The Benefits of Saccharomyces boulardii

ASaccharomyces boulardii strain, which does not carry any auxotrophic markers, was transformed with knockout constructs for the genes HIS3 and ADE2 using the dominant antibiotic marker genes encoding for kanamycin/G418- and nourseothricin/NATR resistance. Thereby, homozygous derivatives that were histidine or adenine deficient were obtained. Histidine prototrophy was easily reconstituted by transforming his-defective diploid derivatives with yeast plasmids carrying the HIS3 gene. Despite different attempts, for example, by creating a rme1::KANX rme1::NATR double-deleted S. boulardii yeast strain (RME1 encodes for Regulator of Meiosis), no visible sporulation to obtain haploid derivatives could be obtained. Besides, no filamentation properties of S. boulardii were observed. As previously mentioned, this yeast strain was confirmed to thrive at 37°C, a temperature disliked by some but not all S. cerevisiae strains used in the laboratory. S. boulardii is a diploid derivative of S. cerevisiae that does not sporulates and survives at temperatures as those found in the human gut. It can be easily manipulated by using conventional yeast methods to introduce auxotrophic markers and obtain heterozygous diploid knockout derivatives that can be transformed with yeast plasmids following conventional yeast protocols, thereby it could be even suited for biochemical and genetic research purposes

Eukaryotic translation elongation factor 1A (eEF1A) domain I from S. cerevisiae is required but not sufficient for inter-species complementation

Ethanolamine phosphoglycerol (EPG) is a protein modification attached exclusively to eukaryotic elongation factor 1A (eEF1A). In mammals and plants, EPG is linked to conserved glutamate residues located in eEF1A domains II and III, whereas in the unicellular eukaryote Trypanosoma brucei, only domain III is modified by a single EPG. A biosynthetic precursor of EPG and structural requirements for EPG attachment to T. brucei eEF1A have been reported, but nothing is known about the EPG modifying enzyme(s). By expressing human eEF1A in T. brucei, we now show that EPG attachment to eEF1A is evolutionarily conserved between T. brucei and Homo sapiens. In contrast, S. cerevisiae eEF1A, which has been shown to lack EPG is not modified in T. brucei. Furthermore, we show that eEF1A cannot functionally complement across species when using T. brucei and S. cerevisiae as model organisms. However, functional complementation in yeast can be obtained using eEF1A chimera containing domains II or III from other species. In contrast, yeast domain I is strictly required for functional complementation in S. cerevisia

XenDB: Full length cDNA prediction and cross species mapping in Xenopus laevis

BACKGROUND: Research using the model system Xenopus laevis has provided critical insights into the mechanisms of early vertebrate development and cell biology. Large scale sequencing efforts have provided an increasingly important resource for researchers. To provide full advantage of the available sequence, we have analyzed 350,468 Xenopus laevis Expressed Sequence Tags (ESTs) both to identify full length protein encoding sequences and to develop a unique database system to support comparative approaches between X. laevis and other model systems. DESCRIPTION: Using a suffix array based clustering approach, we have identified 25,971 clusters and 40,877 singleton sequences. Generation of a consensus sequence for each cluster resulted in 31,353 tentative contig and 4,801 singleton sequences. Using both BLASTX and FASTY comparison to five model organisms and the NR protein database, more than 15,000 sequences are predicted to encode full length proteins and these have been matched to publicly available IMAGE clones when available. Each sequence has been compared to the KOG database and ~67% of the sequences have been assigned a putative functional category. Based on sequence homology to mouse and human, putative GO annotations have been determined. CONCLUSION: The results of the analysis have been stored in a publicly available database XenDB . A unique capability of the database is the ability to batch upload cross species queries to identify potential Xenopus homologues and their associated full length clones. Examples are provided including mapping of microarray results and application of 'in silico' analysis. The ability to quickly translate the results of various species into 'Xenopus-centric' information should greatly enhance comparative embryological approaches. Supplementary material can be found at

Fast online 3D reconstruction of dynamic scenes from individual single-photon detection events

In this paper, we present an algorithm for online 3D reconstruction of dynamic scenes using individual times of arrival (ToA) of photons recorded by single-photon detector arrays. One of the main challenges in 3D imaging using single-photon Lidar is the integration time required to build ToA histograms and reconstruct reliable 3D profiles in the presence of non-negligible ambient illumination. This long integration time also prevents the analysis of rapid dynamic scenes using existing techniques. We propose a new method which does not rely on the construction of ToA histograms but allows, for the first time, individual detection events to be processed online, in a parallel manner in different pixels, while accounting for the intrinsic spatiotemporal structure of dynamic scenes. Adopting a Bayesian approach, a Bayesian model is constructed to capture the dynamics of the 3D profile and an approximate inference scheme based on assumed density filtering is proposed, yielding a fast and robust reconstruction algorithm able to process efficiently thousands to millions of frames, as usually recorded using single-photon detectors. The performance of the proposed method, able to process hundreds of frames per second, is assessed using a series of experiments conducted with static and dynamic 3D scenes and the results obtained pave the way to a new family of real-time 3D reconstruction solutions

Using Connectionist Networks to Examine thue Role of Prior Constraints in Human Learning

This research investigated the effects of prior knowledge on learning in psychologically-plausible connectionist networks. This issue was examined with respect to the benchmark orthography-to-phonology mapping task (Sejnowski & Rosenberg, 1986; Seidenberg & McClelland, 1989). Learning about the correspondences between orthography and phonology is a critical step in learning to read. Children (unlike the networks mentioned above) bring to this task extensive knowledge about the sound-structure of their language. We first describe a simple neural network that acquired some of this phonological knowledge. W e then summarize simulations showing that having this knowledge in place facilitates the acquisition of orthographicphonological correspondences, producing a higher level of asymptotic performance with fewer implausible errors and better nonword generalization. The results suggest that connectionist networks may provide closer approximations to human performance if they incorporate more realistic assumptions about relevant sorts of background knowledge

A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis

With recent advancements in quantum computing technology, optimizing quantum circuits and ensuring reliable quantum state preparation have become increasingly vital. Traditional methods often demand extensive expertise and manual calculations, posing challenges as quantum circuits grow in qubit- and gate-count. Therefore, harnessing machine learning techniques to handle the growing variety of gate-to-qubit combinations is a promising approach. In this work, we introduce a comprehensive reinforcement learning environment for quantum circuit synthesis, where circuits are constructed utilizing gates from the the Clifford+T gate set to prepare specific target states. Our experiments focus on exploring the relationship between the depth of synthesized quantum circuits and the circuit depths used for target initialization, as well as qubit count. We organize the environment configurations into multiple evaluation levels and include a range of well-known quantum states for benchmarking purposes. We also lay baselines for evaluating the environment using Proximal Policy Optimization. By applying the trained agents to benchmark tests, we demonstrated their ability to reliably design minimal quantum circuits for a selection of 2-qubit Bell states

Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures

Quantum computing offers the potential for superior computational capabilities, particularly for data-intensive tasks. However, the current state of quantum hardware puts heavy restrictions on input size. To address this, hybrid transfer learning solutions have been developed, merging pre-trained classical models, capable of handling extensive inputs, with variational quantum circuits. Yet, it remains unclear how much each component -- classical and quantum -- contributes to the model's results. We propose a novel hybrid architecture: instead of utilizing a pre-trained network for compression, we employ an autoencoder to derive a compressed version of the input data. This compressed data is then channeled through the encoder part of the autoencoder to the quantum component. We assess our model's classification capabilities against two state-of-the-art hybrid transfer learning architectures, two purely classical architectures and one quantum architecture. Their accuracy is compared across four datasets: Banknote Authentication, Breast Cancer Wisconsin, MNIST digits, and AudioMNIST. Our research suggests that classical components significantly influence classification in hybrid transfer learning, a contribution often mistakenly ascribed to the quantum element. The performance of our model aligns with that of a variational quantum circuit using amplitude embedding, positioning it as a feasible alternative

Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization

Multi-Agent Reinforcement Learning is becoming increasingly more important in times of autonomous driving and other smart industrial applications. Simultaneously a promising new approach to Reinforcement Learning arises using the inherent properties of quantum mechanics, reducing the trainable parameters of a model significantly. However, gradient-based Multi-Agent Quantum Reinforcement Learning methods often have to struggle with barren plateaus, holding them back from matching the performance of classical approaches. We build upon an existing approach for gradient free Quantum Reinforcement Learning and propose three genetic variations with Variational Quantum Circuits for Multi-Agent Reinforcement Learning using evolutionary optimization. We evaluate our genetic variations in the Coin Game environment and also compare them to classical approaches. We showed that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters. Compared to the larger neural network, our approaches archive similar results using $97.88\%$ less parameters

Protein disulfide isomerase acts as an injury response signal that enhances fibrin generation via tissue factor activation

The activation of initiator protein tissue factor (TF) is likely to be a crucial step in the blood coagulation process, which leads to fibrin formation. The stimuli responsible for inducing TF activation are largely undefined. Here we show that the oxidoreductase protein disulfide isomerase (PDI) directly promotes TF-dependent fibrin production during thrombus formation in vivo. After endothelial denudation of mouse carotid arteries, PDI was released at the injury site from adherent platelets and disrupted vessel wall cells. Inhibition of PDI decreased TF-triggered fibrin formation in different in vivo murine models of thrombus formation, as determined by intravital fluorescence microscopy. PDI infusion increased — and, under conditions of decreased platelet adhesion, PDI inhibition reduced — fibrin generation at the injury site, indicating that PDI can directly initiate blood coagulation. In vitro, human platelet–secreted PDI contributed to the activation of cryptic TF on microvesicles (microparticles). Mass spectrometry analyses indicated that part of the extracellular cysteine 209 of TF was constitutively glutathionylated. Mixed disulfide formation contributed to maintaining TF in a state of low functionality. We propose that reduced PDI activates TF by isomerization of a mixed disulfide and a free thiol to an intramolecular disulfide. Our findings suggest that disulfide isomerases can act as injury response signals that trigger the activation of fibrin formation following vessel injury