Functional and Compositional Changes in Sirex noctilio Gut Microbiome in Different Habitats: Unraveling the Complexity of Invasive Adaptation.

The mutualistic symbiosis relationship between the gut microbiome and their insect hosts has attracted much scientific attention. The native woodwasp, Sirex nitobei, and the invasive European woodwasp, Sirex noctilio, are two pests that infest pines in northeastern China. Following its encounter with the native species, however, there is a lack of research on whether the gut microbiome of S. noctilio changed, what causes contributed to these alterations, and whether these changes were more conducive to invasive colonization. We used high-throughput and metatranscriptomic sequencing to investigate S. noctilio larval gut and frass from four sites where only S. noctilio and both two Sirex species and investigated the effects of environmental factors, biological interactions, and ecological processes on S. noctilio gut microbial community assembly. Amplicon sequencing of two Sirex species revealed differential patterns of bacterial and fungal composition and functional prediction. S. noctilio larval gut bacterial and fungal diversity was essentially higher in coexistence sites than in separate existence sites, and most of the larval gut bacterial and fungal community functional predictions were significantly different as well. Moreover, temperature and precipitation positively correlate with most of the highly abundant bacterial and fungal genera. Source-tracking analysis showed that S. noctilio larvae at coexistence sites remain dependent on adult gut transmission (vertical transmission) or recruitment to frass (horizontal transmission). Meanwhile, stochastic processes of drift and dispersal limitation also have important impacts on the assembly of S. noctilio larval gut microbiome, especially at coexistence sites. In summary, our results reveal the potential role of changes in S. noctilio larval gut microbiome in the successful colonization and better adaptation of the environment.

Anthropogenic noise and habitat structure shaping dominant frequency of bird sounds along urban gradients.

The shifts of bird song frequencies in urbanized areas provide a unique system to understand avian acoustic responses to urbanization. Using passive acoustic monitoring and automatic bird sound recognition technology, we explored the frequency variations of six common urban bird species and their associations with habitat structures. Our results demonstrated that bird song frequencies in urban areas were significantly higher than those in peri-urban and rural areas. Anthropogenic noise and habitat structure were identified as crucial factors shaping the acoustic space for birds. We found that noise, urbanization, and open understory spaces are factors contributing to the increase in the dominant frequency of bird sounds. However, habitat variables such as vegetation density and tree height can potentially slow down this upward trend. These findings offer essential insights into the behavioral response of birds in a variety of urban forest habitats, with implications for urban ecosystem management and habitat restoration.

Large second-order susceptibility from a quantized indium tin oxide monolayer.

Due to their high optical transparency and electrical conductivity, indium tin oxide thin films are a promising material for photonic circuit design and applications. However, their weak optical nonlinearity has been a substantial barrier to nonlinear signal processing applications. In this study, we show that an atomically thin (~1.5 nm) indium tin oxide film in the form of an air/indium tin oxide/SiO2 quantum well exhibits a second-order susceptibility χ2 of ~1,800 pm V-1. First-principles calculations and quantum electrostatic modelling point to an electronic interband transition resonance in the asymmetric potential energy of the quantum well as the reason for this large χ2 value. As the χ2 value is more than 20 times higher than that of the traditional nonlinear LiNbO3 crystal, our indium tin oxide quantum well design can be an important step towards nonlinear photonic circuit applications.

Sensitive and Specific Detection of Estrogens Featuring Doped Silicon Nanowire Arrays.

Estrogens and estrogen-mimicking compounds in the aquatic environment are known to cause negative impacts to both ecosystems and human health. In this initial proof-of-principle study, we developed a novel vertically oriented silicon nanowire (vSiNW) array-based biosensor for low-cost, highly sensitive and selective detection of estrogens. The vSiNW arrays were formed using an inexpensive and scalable metal-assisted chemical etching (MACE) process. A vSiNW array-based p-n junction diode (vSiNW-diode) transducer design for the biosensor was used and functionalized via 3-aminopropyltriethoxysilane (APTES)-based silane chemistry to bond estrogen receptor-alpha (ER-α) to the surface of the vSiNWs. Following receptor conjugation, the biosensors were exposed to increasing concentrations of estradiol (E2), resulting in a well-calibrated sensor response (R 2 ≥ 0.84, 1-100 ng/mL concentration range). Fluorescence measurements quantified the distribution of estrogen receptors across the vSiNW array compared to planar Si, indicating an average of 7 times higher receptor presence on the vSiNW array surface. We tested the biosensor's target selectivity by comparing it to another estrogen (estrone [E1]) and an androgen (testosterone), where we measured a high positive electrical biosensor response after E1 exposure and a minimal response after testosterone. The regeneration capacity of the biosensor was tested following three successive rinses with phosphate buffer solution (PBS) between hormone exposure. Traditional horizontally oriented Si NW field effect transistor (hSiNW-FET)-based biosensors report electrical current changes at the nanoampere (nA) level that require bulky and expensive measurement equipment making them unsuitable for field measurements, whereas the reported vSiNW-diode biosensor exhibits current changes in the microampere (µA) range, demonstrating up to 100-fold electrical signal amplification, thus enabling sensor signal measurement using inexpensive electronics. The highly sensitive and specific vSiNW-diode biosensor developed here will enable the creation of low-cost, portable, field-deployable biosensors that can detect estrogenic compounds in waterways in real-time.

Sensitive detection of SARS-CoV-2 spike protein using vertically-oriented silicon nanowire array-based biosensor.

The COVID-19 pandemic has caused tremendous damage to the world. In order to quickly and accurately diagnose the virus and contain the spread, there is a need for rapid, sensitive, accurate, and cost-effective SARS-CoV-2 biosensors. In this paper, we report on a novel biosensor based on angiotensin converting enzyme 2 (ACE-2)-conjugated vertically-oriented silicon nanowire (vSiNW) arrays that can detect the SARS-CoV-2 spike protein with high sensitivity and selectivity relative to negative controls. First, we demonstrate the efficacy of using ACE-2 receptor to detect the SARS-CoV-2 spike protein via a capture assay test, which confirms high specificity of ACE-2 against the mock protein, and high affinity between the spike and ACE-2. We then report on results for ACE-2-conjugated vSiNW arrays where the biosensor device architecture is based on a p-n junction transducer. We confirm via analytical modeling that the transduction mechanism of the biosensor involves induced surface charge depletion of the vSiNWs due to negative electrostatic surface potential induced by the spike protein after binding with ACE-2. This vSiNW surface charge modulation is measured via current-voltage characteristics of the functionalized biosensor. Calibrated concentration dependent electrical response of the vSiNW sensor confirms the limit-of-detection for virus spike concentration of 100 ng/ml (or 575 pM). The vSiNW sensor also exhibits highly specific response to the spike protein with respect to negative controls, offering a promising point-of-care detection method for SARS-CoV-2.

Early detection of pine wilt disease tree candidates using time-series of spectral signatures.

Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detection of PWD. Unmanned aerial vehicle (UAV)-based hyperspectral images (HSI) can detect tree-level changes and are thus an effective tool for forest change detection. However, previous studies mainly used single-date UAV-based HSI data, which could not monitor the temporal changes of disease distribution and determine the optimal detection period. To achieve these purposes, multi-temporal data is required. In this study, Pinus koraiensis stands were surveyed in the field from May to October during an outbreak of PWD. Concurrently, multi-temporal UAV-based red, green, and blue bands (RGB) and HSI data were also obtained. During the survey, 59 trees were confirmed to be infested with PWD, and 59 non-infested trees were used as control. Spectral features of each tree crown, such as spectral reflectance, first and second-order spectral derivatives, and vegetation indices (VIs), were analyzed to identify those useful for early monitoring of PWD. The Random Forest (RF) classification algorithm was used to examine the separability between the two groups of trees (control and infested trees). The results showed that: (1) the responses of the tree crown spectral features to PWD infestation could be detected before symptoms were noticeable in RGB data and field surveys; (2) the spectral derivatives were the most discriminable variables, followed by spectral reflectance and VIs; (3) based on the HSI data from July to October, the two groups of trees were successfully separated using the RF classifier, with an overall classification accuracy of 0.75-0.95. Our results illustrate the potential of UAV-based HSI for PWD early monitoring.