Fully synthetic platform to rapidly generate tetravalent bispecific nanobody-based immunoglobulins.

Nanobodies bind a target antigen with a kinetic profile similar to a conventional antibody, but exist as a single heavy chain domain that can be readily multimerized to engage antigen via multiple interactions. Presently, most nanobodies are produced by immunizing camelids; however, platforms for animal-free production are growing in popularity. Here, we describe the development of a fully synthetic nanobody library based on an engineered human VH3-23 variable gene and a multispecific antibody-like format designed for biparatopic target engagement. To validate our library, we selected nanobodies against the SARS-CoV-2 receptor-binding domain and employed an on-yeast epitope binning strategy to rapidly map the specificities of the selected nanobodies. We then generated antibody-like molecules by replacing the VH and VL domains of a conventional antibody with two different nanobodies, designed as a molecular clamp to engage the receptor-binding domain biparatopically. The resulting bispecific tetra-nanobody immunoglobulins neutralized diverse SARS-CoV-2 variants with potencies similar to antibodies isolated from convalescent donors. Subsequent biochemical analyses confirmed the accuracy of the on-yeast epitope binning and structures of both individual nanobodies, and a tetra-nanobody immunoglobulin revealed that the intended mode of interaction had been achieved. This overall workflow is applicable to nearly any protein target and provides a blueprint for a modular workflow for the development of multispecific molecules.

Dietary patterns and child, parental, and societal factors associated with being overweight and obesity in Vietnamese children living in Ho Chi Minh city.

Childhood overweight and obesity are rapidly increasing in urban Vietnam. Dietary patterns are understudied for their association with obesity risk in these children, and it is unclear which parental and societal factors should be targeted in prevention efforts. The study assessed child characteristics, dietary patterns, parental and societal factors for associations with childhood overweight and obesity status in Ho Chi Minh City, Vietnam. A sample of 221 children aged 9-11 years was randomly selected from four Ho Chi Minh City primary schools. Weight, height and waist circumference were measured using standardized methods. Three 24-h dietary recalls were collected from 124 children, which were used to assess dietary patterns using principal component analysis (PCA). Parents completed a questionnaire about child, parental and societal factors. The overall prevalence of obesity was 31.7% and of combined overweight and obesity 59.3%. Three main dietary patterns from 10 food groups were identified by PCA: traditional (grains, vegetables, meat and meat alternatives), discretionary (snacks and sweetened beverages), and industrialized (fast food and processed meat). Children with higher discretionary diet scores had higher odds of being overweight. Being a boy, screen time over 2 h/day, parental underestimation of child weight status, father's obesity, and household income in the lowest quintile were positively associated with childhood obesity. Future intervention programmes in Vietnam need to consider targeting children's unhealthy diets and parental perceptions of child weight status, as well as focusing on upstream approaches that reduce inequities contributing to childhood obesity and concomitant dietary patterns.

Reconstruction of spectral irradiance in a real application with a multi-channel spectral sensor using convolutional neural networks.

Lighting is not only a key mediator for the perception of the architectural space but also plays a crucial role regarding the long-term well-being of its human occupants. Future lighting solutions must therefore be capable of monitoring lighting parameters to allow for a dynamic compensation of temporal changes from the optimal or intended conditions. Although mostly based on synthetic data, previous studies adopting small, low-cost, multi-band color sensors for this kind of parameter estimation have reported some promising preliminary results. Building up on these findings, the present work introduces a new methodology for estimating the absolute spectral irradiances of real-world lighting scenarios from the responses of a 10-channel spectral sensor by using a convolutional neural network approach. The lighting scenarios considered here are based on a tunable white floor lamp system set up at three different indoor locations and comprise combinations of LED, fluorescent, tungsten, and daylight lighting conditions. For white light mixtures of the various spectral components, the proposed reconstruction methodology yields estimates of the spectral power distribution with an average root-mean-square error of 1.6%, an average Δu'v' of less than 0.001, and an average illuminance accuracy of 2.7%. Sensor metamerism is discussed as a limiting factor for the achievable spectral reconstruction accuracy with certain light mixtures.

Artificial Intelligence in Hypertension: Seeing Through a Glass Darkly.

Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research efforts over the past 100 years. Hypertension requires transformative solutions that can help reduce the global burden of the disease. Artificial intelligence and machine learning, which have made a substantial impact on our everyday lives over the last decade may be the route to this transformation. However, artificial intelligence in health care is still in its nascent stages and realizing its potential requires numerous challenges to be overcome. In this review, we provide a clinician-centric perspective on artificial intelligence and machine learning as applied to medicine and hypertension. We focus on the main roadblocks impeding implementation of this technology in clinical care and describe efforts driving potential solutions. At the juncture, there is a critical requirement for clinical and scientific expertise to work in tandem with algorithmic innovation followed by rigorous validation and scrutiny to realize the promise of artificial intelligence-enabled health care for hypertension and other chronic diseases.

Distinct glycoconjugate cell surface structures make the pelagic diatom Thalassiosira rotula an attractive habitat for bacteria.

Interactions between marine diatoms and bacteria have been studied for decades. However, the visualization of physical interactions between these diatoms and their colonizers is still limited. To enhance our understanding of these specific interactions, a new Thalassiosira rotula isolate from the North Sea (strain 8673) was characterized by scanning electron microscopy and confocal laser scanning microscopy (CLSM) after staining with fluorescently labeled lectins targeting specific glycoconjugates. To investigate defined interactions of this strain with bacteria the new strain was made axenic and co-cultivated with a natural bacterial community and in two- or three-partner consortia with different bacteria of the Roseobacter group, Gammaproteobacteria and Bacteroidetes. The CLSM analysis of the consortia identified six out of 78 different lectins as very suitable to characterize glycoconjugates of T. rotula. The resulting images show that fucose-containing threads were the dominant glycoconjugates secreted by the T. rotula cells but chitin and to a lesser extent other glycoconjugates were also identified. Bacteria attached predominantly to the fucose glycoconjugates. The colonizing bacteria showed various attachment patterns such as adhering to the diatom threads in aggregates only or attaching to both the surfaces and the threads of the diatom. Interestingly the colonization patterns of single bacteria differed strikingly from those of bacterial co-cultures, indicating that interactions between two bacterial species impacted the colonization of the diatom. Our observations help to better understand physical interactions and specific colonization patterns of distinct bacterial mono- and co-cultures with an abundant diatom of costal seas.

Comorbidities of diabetes and hypertension in Vietnam: current burden, trends over time, and correlated factors.

BACKGROUND: Vietnam conducted the national Noncommunicable Disease Risk-Factor Surveillance (STEPs) surveys in the years 2010, 2015, and 2021. This study aims to use STEPs data to assess the burden of comorbidity between diabetes and hypertension, analyze trends over time, and identify factors associated with this comorbidity. METHODS: The study extracted data for the population aged 25-64 years old from three STEPs surveys. Survey weight was used for all estimations of prevalence and 95% CI. Correlated factors with comorbidity were examined by a multivariate logistics model. RESULTS: The prevalence of comorbidity in 2021 was about 3.92% among Vietnamese people aged 25-64. In the last 10 years, this prevalence has increased more than 8 times (from 0.44% to 3.92%). Sub-populations demonstrating the most significant changes included the male population, people living in urban areas, and older people. Significant factors correlated with comorbidity included demographic factors, body mass index (BMI), and clustering of 4 noncommunicable diseases (NCDs) behavioral risk factors (OR = 3.48, p < 0.05). CONCLUSION: The high comorbidity between hypertension and diabetes underscores the imperative for integrated treatment and management approaches in Vietnam. Coordinated care is essential for addressing the complex interplay between these two prevalent conditions.

Assessing data availability of NCD prevention and control in six ASEAN countries based on WHO global monitoring framework and the progress monitor indicators.

BACKGROUND: To tackle noncommunicable disease (NCD) burden globally, two sets of NCD surveillance indicators were established by the World Health Organization: 25 Global Monitoring Framework (GMF) indicators and 10 Progress Monitoring Indicators (PMI). This study aims to assess the data availability of these two sets of indicators in six ASEAN countries: Cambodia, Lao PDR, Malaysia, Myanmar, Thailand, and Vietnam. METHODS: As data on policy indicators were straightforward and fully available, we focused on studying 25 non-policy indicators: 23 GMFs and 2 PMIs. Gathering data availability of the target indicators was conducted among NCD surveillance experts from the six selected countries during May-June 2020. Our research team found information regarding whether the country had no data at all, was using WHO estimates, was providing 'expert judgement' for the data, or had actual data available for each target indicator. We triangulated their answers with several WHO data sources, including the WHO Health Observatory Database and various WHO Global Reports on health behaviours (tobacco, alcohol, diet, and physical activity) and NCDs. We calculated the percentages of the indicators that need improvement by both indicator category and country. RESULTS: For all six studied countries, the health-service indicators, based on responses to the facility survey, are the most lacking in data availability (100% of this category's indicators), followed by the health-service indicators, based on the population survey responses (57%), the mortality and morbidity indicators (50%), the behavioural risk indicators (30%), and the biological risk indicators (7%). The countries that need to improve their NCD surveillance data availability the most are Cambodia (56% of all indicators) and Lao PDR (56%), followed by Malaysia (36%), Vietnam (36%), Myanmar (32%), and Thailand (28%). CONCLUSION: Some of the non-policy GMF and PMI indicators lacked data among the six studied countries. To achieve the global NCDs targets, in the long run, the six countries should collect their own data for all indicators and begin to invest in and implement the facility survey and the population survey to track NCDs-related health services improvements once they have implemented the behavioural and biological Health Risks Population Survey in their countries.

A continuous reaction network that produces RNA precursors.

Continuous reaction networks, which do not rely on purification or timely additions of reagents, serve as models for chemical evolution and have been demonstrated for compounds thought to have played important roles for the origins of life such as amino acids, hydroxy acids, and sugars. Step-by-step chemical protocols for ribonucleotide synthesis are known, but demonstrating their synthesis in the context of continuous reaction networks remains a major challenge. Herein, compounds proposed to be important for prebiotic RNA synthesis, including glycolaldehyde, cyanamide, 2-aminooxazole, and 2-aminoimidazole, are generated from a continuous reaction network, starting from an aqueous mixture of NaCl, NH4Cl, phosphate, and HCN as the only carbon source. No well-timed addition of any other reagents is required. The reaction network is driven by a combination of γ radiolysis and dry-down. γ Radiolysis results in a complex mixture of organics, including the glycolaldehyde-derived glyceronitrile and cyanamide. This mixture is then dried down, generating free glycolaldehyde that then reacts with cyanamide/NH3 to furnish a combination of 2-aminooxazole and 2-aminoimidazole. This continuous reaction network models how precursors for generating RNA and other classes of compounds may arise spontaneously from a complex mixture that originates from simple reagents.

Traditional Vietnamese medicine Kovir capsule for non-severe COVID-19 patients: A phase III double-blind randomized controlled trial.

The number of COVID-19 infections is still increasing with the omicron variant. Although vaccination has shown its effectiveness, efficacious treatments are still required. Kovir, a Vietnamese herbal medicine, has shown potential effects for non-severe COVID-19 patients in terms of symptom resolution and prevention of disease progression in previous studies. This phase-3 trial evaluated the safety and efficacy of Kovir for non-severe COVID-19 adults. Participants were randomized to the Kovir (381 patients) or placebo (192 patients) groups. Outcomes were progression to severe/critical COVID-19, a daily symptom score based on 11 pre-defined symptoms, time to symptom resolution, a negative reverse transcription polymerase chain reaction, an EQ-5D-5L quality of life (QOL) score, and serious adverse events. Only one patient (in the placebo group) progressed to severe COVID-19, thus we could not conclude the effect of Kovir on the prevention of disease progression. Kovir significantly reduced time to symptom resolution (median: 7 vs. 11 days, hazard ratio [95% confidence interval]: 2.03 [1.66-2.48]) compared to placebo. Kovir also increased the QOL score on days 7 and 14. No safety concerns were observed. To conclude, Kovir is safe and facilitates symptom relief for non-severe COVID-19 patients. We advocate using Kovir in the early phase of COVID-19 for non-severe adult patients.

Determination and Measurement of Melanopic Equivalent Daylight (D65) Illuminance (mEDI) in the Context of Smart and Integrative Lighting.

In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells (ipRGCs) and their function, which were first proposed in 1927. The melanopsin action spectrum has been published in CIE S 026/E: 2018 with the corresponding melanopic equivalent daylight (D65) illuminance (mEDI), melanopic daylight (D65) efficacy ratio (mDER), and four other parameters. Due to the importance of mEDI and mDER, this work synthesizes a simple computational model of mDER as the main research objective, based on a database of 4214 practical spectral power distributions (SPDs) of daylight, conventional, LED, and mixed light sources. In addition to the high correlation coefficient R2 of 0.96795 and the 97% confidence offset of 0.0067802, the feasibility of the mDER model in intelligent and integrated lighting applications has been extensively tested and validated. The uncertainty between the mEDI calculated directly from the spectra and that obtained by processing the RGB sensor and applying the mDER model reached ± 3.3% after matrix transformation and illuminance processing combined with the successful mDER calculation model. This result opens the potential for low-cost RGB sensors for applications in intelligent and integrative lighting systems to optimize and compensate for the non-visual effective parameter mEDI using daylight and artificial light in indoor spaces. The goal of the research on RGB sensors and the corresponding processing method are also presented and their feasibility is methodically demonstrated. A comprehensive investigation with a huge amount of color sensor sensitivities is necessary in a future work of other research.

Extending the color discrimination metric with consideration of illuminance level.

Color discrimination is a crucial dimension for the visual assessment of white light sources. In this Letter, we took full advantage of data comparability among Farnsworth-Munsell 100 Hue (FM-100) color vision tests and modified our recent proposed color discrimination metric [Opt. Lett.45, 6062 (2020)10.1364/OL.400422] to make it effective in quantifying the color discrimination capability of lighting under multiple illuminance levels. The updated metric was developed based on a meta-analysis of 100 sets of combined visual data derived from three groups of visual studies containing 29 FM-100 trials and its superiority was comprehensively validated by 140 sets of combined data from five groups of studies with 42 FM-100 trials.

Genetic potential for changes in breeding systems: Predicted and observed trait changes during artificial selection for male and female allocation in a gynodioecious species.

PREMISE: Evolution of separate sexes from hermaphroditism often proceeds through gynodioecy, but genetic constraints on this process are poorly understood. Genetic (co-)variances and between-sex genetic correlations were used to predict evolutionary responses of multiple reproductive traits in a sexually dimorphic gynodioecious species, and predictions were compared with observed responses to artificial selection. METHODS: Schiedea (Caryophyllaceae) is an endemic Hawaiian lineage with hermaphroditic, gynodioecious, subdioecious, and dioecious species. We measured genetic parameters of Schiedea salicaria and used them to predict evolutionary responses of 18 traits in hermaphrodites and females in response to artificial selection for increased male (stamen) biomass in hermaphrodites or increased female (carpel, capsule) biomass in females. Observed responses over two generations were compared with predictions in replicate lines of treatments and controls. RESULTS: In only two generations, both stamen biomass in hermaphrodites and female biomass in females responded markedly to direct selection, supporting a key assumption of models for evolution of dioecy. Other biomass traits, pollen and ovule numbers, and inflorescence characters important in wind pollination evolved indirectly in response to selection on sex allocation. Responses generally followed predictions from multivariate selection models, with some responses unexpectedly large due to increased genetic correlations as selection proceeded. CONCLUSIONS: Results illustrate the power of artificial selection and utility of multivariate selection models incorporating sex differences. They further indicate that pollen and ovule numbers and inflorescence architecture could evolve in response to selection on biomass allocation to male versus female function, producing complex changes in plant phenotype as separate sexes evolve.

Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water.

A popular approach to select optimal adsorbents is to perform parallel experiments on adsorbents based on an initially decided goal such as specified product purity, efficiency, or binding capacity. To screen optimal adsorbents, we focused on the max adsorption capacity of the candidates at equilibrium in this work because the adsorption capacity of each adsorbent is strongly dependent on certain conditions. A data-driven machine learning tool for predicting the max adsorption capacity (Qm) of 19 pharmaceutical compounds on 88 biochars was developed. The range of values of Qm (mean 48.29 mg/g) was remarkably large, with a high number of outliers and large variability. Modified biochars enhanced the Qm and surface area values compared with the original biochar, with a statistically significant difference (Chi-square value = 7.21-18.25, P < 0.005). K- nearest neighbors (KNN) was found to be the most optimal algorithm with a root mean square error (RMSE) of 23.48 followed by random forest and Cubist with RMSE of 26.91 and 29.56, respectively, whereas linear regression and regularization were the worst algorithms. KNN model achieved R2 of 0.92 and RMSE of 16.62 for the testing data. A web app was developed to facilitate the use of the KNN model, providing a reliable solution for saving time and money in unnecessary lab-scale adsorption experiments while selecting appropriate biochars for pharmaceutical adsorption.

Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images.

Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, we see that semantic segmentation is a process of recognizing or classifying each pixel in an image, and it can help machines recognize and localize tomato suckers. This paper proposes a semantic segmentation neural network that can detect tomato suckers quickly by the tomato plant images. We choose RGB-D images which capture not only the visual of objects but also the distance information from objects to the camera. We make a tomato RGB-D image dataset for training and evaluating the proposed neural network. The proposed semantic segmentation neural network can run in real-time at 138.2 frames per second. Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the accuracy, time of execution, and sucker detection to prove its better performance.

Sirt3 Pharmacologically Promotes Insulin Sensitivity through PI3/AKT/mTOR and Their Downstream Pathway in Adipocytes.

Sirtuin-3 (Sirt3) is a major mitochondrial deacetylase enzyme that regulates multiple metabolic pathways, and its expression is decreased in diabetes type 1 and type 2 diabetes. This study aimed to elucidate Sirt3's molecular mechanism in regulating insulin sensitivity in adipocytes that can contribute to the effort of targeting Sirt3 for the treatment of obesity and type 2 diabetes. We found that the Sirt3 activator honokiol (HNK) induced adipogenesis compared to the control, in contrast to Sirt3 inhibitor, 3-TYP. Accordingly, HNK increased expression of adipocyte gene markers, gene-involved lipolysis and glucose transport (GLUT4), while 3-TYP reduced expression of those genes. Interestingly, 3-TYP caused an increase in gene expression of adipocyte-specific cytokines including IL6, resistin, and TNF-α. However, changes in adipocyte-specific cytokines in HNK treated cells were not significant. In addition, HNK stimulated insulin pathway by promoting insulin receptor beta (IRß) and PI3K/AKT/mTOR pathways, resulting in an increase in phosphorylation of the forkhead family FoxO1/FoxO3a/FoxO4 and glycogen synthase kinase-3 (GSK-3ß), opposing 3-TYP. In line with these findings, HNK increased free fatty acid and glucose uptake, contrary to 3-TYP. In conclusion, Sirt3 activator-HNK induced adipogenesis and lipolysis reduced adipocytes specific cytokines. Intriguingly, HNK activated insulin signaling pathway and increased free fatty acid as well as glucose uptake and transport, in sharp contrast to 3-TYP. These results indicate that, via insulin signaling regulation, Sirt3 activation by HNK improves insulin resistance, while Sirt3 inhibition by 3-TYP might precipitate insulin resistance.

Effects of Plant Elicitors on Growth and Gypenosides Biosynthesis in Cell Culture of Giao co lam (Gynostemma pentaphyllum).

Giao co lam (Gynostemma pentaphyllum (Thunb.) Makino) is used in Northeast and Southeast Asia countries for the treatment of various diseases, including hepatitis, diabetes, and cardiovascular disease. G. pentaphyllum saponins (gypenosides) are the major components responsible for the pharmacological activities. In this study, different concentrations of abiotic (25-200 µM methyl jasmonate-MeJA and salicylic acid-SA) or biotic elicitors (1-5 g/L yeast extract-YE and Fusarium biomass) were used as plant elicitors, in order to investigate their influences on cell growth and gypenosides accumulation in G. pentaphyllum suspension cells. Suspension cells were grown on a MS medium containing 2.0 mg/L KIN and 0.5 mg/L IBA, with initial inoculum sizes of 3 g and shaking speeds of 120 rpm for 18 days. Gypenoside and Rb1 contents were measured by colorimetric and HPLC methods. Among three elicitors, SA was suitable for gypenosides accumulation in individual treatment. The cell biomass had the same values in elicitated and control suspension cells. Gypenosides content in cells treated with 100 µM salicylic acid after 6 days of culture reached a maximum value of 79.721 mg gypenoside/g dry biomass (including 0.093 mg ginsenoside Rb1/mg dry weight), which was 2.18-folds higher than that of the natural product. The elicitation promises an efficiency strategy for the production gypenosides in Gynostemma pentaphyllum suspension cells.

Effects of Supplemental Light Spectra on the Composition, Production and Antimicrobial Activity of Ocimum basilicum L. Essential Oil.

This study was performed to investigate the effects of different supplemental light spectra and doses (duration and illuminance) on the essential oil of basil (Ocimum basilicum L.) cultivated in the net-house in Vietnam during four months. Ten samples of basil aerial parts were hydrodistilled to obtain essential oils which had the average yields from 0.88 to 1.30% (v/w, dry). The oils analyzed using GC-FID and GC-MS showed that the main component was methyl chavicol (87.4−90.6%) with the highest values found in the oils of basil under lighting conditions of 6 h/day and 150−200 µmol·m−2·s−1. Additional lighting conditions caused the significant differences (p < 0.001) in basil biomass and oil production with the highest values found in the oils of basil under two conditions of (1) 71% Red: 20% Blue: 9.0% UVA in at 120 µmol·m−2·s−1 in 6 h/day and (2) 43.5% Red: 43.5% Blue: 8.0% Green: 5.0% Far-Red at 100 µmol·m−2·s−1 in 6 h/day. The oils of basil in some formulas showed weak inhibitory effects on only the Bacillus subtilis strain. Different light spectra affect the biomass and essential oil production of basil, as well as the concentrations of the major components in the oil.

Memory colors and the assessment of color quality in lighting applications.

Due to their potential use as an internal reference, memory colors may provide an excellent approach for the color rendition evaluation of white light sources in terms of predicting visual appreciation. Because of certain limitations in the design of existing memory-related color quality measures, a new metric based on the outcome of a series of recently conducted memory color appearance rating experiments is proposed in this work. In order to validate its predictive performance, a meta-correlation analysis on a comprehensive set of preference rating data collected from literature is performed. Results indicate that the new metric proposal outperforms established color quality measures and is capable of correctly predicting the rank order of light sources in different lighting scenarios. The future inclusion of this new metric into a comprehensive lighting quality model may serve as a valuable tool for the lighting designer to create optimally lit environments for humans that do not only support the visual task fulfillment but also increase the users' well-being and emotional comfort by rendering the perceived space in such a way that it complies with the people's inherent memory references.

Quantifying observer metamerism of LED spectra which chromatically mimic natural daylight.

When LEDs are used to mimic daylight, a side-by-side comparison of the chromaticity difference between the LED spectrum and natural daylight will be perceived differently by individual observers. The magnitude of this effect depends on the LED light's spectral power distribution and can be assessed by using individual observer functions. To minimize the computational effort, an observer metamerism index can be utilized. Here, we compare three methods from the literature to define an observer metamerism index by carrying out a correlation analysis, in which reference spectra of the whole daylight range (1600 K to 88000 K) are used together with an empirical study. The recommended metric is based on a principal component analysis of 1000 individual observers' color matching functions to define a deviate observer. Using the proposed metamerism index significantly simplifies the calculation of the observer metamerism evaluation. Thus, this metric can be applied in spectral optimization pipelines, which are embedded in smart and adaptive multi-primary LED luminaires.

Learning Temporal Quantum Tomography.

Quantifying and verifying the control level in preparing a quantum state are central challenges in building quantum devices. The quantum state is characterized from experimental measurements, using a procedure known as tomography, which requires a vast number of resources. However, tomography for a quantum device with temporal processing, which is fundamentally different from standard tomography, has not been formulated. We develop a practical and approximate tomography method using a recurrent machine learning framework for this intriguing situation. The method is based on repeated quantum interactions between a system called quantum reservoir with a stream of quantum states. Measurement data from the reservoir are connected to a linear readout to train a recurrent relation between quantum channels applied to the input stream. We demonstrate our algorithms for representative quantum learning tasks, followed by the proposal of a quantum memory capacity to evaluate the temporal processing ability of near-term quantum devices.