Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive beam forming using split-polarity transformation for coherent signal and interference

Split-polarity transformation (SPT), which is incorporated into conventional linearly constrained minimum variance (LCMV) beamformers to decorrelate the desired signal from interference, is presented. The SPT processor does not distort the direction vectors associated with wave fronts impinging on the array, but it does reverse the phase of the signal coming from a specified direction. With the aid of SPT processing, the signal cancellation due to correlation between the desired signal and interference is almost eliminated. The design of a robust SPT processor for combating the effects of mismatch between idealized array model and actual scenario arising from causes such as sensor location, amplitude, and phase errors is discussed. A detailed performance study of the SPT-LCMV beamformer shows it to be robust against direction uncertainty in the assumed look direction. Numerical results are presented to demonstrate the effectiveness of the SPT-LCMV beamforming scheme is a coherent interference environment. >
Effective collision strengths for electron impact excitation of C II

We extend a previous calculation of effective collision strengths for electron impact excitation of the boron-like ion C . The earlier work was restricted to the excitation within the 2s 2 2p ground configuration and the sixteen 2s 2 2p-2s2p 2 fine-structure transitions whilst the present paper considers all fine-structure transitions among the levels arising from the lowest 16 LS states. The collisional cross sections are computed in the multichannel close-coupling R-matrix approximation, in which sophisticated configuration-interaction wave functions are used to represent the target states. The 16 states included have the following basis configurations: the 2s 2 2p ground state, four 2s2p 2 states, three 2p 3 states, one 2s2p3s state as well as the 2s 2 3l(l = s, p, d) and 2s 2 4l(l = s, p, d, f) states. The 16 LS target states correspond to 30 fine-structure levels and lead to a total of 435 fine-structure transitions. In this paper we tabulate effective collision strengths, obtained by averaging the electron collision strengths over a Maxwellian distribution of velocities, for electron temperatures in the range given by log10 Te(K) = 3.0−5.5, suitable for most astrophysical applications and laboratory plasma diagnostics. Significant differences from a previous calculation are noted and comparisons are made with some recent experimental data.
AudiFace: Multimodal Deep Learning for Depression Screening

Depression is a very common mental health disorder with a devastating social and economic impact. It can be costly and difficult to detect, traditionally requiring a significant number of hours by a trained mental health professional. Recently, machine learning and deep learning models have been trained for depression screening using modalities extracted from videos of clinical interviews conducted by a virtual agent. This complex task is challenging for deep learning models because of the multiple modalities and limited number of participants in the dataset. To address these challenges we propose AudiFace, a multimodal deep learning model that inputs temporal facial features, audio, and transcripts to screen for depression. To incorporate all three modalities, AudiFace combines multiple pre-trained transfer learning models and bidirectional LSTM with self-Attention. When compared with the state-of-the-art models, AudiFace achieves the highest F 1 scores for thirteen of the fifteen different datasets. AudiFace notably improves the depression screening capabilities of general wellbeing questions. Eye gaze proved to be the most valuable of the temporal facial features, both in the unimodal and multimodal models. Our results can be used to determine the best combination of modalities, temporal facial features, as well as clinical interview questions for future depression screening applications.
Intracellular Amyloid-β in the Normal Rat Brain and Human Subjects and Its relevance for Alzheimer’s Disease

Background: Amyloid-β (Aβ) is a normal product of neuronal activity, including that of the aggregation-prone Aβ42 variant that is thought to cause Alzheimer’s disease (AD). Much knowledge about AD comes from studies of transgenic rodents expressing mutated human amyloid-β protein precursor (AβPP) to increase Aβ production or the Aβ42/40 ratio. Yet, little is known about the normal expression of Aβ42 in rodent brains. Objective: To characterize the brain-wide expression of Aβ42 throughout the life span of outbred Wistar rats, and to relate these findings to brains of human subjects without neurological disease. Methods: Aβ42 immunolabeling of 12 Wistar rat brains (3–18 months of age) and brain sections from six human subjects aged 20–88 years. Results: In healthy Wistar rats, we find intracellular Aβ42 (iAβ42) in neurons throughout the brain at all ages, but levels vary greatly between brain regions. The highest levels are in neurons of entorhinal cortex layer II, alongside hippocampal neurons at the CA1/subiculum border. Concerning entorhinal cortex layer II, we find similarly high levels of iAβ42 in the human subjects. Conclusion: Expression of iAβ42 in healthy Wistar rats predominates in the same structures where iAβ accumulates and Aβ plaques initially form in the much used, Wistar based McGill-R-Thy1-APP rat model for AD. The difference between wild-type Wistar rats and these AD model rats, with respect to Aβ42, is therefore quantitative rather that qualitative. This, taken together with our human results, indicate that the McGill rat model in fact models the underlying wild-type neuronal population-specific vulnerability to Aβ42 accumulation.
TopoFit: Rapid Reconstruction of Topologically-Correct Cortical Surfaces

Mesh-based reconstruction of the cerebral cortex is a fundamental component in brain image analysis. Classical, iterative pipelines for cortical modeling are robust but often time-consuming, mostly due to expensive procedures that involve topology correction and spherical mapping. Recent attempts to address reconstruction with machine learning methods have accelerated some components in these pipelines, but these methods still require slow processing steps to enforce topological constraints that comply with known anatomical structure. In this work, we introduce a novel learning-based strategy, TopoFit, which rapidly fits a topologically-correct surface to the white-matter tissue boundary. We design a joint network, employing image and graph convolutions and an efficient symmetric distance loss, to learn to predict accurate deformations that map a template mesh to subject-specific anatomy. This technique encompasses the work of current mesh correction, fine-tuning, and inflation processes and, as a result, offers a 150× faster solution to cortical surface reconstruction compared to traditional approaches. We demonstrate that TopoFit is 1.8× more accurate than the current state-of-the-art deep-learning strategy, and it is robust to common failure modes, such as white-matter tissue hypointensities.
12 CO(1-0) observation of isolated late-type galaxies

We present 12 CO(J= 1-0) line observations of 99 galaxies obtained with the SEST 15 m, the Kitt Peak 12 m and the IRAM 30 m telescopes. The target galaxies were selected from the catalogue of isolated galaxies of Karachentseva (1973). These data are thus representative of the CO properties of isolated late-type galaxies. All objects were observed in their central position, those with large angular sizes were mapped. These new measurements are used to estimate the molecular gas mass of the target galaxies. The molecular gas is on average18% of the atomic gas mass.
NetSyn: genomic context exploration of protein families

Background The growing availability of large genomic datasets presents an opportunity to discover novel metabolic pathways and enzymatic reactions profitable for industrial or synthetic biological applications. Efforts to identify new enzyme functions in this substantial number of sequences cannot be achieved without the help of bioinformatics tools and the development of new strategies. The classical way to assign a function to a gene uses sequence similarity. However, another way is to mine databases to identify conserved gene clusters (i.e. syntenies) as, in prokaryotic genomes, genes involved in the same pathway are frequently encoded in a single locus with an operonic organisation. This Genomic Context (GC) conservation is considered as a reliable indicator of functional relationships, and thus is a promising approach to improve the gene function prediction. Methods Here we present NetSyn (Network Synteny), a tool, which aims to cluster protein sequences according to the similarity of their genomic context rather than their sequence similarity. Starting from a set of protein sequences of interest, NetSyn retrieves neighbouring genes from the corresponding genomes as well as their protein sequence. Homologous protein families are then computed to measure synteny conservation between each pair of input sequences using a GC score. A network is then created where nodes represent the input proteins and edges the fact that two proteins share a common GC. The weight of the edges corresponds to the synteny conservation score. The network is then partitioned into clusters of proteins sharing a high degree of synteny conservation. Results As a proof of concept, we used NetSyn on two different datasets. The first one is made of homologous sequences of an enzyme family (the BKACE family, previously named DUF849) to divide it into sub-families of specific activities. NetSyn was able to go further by providing additional subfamilies in addition to those previously published. The second dataset corresponds to a set of non-homologous proteins consisting of different Glycosyl Hydrolases (GH) with the aim of interconnecting them and finding conserved operon-like genomic structures. NetSyn was able to detect the locus of Cellvibrio japonicus for the degradation of xyloglucan. It contains three non-homologous GH and was found conserved in fourteen bacterial genomes. Discussion NetSyn is able to cluster proteins according to their genomic context which is a way to make functional links between proteins without taking into count their sequence similarity only. We showed that NetSyn is efficient in exploring large protein families to define iso-functional groups. It can also highlight functional interactions between proteins from different families and predicts new conserved genomic structures that have not yet been experimentally characterised. NetSyn can also be useful in pinpointing mis-annotations that have been propagated in databases and in suggesting annotations on proteins currently annotated as “unknown”. NetSyn is freely available at https://github.com/labgem/netsyn.
Barley shows reduced Fusarium Head Blight under drought and modular expression of differential expressed genes under combined stress

Plants often face simultaneous abiotic and biotic stress conditions. However, physiological and transcriptional responses of plants under combined stress situations are little understood. Spring barley is susceptible to Fusarium Head Blight (FHB), which is strongly affected by weather conditions. We therefore studied the potential influence of drought on FHB severity and responses in three differently susceptible spring barley varieties and found strongly reduced FHB severity in susceptible varieties under drought. Quantity of differentially expressed genes (DEGs) and strength of transcriptomic regulation reflected the concentration of physiological stress markers such as abscisic acid or fungal DNA contents. Infection-related gene expression associated rather with susceptibility than resistance. Weighted gene correlation network analysis uncovered 18 modules of co-expressed genes, which reflect the pathogen or drought response in the used varieties. A generally infection-related module contained co-expressed genes for defence, programmed cell death and mycotoxin-detoxification indicating that diverse genotypes use a similar defence strategy towards FHB albeit with different success. Further DEGs showed co-expression in drought or genotype-associated modules correlating with measured phytohormones or the osmolyte proline. The combination of drought stress with infection lead to highest numbers of DEGs and provoked a modular composition of single stress responses rather than a specific transcriptional readout. Highlight Co-expression network analysis reveals association of physiological stress markers and gene expression modules under biotic and abiotic stress
No evidence for angiosperm mass extinction at the Cretaceous–Paleogene (K-Pg) boundary

The Cretaceous-Paleogene mass extinction event (K-Pg) witnessed up to 75% of animal species going extinct, most notably among these are the non-avian dinosaurs. A major question in macroevolution is whether this extinction event influenced the rise of flowering plants (angiosperms). The fossil record suggests that the K-Pg event had a minor impact on the extinction rates of angiosperm lineages, yet the diversification of extant angiosperms was delayed and started after the K-Pg boundary. However, phylogenetic evidence for angiosperm extinction dynamics remains unexplored. Through the analyses of two angiosperm mega-phylogenies containing ~32,000–74,000 extant species, here we show relatively constant extinction rates throughout geological time and no evidence for a mass extinction at the K-Pg boundary. Despite uncertainty of earliest angiosperm branching times, their staggering diversity, and complex evolutionary dynamics, our preliminary analyses provide congruent results with the fossil record and support the macroevolutionary resilience of angiosperms to the K-Pg mass extinction.
Future climate change impacts on anchoveta (Engraulis ringens) in the Northern Peru Current Ecosystem

The Northern Peru Current Ecosystem (NPCE) is the most productive ecosystem in terms of fish biomass and sustains the world’s largest small pelagic fishery, the Peruvian anchovy fishery. A cooling of this system has been observed during recent decades but the potential regional impacts of rising atmospheric CO2 concentrations on upwelling dynamics and productivity are still unknown. We used the ecosystem model OSMOSE to forecast the impacts of several scenarios of climate change on Peruvian anchovy in the NPCE. The OSMOSE model was forced by plankton production and climate drivers from the Earth System Models IPSL CM5A-LR and GFDL-ESM2M for the period 2009-2100. For each earth system model, representative concentration pathways (RCP) scenarios 2.6, 4.5, 6.0 and 8.5 were run. Our results showed that an optimistic trajectory for anchovy is a reduction of biomass at a rate of 14% per decade until mid-21st century, followed by a collapse and late recovery by the end of the 21st century, while no changes in the spatial distribution of the population were observed. The pessimistic trajectory for anchovy is a reduction of biomass of 22% per decade, with a collapse after 2020 and near extinction by 2060, with a spatial displacement of the population to the south and to more coastal areas. Further research is needed to include additional key environmental variables such as oxygen as well as more realistic fisheries management intervention scenarios to evaluate future options for the sustainability of anchovy resource and fisheries.
A hyper-quiescent chromatin state formed during aging is reversed by regeneration

Epigenetic alterations are a key hallmark of aging but have been limitedly explored in tissues. Here, using naturally aged murine liver as a model and extending to other quiescent tissues, we find that aging is driven by temporal chromatin alterations that promote a refractory cellular state and compromise cellular identity. Using an integrated multi-omics approach, and the first direct visualization of aged chromatin we find that globally, old cells show H3K27me3-driven broad heterochromatinization and transcription suppression. At the local level, site-specific loss of H3K27me3 over promoters of genes encoding developmental transcription factors leads to expression of otherwise non-hepatocyte markers. Interestingly, liver regeneration reverses H3K27me3 patterns and rejuvenates multiple molecular and physiological aspects of the aged liver.
Deep Kernel Learning with Temporal Gaussian Processes for Clinical Variable Prediction in Alzheimer's Disease

Longitudinal prediction of Alzheimer’s disease progression is of high importance for early diagnosis and clinical trial design. We propose to predict the longitudinal changes of neuroimaging biomarkers and cognitive scores by leveraging the expressivity of Deep Kernel Learning with single-task Gaussian Processes. The temporal function that describes the progression of the biomarker is learned through a Gaussian Process. By learning these temporal functions we can predict any future value of a clinical variable. We apply our method for extrapolation of neuroimaging biomarkers, SPARE-AD index, and cognitive metric Adas-Cog13, both significant predictors for the pathological and cognitive changes of Alzheimer’s Disease. The method has been validated in two cohorts, ADNI and BLSA, where the results show that the proposed method significantly out-performs baselines and state-of-the-art models in AD progression prediction both on providing point estimates and quantifying uncertainty.
Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.
Effective Higher-Order Link Prediction and Reconstruction From Simplicial Complex Embeddings

Methods that learn graph topological representations are becoming the usual choice to extract features to help solve machine learning tasks on graphs. In particular, low-dimensional encoding of graph nodes can be exploited in tasks such as link prediction and network reconstruction, where pairwise node embedding similarity is interpreted as the likelihood of an edge incidence. The presence of polyadic interactions in many real-world complex systems is leading to the emergence of representation learning techniques able to describe systems that include such polyadic relations. Despite this, their application on estimating the likelihood of tuple-wise edges is still underexplored. Here we focus on the reconstruction and prediction of simplices (higher-order links) in the form of classification tasks, where the likelihood of interacting groups is computed from the embedding features of a simplicial complex. Using similarity scores based on geometric properties of the learned metric space, we show how the resulting node-level and group-level feature embeddings are beneficial to predict unseen simplices, as well as to reconstruct the topology of the original simplicial structure, even when training data contain only records of lower-order simplices.
SynEcoSys: a multifunctional platform of large-scale single-cell omics data analysis

Next generation sequencing technologies enable the analysis of the transcriptomes of individual cells, providing a higher resolution of gene expression and function at the single cell level. Various single-cell data are continuously generated every year, covering fields from scientific research to clinical development. The fast-growing public datasets are collected by distinctive platforms, which are designed to facilitate biological discoveries, disease diagnosis, and new treatments. However, these platforms are hard to meet the urgency of having a unified data integration pipeline to improve comparability between datasets. Here we present SynEcoSys, an online multifunctional platform for single-cell transcriptomic data analysis, visualization, and exploration. SynEcoSys by Singleron Biotechnologies currently provides a massive collection of publicly available single-cell sequencing dataset, involving 46,326,175 cells from 731 datasets across multiple platforms and species. All datasets are generated with a strict and uniform data analysis pipeline and cell marker-based manual annotation, thus facilitating more comprehensive and reliable data mining. SynEcoSys is available at: https://www.synecosys. com.
Robust morphological representation of binary images

A general theory for the morphological representation of discrete and binary images is presented. Particular cases of the general scheme are shown to yield a number of useful image representations. The effect of noise degradation is studied. It is proven that, under certain assumptions, the general reduced morphological skeleton is the best morphological representation among a collection of invertible morphological image representations. This representation results in a minimal upper-bound on the average probability of error of reconstructing a binary image from its noisy representation. <<ETX>>
Photoreflectance characterization on the InAlAs-InGaAs heterojunction bipolar transistors

Characterization of InAlAs-InGaAs heterojunction bipolar transistors (HBT's) with different spacer thickness inserted between emitter and base grown by molecular beam epitaxy has been performed by photoreflectance (PR) measurement. Analysis of the PR spectra above the fundamental band edge of InAlAs has enabled us to deduce the built-in electric field at the emitter-base junction, so the energy spike at the emitter side can be obtained. The two-dimensional electron gas (2-DEG) accumulated in the spacer channel has also been characterized. Both parameters mentioned above are very important for the performance of HBT's electrical characteristics.