The complete mitochondrial genome of the ice krill Euphausia crystallorophias Holt & Tattersall, 1906 (Euphausiacea, Euphausiidae), from the Ross Sea, Antarctica.

The mitogenome of Euphausia crystallorophias collected from the Ross Sea Region Marine Protected Area (RSR MPA) is described for the first time. The assembled mitogenome was 17,291 bp in length and consisted of two ribosomal RNAs (rRNAs), 22 transfer RNAs (tRNAs), 13 protein-coding genes (PCGs), and noncoding regions, all of which were identical to those of other euphausiid species. The most common start codon for the 13 PCGs was ATG, and the most common termination codon was TAA. The overall G + C content was 33.2% in the heavy strand. Euphausia crystallorophias was sister to E. superba in the phylogenetic analysis. The mitogenome of E. crystallorophias provided significant DNA molecular data for further identification and phylogenetic analysis within the euphausiids.

Interspecific Variations in the Internal Mercury Isotope Dynamics of Antarctic Penguins: Implications for Biomonitoring.

Mercury (Hg) biomonitoring requires a precise understanding of the internal processes contributing to disparities between the Hg sources in the environment and the Hg measured in the biota. In this study, we investigated the use of Hg stable isotopes to trace Hg accumulation in Adélie and emperor penguin chicks from four breeding colonies in Antarctica. Interspecific variation of Δ199Hg in penguin chicks reflects the distinct foraging habitats and Hg exposures in adults. Chicks at breeding sites where adult penguins predominantly consumed mesopelagic prey showed relatively lower Δ199Hg values than chicks that were primarily fed epipelagic krill. Substantial δ202Hg variations in chick tissues were observed in both species (Adélie: -0.11 to 1.13‰, emperor: -0.27 to 1.15‰), whereas only emperor penguins exhibited the lowest δ202Hg in the liver and the highest in the feathers. Our results indicate that tissue-specific δ202Hg variations and their positive correlations with % MeHg resulted from MeHg demethylation in the liver and kidneys of emperor penguin chicks, whereas Adélie penguin chicks showed different internal responses depending on their exposure to dietary MeHg. This study highlights the importance of considering intra- and interspecific variations in adult foraging ecology and MeHg demethylation when selecting penguin chicks for Hg biomonitoring.

Fragment molecular orbital-based variational quantum eigensolver for quantum chemistry in the age of quantum computing.

Quantum computers offer significant potential for complex system analysis, yet their application in large systems is hindered by limitations such as qubit availability and quantum hardware noise. While the variational quantum eigensolver (VQE) was proposed to address these issues, its scalability remains limited. Many efforts, including new ansätze and Hamiltonian modifications, have been made to overcome these challenges. In this work, we introduced the novel Fragment Molecular Orbital/Variational Quantum Eigensolver (FMO/VQE) algorithm. This method combines the fragment molecular orbital (FMO) approach with VQE and efficiently utilizes qubits for quantum chemistry simulations. Employing the UCCSD ansatz, the FMO/VQE achieved an absolute error of just 0.053 mHa with 8 qubits in a [Formula: see text] system using the STO-3G basis set, and an error of 1.376 mHa with 16 qubits in a [Formula: see text] system with the 6-31G basis set. These results indicated a significant advancement in scalability over conventional VQE, maintaining accuracy with fewer qubits. Therefore, our FMO/VQE method exemplifies how integrating fragment-based quantum chemistry with quantum algorithms can enhance scalability, facilitating more complex molecular simulations and aligning with quantum computing advancements.

High-density transparent graphene arrays for predicting cellular calcium activity at depth from surface potential recordings.

Optically transparent neural microelectrodes have facilitated simultaneous electrophysiological recordings from the brain surface with the optical imaging and stimulation of neural activity. A remaining challenge is to scale down the electrode dimensions to the single-cell size and increase the density to record neural activity with high spatial resolution across large areas to capture nonlinear neural dynamics. Here we developed transparent graphene microelectrodes with ultrasmall openings and a large, transparent recording area without any gold extensions in the field of view with high-density microelectrode arrays up to 256 channels. We used platinum nanoparticles to overcome the quantum capacitance limit of graphene and to scale down the microelectrode diameter to 20 µm. An interlayer-doped double-layer graphene was introduced to prevent open-circuit failures. We conducted multimodal experiments, combining the recordings of cortical potentials of microelectrode arrays with two-photon calcium imaging of the mouse visual cortex. Our results revealed that visually evoked responses are spatially localized for high-frequency bands, particularly for the multiunit activity band. The multiunit activity power was found to be correlated with cellular calcium activity. Leveraging this, we employed dimensionality reduction techniques and neural networks to demonstrate that single-cell and average calcium activities can be decoded from surface potentials recorded by high-density transparent graphene arrays.

Indoor air quality and its determinants in underground shopping malls in Korea.

Underground shopping malls (USMs) are often open or semi-open environments with interconnected passageways, resulting in the sharing of indoor air. However, indoor air quality (IAQ) within these spaces can vary due to many diverse emission sources. We investigated the relationships between IAQ and geographical areas, as well as IAQ and store types, within USMs, in Korea. In 2020, we studied 10 different USMs, with a total of 128 outlets. We conducted comprehensive IAQ assessments (including measurements of fine particles (PM2.5), aldehydes, and volatile organic compounds (VOCs)) in stores, passages, and outdoor areas. The stores were categorized into three types: clothing, fashion accessories, and food services. Additionally, we measured environmental factors such as CO2 levels and presence of storefront walls. PM2.5 levels were higher outdoors, whereas aldehyde and VOC levels exhibited elevations within passages and the interior of stores than in the outdoor environment. The store-to-passage ratios for PM2.5, individual aldehydes, and VOC concentrations ranged from 1.06 to 4.93. Formaldehyde and total VOC (TVOC) concentrations were found to be elevated in clothing and fashion accessory stores, whereas PM2.5 concentrations were more prominent in food service establishments. Specific individual compounds, including propionaldehyde, hexaldehyde, benzene, n-heptane, toluene, n-octane, xylene, d-limonene, n-undecane, n-dodecane, and ethylbenzene concentrations exhibited associations with store types. Multivariate regression models demonstrated positive associations between most aldehydes and VOCs with CO2 concentrations and presence of storefront walls. This study underscored variations within USMs based on area and store type. Aldehyde and VOC concentrations were notably higher in clothing and fashion accessory stores than in food service outlets; these elevations were closely linked to CO2 levels and presence of storefront walls. These findings suggest that monitoring CO2 levels within USM stores, optimizing air-conditioning systems, and designing future stores without storefront walls can collectively contribute to an overall improvement of IAQ within USMs.

More Than 30 000-fold Field Enhancement of Terahertz Nanoresonators Enabled by Rapid Inverse Design.

The rapid development of 6G communications using terahertz (THz) electromagnetic waves has created a demand for highly sensitive THz nanoresonators capable of detecting these waves. Among the potential candidates, THz nanogap loop arrays show promising characteristics but require significant computational resources for accurate simulation. This requirement arises because their unit cells are 10 times smaller than millimeter wavelengths, with nanogap regions that are 1 000 000 times smaller. To address this challenge, we propose a rapid inverse design method using physics-informed machine learning, employing double deep Q-learning with an analytical model of the THz nanogap loop array. In ∼39 h on a middle-level personal computer, our approach identifies the optimal structure through 200 000 iterations, achieving an experimental electric field enhancement of 32 000 at 0.2 THz, 300% stronger than prior results. Our analytical model-based approach significantly reduces the amount of computational resources required, offering a practical alternative to numerical simulation-based inverse design for THz nanodevices.

Production of Mono-Hydroxylated Derivatives of Terpinen-4-ol by Bacterial CYP102A1 Enzymes.

CYP102A1 from Bacillus megaterium is an important enzyme in biotechnology, because engineered CYP102A1 enzymes can react with diverse substrates and produce human cytochrome P450-like metabolites. Therefore, CYP102A1 can be applied to drug metabolite production. Terpinen-4-ol is a cyclic monoterpene and the primary component of essential tea tree oil. Terpinen-4-ol was known for therapeutic effects, including antibacterial, antifungal, antiviral, and anti-inflammatory. Because terpenes are natural compounds, examining novel terpenes and investigating the therapeutic effects of terpenes represent responses to social demands for eco-friendly compounds. In this study, we investigated the catalytic activity of engineered CYP102A1 on terpinen-4-ol. Among CYP102A1 mutants tested here, the R47L/F81I/F87V/E143G/L188Q/N213S/E267V mutant showed the highest activity to terpinen-4-ol. Two major metabolites of terpinen-4-ol were generated by engineered CYP102A1. Characterization of major metabolites was confirmed by liquid chromatography-mass spectrometry (LC-MS), gas chromatography-MS, and nuclear magnetic resonance spectroscopy (NMR). Based on the LC-MS results, the difference in mass-to-charge ratio of an ion (m/z) between terpinen-4-ol and its major metabolites was 16. One major metabolite was defined as 1,4-dihydroxy-p-menth-2-ene by NMR. Given these results, we speculate that another major metabolite is also a mono-hydroxylated product. Taken together, we suggest that CYP102A1 can be applied to make novel terpene derivatives.

Chromosome-level genome assembly and annotation of the Antarctica whitefin plunderfish Pogonophryne albipinna.

The Antarctic whitefin plunderfish Pogonophryne albipinna belongs to the family Artedidraconidae, a key component of Antarctic benthic ecosystems within the order Perciformes and the suborder Notothenioidei. While genome research on P. albipinna using short-read sequencing is available, high-quality genome assembly and annotation employing long-read sequencing have yet to be performed. This study presents a chromosome-scale genome assembly and annotation for P. albipinna, utilizing a combination of Illumina short-read, PacBio long-read, and Hi-C sequencing technologies. The resulting genome assembly spans approximately 1.07 Gb, with a longest scaffold measuring 59.39 Mb and an N50 length of 41.76 Mb. Of the 1,111 Hi-C scaffolds, 23 exceeded 10 Mb and were thus classified as chromosome-level. BUSCO completeness was assessed at 95.6%. The assembled genome comprises 50.68% repeat sequences, and a total of 31,128 protein-coding genes were predicted. This study will enhance our understanding of the genomic characteristics of cryonotothenioids and facilitate comparative analyses of their adaptation and evolution in extreme environments.

Effect of combined administration of Acyl-CoA: Cholesterol acyltransferase 1 inhibitor and glucagon-like peptide 1 receptor agonist on a rodent model of diet-induced obesity.

A glucagon-like peptide 1 receptor agonist (GLP-1 RA) semaglutide was approved for the treatment of obesity by the Food and Drug Administration. However, it can cause gastrointestinal events at high doses, limiting its broader use. Combining drugs with multiple mechanisms of action could enhance the weight-reducing effects while minimizing side effects. To this end, we investigated the combined effects of semaglutide and avasimibe, an acyl-CoA:cholesterol acyltransferase 1 (ACAT1) inhibitor, on weight reduction in diet-induced obesity mice. Two cohorts of mice were used: In cohort 1, mice were fed a high-fat (HF) diet for 12 weeks and then randomly assigned to the vehicle, avasimibe [10 mg/kg body weight (BW)], semaglutide (0.4 mg/kg BW), or combination groups. The drugs were administered via subcutaneous (sc) injections on a daily basis. In cohort 2, mice were fed an HF diet for 8 weeks and randomly assigned to the same four groups, but avasimibe was administered at a dose of 20 mg/kg BW, and the drugs were administered every 3 days. In cohort 1, semaglutide initially reduced food intake initially, but this effect was diminished with prolonged administration. Avasimibe, on the other hand, did not affect food intake but prevented weight gain to a lesser extent than semaglutide. Importantly, the combination treatment resulted in the greatest percentage of body weight reduction, along with lower plasma glucose and leptin levels compared to the semaglutide single-treatment group. Cohort 2 confirmed that the superior weight loss in the combination group compared to the other three groups was largely due to a significant reduction in fat mass. Histological analysis of inguinal adipose tissue showed smaller adipocyte size across all treatment groups compared to the vehicle group, with no significant differences among the treatment groups. Collectively, these findings suggest combining semaglutide and avasimibe could be an effective approach to weight management.

Infection prediction in swine populations with machine learning.

The pork industry is an essential part of the global food system, providing a significant source of protein for people around the world. A major factor restraining productivity and compromising animal wellbeing in the pork industry is disease outbreaks in pigs throughout the production process: widespread outbreaks can lead to losses as high as 10% of the U.S. pig population in extreme years. In this study, we present a machine learning model to predict the emergence of infection in swine production systems throughout the production process on a daily basis, a potential precursor to outbreaks whose detection is vital for disease prevention and mitigation. We determine features that provide the most value in predicting infection, which include nearby farm density, historical test rates, piglet inventory, feed consumption during the gestation period, and wind speed and direction. We utilize these features to produce a generalizable machine learning model, evaluate the model's ability to predict outbreaks both seven and 30 days in advance, allowing for early warning of disease infection, and evaluate our model on two swine production systems and analyze the effects of data availability and data granularity in the context of our two swine systems with different volumes of data. Our results demonstrate good ability to predict infection in both systems with a balanced accuracy of [Formula: see text] on any disease in the first system and balanced accuracies (average prediction accuracy on positive and negative samples) of [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] on porcine reproductive and respiratory syndrome, porcine epidemic diarrhea virus, influenza A virus, and Mycoplasma hyopneumoniae in the second system, respectively, using the six most important predictors in all cases. These models provide daily infection probabilities that can be used by veterinarians and other stakeholders as a benchmark to more timely support preventive and control strategies on farms.

Hypoxia stabilizes SETDB1 to maintain genome stability.

Von Hippel-Lindau (VHL) is a tumor suppressor that functions as the substrate recognition subunit of the CRL2VHL E3 complex. While substrates of VHL have been identified, its tumor suppressive role remains to be fully understood. For further determination of VHL substrates, we analyzed the physical interactome of VHL and identified the histone H3K9 methyltransferase SETBD1 as a novel target. SETDB1 undergoes oxygen-dependent hydroxylation by prolyl hydroxylase domain proteins and the CRL2VHL complex recognizes hydroxylated SETDB1 for ubiquitin-mediated degradation. Under hypoxic conditions, SETDB1 accumulates by escaping CRL2VHL activity. Loss of SETDB1 in hypoxia compared with that in normoxia escalates the production of transposable element-derived double-stranded RNAs, thereby hyperactivating the immune-inflammatory response. In addition, strong derepression of TEs in hypoxic cells lacking SETDB1 triggers DNA damage-induced death. Our collective results support a molecular mechanism of oxygen-dependent SETDB1 degradation by the CRL2VHL E3 complex and reveal a role of SETDB1 in genome stability under hypoxia.

Design of hypoxia responsive CRISPR-Cas9 for target gene regulation.

The CRISPR-Cas9 system is a widely used gene-editing tool, offering unprecedented opportunities for treating various diseases. Controlling Cas9/dCas9 activity at specific location and time to avoid undesirable effects is very important. Here, we report a conditionally active CRISPR-Cas9 system that regulates target gene expression upon sensing cellular environmental change. We conjugated the oxygen-sensing transcription activation domain (TAD) of hypoxia-inducing factor (HIF-1α) with the Cas9/dCas9 protein. The Cas9-TAD conjugate significantly increased endogenous target gene cleavage under hypoxic conditions compared with that under normoxic conditions, whereas the dCas9-TAD conjugate upregulated endogenous gene transcription. Furthermore, the conjugate system effectively downregulated the expression of SNAIL, an essential gene in cancer metastasis, and upregulated the expression of the tumour-related genes HNF4 and NEUROD1 under hypoxic conditions. Since hypoxia is closely associated with cancer, the hypoxia-dependent Cas9/dCas9 system is a novel addition to the molecular tool kit that functions in response to cellular signals and has potential application for gene therapeutics.

Effects of SKCPT on Osteoarthritis in Beagle Meniscectomy and Cranial Cruciate Ligament Transection Models.

Osteoarthritis (OA) affects >500 million people globally, and this number is expected to increase. OA management primarily focuses on symptom alleviation, using non-steroidal anti-inflammatory drugs, including Celecoxib. However, such medication has serious side effects, emphasizing the need for disease-specific treatment. The meniscectomy and cranial cruciate ligament transection (CCLx)-treated beagle dog was used to investigate the efficacy of a modified-release formulation of SKI306X (SKCPT) from Clematis mandshurica, Prunella vulgaris, and Trichosanthes kirilowii in managing arthritis. SKCPT's anti-inflammatory and analgesic properties have been assessed via stifle circumference, gait, incapacitance, histopathology, and ELISA tests. The different SKCPT concentrations and formulations also affected the outcome. SKCPT improved the gait, histopathological, and ELISA OA assessment parameters compared to the control group. Pro-inflammatory cytokines and matrix metalloproteinases were significantly lower in the SKCPT-treated groups than in the control group. This study found that SKCPT reduces arthritic lesions and improves abnormal gait. The 300 mg modified-release formulation was more efficacious than others, suggesting a promising approach for managing OA symptoms and addressing disease pathogenesis. A high active ingredient level and a release pattern make this formulation effective for twice-daily arthritis treatment.

A chromosome-level reference genome of the Antarctic blackfin icefish Chaenocephalus aceratus.

The blackfin Icefish (Chaenocephalus aceratus) belongs to the family Channichthyidae and the suborder Notothenioidei which lives in the Antarctic. We corrected the mis-scaffolds in the previous linkage map results by Hi-C analysis to obtain improved results for chromosome-level genome assembly. The final assembly analysis resulted in a total of 3,135 scaffolds, a genome size of 1,065.72 Mb, and an N50 of 33.46 Mb. 820.24 Mb, representing 88.88% of the total genome, is anchored to 24 chromosomes. The final gene set of 38,024 genes, including AFGPs, was annotated using RNA evidence, proteins, and ab-initio predictions. The complete percentage of BUSCO analysis is 92.7%. In this study, we aim to contribute to the study of polar fishes by improving the genome sequences of the blackfin icefish with the AFGP genes belonging to the Notothenoidei.

Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods.

Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of 24 anomaly detection methods based on machine learning, regression, time series techniques and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests and laboratory requests. We introduced synthetic outbreaks of different size and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w) and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data but POD, Se, POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergency of cases in multiple farms, but more work is required to improve the detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases.

Production of an O-desmethylated product, a major human metabolite, of rabeprazole sulfide by bacterial P450 enzymes.

Rabeprazole is a common type of proton pump inhibitor (PPI) used to treat various peptic disorders. Unlike most PPI drugs, rabeprazole is spontaneously reduced to rabeprazole sulfide (thioether) when it is given to patients. As a result, rabeprazole sulfide is considered one of the active metabolites of rabeprazole. Rabeprazole sulfide is mainly metabolized to desmethyl rabeprazole sulfide by CYP2C19 and CYP2D6 in people. However, the pharmacological efficacy and safety of desmethyl rabeprazole sulfide have not yet been investigated. Its usage is challenging due to the high cost associated with the drug. In this study, we found CYP102A1 mutants that can produce desmethyl rabeprazole sulfide as a major metabolite of rabeprazole sulfide. The chemical characteristics of the major product were confirmed using high-performance liquid chromatography, LC-mass spectrometry, and nuclear magnetic resonance spectroscopy. CYP102A1 mutants R47L/F87V/L188Q, R47L/F87V/L188Q/A335V/Q359R, and R47L/F87V/L188Q/I254V/D351E showed kcat values of 39, 93, and 88 min-1, respectively, for O-desmethylation of rabeprazole sulfide. Furthermore, the highest concentration of desmethyl rabeprazole sulfide product from 2 mM rabeprazole sulfide at optimal conditions was obtained in bacterial whole-cell biotransformation with the R47L/F87V/L188Q mutant, reaching 0.63 mM at 4-h incubation. In conclusion, we present a platform that facilitates the efficient and sustainable production of the desmethylated product from rabeprazole sulfide for use in the biopharmaceutical industry.

Spatio-temporal variations in organic carbon composition driven by two different major phytoplankton communities in the Ross Sea, Antarctica.

The compositions of organic carbon could be important in determining biological carbon pump efficiency. However, little information on them in relation to each algal assemblage is currently available in the Ross Sea. Here, we investigated the seasonal variations in organic carbon composition and the relative abundance of each organic carbon, including particulate organic carbon (POC), dissolved organic carbon (DOC), and transparent exopolymer particles (TEPs), characterized by different algal groups in the Ross Sea. The average POC and DOC contributions to the total organic carbon (TOC = POC + DOC) were 13.8 ± 3.7 % and 86.2 ± 3.7 % in mid-January 2019 and 20.9 ± 4.1 % and 79.1 ± 4.1 % in February-March 2018, respectively. The carbon content of TEP (TEP-C) contributed 19.6 ± 11.7 % and 4.6 ± 7.0 % of POC and TOC in mid-January and 36.2 ± 14.8 % and 9.0 ± 6.7 % in February-March, respectively. We found that the organic carbon compositions were affected by seasonal variations in the phytoplankton bloom phase, physical characteristics, and phytoplankton community structure. DOC concentrations and contributions to the TOC increased as phytoplankton cells became senescent in mid-January and decreased in February-March when phytoplankton were relatively active. From February-March, the deepened mixed layer depth encouraged TEP formation, subsequently increasing the TEP contributions. Regardless of the sampling season, all organic carbon concentrations per unit Chl-a were significantly higher in P. antarctica-abundant groups. The DOC contributions to the TOC were correspondingly higher at the P. antarctica-abundant stations in mid-January, which indicates that P. antarctica could be also important in the DOC contributions in the Ross Sea. The rapid alteration in environmental characteristics and phytoplankton community structures in the Ross Sea due to climate change could affect the organic carbon pool at the euphotic layer which consequently could determine the efficiency of the biological pump.

Predicting antimicrobial resistance of bacterial pathogens using time series analysis.

Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida, and Bordetella bronchiseptica). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens.