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We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are often only partially available when decisions need to be made. We propose Latent World Recovery (LWR), a framework built on two key ideas: (i) modality-specific embeddings from different modalities are aligned in a shared latent space, and (ii) a unified representation is constructed by fusing only the embeddings of the modalities that are actually available at both training and inference time. Rather than imputing missing modalities or requiring a fixed modality set, LWR treats each modality as a partial perception of an underlying latent state and performs availability-aware representation learning directly from the observed modalities. This combination of neighbor-based latent alignment and availability-aware modality fusion enables robust multimodal prediction under partial observation, while avoiding error propagation from explicit reconstruction of missing modalities. We evaluate the proposed framework on real-world incomplete multi-omics benchmarks and demonstrate that it provides an effective approach to downstream tasks such a

We report several technical approaches that significantly improve the performance of a vapor-cell atomic electrometer operating in the quasi-DC frequency domain ($\ll$ 1 kHz). With a very small active volume of approximately 11 mm$^3$ inside the vapor cell, we demonstrated a noise floor for electric field (E-field) sensitivity ranging from 0.2 to 7.7 mV/m$\sqrt{\rm Hz}$ for a frequency band of 1--100 Hz. Our work utilizes only a bare vapor cell for electrometry, without any metal parts or electrodes, to ensure minimal distortion of the measured E-field and to minimize the effective sensing volume for high spatial resolution. The E-field-sensitive atomic state (Rydberg state) is excited and read out optically, maximizing the simplicity of the system design and enabling the miniaturization of quasi-DC E-field sensors for potential applications, such as diagnostics of electronics without physical contact, communications in and below the super-low frequency (SLF) band, proximity detection, remote activity surveillance, tracing charge signatures, and research in bioscience and geoscience.

Adaptive AI ethics instruction in graduate research training benefits from intake measures that reflect differences in prior LLM experience. Prior coursework or workshop attendance is an obvious candidate, but it is not clear whether it is associated with pre-instruction ratings on key AI perception items. We compare three candidate intake features, self-reported usage frequency, self-rated LLM familiarity, and prior AI education, across five baseline perception outcomes in 93 bioscience graduate and postdoctoral trainees enrolled in a required research ethics course. Usage frequency shows Holm-corrected associations with all five outcomes, self-rated familiarity with three, and prior AI education with none. A threshold-like pattern at the lower end of the scale is most visible for training interest and accuracy trust rather than appearing as a uniform gradient across all five outcomes. In a short intake survey, reported LLM use is more consistently associated with these perceptions than prior coursework or workshops, with self-rated familiarity serving as a secondary indicator. These results suggest that simple pre-instruction behavioral signals can inform lightweight intake profi

We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage the multimodal capabilities of Gemini to produce embeddings for arbitrary combinations of interleaved inputs across all these modalities that generalize well across a wide variety of tasks. Applying large-scale contrastive learning in a multi-task multi-stage training setup, we achieve state-of-the-art performance on key embedding benchmarks including unimodal, cross-modal, and multimodal retrieval spanning a diverse set of tasks. We show that our embedding model demonstrates strong performance (with a score of 62.9 R@1 on MSCOCO, 68.8 NDCG@10 on Vatex, 69.9 on MTEB multilingual and 84.0 on MTEB Code) across a variety of tasks surpassing the performance of specialized models. These unified capabilities make Gemini Embedding 2 a promising candidate for downstream use cases such as RAG, recommendation and search. Furthermore, its robust zero-shot performance across distinct fields - from astronomy and bioscience to fine arts and the culinary arts - establishes it as a highly reliable, out-of-the-box represen

In high-dimensional genomic data, the curse of dimensionality (d >> n) and limited sampling make feature selection inherently unstable - a critical barrier to biomarker discovery. We introduce StackFeat, an iterative algorithm that accumulates two statistics across repeated cross-validation: signed coefficients (measuring effect strength and direction) and selection frequencies (estimating selection probability). Only features ranking highly by both criteria are retained. On a COVID-19 miRNA dataset (GSE240888), StackFeat identified a stable 5-miRNA signature from 332 features (98.5% reduction), achieving AUC 0.922, significantly outperforming the benchmark 9-gene set (AUC 0.907, p = 0.0016). The signature includes hsa-miR-150-5p, a marker implicated in both COVID-19 survival and Dengue infection. This dual-criterion approach provides convergence guarantees absent in single-criterion methods, enabling discovery of known biomarkers, novel candidates, and previously unknown relationships. Keywords: marker selection, feature selection, bioinformatics, dimensionality reduction, robust algorithm, stacking, miRNA, COVID-19

A consensus tree is a phylogenetic tree that synthesizes a given collection of phylogenetic trees, all of which share the same leaf labels but may have different topologies, typically obtained through bootstrapping. Our research focuses on creating a consensus tree from a collection of phylogenetic trees, each detailed with branch-length data. We integrate branch lengths into the consensus to encapsulate the progression rate of genetic mutations. However, traditional consensus trees, such as the strict consensus tree, primarily focus on the topological structure of these trees, often neglecting the informative value of branch lengths. This oversight disregards a crucial aspect of evolutionary study and highlights a notable gap in traditional phylogenetic approaches. In this paper, we extend \textit{PrimConsTree}\footnote{A preliminary version of this article was presented at \emph{the Fifteenth International Conference on Bioscience, Biochemistry, and Bioinformatics (ICBBB~2025)}~(reference~\cite{torquet2005icbbb}).}, a graph-based method for constructing consensus trees. This algorithm incorporates topological information, edge frequency, clade frequency, and branch length to cons

Understanding the changing structure of science over time is essential to elucidating how science evolves. We develop diachronic embeddings of scholarly periodicals to quantify "semantic changes" of periodicals across decades, allowing us to track the evolution of research topics and identify rapidly developing fields. By mapping periodicals within a physical-life-health triangle, we reveal an evolving interdisciplinary science landscape, finding an overall trend toward specialization for most periodicals but increasing interdisciplinarity for bioscience periodicals. Analyzing a periodical's trajectory within this triangle over time allows us to visualize how its research focus shifts. Furthermore, by monitoring the formation of local clusters of periodicals, we can identify emerging research topics such as AIDS research and nanotechnology in the 1980s. Our work offers novel quantification in the science of science and provides a quantitative lens to examine the evolution of science, which may facilitate future investigations into the emergence and development of research fields.

Computing the similarity between two DNA sequences is of vital importance in bioscience, yet it can be computationally expensive on classical hardware. For example, the edit distance with move operations (EDM), a DNA similarity measure of interest in biology, is proven to be NP-Complete to compute exactly on classical hardware. Recently, applied quantum algorithms have been anticipated to offer potential advantages over classical approaches. In this paper, we propose a novel variational quantum kernel model served as a surrogate model for estimating similarity between DNA sequences defined by EDM. Since the EDM metric exhibits a pairwise permutation-insensitive property, we incorporate a permutation-invariant structure into the variational quantum kernel to approximate this symmetry. Furthermore, to encode the four nucleotide bases as quantum states, we introduce a theoretically motivated encoding scheme based on symmetric informationally complete positive operator-valued measure (SIC-POVM) states. This encoding ensures mutual equivalence among bases, as each pair of symbols is mapped to quantum states that are equidistant on the Bloch sphere. We experimentally show that, equipped

Student beliefs in introductory physics courses can influence their course outcomes and retention in STEM disciplines and future career aspirations. This study used survey data from 501 students in the first of two-semester algebra-based introductory physics courses primarily taken by bioscience majors, in which women make up approximately 65% of the class. We investigated how the learning environment including perceived recognition, peer interaction, and sense of belonging correlate with students' physics outcomes, including their physics self-efficacy, interest, and identity. We found that in general, women had lower physics beliefs than men and the learning environment plays a major role in explaining student outcomes. We also found that perceived recognition played an important role in predicting students' physics identity and students' sense of belonging played an important role in predicting students' physics self-efficacy in the first algebra-based introductory physics course investigated. These findings can be useful to contemplate strategies to create an equitable and inclusive learning environment to help all students to excel in these physics courses.

Advances in molecular technologies underlie an enormous growth in the size of data sets pertaining to biology and biomedicine. These advances parallel those in the deep learning subfield of machine learning. Components in the differentiable programming toolbox that makes deep learning possible are allowing computer scientists to address an increasingly large array of problems with flexible and effective tools. However many of these tools have not fully proliferated into the computational biology and bioinformatics fields. In this perspective we survey some of these advances and highlight exemplary examples of their utilization in the biosciences, with the goal of increasing awareness among practitioners of emerging opportunities to blend expert knowledge with newly emerging deep learning architectural tools.

Causal learning from data has received much attention recently. Bayesian networks can be used to capture causal relationships. There, one recovers a weighted directed acyclic graph in which random variables are represented by vertices, and the weights associated with each edge represent the strengths of the causal relationships between them. This concept is extended to capture dynamic effects by introducing a dependency on past data, which may be captured by the structural equation model. This formalism is utilized in the present contribution to propose a score-based learning algorithm. A mixed-integer quadratic program is formulated and an algorithmic solution proposed, in which the pre-generation of exponentially many acyclicity constraints is avoided by utilizing the so-called branch-and-cut (``lazy constraint'') method. Comparing the novel approach to the state-of-the-art, we show that the proposed approach turns out to produce more accurate results when applied to small and medium-sized synthetic instances containing up to 80 time series. Lastly, two interesting applications in bioscience and finance, to which the method is directly applied, further stress the importance of de

Studying the dynamics of small-scale beams with attached particles is crucial for sensing applications in various fields, such as bioscience, material science, energy storage devices, and environmental monitoring. Here, a stress-driven nonlocal model is presented for the free transverse vibration of small-scale beams carrying multiple masses taking into account the eccentricity of the masses relative to the beam axis. The results show excellent agreement with the experimental and numerical data in the literature. New insights into the frequency shifts and mode shapes of the first four vibrational modes of stress-driven nonlocal beams with up to three attached particles are presented. The study investigates the inverse problem of detecting the location and mass of an attached particle based on natural frequency shifts. The knowledge acquired from the present study provides valuable guidance for the design and analysis of ultrasensitive mechanical mass sensors.

We have previously described the reformed introductory physics course, Collaborative Learning through Active Sense-Making in Physics (CLASP), for bioscience students at a large public research one university (Original University) and presented evidence that the course was more successful and more equitable than the course it replaced by several measures. Now we compare the original success of CLASP with an implementation at a second institution. We find that the original results hold at another institution despite some changes to the original curriculum and a somewhat different student population. We find that students who take CLASP are 1) less likely to drop, 2) less likely to fail, and 3) do as well in later coursework when compared to students who took the courses that CLASP replaced, even if that coursework is not similarly reformed. We find the above items to be independently true for historically marginalized students and remarkably, also find that 4) marginalized students who take CLASP are more likely to graduate from a STEM field. We use a course deficit model perspective to examine these results, and discuss some of the factors that may have contributed to this success.