Remote optogenetic control of the enteric nervous system and brain-gut axis in freely-behaving mice enabled by a wireless, battery-free optoelectronic device.

Wireless activation of the enteric nervous system (ENS) in freely moving animals with implantable optogenetic devices offers a unique and exciting opportunity to selectively control gastrointestinal (GI) transit in vivo, including the gut-brain axis. Programmed delivery of light to targeted locations in the GI-tract, however, poses many challenges not encountered within the central nervous system (CNS). We report here the development of a fully implantable, battery-free wireless device specifically designed for optogenetic control of the GI-tract, capable of generating sufficient light over large areas to robustly activate the ENS, potently inducing colonic motility ex vivo and increased propulsion in vivo. Use in in vivo studies reveals unique stimulation patterns that increase expulsion of colonic content, likely mediated in part by activation of an extrinsic brain-gut motor pathway, via pelvic nerves. This technology overcomes major limitations of conventional wireless optogenetic hardware designed for the CNS, providing targeted control of specific neurochemical classes of neurons in the ENS and brain-gut axis, for direct modulation of GI-transit and associated behaviours in freely moving animals.

Bioresorbable shape-adaptive structures for ultrasonic monitoring of deep-tissue homeostasis.

Monitoring homeostasis is an essential aspect of obtaining pathophysiological insights for treating patients. Accurate, timely assessments of homeostatic dysregulation in deep tissues typically require expensive imaging techniques or invasive biopsies. We introduce a bioresorbable shape-adaptive materials structure that enables real-time monitoring of deep-tissue homeostasis using conventional ultrasound instruments. Collections of small bioresorbable metal disks distributed within thin, pH-responsive hydrogels, deployed by surgical implantation or syringe injection, allow ultrasound-based measurements of spatiotemporal changes in pH for early assessments of anastomotic leaks after gastrointestinal surgeries, and their bioresorption after a recovery period eliminates the need for surgical extraction. Demonstrations in small and large animal models illustrate capabilities in monitoring leakage from the small intestine, the stomach, and the pancreas.

Scbean: a python library for single-cell multi-omics data analysis.

SUMMARY: Single-cell multi-omics technologies provide a unique platform for characterizing cell states and reconstructing developmental process by simultaneously quantifying and integrating molecular signatures across various modalities, including genome, transcriptome, epigenome, and other omics layers. However, there is still an urgent unmet need for novel computational tools in this nascent field, which are critical for both effective and efficient interrogation of functionality across different omics modalities. Scbean represents a user-friendly Python library, designed to seamlessly incorporate a diverse array of models for the examination of single-cell data, encompassing both paired and unpaired multi-omics data. The library offers uniform and straightforward interfaces for tasks, such as dimensionality reduction, batch effect elimination, cell label transfer from well-annotated scRNA-seq data to scATAC-seq data, and the identification of spatially variable genes. Moreover, Scbean's models are engineered to harness the computational power of GPU acceleration through Tensorflow, rendering them capable of effortlessly handling datasets comprising millions of cells. AVAILABILITY AND IMPLEMENTATION: Scbean is released on the Python Package Index (PyPI) (https://pypi.org/project/scbean/) and GitHub (https://github.com/jhu99/scbean) under the MIT license. The documentation and example code can be found at https://scbean.readthedocs.io/en/latest/.

Honeycomb-like 3D ordered macroporous SiOx/C nanoarchitectures with carbon coating for high-performance lithium storage.

SiOx anodes are garnering significant interest in lithium-ion batteries (LIBs) due to theirs low voltage plateau and high capacity. However, critical drawbacks, including high expansion rate and low electronic conductivity, severely limit their practical applications. While 0D, 1D, and 2D scale nanostructures have been proven to mitigate these issues, these materials tend to accumulate after prolonged cycling, leading to adverse effects on the mass transfer processes within the electrode. Herein, we have developed a honeycomb-like SiOx/C nanoarchitecture with carbon coating based on a 3D ordered macroporous (3DOM) structure. The 3D interconnected pore windows facilitate the diffusion and transport of lithium ions (Li+) in the electrolyte, and the extremely thin walls (<15 nm) provide a shorter transport path for Li+ in the solid. The carbon cladding buffers volume expansion and enhances electronic conductivity. The as-prepared anode demonstrates a high reversible capacity of 1068 mAh/g and an initial coulombic efficiency of 70.7 %. It maintains a capacity of 644 mAh/g (capacity retention of 84.63 %) even at a high current of 1.0 A/g after 700 cycles. The unique honeycomb-like structure offers enormous insights into the study of energy storage in 3D materials.

Reversibly growing crosslinked polymers with programmable sizes and properties.

Growth constitutes a powerful method to post-modulate materials' structures and functions without compromising their mechanical performance for sustainable use, but the process is irreversible. To address this issue, we here report a growing-degrowing strategy that enables thermosetting materials to either absorb or release components for continuously changing their sizes, shapes, compositions, and a set of properties simultaneously. The strategy is based on the monomer-polymer equilibrium of networks in which supplying or removing small polymerizable components would drive the networks toward expansion or contraction. Using acid-catalyzed equilibration of siloxane as an example, we demonstrate that the size and mechanical properties of the resulting silicone materials can be significantly or finely tuned in both directions of growth and decomposition. The equilibration can be turned off to yield stable products or reactivated again. During the degrowing-growing circle, material structures are selectively varied either uniformly or heterogeneously, by the availability of fillers. Our strategy endows the materials with many appealing capabilities including environment adaptivity, self-healing, and switchability of surface morphologies, shapes, and optical properties. Since monomer-polymer equilibration exists in many polymers, we envision the expansion of the presented strategy to various systems for many applications.

A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data.

MOTIVATION: Single-cell multimodal assays allow us to simultaneously measure two different molecular features of the same cell, enabling new insights into cellular heterogeneity, cell development and diseases. However, most existing methods suffer from inaccurate dimensionality reduction for the joint-modality data, hindering their discovery of novel or rare cell subpopulations. RESULTS: Here, we present VIMCCA, a computational framework based on variational-assisted multi-view canonical correlation analysis to integrate paired multimodal single-cell data. Our statistical model uses a common latent variable to interpret the common source of variances in two different data modalities. Our approach jointly learns an inference model and two modality-specific non-linear models by leveraging variational inference and deep learning. We perform VIMCCA and compare it with 10 existing state-of-the-art algorithms on four paired multi-modal datasets sequenced by different protocols. Results demonstrate that VIMCCA facilitates integrating various types of joint-modality data, thus leading to more reliable and accurate downstream analysis. VIMCCA improves our ability to identify novel or rare cell subtypes compared to existing widely used methods. Besides, it can also facilitate inferring cell lineage based on joint-modality profiles. AVAILABILITY AND IMPLEMENTATION: The VIMCCA algorithm has been implemented in our toolkit package scbean (≥0.5.0), and its code has been archived at https://github.com/jhu99/scbean under MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Calcined Bean Dregs-Hydrocalumite Composites as Efficient Adsorbents for the Removal of Ofloxacin.

Calcined bean dregs-hydrocalumite composites were prepared through in situ self-assembly of hydrocalumite on the surface of bean dregs and used for the adsorption of ofloxacin from water. The adsorbents were characterized by scanning electron microscopy, X-ray powder diffraction, and N2 physical adsorption. The results showed that the adsorption performance of calcined bean dregs-hydrocalumite composites for ofloxacin was much better than that of a single bean dreg carbon or calcined hydrocalumite. The effects of preparation and adsorption conditions on the adsorption property of calcined bean dregs-hydrocalumite for ofloxacin were also investigated. The adsorption ratio of ofloxacin reached up to 99.93% using 4 g·L-1 adsorbent dosage with 20 mg·L-1 initial concentration of ofloxacin at 30 °C in 2 h. The adsorption process mainly occurred in the first 5 min. In addition, the adsorption of ofloxacin by calcined bean dregs-hydrocalumite was more in line with pseudo-second-order dynamics and the Langmuir isotherm model.

Study of Concrete Surface Coatings Using Thermosensitive Fluorescent Microcapsules Capable of Indicating Damage.

A new type of concrete surface gel coating using thermosensitive fluorescent (TSF) microcapsules was proposed to monitor micro-cracks of cement-based materials. The gel materials can adhere other materials, and the incorporation of microcapsules into the gel coating can be cured on various structural surfaces. Zinc sulfide and phenyl acetate were encapsulated into a polymethyl methacrylate shell to prepare the TSF microcapsules by a solvent evaporation method. When micro-cracks are generated on the surface of the gel coating, the ruptured TSF microcapsules burst out, fill the damaged area, and then emit fluorescence after being excited at ambient temperature. It was found that the brightness of the fluorescence increased with increasing temperature from 80-110 °C. When the concentration of TSF microcapsules was 15% of the mass of the gel coating, the cement-based damage-sensing material had sufficient damage-indicating effects, and the fluorescence brightness of the crack location remained even after a long time. It is expected that this study will provide an effective and intuitive method for crack location detection of cement-based materials.

Estimation of the age of human semen stains by attenuated total reflection Fourier transform infrared spectroscopy: a preliminary study.

Semen stain is one of the most important biological evidence at sexual crime scenes. Age estimation of human semen stains plays an important role in forensic work, and it is rarely studied due to lack of well-established methods. In this study, the technique called attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) coupled with advanced chemometric methods was employed to determine the age of semen stains on three different substrates: glass slides, tissues and fabric made of regenerated cellulose fibres up to 6 d. Partial least squares regression (PLSR) was used in conjunction with spectral analysis for age estimation, and the results generated high R 2 values (cross-validation: 0.81, external validation: 0.74) but a narrow margin of error for root mean square error (RMSE) (RMSE of cross-validation: 0.77 d, RMSE of prediction: 1.02 d). Additionally, our results indicated the robustness of PLSR model was not weaken by the influence of different substrates in this study. Our results indicate that ATR-FTIR, combined with chemometric methods, shows great potential as a convenient and efficient tool for age estimation of semen stains. Moreover, the method could be applied to routine forensic investigations in the future.

Identifying muscle hemorrhage in rat cadavers with advanced decomposition by FT-IR microspectroscopy combined with chemometrics.

The identification of muscle hemorrhage in a cadaver that is in an advanced stage of decomposition is an important but challenging task. Our study investigated whether Fourier transform infrared (FT-IR) microspectroscopy in conjunction with chemometrics could identify muscle hemorrhage using rat cadavers with advanced decomposition. In this study, an intramuscular blood injection method, instead of a mechanical injury method, was used to construct a muscle hemorrhage model, and the modeling idea of muscle hemorrhage identification was to discriminate and classify hemoglobin-leaking myofibrils from negative myofibrils. First, the optical images of hematoxylin/eosin (H&E) stained hemorrhagic muscle at different postmortem intervals (PMIs) were observed and showed that the morphological features of whole erythrocytes disappeared since the PMI of 4 d. Subsequently, principle component analysis (PCA) was performed and indicated that the biochemical differences in protein structures between fresh erythrocytes and myofibrils can be detected by the IR spectroscopic method. Ultimately, several classification models based on the partial least square discriminant analysis (PLS-DA) algorithm were successfully constructed for different PMIs and PMI ranges and achieved great prediction performances in external validations. This preliminary study demonstrates the feasibility of using FT-IR microspectroscopy combined with chemometrics as a potential approach for identifying muscle hemorrhage in cadavers with advanced decomposition for offering vital evidences in judicial process.

A Photoresponsive Hydrogel with Enhanced Photoefficiency and the Decoupled Process of Light Activation and Shape Changing for Precise Geometric Control.

Traditional shape-morphing hydrogels rely on structural implementation of inhomogeneity inside the material during fabrication to realize predetermined complex shape change upon activation. In recent years, several systems with reprogrammable shape-morphing capabilities have been developed. Among those, the photoresponsive hydrogels offer the best spatial and temporal control. However, for most photoresponsive hydrogels, upon light irradiation, they simultaneously deform, which requires the projection of the light pattern to be continuously adaptive to the deforming gel. It is impractical for complex 3D morphing. In this paper, by incorporating two photodissociable molecules that can form a reactive ion couple upon light activation into one hydrogel, the light irradiation process is decoupled with the morphing process, and the consumption of the reactive ion couple drives the reversible photochemical reaction forward. Consequently, the photochemical reaction efficiency is improved, and the photoresponsive molecules are locked in the activated state until a recovery stimulus is applied. Based on the proposed general scheme, a specific example is given by incorporating the triphenylmethane leucohydroxide and 2-nitrobenzaldehyde molecules into a polyacrylamide hydrogel. The swelling behavior is characterized, and the reprogrammable morphing with precisely controlled geometry is demonstrated.

Predicting postmortem interval based on microbial community sequences and machine learning algorithms.

Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis, Anaerosalibacter bizertensis, Lactobacillus reuteri, and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 ± 0.8 h within 24-h decomposition and 14.5 ± 4.4 h within 15-day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.

Controllable Liquid-Liquid Printing with Defect-free, Corrosion-Resistance, Unrestricted Wetting Condition.

Conventional printing is worth revisiting because of its established procedures in meeting the surging demand of manufacturing printed electronics, 3D products, etc. However, one goal in penetrating printing into these is to control pattern transfer with no limitation of wettability. Here we introduce a miscible liquid-liquid transfer printing mechanism that can synchronize material preparation and material patterning with desirable properties including limitless selection of raw materials, corrosion resistance, no wetting constraint, and ability to prepare large-area defect-free materials for multi-function applications. Theoretical modeling and experiments demonstrate that donor liquid could be used to make patterns within the bulk of a receiver material, allowing the obtained intrinsically patterned functional materials to be resistant to harsh conditions. Different from current liquid printing technologies, this printing approach enables stable and defect-free material preparation and is expected to prove useful in flexible display, soft electronics, 4D printing, and beyond.

Human and non-human bone identification using FTIR spectroscopy.

Human and non-human identification of unknown skeletal remains is of great importance in forensic and anthropologic contexts. However, the traditional morphological methods for bone species identification are subjective or time-consuming. Here, we utilized Fourier transform infrared (FTIR) spectroscopy and chemometric methods to determinate the spectral variances between human and non-human (i.e., pig, goat, and cow) bones. To simulate real forensic situations as much as possible, fresh, boiled, and decomposed bones were included in this study. Principal component analysis (PCA) results illustrated pig bones were more sensitive to the environmental and external factors than other species studied in this work. Thus, pig bone might not be a suitable proxy for human bone in the study of postmortem changes. More importantly, score plots of PCA results showed clear separation with a slight overlap between the human and non-human fresh bones, but it failed to distinguish the boiled and decomposed bones. Then, partial least squares discriminant analysis (PLS-DA) was employed, and both internal and external validations were conducted to assess its classification ability, which resulted in 99.72 and 99.53% accuracy, respectively. According to the loading plots of PCA and PLS-DA, the spectral diversity was mainly due to the inorganic portion (i.e., carbonates and phosphates), which can remain relatively stable under various conditions. As such, our results illustrate that FTIR spectroscopy could serve as a reliable tool to assist in bone species determination and also has great potential in real forensic cases with natural conditions.