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Understanding how students regulate their learning, especially in open-ended learning environments, requires looking at both their thinking processes and emotional states. Emotions like excitement, engagement, boredom, frustration, and confusion play a big role in learning, but it’s hard to detect these feelings through facial expressions in middle school students, mainly because there aren’t enough relevant databases. This study introduces a new method using the EmoNet framework, which has been enhanced with advanced self-attention networks, to better detect and understand these learning-related emotions. We studied the emotional and thinking patterns of 41 middle school students in an open-ended learning environment and found clear emotional patterns that were strongly connected to their performance in various cognitive tasks. By creating a dataset from ten students, our model achieved an accuracy of 85%, which is a significant improvement over previous models. These findings open the door to creating future educational tools that can adjust to both students’ emotions and cognitive states, helping to improve their overall learning experiences and academic results.��

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Bhatt, Neel S.; Harris, Andrew C.; Gorfinkel, Lev; Ibanez, Katarzyna; Tkaczyk, Eric R.; Mitchell, Sandra A.; Albuquerque, Stacey; Schechter, Tal; Pavletic, Steven; Duncan, Christine N.; Rotz, Seth J.; Williams, Kirsten; Carpenter, Paul A.; Cuvelier, Geoffrey D.E. ��Transplantation and Cellular Therapy, 2025,��.��

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Chronic graft-versus-host disease (cGVHD) is a major complication that can occur after children and adolescents undergo hematopoietic cell transplantation (HCT), a treatment for certain cancers and blood disorders. However, there is limited information on how cGVHD affects long-term survival and quality of life in pediatric patients. This is an important issue since children who receive HCT have a longer life expectancy compared to adults. To address this gap in knowledge, experts in pediatric cGVHD and late effects of HCT formed a group called RESILIENT, which stands for Research and Education towards Solutions for Late effects to Innovate, Excel, and Nurture after cGVHD. The group aimed to better understand how cGVHD affects children after transplant, improve care for long-term survivors, and develop a research plan for the future.��

The task force split into four working groups, each focusing on a specific area: (1) understanding the stages of cGVHD and how it affects the ability to safely reduce or stop treatment, (2) organ damage and immune system recovery, (3) how cGVHD and its treatments impact growth and development, and (4) the psychological health of patients. These groups met in advance of a large medical conference to discuss their findings and seek feedback. The first manuscript from the group focuses on the challenges clinicians face when trying to determine when to stop or reduce the treatment for cGVHD. One major issue is distinguishing between active cGVHD (which needs treatment) and quiescent cGVHD (which is less active and might not need treatment). To address these challenges, the group recommends new ways to categorize long-term outcomes of cGVHD and offers practical guidance for clinicians on how to monitor and manage these patients over the long term, including when to adjust treatment and how to use supportive care.��

Figure 1.��The long-term goal of pediatric allogeneic HCT is to achieve not only disease eradication but also normal organ function, optimal growth and development, improve functional status, lessen symptom burden and promote high quality of life. Chronic GVHD can be a significant barrier to these goals. The RESILIENT consortium examines various aspects of chronic GVHD and long-term survivorship to achieve resilience for infants, children, and adolescents requiring allogeneic HCT.��

Tuft cells are rare cells in the lining of the intestine that help detect and respond to substances in the gut. These cells have a unique “tuft” of fingerlike structures at their top, made of tightly organized bundles of actin (a structural protein) and specialized proteins arranged in specific patterns. Beneath the tuft, these actin bundles align with a network of microtubules (another structural component), creating a sturdy framework. This structure supports the cell’s ability to transport materials and perform its sensing functions, which are essential for maintaining gut health.��

Figure 1. ��

The tuft cell “tuft” contains over a hundred of core actin bundles. (A)��Cartoon depicting nomenclature for core actin bundles in tuft cells.��(B)��Inverted single channel, single z-slice, Airyscan image of a lateral tissue section of tuft cell. Actin marked with phalloidin (scalebar = 5 µm, zoom = 1 µm).��(C)��Frequency diagram of core bundle length as depicted in B (n��= 27 tuft cells over 3 mice).��(D)��Max intensity projection (MaxIP) spinning disk confocal (SDC) image using en face whole-mount tissue of a tuft cell captured beneath the apical membrane with phalloidin staining (scalebar = 2 µm).��(E)��Frequency diagram of the number of core actin bundles in tuft cells (n��= 81 tuft cells over 3 mice).��(F)��Using lateral sections of frozen tissue, linescans of phalloidin intensity were drawn from the tip to the base of core actin bundles. Raw linescan data is shown in gray with a curve fit to data shown in green. (n��= 218 bundles over 3 mice).��(G)��Single z-slice SDC image of en face whole-mount tissue (scalebar = 5 µm).��(H)��Simple linear regression measuring cell or bundle area (as shown in G) at 1.5, 3, and 4.5 µm beneath the apical surface. Slope bundle area = 2.013. Slope cell area = 5.412 (n��= 54 tuft cells over 3 mice).��(I)��3D projection of core actin bundles in a tuft cell using Trainable WEKA segmentation.��(J)��Pitch of individual actin bundles relative to the long axis of the cell (90° = vertical orientation) using WEKA segmented data from I (n��= 35 tuft cells over 3 mice).��(K)MaxIP SDC image of en face whole-mount tissue section with showing sub-apical core bundles (scalebar = 2 µm, inset box scalebar = 500 nm).��(L)��Frequency diagram of angle measurements between neighboring bundles as shown in zoom inset K (n��= 3,453 measurements made in 25 tuft cells over 3 mice).��(M)��MaxIP SDC image of en face whole-mount tissue with ZO-1 (magenta) and actin marked by phalloidin (green). Arrows point to tuft cells. (scalebar = 5 µm).��(N)��MaxIP SDC image of en face frozen tissue section with NM2A (magenta) and actin marked with phalloidin (green) (scalebar = 5 µm).��(O)��Above: single z-slice image of a lateral frozen tissue section with NM2A (magenta) and actin marked with phalloidin (green) (scalebar = 5 µm). Below: linescans measuring the intensity of NM2A across the apical surface of tuft cells as shown by the pink arrow in the image above (n��= 33 tuft cells over 3 mice).��

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Understanding when and how cells change over time is crucial for studying biology. Traditionally, this involves continuous observation, but another method uses permanent genetic changes, like mutations, as “timestamps” to track events after they happen. Researchers developed a “molecular clock” using CRISPR technology to record the timing of cell changes along with information about cell type and lineage (family tree). This approach revealed the timing of specific cell growth during mouse development, unexpected connections between different cell types, and new states of epithelial cells based on their genetic history.��

The method was also applied to study the early stages of colon cancer in mice and humans. By analyzing 418 human precancerous polyps, researchers discovered that 15–30% originated from multiple normal cells rather than a single cell. This innovative framework combines genetic “timestamps” with single-cell analysis, providing new insights into the timing and origins of development and diseases like cancer.��

Fig. 1: Optimization of a multipurpose, single-cell capture platform.

a, gRNA capture schematic for the NSC–seq platform. The target site of gRNA scaffold anneals to NSC–seq capture sequence (CS) with a cellular barcode (blue) and unique molecular identifier (green). An additional sequence (grey) is added to the 3′-end of the complementary DNA via template switching during reverse transcription to enable downstream library amplification. This gRNA capture approach is compatible with any type of gRNA (single-guide RNA (sgRNA), hgRNA and self-targeting guide RNA) that contains the target site sequence in the scaffold (Extended Data Fig.��).��b, Cas9-induced mutation recovery by direct hgRNA capture as compared with mutations detected in DNA of the same samples.��c, gRNA capture efficiency by NSC–seq assessed in an experiment in which all cells from a drug-selected cell line should contain sgRNAs.��d, Comparative transcriptome capture efficiency between standard inDrops and NSC–seq experiments.��e, NSC–seq experiments performed on developmentally barcoded whole embryos in which Cas9 is constitutively expressed (top). Accumulative mutations on homing barcode regions increase over time (bottom),.��f, Average mutation density over embryonic time points (Extended Data Fig.��). Black dots represent geometric mean for each time point, and��P values are derived from unpaired two-tailed��t-tests.��g, Somatic mtVar calling from mitochondrial RNA (mtRNA) (top). Approach to filtering informative mtVars for lineage tracking using hgRNA mutations as ground truth (bottom) (Extended Data Fig.��).��h, Number of somatic mtVars per cell over embryonic time points. Black dot represents geometric mean for each time point, and��P values were derived from unpaired two-tailed��t-tests.��i, Pearson correlation coefficient heat map of variant proportions combining hgRNAs and mtVars for selected tissue types, presented as pseudobulk from an E9.5 embryo (Extended Data Fig.��).��j, Multimodal application of the NSC–seq platform.��a,e,g,j, Schematics created using BioRender (). a.u., arbitrary units; AUC, area under the curve; rep., replicate; prog., progenitor; bp, base pairs.��

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Human neural organoids are used to study brain biology, but making them more accurate in representing different types of brain cells is still a challenge. In this study, the researchers compared two materials for growing these organoids: Matrigel (a common material) and a new material called GelMA-Cad, which is made from a special gel that helps guide cell development. They found that the GelMA-Cad material helped the organoids develop more like human fetal brain tissue and produced neurons that were more active compared to those grown in Matrigel.��

These findings suggest that GelMA-Cad could be a better material for growing neural organoids, allowing researchers to control how the cells develop more precisely. It also offers a simpler and more reliable alternative to Matrigel for research on brain development.��

Figure��1.��Functionalized gelatin matrix parameters for cortical organoid culture��

(A) Schematic of GelMA-Cad, major tunable parameters, and associated organoid culture timeline. GelMA-Cad utilizes a gelatin-based backbone, with a conjugated methacryloyl group (blue), allowing photo-initiated crosslinking. The methacryloyl can be further modified by the addition of an N-cadherin (Cad) peptide mimetic (orange). Adjusted parameters include the LAP crosslinker concentration and N-cadherin peptide presence.��

(B) Representative��1H-NMR spectra of gelatin, GelMA, and GelMA-Cad displaying the characteristic peaks associated with methacryloyl.��

(C) Atomic force microscope characterization of Young’s modulus.��N��= 5 measurements, each the average of 512 technical replicates. Bars represent data mean. Statistical significance was calculated with a two-way ANOVA, modeled on crosslinker concentration and peptide presence.��

(D) Average storage modulus traces from GelMA and GelMA-Cad hydrogels.��N��= 5 measurements, error bars represent the standard deviation.��

(E) Rheological characterization of average storage modulus across tested frequencies.��N��= 5 measurements, bar represents data mean. Statistical significance was calculated with a two-way ANOVA, modeled on crosslinker concentration and peptide presence.��

(F) Rheological characterization of average loss modulus across tested frequencies.��N��= 5 measurements, bar represents data mean. Statistical significance was calculated with a two-way ANOVA, modeled on crosslinker concentration and peptide presence.��

(G) Representative scanning electron micrographs of lyophilized, crosslinked hydrogels. Scale bar: 100��μm.��

(H) Quantification of pore size from scanning electron micrographs. Each point is the mean of measurements made from a single image,��N��= 6–7 images per condition, each image from a separate preparation. Bars represent data mean.��

(I) Apparent permeability coefficient of 3��kDa FITC-dextran in each hydrogel.��N��= 6 measurements, bar represents data mean. Statistical significance was calculated with a two-way ANOVA, modeled on crosslinker concentration and peptide presence.��

This study focuses on designing a patient-reported outcome (PRO) and decision support system to help postpartum patients decide when to seek medical care for concerning symptoms. The researchers investigated how race, ethnicity, and preferred language influence preferences for using such an app. The study surveyed 446 participants who had given birth within the past year, divided into four demographic groups based on language, race, and ethnicity.

Key findings revealed that Black participants were less likely to have previously used symptom-reporting apps and expressed less interest in downloading the described app compared to other groups. They also placed less importance on sharing warning signs with friends and family. Black participants, along with non-Hispanic Black participants, preferred reporting symptoms less frequently than Hispanic participants. Additionally, Spanish-speaking Hispanic participants favored calling a healthcare professional for urgent issues, while Black and English-speaking Hispanic participants were more interested in using online chat or patient portals.

These results highlight the importance of tailoring health apps to the preferences of different demographic groups. Designing flexible tools that accommodate varying preferences based on race, ethnicity, and language can improve patient engagement and the effectiveness of postpartum care systems.

In this study, researchers examined how our ability to control and redirect our attention develops as we grow older. They conducted their investigation by studying monkeys that were trained to disregard distractions and redirect their attention to a specific target (referred to as the antisaccade task).Through behavioral evaluations and monitoring neural activity in the prefrontal cortex, they compared the monkeys’ performance before and after puberty.

The findings revealed that adult monkeys showed significant improvements in processing the stimulus quickly, resisting the involuntary urge to look at it, and consistently following the task rules compared to when they were younger. The researchers also observed changes in the prefrontal cortex, which played a crucial role in the monkeys’ improved performance. The specific neurons in this brain region showed enhanced activity and provided neural markers of the behavioral changes, suggesting a shift from stimulus-driven to goal-driven control during each trial.

These results not only shed light on the cognitive development of monkeys but also offer important insights into how our own attentional abilities mature as we transition from adolescence to adulthood. It appears that effective allocation of attention plays a key role in achieving better response control. This study deepens our understanding of how our ability to focus and resist distractions improves over time, providing valuable knowledge about the development of cognitive skills.

Prenatal alcohol exposure (PAE) affects about 11% of pregnancies in North America and is the leading known cause of neurodevelopmental disabilities, such as fetal alcohol spectrum disorder (FASD), which affects around 2-5% of the population. PAE has been linked to smaller gray matter volumes in individuals across different age groups. However, the specific developmental patterns in early childhood and potential sex differences have not been well studied.

This study used longitudinal T1-weighted MRI to examine gray matter volume development in young children aged 3-8 years with PAE (42 children, 84 scans) compared to unexposed children aged 2-8.5 years (127 children, 450 scans). The results showed that children with PAE had different gray matter development trajectories, with less increase and more decrease in gray matter volume compared to unexposed children. Notably, sex differences were more pronounced in the PAE group, with females showing the smallest gray matter volumes and the least change with age.

These findings suggest that children with PAE may have reduced brain plasticity or accelerated maturation, contributing to the cognitive and behavioral challenges they often face. The study also indicates that gray matter volume differences associated with PAE become more evident as children grow older, supporting previous research on older age groups.

A comprehensive two-part study utilizing electronic medical records from 91������ Medical Center investigated health conditions in individuals with Down syndrome (DS), particularly those with congenital heart disease (CHD). The first part of the study examined a large cohort of DS individuals, revealing a higher prevalence of specific health issues such as heart failure, pulmonary heart disease, and hypothyroidism compared to controls and those with other intellectual and developmental disabilities. The second part focused on DS patients with CHD, identifying conditions like congestive heart failure and valvular heart disease that increased the likelihood of surgical interventions. These findings highlight the complex health profiles of individuals with DS, suggesting the need for tailored medical approaches to better manage their multiple health challenges.