Advanced thermal management technologies represent an important research frontier because such materials and systems show promise for enhancing personal physiological comfort and reducing building energy consumption. These technologies typically offer the advantages of excellent portability, user-friendly tunability, energy efficiency, and straightforward manufacturability, but they frequently suffer from critical challenges associated with poor breathability, inadequate wash stability, and difficult fabric integration. Within this broader context, our laboratory has previously developed heat-managing composite materials by drawing inspiration from the color-changing skin of the common squid. Herein, we describe the design, fabrication, and testing of breathable, washable, and fabric-integrated variants of our composite materials, which demonstrate state-of-the-art adaptive infrared properties and dynamic thermoregulatory functionalities. The combined findings directly advance the performance and applications scope of our bioinspired thermoregulatory composites and ultimately may guide the incorporation of desirable multifunctionality into other wearable technologies.

The removal mechanism of refractory deep-ocean dissolved organic carbon (deep-DOC) is poorly understood. The Amundsen Sea Polynya (ASP) serves as a natural test basin for assessing the fate of deep-DOC when it is supplied with a large amount of fresh-DOC and exposed to strong solar radiation during the polynya opening in austral summer. We measured the radiocarbon content of DOC in the water column on the western Amundsen shelf. The radiocarbon content of DOC in the surface water of the ASP reflected higher primary production than in the region covered by sea ice. The radiocarbon measurements of DOC, taken two years apart in the ASP, were different, suggesting rapid cycling of DOC. The increase in DOC concentration was less than expected from the observed increase in radiocarbon content from those at the greatest depths. Based on a radiocarbon mass balance, we show that deep-DOC is consumed along with fresh-DOC in the ASP. Our observations imply that water circulation through the surface layer, where fresh-DOC is produced, may play an important role in global DOC cycling.

Background

This study evaluated the real-world impact of acupuncture on analgesics and healthcare resource utilization among breast cancer survivors.

Methods

From a United States (US) commercial claims database (25% random sample of IQVIA PharMetrics® Plus for Academics), we selected 18-63 years old malignant breast cancer survivors experiencing pain and ≥ 1 year removed from cancer diagnosis. Using the difference-in-difference technique, annualized changes in analgesics [prevalence, rates of short-term (< 30-day supply) and long-term (≥ 30-day supply) prescription fills] and healthcare resource utilization (healthcare costs, hospitalizations, and emergency department visits) were compared between acupuncture-treated and non-treated patients.

Results

Among 495 (3%) acupuncture-treated patients (median age: 55 years, stage 4: 12%, average 2.5 years post cancer diagnosis), most had commercial health insurance (92%) and experiencing musculoskeletal pain (98%). Twenty-seven percent were receiving antidepressants and 3% completed ≥ 2 long-term prescription fills of opioids. Prevalence of opioid usage reduced from 29 to 19% (P < 0.001) and NSAID usage reduced from 21 to 14% (P = 0.001) post-acupuncture. The relative prevalence of opioid and NSAID use decreased by 20% (P < 0.05) and 19% (P = 0.07), respectively, in the acupuncture-treated group compared to non-treated patients (n = 16,129). However, the reductions were not statistically significant after adjustment for confounding. Patients receiving acupuncture for pain (n = 264, 53%) were found with a relative decrease by 47% and 49% (both P < 0.05) in short-term opioid and NSAID fills compared to those treated for other conditions. High-utilization patients (≥ 10 acupuncture sessions, n = 178, 36%) were observed with a significant reduction in total healthcare costs (P < 0.001) unlike low-utilization patients.

Conclusions

Although adjusted results did not show that patients receiving acupuncture had better outcomes than non-treated patients, exploratory analyses revealed that patients treated specifically for pain used fewer analgesics and those with high acupuncture utilization incurred lower healthcare costs. Further studies are required to examine acupuncture effectiveness in real-world settings.

Prolonged cold exposure stimulates the recruitment of beige adipocytes within white adipose tissue. Beige adipocytes depend on mitochondrial oxidative phosphorylation to drive thermogenesis. The transcriptional mechanisms that promote remodeling in adipose tissue during the cold are not well understood. Here we demonstrate that the transcriptional coregulator transducin-like enhancer of split 3 (TLE3) inhibits mitochondrial gene expression in beige adipocytes. Conditional deletion of TLE3 in adipocytes promotes mitochondrial oxidative metabolism and increases energy expenditure, thereby improving glucose control. Using chromatin immunoprecipitation and deep sequencing, we found that TLE3 occupies distal enhancers in proximity to nuclear-encoded mitochondrial genes and that many of these binding sites are also enriched for early B-cell factor (EBF) transcription factors. TLE3 interacts with EBF2 and blocks its ability to promote the thermogenic transcriptional program. Collectively, these studies demonstrate that TLE3 regulates thermogenic gene expression in beige adipocytes through inhibition of EBF2 transcriptional activity. Inhibition of TLE3 may provide a novel therapeutic approach for obesity and diabetes.

A bottleneck in efficiently connecting new materials discoveries to established literature has arisen due to an increase in publications. This problem may be addressed by using named entity recognition (NER) to extract structured summary-level data from unstructured materials science text. We compare the performance of four NER models on three materials science datasets. The four models include a bidirectional long short-term memory (BiLSTM) and three transformer models (BERT, SciBERT, and MatBERT) with increasing degrees of domain-specific materials science pre-training. MatBERT improves over the other two BERT_BASE-based models by 1%∼12%, implying that domain-specific pre-training provides measurable advantages. Despite relative architectural simplicity, the BiLSTM model consistently outperforms BERT, perhaps due to its domain-specific pre-trained word embeddings. Furthermore, MatBERT and SciBERT models outperform the original BERT model to a greater extent in the small data limit. MatBERT's higher-quality predictions should accelerate the extraction of structured data from materials science literature.

Although gold nanorods have been the subject of much research, the pathways for controlling their shape and thereby their optical properties remain largely heuristically understood. Although it is apparent that the simultaneous presence of and interaction between various reagents during synthesis control these properties, computational and experimental approaches for exploring the synthesis space can be either intractable or too time-consuming in practice. This motivates an alternative approach leveraging the wealth of synthesis information already embedded in the body of scientific literature by developing tools to extract relevant structured data in an automated, high-throughput manner. To that end, we present an approach using the powerful GPT-3 language model to extract structured multi-step seed-mediated growth procedures and outcomes for gold nanorods from unstructured scientific text. GPT-3 prompt completions are fine-tuned to predict synthesis templates in the form of JSON documents from unstructured text input with an overall accuracy of 86% aggregated by entities and 76% aggregated by papers. The performance is notable, considering the model is performing simultaneous entity recognition and relation extraction. We present a dataset of 11 644 entities extracted from 1137 papers, resulting in 268 papers with at least one complete seed-mediated gold nanorod growth procedure and outcome for a total of 332 complete procedures.

Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters. Data-driven methods can offer insight to help guide understanding of these underlying mechanisms, so long as sufficient synthesis data are available. To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codified gold nanoparticle synthesis protocols and outcomes extracted directly from the nanoparticle materials science literature using natural language processing and text-mining techniques. This dataset contains 5,154 data records, each representing a single gold nanoparticle synthesis article, filtered from a database of 4,973,165 publications. Each record contains codified synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs.

Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

Melasma is a skin disorder characterized by hyperpigmented patches due to increased melanin production and deposition. In this pilot study, we evaluate the potential of multiphoton microscopy (MPM) to characterize non-invasively the melanin content, location, and distribution in melasma and assess the elastosis severity. We employed a clinical MPM tomograph to image in vivo morphological features in melasma lesions and adjacent normal skin in 12 patients. We imaged dermal melanophages in most dermal melasma lesions and occasionally in epidermal melasma. The melanin volume fraction values measured in epidermal melasma (14% ± 4%) were significantly higher (p < 0.05) than the values measured in perilesional skin (11% ± 3%). The basal keratinocytes of melasma and perilesions showed different melanin distribution. Elastosis was predominantly more severe in lesions than in perilesions and was associated with changes in melanin distribution of the basal keratinocytes. These results demonstrate that MPM may be a non-invasive imaging tool for characterizing melasma.

Brown adipose tissue (BAT) is composed of thermogenic cells that convert chemical energy into heat to maintain a constant body temperature and counteract metabolic disease. The metabolic adaptations required for thermogenesis are not fully understood. Here, we explore how steady state levels of metabolic intermediates are altered in brown adipose tissue in response to cold exposure. Transcriptome and metabolome analysis revealed changes in pathways involved in amino acid, glucose, and TCA cycle metabolism. Using isotopic labeling experiments, we found that activated brown adipocytes increased labeling of pyruvate and TCA cycle intermediates from U¹³C-glucose. Although glucose oxidation has been implicated as being essential for thermogenesis, its requirement for efficient thermogenesis has not been directly tested. We show that mitochondrial pyruvate uptake is essential for optimal thermogenesis, as conditional deletion of Mpc1 in brown adipocytes leads to impaired cold adaptation. Isotopic labeling experiments using U¹³C-glucose showed that loss of MPC1 led to impaired labeling of TCA cycle intermediates. Loss of MPC1 in BAT increased 3-hydroxybutyrate levels in blood and BAT in response to the cold, suggesting that ketogenesis provides an alternative fuel source to compensate. Collectively, these studies highlight that complete glucose oxidation is essential for optimal brown fat thermogenesis.