Miracle-dz/UniFAR-FTUs huggingface dataset:
AI Based Solution to Optimize the Fertilizer Composition in Hydroponics Agriculture

Precision agriculture is one of the trending research areas in the world. AI technologies have been applied to greenhouse hydroponics agriculture (which comes under precision agriculture) to control the requirements of the plants in greenhouse agriculture. Apart from the environmental conditions such as temperature, humidity, and light intensity, the optimum combination of the fertilizer components, EC, and pH value provided during the plants’ growth phase is vital to obtaining a higher yield during the harvesting period. However, finding the optimal fertilizer composition is challenging due to the fact that the required fertilizer composition is different for different plant types and the experimental cost is higher. Sparse data samples will be received in data-driven experiments because of the large search space (number of permutations) of the fertilizer combinations. This paper presents an experimental design and AI based solution to find the optimal fertilizer composition for the lettuce plants in hydroponics agriculture. Further, this paper presents preliminary ANOVA analysis results and the designed AI algorithm which is based on Bayesian Neural Network, kNN algorithm, and Particle Swarm Optimization algorithm.
Evolutionary Processes and Biodiversity

Freshwater fishes are the world’s most speciose vertebrates and their variety staggers the imagination. There are species with suction-cap oral disks, saw-like rostrums, flamboyant barbels, poisonous spines and even lungs. Some lack eyes and scales; some have fins better adapted to climbing than swimming. Others guard their eggs or young; some keep them in their mouths, in a pouch, or even attached to their heads! Flashes of colour are a feature of many species, but even dull or camouflaged species can have spectacular nuptial displays. Movements can be local sorties, regular forays between fresh water and sea water or opportunistic journeys over thousands of kilometres during floods, and might include burrowing into sediments to survive dry times. Within and between populations there are likely to be variations in such biological traits as appearance, habitat use, diet, spawning, disease resistance, movements and behaviour. The remarkable variability of fishes contributes to ecologically diverse aquatic ecosystems and to diversity in populations, families, genera, species and individuals, collectively ‘biological diversity’ or simply ‘biodiversity’ (Fig. 3.1). This chapter is a primer on genetics for fish ecologists, recognising that advances in genetics are playing an increasing role in our understanding of patterns of biodiversity and underlying evolutionary processes. It describes approaches that (a) document biodiversity, relationships and identity, (b) explore processes such as dispersal and population dynamics and (c) provide data on species, populations and individuals to inform management and conservation planning of Australian freshwater fishes. These approaches are readily translated into questions. When did these species diverge? Are these two forms members of the same species? Is this species diadromous? How many subpopulations, stocks or conservation units are present? Is there evidence of current or historical dispersal between these populations or regions? What larva is that, and what are these fish remains in the gut contents? Is there a different way to tag fish? Did the colourful or the subordinate male sire these offspring? Which broodstock should we spawn? Genetic data can help to answer questions like these.
DSIRBS : A Layer-wise Balanced DNN Weight Pruning Method

Compression has recently become an ever-important step before deploying the deep neural networks into resource-limited hardware. Previous unstructured pruning methods could produce high sparsity, but also suffer from data irregularity which becomes the bottleneck of sparsity computation speedup and bandwidth saving. Share Index Row-Balanced Sparsity (SIRBS) is a compression method solving this problem which shares the indexes between rows in one row cluster. When applied SIRBS to some DNN models, we observe the fact that using a fixed row-cluster size among all layers ignores the variable size of layers and different significance they have. Therefore, a three-stage layer-wise unstructured weights pruning framework called Dynamic Share Index Row-Balanced Sparsity (DSIRBS) is presented in this paper based on past successful practices. DSIRBS could search an appropriate row-cluster size for each layer in order to get a better trade-off of model performance and compression ratio. In the pruning training stage, ADMM method is adopted to solve the sparsity constrained optimization problem. And we propose a criteria called pruning magnitude to estimate accuracy of pruned model based on weight matrix similarity and layer’s significance in the search algorithm, which could reduce the search time about 88 ×. We implement DSIRBS on YOLOv5 and Resnet, the results show that DSIRBS outperforms SIRBS-4 in both compression ratio and model performance.
Enhancing anti-EGFRvIII CAR T cell therapy against glioblastoma with a paracrine SIRPγ-derived CD47 blocker

A major challenge for chimeric antigen receptor (CAR) T cell therapy against glioblastoma (GBM) is its immunosuppressive tumor microenvironment (TME), which is densely populated and supported by protumoral glioma-associated microglia and macrophages (GAMs). Targeting of CD47, a “don’t-eat-me” signal overexpressed by tumor cells, disrupts the CD47-SIRPα axis and induces GAM phagocytic function. However, antibody-mediated CD47 blockade monotherapy is associated with toxicity and low bioavailability in solid tumors. To overcome these limitations, we combined local CAR T cell therapy with paracrine GAM modulation for more effective elimination of GBM. To this end, we engineered a new CAR T cell against epidermal growth factor receptor variant III (EGFRvIII) that constitutively secretes a SIRPγ-related protein (SGRP) with high affinity to CD47. Anti-EGFRvIII-SGRP CAR T cells eliminated EGFRvIII+ GBM in a dose-dependent manner in vitro and eradicated orthotopically xenografted EGFRvIII-mosaic GBM by locoregional application in vivo. This resulted in significant tumor-free long-term survival, followed by partial tumor control upon tumor re-challenge. The combination of anti-CD47 antibodies with anti-EGFRvIII CAR T cells failed to achieve a similar therapeutic effect, underscoring the importance of sustained paracrine GAM modulation. Multidimensional brain immunofluorescence microscopy and in-depth spectral flow cytometry on GBM-xenografted brains showed that anti-EGFRvIII-SGRP CAR T cells accelerated GBM clearance, increased CD68+ cell trafficking to tumor scar sites, and induced myeloid-mediated tumor cell uptake. Additionally, in a peripheral lymphoma mouse xenograft model, anti-CD19-SGRP CAR T cells had superior efficacy compared to conventional anti-CD19 CAR T cells. Validation on human GBM explants revealed that anti-EGFRvIII-SGRP CAR T cells had similar tumor-killing capacity to anti-EGFRvIII CAR monotherapy, but showed a slight improvement in maintenance of tumor-infiltrated CD14+ myeloid cells. Thus, local anti-EGFRvIII-SGRP CAR T cell therapy combines the potent antitumor effect of engineered T cells with the modulation of the surrounding innate immune TME, resulting in the additive elimination of bystander EGFRvIII- tumor cells in a manner that overcomes major mechanisms of CAR T cell therapy resistance, including tumor innate immune suppression and antigen escape.
Prosocial Decision-Making and Human Brain: A Graph Theory Analysis on Resting-State Functional Magnetic Resonance Image

Prosocial behavior is the cornerstone of a harmonious society. However, the functional organization of the brain underlying prosocial decision-making needs to be further explored. Here, we used graph theory analysis to investigate the brain functional connectivity derived from functional magnetic resonance imaging (fMRI) scans during rest in 55 female Chinese university students. The behavioral responses were collected in another fMRI scan during which participants completed an economic exchange game task by making multiple choices between a prosocial and a selfish option against either human counterparts (i.e., social environment) or robot counterparts (i.e., non-social environment). We found that making more prosocial decisions is accompanied by a longer path length in the right anterior superior temporal gyrus (aSTG), higher degree centrality in the posterior cingulate cortex (PCC), and higher betweenness centrality in the left aSTG. Our results suggest that human prosocial decisions are associated with greater inter-regional collaborations that are dominated by a few core nodes within the brain network of Theory of Mind (ToM). Moreover, an individual’s preference for making prosocial decisions could be uncovered by graph theory analysis of the functional brain network even without explicit task requirements.
Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region

Abstract The microbiological characteristics of the grapes are made up of a wide variety of microorganisms, including filamentous fungi. Their presence in grapes is traditionally associated with deterioration in quality. The health of the grapes is very important for obtaining quality wine. The objective of this study was to investigate the diversity of mycobiota on the surface and inside of different grapevine varieties at harvest time in the temperate climate of Slovakia and to identify potentially pathogenic isolates of Aspergillus and Penicillium producing selected mycotoxins. During the 2021 grape harvest, grapes were collected from the Small Carpathians wine region. Eleven grape samples were analyzed by the plating method and plating method with surface disinfection. Emphasis was placed on Aspergillus and Penicillium species because of their importance in mycotoxin production. Of the 605 fungal strains detected, 11 genera were identified in the exogenous mycobiota. The most common and abundant genera were Alternaria and Botrytis. In the genus Aspergillus, A. section Nigri is the most abundant, while in the genus Penicillium, P. raistrickii reached the highest frequency and abundance. Of the 379 strains detected and identified from the endogenous mycobiota, the most common genera were again Alternaria and Botrytis and the most abundant genus was Botrytis. Penicillium species were detected in 17% of all fungi found, with P. raistrickii dominating. The A. section Nigri reached only 4% of the relative density of all isolates. Potentially toxigenic Aspergillus and Penicillium species were tested for toxinogenity by thin layer chromatography. The most important mycotoxin-producing species found were A. section Nigri but without ochratoxin A production.
Lumican silencing ameliorates β-glycerophosphate-mediated vascular smooth muscle cell calcification by attenuating the inhibition of APOB on KIF2C activity

Abstract Adverse cardiovascular events are associated with vascular calcification (VC) process, where vascular smooth muscle cells (VSMCs) differentiate into osteoblastic phenotype and deposit hydroxyapatite crystals. Microtubule-associated protein kinesin family member 2C (KIF2C) expression is decreased obviously in VSMC during calcification induction. Accordingly, we investigate the role and potential mechanism of KIF2C on VSMC calcification. The effects of β-glycerophosphate (β-GP)/KIF2C/lumican (LUM) on calcification, calcium content, alkaline phosphatase (ALP) activity, calcification-related markers, Tubulin, the ratio of polymerized (Po) to free (Fr) tubulin, as well as levels of LUM, apolipoprotein B (APOB), and KIF2C were assessed by Alizarin red S staining, calcium assay kit, ALP assay kit, Western blot, immunofluorescence, and quantitative real-time PCR. The interplay between LUM and APOB was estimated using co-immunoprecipitation and immunofluorescence. As a result, β-GP promoted calcification of human VMSCs (HVMSCs) and repressed KIF2C expression. KIF2C overexpression reversed the effect of β-GP on HVSMCs. LUM silencing attenuated β-GP-induced promotion on HVSMC calcification and increased KIF2C expression by interacting with APOB. Collectively, LUM silencing can alleviate β-GP-induced VSMC calcification through mitigating the repression of APOB on KIF2C expression.
Direct Antigen Presentation is the Canonical Pathway of Cytomegalovirus CD8 T-cell Priming Regulated by Balanced Immune Evasion Mounting a Strong Antiviral Response

CD8 T cells are the main antiviral effectors of the adaptive immune response to cytomegaloviruses (CMVs) in confining acute infection and in long-term surveillance of latent infection. An unresolved issue of debate, with diametrically opposite conclusions, is the mechanism by which CMV-specific naïve CD8 T cells are primed. The idea of priming by antigen cross-presentation through uninfected professional antigen presenting cells taking up viral proteins derived from infected cells was based on the observation that the net response is not improved when direct presentation is enhanced by deletion of viral ‘immune evasion’ genes affecting the classical MHC class-I pathway of antigen presentation. Redundance of priming mechanisms has been demonstrated by experimental models in which either pathway has been rendered inaccessible, so that both pathways are, in principle, capable of priming CD8 T cells. For studying CMV-specific priming in the normal host competent in both antigen presentation pathways, we took the novel approach to enhance instead of delete immune evasion protein expression. Surprisingly, the net magnitude of the CD8 T-cell response in the regional lymph node draining a local site of infection was identical for the contrasts of reduced or enhanced direct antigen presentation. At first glance, this may lead one to conclude that direct presentation plays no role in priming. This interpretation, however, is incompatible with the finding that an intermediate extent of direct antigen presentation, as it exists for wild-type virus, resulted in the best CD8 T-cell response. Our findings thus revealed an essential positive role of a balanced viral immune evasion in mounting a strong antiviral immune response. This novel insight sheds a completely new light on the acquisition of viral immune evasion genes during virus-host co-evolution.
Awakening Giants, Feet of Clay: Assessing the Economic Rise of China and India

Preface vii Chapter 1 Introduction: The Myths Floating around the Giants 1 Chapter 2 Economic Reform and Growth: Differing Patterns and Institutions 19 Chapter 3 Agriculture: Still the Most Crowded Sector 42 Chapter 4 Infrastructure: The Dazzling Difference 54 Chapter 5 High Saving, Low Financial Intermediation 65 Chapter 6 The Pattern of Burgeoning Capitalism 78 Chapter 7 Poverty and Inequality: How Is the Growth Shared? 90 Chapter 8 The Social Sector: The Relevance of the Socialist Legacy 104 Chapter 9 Environment: The Alarming Signs 117 Chapter 10 Looking to the Future: Through the Lens of Political Economy 125 References 161 Index 169
Sexually discordant selection is associated with trait specific morphological changes and a complex genomic response

Sexes often have differing fitness optima, potentially generating intra-locus sexual conflict, as each sex bears a genetic ‘load’ of alleles beneficial to the other sex. One strategy to evaluate conflict in the genome is to artificially select populations discordantly, against established sexual dimorphism, reintroducing attenuated conflict. We investigate a long-term artificial selection experiment reversing sexual size dimorphism in Drosophila melanogaster during ∼350 generations of sexually discordant selection. We explore morphological and genomic changes to identify loci under selection between the sexes in discordantly and concordantly size selected treatments. Despite substantial changes to overall size, concordant selection maintained ancestral sexual dimorphism. However, discordant selection altered size dimorphism in a trait-specific manner. We observe multiple, possible soft selective sweeps in the genome, with size related genes showing signs of selection. Patterns of genomic differentiation between the sexes within lineages identified potential sites maintained by sexual conflict. One discordant selection lineage shows a pattern of elevated genomic differentiation on chromosome 3L, consistent with the maintenance of sexual conflict. Our results suggest measurable signs of conflict and differentially segregating alleles between the sexes due to discordant selection.
A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility (Short Paper)

Flash floods, as a type of devastating natural disasters, can cause significant damage to infrastructure, agriculture, and people’s livelihoods. Mapping flash flood susceptibility has long been an effective measure to help with the development of flash flood risk reduction and management strategies. Recent studies have shown that machine learning (ML) techniques perform better than traditional statistical and process-based models in estimating flash flood susceptibility. However, a major limitation of standard ML models is that they ignore the local geographic context where flash floods occur. To address this limitation, we developed a local Geographically Weighted Random Forest (GWRF) model and compared its performance against other global and local statistical and ML alternatives using an empirical flash floods model of Jiangxi Province, China
Structure et déformation du manteau supérieur de la région Egée-Anatolie par tomographie en ondes de Rayleigh

La tectonique actuelle et recente (~40 Ma) de la region Egee-Anatolie en fait un laboratoire naturel privilegie pour l'etude de la deformation continentale. L'analyse de la structure detaillee et de la deformation du manteau de cette zone de collision continentale immature constitue une etape indispensable pour comprendre les relations entre la cinematique de surface et la dynamique du manteau. La structure du manteau superieur a ete etudiee a l'echelle de la region en realisant un modele 3-D de la vitesse des ondes S par tomographie telesismique en ondes de surface. Les resolutions laterale (~100-200 km) et verticale (~50 km) des images obtenues, grâce a l'utilisation d'un reseau sismologique large bande compose de ~150 stations (permanentes et temporaires (experience SIMBAAD), apporte de nouvelles contraintes sur la structure du manteau de la Grece continentale a l'Anatolie centrale. Cette tomographie revele notamment des correlations verticales claires entre les geometries des dechirures affectant le slab Hellenique et celles des zones de cisaillement trans-tensives qui accommodent le mouvement rapide du bloc Egeen vers le SW. Le slab Chypriote est clairement image dans le modele 3-D comme plisse et dechire en plusieurs segments sous l'Anatolie. L'analyse de l'anisotropie azimutale par methode de reseau sur les ondes de Rayleigh a permis de proposer l'existence de deux couches anisotropes dans le manteau superieur. L'etude de variations laterales de l'anisotropie sous la region suggere que la deformation des slabs et la cinematique de surface sont controlees par des flux mantelliques toroidaux de differentes echelles.
Adaptive polling MAC schemes for IEEE 802.11 wireless LANs

The IEEE 802.11 standard specifies the point coordination function (PCF) in the MAC sublayer for the transfer of real-time traffic, such as voice. The transfer of real-time traffic over wireless LANs is rapidly gaining acceptance, but little work has been done concerning the efficient use of IEEE 802.11 PCF mode to support voice services. In this paper, we propose adaptive polling MAC schemes for IEEE 802.11 PCF. Our proposed schemes reduce the polling overhead significantly, and provide efficient QoS guarantees as other polling protocols do. Our schemes use statistical behaviors of voice traffic sources to increase the performance of PCF mode. We analyze the applicability of our schemes to IEEE 802.11 WLANs and compare them with other related works.
Network coding: An overview

In recent years, network coding has generated much interest in information theory, coding theory, networking, wireless communications, cryptography, and computer science. Consider a point-to-point communication network on which a number of information sources are to be mulitcast to certain sets of destination nodes. The problem is to characterize the maximum possible throughputs. Contrary to one's intuition, network coding theory reveals that it is in general not optimal to regard the information to be multicast as a “fluid” that can simply be routed or replicated. Rather, by employing coding at the nodes, bandwidth can in general be saved. In this talk, we will give an overview of the recent developments in the field and discuss how they can eventually lead to a new information infrastructure.
Edgeworth Expansions for Realized Volatility and Related Estimators ∗

This paper shows that the asymptotic normal approximation is often insufficiently accurate for volatility estimators based on high frequency data. To remedy this, we compute Edgeworth expansions for such estimators. Unlike the usual expansions, we have found that in order to obtain meaningful terms, one needs to let the size of the noise to go zero asymptotically. The results have application to Cornish-Fisher inversion and bootstrapping.
A Conversational Interface Agent for the Export Business Acceleration: A Syﬆematic Review

: Conversational agents, which underﬆand, respond to, and learn from each interaction using Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Multi Dialog Management, and Machine Learning (ML), have become more common in recent years. Conversational agents, also called chatbots, are used to have real-time conversations with individuals. As a result, conversational agents are now being used in various sectors, including education, healthcare, marketing, cuﬆomer assiﬆance, and entertainment. Conversational agents, frequently used as chatbots and virtual or AI helpers, show how computational linguiﬆics is used in everyday life. It can be challenging to pinpoint the variables that aﬀect the use of conversational agents for business acceleration and defend their utility to enhance export companies. This paper summarises the evolution of conversational agents from a ﬆraightforward model to a sophiﬆicated, intelligent syﬆem and how they are applied in various practical contexts. This ﬆudy contributes to the literature on information syﬆems by contraﬆing the diﬀerent conversational agent types based on the export business acceleration interface. This paper also identiﬁes the challenges conversational applications experience today and recommends further research.