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Tin is an indispensable metal in the modern industry. The current tin smelting processes, however, have the disadvantages of high smelting temperature, long smelting time, especially high tin loss ratio (>10 wt%). The tin loss attributes to the volatilization as gaseous SnO and stannous silicate (SnO•SiO2) residual in the slag. An innovative approach for preparing metallic tin effectively from cassiterite in the presence of Na2CO3, named gas-based reduction roasting followed by water leaching process, is under development in Central South University, China. The present study, using chemically pure SnO2 and SiO2, aims to determine the impact of Na2CO3 on the metallic tin preparation from cassiterite by the novel process using XRD, SEM-EDS, chemical analysis, etc. It was found that Na2CO3 effectively restrained the tin volatilization as SnO and the formation of hardly reductive SnO•SiO2 during the reduction roasting process. In the presence of Na2CO3, most of SnO2 in the raw materials (mixture of SnO2+SiO2) was directly reduced to metallic tin, and part of SnO2 reacted with Na2CO3 to form intermediate Na2SnO3, which was then reduced to metallic tin. The SiO2 was transformed into Na2SiO3 and then went into the water solution in the following water-leaching process. The main reactions of the SnO2 + SiO2 system in the presence of Na2CO3 under reductive atmosphere were ascertained.

Although neurons within intact nervous systems can be classified as ‘sensory’ or ‘motor,’ it is not known whether there is any general distinction between sensory and motor neurons at the cellular or molecular levels. Here we extend and test a theory according to which activation of certain subtypes of voltage-gated ion channel (VGC) generate patterns of spikes in neurons of motor systems, whereas VGC are proposed to counteract patterns in sensory neurons. We previously reported experimental evidence for the theory from visual thalamus, where we found that T-type calcium channels (TtCC) did not cause bursts of spikes but instead served the function of ‘predictive homeostasis’ to maximize the causal and informational link between retinogeniculate excitation and spike output. Here we have recorded neurons in brain slices from 8 sensory and motor regions of rat thalamus while mimicking key features of natural excitatory and inhibitory postsynaptic potentials. As predicted by theory, TtCC caused bursts of spikes in motor but not sensory thalamus. TtCC-mediated responses in motor thalamus were activated at more hyperpolarized potentials and caused larger depolarizations with more spikes than in visual and auditory thalamus. Somatosensory thalamus is known to be more closely connected to motor regions relative to auditory and visual thalamus, and likewise the strength of its TtCC responses was intermediate between these regions and motor thalamus. We also observed lower input resistance, as well as limited evidence of stronger hyperpolarization-induced (‘H-type’) depolarization, in nuclei closer to motor output. These findings support our theory of a specific difference between sensory and motor neurons at the cellular level.

A general theory views the function of all neurons as prediction, and one component of this theory is that of predictive homeostasis or prediction error. It is well established that sensory systems adapt so that neuronal output maintains sensitivity to sensory input, in accord with information theory. Predictive homeostasis applies the same principle at the cellular level, where the challenge is to maintain membrane excitability at the optimal homeostatic level so that spike generation is maximally sensitive to small gradations in synaptic drive. Negative feedback is a hallmark of homeostatic mechanisms, as exemplified by depolarization-activated potassium channels. However, T-type calcium channels exhibit positive feedback that appears at odds with the theory. In thalamocortical neurons of lateral geniculate nucleus (LGN), T-type channels are capable of causing bursts of spikes with an all-or-none character in response to excitation from a hyperpolarized potential. This burst mode would partially uncouple visual input from spike output and reduce the information spikes convey about gradations in visual input. However, past observations of T-type-driven bursts may have resulted from unnaturally high membrane excitability. By mimicking natural patterns of synaptic conductance that occur during vision, we found that T-type channels in rat brain slices did not cause bursts, but rather enabled retinogeniculate excitation to cause spikes despite sustained hyperpolarization, thereby restoring the homeostatic input-output relation observed at depolarized potentials. Our results suggest that T-type channels help to maintain a single optimal mode of transmission rather than creating a second mode. In addition, our results provide evidence for the general theory, which seeks to predict the properties of a neuron’s ion channels and synapses given knowledge of natural patterns of synaptic input.

The conventional interpretation of spikes is from the perspective of an external observer with knowledge of a neuron’s inputs and outputs who is ignorant of the contents of the black box that is the neuron. Here we consider a neuron to be an observer and we interpret spikes from the neuron’s perspective. We propose both a descriptive hypothesis based on physics and logic, and a prescriptive hypothesis based on biological optimality. Our descriptive hypothesis is that a neuron’s membrane excitability is known and the amplitude of a future excitatory postsynaptic conductance (EPSG) is unknown. Therefore excitability is an expectation of EPSG amplitude and a spike is generated only when EPSG amplitude exceeds its expectation (prediction error). Our prescriptive hypothesis is that a diversity of synaptic inputs and voltage-regulated ion channels implement predictive homeostasis, working to insure that the expectation is accurate. The homeostatic ideal and optimal expectation would be achieved when an EPSP reaches precisely to spike threshold, so that spike output is exquisitely sensitive to small variations in EPSG input. To an external observer who knows neither EPSG amplitude nor membrane excitability, spikes would appear random if the neuron is making accurate predictions. We review experimental evidence that spike probabilities are indeed maintained near an average of 0.5 under natural conditions, and we suggest that the same principles may also explain why synaptic vesicle release appears to be stochastic. Whereas the present hypothesis accords with principles of efficient coding dating back to Barlow (1961), it contradicts decades of assertions that neural activity is substantially random or noisy. The apparent randomness is by design, and like many other examples of apparent randomness, it corresponds to the ignorance of external macroscopic observers about the detailed inner workings of a microscopic system.

Reinforcement allows organisms to learn which stimuli predict subsequent biological relevance. Hebbian mechanisms of synaptic plasticity are insufficient to account for reinforced learning because neuromodulators signaling biological relevance are delayed with respect to the neural activity associated with the stimulus. A theoretical solution is the concept of eligibility traces (eTraces), silent synaptic processes elicited by activity which upon arrival of a neuromodulator are converted into a lasting change in synaptic strength. Previously we demonstrated in visual cortical slices the Hebbian induction of eTraces and their conversion into LTP and LTD by the retroactive action of norepinephrine and serotonin Here we show in vivo in mouse V1 that the induction of eTraces and their conversion to LTP/D by norepinephrine and serotonin respectively potentiates and depresses visual responses. We also show that the integrity of this process is crucial for ocular dominance plasticity, a canonical model of experience-dependent plasticity.

AbstractThe Third Pole (TP) is experiencing rapid warming and is currently in its warmest period in the past 2,000 years. This paper reviews the latest development in multidisciplinary TP research associated with this warming. The rapid warming facilitates intense and broad glacier melt over most of the TP, although some glaciers in the northwest are advancing. By heating the atmosphere and reducing snow/ice albedo, aerosols also contribute to the glaciers melting. Glacier melt is accompanied by lake expansion and intensification of the water cycle over the TP. Precipitation has increased over the eastern and northwestern TP. Meanwhile, the TP is greening and most regions are experiencing advancing phenological trends, although over the southwest there is a spring phenological delay mainly in response to the recent decline in spring precipitation. Atmospheric and terrestrial thermal and dynamical processes over the TP affect the Asian monsoon at different scales. Recent evidence indicates substantial roles that mesoscale convective systems play in the TP’s precipitation as well as an association between soil moisture anomalies in the TP and the Indian monsoon. Moreover, an increase in geohazard events has been associated with recent environmental changes, some of which have had catastrophic consequences caused by glacial lake outbursts and landslides. Active debris flows are growing in both frequency of occurrences and spatial scale. Meanwhile, new types of disasters, such as the twin ice avalanches in Ali in 2016, are now appearing in the region. Adaptation and mitigation measures should be taken to help societies’ preparation for future environmental challenges. Some key issues for future TP studies are also discussed.

Under the influence of climate warming, the frequency and intensity of extreme weather events are increasing, posing a greater risk of exposing the ecological environment to extreme climatic conditions. Ecological land, as a land use type of paramount importance to both the natural environment and humanity, has not been examined regarding its exposure under extreme climatic conditions. Therefore, this study focuses on the ecologically significant Southwest China, characterized by frequent extreme temperature events. Based on CMIP6 climate data and land use data, and utilizing ArcGIS software, the exposure of ecological land to extreme temperature conditions is analyzed. The results indicate that, compared to historical periods, the area of ecological land exposed to extreme high temperatures is rapidly increasing, while the area exposed to extreme low temperatures is decreasing. Various types of ecological land show differences in area changes under extreme temperature exposure, and there is significant spatial heterogeneity in the changes in ecological land exposure, primarily due to the different spatial distributions of ecological land and extreme temperature indicators. The changes in exposure area of ecological land under extreme temperatures exhibit prominent topographical gradients. The findings of this study can provide crucial and clear information for the formulation of disaster prevention mitigation policies and land planning. [ABSTRACT FROM AUTHOR]

Metagenomics, the study of all microbial species cohabitants in an environment, often produces large amount of sequence data varying from several GBs to a few TBs. Analyzing metagenomics data includes both data-intensive and compute-intensive steps, making the entire process hard to scale. Here we aim to optimize a metagenomics application that partitions the shortgun metagenomics sequences based on their species of origin. Our solution combines MapReduce-based BioPig analytic toolkit with MPI to provide scalability in respective to both data and compute. We also made some improvements to the existing BioPig toolkit by using simplified data types and compressed k-mer storage. These optimizations leads up to 193× speedup for the computing-intensive step and 9.6× speedup over the entire pipeline. Our optimized application is also capable of processing datasets that are 16 times larger on the same hardware platform. These results suggest integrating heterogeneous technologies such as Hadoop and MPI is quite efficient to solve large genomics problems that are both data-intensive and compute-intensive.

Nonlinear features extracted from Lamb wave signals (e.g., second harmonic generation) are demonstrably sensitive to microscopic damage, such as fatigue and material thermal degradation. While a majority of the existing studies in this context is focused on detecting undersized damage in metallic materials, the present study is aimed at expanding such a detection philosophy to the domain of composites, by linking the relative acoustic nonlinearity parameter (RANP) – a prominent nonlinear signal feature of Lamb waves – to barely visible impact damage (BVID) in composites. Nevertheless, considering immense uncertainties inevitably embedded in acquired signals (due to instrumentation, environment, operation, computation/estimation, etc.) which can adversely obfuscate nonlinear features, it is necessary to quantify the uncertainty of the RANP (i.e., its statistics) in order to enhance decision-making associated with its use as a detection feature. A probabilistic model is established to numerically evaluate the statistical distribution of the RANP. Using piezoelectric wafers, Lamb waves are acquired and processed to produce histograms of RANP estimates in both the healthy and damaged conditions of a CF/EP laminate, to which the model is compared, with good agreement observed between the model-predicted and experimentally-obtained statistic distributions of the RANP. With the model, BVID in the laminate is predicted. The model is further made use of to quantify the level of confidence in damage prediction results based on the concept of a receiver operating characteristic, enabling the practitioners to better understand the obtained results in the presence of uncertainties.

The rise in extreme weather conditions poses a formidable menace to the ecosystem services (ES) bestowed by ecosystems upon humanity. A precise comprehension of the ramifications of extreme weather events on ES is imperative to establish a robust scientific foundation for mitigating the adverse consequences of such phenomena. Despite the extensive corpus of international studies on the impact of extreme weather events on ES, there is a lack of literature reviews and nuanced discussions regarding forthcoming research aims. Therefore, this paper selects to focus on drought, extreme precipitation, heatwaves, and extreme cold--representatives of extreme weather with substantial global ramifications and notable threats. It summarizes the effects of extreme weather events with substantial global on ES, identifies current research issues, and outlines future prospects. Research indicates that the impact of extreme weather events on ES is predominantly negative. Extreme weather affects ecological processes and human needs, subsequently influencing the supply-demand relationship and trade-offs between ES. The diversity of research methods is influenced by differences in research objectives and scales. The interaction of various extreme weather events and the complex coupling with human activities make the mechanisms of extreme weather events impact on ES more intricate. A deficiency exists in the systematic exploration of the dynamic characterization of supply-demand relationships and trade-off dynamics under the sway of extreme weather. Additionally, there is a paucity of research addressing the recovery processes and underlying mechanisms governing ecosystem services (ES) in the aftermath of extreme weather events. Additionally, there is a need for in-depth attention to the spatial scale characteristics of ES under the influence of extreme weather, which is crucial for understanding the mechanism of impacts. [ABSTRACT FROM AUTHOR]

Thermobaric convection (type II convection) and thermobaric cabbeling (type III convection) might substantially contribute to vertical mixing, vertical heat transport, and deep-water formation in the World Ocean. However, the extent of this contribution remains poorly constrained. The concept of ocean convective available potential energy (OCAPE), the thermobaric energy source for type II and type III convection, is introduced to improve the diagnosis and prediction of these convection events. OCAPE is analogous to atmospheric CAPE, which is a key energy source for atmospheric moist convection and has long been used to forecast moist convection. OCAPE is the potential energy (PE) stored in an ocean column arising from thermobaricity, defined as the difference between the PE of the ocean column and its minimum possible PE under adiabatic vertical parcel rearrangements. An ocean column may be stably stratified and still have nonzero OCAPE. The authors present an efficient strategy for computing OCAPE accurately for any given column of seawater. They further derive analytical expressions for OCAPE for approximately two-layer ocean columns that are widely observed in polar oceans. This elucidates the dependence of OCAPE on key physical parameters. Hydrographic profiles from the winter Weddell Sea are shown to contain OCAPE (0.001-0.01 J kg-1), and scaling analysis suggests that OCAPE may be substantially enhanced by wintertime surface buoyancy loss. The release of this OCAPE may substantially contribute to the kinetic energy of deep convection in polar oceans.

The energetics of thermobaricity- and cabbeling-powered deep convection occurring in oceans with cold freshwater overlying warm salty water are investigated here. These quasi-two-layer profiles are widely observed in wintertime polar oceans. The key diagnostic is the ocean convective available potential energy (OCAPE), a concept introduced in a companion piece to this paper (Part I). For an isolated ocean column, OCAPE arises from thermobaricity and is the maximum potential energy (PE) that can be converted into kinetic energy (KE) under adiabatic vertical parcel rearrangements. This study explores the KE budget of convection using two-dimensional numerical simulations and analytical estimates. The authors find that OCAPE is a principal source for KE. However, the complete conversion of OCAPE to KE is inhibited by diabatic processes. Further, this study finds that diabatic processes produce three other distinct contributions to the KE budget: (i) a sink of KE due to the reduction of stratification by vertical mixing, which raises water column's center of mass and thus acts to convert KE to PE; (ii) a source of KE due to cabbeling-induced shrinking of the water column's volume when water masses with different temperatures are mixed, which lowers the water column's center of mass and thus acts to convert PE into KE; and (iii) a reduced production of KE due to diabatic energy conversion of the KE convertible part of the PE to the KE inconvertible part of the PE. Under some simplifying assumptions, the authors also propose a theory to estimate the maximum depth of convection from an energetic perspective. This study provides a potential basis for improving the convection parameterization in ocean models.

In reward-based learning, synaptic modifications depend on a brief stimulus and a temporally delayed reward, which poses the question of how synaptic activity patterns associate with a delayed reward. A theoretical solution to this so-called distal reward problem has been the notion of activity-generated "synaptic eligibility traces," silent and transient synaptic tags that can be converted into long-term changes in synaptic strength by reward-linked neuromodulators. Here we report the first experimental demonstration of eligibility traces in cortical synapses. We demonstrate the Hebbian induction of distinct traces for LTP and LTD and their subsequent timing-dependent transformation into lasting changes by specific monoaminergic receptors anchored to postsynaptic proteins. Notably, the temporal properties of these transient traces allow stable learning in a recurrent neural network that accurately predicts the timing of the reward, further validating the induction and transformation of eligibility traces for LTP and LTD as a plausible synaptic substrate for reward-based learning.