Miracle-dz/UniFAR-FTUs huggingface dataset:
A matter of life and death – Hunting tactics of Southern elephant seals (Mirounga leonina) and anti-predatory behaviours of their prey

For air-breathing marine predators that must save energy during dives, the ability to adopt hunting tactics that minimise the risk of triggering an escape reaction from the prey is crucial for an efficient foraging. Female Southern elephant seals (Mirounga leonina, SES hereafter) forage on small mesopelagic prey that they must hunt almost continuously to meet their high-energy requirements. Here we aimed at understanding how these large time-limited deep-divers can efficiently exploit their small sized prey. To do so, we used data recorded by a new biologger, the sonar tag, deployed on SES during their post-breeding foraging trips. This tag combines an active acoustic sensor with ultra-high-resolution movement and bioluminescence sensors, offering a unique opportunity to simultaneously describe SES’ hunting tactics to capture their prey and defence mechanisms employed by prey. We analysed more than 5,800 prey capture events in nine SES and show that they adopt a “stalking” hunting behaviour allowing them to get as close as possible to their prey before attacking. The ability of SES to adopt stealth approach tactics, minimising the risk of initiating a flight reaction from their prey, might be a key factor in the success of this far-ranging generalist predator.
VRP Assembler: haplotype-resolved de novo assembly of diploid and polyploid genomes using quantum computing

Precision medicine’s emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the VRP assembler: a novel approach that transforms this task into a vehicle routing problem, a combinatorial optimization problem solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Google’s OR-Tools, achieving a haplotype-resolved assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to hifiasm with the phasing accuracy first approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.
Low-cost camera-based estrous tracking enables transgenesis in Peromyscus leucopus, the primary reservoir for Lyme disease

CRISPR/Cas9 technology has revolutionized the production of animal models by reducing experimental timelines, slashing costs and streamlining gene editing, leading to a rapid expansion in the number of unique models for the study of human disease and gene function. However, most non-model animals, many of which are important in cancer and aging research, remain recalcitrant to genome engineering due to our limited understanding of their reproductive biology. Many wild rodents that transmit human diseases remain particularly challenging to engineer due to low pregnancy rates, the lack of external copulatory plugs, and susceptibility to premature termination of pregnancy. Here, we present low-cost activity-based estrous tracking for the efficient generation of timed pregnant and pseudopregnant white-footed mice and extend this tracking method to both lab mice and hamsters. Leveraging this technology, we demonstrate the generation of engineered Peromyscus leucopus, the primary reservoir for Lyme disease-causing bacteria and a putative model organism for studies of aging. These tools have broad implications for biomedical research and ecological engineering.
Role of the prefrontal cortex in musical and verbal short-term memory: A functional near-infrared spectroscopy study

Auditory short-term memory (STM) is a key process in auditory cognition, with evidence for partly distinct networks subtending musical and verbal STM. The delayed matching-to-sample task (DMST) paradigm has been found suitable for comparing musical and verbal STM and for manipulating memory load. In this study, musical and verbal DMSTs were investigated with measures of activity in frontal areas with functional near-infrared spectroscopy (fNIRS): Experiment 1 compared musical and verbal DMSTs with a low-level perception task (that does not entail encoding, retention, or retrieval of information), to identify frontal regions involved in memory processes. Experiment 2 manipulated memory load for musical and verbal materials to uncover frontal brain regions showing parametric changes in activity with load and their potential differences between musical and verbal materials. A FIR model was used to deconvolute fNIRS signals across successive trials without making assumptions with respect to the shape of the hemodynamic response in a DMST. Results revealed the involvement of the dorso-lateral prefrontal cortex (dlPFC) and inferior frontal gyri (IFG), but not of the superior frontal gyri (SFG) in both experiments, in keeping with previously reported neuroimaging data (including fMRI). Experiment 2 demonstrated a parametric variation of activity with memory load in bilateral IFGs during the maintenance period, with opposite directions for musical and verbal materials. Activity in the IFGs increased with memory load for verbal sound sequences, in keeping with previous results with n-back tasks. The decreased activity with memory load observed with musical sequences is discussed in relation to previous research on auditory STM rehearsal strategies. This study highlights fNIRS as a promising tool for investigating musical and verbal STM not only for typical populations, but also for populations with developmental language disorders associated with functional alterations in auditory STM.
Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes

Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ∼800 disease reactions constituting ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactome’s pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.
Ice Shelf Response To The Change In OceanTemperature

Ice Shelf response to Global Ocean temperature change is examined. The thermohaline circulation forced by the mass and energy exchange at the ice-ocean boundary is studied with a two-dimensional model. The melting/freezing rate has been estimated. Different boundary conditions are considered. Maximum melting rate is 2m/y, it is reached at the grounding line. Accumulation of ice takes place at the ice shelf edge with rate on the order of 0.1 m/y. If the underlying water warm by 0.001 °C/y the melting rate would increase to 2.2 m/y and the freezing rate would change by 0.01 m/y. The dimension of the region with supercooled water has been estimated.
Closed-Loop Estimation of Neurostimulation Strength-Duration Curve Using Fisher Information Optimization and Comparison With Uniform and Random Methods

Background Strength-duration (SD) curve, rheobase and chronaxie parameters provide insights about the interdependence between stimulus strength and stimulus duration (or pulse width), and the neural activation dynamics such as the membrane time constant, which are useful for diagnostics and therapeutic applications. The existing SD curve estimation methods are based on open-loop uniform and/or random selection of the pulse widths. Objective To develop a method for closed-loop estimation of the SD curve. Method In the proposed method, after the selection of each pulse width through Fisher information matrix (FIM) optimization, the corresponding motor threshold (MT) is computed, the SD curve estimation is updated, and the process continues until satisfaction of a stopping rule based on the successive convergence of the SD curve parameters. The results are compared with various uniform methods where pulse widths are chosen in ascending, descending and random orders, and with methods with two and all non-uniform random pulse widths. Results 160 simulation cases were run. The FIM method satisfied the stopping rule in 144 runs, and estimated the rheobase (chronaxie in parenthesis) with an average absolute relative error (ARE) of 1.73% (2.46%), with an average of 82 samples. At this point, methods with two and all random pulse widths, and uniform methods with descending, ascending and random orders led to 5.66% (20.27%), 2.15% (4.51%), 8.57% (54.96%), 3.52% (5.45%), and 2.19% (4.40%) AREs, which are greater than that achieved through the FIM method. In all 160 runs, The FIM method has chosen the minimum and maximum pulse widths as the optimal pulse widths. Conclusions The SD curve is identifiable by acquiring the SD data from the minimum and maximum pulse widths achieved through the FIM optimization. The SD data at random or uniform pulse widths from only the vertical area or lower plateau of the curve might not result in satisfactory estimation. Significance This paper provides insights about pulse widths selection in closed-loop and open-loop SD curve estimation methods.
KCC2 Enhancers Normalize Reflex Responses and Improve Locomotor Function after Chronic Spinal Cord Injury

Within a year after a spinal cord injury (SCI), 75% of individuals develop spasticity. While normal movement relies on the ability to adjust reflexes appropriately, and on reciprocal inhibition of antagonistic muscles, spastic individuals display hyperactive spinal reflexes and involuntary muscle co-contractions. Current anti-spastic medications can suppress uncontrolled movements, but by acting on GABAergic signalling, these medications lead to severe side-effects and weakened muscle force, making them incompatible with activity-based therapies. We have previously shown that pharmacologically enhancing activity of KCC2, a chloride cotransporter, reduces signs of spasticity in anesthetized chronic SCI rats. Here, we examine the effect of enhancing KCC2 in awake animals, using a battery of tests to assess multiple reflex pathways required for normal movement as well as locomotor function. Sprague-Dawley rats were implanted with chronic EMG electrodes bilaterally in ankle flexor and ankle extensor muscles and received a complete spinal transection at T12. Four weeks following SCI, the stretch reflex, the non-nociceptive cutaneous reflex pathway, the flexor withdrawal reflex, and the crossed-extensor reflex pathway as well as locomotor function were evaluated before and after receiving the KCC2 enhancer, CLP290. Our results show that enhancing KCC2 activity normalizes reflex responses in multiple pathways and reduces muscle co-contraction without weakening motor output, thereby improving stepping ability. This work reveals the substantial potential for KCC2 enhancers as a novel antispastic treatment.
Spies of the deep: an animal-borne active sonar and bioluminescence tag to characterise mesopelagic prey size and behaviour in distinct oceanographic domains

Mesopelagic fish, a central component of marine trophic networks, play a fundamental role in marine ecosystems. However, as they live in highly inaccessible environments, little information is currently available on their ecology, especially on the influence of oceanographic parameters on their distribution. The emergence of biologging technologies has made it possible to use deep-diving predators as bio-samplers of their environment in under-sampled regions. In this work, we deployed an innovati ve miniaturised sonar tag that combines active acoustics with high-resolution GPS, pressure, movement and light sensors on Southern elephant seals, a deep-diving predator feeding on mesopelagic prey. Seals were also equipped with oceanographic tags, allowing us to explore the functional relationships between oceanographic parameters, distribution and ecology of mesopelagic prey targeted by seals and the seals’ foraging behaviour. We highlighted strong vertical differences in prey characteristics and behaviour, with larger, more evasive and less bioluminescent prey in deeper waters. Moreover, prey encountered in warmer waters were found deeper, were more evasive and displayed a more marked diel vertical migration behaviour compared to prey encountered in colder waters, suggesting that prey accessibility and characteristics differ according to oceanographic domains. This study highlights the usefulness of the sonar-bioluminescence tag to infer mesopelagic prey distribution and habitat when deployed on deep-diving predators such as elephant seals.
Sex differences in aggression and its neural substrate in a cichlid fish

Aggression is ubiquitous among social species and functions to maintains social dominance hierarchies. The African cichlid fish Astatotilapia burtoni is an ideal study species for studying aggression due to their unique and flexible dominance hierarchy. However, female aggression in this species and the neural mechanisms of aggression in both sexes is not well understood. To further understand the potential sex differences in aggression in this species, we characterized aggression in male and female A. burtoni in a mirror assay. We then quantified neural activation patterns in brain regions of the social behavior network (SBN) to investigate if differences in behavior are reflected in the brain with immunohistochemistry by detecting the phosphorylated ribosome marker phospho-S6 ribosomal protein (pS6), a marker for neural activation. We found that A. burtoni perform both identical and sex-specific aggressive behaviors in response to a mirror assay. We observed sex differences in pS6 immunoreactivity in the Vv, a homolog of the lateral septum in mammals. Males but not females had higher ps6 immunoreactivity in the ATn after the aggression assay. The ATn is a homolog of the ventromedial hypothalamus in mammals, which is strongly implicated in the regulation of aggression in males. Several regions also have higher pS6 immunoreactivity in negative controls than fish exposed to a mirror, implicating a role for inhibitory neurons in suppressing aggression until a relevant stimulus is present. Male and female A. burtoni display both similar and sexually dimorphic behavioral patterns in aggression in response to a mirror assay. There are also sex differences in the corresponding neural activation patterns in the SBN. In mirror males but not females, the ATn clusters with the POA, revealing a functional connectivity of these regions that is triggered in an aggressive context in males. These findings suggest that distinct neural circuitry underlie aggressive behavior in male and female A. burtoni, serving as a foundation for future work investigating the molecular and neural underpinnings of sexually dimorphic behaviors in this species to reveal fundamental insights into understanding aggression.
Do Domain-Specific Protein Language Models Outperform General Models on Immunology-Related Tasks?

Deciphering the antigen recognition capabilities by T cell and B cell receptors (antibodies) is essential for advancing our understanding of adaptive immune system responses. In recent years, the development of protein language models (PLMs) has facilitated the development of bioinformatic pipelines where complex amino acid sequences are transformed into vectorized embeddings, which are then applied to a range of downstream analytical tasks. With their success, we have witnessed the emergence of domain-specific PLMs tailored to specific proteins, such as immune receptors. Domain-specific models are often assumed to possess enhanced representation capabilities for targeted applications, however, this assumption has not been thoroughly evaluated. In this manuscript, we assess the efficacy of both generalist and domain-specific transformer-based embeddings in characterizing B and T cell receptors. Specifically, we assess the accuracy of models that leverage these embeddings to predict antigen specificity and elucidate the evolutionary changes that B cells undergo during an immune response. We demonstrate that the prevailing notion of domain-specific models outperforming general models requires a more nuanced examination. We also observe remarkable differences between generalist and domain-specific PLMs, not only in terms of performance but also in the manner they encode information. Finally, we observe that the choice of the size and the embedding layer in PLMs are essential model hyperparameters in different tasks. Overall, our analyzes reveal the promising potential of PLMs in modeling protein function while providing insights into their information-handling capabilities. We also discuss the crucial factors that should be taken into account when selecting a PLM tailored to a particular task.
A neural modeling approach to study mechanisms underlying the heterogeneity of visual spatial frequency sensitivity in schizophrenia

Patients with schizophrenia exhibit abnormalities in spatial frequency sensitivity, and it is believed that these abnormalities indicate more widespread dysfunction and dysregulation of bottom-up processing. The early visual system, including the first-order Lateral Geniculate Nucleus of the thalamus (LGN) and the primary visual cortex (V1), are key contributors to spatial frequency sensitivity. Medicated and unmedicated patients with schizophrenia exhibit contrasting changes in spatial frequency sensitivity, thus making it a useful probe for examining potential effects of the disorder and antipsychotic medications in neural processing. We constructed a parameterized, rate-based neural model of on-center/off-surround neurons in the early visual system to investigate the impacts of changes to the excitatory and inhibitory receptive field subfields. By incorporating changes in both the excitatory and inhibitory subfields that are associated with pathophysiological findings in schizophrenia, the model successfully replicated perceptual data from behavioral/functional studies involving medicated and unmedicated patients. Among several plausible mechanisms, our results highlight the dampening of excitation and/or increase in the spread and strength of the inhibitory subfield in medicated patients and the contrasting decreased spread and strength of inhibition in unmedicated patients. Given that the model was successful at replicating results from perceptual data under a variety of conditions, these elements of the receptive field may be useful markers for the imbalances seen in patients with schizophrenia.
Strategic Analyses of Applying an Option-Based Hedging Mechanism in Parallel Airline Alliances

The extant literature proposes an option-based hedging mechanism for airlines in a parallel alliance to transfer bumped passengers to their alliance partner’s flight. This paper extends this literature by conducting strategic analyses and developing a two-stage simulation-based algorithm to identify the best strategy for applying the hedging mechanism. Specifically, the best strategy refers to the best number of options for the allied carriers to transact. The authors show that there exists a robust result of the best number of options, and it is obtained under the objective of maximizing alliance-wide revenue. The result of this paper can provide direct guidance to the management of airlines on the best practice of hedging ex-post overbooking risks and matching supply with demand.
LLM4SGG: Large Language Model for Weakly Supervised Scene Graph Generation

Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations. In this regard, studies on WSSGG have utilized image captions to obtain unlocalized triplets while primarily focusing on grounding the unlocalized triplets over image regions. However, they have overlooked the two issues involved in the triplet formation process from the captions: 1) Semantic over-simplification issue arises when extracting triplets from captions, where fine-grained predicates in captions are undesirably converted into coarse-grained predicates, resulting in a long-tailed predicate distribution, and 2) Low-density scene graph issue arises when aligning the triplets in the caption with entity/predicate classes of interest, where many triplets are discarded and not used in training, leading to insufficient supervision. To tackle the two issues, we propose a new approach, i.e., Large Language Model for weakly-supervised SGG (LLM4SGG), where we mitigate the two issues by leveraging the LLM's in-depth understanding of language and reasoning ability during the extraction of triplets from captions and alignment of entity/predicate classes with target data. To further engage the LLM in these processes, we adopt the idea of Chain-of-Thought and the in-context few-shot learning strategy. To validate the effectiveness of LLM4SGG, we conduct extensive experiments on Visual Genome and GQA datasets, showing significant improvements in both Recall@K and mean Recall@K compared to the state-of-the-art WSSGG methods. A further appeal is that LLM4SGG is data-efficient, enabling effective model training with a small amount of training images.
Soft-output (SO) GRAND and long, low rate codes to outperform 5 LDPCs

We establish that a large and flexible class of long, high redundancy error correcting codes can be efficiently and accurately decoded with guessing random additive noise decoding (GRAND). Performance evaluation demonstrates that it is possible to construct simple concatenated codes that outperform low-density parity-check (LDPC) codes found in the 5G New Radio standard. The concatenated structure enables many desirable features, including: low-complexity hardware-friendly encoding and decoding; high levels of flexibility in length and rate through modularity; and high levels of parallelism in encoding and decoding that enable low latency. Central to this is the development of a method through which any soft-input (SI) GRAND algorithm can provide soft-output (SO) in the form of an accurate a-posteriori estimate of the likelihood that a decoding is correct or, in the case of list decoding, the likelihood that each element of the list is correct. The key distinguishing feature of SOGRAND in comparison to other methods is the provision of an estimate that the correct decoding has not been found, even when providing a single decoding. That per-block SO can be converted into accurate per-bit SO by a weighted sum that includes a term for the SI. Crucially, implementing SOGRAND adds negligible computation and memory to the existing decoding process, and using it results in a practical alternative to LDPC codes.