Differential diagnosis of Crohn's disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning.

BACKGROUND: Crohn's disease (CD) is often misdiagnosed as intestinal tuberculosis (ITB). However, the treatment and prognosis of these two diseases are dramatically different. Therefore, it is important to develop a method to identify CD and ITB with high accuracy, specificity, and speed. AIM: To develop a method to identify CD and ITB with high accuracy, specificity, and speed. METHODS: A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB. Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis. RESULTS: The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm-1 and 1234 cm-1 bands, and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy, specificity, and sensitivity of 91.84%, 92.59%, and 90.90%, respectively, for the differential diagnosis of CD and ITB. CONCLUSION: Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level, and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

Insights into enhanced immobilization of uranyl carbonate from seawater by Fe-doped MXene.

Extracting uranium from seawater not only reduces radioactive contamination in seawater but also provides a source of uranium energy. However, due to the low concentration of uranium in seawater and the high salinity of seawater, extraction of uranium from seawater is challenging. In this work, we demonstrated a simple strategy to synthesize Fe-doped MXene (Fe@Ti3C2Tx) via a hydrothermal method and applied for uranium enrichment in seawater. The Fe@Ti3C2Tx exhibited excellent adsorption performance in high salinity environments. The removal capacity of Fe@Ti3C2Tx was determined to be 526.6 mg/g for UO2(CO3)22- at 328 K with quick reaction equilibrium (∼ 30 min). Kinetic and thermodynamic analyses of UO2(CO3)22- elimination process on Fe@Ti3C2Tx surface revealed it to be a spontaneous and endothermic single-phase elimination process. FT-IR and XPS analyses further indicated that the removal mechanism of UO2(CO3)22- by Fe@Ti3C2Tx was surface complexation. Our study suggests that Fe@Ti3C2Tx can provide a feasible solution for uranium enrichment in seawater.

Application scenario-oriented molecule generation platform developed for drug discovery.

Design of molecules for candidate compound selection is one of the central challenges in drug discovery due to the complexity of chemical space and requirement of multi-parameter optimization. Here we present an application scenario-oriented platform (ID4Idea) for molecule generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE, RNN, GAN, etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, active learning, etc.) and input representations (1D SMILES, 2D graph, 3D shape, binding site, pharmacophore, etc.), to enable customized solutions for a given molecular design scenario. Besides the usual generation followed screening protocol, goal-directed molecule generation can also be conducted towards predefined goals, enhancing the efficiency of hit identification, lead finding, and lead optimization. We demonstrate the effectiveness of ID4Idea platform through case studies, showcasing customized solutions for different design tasks using various input information, such as binding pockets, pharmacophores, and compound representations. In addition, remaining challenges are discussed to unlock the full potential of AI models in drug discovery and pave the way for the development of novel therapeutics.

Application of IRI Visualization to Terahertz Vibrational Spectroscopy of Hydroxybenzoic Acid Isomers.

The characteristic absorption spectra of three positional isomers of hydroxybenzoic acid are measured using a terahertz time-domain spectroscopy system (THz-TDS) in the 0.6-2.0 THz region at room temperature. Significant differences in their terahertz spectra are discovered, which indicates that THz-TDS is an effective means to identify positional isomers. In order to simulate their spectra, the seven molecular clusters of 2-, 3-, and 4-hydroxybenzoic acid (2-, 3-, and 4-HA) are calculated using the DFT-D3 method. Additionally, the potential energy distribution (PED) method is used to analyze their vibration modes. The analysis indicates that the vibration modes of 2-HA are mainly out-of-plane angle bending and bond angle bend in plane. The vibration modes of 3-HA are mainly bond length stretch and dihedral angle torsion. The vibration modes of 4-HA are mainly bond angle bend in plane and dihedral angle torsion. Interaction region indicator (IRI) analysis is used to visualize the location and type of intermolecular interactions in 2-, 3-, and 4-HA crystals. The results show that the weak interaction type of 2-, 3-, and 4-HA is dominated by van der Waals (vdW) interaction. Therefore, we can confirm that terahertz spectroscopy detection technology can be used as an effective means to identify structural isomers and detect the intermolecular interactions in these crystals. In addition, it can explain the absorption mechanism of terahertz waves interacting with matter.

Early osteoarthritis diagnosis based on near-infrared spectroscopy combined with aquaphotomics.

Osteoarthritis (OA) is the most common joint disease and the leading cause of disability in elderly individuals. Despite rapid advances in imaging techniques, early OA diagnosis remains a clinical challenge. In the present study, the feasibility of early OA diagnosis was explored via near-infrared spectroscopy (NIRS) combined with aquaphotomics. Synovial fluid samples from 65 cases of OA categorized as mild, moderate, and severe according to theKellgrenandLawrence classification criteria were analyzed via NIRS. The 1st overtone of water (1300-1600 nm) was considered as the research object for an aquaphotomics model, and aquagrams of the mild, moderate, and severe OA cases were generated using 12 water absorption patterns for early OA diagnosis.The aquaphotomics results exhibited clear differences in the region of 1300-1500 nm, and the number of hydrogen bonds of different water species (1412,1424, 1482, and 1496 nm) evidently correlated with OA occurrence and development. With OA progression, the absorption intensity of water molecules without hydrogen bonds (1412 nm/1424 nm) became stronger, while the absorption intensity of water molecules with four hydrogen bonds (1482 nm/1496 nm) decreased.These results together reveal that the established accurate and rapid early OA diagnosis model based on NIRS combined with aquaphotomics is effective and feasible, and that the number of hydrogen bonds can be used as a biomarker for early OA diagnosis.

Rapid detection of hepatitis B virus DNA level based on interval-point data fusion of infrared spectra.

Hepatitis B is an infectious disease cause by the hepatitis B virus (HBV). In recent years, HBV-DNA level clinically gets more attention for its detailed information than other serological markers. Unfortunately, common clinical method for HBV-DNA level detection is limited for its hours consuming. This study combined infrared spectroscopy with machine learning to investigate the feasibility of near-infrared (NIR) and mid-infrared (MIR) spectra for rapid detection of HBV-DNA level. Based on partial least squares-discriminant analysis (PLS-DA) modeling method, the optimal NIR and MIR models and traditional data fusion models were constructed, respectively. Considering inequal weight between interval and point data in machine learning, interval-point data fusion method was used to compare with other traditional date fusion methods. The results of the study illustrate that interval-point data fusion of NIR and MIR spectra combined with PLS-DA modeling can provide a rapid method for HBV-DNA level detection.

Identification of mercury species in coal combustion by-products from power plants using thermal desorption-atomic fluorescence spectrometry on-line coupling system.

Along with the environmental protection policies becoming strict in China, the air pollution control devices (especially selective catalytic reduction (SCR)) are widely equipped in coal-fired power plants. The installation and run of these devices will inevitably affect mercury (Hg) species distribution in coal fired by-products such like fly ash (FA) and gypsum. In this work, a new on-line coupling system based on atomic fluorescence spectrometry (AFS) with a home-made chromatographic workstation was successfully developed to identify Hg species through thermal programmed desorption (TPD). The influences of matrix, furnace temperature, and carrier gas flow on analytical performance were investigated and the parameters were optimized. The FA and gypsum samples from coal-fired power plants equipped with SCR were collected and the mercury species were analyzed by the developed coupling system. HgCl2 and HgO were the main species in FA, while Hg2Cl2 and HgO were the main species in gypsum. All of Hg species in the studied FA and gypsum samples were released below 400 °C. A sequential extraction procedure was applied to further verify the operational Hg species including mobile and non-mobile fractions in FA and gypsum samples. This study demonstrated that AFS coupled with TPD procedure was an effective method to analyze Hg species in coal combustion by-products from power plants.

Microbial Fertilization Improves Soil Health When Compared to Chemical Fumigation in Sweet Lily.

Lanzhou Lily(Lilium davidii) var. unicolor, which is also known as sweet lily in China, is used as a type of food. This lily is distributed in narrow regions, propagates asexually, cultivates perennially, and cultivates commonly in serious consecutive replant problems (CRPs). Soil fumigation is commonly used to control soil-borne disease to alleviate crops' consecutive replant problems (CRPs). However, due to the improper fumigation application, it is common to cause chemical hazard to crops. In this study, we designed a two-factor experiment to explore the bacterial and fungal community structure and some specific microbial groups in the lily rhizosphere soil after chemical versus bacterial fertilizer treatments, by using a metagenomic analysis of the treated soils. The results showed that metham-sodium soil fumigation (SMF treatment) significantly decreased plant growth, as well as it significantly decreased both soil fungal diversity and abundance at the OTUs levels, while Special 8™ microbial fertilizer supplement (MF treatment) significantly improved plant growth and increased fungal diversity and abundance. Under FM treatment, Chao1 richness and Shannon's diversity increased by 6.70% and 35.09% compared to CK (no treatment). However, the bacterial diversity and abundance were not significantly changed among these treatments. The fungal and bacterial community structure were different in all treatments. In SMF treatment, the pathogenic fungal species Fusarium oxysporum increased compared to CK, but it significantly decreased in MF treatment; in MF and MMF treatments, some beneficial bacteria groups such as the bacterial phylum Proteobacteria and its member genus Sphingomonas, as well as the fungal genus Mortierella, increased compared to CK and SFM treatments, but the harmful bacterial genera Gemmatimona was decreased, as well as the harmful fungal genus Cryptococcus. Thus, we concluded that under chemical fumigation conditions, both fungal diversity loss and overall microorganism reduction, which impair multiple ecosystem function, in conjunction with the increase of harmful fungal species such as Fusarium oxysporum, are causes for soil degradation. On the other hand, under microbial fertilizer supplement, it was the fungal diversity increase, as well as these beneficial microorganisms groups' accumulation, together with those harmful groups' depletion, played important roles in restoring and improving soil health that suffered from the chemical fumigant hazard. In addition, the bacterial phylum Proteobacteria and its member genus Sphingomonas are involved in soil health recovery and promotion. The results also emphasized that whether soil is chemically fumigated or not, beneficial microorganism supplementary is effective in ensuring soil productivity.

Rapid identification of flaxseed oil based on portable fiber optic Raman spectroscopy combined with an oil microscopy method.

To explore a fast, simple, and accurate method to identify adulteration in flaxseed oil, the Raman spectral data of 130 samples containing flaxseed, canola, cottonseed, and adulterated oils were obtained using a portable fiber optic Raman spectrometer. The Raman spectral results showed that the Raman spectra of the flaxseed and canola oils had noticeable peak shifts, whereas the peak positions of the flaxseed and cottonseed oils were relatively similar. Clear peak intensity differences were observed in the flaxseed, cottonseed, and canola oils, mainly at 868 cm-1 , 1022 cm-1 , 1265 cm-1 , and 1655 cm-1 , with Raman shift intensities in the following order: Iflaxseed oil  > Icottonseed oil  > Icanola oil . Similarly, the peak intensity of the flaxseed and adulterated oils also exhibited certain differences (at 868 cm-1 , 1022 cm-1 , 1265 cm-1 , and 1655 cm-1 ), and the Raman shift intensity tended to decrease gradually with the increasing content of canola and cottonseed oils in the flaxseed oil. Additionally, the results of Raman spectroscopy combined with the "oil microscopy" method exhibited large variations in the radar patterns of the flaxseed, canola, and cottonseed oils, whereas the radar patterns of the flaxseed and adulterated oils closely resembled each other. The results indicated that Raman spectroscopy in combination with oil microscopy more effectively revealed the subtle differences in the Raman shift intensity, serving as a more visual and comprehensive approach for differentiating the quality variations between pure flaxseed oil and other oil species and adulterated oil. PRACTICAL APPLICATION: This study analyzed the Raman spectra of flaxseed, canola, cottonseed, and adulterated oils using fiber optic Raman spectroscopy. Combined with the oil microscopy method for comprehensive evaluation and analysis, it is feasible to effectively identify the quality differences among flaxseed, canola, cottonseed, and adulterated oils.

A new technology for rapid determination of isomers of hydroxybenzoic acid by terahertz spectroscopy.

This study investigated the feasibility of using terahertz (THz) technology for the rapid identification of isomers. The time-domain spectra of 2-hydroxybenzoic acid (2-HA), 3-hydroxybenzoic acid (3-HA), and 4-hydroxybenzoic acid (4-HA) were measured by a THz time-domain spectroscopy system (THz-TDS) in the range of 0.3-1.8 THz. Aiming at the isomer classification problem, a THz spectral data classification model based on a variational mode decomposition-particle swarm optimization-support vector machine (VMD-PSO-SVM) method was proposed. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) were used to extract the first eight intrinsic mode functions (IMFs) of the time-domain signal. Principal component analysis (PCA) was used to extract the first 80 principal components of each modal component as the classification feature vector. The particle swarm optimization (PSO) and support vector machine (SVM) algorithms were used to construct 2-, 3-, and 4-HA classification models. We found that the prediction accuracy of the VMD-PSO-SVM model was significantly higher than that of EMD-PSO-SVM model regardless of the modal components. For both EMD and VMD, with the increase in the IMF number, the corresponding classification recognition accuracy tended to decrease. The results showed that the rapid identification model of hydroxybenzoic acid isomers based on THz spectroscopy and SVM was effective and feasible, providing an accurate and rapid method for the chemical synthesis and quality monitoring of biomedicine.

Vacancy tuned coupling in terahertz metamaterial arrays.

Metamaterials have shown great potential for modulation on the amplitude, phase and polarization of the terahertz wave. Here vacancies were introduced into the metamaterial arrays to tune the mutual interaction between the constituent resonators, which could heavily affect the electromagnetic response of the whole metamaterial arrays. We show that the introduced vacancies in the metamaterial arrays can effectively affect the resonance mode of the metamaterial arrays. Based upon the vacancy mediated coupling, a silicon-metal hybrid metamaterial arrays were designed to achieve active modulation of propagating terahertz waves.

Machine learning methods to predict the cultivation age of Panacis Quinquefolii Radix.

BACKGROUND: American ginseng (AG) is a valuable medicine widely consumed as a herbal remedy throughout the world. Huge price difference among AG with different growth years leads to intentional adulteration for higher profits. Thus, developing reliable approaches to authenticate the cultivation ages of AG products is of great use in preventing age falsification. METHODS: A total of 106 batches of AG samples along with their 9 physicochemical features were collected and measured from experiments, which was then split into a training set and two test sets (test set 1 and 2) according to the cultivation regions. Principle component analysis (PCA) was carried out to examine the distribution of the three data sets. Four machine learning (ML) algorithms, namely elastic net, k-nearest neighbors, support vector machine and multi-layer perception (MLP) were employed to construct predictive models using the features as inputs and their growth years as outputs. In addition, a similarity-based applicability domain (AD) was defined for these models to ensure the reliability of the predictive results for AG samples produced in different regions. RESULTS: A positive correlation was observed between the several features and the growth years. PCA revealed diverse distributions among different cultivation regions. The most accurate model derived from MLP shows good prediction power for the fivefold cross validation and the test set 1 with mean square error (MSE) of 0.017 and 0.016 respectively, but a higher MSE value of 1.260 for the test set 2. After applying the AD, all models showed much lower prediction errors for the test samples within AD (IDs) than those outside the AD (ODs). MLP remains the best predictive model with an MSE value of 0.030 for the IDs. CONCLUSION: Cultivation years have a close relationship with bioactive components of AG. The constructed models and AD are also able to predict the cultivation years and discriminate samples that have inaccurate prediction results. The AD-equipped models used in this study provide useful tools for determining the age of AG in the market and are freely available at https://github.com/dreadlesss/Panax_age_predictor .

Nicotine and its metabolite cotinine target MD2 and inhibit TLR4 signaling.

Nicotine is the principal alkaloid of tobacco often manufactured into cigarettes and belongs to a highly addictive class of drugs. Nicotine attenuates the neuroinflammation induced by microglial activation. However, the molecular target(s) underlying anti-inflammatory action of nicotine has not been fully understood. Considering the psychoactive substances morphine, cocaine, and methamphetamine act as xenobiotic-associated molecular patterns and can be specifically sensed by the innate immune receptor Toll-like receptor 4 (TLR4), here we sought to delineate whether nicotine and/or its metabolite cotinine may be recognized by the innate immune system via myeloid differentiation protein 2 (MD2), an accessory protein of TLR4 that is responsible for ligand recognition. MD2-intrinsic fluorescence titrations, surface plasmon resonance, and competitive displacement binding assays with curcumin (MD2 probe) demonstrated that both nicotine and cotinine targeted the lipopolysaccharide (LPS; TLR4 agonist) binding pocket of MD2 with similar affinities. The cellular thermal shift assay indicated that nicotine binding increased, while cotinine binding decreased, MD2 stability. These biophysical binding results were further supported by in silico simulations. In keeping with targeting MD2, both nicotine and cotinine inhibited LPS-induced production of nitric oxide and tumor necrosis factor alpha (TNF-α) and blocked microglial activation. Neither a pan nicotinic acetylcholine receptor (nAChR) inhibitor nor RNAi for nAChRs abolished the suppressive effect of nicotine- and cotinine-induced neuroinflammation. These data indicate that TLR4 inhibition by nicotine and cotinine at the concentrations tested in BV-2 cells is independent of classic neuronal nAChRs and validate that MD2 is a direct target of nicotine and cotinine in the inhibition of innate immunity.

Ferroptosis: A Novel Mechanism of Artemisinin and its Derivatives in Cancer Therapy.

BACKGROUND: Artemisinin is a sesquiterpene lactone compound with a special peroxide bridge that is tightly linked to the cytotoxicity involved in fighting malaria and cancer. Artemisinin and its derivatives (ARTs) are considered to be potential anticancer drugs that promote cancer cell apoptosis, induce cell cycle arrest and autophagy, inhibit cancer cell invasion and migration. Additionally, ARTs significantly increase intracellular Reactive Oxygen Species (ROS) in cancer cells, which result in ferroptosis, a new form of cell death, depending on the ferritin concentration. Ferroptosis is regarded as a cancer suppressor and as well as considered a new mechanism for cancer therapy. METHODS: The anticancer activities of ARTs and reference molecules were compared by literature search and analysis. The latest research progress on ferroptosis was described, with a special focus on the molecular mechanism of artemisinin-induced ferroptosis. RESULTS: Artemisinin derivatives, artemisinin-derived dimers, hybrids and artemisinin-transferrin conjugates, could significantly improve anticancer activity, and their IC50 values are lower than those of reference molecules such as doxorubicin and paclitaxel. The biological activities of linkers in dimers and hybrids are important in the drug design processes. ARTs induce ferroptosis mainly by triggering intracellular ROS production, promoting the lysosomal degradation of ferritin and regulating the System Xc-/Gpx4 axis. Interestingly, ARTs also stimulate the feedback inhibition pathway. CONCLUSION: Artemisinin and its derivatives could be used in the future as cancer therapies with broader applications due to their induction of ferroptosis. Meanwhile, more attention should be paid to the development of novel artemisinin-related drugs based on the mechanism of artemisinininduced ferroptosis.

Bamboo-like microfiber structures fabricated by one-step-tapering a fiber preform.

The microfiber-based optical structures have been attracting increasing research interests in communications and sensing fields. However, the fabrication of forming structures on fragile microfibers requires delicate operations, which limits the developments of their practical applications. In this work, a one-step-tapering technique is proposed to manufacture structures on microfibers. As a demonstration, the fiber preform, consisting of sawtooth shaped solid-air interfaces with designed dimensions, is obtained using a femtosecond laser milling technique. By one-step tapering the preform, periodic bumps are formed, resulting in a bamboo-like microfiber device. The fabricated structure shows spectral characteristics of a long-period grating, of which extinction ratio is up to 18.2 dB around 1553.3 nm. The response to refractive index is measured to be ∼875.02 nm/RIU and the temperature coefficient is ∼5.78 pm/°C. The theoretical analysis shows good agreement with the experimental results. The microfiber-based structure fabricated using the one-step-tapering-preform technique is featured with flexibility of design, reproducibility, and structural stability.

Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning.

A transmission hyperspectral microscopic imager (THMI) that utilizes machine learning algorithms for hyperspectral detection of microalgae is presented. The THMI system has excellent performance with spatial and spectral resolutions of 4 µm and 3 nm, respectively. We performed hyperspectral imaging (HSI) of three species of microalgae to verify their absorption characteristics. Transmission spectra were analyzed using principal component analysis (PCA) and peak ratio algorithms for dimensionality reduction and feature extraction, and a support vector machine (SVM) model was used for classification. The average accuracy, sensitivity and specificity to distinguish one species from the other two species were found to be 94.4%, 94.4% and 97.2%, respectively. A species identification experiment for a group of mixed microalgae in solution demonstrates the usability of the classification method. Using a random forest (RF) model, the growth stage in a phaeocystis growth cycle cultivated under laboratory conditions was predicted with an accuracy of 98.1%, indicating the feasibility to evaluate the growth state of microalgae through their transmission spectra. Experimental results show that the THMI system has the capability for classification, identification and growth stage estimation of microalgae, with strong potential for in-situ marine environmental monitoring and early warning detection applications.

Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game.

Predicting and modeling human behavior and finding trends within human decision-making processes is a major problem of social science. Rock Paper Scissors (RPS) is the fundamental strategic question in many game theory problems and real-world competitions. Finding the right approach to beat a particular human opponent is challenging. Here we use an AI (artificial intelligence) algorithm based on Markov Models of one fixed memory length (abbreviated as "single AI") to compete against humans in an iterated RPS game. We model and predict human competition behavior by combining many Markov Models with different fixed memory lengths (abbreviated as "multi-AI"), and develop an architecture of multi-AI with changeable parameters to adapt to different competition strategies. We introduce a parameter called "focus length" (a positive number such as 5 or 10) to control the speed and sensitivity for our multi-AI to adapt to the opponent's strategy change. The focus length is the number of previous rounds that the multi-AI should look at when determining which Single-AI has the best performance and should choose to play for the next game. We experimented with 52 different people, each playing 300 rounds continuously against one specific multi-AI model, and demonstrated that our strategy could win against more than 95% of human opponents.

[Bioavailability Characteristics of Chromophoric Dissolved Organic Matter in Lake Gaoyou, Lake Nansi, and Lake Dongping Under Different Hydrological Scenarios].

The bio-lability of chromophoric dissolved organic matter (CDOM) directly reflects its biodegradability potential, and also affects the migration and conversion of pollutants and impacts water quality. This study combines excitation-emission matrices and parallel factor analysis (EEMs-PARAFAC) with laboratory 28 days of bio-incubation experiments, and analyzed the bioavailability characteristics of CDOM samples collected from Lake Gaoyou, Lake Nansi and Lake Dongping in flood season and dry season. Our results showed that:① four fluorescent components were obtained using EEMs-PARAFAC, including a microbial humic-like C1, a terrestrial humic-like C4, a tryptophan-like C2, and a tyrosine-like C3. ② The differences of CDOM absorption pre-and post-incubation, i.e. Δa(254) of the three lakes were positive in the three lakes in the flood season, while partially negative in the dry season, indicating a quite different response of CDOM bioavailability to hydrological seasons. ③ Under different hydrological scenarios, the two humic-like components C1 and C4 increased post-bio-incubation compared with that pre-incubation for the samples collected from Lake Nansi and Lake Dongping, and the two protein-like components in Lake Nansi in both the flood and dry seasons and in Lake Dongping in the flood season (t-test, P<0.001, P=0.005) were lower in the post-than those pre-incubation. In Lake Gaoyou, C1-C3 post-incubation were significantly lower than pre-incubation (t-test, P=0.008, P=0.005). In the dry season, in comparison, C1-C4 except for C2 increased post-incubation than pre-incubation for Lake Gaoyou. This indicated that the protein-like components are unstable and more easily uptaken by microorganisms and may be potentially converted into more stable humic-like components. HIX and IC:IT of the three lakes increased post-incubation while the spectral slope S275-295 decreased, which further confirmed the aforementioned conclusion. ④ During both the flood and dry seasons, the bioavailability of the protein-like components C2-C3 and the fluorescence intensity of C1 and C4 in the inflowing river mouths of the three lakes were higher than in the remaining lake regions. It is therefore necessary to strengthen the water quality management in the inflowing river mouths of the three lakes to maintain the water quality of the lakes.

[Response of Chromophoric Dissolved Organic Matter Dynamics to Different Hydrological Scenarios in the Two Largest Freshwater Lakes Connected to the Yangtze River].

Poyang Lake and Dongting Lake are the two largest freshwater lakes in China connected to the Yangtze River. Changes in the water quality of the two lakes are critical to the water security of the residents surrounding the lakes. Analyses of the optical properties, including chromophoric dissolved organic matter (CDOM) absorption and fluorescence spectroscopy coupled by parallel factor analysis (PARAFAC), were carried out to investigate the dynamics of CDOM in the two lakes in different hydrological scenarios. Our results indicated that different hydrological scenarios have more notable effects on the CDOM dynamics in Poyang Lake compared to those in Dongting Lake. In Poyang Lake, the mean CDOM absorption a(254) and dissolved organic carbon (DOC) were higher in the wet season than in the dry-to-wet transition season, and higher still than in the dry season (t-test, P<0.01), and the mean of the CDOM absorption spectral slope S275-295 was higher in the dry season than in the dry-to-wet transition season and higher still than in the wet season (t-test, P<0.01). In Dongting Lake, the mean of a(254) was not significantly different between different hydrological periods, and SUVA254 reached its maximum in the dry-to-wet transition season. Four fluorescent components were identified using parallel factor analysis. The contribution percentage of CDOM protein-like components in the two lakes was higher during the dry season, and the protein-like components and humic-like components contributed roughly the same amount in the dry-to-wet season, whereas the humic-like components accounted for the main proportion in both lakes during the wet season. From the perspective of spatial distribution, the fluorescence intensity of the four components of Poyang Lake was lower in the southern upstream than in the northern downstream lake regions during the dry season, whereas in the wet season a contrast pattern was found, i.e., with high values found in the upstream lake regions. The spatial difference of fluorescence intensity of the four components in the east of Dongting Lake during the dry season was greater than that in the wet season. We found that DOC increased with increasing water level (r2=0.99, P<0.01) in Poyang Lake and tryptophan-like C2 decreased with increasing water level (r2=0.99, P<0.05) in Dongting Lake. Therefore, the water quality of the two lakes should be managed in a targeted manner according to the response characteristics of CDOM in the two lakes under different hydrological scenarios.

Superstructure microfiber grating characterized by temperature, strain, and refractive index sensing.

Microfiber gratings with diameters in the subwavelength scale have recently attracted much attention for developments of sensitive sensors; however, a specific structure is usually chosen for sensing one parameter according to the optical response. In this work, a superstructure microfiber grating combined with microfiber Bragg grating and long-period microfiber grating is reported for the first time. The proposed superstructure is formed by ultraviolet laser inscription and femtosecond laser scratching techniques, which simultaneously endows the unique properties of the two individual gratings. The reflection and transmission spectral characteristics differing to conventional counterparts are demonstrated. The responsivities of the two gratings to temperature, strain and refractive index are investigated, providing a possibility for simultaneous multi-parameter sensing.