Structural and functional abnormalities in the medial prefrontal cortex were associated with pain and depressive symptoms in patients with adhesive capsulitis.

Introduction: Chronic pain and depression have been shown to coexist in patients with adhesive capsulitis (AC). Recent studies identified the shared brain plasticity between pain and depression; however, how such neuroplasticity contributes to AC remains unclear. Here, we employed a combination of psychophysics, structural MRI, and functional MRI techniques to examine the brain's structural and functional changes in AC. Methods: Fifty-two patients with AC and 52 healthy controls (HCs) were included in our study. Voxelwise comparisons were performed to reveal the differences in grey matter volume (GMV) and regional homogeneity (ReHo) between AC and HCs. Furthermore, region of interest to whole brain functional connectivity (FC) was calculated and compared between the groups. Finally, Pearson correlation coefficients were computed to reveal the association between clinical data and brain alterations. Mediation analyses were performed to investigate the path association among brain alterations and clinical measures. Results: Three main findings were observed: (1) patients with AC exhibited a higher depression subscale of hospital anxiety and depression scale (HADS-D) score correlating with the GMV within the right medial prefrontal cortices (mPFC) compared with HCs; (2) relative to HCs, patients with AC exhibited lower ReHo within the right mPFC, which largely overlapped with the structural abnormalities; (3) the impact of pain duration on HADS-D score was mediated by ventral part of medial prefrontal cortices (vmPFC) GMV in patients with AC. Conclusion: In summary, our current findings suggest that vmPFC alterations correlate with both the pain duration and the emotional comorbidities experienced by patients with AC. Our research provides an enhanced comprehension of the underlying mechanisms of AC, thereby facilitating the development of more effective treatment approaches for AC.

Predicting the mechanism of action of YQYYJD prescription in the treatment of non-small cell lung cancer using transcriptomics analysis.

ETHNOPHARMACOLOGICAL RELEVANCE: The efficacy of the herbal formula Yiqi Yangyin Jiedu (YQYYJD) in the treatment of advanced lung cancer has been reported in clinical trials. However, the key anti-lung cancer herbs and molecular mechanisms underlying its inhibition of lung cancer are not well-understood. AIM OF THE STUDY: To identify the key anti-lung cancer herbs in the YQYYJD formula and investigate their therapeutic effect and potential mechanism of action in non-small cell lung cancer (NSCLC) using transcriptomics and bioinformatics techniques. MATERIALS AND METHODS: A mouse Lewis lung carcinoma (LLC) subcutaneous inhibitory tumor model was established with 6 mice in each group. Mice were treated with the YQYYJD split formula: Yiqi Formula (YQ), Yangyin Formula (YY), and Ruanjian Jiedu Formula (RJJD) for 14 days. The tumor volume and mouse weight were recorded, and the status of tumor occurrence was further observed by taking photos. The tumor was stained with hematoxylin-eosin to observe its histopathological changes. Immunohistochemistry was used to detect the expression of the proliferation marker Ki67 and the apoptotic marker Caspase-3 in tumor tissues. Flow cytometry was used to detect the number of CD4+ and CD8+ T cells and cytokines interleukin-2 (IL-2) and interferon-gamma (IFN-γ) in the spleen and tumor tissues. The differential genes of key drugs against tumors were obtained by transcriptome sequencing of tumors. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed on differential genes to obtain pathways and biological processes where targets were aggregated. TIMER2.0 and TISIDB databases were used to evaluate the impact of drugs on immune cell infiltration and immune-related genes. The binding activity of the key targets and compounds was verified by molecular docking. RESULTS: YQ, YY, and RJJD inhibited the growth of subcutaneous transplanted tumors in LLC mice to varying degrees and achieved antitumor effects by inhibiting the expression of tumor cell proliferation, apoptosis, and metastasis-related proteins. Among the three disassembled prescriptions, YQ better inhibited the growth of subcutaneous transplanted tumors in LLC mice, significantly promoted tumor necrosis, significantly increased the expression of Caspase-3 protein in tumor tissue, and significantly decreased the expression of Ki-67 (P < 0.05), thereby increasing the infiltration of CD8+ T cells. YQ significantly increased the expression of CD4+ and CD8+ T cells in tumor and splenic tissues of tumor-bearing mice and up-regulated the expression of IL-2 and IFN-γ. Transcriptome sequencing and bioinformatics results showed that after YQ intervention, differentially expressed genes were enriched in more than one tumor-related pathway and multiple immune regulation-related biological functions. There were 12 key immune-related target genes. CONCLUSION: YQ was the key disassembled prescription of YQYYJD, exerting significant antitumor effects and immune regulation effects on NSCLC. It may have relieved T cell exhaustion and regulated the immune microenvironment to exert antitumor effects by changing lung cancer-related targets, pathways, and biological processes.

Upconversion-Powered Photoelectrochemical Bioanalysis for DNA Sensing.

In this work, we report a new concept of upconversion-powered photoelectrochemical (PEC) bioanalysis. The proof-of-concept involves a PEC bionanosystem comprising a NaYF4:Yb,Tm@NaYF4 upconversion nanoparticles (UCNPs) reporter, which is confined by DNA hybridization on a CdS quantum dots (QDs)/indium tin oxide (ITO) photoelectrode. The CdS QD-modified ITO electrode was powered by upconversion absorption together with energy transfer effect through UCNPs for a stable photocurrent generation. By measuring the photocurrent change, the target DNA could be detected in a specific and sensitive way with a wide linear range from 10 pM to 1 µM and a low detection limit of 0.1 pM. This work exploited the use of UCNPs as signal reporters and realized upconversion-powered PEC bioanalysis. Given the diversity of UCNPs, we believe it will offer a new perspective for the development of advanced upconversion-powered PEC bioanalysis.

Synthesizing Knowledge-Enhanced Features for Real-World Zero-Shot Food Detection.

Food computing brings various perspectives to computer vision like vision-based food analysis for nutrition and health. As a fundamental task in food computing, food detection needs Zero-Shot Detection (ZSD) on novel unseen food objects to support real-world scenarios, such as intelligent kitchens and smart restaurants. Therefore, we first benchmark the task of Zero-Shot Food Detection (ZSFD) by introducing FOWA dataset with rich attribute annotations. Unlike ZSD, fine-grained problems in ZSFD like inter-class similarity make synthesized features inseparable. The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories. To address these problems, we propose a novel framework ZSFDet to tackle fine-grained problems by exploiting the interaction between complex attributes. Specifically, we model the correlation between food categories and attributes in ZSFDet by multi-source graphs to provide prior knowledge for distinguishing fine-grained features. Within ZSFDet, Knowledge-Enhanced Feature Synthesizer (KEFS) learns knowledge representation from multiple sources (e.g., ingredients correlation from knowledge graph) via the multi-source graph fusion. Conditioned on the fusion of semantic knowledge representation, the region feature diffusion model in KEFS can generate fine-grained features for training the effective zero-shot detector. Extensive evaluations demonstrate the superior performance of our method ZSFDet on FOWA and the widely-used food dataset UECFOOD-256, with significant improvements by 1.8% and 3.7% ZSD mAP compared with the strong baseline RRFS. Further experiments on PASCAL VOC and MS COCO prove that enhancement of the semantic knowledge can also improve the performance on general ZSD. Code and dataset are available at https://github.com/LanceZPF/KEFS.

Exploring the Sheep MAST4 Gene Variants and Their Associations with Litter Size.

The economic efficiency of sheep breeding can be improved by enhancing sheep productivity. A recent genome-wide association study (GWAS) unveiled the potential impact of the MAST4 gene on prolificacy traits in Australian White sheep (AUW)). Herein, whole-genome sequencing (WGS) data from 26 different sheep breeds worldwide (n = 1507), including diverse meat, wool, milk, or dual-purpose sheep breed types from China, Europe, and Africa, were used. Moreover, polymerase chain reaction (PCR) genotyping of the MAST4 gene polymorphisms in (n = 566) Australian white sheep (AUW) was performed. The 3 identified polymorphisms were not homogeneously distributed across the 26 examined sheep breeds. Findings revealed prevalent polymorphisms (P3-ins-29 bp and P6-del-21 bp) with varying frequencies (0.02 to 0.97) across 26 breeds, while P5-del-24 bp was presented in 24 out of 26 breeds. Interestingly, the frequency of the P3-ins-29 bp variant was markedly higher in Chinese meat or dual-purpose sheep breeds, while the other two variants also showed moderate frequencies in meat breeds. Notably, association analysis indicated that all InDels were associated with AUW sheep litter size (p < 0.05). These results suggest that these InDels within the MAST4 gene could be useful in marker-assisted selection in sheep breeding.

Large-Area Fabrication of Hexaazatrinaphthylene-Based 2D Metal-Organic Framework Films for Flexible Photodetectors and Optoelectronic Synapses.

2D conjugated metal-organic frameworks (c-MOFs) have emerged as promising materials for (opto)electronic applications due to their excellent charge transport properties originating from the unique layered-stacked structures with extended in-plane conjugation. The further advancement of MOF-based (opto)electronics necessitates the development of novel 2D c-MOF thin films with high quality. Cu-HHHATN (HHHATN: hexahydroxyl-hexaazatrinaphthylene) is a recently reported 2D c-MOF featuring high in-plane conjugation, strong interlayer π-π stacking, and multiple coordination sites, while the production of its thin-film form has not yet been reported. Herein, large-area Cu-HHHATN thin films with preferential orientation, high uniformity, and smooth surfaces are realized by using a convenient layer-by-layer growth method. Flexible photodetectors are fabricated, showing broadband photoresponse ranging from UV to short-wave infrared (370 to 1450 nm). The relatively long relaxation time of photocurrent, which arises from the trapping of photocarriers, renders the device's synaptic plasticity similar to that of biological synapses, promising its use in neuromorphic visual systems. This work demonstrates the great potential of Cu-HHHATN thin films in flexible optoelectronic devices for various applications.

Divergent thinking benefits from functional antagonism of the left IFG and right TPJ: a transcranial direct current stimulation study.

Divergent thinking is assumed to benefit from releasing the constraint of existing knowledge (i.e. top-down control) and enriching free association (i.e. bottom-up processing). However, whether functional antagonism between top-down control-related and bottom-up processing-related brain structures is conducive to generating original ideas is largely unknown. This study was designed to investigate the effect of functional antagonism between the left inferior frontal gyrus and the right temporoparietal junction on divergent thinking performance. A within-subjects design was adopted for three experiments. A total of 114 participants performed divergent thinking tasks after receiving transcranial direct current stimulation over target regions. In particular, cathodal stimulation over the left inferior frontal gyrus and anodal stimulation over the right inferior frontal gyrus (Experiment 1), anodal stimulation over the right temporoparietal junction (Experiment 2), and both cathodal stimulation over the left inferior frontal gyrus and anodal stimulation over the right temporoparietal junction (Experiment 3) were manipulated. Compared with sham stimulation, the combination of hyperpolarization of the left inferior frontal gyrus and depolarization of the right temporoparietal junction comprehensively promoted the fluency, flexibility, and originality of divergent thinking without decreasing the rationality of generated ideas. Functional antagonism between the left inferior frontal gyrus (hyperpolarization) and right temporoparietal junction (depolarization) has a "1 + 1 > 2" superposition effect on divergent thinking.

Organic Electrochemical Transistors for Biomarker Detections.

The improvement of living standards and the advancement of medical technology have led to an increased focus on health among individuals. Detections of biomarkers are feasible approaches to obtaining information about health status, disease progression, and response to treatment of an individual. In recent years, organic electrochemical transistors (OECTs) have demonstrated high electrical performances and effectiveness in detecting various types of biomarkers. This review provides an overview of the working principles of OECTs and their performance in detecting multiple types of biomarkers, with a focus on the recent advances and representative applications of OECTs in wearable and implantable biomarker detections, and provides a perspective for the future development of OECT-based biomarker sensors.

Effects of three different polysaccharides on the sol gel-behavior, rheological, and structural properties of tapioca starch.

The sol-gel behavior of tapioca starch (TS) plays a crucial role in the processing and quality control of flour-based products. However, natural tapioca starch has low gel strength and poor viscosity, which tremendously limits its application. To solve this problem, this study investigated the effects of κ-carrageenan (KC), konjac gum (KGM), and Mesona chinensis Benth polysaccharide (MCP) on the pasting behavior, rheological, and structural properties of tapioca starch. KC, KGM, and MCP significantly increased the viscosity of TS. With the exception of high-concentration KGM (0.5 %), all other blending systems showed decrease in setback. This may be attributed to the stronger effect of the high-concentration KC (0.5 %) and MCP (0.5 %) functional groups interacting with starch. KC, KGM, and MCP effectively improved the dynamic modulus (G' and G") of TS gel and were effective in increasing the gel strength and hardness of TS. The FT-IR analysis indicated that the short-range order of TS was mainly influenced by polysaccharides through non-covalent bonding interactions. Furthermore, it was confirmed that three polysaccharides could form dense structures by hydrogen bonding with TS. Similarly, more stable structure and pore size were observed in the microstructure diagram.

Developing predictive models for surgical outcomes in patients with degenerative cervical myelopathy: a comparison of statistical and machine learning approaches.

BACKGROUND CONTEXT: Machine learning (ML) is widely used to predict the prognosis of numerous diseases. PURPOSE: This retrospective analysis aimed to develop a prognostic prediction model using ML algorithms and identify predictors associated with poor surgical outcomes in patients with degenerative cervical myelopathy (DCM). STUDY DESIGN: A retrospective study. PATIENT SAMPLE: A total of 406 symptomatic DCM patients who underwent surgical decompression were enrolled and analyzed from three independent medical centers. OUTCOME MEASURES: We calculated the area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model. METHODS: The Japanese Orthopedic Association (JOA) score was obtained before and 1 year following decompression surgery, and patients were grouped into good and poor outcome groups based on a cut-off value of 60% based on a previous study. Two datasets were fused for training, 1 dataset was held out as an external validation set. Optimal feature-subset and hyperparameters for each model were adjusted based on a 2,000-resample bootstrap-based internal validation via exhaustive search and grid search. The performance of each model was then tested on the external validation set. RESULTS: The Support Vector Machine (SVM) model showed the highest predictive accuracy compared to other methods, with an AUC of 0.82 and an accuracy of 75.7%. Age, sex, disease duration, and preoperative JOA score were identified as the most commonly selected features by both the ML and statistical models. Grid search optimization for hyperparameters successfully enhanced the predictive performance of each ML model, and the SVM model still had the best performance with an AUC of 0.93 and an accuracy of 86.4%. CONCLUSIONS: Overall, the study demonstrated that ML classifiers such as SVM can effectively predict surgical outcomes for patients with DCM while identifying associated predictors in a multivariate manner.

Abnormal preoperative fMRI signal variability in the pain ascending pathway is associated with the postoperative axial pain intensity in degenerative cervical myelopathy patients.

BACKGROUND CONTEXT: The moment-to-moment variability of resting-state brain activity has been suggested to play an active role in chronic pain. PURPOSE: To investigate preoperative alterations in regional blood-oxygen-level-dependent signal variability (BOLDsv) and inter-regional dynamic functional connectivity (dFC) in individuals with degenerative cervical myelopathy (DCM), and their potential association with postoperative axial pain severity. STUDY DESIGN: Cross-sectional study. PATIENT SAMPLE: Resting-state functional magnetic resonance imaging was obtained in 42 migraine individuals and 40 healthy controls (HCs). OUTCOME MEASURES: We calculated the standard deviation (SD) of the BOLD time-series at each voxel and the SD and mean of the dynamic conditional correlation between the brain regions which showed significant group differences in BOLDsv. METHODS: A group comparison was conducted using whole-brain voxel-wise analysis of the standard deviation (SD) of the BOLD time-series which was a measure of the BOLDsv. The brain areas displaying notable group discrepancies in BOLDsv were utilized to outline regions of interest (ROIs). To determine the strength/variability of the dFC, the mean and SD of the dynamic conditional correlation were calculated within these ROIs. Moreover, the postoperative axial pain (PAP) severity of patients was evaluated. RESULTS: Our results revealed that DCM patients with postoperative axial pain (PAP) demonstrated considerably increased BOLDsv in the bilateral thalamus and right insular, but significantly lower BOLDsv in the right S1. By applying dynamic functional connectivity (dFC) analysis, we found that DCM patients with PAP exhibited greater fluctuation of dFC in the thalamo-cortical pathway (specifically, thalamus-S1), when compared to HCs and patients without PAP (nPAP). Lastly, we established that dysfunctional BOLDsv and dFC in the ascending pain pathway were positively associated with the severity of PAP in DCM patients. CONCLUSION: Our results indicate a potential correlation between impaired pain ascending pathway and postoperative axial pain in DCM patients. These findings could potentially spark novel treatment approaches for individuals experiencing preoperative axial pain.

Transcriptional correlates of frequency-dependent brain functional activity associated with symptom severity in degenerative cervical myelopathy.

BACKGROUND: Neuroimaging techniques provide insights into the brain abnormalities secondary to degenerative cervical myelopathy (DCM) and their association with neurological deficits. However, the neural correlates underlying the discrepancy between symptom severity and the degree of spinal cord compression, as well as the transcriptional correlates of these cortical abnormalities, remain unknown in DCM patients. METHODS: In this cross-sectional study, which collected resting-state functional MRI (rs-fMRI) images and the Japanese Orthopedic Association (JOA) score, enrolled 104 participants (54 patients and 50 healthy controls). The frequency-dependent amplitude of low-frequency fluctuation (ALFF) was obtained for all participants. We investigated the ALFF differences between mild-symptom DCM patients and severe-symptom DCM patients while carefully matching the degree of compression between these two groups via both univariate comparison and searchlight classification for three frequency bands (e.g., Slow-4, Slow-5, and Full-band). Additionally, we identified genes associated with symptom severity in DCM patients by linking the spatial patterns of gene expression of Allen Human Brain Atlas and brain functional differences between mild symptom and severe symptom groups. RESULTS: (1) We found that the frequency-specific brain activities within the sensorimotor network (SMN), visual network (VN), and default mode network (DMN) were associated with the varying degrees of functional impairment in DCM patients; (2) the frequency-specific brain activity within the SMN correlated with the functional recovery in patients with DCM; (3) a spatial correlation between the brain-wide expression of genes involved in neuronal migration and the brain functional activities associated with symptom severity was identified in DCM patients. CONCLUSION: In conclusion, our study bridges gaps between genes, cell classes, biological processes, and brain functional correlates of DCM. While our findings are correlational in nature, they suggest that the neural activities of sensorimotor cortices in DCM are associated with the severity of symptoms and might be associated with neuronal migration within the brain.

Ingredient Prediction via Context Learning Network With Class-Adaptive Asymmetric Loss.

Ingredient prediction has received more and more attention with the help of image processing for its diverse real-world applications, such as nutrition intake management and cafeteria self-checkout system. Existing approaches mainly focus on multi-task food category-ingredient joint learning to improve final recognition by introducing task relevance, while seldom pay attention to making good use of inherent characteristics of ingredients independently. Actually, there are two issues for ingredient prediction. First, compared with fine-grained food recognition, ingredient prediction needs to extract more comprehensive features of the same ingredient and more detailed features of various ingredients from different regions of the food image. Because it can help understand various food compositions and distinguish the differences within ingredient features. Second, the ingredient distributions are extremely unbalanced. Existing loss functions can not simultaneously solve the imbalance between positive-negative samples belonging to each ingredient and significant differences among all classes. To solve these problems, we propose a novel framework named Class-Adaptive Context Learning Network (CACLNet) for ingredient prediction. In order to extract more comprehensive and detailed features, we introduce Ingredient Context Learning (ICL) to reduce the negative impact of complex background in food images and construct internal spatial connections among ingredient regions of food objects in a self-supervised manner, which can strengthen the contacts of the same ingredients through region interactions. In order to solve the imbalance of different classes among ingredients, we propose one novel Class-Adaptive Asymmetric Loss (CAAL) to focus on various ingredient classes adaptively. Besides, considering that the over-suppression of negative samples will over-fit positive samples of those rare ingredients, CAAL alleviates this continuous suppression according to the imbalanced ratios based on gradients while maintaining the contribution of positive samples by lesser suppression. Extensive evaluation on two popular benchmark datasets (Vireo Food-172, UEC Food-100) demonstrates our proposed method achieves the state-of-the-art performance. Further qualitative analysis and visualization show the effectiveness of our method. Code and models are available at https://123.57.42.89/codes/CACLNet/index.html.

High-power femtosecond regenerative amplifier based on Yb:CaYAlO4 dual-crystal configuration.

A thermal lens insensitive regenerative amplifier (RA) with a dual Yb:CaYAlO4 (Yb:CYA) crystal configuration for extending gain spectra is demonstrated for the first time, to the best of our knowledge. By orthogonalizing the orientation of two a-cut Yb:CYA crystals in one RA, the Q switched spectrum with a full width at half maximum of 15.4 nm is generated, which is 1.5 and 1.6 times of the Q switched spectral bandwidth with π- and σ-polarization, respectively. With chirped pulses injection, this RA can deliver laser pulses with an average power exceeding 10 W at the repetition rate of 20-800 kHz and pulse energy of 1.5 mJ at 1 kHz. This is the highest average power from the Yb:CYA RA to the best of our knowledge. Finally, compressed pulses of 163 fs with 92% overall efficiency are realized. Thanks to the heat insensitive cavity design and excellent thermodynamic properties of the Yb:CYA crystal, the output laser beam is close to the diffraction limit with an M2 value of 1.07 × 1.07.

Tea polyphenols and catechins postpone evolution of antibiotic resistance genes and alter microbial community under stress of tetracycline.

Relying on the high mobility of water flow, the dissemination of antibiotic resistance genes (ARGs) in the water tends to be exacerbated and enlarged. It caused negative impacts on a wider scope of the environment. The ARGs dissemination monitoring and the methods efficiently reducing their concentration in water became the focus of interest. Green chemicals with antibacterial effects such as tea polyphenols (TPs) and catechins (CA) have been considered as auxiliary disinfectants for ARGs removal in the water environment. However, the antibacterial performance of TPs and CA under the stress of external antibiotics still lacks sufficient research. The results show that more operational taxonomic units can be observed in water samples with TPs and CA than in those without the ingredients under pressure of tetracycline. An unexpected increase along with the increase of ARGs concentrations and the diversity of microbial communities under the low-concentration TPs or CA (1 mg/L). Besides, under the stress of tetracycline, the inhibition of TPs was detected to be strengthened for increase of inti1 and tetC but weakened towards for the increase of tetA. Whilst CA substantially diminished abundances of tetC and tetA under tetracycline pressure. This research demonstrated that TPs and CA are able to assuage development of ARGs under the pressure of antibiotic in water system.

Flexible Organic Transistors for Biosensing: Devices and Applications.

Flexible and stretchable biosensors can offer seamless and conformable biological-electronic interfaces for continuously acquiring high-fidelity signals, permitting numerous emerging applications. Organic thin film transistors (OTFTs) are ideal transducers for flexible and stretchable biosensing due to their soft nature, inherent amplification function, biocompatibility, ease of functionalization, low cost, and device diversity. In consideration of the rapid advances in flexible-OTFT-based biosensors and their broad applications, herein, a timely and comprehensive review is provided. It starts with a detailed introduction to the features of various OTFTs including organic field-effect transistors and organic electrochemical transistors, and the functionalization strategies for biosensing, with a highlight on the seminal work and up-to-date achievements. Then, the applications of flexible-OTFT-based biosensors in wearable, implantable, and portable electronics, as well as neuromorphic biointerfaces are detailed. Subsequently, special attention is paid to emerging stretchable organic transistors including planar and fibrous devices. The routes to impart stretchability, including structural engineering and material engineering, are discussed, and the implementations of stretchable organic transistors in e-skin and smart textiles are included. Finally, the remaining challenges and the future opportunities in this field are summarized.

2D metal-organic frameworks for ultraflexible electrochemical transistors with high transconductance and fast response speeds.

Electrochemical transistors (ECTs) have shown broad applications in bioelectronics and neuromorphic devices due to their high transconductance, low working voltage, and versatile device design. To further improve the device performance, semiconductor materials with both high carrier mobilities and large capacitances in electrolytes are needed. Here, we demonstrate ECTs based on highly oriented two-dimensional conjugated metal-organic frameworks (2D c-MOFs). The ion-conductive vertical nanopores formed within the 2D c-MOFs films lead to the most convenient ion transfer in the bulk and high volumetric capacitance, endowing the devices with fast speeds and ultrahigh transconductance. Ultraflexible device arrays are successfully used for wearable on-skin recording of electrocardiogram (ECG) signals along different directions, which can provide various waveforms comparable with those of multilead ECG measurement systems for monitoring heart conditions. These results indicate that 2D c-MOFs are excellent semiconductor materials for high-performance ECTs with promising applications in flexible and wearable electronics.

Achieving robust nitritation in a modified continuous-flow reactor: From micro-granule cultivation to nitrite-oxidizing bacteria elimination.

In this study, a modified continuous-flow nitrifying reactor was successfully operated for rapid cultivation of micro-granules and achieving robust nitritation. Results showed that sludge granulation with mean size of ca. 100 µm was achieved within three weeks by gradually increasing settling velocity-based selection pressure from 0.48 to 0.9 m/hr. Though Nitrospira like nitrite-oxidizing bacteria (NOB) were enriched in the micro-granules with a ratio between ammonia-oxidizing bacteria (AOB) and NOB of 5.7%/6.5% on day 21, fast nitritation was achieved within one-week by gradually increasing of influent ammonium concentration (from 50 to 200 mg/L). Maintaining ammonium in-excess was the key for repressing NOB in the micro-granules. Interestingly, when the influent ammonium concentration switched back to 50 mg/L still with the residual ammonium of 15-25 mg/L, the nitrite accumulation efficiency increased from 90% to 98%. Experimental results suggested that the NOB repression was intensified by both oxygen and nitrite unavailability in the inner layers of micro-granules. Unexpectedly, continuous operation with ammonium in excess resulted in overproduction of extracellular polysaccharides and overgrowth of some bacteria (e.g., Nitrosomonas, Arenimonas, and Flavobacterium), which deteriorated the micro-granule stability and drove the micro-granules aggregation into larger ones with irregular morphology. However, efficient nitritation was stably maintained with extremely high ammonium oxidation potential (> 50 mg/g VSS/hr) and nearly complete washout of NOB was obtained. This suggested that smooth and spherical granule was not a prerequisite for achieving NOB wash-out and maintaining effective nitritation in the granular reactor. Overall, the micro-granules exhibited a great practical potential for high-rate nitritation.

Perovskite Solar Cell-Gated Organic Electrochemical Transistors for Flexible Photodetectors with Ultrahigh Sensitivity and Fast Response.

Photodetectors (PDs) are the building block of various imaging and sensing applications. However, commercially available PDs based on crystalline inorganic semiconductors cannot meet the requirements of emerging wearable/implantable applications due to their rigidity and fragility, which creates the need for flexible devices. Here, a high-performance flexible PD is presented by gating an organic electrochemical transistor (OECT) with a perovskite solar cell. Due to the ultrahigh transconductance of the OECT, the device demonstrates a high gain of ≈106 , a fast response time of 67 µs and an ultrahigh detectivity of 6.7 × 1017 Jones to light signals under a low working voltage (≤0.6 V). Thanks to the ultrahigh sensitivity and fast response, the device can track photoplethysmogram signals and peripheral oxygen saturation under ambient light and even provide contactless remote sensing, offering a low-power and convenient way for continuous vital signs monitoring. This work offers a novel strategy for realizing high-performance flexible PDs that are promising for low-power, user-friendly and wearable optoelectronics.

Temporal contrast enhancement via nonlinear elliptical polarization rotation in a solid thin plate.

We demonstrate the simultaneous temporal contrast enhancement and spectral broadening via nonlinear elliptical polarization rotation in a solid thin plate. The efficiency, temporal contrast enhancement, spectral broadening, pulse compression and power stability are experimentally investigated. With this simple and efficient scheme, the temporal cleaned pulses with energy of 325 µJ and total efficiency of 30% are obtained. The temporal contrast and spectral bandwidth of the filtered pulse are 1011 and 104 nm, respectively. The pulse compressed from 180 fs to 45.8 fs is realized by utilizing chirped mirrors, corresponding to a compression factor of 3.93. With stable output power, presented scheme could be implemented in the ultra-intense femtosecond laser facilities.