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Abstract Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity. 

RNA structure and functional dynamics play fundamental roles in controlling biological systems. Molecular dynamics simulation, which can characterize interactions at an atomistic level, can advance the understanding on newdrug discovery, manufacturing, and delivery mechanisms. However, it is computationally unattainable to support the development of a digital twin for enzymatic reaction network mechanism learning, and endto-end bioprocess design and control. Thus, we create a hybrid (“mechanistic + machine learning") model characterizing the interdependence of RNA structure and functional dynamics from atomistic to macroscopic levels. To assess the proposed modeling strategy, we consider RNA degradation which is a critical process in cellular biology that affects gene expression. The empirical study of RNA lifetime prediction demonstrates the promising performance of the proposed multi-scale bioprocess hybrid modeling strategy. 

In the context of keyword spotting (KWS), the replacement of handcrafted speech features by learnable features has not yielded superior KWS performance. In this study, we demonstrate that filterbank learning outperforms handcrafted speech features for KWS whenever the number of filterbank channels is severely decreased. Reducing the number of channels might yield certain KWS performance drop, but also a substantial energy consumption reduction, which is key when deploying common always-on KWS on low-resource devices. Experimental results on a noisy version of the Google Speech Commands Dataset show that filterbank learning adapts to noise characteristics to provide a higher degree of robustness to noise, especially when dropout is integrated. Thus, switching from typically used 40-channel log-Mel features to 8-channel learned features leads to a relative KWS accuracy loss of only 3.5% while simultaneously achieving a 6.3× energy consumption reduction. 

Gross domestic product (GDP) summarizes a vast amount of economic information in a single monetary metric that is widely used by decision makers around the world. However, GDP fails to capture fully the contributions of nature to economic activity and human well-being. To address this critical omission, we develop a measure of gross ecosystem product (GEP) that summarizes the value of ecosystem services in a single monetary metric. We illustrate the measurement of GEP through an application to the Chinese province of Qinghai, showing that the approach is tractable using available data. Known as the “water tower of Asia,” Qinghai is the source of the Mekong, Yangtze, and Yellow Rivers, and indeed, we find that water-related ecosystem services make up nearly two-thirds of the value of GEP for Qinghai. Importantly most of these benefits accrue downstream. In Qinghai, GEP was greater than GDP in 2000 and three-fourths as large as GDP in 2015 as its market economy grew. Large-scale investment in restoration resulted in improvements in the flows of ecosystem services measured in GEP (127.5%) over this period. Going forward, China is using GEP in decision making in multiple ways, as part of a transformation to inclusive, green growth. This includes investing in conservation of ecosystem assets to secure provision of ecosystem services through transregional compensation payments. 

Abstract The Jiangmen Underground Neutrino Observatory (JUNO) is a large-scale neutrino experiment with multiple physics goals including determining the neutrino mass hierarchy, the accurate measurement of neutrino oscillation parameters, the neutrino detection from supernovae, the Sun, and the Earth, etc. JUNO puts forward physically and technologically stringent requirements for its central detector (CD), including a large volume and target mass (20 kt liquid scintillator, LS), a high-energy resolution (3% at 1 MeV), a high light transmittance, the largest possible photomultiplier (PMT) coverage, the lowest possible radioactive background, etc. The CD design, using a spherical acrylic vessel with a diameter of 35.4 m to contain the LS and a stainless steel structure to support the acrylic vessel and PMTs, was chosen and optimized. The acrylic vessel and the stainless steel structure will be immersed in pure water to shield the radioactive background and bear great buoyancy. The challenging requirements of the acrylic sphere have been achieved, such as a low intrinsic radioactivity and high transmittance of the manufactured acrylic panels, the tensile and compressive acrylic node design with embedded stainless steel pad, and one-time polymerization for multiple bonding lines. Moreover, several technical challenges of the stainless steel structure have been solved: the production of low radioactivity stainless steel material, the deformation and precision control during production and assembly, and the usage of high-strength stainless steel rivet bolt and of high friction efficient linkage plate. Finally, the design of the ancillary equipment such as the LS filling, overflowing, and circulating system was done. 

Abstract We explore the decay of bound neutrons in the JUNO liquid scintillator detector into invisible particles (e.g.,$$n\rightarrow 3 \nu $$ $n\to 3\nu$or$$nn \rightarrow 2 \nu $$ $nn\to 2\nu$), which do not produce an observable signal. The invisible decay includes two decay modes:$$ n \rightarrow { inv} $$ $n\to \mathrm{inv}$and$$ nn \rightarrow { inv} $$ $nn\to \mathrm{inv}$. The invisible decays ofs-shell neutrons in$$^{12}\textrm{C}$$ $_{}^{12}\text{C}$will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino$${\bar{\nu }}_e$$ ${\overline{\nu }}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are$$\tau /B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, \textrm{years}$$ $\tau /B(n\to \mathrm{inv})>5.0\times {10}^{31}\text{years}$and$$\tau /B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, \textrm{years}$$ $\tau /B(nn\to \mathrm{inv})>1.4\times {10}^{32}\text{years}$. 

Abstract The physics potential of detecting⁸B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model-independent manner by using three distinct channels of the charged current (CC), neutral current (NC), and elastic scattering (ES) interactions. Due to the largest-ever mass of¹³C nuclei in the liquid scintillator detectors and the expected low background level,⁸B solar neutrinos are observable in the CC and NC interactions on¹³C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC, and ES channels to guarantee the observation of the⁸B solar neutrinos. From the sensitivity studies performed in this work, we show that JUNO, with 10 yr of data, can reach the 1σprecision levels of 5%, 8%, and 20% for the⁸B neutrino flux, ${\sin }^2{\theta }_{12}$, and $\mathrm{\Delta }{m}_{21}^2$, respectively. Probing the details of both solar physics and neutrino physics would be unique and helpful. In addition, when combined with the Sudbury Neutrino Observatory measurement, the world's best precision of 3% is expected for the measurement of the⁸B neutrino flux. 

Abstract The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detection of neutrinos before (pre-SN) and during the supernova (SN) burst presents a unique opportunity for multi-messenger observations of CCSN events. In this study, we describe the monitoring concept and present the sensitivity of the system to pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton liquid scintillator detector currently under construction in South China.The real-time monitoring system is designed to ensure both prompt alert speed and comprehensive coverage of progenitor stars. It incorporates prompt monitors on the electronic board as well as online monitors at the data acquisition stage.Assuming a false alert rate of 1 per year, this monitoring system exhibits sensitivity to pre-SN neutrinos up to a distance of approximately 1.6 (0.9) kiloparsecs and SN neutrinos up to about 370 (360) kiloparsecs for a progenitor mass of 30 solar masses, considering both normal and inverted mass ordering scenarios.The pointing ability of the CCSN is evaluated by analyzing the accumulated event anisotropy of inverse beta decay interactions from pre-SN or SN neutrinos. This, along with the early alert, can play a crucial role in facilitating follow-up multi-messenger observations of the next galactic or nearby extragalactic CCSN.