Simulating chemical reaction dynamics on quantum computer.

The electronic energies of molecules have been successfully evaluated on quantum computers. However, more attention is paid to the dynamics simulation of molecules in practical applications. Based on the variational quantum eigensolver (VQE) algorithm, Fedorov et al. proposed a correlated sampling (CS) method and demonstrated the vibrational dynamics of H2 molecules [J. Chem. Phys. 154, 164103 (2021)]. In this study, we have developed a quantum approach by extending the CS method based on the VQE algorithm (labeled eCS-VQE) for simulating chemical reaction dynamics. First, the CS method is extended to the three-dimensional cases for calculation of first-order energy gradients, and then, it is further generalized to calculate the second-order gradients of energies. By calculating atomic forces and vibrational frequencies for H2, LiH, H+ + H2, and Cl- + CH3Cl systems, we have seen that the approach has achieved the CCSD level of accuracy. Thus, we have simulated dynamics processes for two typical chemical reactions, hydrogen exchange and chlorine substitution, and obtained high-precision reaction dynamics trajectories consistent with the classical methods. Our eCS-VQE approach, as measurement expectations and ground-state wave functions can be reused, is less demanding in quantum computing resources and is, therefore, a feasible means for the dynamics simulation of chemical reactions on the current noisy intermediate-scale quantum-era quantum devices.

Peripheral peroxisomal ß-oxidation engages neuronal serotonin signaling to drive stress-induced aversive memory in C. elegans.

Physiological dysfunction confers negative valence to coincidental sensory cues to induce the formation of aversive associative memory. How peripheral tissue stress engages neuromodulatory mechanisms to form aversive memory is poorly understood. Here, we show that in the nematode C. elegans, mitochondrial disruption induces aversive memory through peroxisomal ß-oxidation genes in non-neural tissues, including pmp-4/very-long-chain fatty acid transporter, dhs-28/3-hydroxylacyl-CoA dehydrogenase, and daf-22/3-ketoacyl-CoA thiolase. Upregulation of peroxisomal ß-oxidation genes under mitochondrial stress requires the nuclear hormone receptor NHR-49. Importantly, the memory-promoting function of peroxisomal ß-oxidation is independent of its canonical role in pheromone production. Peripheral signals derived from the peroxisomes target NSM, a critical neuron for memory formation under stress, to upregulate serotonin synthesis and remodel evoked responses to sensory cues. Our genetic, transcriptomic, and metabolomic approaches establish peroxisomal lipid signaling as a crucial mechanism that connects peripheral mitochondrial stress to central serotonin neuromodulation in aversive memory formation.

A Novel 3D Culture Scaffold to Shorten Development Time for Multicellular Tumor Spheroids.

Multicellular tumor spheroids and tumoroids are considered ideal in vitro models that reflect the features of the tumor microenvironment. Biomimetic components resembling the extracellular matrix form scaffolds to provide structure to 3-dimensional (3D) culture systems, supporting the growth of both spheroids and tumoroids. Although Matrigel has long been used to support 3D culture systems, batch variations, component complexity, and the use of components derived from tumors are complicating factors. To address these issues, we developed the ACD 3D culture system to provide better control and consistency. We evaluated spheroid and tumoroid formation using the ACD 3D culture system, including the assessment of cell viability and cancer marker expression. Under ACD 3D culture conditions, spheroids derived from cancer cell lines exhibited cancer stem cell characteristics, including a sphere-forming size and the expression of stem cell marker genes. The ACD 3D culture system was also able to support patient-derived primary cells and organoid cell cultures, displaying adequate cell growth, appropriate morphology, and resistance to oxaliplatin treatment. These spheroids could also be used for drug screening purposes. In conclusion, the ACD 3D culture system represents an efficient tool for basic cancer research and therapeutic development.

A unified framework of transformations based on the Jordan-Wigner transformation.

Quantum simulation of chemical Hamiltonians enables the efficient calculation of chemical properties. Mapping is one of the essential steps in simulating fermionic systems on quantum computers. In this work, a unified framework of transformations mapping fermionic systems to qubit systems is presented and many existing transformations-such as Jordan-Wigner, Bravyi-Kitaev, and parity transformations-are included in this framework. Based on this framework, the Multilayer Segmented Parity (MSP) transformation is proposed. The MSP transformation is a general mapping with an adjustable parameter vector, which can be viewed as a generalization of the above-mentioned mappings. Furthermore, the MSP transformation can adjust flexibly when dealing with different systems. Applying these mappings to the electronic structure Hamiltonians of various molecules, the MSP transformation is found to perform better on a number of Pauli operators and gates needed in the circuit of Hamiltonian simulation. The MSP transformation will reduce the qubit gate requirement for Hamiltonian simulation on noisy intermediate-scale quantum devices, and it will provide a much wider choice of mappings for researchers.

Iridescent Colloidal Crystals Composed of SiO2 Porous Hollow Sphere for SERS Application.

In recent years, the application of low-refractive-index materials in the optical field has attracted considerable attention due to it high transmittance and high optical sensitivity. In this study, we synthesized SiO2 porous hollow spheres (SPHS) with an ultralow refractive index (n = 1.05) by using a templating method. Their refractive indices could be easily controlled from 1.05 to 1.08 by tuning the thickness of shell. In addition, a droplet coatings method is proposed for SPHS colloidal crystal (CC) by controlling the temperature and humidity. The SPHS CCs displayed distinct structural colors when the incident angle was adjusted and demonstrated high angular resolution. Moreover, the iridescent color changes could be observed with the naked eye. For surface-enhanced Raman scattering application, more analyte could be absorbed by the porous shells, and metal nanoparticles were coated on the SPHSs surface to increase the hot spot density for improving the SERS intensity.

Highly Efficient Adsorption of Sr2+ and Co2+ Ions by Ambient Prepared Alkali Activated Metakaolin.

This study aimed to explore a low cost and sustainable adsorbent to remove Sr2+ and Co2+ ions, which are major radioactive ions in nuclear wastewater. The material properties of the alkali-activated metakaoline as a function of soaking time at ambient temperature from 1 day to 7 days were examined by XRD, XRF, SEM, and solid-state NMR. Adsorption isotherms were used to evaluate the appropriate soaking time for the optimal sorption performance for both Sr2+ and Co2+ ions. The alkali-activated metakaolin soaked for 3 days (BK3) presented the maximum adsorption capacities of 3.81 meq/g (167.5 mg/g) and 4.02 meq/g (118.5 mg/g) for Sr2+ and Co2+, respectively. The sorption mechanisms for Sr2+ and Co2+ in the BK3 sample were investigated, and the experimental results indicated that adsorption for Sr2+ was achieved via ion exchange. By contrast, surface complexation in combination with ion exchange contributed to the sorption mechanisms for the removal of Co2+. Competitive adsorption experiments revealed that the alkali-activated metakaolin favored the adsorption for divalent ions (i.e., Sr2+ and Co2+), and it was less effective for Cs+. Finally, the used adsorbent could be directly mineralized and vitrified by heat treatment to immobilize the Sr2+ and Co2+ ions.

Impact of recording length and other arrhythmias on atrial fibrillation detection from wrist photoplethysmogram using smartwatches.

This study aimed to evaluate whether quantitative analysis of wrist photoplethysmography (PPG) could detect atrial fibrillation (AF). Continuous electrocardiograms recorded using an electrophysiology recording system and PPG obtained using a wrist-worn smartwatch were simultaneously collected from patients undergoing catheter ablation or electrical cardioversion. PPG features were extracted from 10, 25, 40, and 80 heartbeats of the split segments. Machine learning with a support vector machine and random forest approach were used to detect AF. A total of 116 patients were evaluated. We annotated > 117 h of PPG. A total of 6475 and 3957 segments of 25-beat pulse-to-pulse intervals (PPIs) were annotated as AF and sinus rhythm, respectively. The accuracy of the 25 PPIs yielded a test area under the receiver operating characteristic curve (AUC) of 0.9676, which was significantly better than the AUC for the 10 PPIs (0.9453; P < .001). PPGs obtained from another 38 patients with frequent premature ventricular/atrial complexes (PVCs/PACs) were used to evaluate the impact of other arrhythmias on diagnostic accuracy. The new AF detection algorithm achieved an AUC of 0.9680. The appropriate data length of PPG for optimizing the PPG analytics program was 25 heartbeats. Algorithm modification using a machine learning approach shows robustness to PVCs/PACs.

Quantum Advantages of Communication Complexity from Bell Nonlocality.

Communication games are crucial tools for investigating the limitations of physical theories. The communication complexity (CC) problem is a typical example, for which several distributed parties attempt to jointly calculate a given function with limited classical communications. In this work, we present a method to construct CC problems from Bell tests in a graph-theoretic way. Starting from an experimental compatibility graph and the corresponding Bell test function, a target function that encodes the information of each edge can be constructed; then, using this target function, we can construct a CC function, and by pre-sharing entangled states, its success probability exceeds that of the arbitrary classical strategy. The non-signaling protocol based on the Popescu-Rohrlich box is also discussed, and the success probability in this case reaches one.

Neuronal mitochondrial dynamics coordinate systemic mitochondrial morphology and stress response to confer pathogen resistance in C. elegans.

Mitochondrial functions across different tissues are regulated in a coordinated fashion to optimize the fitness of an organism. Mitochondrial unfolded protein response (UPRmt) can be nonautonomously elicited by mitochondrial perturbation in neurons, but neuronal signals that propagate such response and its physiological significance remain incompletely understood. Here, we show that in C. elegans, loss of neuronal fzo-1/mitofusin induces nonautonomous UPRmt through multiple neurotransmitters and neurohormones, including acetylcholine, serotonin, glutamate, tyramine, and insulin-like peptides. Neuronal fzo-1 depletion also triggers nonautonomous mitochondrial fragmentation, which requires autophagy and mitophagy genes. Systemic activation of UPRmt and mitochondrial fragmentation in C. elegans via perturbing neuronal mitochondrial dynamics improves resistance to pathogenic Pseudomonas infection, which is supported by transcriptomic signatures of immunity and stress-response genes. We propose that C. elegans surveils neuronal mitochondrial dynamics to coordinate systemic UPRmt and mitochondrial connectivity for pathogen defense and optimized survival under bacterial infection.

Wnt signalling and endocytosis: Mechanisms, controversies and implications for stress responses.

Wnt signalling is one of a few conserved pathways that control diverse aspects of development and morphogenesis in all metazoan species. Endocytosis is a key mechanism that regulates the secretion and graded extracellular distribution of Wnt glycoproteins from the source cells, as well as Wnt signal transduction in the receiving cells. However, controversies exist regarding the requirement of clathrin-dependent endocytosis in Wnt signalling. Various lines of evidence from recent studies suggest that Wnt-ß-catenin signalling is also involved in the regulation of cellular stress responses in adulthood, a role that is beyond its canonical functions in animal development. In this review, we summarise recent advances in the molecular and cellular mechanisms by which endocytosis modulates Wnt signalling. We also discuss how Wnt signalling could be repurposed to regulate mitochondrial stress response in the nematode Caenorhabditis elegans.

Rolling-shutter-effect camera-based visible light communication using RGB channel separation and an artificial neural network.

We propose and demonstrate a light-panel and rolling-shutter-effect (RSE) camera-based visible light communication (VLC) system using Z-score normalization, red/green/blue (RGB) color channel separation, and 1-D artificial neural network (ANN). The proposed scheme can mitigate the high inter-symbol interference (ISI) generated by the RSE pattern due to the low pixel-per-bit and high noise-ratio (NR) of the display contents.

Visible light positioning (VLP) system using low-cost organic photovoltaic cell (OPVC) for low illumination environments.

We propose and demonstrate a received-signal-strength (RSS) based visible light positioning (VLP) system using a low-cost organic photovoltaic cell (OPVC) receiver (Rx). The OPVC is a passive device without the need of external power supply. It could detect VLC signal and harvest energy. Our developed OPVC has a high power conversion efficiency (PCE) of 9.8%. The VLP system can be operated at a low illumination of 130 lux. The regression machine learning (ML) algorithm is used to enhance the positioning accuracy.

Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study.

BACKGROUND: Changeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except for timely monitoring, epidemic forecasting using the national influenza surveillance data can provide pivotal information for public health response. OBJECTIVE: We aimed to develop predictive models using machine learning to provide real-time influenza-like illness forecasts. METHODS: Using surveillance data of influenza-like illness visits from emergency departments (from the Real-Time Outbreak and Disease Surveillance System), outpatient departments (from the National Health Insurance database), and the records of patients with severe influenza with complications (from the National Notifiable Disease Surveillance System), we developed 4 machine learning models (autoregressive integrated moving average, random forest, support vector regression, and extreme gradient boosting) to produce weekly influenza-like illness predictions for a given week and 3 subsequent weeks. We established a framework of the machine learning models and used an ensemble approach called stacking to integrate these predictions. We trained the models using historical data from 2008-2014. We evaluated their predictive ability during 2015-2017 for each of the 4-week time periods using Pearson correlation, mean absolute percentage error (MAPE), and hit rate of trend prediction. A dashboard website was built to visualize the forecasts, and the results of real-world implementation of this forecasting framework in 2018 were evaluated using the same metrics. RESULTS: All models could accurately predict the timing and magnitudes of the seasonal peaks in the then-current week (nowcast) (ρ=0.802-0.965; MAPE: 5.2%-9.2%; hit rate: 0.577-0.756), 1-week (ρ=0.803-0.918; MAPE: 8.3%-11.8%; hit rate: 0.643-0.747), 2-week (ρ=0.783-0.867; MAPE: 10.1%-15.3%; hit rate: 0.669-0.734), and 3-week forecasts (ρ=0.676-0.801; MAPE: 12.0%-18.9%; hit rate: 0.643-0.786), especially the ensemble model. In real-world implementation in 2018, the forecasting performance was still accurate in nowcasts (ρ=0.875-0.969; MAPE: 5.3%-8.0%; hit rate: 0.582-0.782) and remained satisfactory in 3-week forecasts (ρ=0.721-0.908; MAPE: 7.6%-13.5%; hit rate: 0.596-0.904). CONCLUSIONS: This machine learning and ensemble approach can make accurate, real-time influenza-like illness forecasts for a 4-week period, and thus, facilitate decision making.

CMOS camera based visible light communication (VLC) using grayscale value distribution and machine learning algorithm.

We demonstrate a visible light communication (VLC) system using light emitting diode (LED) backlight display panel and mobile-phone complementary-metal-oxide-semiconductor (CMOS) camera. The panel is primarily used for displaying advertisements. By modulating its backlight, dynamic contents (i.e. secondary information) can be transmitted wirelessly to users based on rolling shutter effect (RSE) of the CMOS camera. As different display content will be displayed on the panel, the VLC performance is significantly limited if the noise-ratio (NR) is too high. Here, we propose and demonstrate a CMOS RSE pattern demodulation scheme using grayscale value distribution (GVD) and machine learning algorithm (MLA) to significantly enhance the demodulation.

Energy Harvesting Under Dim-Light Condition With Dye-Sensitized and Perovskite Solar Cells.

We demonstrate highly efficient energy harvesting devices for dim-light application under 200 lux irradiation using dye-sensitized solar cells (DSCs) and perovskite solar cells (PSCs). The high-efficiency DSCs are composed of cobalt-based redox mediators in 3-methoxypropionitrile (MPN) solvent with MK-2 sensitizer. With the introduction of under layer treatment and fine-tuning of compositions in cobalt-based electrolyte, the power conversion efficiency of cobalt-based DSCs achieves 16.0% under 200 lux illumination. That outperforms the best device using the conventional iodine-based electrolyte illuminated with the same light intensity. Especially, cobalt-based electrolyte system exhibits a higher open circuit voltage than iodine-based electrolyte counterpart. We also investigate perovskite solar cells under dim-light condition. PSCs show higher open circuit voltage and short circuit current density than DSCs with efficiency up to 23.4%. In this work, our results demonstrate the promising potential of DSCs and PSCs in the dim-light applications.

Glycol chitin/PAA hydrogel composite incorporated bio-functionalized PLGA microspheres intended for sustained release of anticancer drug through intratumoral injection.

A novel anti-hepatoma drug release hybrid system is prepared by using poly(acrylic acid) (PAA) and glycol chitin as substrate in combination with Paclitaxel (PTX)-loaded bio-biofunctionalized poly(lactic-co-glycolic acid) (PLGA) micro-particles, which is intended for cancer therapy through intratumoral injection. The rheological behavior of glycol chitin (7 wt%)/PAA illustrates that it has low gelling temperature (i.e. 17 °C at pH 7.56) which ensures that the formulation turns to gel at physiological condition. The gelling time of glycol chitin/PAA is 16 minutes at 25 °C and 3 minutes at 37 °C, which is convenient for doctors to inject the in-situ gel formulations into the tumor location of patient. The drug release behavior reveals that the system can dramatically postpone the drug release. The cell viability test indicates that the micro-particles with drug still have 62% inhibitory effect on hepatoma cells in the fourteenth day after combing with hydrogel. This system is a promising approach for cancer therapy through intratumoral injection of in-situ gel formulations to extend retention time at tumor sites.

Experimentally Robust Self-testing for Bipartite and Tripartite Entangled States.

Self-testing is a method with which a classical user can certify the state and measurements of quantum systems in a device-independent way. In particular, self-testing of entangled states is of great importance in quantum information processing. An understandable example is that the maximal violation of the Clauser-Horne-Shimony-Holt inequality necessarily implies that the bipartite system shares a singlet. One essential question in self-testing is that, when one observes a nonmaximum violation, how far is the tested state from the target state (which maximally violates a certain Bell inequality)? The answer to this question describes the robustness of the used self-testing criterion, which is highly important in a practical sense. Recently, J. Kaniewski derived two analytic self-testing bounds for bipartite and tripartite systems. In this Letter, we experimentally investigate these two bounds with high-quality two-qubit and three-qubit entanglement sources. The results show that these bounds are valid for various entangled states that we prepared. Thereby, a proof-of-concept demonstration of robust self-testing is achieved, which improves on the previous results significantly.

Simultaneous observation of quantum contextuality and quantum nonlocality.

Quantum nonlocality and quantum contextuality are the most curious properties that change our understanding of nature, and were observed independently in recent decades. One important question is whether both properties can be observed simultaneously. In this paper, we show that in a qutrit-qutrit system we can observe quantum nonlocality and quantum contextuality at the same time. From the perspective of quantum information, our experiment proves in principle that the two resources, quantum nonlocality and quantum contextuality, can be utilized simultaneously.

miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions.

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.

Experimental Quantification of Asymmetric Einstein-Podolsky-Rosen Steering.

Einstein-Podolsky-Rosen (EPR) steering describes the ability of one observer to nonlocally "steer" the other observer's state through local measurements. EPR steering exhibits a unique asymmetric property; i.e., the steerability can differ between observers, which can lead to one-way EPR steering in which only one observer obtains steerability in the steering process. This property is inherently different from the symmetric concepts of entanglement and Bell nonlocality, and it has attracted increasing interest. Here, we experimentally demonstrate asymmetric EPR steering for a class of two-qubit states in the case of two measurement settings. We propose a practical method to quantify the steerability. We then provide a necessary and sufficient condition for EPR steering and clearly demonstrate one-way EPR steering. Our work provides new insight into the fundamental asymmetry of quantum nonlocality and has potential applications in asymmetric quantum information processing.