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Optical spectroscopic sensors are a powerful tool to reveal light-matter interactions in many fields. Miniaturizing the currently bulky spectrometers has become imperative for the wide range of applications that demand in situ or even in vitro characterization systems, a field that is growing rapidly. In this paper, we propose a novel integrated reconstructive spectrometer with programmable photonic circuits by simply using a few engineered MZI elements. This design effectively creates an exponentially scalable number of uncorrelated sampling channels over an ultra-broad bandwidth without incurring additional hardware costs, enabling ultra-high resolution down to single-digit picometers. Experimentally, we implement an on-chip spectrometer with a 6-stage cascaded MZI structure and demonstrate <10 pm resolution with >200 nm bandwidth using only 729 sampling channels. This achieves a bandwidth-to-resolution ratio of over 20,000, which is, to our best knowledge, about one order of magnitude greater than any reported miniaturized spectrometers to date. Recent years have seen a growing need for miniaturized spectroscopic tools. Here, authors present a novel integrated spectrometer with programmable photonic circuits, achieving record-high resolution and bandwidth via only a few filtering components.

Miniaturization of optical spectrometers is important to enable spectroscopic analysis to play a role in in situ, or even in vitro and in vivo characterization systems. However, scaled-down spectrometers generally exhibit a strong trade-off between spectral resolution and operating bandwidth, and are often engineered to identify signature spectral peaks only for specific applications. In this paper, we propose and demonstrate a novel global sampling strategy with distributed filters for generating ultra-broadband pseudo-random spectral responses. The geometry of all-pass ring filters is tailored to ensure small self- and cross-correlation for effective information acquisition across the whole spectrum, which dramatically reduces the requirement on sampling channels. We employ the power of reconfigurable photonics in spectrum shaping by embedding the engineered distributed filters. Using a moderate mesh of MZIs, we create 256 diverse spectral responses on a single chip and demonstrate a resolution of 20 pm for single spectral lines and 30 pm for dual spectral lines over a broad bandwidth of 115 nm, to the best of our knowledge achieving a new record of bandwidth-to-resolution ratio. Rigorous simulations reveal that this design will readily be able to achieve single-picometer-scale resolution. We further show that the reconfigurable photonics provides an extra degree of programmability, enabling user-defined features on resolution, computation complexity, and relative error. The use of SiN integration platform enables the spectrometer to exhibit excellent thermal stability of ±2.0 °C, effectively tackling the challenge of temperature variations at picometer-scale resolutions. We propose an on-chip spectrometer design that leverages reconfigurable networks with distributed broadband filters, demonstrating a 30 pm resolution over a 115 nm bandwidth that can be readily scaled further.

Photonic neural networks (PNNs) are fast in-propagation and high bandwidth paradigms that aim to popularize reproducible NN acceleration with higher efficiency and lower cost. However, the training of PNN is known to be challenging, where the device-to-device and system-to-system variations create imperfect knowledge of the PNN. Despite backpropagation (BP)-based training algorithms being the industry standard for their robustness, generality, and fast gradient convergence for digital training, existing PNN-BP methods rely heavily on accurate intermediate state extraction or extensive computational resources for deep PNNs (DPNNs). The truncated photonic signal propagation and the computation overhead bottleneck DPNN’s operation efficiency and increase system construction cost. Here, we introduce the asymmetrical training (AsyT) method, tailored for encapsulated DPNNs, where the signal is preserved in the analogue photonic domain for the entire structure. AsyT offers a lightweight solution for DPNNs with minimum readouts, fast and energy-efficient operation, and minimum system footprint. AsyT’s ease of operation, error tolerance, and generality aim to promote PNN acceleration in a widened operational scenario despite the fabrication variations and imperfect controls. We demonstrated AsyT for encapsulated DPNN with integrated photonic chips, repeatably enhancing the performance from in-silico BP for different network structures and datasets. Training photonic neural networks is often a challenging process due to the non-ideal device behaviours. Here, the authors introduce the asymmetrical training method as a lightweight and efficient alternative for promoting photonic acceleration in wider application scenarios.

Autism spectrum disorder (ASD) is a severe brain development disorder that is characterized by deficits in social communication and restricted, repetitive and stereotyped behaviors. Accumulating evidence has suggested that gut microbiota disorders play important roles in gastrointestinal symptoms and neurodevelopmental dysfunction in ASD patients. Manipulation of the gut microbiota by fecal microbiota transplantation (FMT) was recently shown to be a promising therapy for the treatment of various diseases. Here, we performed a clinical trial to evaluate the effect of FMT on gastrointestinal (GI) and ASD symptoms and gut microbiota alterations in children with ASD. We found that there was a large difference in baseline characteristics of behavior, GI symptoms, and gut microbiota between children with ASD and typically developing (TD) control children. FMT could improve GI symptoms and ASD symptoms without inducing any severe complications. Similarly, FMT significantly changed the serum levels of neurotransmitters. We further observed that FMT could promote the colonization of donor microbes and shift the bacterial community of children with ASD toward that of TD controls. The abundance of Eubacterium coprostanoligenes pre-FMT was positively correlated with high GSRS scores, whereas a decrease in Eubacterium coprostanoligenes abundance induced by FMT was associated with the FMT response. Our data suggest that FMT might be a promising therapeutic strategy to improve the GI and behavioral symptoms of patients with ASD, possibly due to its ability to alter gut microbiota and highlight a specific microbiota intervention that targets Eubacterium coprostanoligenes that can enhance the FMT response. This trial was registered at the Chinese Clinical Trial Registry ( www.chictr.org.cn ) (trial registration number ChiCTR1800014745).

Miniaturized spectrometers hold great promise for in situ, in vitro, and even in vivo sensing applications. However, their size reduction imposes vital performance constraints in meeting the rigorous demands of spectroscopy, including fine resolution, high accuracy, and ultra-wide observation window. The prevailing view in the community holds that miniaturized spectrometers are most suitable for coarse identification of signature peaks. Here, we present an integrated reconstructive spectrometer that enables near-infrared (NIR) spectroscopic metrology, and demonstrate a fully packaged sensor with auxiliary electronics. Such a sensor operates over a 520 nm bandwidth together with a resolution below 8 pm, yielding a record-breaking bandwidth-to-resolution ratio of over 65,000. The classification of different types of solid substances and the concentration measurement of aqueous and organic solutions are performed, all achieving approximately 100% accuracy. Notably, the detection limit of our sensor matches that of commercial benchtop counterparts, which is as low as 0.1% (i.e. 100 mg/dL) for identifying the concentration of glucose solution. Here, the authors present a chip-scale reconstructive spectroscopic sensor that achieves >520 nm bandwidth with <8 pm resolution, demonstrating various spectrometric applications and realizing a detection limit comparable to benchtop counterparts.

Photonic integrated circuits have been extensively explored for optical processing with the aim of breaking the speed and energy efficiency bottlenecks of digital electronics. However, the input/output (IO) bottleneck remains one of the key barriers. Here we report a photonic iterative processor (PIP) for matrix-inversion-intensive applications. The direct reuse of inputted data in the optical domain unlocks the potential to break the IO bottleneck. We demonstrate notable IO advantages with a lossless PIP for real-valued matrix inversion and integral-differential equation solving, as well as a coherent PIP with optical loops integrated on-chip, enabling complex-valued computation and a net inversion time of 1.2 ns. Furthermore, we estimate at least an order of magnitude enhancement in IO efficiency of a PIP over photonic single-pass processors and the state-of-the-art electronic processors for reservoir training tasks and multiple-input and multiple-output (MIMO) precoding tasks, indicating the huge potential of PIP technology in practical applications. Integrated photonic iterative processors provide a novel I/O-efficient computing paradigm for matrix-inversion-intensive tasks, achieving higher speed and energy efficiency than state-of-the-art electronic and photonic processors.

The programmable photonic integrated mesh is arising as a powerful tool to deal with crosstalk in the multimode optical communication link.

Background: Patients with ulcerative colitis (UC) usually experience anxiety symptoms that seriously affect their quality of life, treatment, and prognosis. Dysbiosis of the gut microbiota plays an important role in UC and mental illness. However, little is known about the role of the gut microbiota in UC patients with anxiety. Objectives: To identify the gut-microbiome and fecal metabolome profiles uniquely associated with comorbid anxiety in UC patients and to explore potential biomarkers for diagnosis. Design: A cross-sectional, two-group comparative study. Methods: To study the underlying association between them, we recruited 126 UC patients in this study, including 78 with anxiety and 48 without anxiety. A total of 102 fecal samples were collected for metagenomic sequencing and metabolome sequencing. Microbial diversity, differential gut microbiota, functional pathways, and metabolites were analyzed. Multivariable logistic regression was used to identify independent risk factors associated with anxiety in UC patients, while Spearman correlation was employed to explore microbe-metabolite interactions and the performance of potential biomarkers. Results: We found that disease severity, steroid usage, and abdominal pain may promote the occurrence of anxiety. Compared to UC patients without anxiety, UC patients with anxiety had low fecal microbial community diversity, with an increase in the species Haemophilus sp. HMSC71H05 and Corynebacterium durum, and a decrease in the species Roseburia intestinalis (RI), Bifidobacterium longum (BL), and Enterococcus hirae. The metabolic pathways driven by the gut microbiota were disrupted. Moreover, the levels of most metabolites (such as L-kynurenine) were increased in the feces, while the levels of a few metabolites decreased, including indole-2-carboxylic acid, N-demethylmirtazapine, and tauroursodeoxycholic acid. Conclusion: Our research further revealed that these gut microbiota and metabolites are highly correlated. This study provides a new perspective for understanding the occurrence and development of anxiety in UC patients, suggesting that RI and BL may serve as potential candidate biomarkers to diagnose UC patients with anxiety.

The interaction between the host and microbiota is influenced by host circadian rhythm. However, it is unknown what the changes of gut microbiota and metabolites. We conducted a cross-sectional study (n=72) in which participants' fecal DNA was detected by macrogenomic sequencing analysis. The feces, urine and blood were analyzed by widely targeted metabolomics analysis. Pearson correlation analysis showed that most of the clinical symptoms of people with circadian rhythm disorders were moderately positively correlated with gastrointestinal symptoms. By distilling the results of multinomic analysis, we reported a variety of different species (19 species in the gut) and metabolites. In our results, the correlation of multiomics is mostly concentrated in and . Bile acid-related metabolites are the most significant metabolites associated with these species. Our study demonstrates the severity of clinical manifestations caused by circadian rhythm disorder is closely related to microbiota and metabolism. In the future, personalized interventions targeting specific microbial species or metabolites may help alleviate the physical and psychological discomfort induced by circadian rhythm disturbances.

Silicon photonic switches are widely considered as a cost-effective solution for addressing the ever-growing data traffic in datacenter networks, as they offer unique advantages such as low power consumption, low latency, small footprint and high bandwidth. Despite extensive research efforts, crosstalk in large-scale photonic circuits still poses a threat to signal integrity. In this paper, we present two designs of silicon Mach-Zehnder Interferometer (MZI) switches achieving ultra-low-crosstalk, driven thermally and electrically. Each switch fabric is optimized at both the device and circuit level to suppress crosstalk and reduce system complexity. Notably, for the first time to the best of our knowledge, we harness the inherent self-heating effect in a carrier-injection-based MZI switch to create a pair of phase shifters that offers arbitrary phase differences. Such a pair of phase shifters induces matched insertion loss at each arm, thus minimizing crosstalk. Experimentally, an ultra-low crosstalk ratio below −40 dB is demonstrated for both thermo-optic (T-O) and electro-optic (E-O) switches. The T-O switch exhibits an on-chip loss of less than 5 dB with a switching time of 500 µs, whereas the E-O switch achieves an on-chip loss as low as 8.5 dB with a switching time of under 100 ns. In addition, data transmission of a 50 Gb/s on–off keying signal is demonstrated with high fidelity on the E-O switch, showing the great potential of the proposed switch designs.