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<|MaskedSetence|> As a result, we collected a non-redundant, self-distillation dataset with ground-truth secondary structure from the RNAStralign and bpRNA-1M databases. <|MaskedSetence|> RhoFold+ was initially trained using only PDB data, which was then used to generate a self-distillation dataset by inferring pseud... | **A**: We filtered this dataset by removing sequences with more than 256 or fewer than 16 nucleotides, resulting in a dataset of 27,732 sequences.
**B**: During training, we masked out pseudo-label residues with pLDDT scores <0.7 and uniformly sub-sampled the MSAs to augment the distillation dataset.
A structure pr... | CAB | CAB | CAB | CAB | Selection 4 |
Author contributions
SL studied the virial theorem while participating in the “Introduction to Astrophysics” cluster in the COSMOS summer program held at UC Irvine from July 9, 2023 to August 4, 2023. <|MaskedSetence|> LP learned of the virial theorem from SL and, in discussing its proof with SL, realized that it mus... | **A**: Specifically, she used the virial theorem to repeat Zwicky’s Coma cluster mass estimates using modern measurements of velocity dispersion and galaxy positions.
**B**: SL and LP explored the applications and implications of the connection.
**C**: LP drafted the initial manuscript; both SL and LP edited the firs... | ABC | ABC | ABC | BCA | Selection 1 |
Although various methods have been developed to enhance performance estimation in model selection using k-fold CV, their design and implementation have been limited to SO problems. <|MaskedSetence|> <|MaskedSetence|> These algorithms modify fitness estimations but do not change the model selection process; the chos... | **A**: [12] compared double CV, the Tibshirani and Tibshirani method [13], and nested CV in their ability to improve the estimation of the fitness for SO problems.
**B**: Of these, only one had a predictive performance estimation strategy that could adjusts for multiple model validations (limitedly to SO problems and ... | CAB | ACB | CAB | CAB | Selection 3 |
5.2 Lung Segmentation Evaluation
Lung segmentation evaluation results are presented in Figure 5. <|MaskedSetence|> <|MaskedSetence|> We also plotted the mean dice coefficient before applying registration. <|MaskedSetence|> For cases involving major motion, IVIM-Morph succeeded in enhancing the dice coefficient ach... | **A**: We calculated the dice twice, one time using the optimal hyperparameters of group 1 and one time using the optimal hyperparameters of group 2.
**B**: The mean dice coefficient for each compared method is plotted as a boxplot, separately for the major and minor motion cases.
**C**: The mean dice before registra... | BAC | BAC | ACB | BAC | Selection 2 |
<|MaskedSetence|> The simplest GNN using pooling is not much more computationally costly than an MLP that takes distances between Cα atoms as inputs. <|MaskedSetence|> We expect the memory and computational requirements to scale with token number quadratically for SubFormer and subquadratically for SubMixer (dependin... | **A**:
The computational costs for training VAMPnets with different token mixers are shown in Figure 8.
**B**: The GNNs with token mixers are about an order of magnitude more computationally costly but still manageable (hundreds of seconds) even without advanced acceleration techniques such as flash-attention or comp... | ABC | ABC | ABC | ABC | Selection 1 |
Even though a relatively small percentage of patients required critical care services, the surge in cases quickly overwhelmed the healthcare system. At the beginning of the COVID epidemic in India, the Ministry of Health and Family Welfare (MoHFW) suggested that around 2.5% of patients needed intensive care. <|MaskedS... | **A**: The survey indicated difficulties securing COVID-19 ICU beds for family and friends (see Fig 1).
Figure 1.
**B**: However, this might be underestimated due to incomplete reporting in some states [26].
**C**: It was estimated that 15% of patients, translating to about 1.5 million individuals in India, would r... | CBA | BCA | BCA | BCA | Selection 3 |
The proposed model is broadly applicable to various domains, including social interactions, biological systems (e.g., neural or protein interactions), and technological networks (e.g., the spread of computer viruses or resilience of infrastructure systems). <|MaskedSetence|> <|MaskedSetence|> The model not only main... | **A**: This stochastic SIR framework thus provides a versatile tool for modeling infectious diseases and other dynamic processes beyond the scope of traditional SIR models..
**B**: By transforming the SIR model using dynamical survival analysis within the edge-based configuration network framework, the resulting syste... | BCA | ACB | BCA | BCA | Selection 1 |
Other authors have studied biophysical network models of premotor neurons. Rakowski et al. (2013) and (2017) simulated the dynamics of a pre-motor and motor circuit. The stationary distributions of the motor neurons were then used to infer synaptic polarities, i.e., whether a synaptic
connection is excitatory or inhi... | **A**: elegans neuronal network and evaluated the activity of the command neurons [22, 26].
**B**: Lanza et al. (2021) simulated the dynamics of the C.
**C**: Their model predicted that neural activity converges to limit cycles; i.e., all neurons eventually acquire the same periodicity..
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Most humans do not spend their lives on treadmills, so their behavior may not already be energy optimal for such gait transition tasks without considerable learning [19, 20, 21]. Here, in contrast to these treadmill gait transition experiments, we show that overground gait transitions in realistic overground locomotion... | **A**: What might you do? If you start very early and have plenty of time, you might prefer to walk all the way.
**B**: Having this mixture of walking and running instead of a sharp gait transition speed is energy optimal [1, 22, 23], and was earlier observed over short distance tasks in humans [1], so the primary exp... | ACB | ACB | ACB | ACB | Selection 2 |
Van der Zee and Kuo [28] proposed a model of metabolic rate proportional to the second derivative of force, which is equivalent to the metabolic cost per movement being proportional to the first derivative of force. This is a different cost from our model, which they supported by showing an approximate quadratic scalin... | **A**: One reason positive and negative force rate may have different costs may be due to decrease force, the calcium needs to be pumped back to the sarcoplasmic reticulum which incurs a metabolic cost [42, 43].
**B**: But these studies did not perform experiments comprising different activation and relaxation times, ... | ABC | CAB | CAB | CAB | Selection 3 |
Figure 6: The evolution of a mutated pathogen (similar to Figs. <|MaskedSetence|> <|MaskedSetence|> The pathogen begins its evolution when a few nodes are infected with pathogens having higher value of γ𝛾\gammaitalic_γ, that after some time get dominance over the network, and the average pathogen’s value of γ𝛾\ga... | **A**: Since scale free networks have a very small diameter, which is almost independent of the size, the average distance in the Deezer netwrok is very close to the average on our generated scale free network even though it is much smaller.
**B**: The initial pathogen’s parameters are set around the epidemic threshol... | CBA | BAC | CBA | CBA | Selection 1 |
times, etc. but these frequency multiples are each associated with
a single haplotype that is different in each case (h=1)ℎ1(h=1)( italic_h = 1 ). <|MaskedSetence|> <|MaskedSetence|> The ordinate is
linear and the abscissa is logarithmic. <|MaskedSetence|> | **A**: The outcome of a plot in which hnℎ𝑛hnitalic_h italic_n is plotted
against n𝑛nitalic_n is highly informative (Figs.4a,b).
**B**: Most high-frequency
multiples are absent.
**C**: The sum of the hnℎ𝑛hnitalic_h italic_n values.
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<|MaskedSetence|> In the presence of an antigen, immune B cells produce neutralizing antibodies that bind to specific target sites on its surface (called antigenic epitope sites) [63]. In a primary infection, a part of the responding B cells is stored as immune memory to protect against future infections by the same p... | **A**: This process, called antigenic drift, is frequently observed in RNA viruses, including human influenza, norovirus, and SARS-CoV-2 [67, 68, 69, 70].
**B**: VI Complexity of immune recognition
The following example shows how selection on complexity can act in the adaptive immune system, a rapidly evolving recogn... | CBA | BAC | BAC | BAC | Selection 3 |
A. Tanaka: Conceptualization, NGS sample preparation, NGS data analysis, investigation, performing experiments, project administration, generating figures and tables, funding acquisition, and writing original draft. <|MaskedSetence|> H. Ohta and Y. Ishitsuka: Data investigation, methodology, generating figures, and w... | **A**: All authors participated in discussions and interpretation of the data and results.
Acknowledgements..
**B**: J.I. Yasunaga: Collecting clinical samples, data investigation, funding acquisition and experimental advices.
**C**: A. Fujimoto: Assisting NGS data analysis.
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<|MaskedSetence|> Army DEVCOM Army Research Laboratory and was completed under Cooperative Agreement Number W911NF2420013 and by the National Science Foundation grant 2126976. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official polici... | **A**: Government.
**B**: Army DEVCOM Army Research Laboratory or the U.S.
**C**: Acknowledgements.
This research was sponsored by the U.S.
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<|MaskedSetence|> One of the first papers is ESM-1b (Rives et al. 2021) trained on 250 million protein sequences with a BERT-style strategy. Several other PLMs are proposed and perform well on various downstream tasks(Rao et al. 2021; Elnaggar et al. 2021; Brandes et al. 2022). Especially, ESMFold (Lin et al. 2022) an... | **A**:
Many efforts have emerged to develop foundation language models to leverage the massive biological sequence data.
**B**: The RLMs are trained on massive non-coding RNA sequences.
**C**: 2022) show the power of PLMs on protein structure prediction, without multiple sequence alignment information as in AlphaFol... | ACB | ACB | ACB | CBA | Selection 3 |
A robust evaluation metric for generative models must effectively distinguish between varying levels of different noise types. <|MaskedSetence|> This demonstrates its effectiveness in evaluating image quality. The capability to identify corruptions in real images makes our metric a valuable tool for detecting subtle ... | **A**: In our experiments with salt-and-pepper noise and rectangular patch noises, common in histopathology images, our metric, RL2, shows a monotonic increase with rising noise levels as shown in Figure 3 and Figure 4.
**B**: Even when synthetic image data significantly differs from real data, our metric reliably ide... | ABC | ABC | ABC | ABC | Selection 4 |
Somewhat counterintuitively, negative scaling regimes have been observed in density scaling laws, particularly in the context of housing prices [48, 49], where the price of detached housing decreases with increasing population density at high densities in England. In our study, cities exhibiting negative elasticities a... | **A**: These improvements may include more aggressive vaccination campaigns and increased awareness of disease prevention, which can more effectively reduce risky behaviors that facilitate the spread of pertussis than in smaller and more isolated populations.
**B**: This regime is more common among cities of intermedi... | ABC | ABC | BCA | ABC | Selection 1 |
<|MaskedSetence|> However, the high dimensionality and vast scale of PINs pose significant challenges for direct computational analysis. Consequently, researchers often resort to indirect approaches, employing summarizing features like topological metrics (e.g., indegree, betweenness, clustering coefficient) to repres... | **A**: While practical, this utilization leads to a substantial loss of resolution, potentially overlooking subtle but crucial network characteristics essential for identifying druggable genes.
To address this issue, network embedding techniques can be employed [39].
**B**: Shingo Tsuji successfully used a deep neur... | CAB | CAB | BCA | CAB | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> Graphs are a highly general representation for different data types, and can also be used to represent 1D, 2D, and 3D Euclidean data by treating the inputs as a grid. Equivalents of convolution, pooling, and attention operators in Euclidean space data are also used for feature extr... | **A**:
Graph Convolutional Neural Networks (GCNNs) - GCNNs operate on graph data, where sample inputs consists of vertices and edges between them.
**B**: Figure 2 gives a generalized overview of different GNN structures.
Figure 2: A simplified overview of the main classes of GNNs.
**C**: Features are represented ... | ACB | ACB | BAC | ACB | Selection 1 |
For training, we randomly split the available cortical thickness data into a training set of 568568568568 individuals and a test set of 63636363 individuals. The anatomical covariance matrix was estimated from the cortical thickness data of the training set. <|MaskedSetence|> The VNN was trained to predict chronologi... | **A**: The training set was further split into a subset of 498498498498 individuals and a validation set of 70707070 individuals.
**B**: Using this strategy, we trained 10101010 distinct VNN models with different permutations of the training set.
**C**: The configuration with the best performance on the validation se... | ACB | ACB | ACB | ACB | Selection 4 |
7 Conclusions
Inducible defences, a form of phenotypic plasticity, have the ability to significantly influence direct interactions within ecological communities, generating trait-mediated indirect effects [66, 67]. These defences arise when prey exhibit adaptive behavioural, morphological, or physiological traits in r... | **A**: An evolutionary ecological theory posits that inducible defences are vouched for over constitutive ones when these defensive traits impose exceptional costs on prey [69, 70].
**B**: However, such defences often come with associated costs- either through a reduction in prey growth rates (metabolic costs) or by i... | BAC | BAC | BAC | BAC | Selection 2 |
Figure 1:
The Role of pMHC-TCR in Adaptive Immunity and the Correspondence between Our Model Architecture and the Biological Process. <|MaskedSetence|> Antigens are up-taken by the APCs and then bind to the MHC. <|MaskedSetence|>
(b) Recognition of Antigens by T cells. All cells present some peptides via the pMHC.... | **A**: Certain peptides can be recognized by T cells through the pMHC-TCR interaction, leading to their elimination by T cells.
**B**: Subsequently, the pMHC complex displayed on APCs can bind to some TCRs on T cells.
**C**: (More details will be introduced in Section 3.1.)
(a) Antigen Presentation via APCs to activ... | CAB | CBA | CBA | CBA | Selection 4 |
Acknowledgements
The authors would like to thank M. Asker, J. Jiménez, S. Muñoz Montero, M. Pleimling, A. M. <|MaskedSetence|> Swailem for fruitful discussions.
L. <|MaskedSetence|> N. and M. <|MaskedSetence|> gratefully acknowledge funding from the U.K. Engineering and Physical Sciences Research Council (EPSRC) u... | **A**: M.
**B**: H.
**C**: Rucklidge, and M.
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<|MaskedSetence|> <|MaskedSetence|> (2015) generative deep learning methods;Noé et al. (2019) Markov model analyses; Husic and Pande (2018); Nüske et al. (2017) and neural-network based analyses.Fraccalvieri et al. (2011); Ward et al. (2021) For example, DiffNetsWard et al. (2021) successfully identified mutation sit... | **A**: (2021)
However, the available methods are generally computationally costly, difficult to apply, and/or not easily interpretable.
**B**: The necessary analysis is the bottleneck of many biomolecular simulation projects, as it can take weeks of dedicated work if performed by eye and by one-off scripts, and a focu... | BCA | ACB | BCA | BCA | Selection 4 |
Paradigm 2 was an extension of paradigm 1, using similar sequences of twenty discrete spoken words. <|MaskedSetence|> <|MaskedSetence|> The subject was instructed to passively count the number of target events in the attended stream and report the count after the trial. <|MaskedSetence|> Additionally, the attended s... | **A**: In each trial, the subject was asked to pay attention to only the target events in one of the streams and completely ignore the other stream.
**B**: The target events in the twenty trials were balanced between the two classes of events.
**C**: However, instead of a single stream of words, two competing streams... | ACB | CAB | CAB | CAB | Selection 2 |
In self-assembly, our results suggest that disassembly pathways can provide time-efficient error correction when combined with misincorporation-induced pauses seen in natural systems such ribosomal assembly checkpoints [83] and synthetic systems [55, 45, 84]. Our results suggest, in a twist, that classic annealing prot... | **A**: Such ‘canalization’ into a few paths can be seen as a non-equilibrium version of Waddington’s homeorhesis [87].
**B**: As a consequence, complex systems can achieve stereotyped reproducible behaviors, despite living in high-dimensional disordered state spaces, through simple non-equilibrium mechanisms that also... | ABC | ACB | ACB | ACB | Selection 3 |
<|MaskedSetence|> During training, it achieves an RMSE of 0.1360, maintaining its effectiveness in the validation phase with an RMSE of 0.3465. The model’s performance remains stable in the testing phase as well, with an RMSE of 0.2912. <|MaskedSetence|> <|MaskedSetence|> Notably, the Rectilinear interpolation model... | **A**:
IV-B3 Neural CDE interpolation strategies
As shown in Table V, the Cubic Hermite splines model exhibits consistent results throughout the process.
**B**: It starts with a slightly lower RMSE of 0.1278 during training, which carries over into the validation phase with an RMSE of 0.3371.
**C**: In contrast, t... | ACB | ACB | ACB | ABC | Selection 3 |
<|MaskedSetence|> The FI that includes the FPFP5 performed the best (green upside-down triangles). Similarly, when the default FI (red triangles) was compared to leave-one-out NFPFP5 (cyan squares), the FI was superior. Both the NFPFP5 and FI typically out-performed chronological age, except for predicting weakness (d... | **A**: Error bars are standard errors..
**B**: The AUC is the probability that a metric will correctly rank positive individuals as higher than negative individuals [31] (dotted line at 0.5 indicates a random guess).
**C**:
Figure 1: The FI predicts future FPFP5 deficits better than NFPFP5.
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<|MaskedSetence|> The metric used for all evaluations is MCC. <|MaskedSetence|> <|MaskedSetence|> All models were trained with a consistent set of hyperparameters: DNABERT (Zhou et al., 2024) variants undergo full-model fine-tuning, while Nucleotide Transformer (NT) (Dalla-Torre et al., 2023) variants and METAGENE-1... | **A**: The header row reports macro-averaged performance metrics.
**B**:
Table 2: Results on the Pathogen Detection benchmark.
**C**: See Section 5.2 for details.
We evaluate the performance of METAGENE-1 and other genomic foundation models on the pathogen detection datasets, measured using the Matthews correlatio... | BCA | BAC | BAC | BAC | Selection 4 |
We employ two age grouping strategies. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> This strategy divides the age range into four segments: [0-34), [34-60), [60-78), and 78+, aligning with significant biological and proteomic changes that correspond to shifts in aging patterns.
Within each age group, we c... | **A**: The second grouping is based on research by [24], which identified key inflection points in aging at approximately 34,60, and 78 years.
**B**: The first divides the age range into decade-sized intervals: [0−10),[10−20),[20−30),…,[90−100),[100+[0-10),[10-20),[20-30),\ldots,[90-100),[100+[ 0 - 10 ) , [ 10 - 20 ) ... | BCA | BCA | ACB | BCA | Selection 1 |
A key mechanism of our model is the refractoriness of plasticity which prevents a continuous update of the post-synaptic neuron’s incoming weights while it is bursting. <|MaskedSetence|> Interestingly, this refractory period has also been observed in in vitro experiments [9]. Also note that without a refractory period... | **A**: Figure 2G and 2H show the number of synapses updated at each Euler step during a small simulation window.
**B**: 2E).
**C**: Figure 2D shows that refrectoriness is quite important for the asynchronous model to approximate the learning trajectory of the discrete model, as non-existent (1 step) or small (10 step... | CBA | CBA | CBA | ACB | Selection 3 |
<|MaskedSetence|> The key element for achieving this unification is the condition map, which transforms complex geometric conditions to match the diffusion model’s configuration space, thereby enabling self-guidance without the need for external models. <|MaskedSetence|> Moreover, our method is the most versatile, ex... | **A**: With our method, we are able to tackle the same tasks, while overcoming major drawbacks: UniGuide eliminates the need for additional training and, more importantly, avoids constraining the model to specific tasks.
We demonstrate the wide applicability of UniGuide by tackling a variety of geometry-constrained d... | BCA | BCA | CAB | BCA | Selection 4 |
Figure 4: Overview of EDM (Equivariant Diffusion Models) and its extensions for molecular generation tasks. The top box represents the foundational EDM model, which uses 3D point cloud representation with E(3) equivariance to handle molecular structures. <|MaskedSetence|> It demonstrates how subsequent models address... | **A**: The figure highlights the key limitations of earlier models (shown in blue boxes).
**B**: Scalability to Complex Molecules: MDM considers covalent bonds and Van der Waals forces but cannot adapt to target-specific molecular pockets.
**C**: MolSnapper improves molecular realism by accurately representing ligand... | ABC | ABC | BCA | ABC | Selection 2 |
<|MaskedSetence|> <|MaskedSetence|> Since ML approaches have discovered two primary evolutionary mechanisms of SARS-CoV-2 [6, 48], they aid in discovering new mechanisms of action and drug targets, providing robust support for the development of innovative anesthetics.
In this study, we constructed a proteomics-bas... | **A**: ML technologies process and analyze vast amounts of biomedical data, thereby enhancing the efficiency of drug design and screening [40], predicting the biological activity and pharmacological properties of new molecules, optimizing drug structures, and improving binding specificity to GABA receptors.
**B**: Mor... | ABC | ABC | CBA | ABC | Selection 2 |
5.1 Broader impact
Neural decoding models, particularly those tasked with reconstructing naturalistic images, significantly deepen our understanding of the relationship between neural activity and the stimuli that evoke it. <|MaskedSetence|> <|MaskedSetence|> Human engagement with the environment extends beyond simp... | **A**: These models hold substantial promise for applications such as visual neuroprosthetics, aiming to restore lost visual experiences.
**B**: However, their deployment must be approached with caution due to the inherent complexities of brain function and environmental interactions.
**C**: Moreover, using these mod... | ABC | ABC | ABC | CAB | Selection 2 |
•
The ability to handle data both globally and locally.
A CTM can surely deal with data both globally and locally. By globally we mean the CTM invokes all processors to handle the data(or to say, the data is available to all processors) and in contrast, by locally we mean it only invokes some of the processors. <|Mas... | **A**: There isn’t an explicit boundary between ’globally’ and ’locally’ in CTM.
**B**: All those data come in the form of chunks conveyed to LTM processors at time t𝑡titalic_t.
**C**: It’s clear that in a probabilistic CTM, those chunks’ weights are comparable in the competition without any unit conversion, therefo... | ABC | ABC | ABC | ACB | Selection 1 |
We structured our analysis into three primary sections. <|MaskedSetence|> We examined the role of local and global bifurcations in shaping these regimes, emphasizing the importance of time scale separation.
Secondly, we explored the diffusively coupled FHN model [Eq. 10], introducing spatial coupling through diffusio... | **A**: This is the broadest category as here one can consider a multitude of different network topologies and coupling terms.
**B**: Additionally, we examined front solutions, localized states, traveling pulses, and pacemaker-driven waves within the oscillatory domain, highlighting the richness of patterns that arose ... | BCA | CBA | CBA | CBA | Selection 2 |
Next, we explore the significance of phase waves across various disciplines, illustrating the versatility of the phenomena observed in our system. The occurrence of these regimes in different geometries suggests that the presence of a driving and a driven system, even with minimal diffusion, is sufficient for these be... | **A**: Although the underlying mechanisms driving these phase phenomena may differ, their widespread applicability is evident, and alternative mechanisms might unveil novel applications for these dynamics.
.
**B**: In chemistry, phase waves have been pivotal in understanding the Belousov–Zhabotinsky reaction’s shift... | CBA | CBA | CBA | ACB | Selection 1 |
<|MaskedSetence|> Foldseek (van Kempen et al., 2024) introduces a quantized autoencoder to encode local protein geometry, demonstrating success in database search tasks. However, as it focuses solely on local features at the residue level, it lacks the capacity to provide global representation of protein structures. T... | **A**: (2024) combines a structural autoencoder with K-means clustering applied to the latent representation of a fixed reference dataset.
.
**B**: Building on the 3Di-alphabet introduced by Foldseek, Su et al.
**C**:
Discrete representation learning for protein structures has recently garnered increasing attentio... | BAC | CBA | CBA | CBA | Selection 2 |
Tuning of these parameters can take place offline, externally to the simulator, or online, inside the simulator, emulating biological homeostatic control [1]. <|MaskedSetence|> Gradients represent how changes in model parameters affect simulation output. Currently, gradient-free methods are the dominant approach for ... | **A**: What is more, online learning methods can also be developed based on gradients.
Unfortunately, existing brain simulators do not provide gradient calculation.
**B**: General parameter-tuning methods can be divided into gradient-free or gradient-based ones.
**C**: However, these are known to suffer from the cu... | BCA | BCA | ABC | BCA | Selection 2 |
Phages infect host cells by adsorbing (attaching) to receptors on the host cell wall and then delivering the genomic content into the host cytoplasm. Phages are much smaller than bacteria and each host cell presents multiple receptors that phages can bind to, so multiple phages can adsorb to a single host cell, though... | **A**: If phage densities are very high, it is possible that multiple phages simultaneously adsorb to and then infect the same host cell.
**B**: The basic premise of phage therapy is to use phages to lyse target bacterial populations (Kortright et al., 2019).
**C**: This creates a potential scenario where many adsorp... | ABC | ABC | ABC | ABC | Selection 2 |
DNA, the carrier of genetic information, naturally suggests itself for such approaches. It is a soft matter system that has evolved specifically for the purpose111Provided one can speak about things like “purposes” of natural objects in a scientific context, see Ref. Hundertmark (forthcoming) for a discussion. <|Mas... | **A**: Moreover, there is an established tradition of using DNA for performing artificial computational tasks in the framework of DNA computing Adleman (1994).
**B**: (2024) have explored the possibility of using DNA as a basis for artificial neural networks.
.
**C**: of storing and processing information.
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<|MaskedSetence|> Negative correlations are observed in the parietal areas, indicating reduced engagement of these regions in low-frequency processing during auditory stimuli. <|MaskedSetence|> Positive correlations are observed in the temporal and parietal regions, indicating theta rhythms’ role in auditory informat... | **A**: In the Delta band (1-4 Hz), the Audio model shows a mix of positive and negative correlations, with positive correlations scattered in the frontal cortex and medial temporal lobe, regions associated with attention and memory processes crucial during auditory tasks.
**B**: The superior temporal gyrus and medial ... | ACB | ACB | ABC | ACB | Selection 4 |
<|MaskedSetence|> Certain bacteria like E.coli, S.typhimurium, B.subtilis are known to show chemotaxis where they can move along a chemical gradient in their environment [18, 19, 20]. <|MaskedSetence|> <|MaskedSetence|> In a homogeneous attractant environment, after a large number of runs and tumbles the net displac... | **A**: This migration happens via run-and-tumble motion, which is characterized by persistent movement along a particular direction (run), punctuated by abrupt change of direction (tumble) [25, 26].
**B**:
In this work, we use reinforcement learning to study a model that has been motivated by the phenomenon of bacte... | BCA | BCA | CAB | BCA | Selection 4 |
<|MaskedSetence|> The dataset was divided into 22,348 training samples and 100 test samples. The linear weights w𝑤witalic_w were initialized to uniform average pooling. Each model is trained 100 epochs with a batch size of 100 samples. <|MaskedSetence|> The architectures and training loops are implemented with the M... | **A**: All models were optimized using Adam with learning rate 0.0002 and batch size 4 and 100 epochs.
**B**:
Training parameters
For our AFRT model, the affine warps 𝒜𝒜\mathcal{A}caligraphic_A were initialized to identity.
**C**: The source code and detailed implementation can be found in our repository 222http... | BAC | BAC | BAC | BAC | Selection 3 |
<|MaskedSetence|> We hypothesize this could be due to the fact that there are no alterations for this cell-type in HD.
Overall, the NN model’s performance evaluated just on HD cells achieves a precision of 0.95 and recall of 0.91, resulting in an F1-score of 0.93. This indicates that the model is highly effective at i... | **A**: In contrast, the NN model shows low classification performance for Perivascular pericytes.
**B**: These metrics suggest a well-balanced performance in identifying WT cells, with a slightly higher recall compared to precision.
**C**: With an overall accuracy of 0.93, the model shows a robust performance.
.
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<|MaskedSetence|> Because of implementation differences of estimates available in these packages, we re-derived population genetic estimates and examined their differences (see Supplement).
Most commonly, our input are sequence reads or read-derived allele counts, as those fully capture the effects of both sources o... | **A**: With these reconstructed allele frequencies, the correction for read depth is less relevant, but the correction for pool size remains important.
**B**: Our implementation however can also be used with inferred or adjusted allele frequencies as input, for instance using information from the haplotype frequencies... | CBA | BAC | CBA | CBA | Selection 1 |
<|MaskedSetence|> Yet, this modular approach, relying on these intermediate descriptors, introduces certain inefficiencies and complexities during both the training phase and sample generation. Approaches like those proposed by [18] and [19] involve predicting interatomic distance matrices and subsequently applying Di... | **A**: Unfortunately, by utilizing DSM (Distance Gradient-based learning), the model was trained using perturbed distance matrices, which had the potential to violate the triangular inequality or contain negative values.
**B**: It estimated the gradient with respect to interatomic distances using denoising score match... | CBA | CBA | CBA | CBA | Selection 3 |
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