Identification of organic pollutants and heavy metals in natural rubber wastewater and evaluation its phytotoxicity and cytogenotoxicity.

The natural rubber industry consumes large volumes of water and annually releases wastewater with rich organic and inorganic loads. This wastewater is allowed for soil irrigation in developing countries. However, the pollutant composition in wastewater and its environmental effects remain unclear. Therefore, we aimed to assess the wastewater's physicochemical parameters, toxic organic pollutants, heavy metals, and phytotoxic and cytogenotoxic. The result revealed that values of comprehensive wastewater parameters were recorded as chemical oxygen demand (187432.1 mg/L), pH (4.23), total nitrogen (1157.1 mg/L), ammonia nitrogen (1113.0 mg/L), total phosphorus (1181.2 mg/L), Zn (593.3 mg/L), Cr (0.6127 mg/L), and Ni (0.2986 mg/L). The organic compounds detected by LC-MS were salbostatin, sirolimus, Gibberellin A34-catabolite, 1-(sn-glycero-3-phospho)-1D-myo-inositol, and methyldiphenylsilane. The toxicity of the identified toxic chemicals and heavy metals was confirmed by onion and mung bean phytotoxicity characterization tests. The wastewater affected the germination of mung bean seeds, reduced or inhibited the growth of onions, and induced various chromosomal aberrations in root apical meristems. Our study shows that the treatment of natural rubber wastewater needs to be improved, and the feasibility of irrigating soil with wastewater needs to be reconsidered.

Proanthocyanidins induce analgesic and anxiolytic effects in spared nerve injured mice by decreasing in vivo firing rate of pyramidal cells in the insular cortex.

Neuropathic pain is one of the most common symptoms of clinical pain that often accompanied by severe emotional changes such as anxiety. However, the treatment for comorbidity of chronic pain and anxiety is limited. Proanthocyanidins (PACs), a group of polyphenols enriched in plants and foods, have been reported to cause pain-alleviating effects. However, whether and how PACs induce analgesic and anxiolytic effects in the central nervous system remain obscure. In the present study, we observed that microinjection of PACs into the insular cortex (IC) inhibited mechanical and spontaneous pain sensitivity and anxiety-like behaviors in mice with spared nerve injury. Meanwhile, PACs application exclusively reduced the FOS expression in the pyramidal cells but not interneurons in the IC. In vivo electrophysiological recording of the IC further showed that PACS application inhibited the firing rate of spikes of pyramidal cells of IC in neuropathic pain mice. In summary, PACs induce analgesic and anxiolytic effects by inhibiting the spiking of pyramidal cells of the IC in mice with neuropathic pain, which should provide new evidence of PACs as the potential clinical treatment of chronic pain and anxiety comorbidity.

Reducing the Risk of Benthic Algae Outbreaks by Regulating the Flow Velocity in a Simulated South-North Water Diversion Open Channel.

The reduction in open-channel flow velocity due to China's South-to-North Water Diversion Project (SNP) increases the risk of benthic algal community blooms resulting in drinking water safety issues. Consequently, it has attracted attention from all walks of life. However, regulatory measures to mitigate the risk of algal blooms and the main risk-causing factors are unclear. This study simulated the river ecosystem of the SNP channel through water diversion. Simulated gradient-increasing river flow velocity affects environmental factors and benthic algal alterations, and can be used to explore the feasibility of regulating the flow velocity to reduce the risk of algal blooms. We found that the algal biomasses in the velocity environments of 0.211 and 0.418 m/s decreased by 30.19% and 39.88%, respectively. Community structure alterations from diatoms to filamentous green algae were 75.56% and 87.53%, respectively. We observed significant differences in biodiversity, especially in terms of richness and evenness. The α diversity index of a species is influenced by physical and chemical environmental factors (especially flow velocity). Our study revealed that flow velocity is the main factor affecting the growth and outbreak of benthic algae. The risk of algal blooms in open channels can be effectively mitigated by regulating the flow velocity. This provides a theoretical basis for ensuring the water safety of large-scale water conservancy projects.

The microbial community and functional indicators response to flow restoration in gradient in a simulated water flume.

Flow reduction has greatly affected the river ecological systems, and it has attracted much attention. However, less attention has been paid to response to flow restoration, especially flow restoration in gradient. Flow regime of rivers may affect river functional indicators and microbial community structure. This study simulated the ecological restoration of the flow-reduced river reach by gradiently controlling the water flow and explores the ecological response of environmental functional indicators and microbial community structure to the water flow. The results showed that gross primary productivity (GPP), ecosystem respiration rate (ER) and some water quality indices such as chemical oxygen demand, total nitrogen, and total phosphorus (TP), exhibited positive ecological responses to flow restoration in gradient. GPP and ER increased by 600.1% and 500.2%, respectively. The alpha diversity indices of the microbial community increased significantly with a flow gradient restoration. Thereinto, Shannon, Simpson, Chao1, and Ace indices, respectively, increased by 16.4%, 5.6%, 8.6%, and 6.2%. Canonical correspondence analysis indicated that water flow, Dissolved oxygen and TP were the main influencing factors for changes in bacterial community structure. Microbial community structure and composition present a positive ecological response to flow restoration in gradient. This study reveals that the main variable in the restoration of the flow-reduced river reach is the flow discharge, and it provides a feasible scheme for its ecological restoration.

Detection of acute ischemic stroke and backtracking stroke onset time via machine learning analysis of metabolomics.

The time window from stroke onset is critical for the treatment decision. However, in unknown onset stroke, it is often difficult to determine the exact onset time because of the lack of assessment methods, which can result in controversial and random treatment decisions. Previous studies have shown that serum biomarkers, in addition to imaging assessment, are useful for determining the stroke onset time. However, as yet there are no specific biomarkers or corresponding methodologies that are accurate and effective for determining the onset time of unknown onset stroke. Herein, we describe our novel advanced metabolites-based machine learning method (termed extreme gradient boost [XGBoost]) combined with recursive feature elimination, which accurately screened five metabolites from 1124 metabolites detected in serum. These metabolites were capable of both detecting acute ischemic stroke and backtracking the acute ischemic stroke onset time. To further investigate the pathological mechanisms of acute ischemic stroke, we also examined characteristic metabolites in different brain regions, and found two metabolites that could distinguish the core infarct area from the ischemic penumbra. Although this study is based on animal experiments, our machine learning framework and selected metabolites may provide a basis for clinical stroke evaluation and treatment.

Bioremediation of tetramethyl thiuram disulfide and resource utilization of natural rubber wastewater by WR-2 Bacillus-dominated microbial community.

Tetramethyl thiuram disulfide (TMTD), an emerging pollutant with ecotoxicity and accumulation in rubber wastewater, is directly discharged by factories into the surrounding soil to save costs, and this disrupts the nearby ecosystem. In this study, an efficient bioremediation microbial community (WR-2) dominated by Bacillus was acclimatized and isolated from soil contaminated by rubber wastewater. After passing through the metabolic process of WR-2, the ecotoxic TMTD decomposes within 14 days. In the pot experiment, WR-2 not only completed the bioremediation of contaminated soil but also significantly improved the crop growth conditions and the product quality. These results show that WR-2 has broad application prospects in the bioremediation of soil contaminated by rubber wastewater. It also provides a theoretical framework for the resource utilization of the effluent at the end of the initial rubber processing.

Glutamatergic synapses from the insular cortex to the basolateral amygdala encode observational pain.

Empathic pain has attracted the interest of a substantial number of researchers studying the social transfer of pain in the sociological, psychological, and neuroscience fields. However, the neural mechanism of empathic pain remains elusive. Here, we establish a long-term observational pain model in mice and find that glutamatergic projection from the insular cortex (IC) to the basolateral amygdala (BLA) is critical for the formation of observational pain. The selective activation or inhibition of the IC-BLA projection pathway strengthens or weakens the intensity of observational pain, respectively. The synaptic molecules are screened, and the upregulated synaptotagmin-2 and RIM3 are identified as key signals in controlling the increased synaptic glutamate transmission from the IC to the BLA. Together, these results reveal the molecular and synaptic mechanisms of a previously unidentified neural pathway that regulates observational pain in mice.

Accelerated Curing for Glass-Based Mortars Using Water at 80 °C.

The substitution of river sand with glass aggregate (GA) and cement with glass powder (GP) is a mainstream method to recycle waste glass. Traditionally, standard curing was widely used for glass-based mortars. However, it is time-consuming and cannot address low mechanical strengths of the early-age mortars. Therefore, the effect of water curing at 80 °C on the properties of GA mortars is investigated. Furthermore, the effect of the GP size is also considered. Results show that compared with the expansion of alkali-silica reaction (ASR), water curing at 80 °C has a negligible effect on the volume change. Moreover, the compressive strength of GA mortars under 1-day water curing at 80 °C is comparable with that under 28-day water curing at 20 °C. Therefore, the 1-day water curing at 80 °C is proposed as an accelerated curing method for GA mortars. On the other hand, the addition of GP with the mean size of 28.3 and 47.9 µm can effectively mitigate the ASR expansion of GA mortars. Compared with the size of 28.3 µm, GA mortars containing GP (47.9 µm) always obtain higher compressive strength. In particular, when applying the 1-day water curing at 80 °C, GA mortars containing GP (47.9 µm) can even gain higher strength than those containing fly ash.

Maximized Frequency Doubling through the Inverse Design of Nonlinear Metamaterials.

The conventional process for developing an optimal design for nonlinear optical responses is based on a trial-and-error approach that is largely inefficient and does not necessarily lead to an ideal result. Deep learning can automate this process and widen the realm of nonlinear geometries and devices. This research illustrates a deep learning framework used to create an optimal plasmonic design for a nonlinear metamaterial. The algorithm produces a plasmonic pattern that can maximize the second-order nonlinear effect of a nonlinear metamaterial. A nanolaminate metamaterial is used as a nonlinear material, and plasmonic patterns are fabricated on the prepared nanolaminate to demonstrate the validity and efficacy of the deep learning algorithm. The optimal pattern produced yielded second-harmonic generation from the nanolaminate with normal incident fundamental light. The deep learning architecture applied in this research can be expanded to other optical responses and light-matter interaction processes.

The increased in vivo firing of pyramidal cells but not interneurons in the anterior cingulate cortex after neuropathic pain.

Chronic pain damages the balance between excitation and inhibition in the sensory cortex. It has been confirmed that the activity of cortical glutamatergic pyramidal cells increases after chronic pain. However, whether the activity of inhibitory interneurons synchronized changed remains obscure, especially in in vivo conditions. In the present study, we checked the firing rate of pyramidal cells and interneurons in the anterior cingulate cortex, a main cortical area for the regulation of nociceptive information in mice with spared nerve injury by using in vivo multi-channel recording system. We found that the firing rate of pyramidal cells but not interneurons increased in the ACC, which was further confirmed by the increased FOS expression in pyramidal cells but not interneurons, in mice with neuropathic pain. Selectively high frequency stimulation of the ACC nociceptive afferent fibers only potentiated the activity of pyramidal cells either. Our results thus suggest that the increased activity of pyramidal cells contributes to the damaged E/I balance in the ACC and is important for the pain hypersensitivity in mice with neuropathic pain.

Determining Critical Thresholds of Environmental Flow Restoration Based on Planktonic Index of Biotic Integrity (P-IBI): A Case Study in the Typical Tributaries of Poyang Lake.

Hydropower construction and climate change have aggravated river hydrological changes, which have reduced the water flow regime in the Ruhe River Basin. The reduced flow of the river seriously affected the water supply of nearby residents and the operation of the river ecosystem. Therefore, in order to alleviate the contradiction between water use for hydropower facilities and environmental water use, the urgent need is to explore the ecological flow-threshold of rivers. This study took the Fuhe River Basin as the research object, and summarized the monitoring data of eight hydrological stations from recent decades. Based on this, we explored the response law of P-IBI and flow, a tool to quickly measure the health of the ecosystem. Through the response relationship between alterations in environmental factors of the river and phytoplankton index of biotic integrity (P-IBI), it was determined that environmental flow was the dominant influencing factor of P-IBI. According to P-IBI, the threshold of environmental discharge in the Fuhe River was limited to 273~826.8 m3/s. This study established a regulatory framework for the river flow of large rivers by constructing P-IBI and determining the critical thresholds of environmental flow by constraining the constitution. These results provide a theoretical basis for better planning and improvement of river ecosystem restoration and river utilization.

Tackling Photonic Inverse Design with Machine Learning.

Machine learning, as a study of algorithms that automate prediction and decision-making based on complex data, has become one of the most effective tools in the study of artificial intelligence. In recent years, scientific communities have been gradually merging data-driven approaches with research, enabling dramatic progress in revealing underlying mechanisms, predicting essential properties, and discovering unconventional phenomena. It is becoming an indispensable tool in the fields of, for instance, quantum physics, organic chemistry, and medical imaging. Very recently, machine learning has been adopted in the research of photonics and optics as an alternative approach to address the inverse design problem. In this report, the fast advances of machine-learning-enabled photonic design strategies in the past few years are summarized. In particular, deep learning methods, a subset of machine learning algorithms, dealing with intractable high degrees-of-freedom structure design are focused upon.

Building Multifunctional Metasystems via Algorithmic Construction.

Flat optics foresees a promising route to ultracompact optical devices, where metasurfaces serve as the foundation. Conventional designs of metasurfaces start with a certain structure as the prototype, followed by extensive parametric sweeps to accommodate the requirements of phase and amplitude of the emerging light. Regardless of how computation consuming the process is, a predefined structure can hardly realize the independent control over polarization, frequency, and spatial channels, which hinders the potential of metasurfaces to be multifunctional. Besides, achieving complicated and multiple functions calls for designing metasystems with multiple cascading layers of metasurfaces, which introduces exponential complexity. In this work, we present a hybrid deep learning framework for designing multilayer metasystems with multifunctional capabilities. We demonstrate examples of a polarization-multiplexed dual-functional beam generator, a second-order differentiator for all-optical computing, and a space-polarization-wavelength multiplexed hologram. These examples are barely achievable by single-layer metasurfaces and unattainable by traditional design processes.

Proanthocyanidins Inhibit the Transmission of Spinal Pain Information Through a Presynaptic Mechanism in a Mouse Inflammatory Pain Model.

Inflammatory pain is one of the most common symptoms of clinical pain that seriously affects patient quality of life, but it currently has limited therapeutic options. Proanthocyanidins, a group of polyphenols enriched in plants and foods, have been reported to exert anti-inflammatory pain-alleviating effects. However, the mechanism by which proanthocyanidins relieve inflammatory pain in the central nervous system is unclear. In the present study, we observed that intrathecal injection of proanthocyanidins inhibited mechanical and thermal pain sensitivity in mice with inflammatory pain induced by Complete Freund's Adjuvant (CFA) injection. Electrophysiological results further showed that proanthocyanidins inhibited the frequency of spontaneous excitatory postsynaptic currents without affecting the spontaneous inhibitory postsynaptic currents or the intrinsic properties of parabrachial nucleus-projecting neurons in the spinal cord. The effect of proanthocyanidins may be mediated by their inhibition of phosphorylated activation of the PI3K/Akt/mTOR pathway molecules in dorsal root ganglia neurons. In summary, intrathecal injection of procyanidin induces an obvious anti-inflammatory pain effect in mice by inhibiting peripheral excitatory inputs to spinal neurons that send nociceptive information to supraspinal areas.

Compounding Meta-Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques.

Molecules composed of atoms exhibit properties not inherent to their constituent atoms. Similarly, metamolecules consisting of multiple meta-atoms possess emerging features that the meta-atoms themselves do not possess. Metasurfaces composed of metamolecules with spatially variant building blocks, such as gradient metasurfaces, are drawing substantial attention due to their unconventional controllability of the amplitude, phase, and frequency of light. However, the intricate mechanisms and the large degrees of freedom of the multielement systems impede an effective strategy for the design and optimization of metamolecules. Here, a hybrid artificial-intelligence-based framework consolidating compositional pattern-producing networks and cooperative coevolution to resolve the inverse design of metamolecules in metasurfaces is proposed. The framework breaks the design of the metamolecules into separate designs of meta-atoms, and independently solves the smaller design tasks of the meta-atoms through deep learning and evolutionary algorithms. The proposed framework is leveraged to design metallic metamolecules for arbitrary manipulation of the polarization and wavefront of light. Moreover, the efficacy and reliability of the design strategy are confirmed through experimental validations. This framework reveals a promising candidate approach to expedite the design of large-scale metasurfaces in a labor-saving, systematic manner.

Generative Model for the Inverse Design of Metasurfaces.

The advent of metasurfaces in recent years has ushered in a revolutionary means to manipulate the behavior of light on the nanoscale. The design of such structures, to date, has relied on the expertise of an optical scientist to guide a progression of electromagnetic simulations that iteratively solve Maxwell's equations until a locally optimized solution can be attained. In this work, we identify a solution to circumvent this conventional design procedure by means of a deep learning architecture. When fed an input set of customer-defined optical spectra, the constructed generative network generates candidate patterns that match the on-demand spectra with high fidelity. This approach reveals an opportunity to expedite the discovery and design of metasurfaces for tailored optical responses in a systematic, inverse-design manner.

Multi-mode of Four and Six Wave Parametric Amplified Process.

Multiple quantum modes in correlated fields are essential for future quantum information processing and quantum computing. Here we report the generation of multi-mode phenomenon through parametric amplified four- and six-wave mixing processes in a rubidium atomic ensemble. The multi-mode properties in both frequency and spatial domains are studied. On one hand, the multi-mode behavior is dominantly controlled by the intensity of external dressing effect, or nonlinear phase shift through internal dressing effect, in frequency domain; on the other hand, the multi-mode behavior is visually demonstrated from the images of the biphoton fields directly, in spatial domain. Besides, the correlation of the two output fields is also demonstrated in both domains. Our approach supports efficient applications for scalable quantum correlated imaging.

Phase Modulation in Rydberg Dressed Multi-Wave Mixing processes.

We study the enhancement and suppression of different multi-waving mixing (MWM) processes in a Rydberg-EIT rubidium vapor system both theoretically and experimentally. The nonlinear dispersion property of hot rubidium atoms is modulated by the Rydberg-Rydberg interaction, which can result in a nonlinear phase shift of the relative phase between dark and bright states. Such Rydberg-induced nonlinear phase shift can be quantitatively estimated by the lineshape asymmetry in the enhancedand suppressed MWM processes, which can also demonstrate the cooperative atom-light interaction caused by Rydberg blockaded regime. Current study on phase shift is applicable to phase-sensitive detection and the study of strong Rydberg-Rydberg interaction.

Periodic inversion and phase transition of finite energy Airy beams in a medium with parabolic potential.

We study periodic inversion and phase transition of normal, displaced, and chirped finite energy Airy beams propagating in a parabolic potential. This propagation leads to an unusual oscillation: for half of the oscillation period the Airy beam accelerates in one transverse direction, with the main Airy beam lobe leading the train of pulses, whereas in the other half of the period it accelerates in the opposite direction, with the main lobe still leading - but now the whole beam is inverted. The inversion happens at a critical point, at which the beam profile changes from an Airy profile to a Gaussian one. Thus, there are two distinct phases in the propagation of an Airy beam in the parabolic potential - the normal Airy and the single-peak Gaussian phase. The length of the single-peak phase is determined by the size of the decay parameter: the smaller the decay, the smaller the length. A linear chirp introduces a transverse displacement of the beam at the phase transition point, but does not change the location of the point. A quadratic chirp moves the phase transition point, but does not affect the beam profile. The two-dimensional case is discussed briefly, being equivalent to a product of two one-dimensional cases.