Isolation of anti-tumor monoclonal antibodies targeting on MICA/B α3 domain by single B cell technology for colon cancer therapy.

Colon cancer (CC) is one of the most common gastrointestinal malignancies. Effectiveness of the existing therapies is limited. Immunotherapy is a promising complementary treatment approach for CC. Major histocompatibility complex class I-related protein A and B (MICA/B) are ligands for NK cells. Shedding of MICA/B from the surface of tumor cells by cleavage of MICA/B at the membrane proxial region in MICA/B α3 structural domain is one of immune evasion strategies leading to escape of cancer cells from immunosurveillance. In this study, we generated a panel of MICA/B monoclonal antibodies (mAbs) and identified one of mAbs, mAb RDM028, that had high binding affinity to MICA/B and recognized a site on MICA/B α3 structural domain that is critically important for cleavage of MICA/B. Our study has further demonstrated that RDM028 augmented the surface expression of MICA/B on HCT-116 human CC cells by inhibiting the MICA/B shedding resulting in the enhanced cyotoxicity of NK cells against HCT-116 human CC cells and mediated anti-tumor activity in nude mouse model of colon cancer. These results indicate that mAb RDM028 could be explored for developing as an effective immuno therapy against CC by targeting the MICA/B α3 domain to promot immunosurveillance mediated by MICA/B-NKG2D interaction.

From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning.

Data scarcity is one of the critical bottlenecks to utilizing machine learning in material discovery. Transfer learning can use existing big data to assist property prediction on small data sets, but the premise is that there must be a strong correlation between large and small data sets. To extend its applicability in scenarios with different properties and materials, here we develop a hybrid framework combining adversarial transfer learning and expert knowledge, which enables the direct prediction of carrier mobility of two-dimensional (2D) materials using the knowledge learned from bulk effective mass. Specifically, adversarial training ensures that only common knowledge between bulk and 2D materials is extracted while expert knowledge is incorporated to further improve the prediction accuracy and generalizability. Successfully, 2D carrier mobilities are predicted with the accuracy over 90% from only crystal structure, and 21 2D semiconductors with carrier mobilities far exceeding silicon and suitable bandgap are successfully screened out. This work enables transfer learning in simultaneous cross-property and cross-material scenarios, providing an effective tool to predict intricate material properties with limited data.

Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory.

The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available.

Adaptive Design of Alloys for CO2 Activation and Methanation via Reinforcement Learning Monte Carlo Tree Search Algorithm.

Data-driven machine learning (ML) has earned remarkable achievements in accelerating materials design, while it heavily relies on high-quality data acquisition. In this work, we develop an adaptive design framework for searching for optimal materials starting from zero data and with as few DFT calculations as possible. This framework integrates automatic density functional theory (DFT) calculations with an improved Monte Carlo tree search via reinforcement learning algorithm (MCTS-PG). As a successful example, we apply it to rapidly identify the desired alloy catalysts for CO2 activation and methanation within 200 MCTS-PG steps. To this end, seven alloy surfaces with high theoretical activity and selectivity for CO2 methanation are screened out and further validated by comprehensive free energy calculations. Our adaptive design framework enables the fast computational exploration of materials with desired properties via minimal DFT calculations.

Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression.

Developing activity descriptors via data-driven machine learning (ML) methods can speed up the design of highly active and low-cost electrocatalysts. Despite the fact that a large amount of activity data for electrocatalysts is stored in the literature, data from different publications are not comparable due to different experimental or computational conditions. In this work, an interpretable ML method, multi-task symbolic regression, was adopted to learn from data in multiple experiments. A universal activity descriptor to evaluate the oxygen evolution reaction (OER) performance of oxide perovskites free of calculations or experiments was constructed and reached high accuracy and generalization ability. Utilizing this descriptor with Bayesian-optimized parameters, a series of compelling double perovskites with excellent OER activity were predicted and further evaluated using first-principles calculations. Finally, the two ML-predicted nickel-based perovskites with the best OER activity were successfully synthesized and characterized experimentally. This work opens a new way to extend machine-learning material design by utilizing multiple data sources.

Inverse design with deep generative models: next step in materials discovery.

Data-driven inverse design for inorganic functional materials is a rapidly emerging field, which aims to automatically design innovative materials with target properties and to enable property-to-structure material discovery.

Formation of Graphene Nanoscrolls and Their Electronic Structures Based on Ab Initio Calculations.

Rolling up two-dimensional (2D) materials can form quasi-one-dimensional nanoscrolls, which are expected to have novel properties due to their larger space of structural parameters. In this Letter, the structural dependence of formation energy was investigated based on more than 90 different graphene nanoscrolls (GNSs) through ab initio calculations. A quantified relationship between formation energy and structural parameters is discovered, which could provide universal description of rolling up 2D materials beyond graphene. Further calculations on electronic structures show the opening of bandgap in GNSs with ultrahigh carrier mobilities up to 107 cm2 V-1 s-1. The structural stability under room temperature was also testified by using molecular dynamic simulations. This work provides general insights into the rolling-up strategy and demonstrates the tunable properties of GNSs, thus extending the scope of the research field for 2D materials.

Coexistence of Semiconducting Ferromagnetics and Piezoelectrics down 2D Limit from Non van der Waals Antiferromagnetic LiNbO3-Type FeTiO3.

Stable two-dimensional (2D) ferromagnetic semiconductors (FMSs) with multifunctional properties have attracted extensive attention in device applications. Non van der Waals (vdW) transition-metal oxides with excellent environmental stability, if ferromagnetic (FM), may open up an unconventional and promising avenue for this subject, but they are usually antiferromagnetic or ferrimagnetic. Herein, we predict an FMS, monolayer Fe2Ti2O9, which can be obtained from LiNbO3-type FeTiO3 antiferromagnetic bulk, has a moderate band gap of 0.87 eV, large perpendicular magnetization (6 µB/fu) and a Curie temperature up to 110 K. The intriguing magnetic properties are derived from the double exchange and negative charge transfer between O_p orbitals and Fe_d orbitals. In addition, a large in-plane piezoelectric (PE) coefficient d11 of 5.0 pm/V is observed. This work offers a competitive candidate for multifunctional spintronics and may stimulate further experimental exploration of 2D non-vdW magnets.

Accelerated design of promising mixed lead-free double halide organic-inorganic perovskites for photovoltaics using machine learning.

Mixed double halide organic-inorganic perovskites (MDHOIPs) exhibit both good stability and high power conversion efficiency and have been regarded as attractive photovoltaic materials. Nevertheless, due to the complexity of structures, large-scale screening of thousands of possible candidates remains a great challenge. In this work, advanced machine learning (ML) techniques and first-principles calculations were combined to achieve a rapid screening of MDHOIPs for solar cells. Successfully, 204 stable lead-free MDHOIPs with optimal bandgaps were selected out of 11 370 candidates. The accuracy of ML models for perovskite structure formability and bandgap is over 94% and 97%, respectively. Moreover, representative MDHOIP candidates, MA2GeSnI4Br2 and MA2InBiI2Br4, stand out with suitable direct bandgaps, light carrier effective masses, small exciton binding energies, strong visible light absorption, and good stability against decomposition.

Invasive Klebsiella pneumoniae Infections in Community-Settings and Healthcare Settings.

OBJECTIVE: To assess clinical characteristics, outcomes and antimicrobial resistance of invasive Klebsiella pneumoniae (KP) infections in Chinese pediatric patients in hospital and community settings. METHODS: This retrospective study was conducted in the nine tertiary hospitals during 2016-2018. The 324 pediatric inpatients who had KP isolated from blood and cerebrospinal fluid and had complete medical records reviewed were included. We analyzed the risk factors, outcomes and antimicrobial resistance pattern of KP-infected patients based on comparison between healthcare-associated KP infections (HAI) and community-acquired infections. RESULTS: Of the 324 enrolled patients, 275 (84.9%) were clinically defined as HAI, including 175 (63.6%) neonates and 100 (36.4%) aged >28 days. The overall prevalence of CRKP was 38.2% (43.4% in HAI verse 8.7% in CAI, P <0.05). Prematurity (odds ratio (OR): 37.07, 95% CI: 8.29-165.84), hematologic malignancies (OR: 15.52, 95% CI: 1.89-127.14) and invasive mechanical ventilation (OR: 13.09, 95% CI: 1.66-103.56) were independent risk factors for HAI. Patients from rural area (OR: 1.94, 95% CI: 1.12-3.35), invasive mechanical ventilation (OR: 2.33, 95% CI: 1.25-4.33), antibiotic therapy prior to admission (OR: 2.33, 95% CI: 1.25-4.33) and prior hospital stay in the past 30 days (OR: 3.46, 95% CI: 1.87-6.41) were associated with healthcare-associated CRKP infections. Organ dysfunction was independently correlated with poor outcomes (OR: 2.92, 95% CI: 1.23-6.95). CONCLUSION: Pediatric invasive KP infections and high prevalence of CRKP infections largely occurred in healthcare settings in China. The adequate and intensified infection control measures should be focused on high-risk hematologic patients, neonatal patients and intubated patients.

Antibiotic Use Among Hospitalized Children and Neonates in China: Results From Quarterly Point Prevalence Surveys in 2019.

Background: Antimicrobial resistance is a significant clinical problem in pediatric practice in China. Surveillance of antibiotic use is one of the cornerstones to assess the quality of antibiotic use and plan and assess the impact of antibiotic stewardship interventions. Methods: We carried out quarterly point prevalence surveys referring to WHO Methodology of Point Prevalence Survey in 16 Chinese general and children's hospitals in 2019 to assess antibiotic use in pediatric inpatients based on the WHO AWaRe metrics and to detect potential problem areas. Data were retrieved via the hospital information systems on the second Monday of March, June, September and December. Antibiotic prescribing patterns were analyzed across and within diagnostic conditions and ward types according to WHO AWaRe metrics and Anatomical Therapeutic Chemical (ATC) Classification. Results: A total of 22,327 hospitalized children were sampled, of which 14,757 (66.1%) were prescribed ≥1 antibiotic. Among the 3,936 sampled neonates (≤1 month), 59.2% (n = 2,331) were prescribed ≥1 antibiotic. A high percentage of combination antibiotic therapy was observed in PICUs (78.5%), pediatric medical wards (68.1%) and surgical wards (65.2%). For hospitalized children prescribed ≥1 antibiotic, the most common diagnosis on admission were lower respiratory tract infections (43.2%, n = 6,379). WHO Watch group antibiotics accounted for 70.4% of prescriptions (n = 12,915). The most prescribed antibiotic ATC classes were third-generation cephalosporins (41.9%, n = 7,679), followed by penicillins/ß-lactamase inhibitors (16.1%, n = 2,962), macrolides (12.1%, n = 2,214) and carbapenems (7.7%, n = 1,331). Conclusion: Based on these data, overuse of broad-spectrum Watch group antibiotics is common in Chinese pediatric inpatients. Specific interventions in the context of the national antimicrobial stewardship framework should aim to reduce the use of Watch antibiotics and routine surveillance of antibiotic use using WHO AWaRe metrics should be implemented.

Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half-Metals/Metals.

2D ferromagnetic (FM) semiconductors/half-metals/metals are the key materials toward next-generation spintronic devices. However, such materials are still rather rare and the material search space is too large to explore exhaustively. Here, an adaptive framework to accelerate the discovery of 2D intrinsic FM materials is developed, by combining advanced machine-learning (ML) techniques with high-throughput density functional theory calculations. Successfully, about 90 intrinsic FM materials with desirable bandgap and excellent thermodynamic stability are screened out and a database containing 1459 2D magnetic materials is set up. To improve the performance of ML models on small-scale datasets like diverse 2D materials, a crystal graph multilayer descriptor using the elemental property is proposed, with which ML models achieve prediction accuracy over 90% on thermodynamic stability, magnetism, and bandgap. This study not only provides dozens of compelling FM candidates for future spintronics, but also paves a feasible route for ML-based rapid screening of diverse structures and/or complex properties.

Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique.

Property-oriented material design is a persistent pursuit for material scientists. Recently, machine learning (ML) as a powerful new tool has attracted worldwide attention in the material design field. Based on statistics instead of solving physical equations, ML can predict material properties faster with lower cost. Because of its data-driven characteristics, the quantity and quality of material data become the keys to the practical applications of this technique. In this Perspective, problems caused by lack of data and diversity of data are discussed. Various approaches, including high-throughput calculations, database construction, feedback loop algorithms, and better descriptors, have been exploited to address these problems. It is expected that this Perspective will bring data itself to the forefront of ML-based material design.

Laser paint removal monitoring based on time-resolved spectroscopy.

Laser paint removal is a new cleaning technology with high efficiency. Dynamic monitoring and closed-loop control of the laser paint removal process are key to reducing the risk of metal substrate damage and to achieving the best cleaning. In this paper, the time-resolved characteristics of the elemental peaks in the laser-induced breakdown spectrum of paints and substrate were studied by using the combination of a monochromator (or a bandpass filter) and a photomultiplier tube (PMT) detector. The results show that the intensity of the elemental spectrum peak of the paint has a sudden drop while the intensity of the elemental spectrum peak of the substrate has a sudden increase when the paint is removed. The time-resolved signals can be fitted by double exponential functions, which are combinations of exponential functions with a longer and a shorter lifetime, respectively. The relative ratios of the coefficients of the shorter and longer lifetimes (A s h o r t /A l o n g ) at the wavelength correspond to the elements in paint increasing suddenly while decreasing suddenly at the wavelength corresponding to the substrate. The intensity of the elemental spectrum peaks of paints and substrate and the ratio (A s h o r t /A l o n g ) can be used to monitor the laser paint removal process in real time to reduce the damage risk of the metal substrate and achieve the purpose of efficient cleaning with low cost.

Ambient Degradation-Induced Spin Paramagnetism in Phosphorene.

The sizeable direct bandgap, high mobility, and long spin lifetimes at room temperature offer black phosphorus (BP) potential applications in spin-based semiconductor devices. Toward these applications, a critical step is creating a magnetic response in BP, which is arousing much interest. It is reported here that ambient degradation of BP, which is immediate and inevitable and greatly changes the semiconducting properties, creates magnetic moments, and any degree of degradation leads to notable paramagnetism. Its Landau factor g measured is ≈1.995, revealing that the magnetization mainly results from spin rather than orbital moments. Such magnetism most likely results from the unsaturated phosphorus in the vacancies which are stabilized by O adatoms. It can be tuned by changing any one of the ambient factors of ambient temperature, humidity, and light intensity, and can be stabilized by exposing BP in argon. The findings highlight the importance of evaluating the effect of ambient degradation-induced magnetism on BP's spin-based devices. The work seems an essential milestone toward the forthcoming research upsurge on BP's magnetism.

Highly Promoted Carrier Mobility and Intrinsic Stability by Rolling Up Monolayer Black Phosphorus into Nanoscrolls.

Rolling up two-dimensional (2D) materials into nanoscrolls could not only retain the excellent properties of their 2D hosts but also display intriguing physical and chemical properties that arise from their 1D tubular structures. Here, we report a new class of black phosphorus nanoscrolls (bPNSs), which are stable at room-temperature and energetically more favorable than 2D bP. Most strikingly, these bPNSs hold tunable direct band gaps and extremely high mobilities (e.g., the mobility of the double-layer bPNS is about 20-fold higher than that of 2D bP monolayer). Their unique self-encapsulation structure and layer-dependent conduction band minimum can largely prevent the entrance of O2 and the production of O2- and thereby suppress the possible environmental degradation as well. The enhanced intrinsic stability and promoted electronic properties render bPNSs great promise in many advanced electronics or optoelectronics applications.

Photo-oxidative Degradation and Protection Mechanism of Black Phosphorus: Insights from Ultrafast Dynamics.

The environmental instability and protection of black phosphorus (BP) is one of the most attractive hotspots in two-dimensional materials. The generation of superoxide is believed to be the key culprit, while the photogenerated electron dynamics is yet to be known. In this work, we carry out time domain ab initio nonadiabatic molecular dynamics to understand the photogenerated electron dynamics at the molecule/BP interface. It is found that oxygen can trap the photogenerated electrons of BP rapidly owing to strong electron-phonon (e-p) coupling and becomes an active superoxide under light illumination. A good protection layer, such as perylene diimide (PDI), has comparable capabilities of trapping photogenerated electrons to oxygen, which can efficiently prevent the formation of superoxide and thus suppress the degradation of BP. Moreover, PDI can enhance the separation of photogenerated electron-hole pairs of BP by prolonging the time of holding photogenerated electrons of BP. In contrast, 7,7,8,8-tetracyano- p-quinodimethane (TCNQ) has weak e-p coupling and a large band edge offset between trapping and donor states and thereby poor trapping ability for photogenerated electrons, leading to poor protection efficiency. This study provides the first in-depth understanding of the BP degradation and protection mechanism from excited-state dynamics and can be applicable to other 2D photo-oxidative degradation.

Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning.

Rapidly discovering functional materials remains an open challenge because the traditional trial-and-error methods are usually inefficient especially when thousands of candidates are treated. Here, we develop a target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs) for photovoltaics. This strategy, combining machine learning techniques and density functional theory calculations, aims to quickly screen the HOIPs based on bandgap and solve the problems of toxicity and poor environmental stability in HOIPs. Successfully, six orthorhombic lead-free HOIPs with proper bandgap for solar cells and room temperature thermal stability are screened out from 5158 unexplored HOIPs and two of them stand out with direct bandgaps in the visible region and excellent environmental stability. Essentially, a close structure-property relationship mapping the HOIPs bandgap is established. Our method can achieve high accuracy in a flash and be applicable to a broad class of functional material design.

Abnormal Near-Infrared Absorption in 2D Black Phosphorus Induced by Ag Nanoclusters Surface Functionalization.

Black phosphorus (BP), as a fast emerging 2D material, shows promising potential in near-infrared (NIR) photodetection owing to its relatively small direct thickness-dependent bandgaps. However, the poor NIR absorption due to the atomically thin nature strongly hinders the practical application. In this study, it is demonstrated that surface functionalization of Ag nanoclusters on 2D BP can induce an abnormal NIR absorption at ≈746 nm, leading to ≈35 (138) times enhancement in 808 (730) nm NIR photoresponse for BP-based field-effect transistors. First-principles calculations reveal that localized bands are introduced into the bandgap of BP, serving as the midgap states, which create new transitions to the conduction band of BP and eventually lead to the abnormal absorption. This work provides a simple yet effective method to dramatically increase the NIR absorption of BP, which is crucial for developing high-performance NIR optoelectronic devices.