Low-input and single-cell methods for Infinium DNA methylation BeadChips.

The Infinium BeadChip is the most widely used DNA methylome assay technology for population-scale epigenome profiling. However, the standard workflow requires over 200 ng of input DNA, hindering its application to small cell-number samples, such as primordial germ cells. We developed experimental and analysis workflows to extend this technology to suboptimal input DNA conditions, including ultra-low input down to single cells. DNA preamplification significantly enhanced detection rates to over 50% in five-cell samples and ∼25% in single cells. Enzymatic conversion also substantially improved data quality. Computationally, we developed a method to model the background signal's influence on the DNA methylation level readings. The modified detection P-value calculation achieved higher sensitivities for low-input datasets and was validated in over 100 000 public diverse methylome profiles. We employed the optimized workflow to query the demethylation dynamics in mouse primordial germ cells available at low cell numbers. Our data revealed nuanced chromatin states, sex disparities, and the role of DNA methylation in transposable element regulation during germ cell development. Collectively, we present comprehensive experimental and computational solutions to extend this widely used methylation assay technology to applications with limited DNA.

Dynamic temperature control in microfluidics for in vivo imaging of cold-sensing in C. elegans.

The ability to perceive temperature is crucial for most animals. It enables them to maintain their body temperature and swiftly react to noxiously cold or hot objects. Caenorhabditis elegans is a powerful genetic model for the study of thermosensation as its simple nervous system is well characterized and its transparent body is suited for in vivo functional imaging of neurons. The behavior triggered by experience-dependent thermosensation has been well studied in C. elegans under temperature-gradient environments. However, how C. elegans senses temperature via its nervous system is not well understood due to the limitations of currently available technologies. One major bottleneck is the difficulty in creating fast temperature changes, especially cold stimuli. Here, we developed a microfluidic-based platform that allowed the in vivo functional imaging of C. elegans responding to well-controlled temporally varying temperature stimulation by rapidly switching fluid streams at different temperatures. We used computational models to enable rational design and optimization of experimental conditions. We validated the design and utility of our system with studies of the functional role of thermosensory neurons. We showed that the responses of PVD polymodal nociceptor neurons observed in previous studies can be recapitulated. Further, we highlighted how this platform may be used to dissect neuronal circuits with an example of activity recording in PVC interneurons. Both of these neuron types show sensitization phenotypes. We envision that both the engineered system and the findings in this work will spur further studies of molecular and cellular mechanisms underlying cold-sensing through the nervous system.

Electron beam-induced demetallation of Fe, Co, Ni, Cu, Zn, Pd, and Pt metalloporphyrins: insights in e-beam chemistry and metal cluster formations.

Electron beams are versatile tools for nanoscale fabrication processes, however, the underlying e-beam chemistry remains in its infancy. Through operando transmission electron microscopy investigations, we elucidate a redox-driven cargo release of individual metal atoms triggered by electron beams. The chosen organic delivery molecule, tetraphenylporphyrin (TPP), proves highly versatile, forming complexes with nearly all metals from the periodic table and being easily processed in solution. A comprehensive cinematographic analysis of the dynamics of single metal atoms confirms the nearly instantaneous ejection of complexed metal atoms under an 80 kV electron beam, underscoring the system's broad versatility. Providing mechanistic insights, we employ density functional theory to support the proposed reductive demetallation pathway facilitated by secondary electrons, contributing novel perspectives to electron beam-mediated chemical reaction mechanisms. Lastly, our findings demonstrate that all seven metals investigated form nanoclusters once ejected from TPP, highlighting the method's potential for studying and developing sustainable single-atom and nanocluster catalysts.

A predicted-loss based active learning approach for robust cancer pathology image analysis in the workplace.

BACKGROUND: Convolutional neural network-based image processing research is actively being conducted for pathology image analysis. As a convolutional neural network model requires a large amount of image data for training, active learning (AL) has been developed to produce efficient learning with a small amount of training data. However, existing studies have not specifically considered the characteristics of pathological data collected from the workplace. For various reasons, noisy patches can be selected instead of clean patches during AL, thereby reducing its efficiency. This study proposes an effective AL method for cancer pathology that works robustly on noisy datasets. METHODS: Our proposed method to develop a robust AL approach for noisy histopathology datasets consists of the following three steps: 1) training a loss prediction module, 2) collecting predicted loss values, and 3) sampling data for labeling. This proposed method calculates the amount of information in unlabeled data as predicted loss values and removes noisy data based on predicted loss values to reduce the rate at which noisy data are selected from the unlabeled dataset. We identified a suitable threshold for optimizing the efficiency of AL through sensitivity analysis. RESULTS: We compared the results obtained with the identified threshold with those of existing representative AL methods. In the final iteration, the proposed method achieved a performance of 91.7% on the noisy dataset and 92.4% on the clean dataset, resulting in a performance reduction of less than 1%. Concomitantly, the noise selection ratio averaged only 2.93% on each iteration. CONCLUSIONS: The proposed AL method showed robust performance on datasets containing noisy data by avoiding data selection in predictive loss intervals where noisy data are likely to be distributed. The proposed method contributes to medical image analysis by screening data and producing a robust and effective classification model tailored for cancer pathology image processing in the workplace.

Electrical Transport Properties Driven by Unique Bonding Configuration in γ-GeSe.

Group IV monochalcogenides have recently shown great potential for their thermoelectric, ferroelectric, and other intriguing properties. The electrical properties of group IV monochalcogenides exhibit a strong dependence on the chalcogen type. For example, GeTe exhibits high doping concentration, whereas S/Se-based chalcogenides are semiconductors with sizable bandgaps. Here, we investigate the electrical and thermoelectric properties of γ-GeSe, a recently identified polymorph of GeSe. γ-GeSe exhibits high electrical conductivity (∼106 S/m) and a relatively low Seebeck coefficient (9.4 µV/K at room temperature) owing to its high p-doping level (5 × 1021 cm-3), which is in stark contrast to other known GeSe polymorphs. Elemental analysis and first-principles calculations confirm that the abundant formation of Ge vacancies leads to the high p-doping concentration. The magnetoresistance measurements also reveal weak antilocalization because of spin-orbit coupling in the crystal. Our results demonstrate that γ-GeSe is a unique polymorph in which the modified local bonding configuration leads to substantially different physical properties.

Navigating Ball Mill Specifications for Theory-to-Practice Reproducibility in Mechanochemistry.

The rising prospects of mechanochemically assisted syntheses hold promise for both academia and industry, yet they face challenges in understanding and, therefore, anticipating respective reaction kinetics. Particularly, dependencies based on variations in milling equipment remain little understood and globally overlooked. This study aims to address this issue by identifying critical parameters through kinematic models, facilitating the reproducibility of mechanochemical reactions across the most prominent mills in laboratory settings, namely planetary and mixer mills. Through a series of selected experiments replicating major classes of organic, organometallic, transition metal-catalyzed, and inorganic reactions from literature, we rationalize the independence of kinematic parameters on reaction kinetics when the accumulated energy criterion is met. As a step forward and to facilitate the practicability of our findings, we provide a freely accessible online tool† that allows the calculation of respective energy parameters for different planetary and mixer mills. Our work advances the current understanding of mechanochemistry and lays the foundation for future rational exploration in this rapidly evolving field.

Q-Learning-Based Pending Zone Adjustment for Proximity Classification.

This paper presents a Q-learning-based pending zone adjustment for received signal strength indicator (RSSI)-based proximity classification (QPZA). QPZA aims to improve the accuracy of RSSI-based proximity classification by adaptively adjusting the size of the pending zone, taking into account changes in the surrounding environment. The pending zone refers to an area in which the previous result of proximity classification is maintained and is expressed as a near boundary and a far boundary. QPZA uses Q-learning to expand the size of the pending zone when the noise level increases and reduce it otherwise. Specifically, it calculates the noise level using the estimation error of a device deployed at a specific location. Then, QPZA adjusts the near boundary and far boundary separately by inputting the noise level into the near and far boundary adjusters, consisting of the Q-learning agent and reward calculator. The Q-learning agent determines the next boundary using the Q-table, and the reward calculator calculates the reward using the noise level. QPZA updates the Q-table of the Q-learning agent using the reward. To evaluate the performance of QPZA, we conducted an experimental implementation and compared the accuracy of QPZA with that of the existing approach. The results showed that QPZA achieves 11.69% higher accuracy compared to the existing approach, on average.

Temporal Estimation of Non-Rigid Dynamic Human Point Cloud Sequence Using 3D Skeleton-Based Deformation for Compression.

This paper proposes an algorithm for transmitting and reconstructing the estimated point cloud by temporally estimating a dynamic point cloud sequence. When a non-rigid 3D point cloud sequence (PCS) is input, the sequence is divided into groups of point cloud frames (PCFs), and a key PCF is selected. The 3D skeleton is predicted through 3D pose estimation, and the motion of the skeleton is estimated by analyzing the joints and bones of the 3D skeleton. For the deformation of the non-rigid human PC, the 3D PC model is transformed into a mesh model, and the key PCF is rigged using the 3D skeleton. After deforming the key PCF into the target PCF utilizing the motion vector of the estimated skeleton, the residual PC between the motion compensation PCF and the target PCF is generated. If there is a key PCF, the motion vector of the target PCF, and a residual PC, the target PCF can be reconstructed. Just as compression is performed using pixel correlation between frames in a 2D video, this paper compresses 3D PCFs by estimating the non-rigid 3D motion of a 3D object in a 3D PC. The proposed algorithm can be regarded as an extension of the 2D motion estimation of a rigid local region in a 2D plane to the 3D motion estimation of a non-rigid object (human) in 3D space. Experimental results show that the proposed method can successfully compress 3D PC sequences. If it is used together with a PC compression technique such as MPEG PCC (point cloud compression) in the future, a system with high compression efficiency may be configured.

STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS2.

Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handling of high-throughput data. Here, we apply a fully convolutional network (FCN) for identification of important structural features of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying sulfur vacancies and polymorph types of MoS2 from atomic resolution STEM images. Efficient models are achieved based on training with simulated images in the presence of different levels of noise, aberrations, and carbon contamination. The accuracy of the FCN models toward extensive experimental STEM images is comparable to that of careful hands-on analysis. Our work provides a guideline on best practices to train a deep learning model for STEM image analysis and demonstrates FCN's application for efficient processing of a large volume of STEM data.

Loliolide in Sargassum horneri Alleviates Ultrafine Urban Particulate Matter (PM 0.1)-Induced Inflammation in Human RPE Cells.

Owing to increasing air pollution due to industrial development, fine dust has been associated with threatening public health. In particular, ultrafine urban particulate matter (uf-UP, PM 0.1) can easily enter our bodies, causing inflammation-related diseases. Therefore, in the present study, we evaluated the effects of hydrothermal extracts of Sargassum horneri and its bioactive compound, loliolide, on uf-UP-induced inflammation as a potential treatment strategy for retinal disorders. Human retinal pigment epithelial cells (ARPE-19) stimulated with TNF-α or uf-UPs were treated with S. horneri extract and loliolide. S. horneri extracts exhibited anti-inflammatory effects on uf-UP-induced inflammation without cell toxicity through downregulating the mRNA expression of MCP-1, IL-8, IL-6, and TNF-α. UPLC-QTOF/MS analysis confirmed that the hydrothermal extract of S. horneri contained loliolide, which has anti-inflammatory effects. Loliolide effectively reduced the mRNA expression and production of proinflammatory chemokines (IL-8) and cytokines (IL-1ß and IL-6) by downregulating the MAPK/NF-ĸB signaling pathway on TNF-α-stimulated inflammatory ARPE-19 cells. These effects were further confirmed in inflammatory ARPE-19 cells after stimulation with uf-UPs. Collectively, these results suggested the application of S. horneri as a functional ingredient for treating ocular disorders caused by particular matters.

Surface-Tailored Medium Entropy Alloys as Radically Low Overpotential Oxygen Evolution Electrocatalysts.

Numerous studies have explored new materials for electrocatalysts, but it is difficult to discover materials that surpass the catalytic activity of current commercially available noble metal electrocatalysts. In contrast to conventional transition metal alloys, high-entropy alloys (HEAs) have immense potential to maximize their catalytic properties because of their high stability and compositional diversity as oxygen evolution reactions (OERs). This work presents medium-entropy alloys (MEAs) as OER electrocatalysts to simultaneously satisfy the requirement of high catalytic activity and long-term stability. The surface of MEA electrocatalyst is tailored to suit the OER via anodizing and cyclic voltammetry activation methods. Optimized electrical properties and hydrophilicity of the surface enable an extremely low overpotential of 187 mV for achieving the current density of 10 mA cm-2 alkaline media. Furthermore, a combined photovoltaic-electrochemical system with MEA electrocatalyst and a perovskite/Si tandem solar cell exhibits a solar-to-hydrogen conversion efficiency of 20.6% for an unassisted hydrogen generation system. These results present a new pathway for designing sustainable high efficiency water splitting cells.

Emerging Two-Dimensional-Based Nanostructured Catalysts: Applications in Sustainable Organic Transformations.

The extension of green and sustainable materials in the preparation of heterogeneous catalysts for organic transformations has increased over the past few decades. Because of their unique and intriguing physical and chemical properties, two-dimensional (2D) nanostructured materials have attracted widespread attention and have been used in a variety of applications, such as catalysis, electronics, and energy storage. A promising pathway to enhance the performance of 2D nanomaterials is their coupling with other functional materials to form heterogeneous or hybrid structures. Herein, we discuss the use of 2D-based nanostructured catalysts for enhancing organic transformations and highlight selected examples to demonstrate the synthesis, advantages, challenges, efficiency, and reusability of the introduced heterogeneous catalysts for cross-coupling and reduction reactions.

A systematic review on papers that study on Single Nucleotide Polymorphism that affects coronavirus 2019 severity.

BACKGROUND: COVID-19, caused by SARS-CoV-2 has become the most threatening issue to all populations around the world. It is, directly and indirectly, affecting all of us and thus, is an emerging topic dealt in global health. To avoid the infection, various studies have been done and are still ongoing. COVID-19 cases are reported all over the globe, and among the millions of cases, genetic similarity may be seen. The genetical common features seen within confirmed cases may help outline the tendency of infection and degree severity of the disease. Here, we reviewed multiple papers on SNPs related to SARS-CoV-2 infection and analyzed their results. METHODS: The PubMed databases were searched for papers discussing SNPs associated with SARS-CoV-2 infection and severity. Clinical studies with human patients and statistically showing the relevance of the SNP with virus infection were included. Quality Assessment of all papers was done with Newcastle Ottawa Scale. RESULTS: In the analysis, 21 full-text literature out of 2956 screened titles and abstracts, including 63,496 cases, were included. All were human-based clinical studies, some based on certain regions gathered patient data and some based on big databases obtained online. ACE2, TMPRSS2, and IFITM3 are the genes mentioned most frequently that are related to SARS-CoV-2 infection. 20 out of 21 studies mentioned one or more of those genes. The relevant genes according to SNPs were also analyzed. rs12252-C, rs143936283, rs2285666, rs41303171, and rs35803318 are the SNPs that were mentioned at least twice in two different studies. CONCLUSIONS: We found that ACE2, TMPRSS2, and IFITM3 are the major genes that are involved in SARS-CoV-2 infection. The mentioned SNPs were all related to one or more of the above-mentioned genes. There were discussions on certain SNPs that increased the infection and severity to certain groups more than the others. However, as there is limited follow-up and data due to a shortage of time history of the disease, studies may be limited.

Learning curve for minimally invasive transforaminal lumbar interbody fusion: a systematic review.

PURPOSE: Minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) is commonly used to treat degenerative lumbar spinal disorders. It facilitates a full-scale spinal decompression and interbody fusion with minimal neural retraction using the tubular retractor system. Despite the benefits of surgical efficiency and minimalism, this technique requires a long learning curve. There is currently no consensus on the learning curve characteristics and proper training methods for MI-TLIF. Thus, this systematic review aimed to discuss the cutoff point at which technical proficiency is achieved and ways to enhance the learning process. METHODS: Major databases, including PubMed, Embase, and Cochrane Library, were searched for learning curve studies that have evaluated the clinical outcome and learning progress of MI-TLIF using quantitative data. The qualities of the selected studies were assessed using the Newcastle‒Ottawa scale. The plateau points in the "learning curve" were analyzed according to various outcome measures. RESULTS: Nine full-text articles, representing 753 cases, were included from 9743 screened studies. The most commonly used outcome measures were the operative time, followed by the complication rate. The mean cutoff point for the operative time was 31.33 ± 11.98 (range 13‒45) cases. CONCLUSION: The plateau point in the learning curve for MI-TLIF may differ according to the outcome measures used. Most studies have demonstrated the learning progress based on simple task efficiency, rather than patient outcomes. Moreover, the learning rate may be affected by the patients' and technical conditions. Therefore, great care is required in interpreting the learning curve and cutoff point for MI-TLIF proficiency.

Multiple Concurrent Slotframe Scheduling for Wireless Power Transfer-Enabled Wireless Sensor Networks.

This paper presents a multiple concurrent slotframe scheduling (MCSS) protocol for wireless power transfer (WPT)-enabled wireless sensor networks. The MCSS supports a cluster-tree network topology composed of heterogeneous devices, including hybrid access points (HAPs) serving as power transmitting units and sensor nodes serving as power receiving units as well as various types of traffic, such as power, data, and control messages (CMs). To this end, MCSS defines three types of time-slotted channel hopping (TSCH) concurrent slotframes: the CM slotframe, HAP slotframe, and WPT slotframe. These slotframes are used for CM traffic, inter-cluster traffic, and intra-cluster traffic, respectively. In MCSS, the length of each TSCH concurrent slotframe is set to be mutually prime to minimize the overlap between cells allocated in the slotframes, and its transmission priority is determined according to the characteristics of transmitted traffic. In addition, MCSS determines the WPT slotframe length, considering the minimum number of power and data cells required for energy harvesting and data transmission of sensor nodes and the number of overprovisioned cells needed to compensate for overlap between cells. The simulation results demonstrated that MCSS outperforms the legacy TSCH medium access control protocol and TSCH multiple slotframe scheduling (TMSS) for the average end-to-end delay, aggregate throughput, and average harvested energy.

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network.

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user's device to the cloud server without the captured original images. This paper proposes a new method to generate 3D (dimensional) keypoints based on a user's mobile device with a commercial RGB camera in a distributed computing environment such as a cloud server. The images are captured with a moving camera and 2D keypoints are extracted from them. After executing feature extraction between continuous frames, disparities are calculated between frames using the relationships between matched keypoints. The physical distance of the baseline is estimated by using the motion information of the camera, and the actual distance is calculated by using the calculated disparity and the estimated baseline. Finally, 3D keypoints are generated by adding the extracted 2D keypoints to the calculated distance. A keypoint-based scene change method is proposed as well. Due to the existing similarity between continuous frames captured from a camera, not all 3D keypoints are transferred and stored, only the new ones. Compared with the ground truth of the TUM dataset, the average error of the estimated 3D keypoints was measured as 5.98 mm, which shows that the proposed method has relatively good performance considering that it uses a commercial RGB camera on a mobile device. Furthermore, the transferred 3D keypoints were decreased to about 73.6%.

Dynamic Reconstruction and Mesh Compression of 4D Volumetric Model Using Correspondence-Based Deformation for Streaming Service.

A sequence of 3D models generated using volumetric capture has the advantage of retaining the characteristics of dynamic objects and scenes. However, in volumetric data, since 3D mesh and texture are synthesized for every frame, the mesh of every frame has a different shape, and the brightness and color quality of the texture is various. This paper proposes an algorithm to consistently create a mesh of 4D volumetric data using dynamic reconstruction. The proposed algorithm comprises remeshing, correspondence searching, and target frame reconstruction by key frame deformation. We make non-rigid deformation possible by applying the surface deformation method of the key frame. Finally, we propose a method of compressing the target frame using the target frame reconstructed using the key frame with error rates of up to 98.88% and at least 20.39% compared to previous studies. The experimental results show the proposed method's effectiveness by measuring the geometric error between the deformed key frame and the target frame. Further, by calculating the residual between two frames, the ratio of data transmitted is measured to show a compression performance of 18.48%.

γ-GeSe: A New Hexagonal Polymorph from Group IV-VI Monochalcogenides.

The family of group IV-VI monochalcogenides has an atomically puckered layered structure, and their atomic bond configuration suggests the possibility for the realization of various polymorphs. Here, we report the synthesis of the first hexagonal polymorph from the family of group IV-VI monochalcogenides, which is conventionally orthorhombic. Recently predicted four-atomic-thick hexagonal GeSe, so-called γ-GeSe, is synthesized and clearly identified by complementary structural characterizations, including elemental analysis, electron diffraction, high-resolution transmission electron microscopy imaging, and polarized Raman spectroscopy. The electrical and optical measurements indicate that synthesized γ-GeSe exhibits high electrical conductivity of 3 × 105 S/m, which is comparable to those of other two-dimensional layered semimetallic crystals. Moreover, γ-GeSe can be directly grown on h-BN substrates, demonstrating a bottom-up approach for constructing vertical van der Waals heterostructures incorporating γ-GeSe. The newly identified crystal symmetry of γ-GeSe warrants further studies on various physical properties of γ-GeSe.

Tailoring Single- and Double-Sided Fluorination of Bilayer Graphene via Substrate Interactions.

While many technologies rely on multilayer heterostructures, most of the studies on chemical functionalization have been limited to monolayer graphene. In order to use functionalization in multilayer systems, we must first understand the interlayer interactions between functionalized and nonfunctionalized (intact) layers and how to selectively functionalize one layer at a time. Here, we demonstrate a method to fabricate single- or double-sided fluorinated bilayer graphene (FBG) by tailoring substrate interactions. Both the top and bottom surfaces of bilayer graphene on the rough silicon dioxide (SiO2) are fluorinated; meanwhile, only the top surface of graphene on hexagonal boron nitride (hBN) is fluorinated. The functionalization type affects electronic properties; double-sided FBG on SiO2 is insulating, whereas single-sided FBG on hBN maintains conducting, showing that the intact bottom layer becomes electrically decoupled from the fluorinated top insulating layer. Our results define a straightforward method to selectively functionalize the top and bottom surfaces of bilayer graphene.

PKR-Mediated Phosphorylation of eIF2a and CHK1 Is Associated with Doxorubicin-Mediated Apoptosis in HCC1143 Triple-Negative Breast Cancer Cells.

Triple-negative breast cancer is more aggressive than other types of breast cancer. Protein kinase R (PKR), which is activated by dsRNA, is known to play a role in doxorubicin-mediated apoptosis; however, its role in DNA damage-mediated apoptosis is not well understood. In this study, we investigated the roles of PKR and its downstream players in doxorubicin-treated HCC1143 triple-negative breast cancer cells. Doxorubicin treatment induces DNA damage and apoptosis. Interestingly, doxorubicin treatment induced the phosphorylation of eukaryotic initiation factor 2 alpha (eIF2α) via PKR, whereas the inhibition of PKR with inhibitor C16 reduced eIF2α phosphorylation. Under these conditions, doxorubicin-mediated DNA fragmentation, cell death, and poly(ADP ribose) polymerase and caspase 7 levels were recovered. In addition, phosphorylation of checkpoint kinase 1 (CHK1), which is known to be involved in doxorubicin-mediated DNA damage, was increased by doxorubicin treatment, but blocked by PKR inhibition. Protein translation was downregulated by doxorubicin treatment and upregulated by blocking PKR phosphorylation. These results suggest that PKR activation induces apoptosis by increasing the phosphorylation of eIF2α and CHK1 and decreasing the global protein translation in doxorubicin-treated HCC1143 triple-negative breast cancer cells.