IOHE in transition metals. Our findings not only suggest a more efficient generation of the charge current via the orbital angular moment channel but also provides crucial insights into the interplay among charge, spin, and orbit.
Min-k-planar Drawings of Graphs

The study of nonplanar drawings of graphs with restricted crossing configurations is a well-established topic in graph drawing, often referred to as beyond-planar graph drawing. One of the most studied types of drawings in this area are the $k$-planar drawings $(k \geq 1)$, where each edge cannot cross more than $k$ times. We generalize $k$-planar drawings, by introducing the new family of min-$k$-planar drawings. In a min-$k$-planar drawing edges can cross an arbitrary number of times, but for any two crossing edges, one of the two must have no more than $k$ crossings. We prove a general upper bound on the number of edges of min-$k$-planar drawings, a finer upper bound for $k=3$, and tight upper bounds for $k=1,2$. Also, we study the inclusion relations between min-$k$-planar graphs (i.e., graphs admitting min-$k$-planar drawings) and $k$-planar graphs. In our setting we only allow simple drawings, that is, any two edges cross at most once, no two adjacent edges cross, and no three edges intersect at a common crossing point.
Randomness and imprecision: from supermartingales to randomness tests

We generalise the randomness test definitions in the literature for both the Martin-L\"of and Schnorr randomness of a series of binary outcomes, in order to allow for interval-valued rather than merely precise forecasts for these outcomes, and prove that under some computability conditions on the forecasts, our definition of Martin-L\"of test randomness can be seen as a special case of Levin's uniform randomness. We show that the resulting randomness notions are, under some computability and non-degeneracy conditions on the forecasts, equivalent to the martingale-theoretic versions we introduced in earlier papers. In addition, we prove that our generalised notion of Martin-L\"of randomness can be characterised by universal supermartingales and universal randomness tests.
Single Photon Quantum Ranging: When Sequential Decoding Meets High Dimensional Entanglement

We consider the quantum ranging problem in the low noise level per mode and low reflectivity (high loss) regime. We focus on single photon transmission strategies and propose a novel approach that combines high dimensional time-bin entanglement at the transmitter with a carefully constructed sequential decision rule at the detector. Our analytical results establish the significant performance gains that can be leveraged from this approach in a range of operating parameters, as compared to the single photon classical approach, the two-mode squeezed vacuum ranging scheme proposed earlier, and even the block-based classical scheme. One can attribute this performance gain to 1) the ability of the high dimensional time-bin entangled signaling to offer a very fine range resolution with a single transmitted photon and 2) the ability of the sequential decision rule to minimize the average number of transmitted photon subject to a constraint on the probability of error. While our analysis is limited to the low energy/low noise regime, we conjecture that the proposed approach's superior performance extends to a wider range of scenarios which should motivate further analytical and experimental investigations.
A Survey of AI Music Generation Tools and Models

In this work, we provide a comprehensive survey of AI music generation tools, including both research projects and commercialized applications. To conduct our analysis, we classified music generation approaches into three categories: parameter-based, text-based, and visual-based classes. Our survey highlights the diverse possibilities and functional features of these tools, which cater to a wide range of users, from regular listeners to professional musicians. We observed that each tool has its own set of advantages and limitations. As a result, we have compiled a comprehensive list of these factors that should be considered during the tool selection process. Moreover, our survey offers critical insights into the underlying mechanisms and challenges of AI music generation.
Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon

With the advent of digital scanners and deep learning, diagnostic operations may move from a microscope to a desktop. Hematoxylin and Eosin (H&E) staining is one of the most frequently used stains for disease analysis, diagnosis, and grading, but pathologists do need different immunohistochemical (IHC) stains to analyze specific structures or cells. Obtaining all of these stains (H&E and different IHCs) on a single specimen is a tedious and time-consuming task. Consequently, virtual staining has emerged as an essential research direction. Here, we propose a novel generative model, Structural Cycle-GAN (SC-GAN), for synthesizing IHC stains from H&E images, and vice versa. Our method expressly incorporates structural information in the form of edges (in addition to color data) and employs attention modules exclusively in the decoder of the proposed generator model. This integration enhances feature localization and preserves contextual information during the generation process. In addition, a structural loss is incorporated to ensure accurate structure alignment between the generated and input markers. To demonstrate the efficacy of the proposed model, experiments are conducted with two IHC markers emphasizing distinct structures of glands in the colon: the nucleus of epithelial cells (CDX2) and the cytoplasm (CK818). Quantitative metrics such as FID and SSIM are frequently used for the analysis of generative models, but they do not correlate explicitly with higher-quality virtual staining results. Therefore, we propose two new quantitative metrics that correlate directly with the virtual staining specificity of IHC markers.