Miracle-dz/UniFAR-FTUs huggingface dataset:
CountMeIn: Adaptive Crowd Estimation with Wi-Fi in Smart Cities

The widespread use of pervasive sensing technologies such as wireless sensors and street cameras allows the deployment of crowd estimation solutions in smart cities. However, existing Wi-Fi-based systems do not provide highly accurate crowd size estimation. Furthermore, these systems do not adapt to the dynamic changes in-the-wild, such as unexpected crowd gatherings. This paper presents a new adaptive machine learning system, called CountMeIn, to address the crowd estimation problem using polynomial regression and neural networks. The approach transfers the calibration task from cameras to machine learning after a short training with people counting from stereoscopic cameras, Wi-Fi probe packets, and temporal features. After the training, CountMeIn calibrates Wi-Fi using the trained model and maintains high accuracy for a longer duration without cameras. We test the approach in our pilot study in Gold Coast, Australia, for about five months. CountMeIn achieves 44% and 72% error reductions in minutely and hourly crowd estimations compared to the state-of-the-art methods.
Joint Doppler shift and channel estimation for UAV mmWave system with massive ULA

A joint Doppler shift and channel estimation method for the millimeter-wave communication system of an unmanned aerial vehicle (UAV) equipped with a large-scale uniform linear antenna (ULA) array has been proposed. Since Doppler shift induces intercarrier interference, the parameters of the channel paths have been decomposed into the Doppler shift and the channel information. In order to obtain the Doppler shift, a new estimation algorithm based on a combination of discrete Fourier transform and phase rotation has been proposed, which can determine the appropriate number of antennas. In addition to estimating the channel information, a low-complexity joint Doppler shift and channel estimation method has been designed that can quickly obtain accurate estimates. Furthermore, the achievable sum rate, the theoretical bounds of the mean squared errors, and the Cramér-Rao lower bounds of the estimation method have been derived. The analysis and simulation results prove that the performance of the proposed approach is close to the theoretical inference.
A 4.6‐GHz low‐phase‐noise differential common‐gate Clapp VCO with simplified common‐mode biasing

We present a new differential Clapp voltage‐controlled oscillator (VCO) suitable for low‐voltage scaled‐down CMOS technology. For loss compensation, the Clapp VCO uses a common‐gate topology that provides higher transconductance than that of the conventional common‐drain configuration. To provide reliable start‐up condition for the Clapp VCO, a small inductor is added at the gate of a transistor in the common‐gate configuration. The new configuration also simplifies DC biasing while providing a suitable common‐mode point for varactor tuning. All of these features allow the Clapp VCO to operate under low‐voltage and low‐power conditions. The fabricated Clapp VCO using 0.18‐μm RFCMOS achieves an excellent phase noise performance of −121 dBc/Hz at a 1 MHz offset from 4.6 GHz. Under a supply voltage of 1.0 V and a power consumption of 4.2 mW, the figure‐of‐merit (FOM) is −188 dBc/Hz.
Proteomics and Co-expression Network Analysis Reveal the Importance of Hub Proteins and Metabolic Pathways in Nicotine Synthesis and Accumulation in Tobacco (Nicotiana tabacum L.)

Nicotine is a unique alkaloid present in tobacco that is widely used in cigarettes and in the agricultural, chemical, and pharmaceutical industries. However, the research on nicotine is mostly limited to its synthesis pathways, and only a few studies have explored the effects of other metabolic pathways on nicotine precursors. Regulating the nicotine content in tobacco can greatly promoting the application of nicotine in other fields. In this study, we performed global data-independent acquisition proteomics analysis of four tobacco varieties. Of the four varieties, one had high nicotine content and three had a low nicotine content. A total of 31,259 distinct peptides and 6,018 proteins across two samples were identified. A total of 45 differentially expressed proteins (DEPs) co-existed in the three comparison groups and were mainly involved in the transport and metallic processes of the substances. Most DEPs were enriched in the biosynthesis of secondary metals, glutathione metabolism, carbon metabolism, and glycolysis/gluconeogenesis. In addition, the weighted gene co-expression network analysis identified an expression module closely related to the nicotine content (Brown, r = 0.74, P = 0.006). Gene Ontology annotation and Kyoto Encyclopaedia of Genes and Genomes enrichment analysis showed that the module proteins were mainly involved in the synthesis and metabolism of nicotine precursors such as arginine, ornithine aspartate, proline, and glutathione. The increased levels of these precursors lead to the synthesis and accumulation of nicotine in plants. More importantly, these proteins regulate nicotine synthesis by affecting the formation of putrescine, which is the core intermediate product in nicotine anabolism. Our results provide a reference for tobacco variety selection with a suitable nicotine content and regulation of the nicotine content. Additionally, the results highlight the importance of other precursor metabolism in nicotine synthesis.
Benefits of Parasitism in Polluted Environments: A Review and Perspectives

The frequency and strength of biotic interactions are thought to be shaped by environmental conditions. In this study, we reviewed and discussed the potential effects of toxic chemicals in driving shifts along the parasite-mutualist continuum. Some parasites have the astonishing capacity to accumulate trace metals and organic pollutants from various taxa within freshwater, marine, and terrestrial ecosystems. Recent studies have provided evidence of clear benefits for the host: when exposed to contaminants, infected organisms exhibited reduced contamination levels, less severe oxidative stress, and histological alterations, as well as higher body condition and survival rate compared with their uninfected conspecifics. Such effects might arise when the costs of parasitism are lower than their benefits in specific environmental conditions. Assessing the potential outcomes for parasites exploiting contaminated hosts is a crucial but neglected issue, since ecotoxicological effects on parasites may alter interspecific relationships. We identified possible avenues for future research using innovative tools and long-term experimental manipulations of both parasitism and pollution to better understand how toxic chemicals can modulate the strength and direction of host-parasite interactions.
A Hydraulic Hybrid Vehicle Simulation Program To Enhance Understanding Of Engineering Fundamentals

Mark Schumack, University of Detroit Mercy Mark Schumack is Professor of Mechanical Engineering at the University of Detroit Mercy, where he teaches courses in heat transfer, thermodynamics, fluid mechanics, and energy systems. His ongoing pedagogical interests include developing ways to teach energy conservation and sustainability principles. He has held several leadership positions in the Energy Conversion and Conservation Division of ASEE. His research interests include thermal/fluid modeling using computational techniques, with applications in the automotive, manufacturing, and energy fields. Dr. Schumack earned his BS, MS, and Ph.D. degrees in Mechanical Engineering from the University of Michigan.
Enteric Glial Cells in Immunological Disorders of the Gut

Enteric glial cells (EGCs) are one of the major cell types of neural crest lineage distributed in the gastrointestinal tract. EGCs represent an integral part of the enteric nervous system (ENS) and significantly outnumber ENS neurons. Studies have suggested that EGCs would exert essential roles in supporting the survival and functions of the ENS neurons. Notably, recent evidence has begun to reveal that EGCs could possess multiple immune functions and thereby may participate in the immune homeostasis of the gut. In this review article, we will summarize the current evidence supporting the potential involvement of EGCs in several important immunological disorders, including inflammatory bowel disease, celiac disease, and autoimmune enteropathy. Further, we highlight critical questions on the immunological aspects of EGCs that warrant future research attention.
Evaluating Cross-Applicability of Weed Detection Models Across Different Crops in Similar Production Environments

Convolutional neural networks (CNNs) have revolutionized the weed detection process with tremendous improvements in precision and accuracy. However, training these models is time-consuming and computationally demanding; thus, training weed detection models for every crop-weed environment may not be feasible. It is imperative to evaluate how a CNN-based weed detection model trained for a specific crop may perform in other crops. In this study, a CNN model was trained to detect morningglories and grasses in cotton. Assessments were made to gauge the potential of the very model in detecting the same weed species in soybean and corn under two levels of detection complexity (levels 1 and 2). Two popular object detection frameworks, YOLOv4 and Faster R-CNN, were trained to detect weeds under two schemes: Detect_Weed (detecting at weed/crop level) and Detect_Species (detecting at weed species level). In addition, the main cotton dataset was supplemented with different amounts of non-cotton crop images to see if cross-crop applicability can be improved. Both frameworks achieved reasonably high accuracy levels for the cotton test datasets under both schemes (Average Precision-AP: 0.83–0.88 and Mean Average Precision-mAP: 0.65–0.79). The same models performed differently over other crops under both frameworks (AP: 0.33–0.83 and mAP: 0.40–0.85). In particular, relatively higher accuracies were observed for soybean than for corn, and also for complexity level 1 than for level 2. Significant improvements in cross-crop applicability were further observed when additional corn and soybean images were added to the model training. These findings provide valuable insights into improving global applicability of weed detection models.
Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention

Maize leaf diseases significantly reduce maize yield; therefore, monitoring and identifying the diseases during the growing season are crucial. Some of the current studies are based on images with simple backgrounds, and the realistic field settings are full of background noise, making this task challenging. We collected low-cost red, green, and blue (RGB) images from our experimental fields and public dataset, and they contain a total of four categories, namely, southern corn leaf blight (SCLB), gray leaf spot (GLS), southern corn rust (SR), and healthy (H). This article proposes a model different from convolutional neural networks (CNNs) based on transformer and self-attention. It represents visual information of local regions of images by tokens, calculates the correlation (called attention) of information between local regions with an attention mechanism, and finally integrates global information to make the classification. The results show that our model achieves the best performance compared to five mainstream CNNs at a meager computational cost, and the attention mechanism plays an extremely important role. The disease lesions information was effectively emphasized, and the background noise was suppressed. The proposed model is more suitable for fine-grained maize leaf disease identification in a complex background, and we demonstrated this idea from three perspectives, namely, theoretical, experimental, and visualization.
Unconventional Pathways of Protein Secretion: Mammals vs. Plants

In eukaryotes, many proteins contain an N-terminal signal peptide that allows their translocation into the endoplasmic reticulum followed by secretion outside the cell according to the classical secretory system. However, an increasing number of secreted proteins lacking the signal peptide sequence are emerging. These proteins, secreted in several alternative ways collectively known as unconventional protein secretion (UPS) pathways, exert extracellular functions including cell signaling, immune modulation, as well as moonlighting activities different from their well-described intracellular functions. Pathways for UPS include direct transfer across the plasma membrane, secretion from endosomal/multivesicular body-related components, release within plasma membrane-derived microvesicles, or use of elements of autophagy. In this review we describe the mammals and plants UPS pathways identified so far highlighting commonalities and differences.
How Do Green Innovation Strategies Contribute to Firm Performance Under Supply Chain Risk? Evidence From China’s Manufacturing Sector

With environmental issues increasingly becoming prominent in today’s business world, firms may need to pay extra attention to developing their environmental strategies and capabilities in response to environmental concerns and achieving sustainable growth. While a broad consensus exists on the value of green innovation, current empirical research on how different types of green innovation strategies may account for the international performance of a firm remains scant. Addressing this gap is important because determining how to better manage a firm’s green innovation strategies nowadays has become increasingly important for firms hoping to achieve and maintain their sustainable performance advantages. This study aims to bridge this gap by systematically examining how various types of green innovation strategies (i.e., green product, green process, and green service innovations) can be beneficial to firms in an emerging market economy. This study also examined the important role that potential risks of supply chain play in shaping the relationships between various types of green innovation strategies and firm performance. This study proposes that the effective management of supply chain risks may be important to the successful implementation of green innovation strategies because green innovation has increasingly become a collaborative effort. This study empirically tested the hypotheses by gathering survey data from a sample of 337 firms in China’s manufacturing industries. Results demonstrate that the green innovation strategies of firms are positively related to their firm performance. Additionally, the potential risks faced by the firms in efficiently and effectively managing their supply chain significantly moderate the impact of green product innovation and green process innovation strategies on their firm performance. This study not only offers useful theoretical implications for the green innovation strategy research and for better and effective supply chain risk management. It also provides important practical guidelines and managerial actions that practicing managers can implement to accelerate their green innovation strategy, assess the effect of supply chain risks, and thus improve firm performance in the post-pandemic era.
Streptomyces marincola sp. nov., a Novel Marine Actinomycete, and Its Biosynthetic Potential of Bioactive Natural Products

Marine actinomycetes are an important source of antibiotics, but many of them are yet to be explored in terms of taxonomy, ecology, and functional activity. In this study, two marine actinobacterial strains, designated SCSIO 64649T and SCSIO 03032, were isolated, and the potential for bioactive natural product discovery was evaluated based on genome mining, compound detection, and antimicrobial activity. Phylogenetic analysis of the 16S rRNA gene sequences showed that strain SCSIO 64649T formed a single clade with SCSIO 03032 (similarity 99.5%) and sister clades with the species Streptomyces specialis DSM 41924T (97.1%) and Streptomyces manganisoli MK44T (96.8%). The whole genome size of strain SCSIO 64649T was 6.63 Mbp with a 73.6% G + C content. The average nucleotide identity and digital DNA–DNA hybridization between strain SCSIO 64649T and its closest related species were well below the thresholds recommended for species delineation. Therefore, according to the results of polyphasic taxonomy analysis, the strains SCSIO 64649T and SCSIO 03032 are proposed to represent a novel species named Streptomyces marincola sp. nov. Furthermore, strains SCSIO 64649T and 03032 encode 37 putative biosynthetic gene clusters, and in silico analysis revealed that this new species has a high potential to produce unique natural products, such as a novel polyene polyketide compounds, two mayamycin analogs, and a series of post-translationally modified peptides. In addition, other important bioactive natural products, such as heronamide F, piericidin A1, and spiroindimicin A, were also detected in strain SCSIO 64649T. Finally, this new species’ metabolic crude extract showed a strong antimicrobial activity. Thanks to the integration of all these analyses, this study demonstrates that the novel species Streptomyces marincola has a unique and novel secondary metabolite biosynthetic potential that not only is beneficial to possible marine hosts but that could also be exploited for industrial applications.
Editorial: A Journey Through 50 Years of Structural Bioinformatics in Memoriam of Cyrus Chothia

Department of Biostatistics and Health Informatics, King’s College London, London, United Kingdom, Department of Basic and Clinical Neuroscience, King’s College London, MauriceWohl Clinical Neuroscience Institute, London, United Kingdom, King’s College London, National Institute for Health Research Biomedical Research Centre and Dementia Unit at South London and Maudsley NHS Foundation Trust King’s College London, London, United Kingdom, Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark, Department of Statistics, University of Oxford, Oxford, United Kingdom, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, United States, European Synchrotron Radiation Facility, Grenoble, France, Wellcome Sanger Institute, Hinxton, United Kingdom, Theory of Condensed Matter Group, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, United Kingdom
White Matter Tracts Associated With Deep Brain Stimulation Targets in Major Depressive Disorder: A Systematic Review

Background Deep brain stimulation (DBS) has been proposed as a last-resort treatment for major depressive disorder (MDD) and has shown potential antidepressant effects in multiple clinical trials. However, the clinical effects of DBS for MDD are inconsistent and suboptimal, with 30–70% responder rates. The currently used DBS targets for MDD are not individualized, which may account for suboptimal effect. Objective We aim to review and summarize currently used DBS targets for MDD and relevant diffusion tensor imaging (DTI) studies. Methods A literature search of the currently used DBS targets for MDD, including clinical trials, case reports and anatomy, was performed. We also performed a literature search on DTI studies in MDD. Results A total of 95 studies are eligible for our review, including 51 DBS studies, and 44 DTI studies. There are 7 brain structures targeted for MDD DBS, and 9 white matter tracts with microstructural abnormalities reported in MDD. These DBS targets modulate different brain regions implicated in distinguished dysfunctional brain circuits, consistent with DTI findings in MDD. Conclusions In this review, we propose a taxonomy of DBS targets for MDD. These results imply that clinical characteristics and white matter tracts abnormalities may serve as valuable supplements in future personalized DBS for MDD.
The Impact of Positive Verbal Rewards on Organizational Citizenship Behavior—The Mediating Role of Psychological Ownership and Affective Commitment

Organizational citizenship behavior (OCB) can foster organizational competitiveness and survival especially, facing a rapidly changing environment. There are some empirical pieces of research that shed light on the effects of OCB on extrinsic rewards, since OCB, through performance appraisal, affects extrinsic rewards which will influence OCB as well. However, researchers have overlooked the reverse effect of extrinsic rewards (i.e., positive verbal rewards) on OCB. It is necessary to explore the mechanism between positive verbal rewards and OCB. This study integrated psychological ownership and affective commitment to form a structural model based on social exchange theory and cognitive evaluation theory. These results show that positive verbal rewards are positively correlated with psychological ownership, psychological ownership is positively correlated with affective commitment and OCB, and affective commitment is positively correlated with OCB. As refers to the mediating effects, psychological ownership fully mediates the relationship between positive verbal rewards and affective commitment. Furthermore, affective commitment plays a partial mediating role in the effect of psychological ownership on OCB. Accordingly, this psychological mechanism between positive verbal rewards and OCB reveals important theoretical and practical implications.
Eugenol Improves Follicular Survival and Development During in vitro Culture of Goat Ovarian Tissue

This study evaluated the effects of different concentrations (10, 20, or 40 μM) of eugenol (EUG 10, EUG 20, or EUG 40), ascorbic acid (50 μg/mL; AA) or anethole (300 μg/mL; ANE 300) on the in-vitro survival and development of goat preantral follicles and oxidative stress in the cultured ovarian tissue. Ovarian fragments from five goats were cultured for 1 or 7 days in Alpha Minimum Essential Medium (α-MEM+) supplemented or not with AA, ANE 300, EUG 10, EUG 20 or EUG 40. On day 7 of culture, when compared to MEM, the addition of EUG 40 had increased the rate of follicular development, as observed by a decrease in the proportion of primordial follicles alongside with an increase in the rate of normally developing follicles. Furthermore, EUG 40 significantly increased both follicular and oocyte diameters. Subsequently, ovarian fragments from three goats were cultured for 1 or 7 days in α-MEM+ supplemented or not with AA, ANE 300 or EUG 40. All tested antioxidants, except ANE 300, were able to significantly decrease the levels of reactive oxygen species in the ovarian tissue, but EUG 40 could most efficiently neutralize free radicals. All ovarian tissues cultured in the presence of antioxidants, especially EUG 40, presented a significant decrease in H3K4me3 labeling, indicating a silencing of genes that play a role in the inhibition of follicular activation and apoptosis induction. When compared to cultured control tissues, both EUG 40 and ANE 300 significantly increased the intensity of calreticulin labeling in growing follicles. The mRNA relative expression of ERP29 and KDM3A was significantly increased when the culture medium was supplemented with EUG 40, indicating a response to ER stress experienced during culture. In conclusion, EUG 40 improved in-vitro follicle survival, activation and development and decreased ROS production, ER stress and histone lysine methylation in goat ovarian tissue.