Paraventricular Thalamus Dynamically Modulates Aversive Memory via Tuning Prefrontal Inhibitory Circuitry.

The impact of stress on the formation and expression of memory is well studied, especially on the contributions of stress hormones. But how stress affects brain circuitry dynamically to modulate memory is far less understood. Here, we used male C57BL6/J mice in an auditory fear conditioning as a model system to examine this question and focused on the impact of stress on dorsomedial prefrontal cortex (dmPFC) neurons which play an important role in probabilistic fear memory. We found that paraventricular thalamus (PVT) neurons are robustly activated by acute restraining stress. Elevated PVT activity during probabilistic fear memory expression increases spiking in the dmPFC somatostatin neurons which in turn suppresses spiking of dmPFC parvalbumin (PV) neurons, and reverts the usual low fear responses associated with probabilistic fear memory to high fear. This dynamic and reversible modulation allows the original memory to be preserved and modulated during memory expression. In contrast, elevated PVT activity during fear conditioning impairs synaptic modifications in the dmPFC PV-neurons and abolishes the formation of probabilistic fear memory. Thus, PVT functions as a stress sensor to modulate the formation and expression of aversive memory by tuning inhibitory functions in the prefrontal circuitry.SIGNIFICANCE STATEMENT The impact of stress on cognitive functions, such as memory and executive functions, are well documented especially on the impact by stress hormone. However, the contributions of brain circuitry are far less understood. Here, we show that a circuitry-based mechanism can dynamically modulate memory formation and expression, namely, higher stress-induced activity in paraventricular thalamus (PVT) impairs the formation and expression of probabilistic fear memory by elevating the activity of somatostatin-neurons to suppress spiking in dorsomedial prefrontal parvalbumin (PV) neurons. This stress impact on memory via dynamic tuning of prefrontal inhibition preserves the formed memory but enables a dynamic expression of memory. These findings have implications for better stress coping strategies as well as treatment options including better drug targets/mechanisms.

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections.

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Towards an ecosystem capacity to stabilise organic carbon in soils.

Soil organic carbon (SOC) accrual, and particularly the formation of fine fraction carbon (OCfine), has a large potential to act as sink for atmospheric CO2. For reliable estimates of this potential and efficient policy advice, the major limiting factors for OCfine accrual need to be understood. The upper boundary of the correlation between fine mineral particles (silt + clay) and OCfine is widely used to estimate the maximum mineralogical capacity of soils to store OCfine, suggesting that mineral surfaces get C saturated. Using a dataset covering the temperate zone and partly other climates on OCfine contents and a SOC turnover model, we provide two independent lines of evidence, that this empirical upper boundary does not indicate C saturation. Firstly, the C loading of the silt + clay fraction was found to strongly exceed previous saturation estimates in coarse-textured soils, which raises the question of why this is not observed in fine-textured soils. Secondly, a subsequent modelling exercise revealed, that for 74% of all investigated soils, local net primary production (NPP) would not be sufficient to reach a C loading of 80 g C kg-1 silt + clay, which was previously assumed to be a general C saturation point. The proportion of soils with potentially enough NPP to reach that point decreased strongly with increasing silt + clay content. High C loadings can thus hardly be reached in more fine-textured soils, even if all NPP would be available as C input. As a pragmatic approach, we introduced texture-dependent, empirical maximum C loadings of the fine fraction, that decreased from 160 g kg-1 in coarse to 75 g kg-1 in most fine-textured soils. We conclude that OCfine accrual in soils is mainly limited by C inputs and is strongly modulated by texture, mineralogy, climate and other site properties, which could be formulated as an ecosystem capacity to stabilise SOC.

Not all soil carbon is created equal: Labile and stable pools under nitrogen input.

Anthropogenic activities have raised nitrogen (N) input worldwide with profound implications for soil carbon (C) cycling in ecosystems. The specific impacts of N input on soil organic matter (SOM) pools differing in microbial availability remain debatable. For the first time, we used a much-improved approach by effectively combining the 13C natural abundance in SOM with 21 years of C3-C4 vegetation conversion and long-term incubation. This allows to distinguish the impact of N input on SOM pools with various turnover times. We found that N input reduced the mineralization of all SOM pools, with labile pools having greater sensitivity to N than stable ones. The suppression in SOM mineralization was notably higher in the very labile pool (18%-52%) than the labile and stable (11%-47%) and the very stable pool (3%-21%) compared to that in the unfertilized control soil. The very labile C pool made a strong contribution (up to 60%) to total CO2 release and also contributed to 74%-96% of suppressed CO2 with N input. This suppression of SOM mineralization by N was initially attributed to the decreased microbial biomass and soil functions. Over the long-term, the shift in bacterial community toward Proteobacteria and reduction in functional genes for labile C degradation were the primary drivers. In conclusion, the higher the availability of the SOM pools, the stronger the suppression of their mineralization by N input. Labile SOM pools are highly sensitive to N availability and may hold a greater potential for C sequestration under N input at global scale.

Impact of Climate on Soil Organic Matter Composition in Soils of Tropical Volcanic Regions Revealed by EGA-MS and Py-GC/MS.

Soil organic matter (SOM) crucially influences the global carbon cycle, yet its molecular composition and determinants are understudied, especially for tropical volcanic regions. We investigated how SOM compounds change in response to climate, vegetation, soil horizon, and soil properties and the relationship between SOM composition and microbial decomposability in Tanzanian and Indonesian volcanic regions. We collected topsoil (0-15 cm) and subsoil (20-40 cm) horizons (n = 22; pH: 4.6-7.6; SOC: 10-152 g kg-1) with undisturbed vegetation and wide mean annual temperature and moisture ranges (14-26 °C; 800-3300 mm) across four elevational transects (340-2210 m asl.). Evolved gas analysis-mass spectrometry (EGA-MS) documented a simultaneous release of SOM compounds and clay mineral dehydroxylation. Subsequently applying double-shot pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) at 200 and 550 °C, we detailed the molecular composition of topsoil and subsoil SOM. A minor portion (2.7 ± 1.9%) of compounds desorbed at 200 °C, limiting its efficacy for investigating overall SOM characteristics. Pyrolyzed SOM closely aligns with the intermediate decomposable SOM pool, with most pyrolysates (550 °C) originating from this pool. Pyrolysates composition suggests tropical SOM is mainly microbial-derived and subsoil contains more degraded compounds. Higher litter inputs and attenuated SOM decomposition due to cooler temperatures and lower soil pH (<5.5) produce less-degraded SOM at higher elevations. Redundancy analyses revealed the crucial role of active Al/Fe (oxalate-extractable Al/Fe), abundant in low-temperature/high-moisture conditions, in stabilizing these less-degraded components. Our findings provide new insights into SOM molecular composition and its determinants, critical for understanding the carbon cycle in tropical ecosystems.

Ultra-high-performance liquid chromatography-quadrupole time of flight tandem mass spectrometry based in vitro metabolite profiling of DK-GV-04P, a novel anticancer molecule under drug discovery.

DK-GV-04P, chemically identified as 3-cinnamyl-2-(4-methoxyphenyl) quinazolin-4(3H)-one, is an investigational molecule synthesized at the Chemical Biology Laboratory of the National Institute of Pharmaceutical Education and Research-Ahmedabad. The compound has shown potential anticancer activity against squamous CAL27 cell lines. Metabolite identification and characterization are critical in drug discovery, providing key insights into a compound's pharmacokinetics, pharmacodynamics safety, and metabolic fate. The primary aim of the study was to identify and characterize the in vitro metabolites of DK-GV-04P. In silico identification of the site of metabolism was also carried out using xenosite online software. The molecule was incubated with human liver microsomes and human S9 liver fraction to generate in vitro metabolites, which were further identified and characterized using ultra-high-performance liquid chromatography-quadrupole time of flight tandem mass spectrometry. A total of nine metabolites (four phase I and five phase II) were identified and characterized through tandem mass spectrometry. The major biotransformation pathways involved in metabolism of DK-GV-04P were hydroxylation, O-demethylation and glucuronidation. In addition to this, a detailed biotransformation pathway of DK-GV-04P has been established in this study.

Multi-AUV Kinematic Task Assignment Based on Self-Organizing Map Neural Network and Dubins Path Generator.

To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by the improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by changing the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. The AUV's yaw angle is limited, which results in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realize the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for a multi-AUV system.

A Self-Rectifying Synaptic Memristor Array with Ultrahigh Weight Potentiation Linearity for a Self-Organizing-Map Neural Network.

Two-terminal self-rectifying (SR)-synaptic memristors are preeminent candidates for high-density and efficient neuromorphic computing, especially for future three-dimensional integrated systems, which can self-suppress the sneak path current in crossbar arrays. However, SR-synaptic memristors face the critical challenges of nonlinear weight potentiation and steep depression, hindering their application in conventional artificial neural networks (ANNs). Here, a SR-synaptic memristor (Pt/NiOx/WO3-x:Ti/W) and cross-point array with sneak path current suppression features and ultrahigh-weight potentiation linearity up to 0.9997 are introduced. The image contrast enhancement and background filtering are demonstrated on the basis of the device array. Moreover, an unsupervised self-organizing map (SOM) neural network is first developed for orientation recognition with high recognition accuracy (0.98) and training efficiency and high resilience toward both noises and steep synaptic depression. These results solve the challenges of SR memristors in the conventional ANN, extending the possibilities of large-scale oxide SR-synaptic arrays for high-density, efficient, and accurate neuromorphic computing.

Twelve-year conversion of rice paddy to wetland does not alter SOC content but decreases C decomposition and N mineralization in Japan.

Land-use change worldwide has been driven by anthropogenic activities, which profoundly regulates terrestrial C and N cycles. However, it remains unclear how the dynamics and decomposition of soil organic C (SOC) and N respond to long-term conversion of rice paddy to wetland. Here, soil samples from five soil depths (0-25 cm, 5 cm/depth) were collected from a continuous rice paddy and an adjacent wetland (a rice paddy abandoned for 12 years) on Shonai Plain in northeastern Japan. A four-week anaerobic incubation experiment was conducted to investigate soil C decomposition and N mineralization. Our results showed that SOC in the wetland and rice paddy decreased with soil depth, from 31.02 to 19.66 g kg-1 and from 30.26 to 18.86 g kg-1, respectively. There was no significant difference in SOC content between wetland and rice paddy at any depth. Soil total nitrogen (TN) content in the wetland (2.61-1.49 g kg-1) and rice paddy (2.91-1.78 g kg-1) showed decreasing trend with depth; TN was significantly greater in the rice paddy than in the wetland at all depths except 20-25 cm. Paddy soil had significantly lower C/N ratios but significantly larger decomposed C (Dec-C, CO2 and CH4 production) and mineralized N (Min-N, net NH4+-N production) than wetland soil across all depths. Moreover, the Dec-C/Min-N ratio was significantly larger in wetland than in rice paddy across all depths. Rice paddy had higher exponential correlation between Dec-C and SOC, Min-N and TN than wetland. Although SOC did not change, TN decreased by 14.1% after the land-use conversion. The Dec-C and Min-N were decreased by 32.7% and 42.2%, respectively, after the12-year abandonment of rice paddy. Conclusively, long-term conversion of rice paddy to wetland did not distinctly alter SOC content but increased C/N ratio, and decreased C decomposition and N mineralization in 0-25 cm soil depth.

Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol-Gasoline Blends by Raman Spectroscopy.

Methanol-gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol-gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol-gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)-PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol-gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.

Analysis of heavy metal and polycyclic aromatic hydrocarbon pollution characteristics of a typical metal rolling industrial site based on data mining.

With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.

A new scheme of PM2.5 and O3 control strategies with the integration of SOM, GA and WRF-CAMx.

Previous air pollution control strategies didn't pay enough attention to regional collaboration and the spatial response sensitivities, resulting in limited control effects in China. This study proposed an effective PM2.5 and O3 control strategy scheme with the integration of Self-Organizing Map (SOM), Genetic Algorithm (GA) and WRF-CAMx, emphasizing regional collaborative control and the strengthening of control in sensitive areas. This scheme embodies the idea of hierarchical management and spatial-temporally differentiated management, with SOM identifying the collaborative subregions, GA providing the optimized subregion-level priority of precursor emission reductions, and WRF-CAMx providing response sensitivities for grid-level priority of precursor emission reductions. With Beijing-Tianjin-Hebei and the surrounding area (BTHSA, "2 + 26" cities) as the case study area, the optimized strategy required that regions along Taihang Mountains strengthen the emission reductions of all precursors in PM2.5-dominant seasons, and strengthen VOCs reductions but moderate NOx reductions in O3-dominant season. The spatiotemporally differentiated control strategy, without additional emission reduction burdens than the 14th Five-Year Plan proposed, reduced the average annual PM2.5 and MDA8 O3 concentrations in 28 cities by 3.2%-8.2% and 3.9%-9.7% respectively in comparison with non-differential control strategies, with the most prominent optimization effects occurring in the heavily polluted seasons (6.9%-18.0% for PM2.5 and 3.3%-14.2% for MDA8 O3, respectively). This study proposed an effective scheme for the collaborative control of PM2.5 and O3 in BTHSA, and shows important methodological implications for other regions suffering from similar air quality problems.

Genome sequences and functional analysis of Levilactobacillus brevis LSF9-1 and Pediococcus acidilactici LSF1-1 from fermented fish cake (Som-fak) with gamma-aminobutyric acid (GABA) production.

Gamma-aminobutyric acid (GABA) is a crucial inhibitory neurotransmitter in the sympathetic nervous system that exerts regulatory effects on the blood, immune, and nervous systems. GABA production in som-fak, a traditional fermented fish of Thailand, has been attributed to the activity of lactic acid bacteria (LAB). The present study aims to characterize the LAB isolates and compare the genomes and GABA synthesis genes of selected isolates capable of GABA production. Thirteen isolates demonstrating GABA synthesis capability were identified based on their phenotypic and genotypic characteristics. Seven isolates (group I: LSF3-3, LSF8-3, LSF9-1, LSF9-3, LSF9-6, LSF9-7, and LSF10-14) were identified as Levilactobacillus brevis with 99.78-100% similarity. LSF2-1, LSF3-2, LSF5-4, and LSF6-5 (group II) were identified as Lactiplantibacillus pentosus with 99.86-100% similarity. Strain LSF1-1 (group III) was identified as Pediococcus acidilactici (99.47%), and LSF10-4 (group IV) was identified as Pediococcus pentosaceus with 99.93% similarity. The GABA production of isolates ranged from 0.087 to 16.935 g/L. The maximum production of 16.935 g/L from 3% monosodium glutamate was obtained from strain LSF9-1. Gene and genome analysis revealed that L. brevis LSF9-1 has multiple gad genes in the genome, such as gadB1, gadB2, gadC1, and gadC2, making it the potential strain for GABA production. Additionally, the genome analysis of P. acidilactici LSF1-1 consists of gadA, gadB, and gadC, which respond to controlling GABA production and export. Furthermore, strain LSF1-1 was considered safe, containing no virulence factors. Thus, Levilactobacillus brevis LSF9-1 and Pediococcus acidilactici LSF1-1 have the potential for GABA production and probiotic use in future studies.

Soil organic matter dynamics mediated by arbuscular mycorrhizal fungi - an updated conceptual framework.

Arbuscular mycorrhizal (AM) fungi play an important role in soil organic matter (SOM) formation and stabilization. Previous studies have emphasized organic compounds produced by AM fungi as persistent binding agents for aggregate formation and SOM storage. This concept overlooks the multiple biogeochemical processes mediated by AM fungal activities, which drive SOM generation, reprocessing, reorganization, and stabilization. Here, we propose an updated conceptual framework to facilitate a mechanistic understanding of the role of AM fungi in SOM dynamics. In this framework, four pathways for AM fungi-mediated SOM dynamics are included: 'Generating', AM fungal exudates and biomass serve as key sources of SOM chemodiversity; 'Reprocessing', hyphosphere microorganisms drive SOM decomposition and resynthesis; 'Reorganizing', AM fungi mediate soil physical changes and influence SOM transport, redistribution, transformation, and storage; and 'Stabilizing', AM fungi drive mineral weathering and organo-mineral interactions for SOM stabilization. Moreover, we discuss the AM fungal role in SOM dynamics at different scales, especially when translating results from small scales to complex larger scales. We believe that working with this conceptual framework can allow a better understanding of AM fungal role in SOM dynamics, therefore facilitating the development of mycorrhiza-based technologies toward soil health and global change mitigation.

Long-term nitrogen deposition inhibits soil priming effects by enhancing phosphorus limitation in a subtropical forest.

It is widely accepted that phosphorus (P) limits microbial metabolic processes and thus soil organic carbon (SOC) decomposition in tropical forests. Global change factors like elevated atmospheric nitrogen (N) deposition can enhance P limitation, raising concerns about the fate of SOC. However, how elevated N deposition affects the soil priming effect (PE) (i.e., fresh C inputs induced changes in SOC decomposition) in tropical forests remains unclear. We incubated soils exposed to 9 years of experimental N deposition in a subtropical evergreen broadleaved forest with two types of 13 C-labeled substrates of contrasting bioavailability (glucose and cellulose) with and without P amendments. We found that N deposition decreased soil total P and microbial biomass P, suggesting enhanced P limitation. In P unamended soils, N deposition significantly inhibited the PE. In contrast, adding P significantly increased the PE under N deposition and by a larger extent for the PE of cellulose (PEcellu ) than the PE of glucose (PEglu ). Relative to adding glucose or cellulose solely, adding P with glucose alleviated the suppression of soil microbial biomass and C-acquiring enzymes induced by N deposition, whereas adding P with cellulose attenuated the stimulation of acid phosphatase (AP) induced by N deposition. Across treatments, the PEglu increased as C-acquiring enzyme activity increased, whereas the PEcellu increased as AP activity decreased. This suggests that P limitation, enhanced by N deposition, inhibits the soil PE through varying mechanisms depending on substrate bioavailability; that is, P limitation regulates the PEglu by affecting soil microbial growth and investment in C acquisition, whereas regulates the PEcellu by affecting microbial investment in P acquisition. These findings provide new insights for tropical forests impacted by N loading, suggesting that expected changes in C quality and P limitation can affect the long-term regulation of the soil PE.

A proof-of-concept study with SOM3355 (bevantolol hydrochloride) for reducing chorea in Huntington's disease.

AIMS: The study's aim is to investigate the efficacy and safety of SOM3355 (bevantolol hydrochloride), a ß1 -adrenoreceptor antagonist with recently identified vesicular monoamine transporter type 2 inhibitory properties, as a repositioned treatment to reduce chorea in Huntington's disease (HD). METHODS: A randomized, placebo-controlled proof-of-concept study was performed in 32 HD patients allocated to 2 arms of 4 sequential 6-week periods each. Patients received placebo and SOM3355 at 100 and 200 mg twice daily in a crossover design. The primary endpoint was improvement by at least 2 points in the total maximal chorea score in any active drug period compared with the placebo period. RESULTS: The primary endpoint was met in 57.1% of the patients. Improvements ≥3, ≥4, ≥5 and ≥6 points vs. placebo treatment were observed in 28.6, 25.0, 17.9 and 10.7% of the patients, respectively. A mixed-model analysis found a significant improvement in the total maximal chorea score of -1.14 (95% confidence interval, -2.11 to -0.16; P = .0224) with 200 mg twice daily SOM3355 treatment compared with placebo treatment. These results were paralleled by Clinical and Patient Global Impression of Change ratings (secondary endpoints). An elevation in plasma prolactin levels by 1.7-1.9-fold was recorded (P < .005), probably reflecting the effect on the dopamine pathway, consistent with vesicular monoamine transporter type 2 inhibition. The most frequent adverse events during SOM3355 administration were mild to moderate. CONCLUSION: Within the limits of this study, the results suggest that SOM3355 reduces chorea in patients with HD and is well-tolerated. Larger studies are necessary to confirm its therapeutic utility as an antichoreic drug. EudraCT number: 2018-000203-16 and ClinicalTrials.gov Identifier: NCT03575676.

Natural-human driving factors of groundwater salinization in a long-term irrigation area.

Salinization of groundwater is a major challenge for groundwater management in long-term irrigation areas, decoupling its complex influencing factors can provide insights for the sustainable development of irrigation areas. In this study, the natural-human driving factors of groundwater salinization in the Yinchuan Plain, a typical irrigated area, were identified using isotope analysis, information entropy, and self-organizing map. Results show that groundwater in the study area is seriously salinized with obvious spatial heterogeneity. Multiple natural conditions and frequent human activities complicate the salinization characteristics of groundwater. On this basis, four typical natural influence units of groundwater were identified, namely, an evaporation and upward leakage zone, a runoff zone, an evaporation zone, and a runoff and upward leakage zone. Information entropy was proposed to quantify the complexity of groundwater resulting from human activities: The complexity difference between densely populated areas and natural dominant areas is mainly reflected in Na+, SO42-, and Cl-. Multiple human-made drivers of complex water environment were further separated into three patterns by the SOM model: blockage-evaporation type, leakage-evaporation type, and irrigation type. The blockage of drainage ditches and obstruction of salt discharge has the highest impact on the salinization of groundwater, followed by irrigation activities and transportation losses. Water excessive stagnation caused by blockage or irrigation is the root cause of groundwater salinization in the irrigated area, and its impact is greater than that of the traditional understanding of groundwater level rise. Based on the evaluation of irrigation water quality, management initiatives for irrigated areas should prioritize dredging and maintaining a healthy soil and groundwater environment in tandem.

Physically separated soil organic matter pools as indicators of carbon and nitrogen change under long-term fertilization in a Chinese Mollisol.

Isolation and quantification of soil organic matter (SOM) pools under the influence of management practices is needed for assessing the changes in soil fertility. However, the knowledge on how the active, slow and passive pools of SOM respond to long-term fertilization is scarce. Therefore, the present study was designed to isolate the active, slow, and passive pools of soil organic matter through physical fractionation under long-term fertilization. The treatments included; inorganic fertilization (NPK) either alone or combined with a normal dose of manure (MNPK) or a high dose of manure (1.5MNPK) with an unfertilized control (CK) for comparison. The isolated pools were analyzed and compared for their sizes, SOC and TN storage and their contribution to total SOC and TN sequestration. The results revealed that the fertilization enhanced the active, slow and passive pools of SOC and TN and their storage under applied treatments was patterned as 1.5MNK > MNPK > NPK > CK. The highest SOC and TN storage was observed in the active pool, while, greater response to fertilization (in terms of response ratio) was associated with the slow pool. Results show that fertilization enhanced the proportion of SOC and TN stocks to bulk SOC and TN stocks in active and slow pools, while a diminishing trend was found for passive pools. Moreover, the highest response ratio was found for TN sequestration in each pool as compared to SOC, suggesting preferential accumulation of TN over SOC in the studied soil. Nevertheless, the highest SOC and TN storage took place in the active pool. The slow pool showed greater response to applied fertilizer, with the highest values being observed under 1.5MNPK. This study concluded that long-term manure + inorganic fertilization is crucial for enhancing C and N sequestration by altering the size and response of SOM pools.

Abstinence-dependent dissociable central amygdala microcircuits control drug craving.

We recently reported that social choice-induced voluntary abstinence prevents incubation of methamphetamine craving in rats. This inhibitory effect was associated with activation of protein kinase-Cδ (PKCδ)-expressing neurons in central amygdala lateral division (CeL). In contrast, incubation of craving after forced abstinence was associated with activation of CeL-expressing somatostatin (SOM) neurons. Here we determined the causal role of CeL PKCδ and SOM in incubation using short-hairpin RNAs against PKCδ or SOM that we developed and validated. We injected two groups with shPKCδ or shCtrlPKCδ into CeL and trained them to lever press for social interaction (6 d) and then for methamphetamine infusions (12 d). We injected two other groups with shSOM or shCtrlSOM into CeL and trained them to lever press for methamphetamine infusions (12 d). We then assessed relapse to methamphetamine seeking after 1 and 15 abstinence days. Between tests, the rats underwent either social choice-induced abstinence (shPKCδ groups) or homecage forced abstinence (shSOM groups). After test day 15, we assessed PKCδ and SOM, Fos, and double-labeled expression in CeL and central amygdala medial division (CeM). shPKCδ CeL injections decreased Fos in CeL PKCδ-expressing neurons, increased Fos in CeM output neurons, and reversed the inhibitory effect of social choice-induced abstinence on incubated drug seeking on day 15. In contrast, shSOM CeL injections decreased Fos in CeL SOM-expressing neurons, decreased Fos in CeM output neurons, and decreased incubated drug seeking after 15 forced abstinence days. Our results identify dissociable central amygdala mechanisms of abstinence-dependent expression or inhibition of incubation of craving.

Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification.

The objective of this article is to develop a methodology for selecting the appropriate number of clusters to group and identify human postures using neural networks with unsupervised self-organizing maps. Although unsupervised clustering algorithms have proven effective in recognizing human postures, many works are limited to testing which data are correctly or incorrectly recognized. They often neglect the task of selecting the appropriate number of groups (where the number of clusters corresponds to the number of output neurons, i.e., the number of postures) using clustering quality assessments. The use of quality scores to determine the number of clusters frees the expert to make subjective decisions about the number of postures, enabling the use of unsupervised learning. Due to high dimensionality and data variability, expert decisions (referred to as data labeling) can be difficult and time-consuming. In our case, there is no manual labeling step. We introduce a new clustering quality score: the discriminant score (DS). We describe the process of selecting the most suitable number of postures using human activity records captured by RGB-D cameras. Comparative studies on the usefulness of popular clustering quality scores-such as the silhouette coefficient, Dunn index, Calinski-Harabasz index, Davies-Bouldin index, and DS-for posture classification tasks are presented, along with graphical illustrations of the results produced by DS. The findings show that DS offers good quality in posture recognition, effectively following postural transitions and similarities.