How our authors are using AI tools in manuscript writing.

Scientific writing is an essential skill for researchers to publish their work in respected peer-reviewed journals. While using AI-assisted tools can help researchers with spelling checks, grammar corrections, and even rephrasing of paragraphs to improve the language and meet journal standards, unethical use of these tools may raise research integrity concerns during this process. In this piece, three authors share their thoughts on three questions: how do you use AI tools ethically during manuscript writing? What benefits and risks do you believe AI tools will bring to scientific writing? Do you have any recommendations for better policies regulating AI tools' use in scientific writing?

Hopfield and Hinton's neural network revolution and the future of AI.

In this opinion piece, the authors, from the fields of artificial intelligence (AI) and psychology, reflect on how the foundational discoveries of Nobel laureates Hopfield and Hinton have influenced their research. They also discuss emerging directions in AI and the challenges that lie ahead for neural networks and machine learning.

Enhancing Automatic Placenta Analysis through Distributional Feature Recomposition in Vision-Language Contrastive Learning.

The placenta is a valuable organ that can aid in understanding adverse events during pregnancy and predicting issues post-birth. Manual pathological examination and report generation, however, are laborious and resource-intensive. Limitations in diagnostic accuracy and model efficiency have impeded previous attempts to automate placenta analysis.

Incorporating simulated spatial context information improves the effectiveness of contrastive learning models.

Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning. We present a unique approach, termed environmental spatial similarity (ESS), that complements existing contrastive learning methods.

Dyslexia Data Consortium Repository: A Data Sharing and Delivery Platform for Research.

Specific learning disability of reading, or dyslexia, affects 5-17% of the population in the United States. Research on the neurobiology of dyslexia has included studies with relatively small sample sizes across research sites, thus limiting inference and the application of novel methods, such as deep learning. To address these issues and facilitate open science, we developed an online platform for data-sharing and advanced research programs to enhance opportunities for replication by providing researchers with secondary data that can be used in their research (https://www.

Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting.

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images.

Bodily expressed emotion understanding through integrating Laban movement analysis.

Bodily expressed emotion understanding (BEEU) aims to automatically recognize human emotional expressions from body movements. Psychological research has demonstrated that people often move using specific motor elements to convey emotions. This work takes three steps to integrate human motor elements to study BEEU.

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion: Drawing Insights From Psychology, Engineering, and the Arts, This Article Provides a Comprehensive Overview of the Field of Emotion Analysis in Visual Media and Discusses the Latest Research, Systems, Challenges, Ethical Implications, and Potential Impact of Artificial Emotional Intelligence on Society.

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion," coupled with the inherently subjective nature of emotions and their intricate nuances.

A Machine Learning Paradigm for Studying Pictorial Realism: How Accurate are Constable's Clouds?

The British landscape painter John Constable is considered foundational for the Realist movement in 19th-century European painting. Constable's painted skies, in particular, were seen as remarkably accurate by his contemporaries, an impression shared by many viewers today. Yet, assessing the accuracy of realist paintings like Constable's is subjective or intuitive, even for professional art historians, making it difficult to say with certainty what set Constable's skies apart from those of his contemporaries.

BBox-Guided Segmentor: Leveraging expert knowledge for accurate stroke lesion segmentation using weakly supervised bounding box prior.

Stroke is one of the leading causes of death and disability in the world. Despite intensive research on automatic stroke lesion segmentation from non-invasive imaging modalities including diffusion-weighted imaging (DWI), challenges remain such as a lack of sufficient labeled data for training deep learning models and failure in detecting small lesions. In this paper, we propose BBox-Guided Segmentor, a method that significantly improves the accuracy of stroke lesion segmentation by leveraging expert knowledge.

Deep learning classification of reading disability with regional brain volume features.

Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.

Automated 3D segmentation of guard cells enables volumetric analysis of stomatal biomechanics.

Automating the three-dimensional (3D) segmentation of stomatal guard cells and other confocal microscopy data is extremely challenging due to hardware limitations, hard-to-localize regions, and limited optical resolution. We present a memory-efficient, attention-based, one-stage segmentation neural network for 3D images of stomatal guard cells. Our model is trained end to end and achieved expert-level accuracy while leveraging only eight human-labeled volume images.

DeepStroke: An efficient stroke screening framework for emergency rooms with multimodal adversarial deep learning.

In an emergency room (ER) setting, stroke triage or screening is a common challenge. A quick CT is usually done instead of MRI due to MRI's slow throughput and high cost. Clinical tests are commonly referred to during the process, but the misdiagnosis rate remains high.

SSAS: Spatiotemporal Scale Adaptive Selection for Improving Bias Correction on Precipitation.

By utilizing physical models of the atmosphere collected from the current weather conditions, the numerical weather prediction model developed by the European Centre for Medium-range Weather Forecasts (ECMWF) can provide the indicators of severe weather such as heavy precipitation for an early-warning system. However, the performance of precipitation forecasts from ECMWF often suffers from considerable prediction biases due to the high complexity and uncertainty for the formation of precipitation. The bias correcting on precipitation (BCoP) was thus utilized for correcting these biases via forecasting variables, including the historical observations and variables of precipitation, and these variables, as predictors, from ECMWF are highly relevant to precipitation.

Multi-region saliency-aware learning for cross-domain placenta image segmentation.

We propose a multi-region saliency-aware learning (MSL) method for cross-domain placenta image segmentation. Unlike most existing image-level transfer learning methods that fail to preserve the semantics of paired regions, our MSL incorporates the attention mechanism and a saliency constraint into the adversarial translation process, which can realize multi-region mappings in the semantic level. Specifically, the built-in attention module serves to detect the most discriminative semantic regions that the generator should focus on.

AI-PLAX: AI-based placental assessment and examination using photos.

Post-delivery analysis of the placenta is useful for evaluating health risks of both the mother and baby. In the U.S.

Clinical utility of multigene analysis in over 25,000 patients with neuromuscular disorders.

Objective: Molecular genetic testing for hereditary neuromuscular disorders is increasingly used to identify disease subtypes, determine prevalence, and inform management and prognosis, and although many small disease-specific studies have demonstrated the utility of genetic testing, comprehensive data sets are better positioned to assess the complexity of genetic analysis.

Methods: Using high depth-of-coverage next-generation sequencing (NGS) with simultaneous detection of sequence variants and copy number variants (CNVs), we tested 25,356 unrelated individuals for subsets of 266 genes.

Results: A definitive molecular diagnosis was obtained in 20% of this cohort, with yields ranging from 4% among individuals with congenital myasthenic syndrome to 33% among those with a muscular dystrophy.

ARBEE: Towards Automated Recognition of Bodily Expression of Emotion in the Wild.

Humans are arguably innately prepared to comprehend others' emotional expressions from subtle body movements. If robots or computers can be empowered with this capability, a number of robotic applications become possible. Automatically recognizing human bodily expression in unconstrained situations, however, is daunting given the incomplete understanding of the relationship between emotional expressions and body movements.

PaDNet: Pan-Density Crowd Counting.

Crowd counting is a highly challenging problem in computer vision and machine learning. Most previous methods have focused on consistent density crowds, i.e.

Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data.

We proposed a probabilistic approach to joint modeling of participants' and humans' in crowdsourced affective studies. Reliability measures how likely a subject will respond to a question seriously; and regularity measures how often a human will agree with other seriously-entered responses coming from a targeted population. Crowdsourcing-based studies or experiments, which rely on human self-reported affect, pose additional challenges as compared with typical crowdsourcing studies that attempt to acquire labels of objects.

The stomatal flexoskeleton: how the biomechanics of guard cell walls animate an elastic pressure vessel.

In plants, stomatal guard cells are one of the most dynamic cell types, rapidly changing their shape and size in response to environmental and intrinsic signals to control gas exchange at the plant surface. Quantitative and systematic knowledge of the biomechanical underpinnings of stomatal dynamics will enable strategies to optimize stomatal responsiveness and improve plant productivity by enhancing the efficiency of photosynthesis and water use. Recent developments in microscopy, mechanical measurements, and computational modeling have revealed new insights into the biomechanics of stomatal regulation and the genetic, biochemical, and structural origins of how plants achieve rapid and reliable stomatal function by tuning the mechanical properties of their guard cell walls.

Mechanical Effects of Cellulose, Xyloglucan, and Pectins on Stomatal Guard Cells of .

Stomata function as osmotically tunable pores that facilitate gas exchange at the surface of plants. Stomatal opening and closure are regulated by turgor changes in guard cells that result in mechanically regulated deformations of guard cell walls. However, how the molecular, architectural, and mechanical heterogeneities that exist in guard cell walls affect stomatal dynamics is unclear.

Balancing Strength and Flexibility: How the Synthesis, Organization, and Modification of Guard Cell Walls Govern Stomatal Development and Dynamics.

Guard cells are pairs of epidermal cells that control gas diffusion by regulating the opening and closure of stomatal pores. Guard cells, like other types of plant cells, are surrounded by a three-dimensional, extracellular network of polysaccharide-based wall polymers. In contrast to the walls of diffusely growing cells, guard cell walls have been hypothesized to be uniquely strong and elastic to meet the functional requirements of withstanding high turgor and allowing for reversible stomatal movements.

Development and validation of Image Stimuli for Emotion Elicitation (ISEE): A novel affective pictorial system with test-retest repeatability.

The aim of this study was to develop a novel set of pictorial stimuli for emotion elicitation. The Image Stimuli for Emotion Elicitation (ISEE), are the first set of stimuli for which there was an unbiased initial selection method and with images specifically selected for high retest correlation coefficients and high agreement across time. In order to protect against a researcher's subjective bias in screening initial pictures, we crawled 10,696 images from the biggest image hosting website (Flickr.

POLYGALACTURONASE INVOLVED IN EXPANSION3 Functions in Seedling Development, Rosette Growth, and Stomatal Dynamics in .

Plant cell separation and expansion require pectin degradation by endogenous pectinases such as polygalacturonases, few of which have been functionally characterized. Stomata are a unique system to study both processes because stomatal maturation involves limited separation between sister guard cells and stomatal responses require reversible guard cell elongation and contraction. However, the molecular mechanisms for how stomatal pores form and how guard cell walls facilitate dynamic stomatal responses remain poorly understood.