Schrödinger's Red Beyond 65,000 Pixel-Per-Inch by Multipolar Interaction in Freeform Meta-Atom through Efficient Neural Optimizer

Freeform nanostructures have the potential to support complex resonances and their interactions, which are crucial for achieving desired spectral responses. However, the design optimization of such structures is nontrivial and computationally intensive. Furthermore, the current "black box" design approaches for freeform nanostructures often neglect the underlying physics. Here, a hybrid data-efficient neural optimizer for resonant nanostructures by combining a reinforcement learning algorithm and Powell's local optimization technique is presented. As a case study, silicon nanostructures with a highly-saturated red color are designed and experimentally demonstrated. Specifically, color coordinates of (0.677, 0.304) in the International Commission on Illumination (CIE) chromaticity diagram - close to the ideal Schrödinger's red, with polarization independence, high reflectance (>85%), and a large viewing angle (i.e., up to ± 25°) is achieved. The remarkable performance is attributed to underlying generalized multipolar interferences within each nanostructure rather than the collective array effects. Based on that, pixel size down to ≈400 nm, corresponding to a printing resolution of 65000 pixels per inch is demonstrated. Moreover, the proposed design model requires only ≈300 iterations to effectively search a thirteen-dimensional (13D) design space - an order of magnitude more efficient than the previously reported approaches. The work significantly extends the free-form optical design toolbox for high-performance flat-optical components and metadevices.

An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response

We present a novel framework combining single-cell phenotypic data with single-cell transcriptomic analysis to identify factors underpinning heterogeneity in antitumor immune response. We developed a pairwise, tumor-immune discretized interaction assay between natural killer (NK-92MI) cells and patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines on a microfluidic cell-trapping platform. Furthermore we generated a deep-learning computer vision algorithm that is capable of automating the acquisition and analysis of a large, live-cell imaging data set (>1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (n = 10). Finally, we combined the response data measured by Kaplan-Meier survival analysis against NK-mediated killing with downstream single-cell transcriptomic analysis to interrogate molecular signatures associated with NK-effector response. As proof-of-concept for the proposed framework, we efficiently identified MHC class I-driven cytotoxic resistance as a key mechanism for immune evasion in nonresponders, while enhanced expression of cell adhesion molecules was found to be correlated with sensitivity against NK-mediated cytotoxicity. We conclude that this integrated, data-driven phenotypic approach holds tremendous promise in advancing the rapid identification of new mechanisms and therapeutic targets related to immune evasion and response.

Promoted Growth and Multiband Emission in Heterostructured Perovskites Through Cs+ -Sublattice Interaction

Precise control of exciton confinement in metal halide perovskites is critical to the development of high-performance, stable optoelectronic devices. A significant hurdle is the swift completion of ionic metathesis reactions, often within seconds, making consistent control challenging. Herein, the introduction of different steric hindrances in a Cs+ sublattice within CsYb2 F7 is reported, which effectively modulates the reaction rate of Cs+ with lead (Pb2+ ) and halide ions in solution, extending the synthesis time for perovskite nanostructures to tens of minutes. Importantly, the Cs+ sublattice provides a crystal facet-dependent preference for perovskite growth and thus exciton confinement, allowing the simultaneous occurrence of up to six emission bands of CsPbBr3 . Moreover, the rigid CsYb2 F7 nano template offers high activation energy and enhances the stability of the resulting perovskite nanostructures. This methodology provides a versatile approach to synthesizing functional heterostructures. Its robustness is demonstrated by in-situ growth of perovskite nanostructures on Cs+ -mediated metal-organic frameworks.

Real-World Systolic and Diastolic Blood Pressure Levels and Cardiovascular Mortality in Patients With Type 2 Diabetes-Results From a Large Registry Cohort in Asia

BACKGROUND

Elevated blood pressure (BP) is associated with increased risk of cardiovascular mortality. However, there is ongoing debate whether intensive BP lowering may paradoxically increase the risk of cardiovascular disease (CVD), especially in patients with type 2 diabetes (T2D). We investigated the association of BP with risk of CVD mortality in patients with T2D.

METHODS AND RESULTS

We used data on 83 721 patients with T2D from a multi-institutional diabetes registry in Singapore from 2013 to 2019. BP was analyzed as categories and restricted cubic splines using Cox multivariable regression analysis stratified by preexisting CVD and age (<65 years versus ≥65 years). The primary outcome was CVD mortality, determined via linkage with the national registry. Among 83 721 patients with T2D (mean age 65.3 years, 50.6% women, 78.9% taking antihypertensive medications), 7.6 per 1000 person-years experienced the primary outcome. Systolic BP had a graded relationship with a significant increase in CVD mortality at levels >120 to 129 mm Hg. Diastolic BP levels >90 mm Hg were significantly associated with CVD mortality in those aged ≥65 years. In addition, diastolic BP <70 mm Hg was associated with a significantly higher risk of CVD mortality in all patients.

CONCLUSIONS

In patients with T2D, clinic systolic BP levels ≥130 mm Hg or diastolic BP levels ≥90 mm Hg are associated with higher risk of CVD mortality. Diastolic BP <70 mm Hg is also associated with the risk of adverse CVD outcomes, although reverse causality cannot be ruled out.

A Leaf-Patchable Reflectance Meter for In Situ Continuous Monitoring of Chlorophyll Content

Plant wearable sensors facilitate the real-time monitoring of plant physiological status. In situ monitoring of the plant chlorophyll content over days can provide valuable information on the photosynthetic capacity, nitrogen content, and general plant health. However, it cannot be achieved by current chlorophyll measuring methods. Here, a miniaturized and plant-wearable chlorophyll meter for rapid, non-destructive, in situ, and long-term chlorophyll monitoring is developed. The reflectance-based chlorophyll sensor with 1.5 mm thickness and 0.2 g weight (1000 times lighter than the commercial chlorophyll meter), includes a light emitting diode (LED) and two symmetric photodetectors (PDs) on a flexible substrate, and is patched onto the leaf upper epidermis with a conformal light guiding layer. A chlorophyll content index (CCI) calculated based on the sensor shows a better linear relationship with the leaf chlorophyll content (r2 > 0.9) than the traditional chlorophyll meter. This meter can wirelessly communicate with a smartphone to monitor the leaf chlorophyll change under various stresses and indicate the unhealthy status of plants for long-term application of plants under various stresses earlier than chlorophyll meter and naked-eye observation. This wearable chlorophyll sensing patch is promising in smart and precision agriculture.

Stereoselective Synthesis of Fluoroalkanes via FLP Mediated Monoselective C─F Activation of Geminal Difluoroalkanes

A method of desymmetrization of geminal difluoroalkanes using frustrated Lewis pair (FLP) mediated monoselective C-F activation where a chiral sulfide is the Lewis base component is reported. The stereoselective reaction provides generally high yields of diastereomeric sulfonium salts with dr of up to 95:5. The distribution of diastereomers is found to be thermodynamically controlled via facile sulfide exchange. The use of enantiopure chiral sulfides allows for high stereospecificity in nucleophilic substitution reactions and the formation of stereoenriched products.

A prospective study on the wound healing and quality of life outcomes of patients with venous leg ulcers in Singapore-Interim analysis at 6 month follow up

Venous leg ulceration results in significant morbidity. However, the majority of studies conducted are on Western populations. This study aims to evaluate the wound healing and quality of life for patients with venous leg ulcers (VLUs) in a Southeast Asian population. This is a multi-centre prospective cohort study from Nov 2019 to Nov 2021. All patients were started on 2- or 4-layer compression bandage and were reviewed weekly or fortnightly. Our outcomes were wound healing, factors predictive of wound healing and the EuroQol 5-dimensional 5-level (EQ-5D-5L) health states. Within our cohort, there were 255 patients with VLU. Mean age was 65.2 ± 11.6 years. Incidence of diabetes mellitus was 42.0%. Median duration of ulcer at baseline was 0.30 years (interquartile range 0.136-0.834). Overall, the median time to wound healing was 4.5 months (95% confidence interval [CI]: 3.77-5.43). The incidence of complete wound healing at 3- and 6-month was 47.0% and 60.9%, respectively. The duration of the wound at baseline was independently associated with worse wound healing (Hazard ratio 0.94, 95% CI: 0.89-0.99, P = .014). Patients with healed VLU had a significantly higher incidence of perfect EQ-5D-5L health states at 6 months (57.8% vs 13.8%, P < .001). We intend to present longer term results in subsequent publications.

A versatile genomic transgenesis platform with enhanced λ integrase for human Expi293F cells

Reliable cell-based platforms to test and/or produce biologics in a sustainable manner are important for the biotech industry. Utilizing enhanced λ integrase, a sequence-specific DNA recombinase, we developed a novel transgenesis platform involving a fully characterized single genomic locus as an artificial landing pad for transgene insertion in human Expi293F cells. Importantly, transgene instability and variation in expression were not observed in the absence of selection pressure, thus enabling reliable long-term biotherapeutics testing or production. The artificial landing pad for λ integrase can be targeted with multi-transgene constructs and offers future modularity involving additional genome manipulation tools to generate sequential or nearly seamless insertions. We demonstrated broad utility with expression constructs for anti PD-1 monoclonal antibodies and showed that the orientation of heavy and light chain transcription units profoundly affected antibody expression levels. In addition, we demonstrated encapsulation of our PD-1 platform cells into bio-compatible mini-bioreactors and the continued secretion of antibodies, thus providing a basis for future cell-based applications for more effective and affordable therapies.

A review of algorithmic approaches for cell culture media optimization

Cell culture media composition and culture conditions play a crucial role in product yield, quality and cost of production. Culture media optimization is the technique of improving media composition and culture conditions to achieve desired product outcomes. To achieve this, there have been many algorithmic methods proposed and used for culture media optimization in the literature. To help readers evaluate and decide on a method that best suits their specific application, we carried out a systematic review of the different methods from an algorithmic perspective that classifies, explains and compares the available methods. We also examine the trends and new developments in the area. This review provides recommendations to researchers regarding the suitable media optimization algorithm for their applications and we hope to also promote the development of new cell culture media optimization methods that are better suited to existing and upcoming challenges in this biotechnology field, which will be essential for more efficient production of various cell culture products.

Machine Learning Customized Novel Material for Energy-Efficient 4D Printing

Existing commercial powders for laser additive manufacturing (LAM) are designed for traditional manufacturing methods requiring post heat treatments (PHT). LAM's unique cyclic thermal history induces intrinsic heat treatment (IHT) on materials during deposition, which offers an opportunity to develop LAM-customized new materials. This work customized a novel Fe-Ni-Ti-Al maraging steel assisted by machine learning to leverage the IHT effect for in situ forming massive precipitates during LAM without PHT. Fast precipitation kinetics in steel, tailored intermittent deposition strategy, and the IHT effect facilitate the in situ Ni₃ Ti precipitation in the martensitic matrix via heterogeneous nucleation on high-density dislocations. The as-built steel achieves a tensile strength of 1538 MPa and a uniform elongation of 8.1%, which is superior to a wide range of as-LAM-processed high-strength steel. In the current mainstream ex situ 4D printing, the time-dependent evolutions (i.e., property or functionality changes) of a 3D printed structure occur after part formation. This work highlights in situ 4D printing via the synchronous integration of time-dependent precipitation hardening with 3D geometry shaping, which shows high energy efficiency and sustainability. The findings provide insight into developing LAM-customized materials by understanding and utilizing the IHT-materials interaction.

Data-Driven Intelligent Manipulation of Particles in Microfluidics

Automated manipulation of small particles using external (e.g., magnetic, electric and acoustic) fields has been an emerging technique widely used in different areas. The manipulation typically necessitates a reduced-order physical model characterizing the field-driven motion of particles in a complex environment. Such models are available only for highly idealized settings but are absent for a general scenario of particle manipulation typically involving complex nonlinear processes, which has limited its application. In this work, the authors present a data-driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. The architecture replaces the difficult-to-derive model by a generally trainable artificial neural network to describe the kinematics of particles, and subsequently identifies the optimal operations to manipulate particles. The authors successfully demonstrate a diverse set of particle manipulations in a numerically emulated microfluidic chamber, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles. The approach achieves both spatial and temporal controllability of high precision for these settings. This achievement revolutionizes automated particle manipulation, showing the potential of data-driven approaches and machine learning in improving microfluidic technologies for enhanced flexibility and intelligence.

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.

Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System

Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non-invasive device for quantitative and real-time evaluation of human respiration. This device simultaneously captures respiration and temperature signatures using customized capacitive and resistive sensors, encapsulated by a breathable layer, and does not limit the user's daily life. Further a machine learning-based respiration classification algorithm with a set of carefully studied features as inputs is proposed and it is deployed into mobile clients. The body status of users, such as being quiet, active and coughing, can be accurately recognized by the algorithm and displayed on clients. Moreover, multiple devices can be linked to a server network to monitor a group of users and provide each user with the statistical duration of physiological activities, coughing alerts, and body health advice. With these devices, individual and group respiratory health status can be quantitatively collected, analyzed, and stored for daily physiological signal detections as well as medical assistance.

Case report: Persistence of residual antigen and RNA of the SARS-CoV-2 virus in tissues of two patients with long COVID

The World Health Organization has defined long COVID-19 (LC) as a condition that occurs in individuals with a history of SARS-CoV-2 infection who exhibit persistent symptoms after its acute phase that last for at least two months and cannot be explained by an alternative diagnosis. Since we had previously reported residual viral antigens in tissues of convalescent patients, we aimed to assess the presence of such antigens in long COVID tissues. Here, we established the presence of the residual virus in the appendix, skin, and breast tissues of 2 patients who exhibited LC symptoms 163 and 426 days after symptom onset. With multiplex immunohistochemistry, we detected viral nucleocapsid protein in all three tissues. The nucleocapsid protein was further observed to colocalize with macrophage marker CD68, suggesting that immune cells were direct targets of SARS-CoV-2. Additionally, using RNAscope, the presence of viral RNA was also detected. Our positive finding in the breast tissue is corroborated by the recent reports of immunocompromised patients experiencing LC symptoms and persistent viral replication. Overall, our findings and emerging LC studies raise the possibility that the gastrointestinal tract may function as a reservoir for SARS-CoV-2.

Comparison of machine learning approaches for structure-function modeling in glaucoma

To evaluate machine learning (ML) approaches for structure-function modeling to estimate visual field (VF) loss in glaucoma, models from different ML approaches were trained on optical coherence tomography thickness measurements to estimate global VF mean deviation (VF MD) and focal VF loss from 24-2 standard automated perimetry. The models were compared using mean absolute errors (MAEs). Baseline MAEs were obtained from the VF values and their means. Data of 832 eyes from 569 participants were included, with 537 Asian eyes for training, and 148 Asian and 111 Caucasian eyes set aside as the respective test sets. All ML models performed significantly better than baseline. Gradient-boosted trees (XGB) achieved the lowest MAE of 3.01 (95% CI: 2.57, 3.48) dB and 3.04 (95% CI: 2.59, 3.99) dB for VF MD estimation in the Asian and Caucasian test sets, although difference between models was not significant. In focal VF estimation, XGB achieved median MAEs of 4.44 [IQR 3.45-5.17] dB and 3.87 [IQR 3.64-4.22] dB across the 24-2 VF for the Asian and Caucasian test sets and was comparable to VF estimates from support vector regression (SVR) models. VF estimates from both XGB and SVR were significantly better than the other models. These results show that XGB and SVR could potentially be used for both global and focal structure-function modeling in glaucoma.

Broadband Enhancement of Cherenkov Radiation Using Dispersionless Plasmons

As one of leading technologies in detecting relativistic particles, Cherenkov radiation plays an essential role in modern high-energy and particle physics. However, the limited photon yield in transparent dielectrics makes efficient Cherenkov radiation only possible with high-energy particles (at least several MeV). This restriction hinders applications of Cherenkov radiation in free-electron light source, bio-imaging, medical therapy, etc. Broadband enhancement of Cherenkov radiation is highly desired for all these applications, but still widely acknowledged as a scientific challenge. To this end, a general approach is reported to enhance the photon yield of Cherenkov radiation using dispersionless plasmons. Broadband dispersionless plasmons can be realized by exploiting either the acoustic nature of terahertz plasmons in a graphene-based heterostructure or the nonlocal property of optical plasmons in a metallodielectric structure. When coupled to moving electrons, such dispersionless plasmons give rise to a radiation enhancement rate more than two orders of magnitude (as compared with conventional Cherenkov radiation) over an ultrabroad frequency band. Moreover, since the phase velocity of dispersionless plasmons can be made as small as the Fermi velocity, giant radiation enhancements can be readily induced by ultralow-energy free electrons (e.g., with a kinetic energy down to 3 eV), without resorting to relativistic particles.

Age-Related Alterations in Macrophage Distribution and Function Are Associated With Delayed Cutaneous Wound Healing

Ageing-related delays and dysregulated inflammation in wound healing are well-documented in both human and animal models. However, cellular and molecular changes underlying this impairment in healing progression are not fully understood. In this study, we characterised ageing-associated changes to macrophages in wounds of young and aged mice and investigated transcriptomic differences that may impact the progression of wound healing. Full-thickness wounds created on the dorsum of C57BL/6J young and aged mice were excised on Days 3 and 7 post-wounding for analysis by immunohistochemistry, flow cytometry, and RNA sequencing. Our data revealed that macrophages were significantly reduced in aged wounds in comparison to young. Functional transcriptomic analyses showed that macrophages from aged wounds exhibited significantly reduced expression of cell cycle, DNA replication, and repair pathway genes. Furthermore, we uncovered an elevated pro-inflammatory gene expression program in the aged macrophages correlated with poor inflammation resolution and excessive tissue damage observed in aged wounds. Altogether, our work provides insights into how poorly healing aged wounds are phenotypically defined by the presence of macrophages with reduced proliferative capacity and an exacerbated inflammatory response, both of which are pathways that can be targeted to improve healing in the elderly.

Enhancing the Discovery of Bioactive Secondary Metabolites From Fungal Endophytes Using Chemical Elicitation and Variation of Fermentation Media

Endophytic microorganisms are an important source of bioactive secondary metabolites. In this study, fungal endophytes obtained from A*STAR’s Natural Product Library (NPL) and previously isolated from different habitats of Singapore were investigated for their diversity, antimicrobial, and cytotoxic activities. A total of 222 fungal strains were identified on the basis of sequence analysis of ITS region of the rDNA gene. The identified fungal strains belong to 59 genera distributed in 20 orders. Majority of the identified strains (99%; 219 strains) belong to the phylum Ascomycota, while two strains belonged to the phylum Basidiomycota, and only one strain was from Mucoromycota phylum. The most dominant genus was Colletotrichum accounting for 27% of all the identified strains. Chemical elicitation using 5-azacytidine and suberoylanilide hydroxamic acid (SAHA) and variation of fermentation media resulted in the discovery of more bioactive strains. Bioassay-guided isolation and structure elucidation of active constituents from three prioritized fungal strains: Lophiotrema sp. F6932, Muyocopron laterale F5912, and Colletotrichum tropicicola F10154, led to the isolation of a known compound; palmarumycin C₈ and five novel compounds; palmarumycin CP₃₀, muyocopronol A-C and tropicicolide. Tropicicolide displayed the strongest antifungal activity against Aspergillus fumigatus with an IC₅₀ value of 1.8 μg/ml but with a weaker activity against the Candida albicans presenting an IC₅₀ of 7.1 μg/ml. Palmarumycin C₈ revealed the best antiproliferative activity with IC₅₀ values of 1.1 and 2.1 μg/ml against MIA PaCa-2 and PANC-1 cells, respectively.

Experimental Models to Study the Pathogenesis of Malaria-Associated Acute Respiratory Distress Syndrome

Malaria-associated acute respiratory distress syndrome (MA-ARDS) is increasingly gaining recognition as a severe malaria complication because of poor prognostic outcomes, high lethality rate, and limited therapeutic interventions. Unfortunately, invasive clinical studies are challenging to conduct and yields insufficient mechanistic insights. These limitations have led to the development of suitable MA-ARDS experimental mouse models. In patients and mice, MA-ARDS is characterized by edematous lung, along with marked infiltration of inflammatory cells and damage of the alveolar-capillary barriers. Although, the pathogenic pathways have yet to be fully understood, the use of different experimental mouse models is fundamental in the identification of mediators of pulmonary vascular damage. In this review, we discuss the current knowledge on endothelial activation, leukocyte recruitment, leukocyte induced-endothelial dysfunction, and other important findings, to better understand the pathogenesis pathways leading to endothelial pulmonary barrier lesions and increased vascular permeability. We also discuss how the advances in imaging techniques can contribute to a better understanding of the lung lesions induced during MA-ARDS, and how it could aid to monitor MA-ARDS severity.

An allied reprogramming, selection, expansion and differentiation platform for creating hiPSC on microcarriers

OBJECTIVES

Induced pluripotent stem cells (iPSCs) generated by monolayer cultures is plagued by low efficiencies, high levels of manipulation and operator unpredictability. We have developed a platform, reprogramming, expansion, and differentiation on Microcarriers, to solve these challenges.

MATERIALS AND METHODS

Five sources of human somatic cells were reprogrammed, selected, expanded and differentiated in microcarriers suspension cultures.

RESULTS

Improvement of transduction efficiencies up to 2 times was observed. Accelerated reprogramming in microcarrier cultures was 7 days faster than monolayer, providing between 30 and 50-fold more clones to choose from fibroblasts, peripheral blood mononuclear cells, T cells and CD34+ stem cells. This was observed to be due to an earlier induction of genes (β-catenin, E-cadherin and EpCAM) on day 4 versus monolayer cultures which occurred on days 14 or later. Following that, faster induction and earlier stabilization of pluripotency genes occurred during the maturation phase of reprogramming. Integrated expansion without trypsinization and efficient differentiation, without embryoid bodies formation, to the three germ-layers, cardiomyocytes and haematopoietic stem cells were further demonstrated.

CONCLUSIONS

Our method can solve the inherent problems of conventional monolayer cultures. It is highly efficient, cell dissociation free, can be operated with lower labor, and allows testing of differentiation efficiency without trypsinization and generation of embryoid bodies. It is also amenable to automation for processing more samples in a small footprint, alleviating many challenges of manual monolayer selection.

Branched PCL-Based Thermogelling Copolymers: Controlling Polymer Architecture to Tune Drug Release Profiles

Temperature-responsive hydrogels, or thermogels, are a unique class of biomaterials that show facile and spontaneous transition from solution to gel when warmed. Their high biocompatibility, and ease of formulation with both small molecule drugs and biologics have made these materials prime candidates as injectable gel depots for sustained local drug delivery. At present, controlling the kinetics and profile of drug release from thermogels is achieved mainly by varying the ratio of hydrophobic: hydrophilic composition and the polymer molecular weight. Herein, we introduce polymer branching as a hitherto-overlooked polymer design parameter that exhibits profound influences on the rate and profile of drug release. Through a family of amphiphilic thermogelling polymers with systematic variations in degree of branching, we demonstrate that more highly-branched polymers are able to pack less efficiently with each other during thermogel formation, with implications on their physical properties and stability towards gel erosion. This in turn resulted in faster rates of release for both encapsulated small molecule hydrophobic drug and protein. Our results demonstrate the possibility of exploiting polymer branching as a hitherto-overlooked design parameter for tailoring the kinetics and profile of drug release in injectable thermogel depots.

Biologically engineered microbes for bioremediation of electronic waste: Wayposts, challenges and future directions

In the face of a burgeoning stream of e-waste globally, e-waste recycling becomes increasingly imperative, not only to mitigate the environmental and health risks it poses but also as an urban mining strategy for resource recovery of precious metals, rare Earth elements, and even plastics. As part of the continual efforts to develop greener alternatives to conventional approaches of e-waste recycling, biologically assisted degradation of e-waste offers a promising recourse by capitalising on certain microorganisms' innate ability to interact with metals or degrade plastics. By harnessing emerging genetic tools in synthetic biology, the evolution of novel or enhanced capabilities needed to advance bioremediation and resource recovery could be potentially accelerated by improving enzyme catalytic abilities, modifying substrate specificities, and increasing toxicity tolerance. Yet, the management of e-waste presents formidable challenges due to its massive volume, high component complexity, and associated toxicity. Several limitations will need to be addressed before nascent laboratory-scale achievements in bioremediation can be translated to viable industrial applications. Nonetheless, vested groups, involving both start-up and established companies, have taken visionary steps towards deploying microbes for commercial implementation in e-waste recycling.

FIONA1-Mediated m6 A Modification Regulates the Floral Transition in Arabidopsis

N⁶ -methyladenosine (m⁶ A) mRNA modification represents the most widespread form of internal modifications in eukaryotic mRNAs. In the model plant Arabidopsis thaliana, those known methyltransferases mainly deposit m⁶ A at their target transcripts near the stop codon or in the 3' untranslated region. Here, it is reported that FIONA1 (FIO1), a human METTL16 ortholog, acts as a hitherto unknown m⁶ A methyltransferase that determines m⁶ A modifications at over 2000 Arabidopsis transcripts predominantly in the coding region. Mutants of FIO1 show a decrease in global m⁶ A mRNA methylation levels and an early-flowering phenotype. Nanopore direct RNA sequencing reveals that FIO1 is required for establishing appropriate levels of m⁶ A preferentially in the coding sequences of a subset of protein-coding transcripts, which is associated with changes in transcript abundance and alternative polyadenylation. It is further demonstrated that FIO1-mediated m⁶ A methylation determines the mRNA abundance of a central flowering integrator SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) and its upstream regulators, thus preventing premature flowering. The findings reveal that FIO1 acts as a unique m⁶ A methyltransferase that mainly modifies the coding regions of transcripts, which underlies the key developmental transition from vegetative to reproductive growth in plants.

Machine-Learning-Assisted Accurate Prediction of Molecular Optical Properties upon Aggregation

For practical applications, molecules often exist in an aggregate state. Therefore, it is of great value if one can predict the performance of molecules when forming aggregates, for example, aggregation-induced emission (AIE) or aggregation-caused quenching (ACQ). Herein, a database containing AIE/ACQ molecules reported in the literature is first established. Through training, these machine learning (ML) models can build up the structure-property relationship and thus implement fast prediction of AIE/ACQ properties. To this end, a multi-modal approach is proposed, multiple prediction methods are compared and designed, and thus an ensemble strategy is developed. First, multiple molecular descriptors are considered at the same time, major features are extracted by dimensionality reduction, and multi-modal features are synthesized. Then, several state-of-the-art methods are designed and compared to analyze the advantages of the different methods. Finally, the ensemble strategy combines the advantages of the multiple methods to obtain the final prediction result. The reliability of this approach in an unknown molecular space is further verified by three newly designed molecules. Reasonable consistency between model predictions and experimental outcomes is obtained. The result indicates that ML can be a powerful tool to predict molecular properties in the aggregated state, thus accelerating the development of solid-state optical materials.

Systems Biology to Understand and Regulate Human Retroviral Proinflammatory Response

The majority of human genome are non-coding genes. Recent research have revealed that about half of these genome sequences make up of transposable elements (TEs). A branch of these belong to the endogenous retroviruses (ERVs), which are germline viral infection that occurred over millions of years ago. They are generally harmless as evolutionary mutations have made them unable to produce viral agents and are mostly epigenetically silenced. Nevertheless, ERVs are able to express by still unknown mechanisms and recent evidences have shown links between ERVs and major proinflammatory diseases and cancers. The major challenge is to elucidate a detailed mechanistic understanding between them, so that novel therapeutic approaches can be explored. Here, we provide a brief overview of TEs, human ERVs and their links to microbiome, innate immune response, proinflammatory diseases and cancer. Finally, we recommend the employment of systems biology approaches for future HERV research.

Space-Time Wave Packets from Smith-Purcell Radiation

Space-time wave packets are electromagnetic waves with strong correlations between their spatial and temporal degrees of freedom. These wave packets have gained much attention for fundamental properties like propagation invariance and user-designed group velocities, and for potential applications like optical microscopy, micromanipulation, and laser micromachining. Here, free-electron radiation is presented as a natural and versatile source of space-time wave packets that are ultra-broadband and highly tunable in frequency. For instance, ab initio theory and numerical simulations show that the intensity profile of space-time wave packets from Smith-Purcell radiation can be directly tailored through the grating properties, as well as the velocity and shape of the electron bunches. The result of this work indicates a viable way of generating space-time wave packets at exotic frequencies such as the terahertz and X-ray regimes, potentially paving the way toward new methods of shaping electromagnetic wave packets through free-electron radiation.

Reconfigurable Parametric Amplifications of Spoof Surface Plasmons

Next-generation inter-chip communication requires ultrafast ultra-compact interconnects. Designer plasmonics offers a possible route towards this goal. Further development of the plasmonic technique to circuit applications requires the direct amplification of plasmonic signals on a compact platform. However, significant signal distortions and limited operational speeds prevent the application of traditional MOS-based amplifiers to plasmonics. Up to day, the amplification of surface plasmons without phase distortion has remained a scientific challenge. In this work, the concept of parametric amplification (PA) is transplanted to the plasmonics and is realized experimentally an ultrathin reconfigurable PA using a spoof surface plasmon polariton (SSPP) waveguide integrated with tunable and nonlinear varactors. The measured parametric gain in the experiment can reach up to 9.14 dB within a short nonlinear propagation length, for example, six SSPP wavelengths, in excellent agreement with the theoretical prediction. By tuning the bias voltage of varactors, the phase-matching condition can be precisely controlled over a broad frequency band, enabling the authors to realize the multi-frequency PA of plasmonic signals. Measured phase responses confirm that the plasmonic parametric amplifier can significantly suppress the signal distortions as compared with the traditional MOS-based amplifier, which is a property highly desired for ultrafast wireless communication systems and integrated circuits.