Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of acute kidney injury in critically ill patients with severe coronavirus disease 2019

Abstract Background Coronavirus disease 2019 (COVID-19)-associated acute kidney injury (AKI) frequency, severity and characterization in critically ill patients has not been reported. Methods Single-centre cohort performed from 3 March 2020 to 14 April 2020 in four intensive care units in Bordeaux University Hospital, France. All patients with COVID-19 and pulmonary severity criteria were included. AKI was defined using Kidney Disease: Improving Global Outcomes (KDIGO) criteria. A systematic urinary analysis was performed. The incidence, severity, clinical presentation, biological characterization (transient versus persistent AKI; proteinuria, haematuria and glycosuria) and short-term outcomes were evaluated. Results Seventy-one patients were included, with basal serum creatinine (SCr) of 69 ± 21 µmol/L. At admission, AKI was present in 8/71 (11%) patients. Median [interquartile range (IQR)] follow-up was 17 (12–23) days. AKI developed in a total of 57/71 (80%) patients, with 35% Stage 1, 35% Stage 2 and 30% Stage 3 AKI; 10/57 (18%) required renal replacement therapy (RRT). Transient AKI was present in only 4/55 (7%) patients and persistent AKI was observed in 51/55 (93%). Patients with persistent AKI developed a median (IQR) urine protein/creatinine of 82 (54–140) (mg/mmol) with an albuminuria/proteinuria ratio of 0.23 ± 20, indicating predominant tubulointerstitial injury. Only two (4%) patients had glycosuria. At Day 7 after onset of AKI, six (11%) patients remained dependent on RRT, nine (16%) had SCr >200 µmol/L and four (7%) had died. Day 7 and Day 14 renal recovery occurred in 28% and 52%, respectively. Conclusion Severe COVID-19-associated AKI is frequent, persistent, severe and characterized by an almost exclusive tubulointerstitial injury without glycosuria.
Modeling Lost Information in Lossy Image Compression

Lossy image compression is one of the most commonly used operators for digital images. Most recently proposed deep-learning-based image compression methods leverage the auto-encoder structure, and reach a series of promising results in this field. The images are encoded into low dimensional latent features first, and entropy coded subsequently by exploiting the statistical redundancy. However, the information lost during encoding is unfortunately inevitable, which poses a significant challenge to the decoder to reconstruct the original images. In this work, we propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem. Specifically, ILC introduces an invertible encoding module to replace the encoder-decoder structure to produce the low dimensional informative latent representation, meanwhile, transform the lost information into an auxiliary latent variable that won't be further coded or stored. The latent representation is quantized and encoded into bit-stream, and the latent variable is forced to follow a specified distribution, i.e. isotropic Gaussian distribution. In this way, recovering the original image is made tractable by easily drawing a surrogate latent variable and applying the inverse pass of the module with the sampled variable and decoded latent features. Experimental results demonstrate that with a new component replacing the auto-encoder in image compression methods, ILC can significantly outperform the baseline method on extensive benchmark datasets by combining with the existing compression algorithms.
An Optimal Transport Kernel for Feature Aggregation and its Relationship to Attention

We introduce a kernel for sets of features based on an optimal transport distance, along with an explicit embedding function. Our approach addresses the problem of feature aggregation, or pooling, for sets that exhibit long-range dependencies between their members. More precisely, our embedding aggregates the features of a given set according to the transport plan between the set and a reference shared across the data set. Unlike traditional hand-crafted kernels, our embedding can be optimized for a specific task or data set. It also has a natural connection to attention mechanisms in neural networks, which are commonly used to deal with sets, yet requires less data. Our embedding is particularly suited for biological sequence classification tasks and shows promising results for natural language sequences. We provide an implementation of our embedding that can be used alone or as a module in larger learning models. Our code is freely available at https://github.com/claying/OTK.
Thermal Regulation of KCNQ2 Potassium Channels

KCNQ2 (KV7.2) plays a key role in the regulation of neuronal excitability. Although temperature is thought to modulate all ion channels by different magnitudes, temperature dependence of KCNQ channels has not been demonstrated. In the present work, we reported that temperature elevation caused an increased expression of KCNQ2. Notably, the increased protein was retained intracellular. Meanwhile, we found that elevated temperature had a significant effect on the voltage-dependence of KCNQ2 channel activation. These data suggested that temperature can increase the rates of activation of KCNQ2 channel and has a critical role in the trafficking of KCNQ2 subunits to the surface membrane.
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights

In the absence of large labelled datasets, self-supervised learning techniques can boost performance by learning useful representations from unlabelled data, which is often more readily available. However, there is often a domain shift between the unlabelled collection and the downstream target problem data. We show that by learning Bayesian instance weights for the unlabelled data, we can improve the downstream classification accuracy by prioritising the most useful instances. Additionally, we show that the training time can be reduced by discarding unnecessary datapoints. Our method, BetaDataWeighter is evaluated using the popular self-supervised rotation prediction task on STL-10 and Visual Decathlon. We compare to related instance weighting schemes, both hand-designed heuristics and meta-learning, as well as conventional self-supervised learning. BetaDataWeighter achieves both the highest average accuracy and rank across datasets, and on STL-10 it prunes up to 78% of unlabelled images without significant loss in accuracy, corresponding to over 50% reduction in training time.
RELATION BETWEEN THE CONCENTRATION OF DINOPHYSIS ACUMINATA AND DIARRHEIC SHELLFISH POISONING TOXINS IN BLUE MUSSELS (MYTILUS EDULIS) DURING A TOXIC EPISODE IN THE LIMFJORD (DENMARK), 2006

Abstract For the first time since 1990 the Limfjord, which is the main Danish production area for mussels, was partly closed for a period of 14 wk during autumn 2006 because of presence of DSP toxins in blue mussels. The causative organism was Dinophysis acuminata. Only the okadaic acid (OA) and OA esters were observed. The maximum concentration level of approximately 600–700 μg OA equivalents kg−1 was reached at approximately 5,000 cells l−1 of D. acuminata. A further increase in the concentration of D. acuminata did not lead to any further increase in the concentration of OA in the blue mussels. The temporal development in the concentration of D. acuminata cells in the water was in good accordance with the development in the content of OA in blue mussels on a weekly basis. The maximum concentration of D. acuminata during the bloom was 14,000 cells l−1 and the maximum concentration of total OA equivalents in the raw blue mussels was 706 μg/kg. Single cell toxicity measurements showed a mean content of 31 pg OA cell−1 (min 19; max 72 pg OA cell−1). The impact of the concentration procedure was investigated using a range of volumes filtered from 0,2 l–10 l filter−1. Toxins were lost during the filtration process as a response to amount of water concentrated on each filter. The relationship between the volume filtered per filter and the content of OA observed in the cells fitted well the equation y = 32.0·e−0.12·x, where y = OA cell−1 (pg OA cell−1) and x = volume of water filtered per filter (l). The OA content per cell measured when concentrating only 0.2 L per filter was a good measure of the theoretical content of toxins calculated in a “no handling” case. The influence of heat-treatment on the concentration of DSP toxins in mussels was investigated. Results of OA concentration in paired samples of raw and heat-treated blue mussels showed a 1.8 times higher concentration in heat-treated mussels than in raw blue mussels. In most official monitoring programs raw mussel tissue is used for the analysis of content of DSP toxins. However, most shellfish are eaten cooked or steamed so heat-treated tissue seems to be the most relevant matrix to analyze. This should be recognized in relation to future risk assessments to establish permissible levels of DSP toxins in shellfish and in relation to the control of the permissible levels.
Memory Transformer

Transformer-based models have achieved state-of-the-art results in many natural language processing (NLP) tasks. The self-attention architecture allows us to combine information from all elements of a sequence into context-aware representations. However, all-to-all attention severely hurts the scaling of the model to large sequences. Another limitation is that information about the context is stored in the same element-wise representations. This makes the processing of properties related to the sequence as a whole more difficult. Adding trainable memory to selectively store local as well as global representations of a sequence is a promising direction to improve the Transformer model. Memory-augmented neural networks (MANNs) extend traditional neural architectures with general-purpose memory for representations. MANNs have demonstrated the capability to learn simple algorithms like Copy or Reverse and can be successfully trained via backpropagation on diverse tasks from question answering to language modeling outperforming RNNs and LSTMs of comparable complexity. In this work, we propose and study two extensions of the Transformer baseline (1) by adding memory tokens to store non-local representations, and (2) creating memory bottleneck for the global information. We evaluate these memory augmented Transformers on machine translation task and demonstrate that memory size positively correlates with the model performance. Attention patterns over the memory suggest that it improves the model's ability to process a global context. We expect that the application of Memory Transformer architectures to the tasks of language modeling, reading comprehension, and text summarization, as well as other NLP tasks that require the processing of long contexts will contribute to solving challenging problems of natural language understanding and generation.
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex

A key challenge in understanding the sensory transformations of the visual system is to obtain a highly predictive model of responses from visual cortical neurons. Deep neural networks (DNNs) provide a promising candidate for such a model. However, DNNs require orders of magnitude more training data than neuroscientists can collect from real neurons because experimental recording time is severely limited. This motivates us to find images that train highly-predictive DNNs with as little training data as possible. We propose gaudy images---high-contrast binarized versions of natural images---to efficiently train DNNs. In extensive simulation experiments, we find that training DNNs with gaudy images substantially reduces the number of training images needed to accurately predict the simulated responses of visual cortical neurons. We also find that gaudy images, chosen before training, outperform images chosen during training by active learning algorithms. Thus, gaudy images overemphasize features of natural images, especially edges, that are the most important for efficiently training DNNs. We believe gaudy images will aid in the modeling of visual cortical neurons, potentially opening new scientific questions about visual processing, as well as aid general practitioners that seek ways to improve the training of DNNs.
Toxic Effects of Groundnut Oil on the Rice Weevil Sitophilus Oryzae (L.)

On exposure of maize grains mixed with groundnut oil at a dose of 20 ml/kg, 100% mortality of the rice weevil, Sitophilus oryzae (L.) was observed within 24 hr and 100% at 10 and 5 ml/kg in 2 and 3 days.Egg-laying and progeny development in the rice weevils were completely prevented at groundnut oil doses other than 1 ml/kg at which the number of eggs laid and progeny developed were 27 and 38% of those in the oil-free control during the same period. When maize treated with oil at 10–20 ml/kg was reinfested after 19 weeks with fresh adult rice weevils, less than 42% of them had died. The total number of progeny that emerged on week 23 from the two higher doses of oil was 38% of that emerging from the control.Groudnut oil does not affect the viability of maize grains and damage after 25 weeks. Viability of maize grains was only 6–13% after oil treatment at 10–20 ml/kg compared to 100% in the untreated control.
Spin-orbit-proximitized ferromagnetic metal by monolayer transition metal dichalcogenide: Atlas of spectral functions, spin textures, and spin-orbit torques in 
Co/MoSe2, Co/WSe2
, and 
Co/TaSe2
 heterostructures

The bilayer heterostructures composed of an ultrathin ferromagnetic metal (FM) and a material hosting strong spin-orbit (SO) coupling are principal resource for SO torque and spin-to-charge conversion nonequilibrium effects in spintronics. We demonstrate how hybridization of wavefunctions of Co layer and a monolayer of transition metal dichalcogenides (TMDs)---such as semiconducting MoSe$_2$ and WSe$_2$ or metallic TaSe$_2$---can lead to dramatic transmutation of electronic and spin structure of Co within some distance away from its interface with TMD, when compared to the bulk of Co or its surface in contact with vacuum. This is due to proximity induced SO splitting of Co bands encoded in the spectral functions and spin textures on its monolayers, which we obtain using noncollinear density functional theory (ncDFT) combined with equilibrium Green function (GF) calculations. In fact, SO splitting is present due to structural inversion asymmetry of the bilayer even if SO coupling within TMD monolayer is artificially switched off in ncDFT calculations, but switching it on makes the effects associated with proximity SO coupling within Co layer about five times larger. Injecting spin-unpolarized charge current through SO-proximitized monolayers of Co generates nonequilibrium spin density over them, so that its cross product with the magnetization of Co determines SO torque. The SO torque computed via first-principles quantum transport methodology, which combines ncDFT with nonequilibrium GF calculations, can be used as the screening parameter to identify optimal combination of materials and their interfaces for applications in spintronics. In particular, we identify heterostructure two-monolayer-Co/monolayer-WSe$_2$ as the most optimal.
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?

Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets, large computing systems, and better neural network models, natural language processing (NLP) technology has made significant strides in understanding, proofreading, and organizing these messages. Thus, there is a significant opportunity to deploy NLP in myriad applications to help web users, social networks, and businesses. Toward this end, we consider smartphones and other mobile devices as crucial platforms for deploying NLP models at scale. However, today’s highly-accurate NLP neural network models such as BERT and RoBERTa are extremely computationally expensive, with BERT-base taking 1.7 seconds to classify a text snippet on a Pixel 3 smartphone. To begin to address this problem, we draw inspiration from the computer vision community, where work such as MobileNet has demonstrated that grouped convolutions (e.g. depthwise convolutions) can enable speedups without sacrificing accuracy. We demonstrate how to replace several operations in self-attention layers with grouped convolutions, and we use this technique in a novel network architecture called SqueezeBERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test set. A PyTorch-based implementation of SqueezeBERT is available as part of the Hugging Face Transformers library: https://huggingface.co/squeezebert
Growth of Cinta Senese piglets as affected by location of the suckled teat

The experimental animals were 18 Cinta Senese sows (7 primiparous and 11 multiparous) and relative purebred offspring. Individual weight of piglets was recorded at birth (or shortly afterwards) to the nearest 50 g and subsequently every 3-5 days up to weaning. At each recording, piglets were ranked in decreasing order according to weight within their respective litters. The behavior of a subsample of 8 sows and litters was observed during suckling, by recording the teat–piglet coupling. The sows had 12 functional teats, equally distributed in the two symmetric rows, that were numbered as pairs in the antero-posterior direction. Starting from the third week, piglets of multiparous sows showed a faster growth rate than those of the primiparous ones. Repeatability of the piglets’ weight during the suckling period was high (r = 0.56) and repeatability of rank was even higher, but decreased up to weaning. Anterior teats were the most occupied and showed the highest suckling fidelity (consistency of suckling position). Various statistical analyses about the dependence of piglet weight (or weight rank within litter) on teat order indicated the highest milk productivity of the first teats and the lowest of the 5th & 6th teat pairs.
A new genus and two new species of cavernicolous amphipods (Crustacea: Typhlogammaridae) from the Western Caucasus

This study deals with the biodiversity and distribution of cavernicolous Amphipoda in caves of the Arabika massif (Western Caucasus). The Sarma, Trojka and Orlinoe Gnezdo caves were explored during speleological expeditions over the years 2011–12. Two new species of Amphipoda were found: a sub-surface dweller Zenkevitchia sandroruffoi sp. nov. is reported from the Sarma, Trojka and Orlinoe Gnezdo caves at depths from -30 m to -350 m; the second one, a deep dweller Adaugammarus pilosus gen. et sp. nov. is reported from the Sarma Cave at depths of -1270 to -1700 m. Adaugammarus gen. nov. shares similarities with Typhlogammarus Schaferna, 1907 and Zenkevitchia Birstein, 1940. The species Anopogammarus birsteini Derzhavin, 1945 is also re-described herein based on new samples that suggest close affinity of this species with the family Gammaridae. The original taxonomic combination is resurrected for Zenkevitchia revazi Birstein & Ljovuschkin, 1970, comb. resurr. (from Anopogammarus Derzhavin, 1945). To accommodate morphologically different species in the genus Zenkevitchia , two new groups are proposed. These are the admirabilis -group ( Z. admirabilis Birstein, 1940 and Z. yakovi Sidorov, 2015) and the sandroruffoi -group ( Z. sandroruffoi sp. nov. and Z. revazi ). An updated molecular (mt-cox1) phylogeny, an identification key to the genera and a distribution map for the typhlogammarid amphipod species of Transcaucasia are provided.
Internalization of Riau Malay Culture in Developing the Morals of Madrasah Ibtidaiyah Students

Abstract This study aims to describe the internalization of Malay culture in developing the morals of madrasah ibtidaiyah students through the role of the teacher. This type of research is a case study, with interviews and observations used as data collection techniques. Researchers conducted interviews with teachers and principals of madrasah, as well as observing the learning process and madrasah activities for one year. Data were analyzed by reducing the results found, making presentations, and attracting new meanings based on the data found. The results of this study showed that the internalization of Riau Malay culture in developing the morals of students was done through giving advice; giving motivation; giving warning; providing understanding; giving example; communicating with parents; protecting and looking after students; giving training; providing assistance; approaching; controlling; inviting; using learning methods; imposing sanctions; and giving reward. This study concludes that the moral development of madrasah ibtidaiyah students can be done by internalizing Riau Malay culture in the learning process both at school and at home. Keywords: Malay culture, morals, madrasah students, learning process. Abstrak Penelitian ini bertujuan untuk mendeskripsikan internalisasi budaya melayu dalam mengembangkan moral siswa madrasah ibtidaiyah melalui peran guru. Jenis penelitian ini adalah studi kasus, dengan wawancara dan observasi dijadikan sebagai teknik pengumpulan data. Peneliti melakukan wawancara kepada guru dan kepala madrasah, serta melakukan observasi proses pembelajaran dan kegiatan madrasah selama satu tahun. Data dianalisis dengan melakukan reduksi terhadap hasil yang ditemukan, melakukan penyajian, dan menarik makna baru berdasarkan data yang ditemukan. Hasil penelitian ini menunjukkan bahwa internalisasi budaya Melayu Riau dalam membentuk moral (akhlak) siswa yaitu dengan memberikan nasihat; memotivasi; memberikan teguran; memahamkan; teladan; berkomunikasi dengan orang tua; mengayomi dan menjaga peserta didik; memberikan latihan; memberikan bantuan; melakukan pendekatan; mengontrol; mengajak; menggunakan metode pembelajaran; memberikan sanksi; dan memberikan reward. Penelitian ini menyimpulkan bahwa pengeembangan moral siswa madrasah ibtidaiyah dapat dilakukan dengan menginternalisasikan budaya Melayu Riau dalam proses pembelajaran, baik di sekolah maupun di rumah. Kata kunci: budaya melayu, akhlak, siswa madrasah, proses pembelajaran.
Epidemiology of Seasonal Coronaviruses: Establishing the Context for the Emergence of Coronavirus Disease 2019

Abstract Public health preparedness for coronavirus (CoV) disease 2019 (COVID-19) is challenging in the absence of setting-specific epidemiological data. Here we describe the epidemiology of seasonal CoVs (sCoVs) and other cocirculating viruses in the West of Scotland, United Kingdom. We analyzed routine diagnostic data for >70 000 episodes of respiratory illness tested molecularly for multiple respiratory viruses between 2005 and 2017. Statistical associations with patient age and sex differed between CoV-229E, CoV-OC43, and CoV-NL63. Furthermore, the timing and magnitude of sCoV outbreaks did not occur concurrently, and coinfections were not reported. With respect to other cocirculating respiratory viruses, we found evidence of positive, rather than negative, interactions with sCoVs. These findings highlight the importance of considering cocirculating viruses in the differential diagnosis of COVID-19. Further work is needed to establish the occurrence/degree of cross-protective immunity conferred across sCoVs and with COVID-19, as well as the role of viral coinfection in COVID-19 disease severity.