Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement in Zebrafish with Diabetes and Alzheimer's Disease Treated with Pasteurized Akkermansia muciniphila

The interaction between intestinal microflora and host affects the progression of diabetes and Alzheimer's disease. As a recognized next-generation probiotic, Akkermansia muciniphila has been shown to play a key role in the progression of diabetes and Alzheimer's disease, but whether A. muciniphila can improve diabetes complicated with Alzheimer's disease and its potential mechanism are unclear. ABSTRACT Diabetes and Alzheimer's disease (AD) are associated with specific changes in the composition of the intestinal flora. Studies have shown that the supplementation with pasteurized Akkermansia muciniphila has therapeutic and preventive effects on diabetes. However, it is not clear whether there is any association with improvement in and prevention of Alzheimer's disease and diabetes with Alzheimer's disease. Here, we found that pasteurized Akkermansia muciniphila can significantly improve the blood glucose, body mass index, and diabetes indexes of zebrafish with diabetes mellitus complicated with Alzheimer's disease and also alleviate the related indexes of Alzheimer's disease. The memory, anxiety, aggression, and social preference behavior of zebrafish with combined type 2 diabetes mellitus (T2DM) and Alzheimer’s disease (TA zebrafish) were significantly improved after pasteurized Akkermansia muciniphila treatment. Moreover, we examined the preventive effect of pasteurized Akkermansia muciniphila on diabetes mellitus complicated with Alzheimer's disease. The results showed that the zebrafish in the prevention group were better in terms of biochemical index and behavior than the zebrafish in the treatment group. These findings provide new ideas for the prevention and treatment of diabetes mellitus complicated with Alzheimer's disease. IMPORTANCE The interaction between intestinal microflora and host affects the progression of diabetes and Alzheimer's disease. As a recognized next-generation probiotic, Akkermansia muciniphila has been shown to play a key role in the progression of diabetes and Alzheimer's disease, but whether A. muciniphila can improve diabetes complicated with Alzheimer's disease and its potential mechanism are unclear. In this study, a new zebrafish model of diabetes mellitus complicated with Alzheimer's disease was established, and the effect of Akkermansia muciniphila on diabetes mellitus complicated with Alzheimer's disease is discussed. The results showed that Akkermansia muciniphila after pasteurization significantly improved and prevented diabetes mellitus complicated with Alzheimer's disease. Treatment with pasteurized Akkermansia muciniphila improved the memory, social preference, and aggressive and anxiety behavior of TA zebrafish and alleviated the pathological characteristics of T2DM and AD. These results provide a new prospect for probiotics in the treatment of diabetes and Alzheimer's disease.
Biofilm formation in Gram‐positives as an answer to combined salt and metal stress

Biofilm formation can lead to tolerance against stressors like antibiotics, toxic metals, salts, and other environmental contaminants. Halo‐ and metal‐tolerant bacilli and actinomycete strains isolated from a former uranium mining and milling site in Germany were shown to form biofilm in response to salt and metal treatment; specifically, Cs and Sr exposition led to biofilm formation. Since the strains were obtained from soil samples, a more structured environment was tested using expanded clay to provide porous structures resembling the natural environment. There, accumulation of Cs could be shown for Bacillus sp. SB53B, and high Sr accumulation ranging from 75% to 90% was seen with all isolates tested. We could, therefore, show that biofilms in a structured environment like soil will contribute to the water purification obtained by the passage of water through the critical zone of soil, providing an ecosystem benefit that can hardly be overestimated.
The influence of Nav1.9 channels on intestinal hyperpathia and dysmotility

ABSTRACT The Nav1.9 channel is a voltage-gated sodium channel. It plays a vital role in the generation of pain and the formation of neuronal hyperexcitability after inflammation. It is highly expressed in small diameter neurons of dorsal root ganglions and Dogiel II neurons in enteric nervous system. The small diameter neurons in dorsal root ganglions are the primary sensory neurons of pain conduction. Nav1.9 channels also participate in regulating intestinal motility. Functional enhancements of Nav1.9 channels to a certain extent lead to hyperexcitability of small diameter dorsal root ganglion neurons. The hyperexcitability of the neurons can cause visceral hyperalgesia. Intestinofugal afferent neurons and intrinsic primary afferent neurons in enteric nervous system belong to Dogiel type II neurons. Their excitability can also be regulated by Nav1.9 channels. The hyperexcitability of intestinofugal afferent neurons abnormally activate entero-enteric inhibitory reflexes. The hyperexcitability of intrinsic primary afferent neurons disturb peristaltic waves by abnormally activating peristaltic reflexes. This review discusses the role of Nav1.9 channels in intestinal hyperpathia and dysmotility.
Novel Gene Clusters for Natural Product Synthesis Are Abundant in the Mangrove Swamp Microbiome

At present, the majority of known clinical drugs originated from cultivated species of a few bacterial lineages. It is vital for the development of new pharmaceuticals to explore the biosynthetic potential of naturally uncultivable microorganisms using new techniques. ABSTRACT Natural microbial communities produce a diverse array of secondary metabolites with ecologically and biotechnologically relevant activities. Some of them have been used clinically as drugs, and their production pathways have been identified in a few culturable microorganisms. However, since the vast majority of microorganisms in nature have not been cultured, identifying the synthetic pathways of these metabolites and tracking their hosts remain a challenge. The microbial biosynthetic potential of mangrove swamps remains largely unknown. Here, we examined the diversity and novelty of biosynthetic gene clusters in dominant microbial populations in mangrove wetlands by mining 809 newly reconstructed draft genomes and probing the activities and products of these clusters by using metatranscriptomic and metabolomic techniques. A total of 3,740 biosynthetic gene clusters were identified from these genomes, including 1,065 polyketide and nonribosomal peptide gene clusters, 86% of which showed no similarity to known clusters in the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) repository. Of these gene clusters, 59% were harbored by new species or lineages of Desulfobacterota-related phyla and Chloroflexota, whose members are highly abundant in mangrove wetlands and for which few synthetic natural products have been reported. Metatranscriptomics revealed that most of the identified gene clusters were active in field and microcosm samples. Untargeted metabolomics was also used to identify metabolites from the sediment enrichments, and 98% of the mass spectra generated were unrecognizable, further supporting the novelty of these biosynthetic gene clusters. Our study taps into a corner of the microbial metabolite reservoir in mangrove swamps, providing clues for the discovery of new compounds with valuable activities. IMPORTANCE At present, the majority of known clinical drugs originated from cultivated species of a few bacterial lineages. It is vital for the development of new pharmaceuticals to explore the biosynthetic potential of naturally uncultivable microorganisms using new techniques. Based on the large numbers of genomes reconstructed from mangrove wetlands, we identified abundant and diverse biosynthetic gene clusters in previously unsuspected phylogenetic groups. These gene clusters exhibited a variety of organizational architectures, especially for nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS), implying the presence of new compounds with valuable activities in the mangrove swamp microbiome.
Compressed sensing pour l’imagerie radar

Ce rapport est le fruit d’un travail effectue lors de la SEME (Semaine d’Etude Maths En- treprise). Le projet presente ici a ete propose par Airbus et provient du domaine de l’imagerie radar. Les couts de calcul de reconstruction d’un signal grâce a ses modes de Fourier sont importants et soumis au theoreme de Nyquist-Shannon qui donne les conditions sur les frequences a echantillonner pour retrouver exactement le signal d’origine [3]. Nous avons etudie une solution alternative, moins couteuse. L’objectif est donc de reconstruire precisement une fonction d’interet a partir de mesures sur ses coefficients de Fourier en limitant au maximum le nombre de mesures.
Impact of Retinal Degeneration on Response of ON and OFF Cone Bipolar Cells to Electrical Stimulation

In retinal degenerative diseases, such as retinitis pigmentosa (RP) and age-related macular degeneration (AMD), the photoreceptors become stressed and start to degenerate in the early stages of the disease. Retinal prosthetic devices have been developed to restore vision in patients by applying electrical stimulation to the surviving retinal cells. However, these devices provide limited visual perception as the therapeutic interventions are generally considered in the later stages of the disease when only inner retinal layer cells are left. A potential treatment option for retinal degenerative diseases in the early stages can be stimulating bipolar cells, which receive presynaptic signals from photoreceptors. In this work, we constructed computational models of healthy and degenerated (both ON and OFF-type) cone bipolar cells (CBCs) with realistic morphologies extracted from connectomes of the healthy and early-stage degenerated rabbit retina. We examined these cells’ membrane potential and axon terminal calcium current differences when subjected to electrical stimulation. In addition, we investigated how differently healthy and degenerated cells behave with respect to various stimulation parameters, including pulse duration and cells’ distance from the stimulating electrode. The results suggested that regardless of the position of the OFF CBCs in the retina model, there is not a significant difference between the membrane potential of healthy and degenerate cells when electrically stimulated. However, the healthy ON CBC axon terminal membrane potential rising time-constant is shorter (0.29 ± 0.03 ms) than the degenerated cells (0.8 ± 0.07 ms). Moreover, the ionic calcium channels at the axon terminals of the cells have a higher concentration and higher current in degenerated cells (32.24 ± 6.12 pA) than the healthy cells (13.64 ± 2.88 pA) independently of the cell’s position.
Reconfiguring smart structures using approximate heteroclinic connections in a spring-mass model

Several new methods are proposed to reconfigure smart structures with embedded computing, sensors and actuators. These methods are based on heteroclinic connections between equal-energy unstable equilibria in an idealised spring-mass smart structure model. Transitions between equal-energy unstable (but actively controlled) equilibria are considered since in an ideal model zero net energy input is required, compared to transitions between stable equilibria across a potential barrier. Dynamical system theory is used firstly to identify sets of equal-energy unstable configurations in the model, and then to connect them through heteroclinic connection in the phase space numerically. However, it is difficult to obtain such heteroclinic connections numerically in complex dynamical systems, so an optimal control method is investigated to seek transitions between unstable equilibria, which approximate the ideal heteroclinic connection. The optimal control method is verified to be effective through comparison with the results of the exact heteroclinic connection. In addition, we explore the use of polynomials of varying order to approximate the heteroclinic connection, and then develop an inverse method to control the dynamics of the system to track the polynomial reference trajectory. It is found that high order polynomials can provide a good approximation to true heteroclinic connections and provide an efficient means of generating such trajectories. The polynomial method is envisaged as being computationally efficient to form the basis for real-time reconfiguration of real, complex smart structures with embedded computing, sensors and actuators.
Identification Of Unique Objects In High Resolution Aerial Images

Accurate and reliable detection of unique objects is an important component of an image understanding system. The objects are considered to have a unique pattern and should be recognized based upon their own characteristics. An object detection approach applicable to the high resolution aerial images is developed in the paper. The methodologies incorporate features such as minimal sensitivity to the backgrounds on which objects may appear, ability to detect objects appearing in arbitrary orientations, and use of a unified set of operators to analyze various levels of details. Specifically the gray level cooccurrence matrix is used to provide the early vision operators. Results of several experiments show the promise for the proposed methods.
Die Rolle der perkutanen Impella-Pumpe in der Anästhesie und Intensivmedizin

Der Einsatz der verschiedenen Impella-Typen ist mittlerweile in der klinischen Routine in ganz unterschiedlichen Bereichen fest etabliert. Neben der hämodynamischen Unterstützung während High-Risk-perkutanen Koronarinterventionen oder verschiedener kardiochirurgischer Operationen wird die Impella allein oder in Kombination mit extrakorporalen Life-Support-Systemen regelhaft bei Patienten im kardiogenen Schock eingesetzt.
Automated 3D Building Modeling Based on Urban Aerial Stereopair

Urban 3D building modeling is one of key technology of virtual city. In this paper, an approach to tackle the problem of 3D building modeling from urban high-resolution aerial image is presented. Automatic 2D building detection technique and 3D height extraction technique has been applied to the test image (stereopair) completely. Then combination between them is carried out, using the result of pyramidal stereo matching and the linear elements from 2D detection, the matching information from the matched point is assigned to the point of the linear element which corresponds to the matched point, so 3D modeling of building is achieved. In addition, more accurate 3D height information is obtained by applying new control strategies to prevent blunder propagation in pyramidal matching which based on the modified Adaptive Least Square Correlation algorithm
Quality of Table Eggs from Different Production Systems

Summary The production system is one of the most significant factors influencing internal and external egg quality. Due to concerns for the welfare of laying hens, and on the basis of Council Directive 1999/74/EC, EU countries banned conventional cages, so it is necessary to determine the influence of alternative systems on the external and internal quality traits of table eggs. Consumers believe that organic eggs are healthier than eggs from the conventional production systems. In addition, consumer awareness of the welfare of laying hens has been raised in the last few decades. This research aimed to determine the quality of table eggs from the cage, free-range and organic systems that are present on the Serbian market. External and internal quality traits were examined for 30 eggs taken from the market from the cage, organic and free-range systems. The value of Haugh units was 77.16 for organic eggs, 66.01 for conventional cages, and 62.01 for free-range system. The results showed that eggs from the organic system had significantly higher egg weight, shape index, albumen height, and Haugh units (p≤0.05) compared to cage and free-range eggs. Eggs from the free-range system had the highest number of meat and blood spots (2.25), while it was the lowest in eggs from the cage system (0.40). Eggs from organic production and conventional cages showed better results in internal and external egg quality. Shell quality traits, yolk color, and yolk pH were not affected by the production system.
Finite element analysis of bearingless permanent magnet motors with Halbach array

Aiming at the influence of air gap magnetic density on the dependability,the torque ripple and the suspension force of bearingless permanent magnet motors,the structure of Halbach array permanent rotor is proposed.From the structure of permanent magnet rotors in bearingless permanent magnet motors,the conventional surface mounted and Halbach array permanent magnet rotors were compared and analyzed.Then a finite element analysis with the software of Ansoft was presented,and magnetic field lines and air gap magnetic density of the two different rotor structures were calculated.Finally,the relations between suspension forces and currents in the suspension windings in bearingless permanent magnet motors with the different rotor structures were compared.The result shows that the air gap magnetic density with fine sinusoidal waveform is enhanced,and suspension force is improved by using Halbach array in bearingless permanent magnet motors.Halbach array has reliability and feasibility when used in bearingless permanent magnet motors.
Are bacteria, fungi, and archaea present in the midtrimester amniotic fluid?

Abstract Objectives This study was conducted to determine whether bacteria, fungi, or archaea are detected in the amniotic fluid of patients who underwent midtrimester amniocentesis for clinical indications. Methods Amniotic fluid samples from 692 pregnancies were tested by using a combination of culture and end-point polymerase chain reaction (PCR) techniques. Intra-amniotic inflammation was defined as an interleukin-6 concentration >2,935 pg/mL. Results Microorganisms were detected in 0.3% (2/692) of cases based on cultivation, 1.73% (12/692) based on broad-range end-point PCR, and 2% (14/692) based on the combination of both methods. However, most (13/14) of these cases did not have evidence of intra-amniotic inflammation and delivered at term. Therefore, a positive culture or end-point PCR in most patients appears to have no apparent clinical significance. Conclusions Amniotic fluid in the midtrimester of pregnancy generally does not contain bacteria, fungi, or archaea. Interpretation of amniotic fluid culture and molecular microbiologic results is aided by the assessment of the inflammatory state of the amniotic cavity. The presence of microorganisms, as determined by culture or a microbial signal in the absence of intra-amniotic inflammation, appears to be a benign condition.
California’s 2021 Gubernatorial Recall: Field Notes

In 2021, the second attempt in California history to recall a governor ended in a resounding victory for the Democratic incumbent. Unlike the recall of Governor Gray Davis in 2003, auspicious political conditions helped enable Gavin Newsom’s survival; these included a large Democratic advantage in voter registration and an all-mail-ballot election that reduced participation costs. Turning points in the process included an extension of the signature-gathering deadline and the emergence of a polemical candidate who helped energize Democratic voters. These developments re-exposed flaws in the state’s recall process and have prompted calls to reform the qualification procedures and the dual ballot format, which permits the election of a potentially anti-majoritarian replacement who receives a small fraction of the plurality vote.
Crop Disease Recognition Based on Attention Mechanism and Residual Structure: A Simpler Model for Crop Disease Recognition

At present, due to the disadvantages of huge parameters and long computing time, the crop recognition model trained by the mainstream network architecture is difficult to deploy on the mobile terminal. Aiming at this problem, a network model based on residual structure and SE attention module is designed. The primary features of the image are extracted through a standard convolution layer, and then the residual block embedded with the attention mechanism is constructed to obtain high-level features. Finally, the classification output is obtained through global average pooling and softmax layer. The network structure designed in this paper is trained on the dataset, and the final result is compared with other mainstream network architectures, and the model complexity is greatly reduced based on ensuring the recognition accuracy.
Planning and Learning in Partially Observable Systems via Filter Stability

Partially Observable Markov Decision Processes (POMDPs) are an important model in reinforcement learning that take into account the agent’s uncertainty about its current state. In the literature on POMDPs, it is customary to assume access to a planning oracle that computes an optimal policy when the parameters are known, even though this problem is known to be computationally hard. The major obstruction is the Curse of History, which arises because optimal policies for POMDPs may depend on the entire observation history thus far. In this work, we revisit the planning problem and ask: Are there natural and well-motivated assumptions that avoid the Curse of History in POMDP planning (and beyond)? We assume one-step observability, which stipulates that well-separated distributions on states lead to well-separated distributions on observations. Our main technical result is a new quantitative bound for filter stability in observable Hidden Markov Models (HMMs) and POMDPs – i.e. the rate at which the Bayes filter for the latent state forgets its initialization. We give the following algorithmic applications: First, a quasipolynomial-time algorithm for planning in one-step observable POMDPs and a matching computational lower bound under the Exponential Time Hypothesis. Crucially, we require no assumptions on the transition dynamics of the POMDP. Second, a quasipolynomial-time algorithm for improper learning of overcomplete HMMs, which does not require full-rank transitions; full-rankness is violated, for instance, when the number of latent states varies over time. Instead we assume multi-step observability, a generalization of observability which allows observations to be informative in aggregate. Third, a quasipolynomial-time algorithm for computing approximate coarse correlated equilibria in one-step observable Partially Observable Markov Games (POMGs). Thus we show that observability gives a blueprint for circumventing computational intractability in a variety of settings with partial observations, including planning, learning and computing equilibria.
Attention-based BiLSTM model for stock price prediction

Abstract: Stock price prediction is a hot issue in the field of quantitative finance. Investors hope to discover the objective laws of stock price fluctuations from historical data, optimize their investment strategies, and avoid risks to obtain better investment returns. With the development of deep learning technology, neural networks have shown good forecasting effects in task of time series data forecasting. Aiming at the temporal correlation of stock data, a bidirectional LSTM network stock price prediction model fused with attention mechanism is proposed. The experimental results show that the bidirectional LSTM network can effectively learn the correlation between data when performing the prediction task, and the attention module helps the model to better capture the key information in the stock data. Compared with other prediction networks, the model has higher prediction accuracy and lower prediction error, and achieves the best prediction performance on different datasets, which can provide help for stock price prediction.