Browse Articles

Spontaneous activity of astrocytes is a stochastic functional signal for memory consolidation

In the absence of explicit neuronal inputs, the glial cell astrocytes exhibit recurring intracellular Ca 2+ fluctuations, primarily localized at thin processes, known as Ca 2+ microdomains (MDs). Although spontaneous Ca 2+ MDs are present throughout the brain, their putative role is unknown. Here, we question whether, owing to their recurring signaling mode, spontaneous Ca 2+ MDs contribute to slowly evolving phenomena in the brain, such as memory consolidation. We demonstrate that, in the perirhinal cortex, a central region in recognition memory, these events promote Ca 2+ -dependent gliotransmission and modulate synaptic strengthening. Their recurring activity extends the release of the gliotransmitter brain-derived neurotrophic factor (BDNF) over time, ensuring the sustained Tropomyosin Receptor Kinase B (TrkB)-signaling required for the consolidation of long-term synaptic potentiation and lasting memories. We also show that Ca 2+ MDs, which are stochastic events, preserve their random behavior during gliotransmission, introducing an element of unpredictability into the process of memory retention. Our study assigns to spontaneous, stochastic activity in astrocytes a unique functional role in shaping and stabilizing memory circuits.

L-SHADE optimized learning framework for sEMG hand gesture recognition

Abstract In recent years, Hand Gesture Recognition (HGR) devices have been designed to recognize gestures in real time using machine-learning classifiers (MLCs). However, the performance of these classifiers heavily relies on the tuning of their hyperparameters on real-time data. In this regard, this study provides a Linear Population Size Reduction Success-History Adaptation Differential Evolution (L-SHADE)-based optimized Extra Tree (ET) MLC framework for HGR. The study includes real-time sEMG signals from two forearm muscles to capture six distinct hand gesture movements. To recognize the gesture, this work employed ten MLCs. Among these ET classifier demonstrates the highest accuracy without optimizing the hyperparameters. To further enhance performance, ten optimization algorithms, along with the ET classifier, are considered, where the L-SHADE optimized ET framework outperforms the others. To validate the proposed framework, a consistent system environment has been used for both acquired and public datasets. On the acquired data, the mean accuracy improves from 84.14% to 87.89% using ET with the L-SHADE optimization framework while the mean computational time is reduced from 8.62 to 3.16 milliseconds. Similarly, the publicly available 15-hand gesture classification dataset demonstrated a mean accuracy improvement of more than 3.0%.

Unisexual reproduction in the global human fungal pathogen <i> <i>Cryptococcus neoformans</i> </i>

The human fungal pathogen Cryptococcus species complex (encompassing Cryptococcus neoformans , Cryptococcus deneoformans , and the Cryptococcus gattii species complexes) exhibits diversity in sexual reproduction, including α- a mating, pseudosexual reproduction, as well as unisexual reproduction initiated from a single isolate or between isolates of the same mating type. A central conundrum is that while most Cryptococcus natural populations exhibit significant α mating-type bias, genetic and genomic analyses show recombination occurs in nature. The discovery of unisexual reproduction in C. deneoformans provided insight; however, thus far, unisexual reproduction has never been directly observed in the predominant global pathogenic species C. neoformans . Here, we provide evidence that mutating the RIC8 gene, which encodes a conserved guanine nucleotide exchange factor (GEF) involved in both chaperoning and activating Gα proteins, enables unisexual reproduction in C. neoformans . Additionally, we show that genetic variation in the natural population promotes unisexual reproduction, and unisexual reproduction in C. neoformans involves canonical meiotic recombination. Finally, we found that deletion of both GPA2 and GPA3 in the MAT α background leads to self-filamentation without sporulation, suggesting that differential modulation of the Gα proteins, likely involving Ric8, could underlie the switch between different modes of sexual reproduction in Cryptococcus . Our study further highlights that the highly conserved Ric8 GEF can act as an important regulator of cellular development in response to environmental stimuli and could modulate sexual reproduction in nature. We hypothesize that unisexual reproduction occurs much more frequently in nature than currently appreciated, and possibly in other fungi and microbial eukaryotes as well.

Sustainability evaluation of the steel industry in belt and road countries using an ESG-MI and obstacle analysis framework

<i>WUSCHEL-D1</i> upregulation enhances grain number by inducing formation of multiovary-producing florets in wheat

Innovative genetic improvements in food crops are needed to maintain global food security. Here, we report the map-based cloning of TaWUSCHEL-D1 ( WUS-D1 ) as the gene responsible for the multiovary phenotype in wheat, which produces three fertile ovaries and grains per floret. We generated a 14.5 Gbp chromosome-level assembly of multiovary wheat line “MOV” that shows unique structural variation in the Mov-1 physical region, resulting in widespread gene upregulation. High-resolution genetic mapping refined the locus to a 135 kbp region that contains two genes. We used nine independent deletion mutants, eight TILLING mutants, and genetic complementation of these genotypes to show that a WUSCHEL ortholog, WUS-D1 , is the causal gene of the Mov-1 locus. Expression studies showed that WUS-D1 is highly expressed during early inflorescence development in MOV, whereas the gene is inactive in wild-type wheat. The higher WUS-D1 expression is associated with the formation of larger meristems and floret primordia that are competent to produce multiple ovaries. These insights provide a foundation to manipulate floral organ numbers to enhance breeding capabilities of bread wheat.

Aphid populations and virus vector potential in potato fields across seasons and regions in Norway

Abstract Several aphid species pose serious treats to potato crops by causing direct damage to the plants and/or indirectly by transmitting viruses. Different morphological forms and phenotypic plasticity among aphids complicates taxonomy and identification and thus makes targeted pest management in potatoes challenging. To obtain an overview of aphids frequenting potato fields in Norway, we investigated seasonal and annual changes in aphid populations in five potato fields (58–64 °N) over a three-year period (2016–2018), using yellow pan traps. In total 2218 of the 6136 collected aphids were identified by traditional barcoding, meaning sequencing a ~ 650 fragment of the mitochondrial COI gene. This revealed 137 different species, of which 111 were identified at the species level. The remaining were identified only to the genus level, indicating potential novel species. The southernmost sampling location yielded the highest number of species and individual counts, although no clear correlations to climate factors (temperature/precipitation) was observed. Of the 111 species identified, at least 39 are potential vectors of potato virus Y (PVY) and nine species may also transmit potato virus A (PVA). Knowledge on virus vector and non-vector aphid abundance and phenology have the potential to improve pest management of potato cultivation.

Fundamental features of social environments determine rate of social affiliation

Humans start new friendships and social connections throughout their lives and such relationships foster mental and physical well-being. While friendship initiation may depend on alignment of subtle and complex personal variables, here we investigated whether it also depends on basic features of social environments. In a preregistered online study (n = 783) using a novel social-affiliation seeking paradigm, we found people were more likely to send friend requests as the density of friendship opportunities decreased and frequency of success increased. Further, we found task-related measures, like overall friend requests, were correlated with mental health dimensions like social thriving and anhedonia. Next, in an ultra-high-field fMRI study (n = 24), we found that both fundamental features of social environments--opportunity density and frequency of success--affected neural activity across a network of regions linked to foraging including dorsal raphe nucleus, substantia nigra, and anterior insula. Thus, humans consider the background statistics of an environment while making social decisions and these decisions are linked to activity in cortico-subcortical circuits mediating the influence of environmental statistics on other aspects of behavior. Moreover, individual differences in how environmental features influence social behavior are associated with variation in mental health dimensions, offering key insights into interindividual variability in social functioning.

Zinc biofortification and yield enhancement in rice with nano- primed seeds and foliar sprays

Citrullination negatively regulates the functions of the p53 protein and opposes its ubiquitination and degradation

This study investigates the regulatory role of peptidylarginine deiminase 4 (PAD4)-mediated citrullination on the tumor suppressor protein p53. We demonstrate that p53 serves as a substrate for PAD4, undergoing citrullination at multiple arginine residues, including critical sites within its DNA-binding domain. Mass spectrometry identified eight citrullination sites, notably R158, R282, and R283, which were further validated in various cancer cell lines. Functional studies revealed that citrullination significantly impairs p53’s ability to form stable tetramers, essential for high-affinity DNA binding. Electrophoretic mobility shift assays and analytical ultracentrifugation confirmed reduced binding to consensus sequences in the p21 and MDM2 promoters. As a result, citrullination led to marked reductions in p21 and MDM2 transcriptional activation and altered regulation of ME2, as demonstrated by reporter assays and quantitative PCR. In addition, citrullination compromised p53’s roles in cell cycle control and apoptosis. Supporting these findings, citrulline-mimic mutants (arginine-to-glutamine substitutions) exhibited diminished transcriptional activity relative to wild-type p53. Furthermore, citrullination disrupted the interaction between p53 and its E3 ubiquitin ligase MDM2, reducing p53 ubiquitination and degradation, as shown by in vitro ubiquitination assays and cycloheximide chase experiments. Importantly, replacing glutamine with lysine at these key sites largely restored p53 activity, indicating that the loss of positive charge is central to the functional consequences of citrullination. Together, these findings identify PAD4-catalyzed citrullination as a regulatory mechanism that modulates p53 function and highlight PAD4 as a potential therapeutic target in cancer.

Advancements in fusion-based deep representation learning for enhanced cervical precancerous lesion classification using biomedical image analysis

Adaptable microplastic classification using similarity learning on µFTIR spectra collected from µFTIR focal plane array imaging

Deep learning on micro-Fourier transform infrared (µFTIR) spectra has the potential to provide a reliable, automated approach to classify and identify microplastics. However, deep learning models often come with certain limitations, including exhaustive dataset requirements, overfitting, and the need to retrain when new classes are introduced or new data are substantially different from the training set. This work explores a similarity learning approach to training deep learning models to address these issues for microplastic classification. A one-dimensional convolutional neural network (CNN) was trained by similarity learning on a dataset of µFTIR spectra acquired from 45 manufactured microplastic samples of 11 plastic compositions and compared with cross-entropy training of the same CNN architecture as well as classical machine learning algorithms. The CNN trained by similarity learning consistently yielded the highest accuracies (up to a 0.973 F1-score) across the multiple classes of microplastics. Notably, despite only training on microplastic spectra collected under pristine conditions, the CNN trained via similarity learning maintained the highest accuracy (up to a 0.905 F1-score) on a “noisy” dataset consisting of microplastics spiked onto filters with high amounts of exogenous background material. Furthermore, similarity learning combined with support-vector classifiers also allowed for the detection and separation of microplastic polymer-composition classes not contained in the training set. Overall, this approach is able to achieve high accuracy in microplastic classification despite challenges posed by the diversity of microplastic polymer compositions, limited time and resources for dataset preparation, and high amounts of background noise that are common in FTIR spectra collected from real-world microplastic samples.

Teamwork and job satisfaction among nurses in pediatric intensive care unit

Mot1 regulation of promoter binding by TBP varies with stress and gene expression levels independently of coactivator dependence

Mot1 in budding yeast regulates transcription by dissociating general initiation factor TBP (TATA-binding protein) from DNA. Previous studies suggested that Mot1 preferentially removes TBP from stress-responsive promoters containing consensus TATA elements that utilize coactivator SAGA while enhancing TBP binding at “house-keeping” genes with TATA-like elements that employ TFIID for TBP recruitment. In stress conditions of amino acid starvation, by contrast, we found that Mot1 promotes TBP binding at genes activated by transcription factor Gcn4, enriched for TATA/SAGA-dependent promoters, and at SAGA-dependent genes expressed constitutively at high-levels, while suppressing TBP binding at SAGA-dependent genes only expressed at lower levels. Importantly, Mot1’s influence on genes induced by starvation or oxidative stress switches from increased to decreased TBP binding when transcribed at low levels in nonstressed cells. Mot1’s role at TFIID-dependent promoters also scales with transcription level, enhancing TBP binding only for the highly expressed subset. Notably, reduced TBP binding on Mot1 depletion impairs transcription of highly expressed TFIID genes but not highly expressed SAGA/stress-activated genes, suggesting that SAGA produces a surfeit of incomplete preinitiation complexes dependent on Mot1 for assembly.

A novel hybrid interval prediction framework integrating multiobjective optimization and quantile deep learning for copper price prediction

Mode-specific low barrier tunneling dynamics in the à state of formaldehyde: The <i>ν</i>1 fundamental and <i>ν</i>4 + <i>ν</i>5 combination levels

We present a joint experimental and theoretical study of mode-specific tunneling splittings in the à state of formaldehyde. We report the first observation of the symmetric CH stretch fundamental level (ν1′), and of the out-of-plane bend with the antisymmetric CH stretch combination level (ν4′+ν5′) including rotational analysis, using infrared–ultraviolet double-resonance laser-induced fluorescence spectroscopy. The experimental results agree very well with full-dimensional ab initio quasi-variational discrete variable representation calculations on a frozen core equation of motion coupled cluster (EOM-CCSDT/ANO1) surface. We compare our findings with various models, including effective potentials, a semiclassical approach, and a quasidiabatic vibronic coupling treatment, to derive insights into the dynamics and coupling between different degrees of freedom in the vicinity of the tunneling barrier.

Electrostatics facilitate midair host attachment in parasitic jumping nematodes

Jumping can be hazardous for entomopathogenic nematodes (EPNs) as those that fail to attach to an insect host face death by predation or starvation. Recently, it has been shown that electrostatic charges on large insects can prompt a close-range detachment of free-living nematodes, which are nonparasitic and unable to jump. However, it remains unclear if static electricity can influence aerial interactions between parasitic jumping worms and their insect hosts. Here, we analyze and model the trajectories of jumping EPNs in still air as they approach fruit flies with varying electrostatic charge. We find that the nematodes’ attachment to the host is facilitated by an electrical potential of a few hundred volts, a magnitude commonly found in flying insects. A model combining electrostatics, aerodynamics, and Bayesian inference indicates that the electrostatic charge on jumping nematodes is ∼ 0.1 pC, which aligns with theoretical predictions for electrostatic induction. Drag coefficients based on host–nematode interactions in the presence of horizontal wind show differences at both low and high jumping velocities. Numerical simulations show that intermediate wind speeds ( ∼ 0.2 m/s) can further increase the likelihood of host attachment, as wind-driven aerial drifting allows the worms to reach hosts at greater distances. Our results suggest that submillimeter parasites that become airborne may exploit the electric charge carried by their host to facilitate attachment and thus enhance survival. The use of quantitative physical models provides valuable insights into understanding complex airborne infectious diseases mediated by natural environmental forces.

A case study on litter management and clean environment index in small tourism cities

Steady-state free precession NMR in the presence of heteronuclear couplings and decoupling: More than meets the eye

Fourier Transform (FT) has been a mainstay of analytical 13C/15N NMR. On the other hand, it has been shown that Steady-State Free Precession (SSFP) experiments, which depart from this scheme, can, under certain conditions, endow 13C/15N small-molecule NMR with comparable sensitivity and resolution. SSFP is one of the earliest and most widely used NMR pulse sequences, yet its analyses have focused on isolated spin-1/2 ensembles such as water. The present study demonstrates that significant deviations from such isolated spin-1/2 behavior may occur when SSFP is applied in the presence of spin–spin couplings. Even in the simplest case supporting such couplings, a single 13C J-coupled to a 1H, departures from the isolated spin-1/2 behavior arise in the 13C SSFP response—both in the absence and in the presence of 1H spin decoupling. In the former case, deviations are produced by the differential relaxation of antiphase two-spin terms generated by the pulse train; in the latter case, magnified interferences may arise between the SSFP pulses and the coherent perturbation arising upon 1H decoupling. Although both phenomena are also known in FT NMR, the spectral distortions that they will originate may be much larger in the SSFP case—particularly if interpulse delays in large flip-angle SSFP pulse trains “resonate” with the coupling perturbations. The origins of these effects are here analyzed for heteronuclear spin-1/2 systems and corroborated with 13C NMR SSFP experiments recorded under different conditions. Additional considerations aimed at magnifying or suppressing these effects, as well as extensions to more complex scenarios, are also briefly discussed.

Mapping subnational gender gaps in internet and mobile adoption using social media data

The digital revolution has ushered in many societal and economic benefits. Yet access to digital technologies such as mobile phones and internet remains highly unequal, especially by gender in the context of low- and middle-income countries (LMICs). While national-level estimates are increasingly available for many countries, reliable, quantitative estimates of digital gender inequalities at the subnational level are lacking. These estimates, however, are essential for monitoring gaps within countries and implementing targeted interventions within the global sustainable development goals, which emphasize the need to close inequalities both between and within countries. We develop estimates of internet and mobile adoption by gender and digital gender gaps at the subnational level for 2,075 regions in 117 LMICs from 2015 through 2025, a context where digital penetration is low and national-level gender gaps disfavoring women are large. We construct these estimates by applying machine-learning algorithms to Facebook user counts, geospatial data, development indicators, and population composition data. We calibrate and assess the performance of these algorithms using ground-truth data from subnationally representative household survey data from 33 LMICs. Our results reveal striking disparities in access to mobile and internet technologies between and within LMICs. These disparities imply that as of 2025, women are 19% less likely to use the internet and 8% less likely to own a mobile phone in LMICs, corresponding to over 190 million fewer women owning a mobile phone and over 320 million fewer women using the internet.

Enhancing gridded climate products with third party weather data in a rainfall study from Western Australia

Abstract Accurate estimation of weather variables is essential for climate science and real-world applications, yet sparse official weather station networks often limit data reliability in many regions. This study highlights the transformative potential of integrating third-party automatic weather station (TPAWS) data to improve gridded climate data products. Daily rainfall, one of the most important yet challenging weather variables to estimate, is used as a case study in southwestern Western Australia. By incorporating quality-controlled TPAWS observations, we reduce the root mean square error (RMSE) of rainfall estimates by over 15% and false no-rain rates by 30%, with notable improvements during extreme events. These results illustrate how TPAWS data can augment official networks, offering a scalable, cost-effective approach to improve the accuracy of diverse weather variables beyond rainfall alone. Our findings provide compelling evidence of the scientific and practical value of leveraging non-traditional datasets to address data sparsity, opening new avenues for research and development in climate data integration worldwide.