TimeTector: A Twin-Branch Approach for Unsupervised Anomaly Detection in Livestock Sensor Noisy Data (TT-TBAD).

Unsupervised anomaly detection in multivariate time series sensor data is a complex task with diverse applications in different domains such as livestock farming and agriculture (LF&A), the Internet of Things (IoT), and human activity recognition (HAR). Advanced machine learning techniques are necessary to detect multi-sensor time series data anomalies. The primary focus of this research is to develop state-of-the-art machine learning methods for detecting anomalies in multi-sensor data. Time series sensors frequently produce multi-sensor data with anomalies, which makes it difficult to establish standard patterns that can capture spatial and temporal correlations. Our innovative approach enables the accurate identification of normal, abnormal, and noisy patterns, thus minimizing the risk of misinterpreting models when dealing with mixed noisy data during training. This can potentially result in the model deriving incorrect conclusions. To address these challenges, we propose a novel approach called "TimeTector-Twin-Branch Shared LSTM Autoencoder" which incorporates several Multi-Head Attention mechanisms. Additionally, our system now incorporates the Twin-Branch method which facilitates the simultaneous execution of multiple tasks, such as data reconstruction and prediction error, allowing for efficient multi-task learning. We also compare our proposed model to several benchmark anomaly detection models using our dataset, and the results show less error (MSE, MAE, and RMSE) in reconstruction and higher accuracy scores (precision, recall, and F1) against the baseline models, demonstrating that our approach outperforms these existing models.

Recovery of isolated lithium through discharged state calendar ageing.

Rechargeable Li-metal batteries have the potential to more than double the specific energy of the state-of-the-art rechargeable Li-ion batteries, making Li-metal batteries a prime candidate for next-generation high-energy battery technology1-3. However, current Li-metal batteries suffer from fast cycle degradation compared with their Li-ion battery counterparts2,3, preventing their practical adoption. A main contributor to capacity degradation is the disconnection of Li from the electrochemical circuit, forming isolated Li4-8. Calendar ageing studies have shown that resting in the charged state promotes further reaction of active Li with the surrounding electrolyte9-12. Here we discover that calendar ageing in the discharged state improves capacity retention through isolated Li recovery, which is in contrast with the well-known phenomenon of capacity degradation observed during the charged state calendar ageing. Inactive capacity recovery is verified through observation of Coulombic efficiency greater than 100% on both Li||Cu half-cells and anode-free cells using a hybrid continuous-resting cycling protocol and with titration gas chromatography. An operando optical setup further confirms excess isolated Li reactivation as the predominant contributor to the increased capacity recovery. These insights into a previously unknown pathway for capacity recovery through discharged state resting emphasize the marked impact of cycling strategies on Li-metal battery performance.

Solvation-property relationship of lithium-sulphur battery electrolytes.

The Li-S battery is a promising next-generation battery chemistry that offers high energy density and low cost. The Li-S battery has a unique chemistry with intermediate sulphur species readily solvated in electrolytes, and understanding their implications is important from both practical and fundamental perspectives. In this study, we utilise the solvation free energy of electrolytes as a metric to formulate solvation-property relationships in various electrolytes and investigate their impact on the solvated lithium polysulphides. We find that solvation free energy influences Li-S battery voltage profile, lithium polysulphide solubility, Li-S battery cyclability and the Li metal anode; weaker solvation leads to lower 1st plateau voltage, higher 2nd plateau voltage, lower lithium polysulphide solubility, and superior cyclability of Li-S full cells and Li metal anodes. We believe that relationships delineated in this study can guide the design of high-performance electrolytes for Li-S batteries.

Pig Movement Estimation by Integrating Optical Flow with a Multi-Object Tracking Model.

Pig husbandry constitutes a significant segment within the broader framework of livestock farming, with porcine well-being emerging as a paramount concern due to its direct implications on pig breeding and production. An easily observable proxy for assessing the health of pigs lies in their daily patterns of movement. The daily movement patterns of pigs can be used as an indicator of their health, in which more active pigs are usually healthier than those who are not active, providing farmers with knowledge of identifying pigs' health state before they become sick or their condition becomes life-threatening. However, the conventional means of estimating pig mobility largely rely on manual observations by farmers, which is impractical in the context of contemporary centralized and extensive pig farming operations. In response to these challenges, multi-object tracking and pig behavior methods are adopted to monitor pig health and welfare closely. Regrettably, these existing methods frequently fall short of providing precise and quantified measurements of movement distance, thereby yielding a rudimentary metric for assessing pig health. This paper proposes a novel approach that integrates optical flow and a multi-object tracking algorithm to more accurately gauge pig movement based on both qualitative and quantitative analyses of the shortcomings of solely relying on tracking algorithms. The optical flow records accurate movement between two consecutive frames and the multi-object tracking algorithm offers individual tracks for each pig. By combining optical flow and the tracking algorithm, our approach can accurately estimate each pig's movement. Moreover, the incorporation of optical flow affords the capacity to discern partial movements, such as instances where only the pig's head is in motion while the remainder of its body remains stationary. The experimental results show that the proposed method has superiority over the method of solely using tracking results, i.e., bounding boxes. The reason is that the movement calculated based on bounding boxes is easily affected by the size fluctuation while the optical flow data can avoid these drawbacks and even provide more fine-grained motion information. The virtues inherent in the proposed method culminate in the provision of more accurate and comprehensive information, thus enhancing the efficacy of decision-making and management processes within the realm of pig farming.

Local refinement mechanism for improved plant leaf segmentation in cluttered backgrounds.

Plant phenotyping is a critical field in agriculture, aiming to understand crop growth under specific conditions. Recent research uses images to describe plant characteristics by detecting visual information within organs such as leaves, flowers, stems, and fruits. However, processing data in real field conditions, with challenges such as image blurring and occlusion, requires improvement. This paper proposes a deep learning-based approach for leaf instance segmentation with a local refinement mechanism to enhance performance in cluttered backgrounds. The refinement mechanism employs Gaussian low-pass and High-boost filters to enhance target instances and can be applied to the training or testing dataset. An instance segmentation architecture generates segmented masks and detected areas, facilitating the derivation of phenotypic information, such as leaf count and size. Experimental results on a tomato leaf dataset demonstrate the system's accuracy in segmenting target leaves despite complex backgrounds. The investigation of the refinement mechanism with different kernel sizes reveals that larger kernel sizes benefit the system's ability to generate more leaf instances when using a High-boost filter, while prediction performance decays with larger Gaussian low-pass filter kernel sizes. This research addresses challenges in real greenhouse scenarios and enables automatic recognition of phenotypic data for smart agriculture. The proposed approach has the potential to enhance agricultural practices, ultimately leading to improved crop yields and productivity.

Proximity Matters: Interfacial Solvation Dictates Solid Electrolyte Interphase Composition.

The composition of the solid electrolyte interphase (SEI) plays an important role in controlling Li-electrolyte reactions, but the underlying cause of SEI composition differences between electrolytes remains unclear. Many studies correlate SEI composition with the bulk solvation of Li ions in the electrolyte, but this correlation does not fully capture the interfacial phenomenon of SEI formation. Here, we provide a direct connection between SEI composition and Li-ion solvation by forming SEIs using polar substrates that modify interfacial solvation structures. We circumvent the deposition of Li metal by forming the SEI above Li+/Li redox potential. Using theory, we show that an increase in the probability density of anions near a polar substrate increases anion incorporation within the SEI, providing a direct correlation between interfacial solvation and SEI composition. Finally, we use this concept to form stable anion-rich SEIs, resulting in high performance lithium metal batteries.

Dissolution of the Solid Electrolyte Interphase and Its Effects on Lithium Metal Anode Cyclability.

At >95% Coulombic efficiencies, most of the capacity loss for Li metal anodes (LMAs) is through the formation and growth of the solid electrolyte interphase (SEI). However, the mechanism through which this happens remains unclear. One property of the SEI that directly affects its formation and growth is the SEI's solubility in the electrolyte. Here, we systematically quantify and compare the solubility of SEIs derived from ether-based electrolytes optimized for LMAs using in-operando electrochemical quartz crystal microbalance (EQCM). A correlation among solubility, passivity, and cyclability established in this work reveals that SEI dissolution is a major contributor to the differences in passivity and electrochemical performance among battery electrolytes. Together with our EQCM, X-ray photoelectron spectroscopy (XPS), and nuclear magnetic resonance (NMR) spectroscopy results, we show that solubility depends on not only the SEI's composition but also the properties of the electrolyte. This provides a crucial piece of information that could help minimize capacity loss due to SEI formation and growth during battery cycling and aging.

In Situ Prelithiation by Direct Integration of Lithium Mesh into Battery Cells.

Silicon (Si)-based anodes are promising for next-generation lithium (Li)-ion batteries due to their high theoretical capacity (∼3600 mAh/g). However, they suffer quantities of capacity loss in the first cycle from initial solid electrolyte interphase (SEI) formation. Here, we present an in situ prelithiation method to directly integrate a Li metal mesh into the cell assembly. A series of Li meshes are designed as prelithiation reagents, which are applied to the Si anode in battery fabrication and spontaneously prelithiate Si with electrolyte addition. Various porosities of Li meshes tune prelithiation amounts to control the degree of prelithiation precisely. Besides, the patterned mesh design enhances the uniformity of prelithiation. With an optimized prelithiation amount, the in situ prelithiated Si-based full cell shows a constant >30% capacity improvement in 150 cycles. This work presents a facile prelithiation approach to improve battery performance.

Corrigendum: Elevated IFNA1 and suppressed IL12p40 associated with persistent hyperinflammation in COVID-19 pneumonia.

[This corrects the article DOI: 10.3389/fimmu.2023.1101808.].

Data-driven electrolyte design for lithium metal anodes.

Improving Coulombic efficiency (CE) is key to the adoption of high energy density lithium metal batteries. Liquid electrolyte engineering has emerged as a promising strategy for improving the CE of lithium metal batteries, but its complexity renders the performance prediction and design of electrolytes challenging. Here, we develop machine learning (ML) models that assist and accelerate the design of high-performance electrolytes. Using the elemental composition of electrolytes as the features of our models, we apply linear regression, random forest, and bagging models to identify the critical features for predicting CE. Our models reveal that a reduction in the solvent oxygen content is critical for superior CE. We use the ML models to design electrolyte formulations with fluorine-free solvents that achieve a high CE of 99.70%. This work highlights the promise of data-driven approaches that can accelerate the design of high-performance electrolytes for lithium metal batteries.

Elevated IFNA1 and suppressed IL12p40 associated with persistent hyperinflammation in COVID-19 pneumonia.

Introduction: Despite of massive endeavors to characterize inflammation in COVID-19 patients, the core network of inflammatory mediators responsible for severe pneumonia stillremain remains elusive. Methods: Here, we performed quantitative and kinetic analysis of 191 inflammatory factors in 955 plasma samples from 80 normal controls (sample n = 80) and 347 confirmed COVID-19 pneumonia patients (sample n = 875), including 8 deceased patients. Results: Differential expression analysis showed that 76% of plasmaproteins (145 factors) were upregulated in severe COVID-19 patients comparedwith moderate patients, confirming overt inflammatory responses in severe COVID-19 pneumonia patients. Global correlation analysis of the plasma factorsrevealed two core inflammatory modules, core I and II, comprising mainly myeloid cell and lymphoid cell compartments, respectively, with enhanced impact in a severity-dependent manner. We observed elevated IFNA1 and suppressed IL12p40, presenting a robust inverse correlation in severe patients, which was strongly associated with persistent hyperinflammation in 8.3% of moderate pneumonia patients and 59.4% of severe patients. Discussion: Aberrant persistence of pulmonary and systemic inflammation might be associated with long COVID-19 sequelae. Our comprehensive analysis of inflammatory mediators in plasmarevealed the complexity of pneumonic inflammation in COVID-19 patients anddefined critical modules responsible for severe pneumonic progression.

Revealing the Multifunctions of Li3N in the Suspension Electrolyte for Lithium Metal Batteries.

Inorganic-rich solid-electrolyte interphases (SEIs) on Li metal anodes improve the electrochemical performance of Li metal batteries (LMBs). Therefore, a fundamental understanding of the roles played by essential inorganic compounds in SEIs is critical to realizing and developing high-performance LMBs. Among the prevalent SEI inorganic compounds observed for Li metal anodes, Li3N is often found in the SEIs of high-performance LMBs. Herein, we elucidate new features of Li3N by utilizing a suspension electrolyte design that contributes to the improved electrochemical performance of the Li metal anode. Through empirical and computational studies, we show that Li3N guides Li electrodeposition along its surface, creates a weakly solvating environment by decreasing Li+-solvent coordination, induces organic-poor SEI on the Li metal anode, and facilitates Li+ transport in the electrolyte. Importantly, recognizing specific roles of SEI inorganics for Li metal anodes can serve as one of the rational guidelines to design and optimize SEIs through electrolyte engineering for LMBs.

Correlating Kinetics to Cyclability Reveals Thermodynamic Origin of Lithium Anode Morphology in Liquid Electrolytes.

The rechargeability of lithium metal batteries strongly depends on the electrolyte. The uniformity of the electroplated Li anode morphology underlies this dependence, so understanding the main drivers of uniform plating is critical for further electrolyte discovery. Here, we correlate electroplating kinetics with cyclability across several classes of electrolytes to reveal the mechanistic influence electrolytes have on morphology. Fast charge-transfer kinetics at fresh Li-electrolyte interfaces correlate well with uniform morphology and cyclability, whereas the resistance of Li+ transport through the solid electrolyte interphase (SEI) weakly correlates with cyclability. These trends contrast with the conventional thought that Li+ transport through the electrolyte or SEI is the main driver of morphological differences between classes of electrolytes. Relating these trends to Li+ solvation, Li nucleation, and the charge-transfer mechanism instead suggests that the Li/Li+ equilibrium potential and the surface energy─thermodynamic factors modulated by the strength of Li+ solvation─underlie electrolyte-dependent trends of Li morphology. Overall, this work provides an insight for discovering functional electrolytes, tuning kinetics in batteries, and explaining why weakly solvating fluorinated electrolytes favor uniform Li plating.

The UGT1A9*22 genotype identifies a high-risk group for irinotecan toxicity among gastric cancer patients.

Several studies have shown associations between irinotecan toxicity and UGT1A genetic variations in colorectal and lung cancer, but only limited data are available for gastric cancer patients. We evaluated the frequencies of UGT1A polymorphisms and their relationship with clinicopathologic parameters in 382 Korean gastric cancer patients. Polymorphisms of UGT1A1*6, UGT1A1*27, UGT1A1*28, UGT1A1*60, UGT1A7*2, UGT1A7*3, and UGT1A9*22 were genotyped by direct sequencing. In 98 patients treated with irinotecan-containing regimens, toxicity and response were compared according to the genotype. The UGT1A1*6 and UGT1A9*22 genotypes showed a higher prevalence in Korean gastric cancer patients, while the prevalence of the UG1A1*28 polymorphism was lower than in normal Koreans, as has been found in other studies of Asian populations. The incidence of severe diarrhea after irinotecan-containing treatment was more common in patients with the UGT1A1*6, UGT1A7*3, and UGT1A9*22 polymorphisms than in controls. The presence of the UGT1A1*6 allele also showed a significant association with grade III-IV neutropenia. Upon haplotype and diplotype analyses, almost every patient bearing the UGT1A1*6 or UGT1A7*3 variant also had the UGT1A9*22 polymorphism, and all severe manifestations of UGT1A polymorphism-associated toxicity were related to the UGT1A9*22 polymorphism. By genotyping UGT1A9*22 polymorphisms, we could identify high-risk gastric cancer patients receiving irinotecan-containing chemotherapy, who would experience severe toxicity. When treating high-risk patients with the UGT1A9*22 polymorphism, clinicians should closely monitor them for signs of severe toxicity such as intense diarrhea or neutropenia.

Establishment of the large-scale longitudinal multi-omics dataset in COVID-19 patients: data profile and biospecimen.

Understanding and monitoring virus-mediated infections has gained importance since the global outbreak of the coronavirus disease 2019 (COVID-19) pandemic. Studies of high-throughput omics-based immune profiling of COVID-19 patients can help manage the current pandemic and future virus-mediated pandemics. Although COVID-19 is being studied since past 2 years, detailed mechanisms of the initial induction of dynamic immune responses or the molecular mechanisms that characterize disease progression remains unclear. This study involved comprehensively collected biospecimens and longitudinal multi-omics data of 300 COVID-19 patients and 120 healthy controls, including whole genome sequencing (WGS), single-cell RNA sequencing combined with T cell receptor (TCR) and B cell receptor (BCR) sequencing (scRNA(+scTCR/BCR)-seq), bulk BCR and TCR sequencing (bulk TCR/BCR-seq), and cytokine profiling. Clinical data were also collected from hospitalized COVID-19 patients, and HLA typing, laboratory characteristics, and COVID-19 viral genome sequencing were performed during the initial diagnosis. The entire set of biospecimens and multi-omics data generated in this project can be accessed by researchers from the National Biobank of Korea with prior approval. This distribution of largescale multi-omics data of COVID-19 patients can facilitate the understanding of biological crosstalk involved in COVID-19 infection and contribute to the development of potential methodologies for its diagnosis and treatment. [BMB Reports 2022; 55(9): 465-471].

Suspension electrolyte with modified Li+ solvation environment for lithium metal batteries.

Designing a stable solid-electrolyte interphase on a Li anode is imperative to developing reliable Li metal batteries. Herein, we report a suspension electrolyte design that modifies the Li+ solvation environment in liquid electrolytes and creates inorganic-rich solid-electrolyte interphases on Li. Li2O nanoparticles suspended in liquid electrolytes were investigated as a proof of concept. Through theoretical and empirical analyses of Li2O suspension electrolytes, the roles played by Li2O in the liquid electrolyte and solid-electrolyte interphases of the Li anode are elucidated. Also, the suspension electrolyte design is applied in conventional and state-of-the-art high-performance electrolytes to demonstrate its applicability. Based on electrochemical analyses, improved Coulombic efficiency (up to ~99.7%), reduced Li nucleation overpotential, stabilized Li interphases and prolonged cycle life of anode-free cells (~70 cycles at 80% of initial capacity) were achieved with the suspension electrolytes. We expect this design principle and our findings to be expanded into developing electrolytes and solid-electrolyte interphases for Li metal batteries.

Steric Effect Tuned Ion Solvation Enabling Stable Cycling of High-Voltage Lithium Metal Battery.

1,2-Dimethoxyethane (DME) is a common electrolyte solvent for lithium metal batteries. Various DME-based electrolyte designs have improved long-term cyclability of high-voltage full cells. However, insufficient Coulombic efficiency at the Li anode and poor high-voltage stability remain a challenge for DME electrolytes. Here, we report a molecular design principle that utilizes a steric hindrance effect to tune the solvation structures of Li+ ions. We hypothesized that by substituting the methoxy groups on DME with larger-sized ethoxy groups, the resulting 1,2-diethoxyethane (DEE) should have a weaker solvation ability and consequently more anion-rich inner solvation shells, both of which enhance interfacial stability at the cathode and anode. Experimental and computational evidence indicates such steric-effect-based design leads to an appreciable improvement in electrochemical stability of lithium bis(fluorosulfonyl)imide (LiFSI)/DEE electrolytes. Under stringent full-cell conditions of 4.8 mAh cm-2 NMC811, 50 µm thin Li, and high cutoff voltage at 4.4 V, 4 M LiFSI/DEE enabled 182 cycles until 80% capacity retention while 4 M LiFSI/DME only achieved 94 cycles. This work points out a promising path toward the molecular design of non-fluorinated ether-based electrolyte solvents for practical high-voltage Li metal batteries.

Potentiometric Measurement to Probe Solvation Energy and Its Correlation to Lithium Battery Cyclability.

The electrolyte plays a critical role in lithium-ion batteries, as it impacts almost every facet of a battery's performance. However, our understanding of the electrolyte, especially solvation of Li+, lags behind its significance. In this work, we introduce a potentiometric technique to probe the relative solvation energy of Li+ in battery electrolytes. By measuring open circuit potential in a cell with symmetric electrodes and asymmetric electrolytes, we quantitatively characterize the effects of concentration, anions, and solvents on solvation energy across varied electrolytes. Using the technique, we establish a correlation between cell potential (Ecell) and cyclability of high-performance electrolytes for lithium metal anodes, where we find that solvents with more negative cell potentials and positive solvation energies-those weakly binding to Li+-lead to improved cycling stability. Cryogenic electron microscopy reveals that weaker solvation leads to an anion-derived solid-electrolyte interphase that stabilizes cycling. Using the potentiometric measurement for characterizing electrolytes, we establish a correlation that can guide the engineering of effective electrolytes for the lithium metal anode.

Correlating Li-Ion Solvation Structures and Electrode Potential Temperature Coefficients.

Temperature coefficients (TCs) for either electrochemical cell voltages or potentials of individual electrodes have been widely utilized to study the thermal safety and cathode/anode phase changes of lithium (Li)-ion batteries. However, the fundamental significance of single electrode potential TCs is little known. In this work, we discover that the Li-ion desolvation process during Li deposition/intercalation is accompanied by considerable entropy change, which significantly contributes to the measured Li/Li+ electrode potential TCs. To explore this phenomenon, we compare the Li/Li+ electrode potential TCs in a series of electrolyte formulations, where the interaction between Li-ion and solvent molecules occurs at varying strength as a function of both solvent and anion species as well as salt concentrations. As a result, we establish correlations between electrode potential TCs and Li-ion solvation structures and further verify them by ab initio molecular dynamics simulations. We show that measurements of Li/Li+ electrode potential TCs provide valuable knowledge regarding the Li-ion solvation environments and could serve as a screening tool when designing future electrolytes for Li-ion/Li metal batteries.

Single-Cell RNA Sequencing of Human Pluripotent Stem Cell-Derived Macrophages for Quality Control of The Cell Therapy Product.

Macrophages exhibit high plasticity to achieve their roles in maintaining tissue homeostasis, innate immunity, tissue repair and regeneration. Therefore, macrophages are being evaluated for cell-based therapeutics against inflammatory disorders and cancer. To overcome the limitation related to expansion of primary macrophages and cell numbers, human pluripotent stem cell (hPSC)-derived macrophages are considered as an alternative source of primary macrophages for clinical application. However, the quality of hPSC-derived macrophages with respect to the biological homogeneity remains still unclear. We previously reported a technique to produce hPSC-derived macrophages referred to as iMACs, which is amenable for scale-up. In this study, we have evaluated the biological homogeneity of the iMACs using a transcriptome dataset of 6,230 iMACs obtained by single-cell RNA sequencing. The dataset provides a valuable genomic profile for understanding the molecular characteristics of hPSC-derived macrophage cells and provide a measurement of transcriptomic homogeneity. Our study highlights the usefulness of single cell RNA-seq data in quality control of the cell-based therapy products.