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We compare alternative computing strategies for solving the constrained lasso problem. As its name suggests, the constrained lasso extends the widely used lasso to handle linear constraints, which allow the user to incorporate prior information into the model. In addition to quadratic programming, we employ the alternating direction method of multipliers (ADMM) and also derive an efficient solution path algorithm. Through both simulations and benchmark data examples, we compare the different algorithms and provide practical recommendations in terms of efficiency and accuracy for various sizes of data. We also show that, for an arbitrary penalty matrix, the generalized lasso can be transformed to a constrained lasso, while the converse is not true. Thus, our methods can also be used for estimating a generalized lasso, which has wide-ranging applications. Code for implementing the algorithms is freely available in both the Matlab toolbox SparseReg and the Julia package ConstrainedLasso . Supplementary materials for this article are available online.

Introduction of synthetic circuits into microbes creates competition between circuit and host genes for shared cellular resources, such as ribosomes. This can lead to the emergence of unwanted coupling between the expression of different circuit genes, complicating the design process and potentially leading to circuit failure. By expressing a synthetic 16S rRNA with altered specificity, we can partition the ribosome pool into host-specific and circuit-specific activities. We show mathematically and experimentally that the effects of resource competition can be alleviated by targeting genes to different ribosomal pools. This division of labour can be used to increase flux through a metabolic pathway. We develop a model of cell physiology which is able to capture these observations and use it to design a dynamic resource allocation controller. When implemented, this controller acts to decouple genes by increasing orthogonal ribosome production as the demand for translational resources by a synthetic circuit increases. Competition between synthetic genetic circuits and host genes for shared resources can complicate circuit design and lead to failure. Here the authors demonstrate, mathematically and experimentally, the use of orthogonal ribosomes to decouple competing genes.

This study focused on developing machine learning models to detect leak size and location in transient state conditions. The model was designed for an onshore methane–hydrogen blending gas pipeline in Canada. Base case simulations revealed significant effects on mass flow and pressure due to leaks, with the system taking approximately 6 h to reach a steady state from transient conditions. This made it essential to analyze the flow characteristics during the transient state. Trend data from the pipeline’s inlet and outlet were examined, considering the leak size and location. To better represent the data over time, a method was used to create two-dimensional images, which were then fed into a CNN (convolutional neural network) for training. The model’s accuracy was assessed using classification accuracy and a confusion matrix. By refining the data acquisition process and implementing targeted screening procedures, the model’s classification accuracy increased to over 80%. In conclusion, this study demonstrates that machine learning can enable rapid and accurate leak detection in transient state conditions. The findings are expected to complement existing leak detection methods and support operators in making faster, more informed decisions.

GABAergic Lhx6 + neurons in the ventral zona incerta promote both rapid eye movement and non-rapid eye movement sleep and inhibit the activity of wake-promoting GABAergic and Hcrt + neurons of the lateral hypothalamus. Sleep-inducing neurons Various populations of neurons that can promote wakefulness have been identified, but only a small number of neuronal populations that promote sleep have been described. Here, Seth Blackshaw and colleagues reveal that specific inhibitory neurons in the zona incerta become more active as sleep need increases, inhibiting the activity of wake-promoting neurons in the lateral hypothalamus. Midbrain deletion of Lhx6, a transcription factor that defines these inhibitory zona incerta neurons, can lead to decreases in both NREM and REM sleep. Further exploration of the gene expression networks that drive the development and function of these Lhx6-expressing neurons may identify other factors that are critical to sleep regulation. Multiple populations of wake-promoting neurons have been characterized in mammals, but few sleep-promoting neurons have been identified 1 . Wake-promoting cell types include hypocretin and GABA (γ-aminobutyric-acid)-releasing neurons of the lateral hypothalamus, which promote the transition to wakefulness from non-rapid eye movement (NREM) and rapid eye movement (REM) sleep 2 , 3 . Here we show that a subset of GABAergic neurons in the mouse ventral zona incerta, which express the LIM homeodomain factor Lhx6 and are activated by sleep pressure, both directly inhibit wake-active hypocretin and GABAergic cells in the lateral hypothalamus and receive inputs from multiple sleep–wake-regulating neurons. Conditional deletion of Lhx6 from the developing diencephalon leads to decreases in both NREM and REM sleep. Furthermore, selective activation and inhibition of Lhx6-positive neurons in the ventral zona incerta bidirectionally regulate sleep time in adult mice, in part through hypocretin-dependent mechanisms. These studies identify a GABAergic subpopulation of neurons in the ventral zona incerta that promote sleep.

Older adults and patients with masticatory and deglutition disorders often experience difficulties consuming tough, fibrous vegetables. The enzymatic and liposomal conditions for softening garlic scapes were optimized while simultaneously enhancing their nutritional value through vitamin E fortification. Enzymes (Plantase UF and Plantase PT) were applied at varying concentrations and incubation times to determine optimal tenderization conditions, followed by the application of vitamin E-loaded liposomes. The physicochemical, microstructural, and color characteristics of the scapes and liposomal systems were evaluated. Enzymatic treatment significantly (p < 0.05) decreased hardness and increased adhesiveness, indicating effective cell wall disruption. Plantase PT hydrolyzes pectin in the middle lamella, promoting cell separation and softening, and maintains higher activity than Plantase UF, confirming its suitability for the consistent tenderization of fibrous vegetables. Its stability ensures reliable and uniform softening for real-world fibrous vegetable processing. Enzyme–vitamin E co-encapsulation balanced texture and nutrition by enlarging particles and lowering the ζ-potential (p < 0.05). Liposomal encapsulation preserved enzyme activity during processing and enabled sustained vitamin E delivery to scape tissues. Compared with untreated control, vitamin E liposomes provided controlled softening and improved nutrient stability. This highlights the potential of enzyme–liposome systems in developing tenderized older adult-friendly diets using fibrous plants.

The flowering of Arabidopsis thaliana winter annuals is delayed until the subsequent spring by the strong floral repressor FLOWERING LOCUS C (FLC). FRIGIDA (FRI) activates the transcription of FLC, but the molecular mechanism remains elusive. The fri mutation causes early flowering with reduced FLC expression similar to frl1, fes1, suf4, and flx, which are mutants of FLC-specific regulators. Here, we report that FRI acts as a scaffold protein interacting with FRL1, FES1, SUF4, and FLX to form a transcription activator complex (FRI-C). Each component of FRI-C has a specialized function. SUF4 binds to a cis-element of the FLC promoter, FLX and FES1 have transcriptional activation potential, and FRL1 and FES1 stabilize the complex. FRI-C recruits a general transcription factor, a TAF14 homolog, and chromatin modification factors, the SWR1 complex and SET2 homolog. Complex formation was confirmed by the immunoprecipitation of FRI-associated proteins followed by mass spectrometric analysis. Our results provide insight into how a specific transcription activator recruits chromatin modifiers to regulate a key flowering gene.

Extracellular vesicles (EVs) are nano-sized particles released by various cell types that facilitate intercellular communication by transferring bioactive molecules. Owing to their biocompatibility, EVs are widely used as drug delivery vehicles. However, freezing EVs at – 70 °C can induce aggregation, reducing their effective concentration and drug delivery efficiency. In this study, we evaluated the use of water-bath sonication to disperse aggregated EVs and compared its effectiveness with regular pipetting. Frozen EVs showed reduced total concentration and increased aggregation relative to fresh EVs. Sonication at power level 3 (40 kHz, 100 W) significantly increased EV concentration and reduced aggregation. Unlike pipetting, only sonication effectively dispersed aggregated EVs, though subsequent pipetting caused reaggregation. In vivo, aggregated EVs were detected in the bronchoalveolar lavage fluid of mice treated with frozen EVs, whereas mice receiving sonicated EVs exhibited fewer aggregates and enhanced cellular uptake. Molecular dynamics simulations supported the effectiveness of sonication in dispersing EVs. In conclusion, water-bath sonication is a simple and effective method to restore the functionality of freeze-thawed EVs, improving their intracellular delivery and therapeutic potential.

In this work, we propose our newly developed wafer-type plasma monitoring sensor based on a floating-type double probe method that can be useful for two-dimensional (2D) in situ plasma diagnosis within a semiconductor processing chamber. A key achievement of this work is the first realization of an ultra-thin plasma monitoring sensor with a system thickness of ~1.4 mm, which supports a fully automated robot arm transfer capability for in situ plasma diagnosis. To the best of our knowledge, it is the thinnest accomplishment among all wafer-type plasma monitoring sensors. Our proposed sensor is assembled with two Si wafers and SiO2-based probes; accordingly, it makes it possible to monitor the actual dynamics of processing plasmas under electrostatic chucking (ESC) conditions. Also, it allows for the prevention of chamber contamination issues after continuously exposing the radio frequency (RF) to various processing gases. Using a test-bed chamber, we successfully demonstrated the feasibility and system performance of the proposed sensor, including robot arm transfer capability, vacuum and thermal stress durability, and data integrity and reproducibility. Consequently, compared with the conventional plasma diagnostic tools, we expect that our proposed sensor will be highly beneficial for tool-to-tool matching (TTTM) and/or for studying various plasma-related items by more accurately providing the parameters of processing plasmas, further saving both time and manpower resources required for preventive maintenance (PM) routines as well.

Adolescent cannabis use increases the risk for cognitive impairments and psychiatric disorders. Cannabinoid receptor type 1 (Cnr1) is expressed not only in neurons and astrocytes, but also in microglia, which shape synaptic connections during adolescence. However, the role of microglia in mediating the adverse cognitive effects of delta-9-tetrahydrocannabinol (THC), the principal psychoactive constituent of cannabis, is not fully understood. Here, we report that in mice, adolescent THC exposure produces microglial apoptosis in the medial prefrontal cortex (mPFC), which was exacerbated in a model of 16p11.2 duplication, a representative copy number variation (CNV) risk factor for psychiatric disorders. These effects are mediated by microglial Cnr1, leading to reduction in the excitability of mPFC pyramidal-tract neurons and deficits in social memory in adulthood. Our findings suggest the microglial Cnr1 may contribute to adverse effect of cannabis exposure in genetically vulnerable individuals. Exposure to cannabis in adolescence is associated with increased risk of psychiatric disorders. Here, in a mouse model of 16p11.2 duplication, the authors identified a role of microglial Cnr1 for mediating the adverse effect of THC exposure on mPFC maturation and social memory.

Dialogue systems must understand children’s utterance intentions by considering their unique linguistic characteristics, such as syntactic incompleteness, pronunciation inaccuracies, and creative expressions, to enable natural conversational engagement in child–robot interactions. Even state-of-the-art large language models (LLMs) for language understanding and contextual awareness cannot comprehend children’s intent as accurately as humans because of their distinctive features. An LLM-based dialogue system should acquire the manner by which humans understand children’s speech to enhance its intention reasoning performance in verbal interactions with children. To this end, we propose a fine-tuning methodology that utilizes the LLM–human judgment discrepancy and interactive response data. The former data represent cases in which the LLM and human judgments of the contextual appropriateness of a child’s answer to a robot’s question diverge. The latter data involve robot responses suitable for children’s utterance intentions, generated by the LLM. We developed a fine-tuned dialogue system using these datasets to achieve human-like interpretations of children’s utterances and to respond adaptively. Our system was evaluated through human assessment using the Robotic Social Attributes Scale (RoSAS) and Sensibleness and Specificity Average (SSA) metrics. Consequently, it supports the effective interpretation of children’s utterance intentions and enables natural verbal interactions, even in cases with syntactic incompleteness and mispronunciations.