Downstream Network Research Articles

Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods.

The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the encoding neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare patterns of responses have been used by neurophysiologists to characterize the accuracy of the sensory responses studied. Among the most widely used analyses, we note methods based on Euclidean distances or on spike metric distances. Methods based on artificial neural networks and machine learning that recognize and/or classify specific input patterns have also gained popularity. Here, we first compare these three strategies using datasets from three different model systems: the moth olfactory system, the electrosensory system of gymnotids, and leaky-integrate-and-fire (LIF) model responses. We show that the input-weighting procedure inherent to artificial neural networks allows the efficient extraction of information relevant to stimulus discrimination. To combine the convenience of methods such as spike metric distances but leverage the advantages of weighting the inputs, we propose a measure based on geometric distances where each dimension is weighted proportionally to how informative it is. We show that the result of this Weighted Euclidian Distance (WED) analysis performs as well or better than the artificial neural network we tested and outperforms the more traditional spike distance metrics. We applied information theoretic analysis to LIF responses and compared their encoding accuracy with the discrimination accuracy quantified through this WED analysis. We show a high degree of correlation between discrimination accuracy and information content, and that our weighting procedure allowed the efficient use of information present to perform the discrimination task. We argue that our proposed measure provides the flexibility and ease of use sought by neurophysiologists while providing a more powerful way to extract relevant information than more traditional methods.

Driving Forces and Influences of Flood Diversion on Discharge Fraction and Peak Water Levels at an H-Shaped Compound River Node in the Pearl River Delta, South China

The SiXianJiao (SXJ) is the first-order exchange node of the West River and the North River and redistributes water (mass) to the downstream river network in the Pearl River Delta (PRD), South China. The lateral SXJ waterway plays a critical role in flow (mass) diversion between the West River and the North River, forming a unique H-shaped compound river node. Previous studies mainly focused on Y-shaped bifurcation and confluence nodes, and there is a lack of research on deltaic H-shaped river nodes. This study established the Delft3D model to investigate the driving forces and influences of flood diversion at the SXJ node. The results showed that the H-shaped SXJ river node was usually in hydraulic equilibrium but was often disturbed by large water level differences between the two rivers, due to unbalanced and asynchronous upstream flood waves. The large water level differences drove mutual flood diversion through the lateral SXJ waterway, which synchronized the downstream discharge and reduced the peak water levels (flood hazards), resulting in similar water levels or hydraulic equilibrium in the two rivers. There exists a critical flow fraction—about 75.9% (West River)—at which the incoming flow from both rivers presents similar water levels at the SXJ node, resulting in little flood diversion. Above the threshold, the flood water will divert from the West River to the North River with a maximum rate of −11,900 m3/s, accounting for 20% of the West River, reducing the peak water level up to 1.48 m at Makou. Below the threshold, the flood water will divert from the North River to the West River with a maximum rate of 11,990 m3/s, accounting for 55% of the North River, reducing the peak water level up to 6.63 m at Sanshui. Meanwhile, the discharge fraction at downstream Makou (Sanshui) maintained a near-constant value during individual floods and fluctuated around 76.6% (23.4%). This critical discharge fraction and the analytical approach are of significance in flood-risk management and hydraulic engineering design in the PRD. The concept model of the H-shaped compound river node clearly elucidates the flood diversion mechanism via the lateral SXJ waterway and may work for other similar river nodes as well.

Deriving efficiency from the major customer network: the role of network connectedness and centeredness

PurposePrior studies on major customer relationships (i.e. embedded ties) focus mostly on the ties between a focal firm and its immediate customers, hindering the understanding of the influence of indirect ties (both upstream and downstream) on a focal firm's operational performance. In this study, the authors analyze how a focal firm's upstream and downstream connectedness and network location affect its productive efficiency.Design/methodology/approachUtilizing Compustat segment files, the authors constructed large-scale major customer networks covering the period 2007–2013. The authors applied a fixed-effect panel stochastic frontier model to conduct estimation. Moreover, the authors applied an endogenous panel stochastic frontier model to ensure the robustness of the main analysis.FindingsThe authors found that a focal firm's upstream and downstream connectedness both have a positive influence on a firm's productive efficiency, whereas a focal firm's centeredness in the major customer network has a negative influence on productive efficiency. Moreover, it was found that centeredness lessens the positive influences of upstream and downstream connectedness on productive efficiency. The post hoc analysis further confirmed that a focal firm's indirect ties, both upstream and downstream, positively influence a focal firm's productive efficiency.Originality/valueThis study contributes to the literature by evaluating the relative effectiveness of a focal firm's direct and indirect major customer ties, both upstream and downstream. More importantly, this study suggests potential exploitation–exploration trade-offs (i.e. productive efficiency vs. innovation) triggered by a firm's network location.

Towards COVID-19 fake news detection using transformer-based models

The COVID-19 pandemic has resulted in a surge of fake news, creating public health risks. However, developing an effective way to detect such news is challenging, especially when published news involves mixing true and false information. Detecting COVID-19 fake news has become a critical task in the field of natural language processing (NLP). This paper explores the effectiveness of several machine learning algorithms and fine-tuning pre-trained transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT) and COVID-Twitter-BERT (CT-BERT), for COVID-19 fake news detection. We evaluate the performance of different downstream neural network structures, such as CNN and BiGRU layers, added on top of BERT and CT-BERT with frozen or unfrozen parameters. Our experiments on a real-world COVID-19 fake news dataset demonstrate that incorporating BiGRU on top of the CT-BERT model achieves outstanding performance, with a state-of-the-art F1 score of 98%. These results have significant implications for mitigating the spread of COVID-19 misinformation and highlight the potential of advanced machine learning models for fake news detection.

CCN-Hippo YAP signaling in vision and its role in neuronal, glial and vascular cell function and behavior.

The retina is a highly specialized tissue composed of a network of neurons, glia, and vascular and epithelial cells; all working together to coordinate and transduce visual signals to the brain. The retinal extracellular matrix (ECM) shapes the structural environment in the retina but also supplies resident cells with proper chemical and mechanical signals to regulate cell function and behavior and maintain tissue homeostasis. As such, the ECM affects virtually all aspects of retina development, function and pathology. ECM-derived regulatory cues influence intracellular signaling and cell function. Reversibly, changes in intracellular signaling programs result in alteration of the ECM and downstream ECM-mediated signaling network. Our functional studies in vitro, genetic studies in mice, and multi omics analyses have provided evidence that a subset of ECM proteins referred to as cellular communication network (CCN) affects several aspects of retinal neuronal and vascular development and function. Retinal progenitor, glia and vascular cells are major sources of CCN proteins particularly CCN1 and CCN2. We found that expression of the CCN1 and CCN2 genes is dependent on the activity of YAP, the core component of the hippo-YAP signaling pathway. Central to the Hippo pathway is a conserved cascade of inhibitory kinases that regulate the activity of YAP, the final transducer of this pathway. Reversibly, YAP expression and/or activity is dependent on CCN1 and CCN2 downstream signaling, which creates a positive or negative feedforward loop driving developmental processes (e.g., neurogenesis, gliogenesis, angiogenesis, barriergenesis) and, when dysregulated, disease progression in a range of retinal neurovascular disorders. Here we describe mechanistic hints involving the CCN-Hippo-YAP regulatory axis in retina development and function. This regulatory pathway represents an opportunity for targeted therapies in neurovascular and neurodegenerative diseases. The CCN-YAP regulatory loop in development and pathology.

HIF-1 signaling: an emerging mechanism for mitochondrial dynamics.

A growing emphasis has been paid to the function of mitochondria in tumors, neurodegenerative disorders (NDs), and cardiovascular diseases. Mitochondria are oxygen-sensitive organelles whose function depends on their structural basis. Mitochondrial dynamics are critical in regulating the structure. Mitochondrial dynamics include fission, fusion, motility, cristae remodeling, and mitophagy. These processes could alter mitochondrial morphology, number, as well as distribution, to regulate complicated cellular signaling processes like metabolism. Meanwhile, they also could modulate cell proliferation and apoptosis. The initiation and progression of several diseases, such as tumors, NDs, cardiovascular disease, were all interrelated with mitochondrial dynamics. HIF-1 is a nuclear protein presented as heterodimers, and its transcriptional activity is triggered by hypoxia. It plays an important role in numerous physiological processes including the development of cardiovascular system, immune system, and cartilage. Additionally, it could evoke compensatory responses in cells during hypoxia through upstream and downstream signaling networks. Moreover, the alteration of oxygen level is a pivotal factor to promote mitochondrial dynamics and HIF-1 activation. HIF-1α might be a promising target for modulating mitochondrial dynamics to develop therapeutic approaches for NDs, immunological diseases, and other related diseases. Here, we reviewed the research progress of mitochondrial dynamics and the potential regulatory mechanism of HIF-1 in mitochondrial dynamics.

An optimization model for the downstream supply chain network, considering consolidated warehouses and the selection of transportation mode

PurposeThe supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.Design/methodology/approachThe model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.FindingsTotal cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.Research limitations/implicationsThe study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.Originality/valueThe issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

Laser capture microdissection-based spatiotemporal transcriptomes uncover regulatory networks during seed abortion in seedless Ponkan (Citrus reticulata).

Seed abortion is an important process in the formation of seedless characteristics in citrus fruits. However, the molecular regulatory mechanism underlying citrus seed abortion is poorly understood. Laser capture microdissection-based RNA-seq combined with Pacbio-seq was used to profile seed development in the Ponkan cultivars 'Huagan No. 4' (seedless Ponkan) (Citrus reticulata) and 'E'gan No. 1' (seeded Ponkan) (C. reticulata) in two types of seed tissue across three developmental stages. Through comparative transcriptome and dynamic phytohormone analyses, plant hormone signal, cell division and nutrient metabolism-related processes were revealed to play critical roles in the seed abortion of 'Huagan No. 4'. Moreover, several genes may play indispensable roles in seed abortion of 'Huagan No. 4', such as CrWRKY74, CrWRKY48 and CrMYB3R4. Overexpression of CrWRKY74 in Arabidopsis resulted in severe seed abortion. By analyzing the downstream regulatory network, we further determined that CrWRKY74 participated in seed abortion regulation by inducing abnormal programmed cell death. Of particular importance is that a preliminary model was proposed to depict the regulatory networks underlying seed abortion in citrus. The results of this study provide novel insights into the molecular mechanism across citrus seed development, and reveal the master role of CrWRKY74 in seed abortion of 'Huagan No. 4'.

A trio of resiliency, reliability, and uncertainty to design and plan the downstream oil supply chain

The main goal of this research is to design and plan a downstream oil supply chain network under mixed uncertainty. To reach this goal, a bi-objective mixed-integer linear model is proposed to make decisions at tactical and strategic levels. Notably, to deal with mixed uncertainty, a hybrid uncertain programming approach including adjustable possibilistic programming, chance-constrained programming, scenario-based programming, and p-robust optimization is developed and applied. Finally, the proposed model is implemented in a real-life case from the National Iranian Oil Products Distribution Company to illustrate the applicability and efficacy of the proposed approach. The analysis of the results indicates that the benefit of the used methods leads to the improvement of network performance against operational and disruption risks. Moreover, the analysis of the results indicates that the tailored approaches improve the total costs, the resiliency of the network, and the reliability of the chain in comparison to the deterministic situation.

Exercise Mitigates Endothelial Pyroptosis and Atherosclerosis by Downregulating NEAT1 Through N6-Methyladenosine Modifications

The benefits of exercise on the cardiovascular system are widely recognized; however, the underlying mechanisms are unknown. Here, we report the effect of the long noncoding RNA NEAT1 (nuclear paraspeckle assembly transcript 1), which is regulated by exercise, on atherosclerosis development after N6-methyladenosine (m6A) modifications. Using clinical cohorts and NEAT1-/- mice, we determined the exercise-mediated expression and role of NEAT1 in atherosclerosis. To investigate the mechanism of epigenetic modification of NEAT1 regulated by exercise, we identified METTL14 (methyltransferase-like 14)-a key m6A modification enzyme under exercise-and found that METTL14 alters the expression and role of NEAT1 through m6A modification and elucidated the specific mechanism of METTL14 in vitro and in vivo. Finally, the NEAT1 downstream regulatory network was investigated. We found that NEAT1 expression was downregulated with exercise and that downregulation of NEAT1 was an important factor in the improvement of atherosclerosis with exercise. Exercise-mediated loss of function of NEAT1 can delay atherosclerosis. Mechanistically, we showed that exercise induced a significant downregulation of m6A modification and METTL14, which binds to the m6A sites of NEAT1 and promotes NEAT1 expression through subsequent YTHDC1 (YTH domain-containing 1) recognition to promote endothelial pyroptosis. Furthermore, NEAT1 induces endothelial pyroptosis by binding KLF4 (Kruppel-like factor 4) to promote the transcriptional activation of the key pyroptotic protein NLRP3 (NOD-like receptor thermal protein domain-associated protein 3), whereas exercise can attenuate NEAT1-mediated endothelial pyroptosis to improve atherosclerosis. Our study of NEAT1 provides new insights into the improvement of atherosclerosis by exercise. This finding demonstrates the role of exercise-mediated NEAT1 downregulation in atherosclerosis while expanding our understanding of the mechanisms by which exercise regulates long noncoding RNA function through epigenetic modifications.

Theoretical upper bound of multiplexing in biological sensory receptors

Biological sensory receptors provide excellent examples of microscopic scale information transduction amidst stochastic noise. We argue that stochasticity is not always a hindrance to sensing. Instead, it could allow a single stochastic sensor to perform multiplexing: simultaneously transducing multiple types of environmental information to the downstream sensory network. Through a Langevin dynamics simulation of a ligand-receptor sensor in a bath of ligands, we demonstrate that a binary-state receptor can simultaneously encode multiple independent environmental variables, such as ligand concentration and the speed of media flow. We develop a general theory of stochastic sensory multiplexing and suggest two theoretical upper bounds. Furthermore, we conjecture that randomly generated sensors typically saturate the tighter upper bound. The theoretical framework developed in this study, which involves a rank-deficient maximum likelihood analysis (rd-MLE), provides a systematic approach to comprehensively assess a sensor's sensory ability without any initial assumptions. This theoretical framework can inspire the design of more efficient artificial sensors.

From degrade to upgrade: Learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution

Over the past few years, single image super-resolution (SR) has become a hotspot in the remote sensing area, and numerous methods have made remarkable progress in this fundamental task. However, they usually rely on the assumption that images suffer from a fixed known degradation process, e.g., bicubic downsampling. To save us from performance drop when real-world distribution deviates from the naive assumption, blind image super-resolution for multiple and unknown degradations has been explored. Nevertheless, the lack of a real-world dataset and the challenge of reasonable degradation estimation hinder us from moving forward. In this paper, a self-supervised degradation-guided adaptive network is proposed to mitigate the domain gap between simulation and reality. Firstly, the complicated degradations are characterized by robust representations in embedding space, which promote adaptability to the downstream SR network with degradation priors. Specifically, we incorporated contrastive learning to blind remote sensing image SR, which guides the reconstruction process by encouraging the positive representations (relevant information) while punishing the negatives. Besides, an effective dual-wise feature modulation network is proposed for feature adaptation. With the guide of degradation representations, we conduct modulation on feature and channel dimensions to transform the low-resolution features into the desired domain that is suitable for reconstructing high-resolution images. Extensive experiments on three mainstream datasets have demonstrated our superiority against state-of-the-art methods. Our source code can be found at https://github.com/XY-boy/DRSR

To the fin and back again: expanding our understanding of congenital limb malformation

Fins are not limbs. However, findings from comparative developmental and genetic analyses show that many of the processes essential in establishing a limb are similar for fin formation. Nevertheless, due to their structural differences, fins are not often used as models to understand the causes or potential treatment of limb disorders.Split hand/foot malformation (SHFM) presents with loss or mis-patterning of elements of hands or feet leading to a ‘clefting-like’ phenotype, often with joining of the digits. This rare disorder is genetically and phenotypically heterogeneous, and knowledge of its etiology remains largely incomplete. Recent work by Truong et al. provides new insight into SHFM by identifying new genetic variants within three different families, predicted to cause changes in function of the PRDM1/Blimp1 gene. PRDM1 is a transcription factor known to be essential for early appendage development in both zebrafish and mice. However, PRDM1 action is nuanced, in part due to its multifaceted roles in integrating signaling, transcription and chromatin state. This complicates the functional characterization of variants in this gene that have unknown significance in SHFM patients. Truong et al. demonstrate the utility of the zebrafish fin as an assay to directly address the function of domain-specific mutations in prdm1a by using the ability of the mutant alleles to rescue fin phenotypes caused by loss of Prdm1a function or, in the case of putative dominant variants, to suppress wildtype zebrafish fin development.A fin may not, or more likely will not, have the complete genetic networks underlying limb development and patterning. However, core mechanisms remain active and, at times, latent in fin development, allowing experimental dissection of mechanisms and gene function. Truong et al. harnessed the experimental malleability of zebrafish to isolate pectoral fin cells in early development and detail Prdm1a-dependent regulation of genes. These data establish aspects of the downstream regulatory network of fin development regulated by Prdm1a, providing insight into the broader array of changes involved in the etiology of this disorder.Core mechanisms and common evolutionary foundations allow for the establishment of defined genetic models of limb disorders in zebrafish and, importantly, accessible experimental models to formulate and test hypotheses on how these disease states are attained. Conversely, identification of disease-associating alleles provides insight into molecular action of genes in development and reveals details of the specificity of their regulation. Fin-in-hand, leading towards new discoveries for congenital limb malformations.

TriBeC: identifying influential users on social networks with upstream and downstream network centrality

The complex heterogeneous nature of social networks generates colossal user data, hence requiring exhaustive efforts to accelerate the propagation of information. This necessitates the identification of central nodes that are considered substantial for information spread and control. Our research proposes a novel centrality metric, TriBeC to identify the significant nodes in online social networks by utilizing the impact of weighted betweenness extended with network quartiles. The proposed approach introduces a user data-driven centrality measure for the discovery of influential nodes in online social networks. This is based on locating the median with the information flowing upstream and downstream, thereby considering the impact of border nodes lying farthest in the network circumference. Experimental outcomes on Twitter, Facebook, BlogCatalog, Scale-free and Random networks show the outperforming results of topmost 1% TriBeC central nodes over existing counterparts in terms of the percentage of the network being infested with information over time.

Few-Shot PolSAR Ship Detection Based on Polarimetric Features Selection and Improved Contrastive Self-Supervised Learning

Deep learning methods have been widely studied in the field of polarimetric synthetic aperture radar (PolSAR) ship detection over the past few years. However, the backscattering of manmade targets, including ships, is sensitive to the relative geometry between target orientation and radar line of sight, which makes the diversity of polarimetric and spatial features of ships. The diversity of scattering leads to a relative increase in the scarcity of PolSAR-labeled samples, which are difficult to obtain. To solve the abovementioned issue and extract the polarimetric and spatial features of PolSAR images better, this paper proposes a few-shot PolSAR ship detection method based on the combination of constructed polarimetric input data selection and improved contrastive self-supervised learning (CSSL) pre-training. Specifically, eight polarimetric feature extraction methods are adopted to construct deep learning network input data with polarimetric features. The backbone is pre-trained with un-labeled PolSAR input data through an improved CSSL method without negative samples, which enhances the representation capability by the multi-scale feature fusion module (MFFM) and implements a regularization strategy by the mix-up auxiliary pathway (MUAP). The pre-trained backbone is applied to the downstream ship detection network; only a few labeled samples are used for fine-tuning and the construction method of polarimetric input data with the best detection effect is studied. The comparison and ablation experiment results on the self-established PolSAR ship detection dataset verify the superiority of the proposed method, especially in the case of few-shot learning.

Cracking the Code of Neuronal Cell Fate.

Transcriptional regulation is fundamental to most biological processes and reverse-engineering programs can be used to decipher the underlying programs. In this review, we describe how genomics is offering a systems biology-based perspective of the intricate and temporally coordinated transcriptional programs that control neuronal apoptosis and survival. In addition to providing a new standpoint in human pathology focused on the regulatory program, cracking the code of neuronal cell fate may offer innovative therapeutic approaches focused on downstream targets and regulatory networks. Similar to computers, where faults often arise from a software bug, neuronal fate may critically depend on its transcription program. Thus, cracking the code of neuronal life or death may help finding a patch for neurodegeneration and cancer.

A novel TF molecular switch-mechanism found in two contrasting ecotypes of a psammophyte, Agriophyllum squarrosum, in regulating transcriptional drought memory

BackgroundPrior drought stress may change plants response patterns and subsequently increase their tolerance to the same condition, which can be referred to as “drought memory” and proved essential for plants well-being. However, the mechanism of transcriptional drought memory in psammophytes remains unclear. Agriophyllum squarrosum, a pioneer species on mobile dunes, is widely spread in Northern China’s vast desert areas with outstanding ability of water use efficiency. Here we conducted dehydration-rehydration treatment on A. squarrosum semi-arid land ecotype AEX and arid land ecotype WW to dissect the drought memory mechanism of A. squarrosum, and to determine the discrepancy in drought memory of two contrasting ecotypes that had long adapted to water heterogeneity.ResultPhysiological traits monitoring unveiled the stronger ability and longer duration in drought memory of WW than that of AEX. A total of 1,642 and 1,339 drought memory genes (DMGs) were identified in ecotype AEX and WW, respectively. Furthermore, shared DMGs among A. squarrosum and the previously studied species depicted that drought memory commonalities in higher plants embraced pathways like primary and secondary metabolisms; while drought memory characteristics in A. squarrosum were mainly related to response to heat, high light intensity, hydrogen peroxide, and dehydration, which might be due to local adaptation to desert circumstances. Heat shock proteins (HSPs) occupied the center of the protein-protein interaction (PPI) network in drought memory transcription factors (TF), thus playing a key regulatory role in A. squarrosum drought memory. Co-expression analysis of drought memory TFs and DMGs uncovered a novel regulating module, whereby pairs of TFs might function as molecular switches in regulating DMG transforming between high and low expression levels, thus promoting drought memory reset.ConclusionBased on the co-expression analysis, protein-protein interaction prediction, and drought memory metabolic network construction, a novel regulatory module of transcriptional drought memory in A. squarrosum was hypothesized here, whereby recurrent drought signal is activated by primary TF switches, then amplified by secondary amplifiers, and thus regulates downstream complicated metabolic networks. The present research provided valuable molecular resources on plants’ stress-resistance basis and shed light on drought memory in A. squarrosum.

The reliability of the trade dependence network in the tungsten industry chain based on percolation

The unbalanced distribution of globe mineral resources can be addressed partly by the worldwide commerce in those resources. To identify significant international trade linkages and assess the reliability of the global tungsten trade, this paper suggests a modified point mutual information (PMI) and percolation approach. To build a directional network of trade dependence between countries (regions) of representative products at each stage of the tungsten industry chain, we calculate the relationships among trade dependence between countries using countries as nodes, trade dependence intensity as connected edges, and dependence intensity as the ability to cope with risk. The percolation process is replicated using various scenarios (risk disruptions) to filter the network's connected edges. The findings indicate that the downstream and overall networks of the tungsten industry chain are more robust to nonsystemic risks than the upstream and midstream trading networks. Furthermore, the countries with significant total imports and exports are near those with high critical link scores. Additionally, a few countries play an essential role in the tungsten industry chain. The critical countries are upstream tungsten-producing countries, including China, Canada, Russia, and Portugal. At the same time, the United States, Germany, and Japan, which rely on a certain level of in-depth processing technology, are critical downstream countries. Finally, increasing the maximum critical link is crucial for enhancing network reliability. Additionally, it is vital to concentrate on the policy directives of countries with high critical link scores because doing so will help the country create relevant policies and plans.

Innovative Out-of-Stock Prediction System Based on Data History Knowledge Deep Learning Processing

Research and development efforts in the field of commercial applications have invested strategic interest in the design of intelligent systems that correctly handle out-of-stock events. An out-of-stock event refers to a scenario in which such customers do not have the availability of the products they want to buy. This scenario generates important economic damage to the producer and to the commercial store. Addressing the out-of-stock problem is currently of great interest in the commercial field as it would allow limiting the economic damages deriving from these events. Furthermore, in the era of online commerce (e-commerce), it would significantly limit out-of-stock events which show a considerable economic impact in the field. For these reasons, the authors proposed a solution based on deep learning for predicting the residual stock amount of a commercial product based on the intelligent analysis of specific visual–commercial data as well as seasonality. By means of a combined deep pipeline embedding convolutional architecture boosted with a self-attention mechanism and a downstream temporal convolutional network, the authors will be able to predict the remaining stock of a particular commodity. By integrating and interpreting climate/seasonal information, customers’ behavior data, and full history data on the dynamics of commercial sales, it will be possible to estimate the residual stock of a certain product and, therefore, define purchase orders efficiently. An accurate prediction of remaining stocks allows an efficient trade order policy which results in a significant reduction in out-of-stock events. The experimental results confirmed the effectiveness of the proposed approach with an accuracy (in the prediction of the remaining stock of such products) greater than 90%.

Manipulation of Signal Gradient and Transcription Factors Recapitulates: Multiple Hypothalamic Identities.

During development, the hypothalamus emerges from the ventral diencephalon and is regionalized into several distinct functional domains. Each domain is characterized by a different combination of transcription factors, including Nkx2.1, Nkx2.2, Pax6, and Rx, which are expressed in the presumptive hypothalamus and its surrounding regions, and play critical roles in defining each area. Here, we recapitulated the molecular networks formed by the gradient of Sonic Hedgehog (Shh) and the aforementioned transcription factors. Using combinatorial experimental systems of directed neural differentiation of mouse embryonic stem (ES) cells, as well as a reporter mouse line and gene overexpression in chick embryos, we deciphered the regulation of transcription factors by different Shh signal intensities. We then used CRISPR/Cas9 mutagenesis to demonstrate the mutual repression between Nkx2.1 and Nkx2.2 in a cell-autonomous manner; however, they induce each other in a non-cell-autonomous manner. Moreover, Rx resides upstream of all these transcription factors and determines the location of the hypothalamic region. Our findings suggest that Shh signaling and its downstream transcription network are required for hypothalamic regionalization and establishment.