Miracle-dz/UniFAR-FTUs huggingface dataset:
Screening of Endophytic Bacteria Isolated from Weed Plant to Biocontrol Stem Rot Disease on Pitaya (Hylocereus undatus)

Endophytic bacteria from weed are emerging as valuable alternatives for biochemical pesticides in sustainable agriculture. This study aims to discover the antagonistic effects of some endophytic bacteria isolated from weed (Lactuca indica) against A. alternata, a casual of stem end rot disease of pitaya. A total of 14 endophytic bacteria were isolated and four of them presented in vitro antagonistic activity against A. alternata. Of four, strain L115 significantly inhibited the pathogenic growth with a mean inhibition diameter of 12.67 ± 0.02 mm, while the other three (strain L110, L111, and L114) showed a weak inhibition. The results indicated that strain L115 could belong to the Bacillaceae family while the other three (strain L110, L111, and L114) had characteristics of the Pseudomonadaceae family. Interestingly, strain L115 showed positive results for phosphate solubilization, indole acetic acid (IAA), and biofilm production, whereas three other strains presented weak capabilities for phosphate solubilization, biofilm production and IAA production. In addition, the filtrate of strain L115 presented antifungal activity on biocontrol tests in vitro. Especially, strain L115 HIGHLIGHTS • First report of endophytic bacteria isolated from weed (Lactuca indica) • Endophytic bacteria, L115 strain, inhibit significantly the development of Alternaria alternata. • L115 strain promotes the growth parameters of multiple plants compared to the control. Trung, D.Q. ; et al. 2 Brazilian Archives of Biology and Technology. Vol.65: e22200749, 2022 www.scielo. br/babt significantly increased seedling biomass of pitaya and tomato compared to the control. Hence, these results suggest strain L115 has the potential to be used as biocontrol agents against A. alternata. More studies should be done in the future to evaluate their efficiency in field conditions.
Laparoscopic management of a giant challenging ovarian dermoid cyst: A case report

Background: Dermoid cyst accounts for 10-20 % of all ovarian tumors. Traditionally giant ovarian cysts have been removed through a midline laparotomy. Here we report a case of laparoscopy-assisted removal of a giant dermoid cyst in an adolescent girl. Case Presentation: A 17-year-old girl presented with a complaint of abdominal distension for 7 months. Abdominal examination revealed a well-defined tense cystic mass arising from the pelvis and extending up to the epigastric region. CT scan of abdomen and pelvis reported a mature teratoma of size 25.5x19. 9x35cm of the right ovary. Laparoscopic excision of the cyst was performed following extracorporeal drainage of the cyst. Conclusion: Laparoscopy-assisted of a giant ovarian dermoid cyst is a feasible mode of treatment irrespective of its size provided malignancy has been ruled out.
Rapid, Sensitive On-Site Detection of Deoxynivalenol in Cereals Using Portable and Reusable Evanescent Wave Optofluidic Immunosensor

This paper develops an improved portable and reusable evanescent wave optofluidic immunosensor (OIP-v2) for rapid and sensitive on-site determination of deoxynivalenol (DON), one of the most frequently detected mycotoxins mainly produced by Fusarium species. Using the bifunctional reagent N,N′-Disuccinimidyl carbonate, deoxynivalenol-bovine-serum-albumin (DON-BSA) were covalently modified onto a bio-probe surface as biorecognition elements, whose robustness allowed it to perform multiple detections without significant activity loss. An indirect competitive immunoassay strategy was applied for DON detection. Under optimal conditions, the limit of detection of 0.11 μg/L and the linear dynamic detection range of 0.43 to 36.61 μg/L was obtained when the concentration of the Cy5.5-anti-DON antibody was 0.25 μg/mL. The OIP-v2 was also applied to detect DON in various cereals, and the recoveries ranged from 81% to 127%. The correlation between OIP-v2 and enzyme-linked immunosorbent assay (ELISA) through the simultaneous detection of maize-positive samples was in good agreement (R2 = 0.9891).
From DARVO to Distress: College Women’s Contact with their Perpetrators after Sexual Assault

ABSTRACT College women face high rates of sexual violence and rarely report their experiences to school officials. When victims do report, their cases infrequently result in their perpetrators’ expulsion. As such, many victims continue to attend school with their perpetrators. We are not aware of any academic research that has explored the experiences of these college women. Furthermore, previous research on how perpetrators behave after acts of violence suggests that individuals who commit sexual assault may try to influence their victims after the assault by denying the assault, attacking the victim verbally, and reversing the victim and offender roles (a pattern referred to by the acronym DARVO). This study explores the experiences of 89 women who were sexually assaulted during college, asking to what extent they experience continued exposure to their perpetrators, whether such contact includes patterns characteristic of DARVO, and how they perceive such contact. Most participants experienced some contact with their perpetrator after their assault, and nearly half indicated experiencing DARVO tactics from their perpetrator. Approximately half of those who experienced contact saw it as having a negative effect on their wellbeing. We also explored participants’ written descriptions via directed content analysis, which underscored the quantitative trends. Victims’ experiences of contact with their perpetrators warrant further exploration, particularly in contexts where contact may be inevitable.
Laplacian face blending

Designing realistic tridimensional facial models is a challenging task, not only due to the effort and artistic abilities required but also because human visual perception is very tuned to the processing of facial features. For this reason, rather than creating face models from scratch, artists usually start from a scanned model of a real person. In this work, we present a novel method for blending human faces in order to create a new one. In a nutshell, our proposal uses Laplacian smoothing to segregate layers of details from one or more faces, which are then integrated into a base face with the help of an interactive and visual editor. In particular, our method supports blending multiple faces and multiple sub‐regions in those faces. Since our approach is intuitive and relatively easy to implement, it can be integrated into artistic pipelines aiming at designing human face models from preexisting ones.
Laccases and Tyrosinases in Organic Synthesis

Laccases (Lac) and tyrosinases (TYR) are mild oxidants with a great potential in research and industry. In this work, we review recent advances in their use in organic synthesis. We summarize recent examples of Lac-catalyzed oxidation, homocoupling and heterocoupling, and TYR-catalyzed ortho-hydroxylation of phenols. We highlight the combination of Lac and TYR with other enzymes or chemical catalysts. We also point out the biological and pharmaceutical potential of the products, such as dimers of piceid, lignols, isorhamnetin, rutin, caffeic acid, 4-hydroxychalcones, thiols, hybrid antibiotics, benzimidazoles, benzothiazoles, pyrimidine derivatives, hydroxytyrosols, alkylcatechols, halocatechols, or dihydrocaffeoyl esters, etc. These products include radical scavengers; antibacterial, antiviral, and antitumor compounds; and building blocks for bioactive compounds and drugs. We summarize the available enzyme sources and discuss the scalability of their use in organic synthesis. In conclusion, we assume that the intensive use of laccases and tyrosinases in organic synthesis will yield new bioactive compounds and, in the long-term, reduce the environmental impact of industrial organic chemistry.
Screening of periodontitis-related diagnostic biomarkers based on weighted gene correlation network analysis and machine algorithms.

BACKGROUND Periodontitis is a common oral immune inflammatory disease and early detection plays an important role in its prevention and progression. However, there are no accurate biomarkers for early diagnosis. OBJECTIVE This study screened periodontitis-related diagnostic biomarkers based on weighted gene correlation network analysis and machine algorithms. METHODS Transcriptome data and sample information of periodontitis and normal samples were obtained from the Gene Expression Omnibus (GEO) database, and key genes of disease-related modules were obtained by bioinformatics. The key genes were subjected to Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and 5 machine algorithms: Logistic Regression (LR), Random Forest (RF), Gradient Boosting Decisio Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). Expression and correlation analysis were performed after screening the optimal model and diagnostic biomarkers. RESULTS A total of 47 candidate genes were obtained, and the LR model had the best diagnostic efficiency. The COL15A1, ICAM2, SLC15A2, and PIP5K1B were diagnostic biomarkers for periodontitis, and all of which were upregulated in periodontitis samples. In addition, the high expression of periodontitis biomarkers promotes positive function with immune cells. CONCLUSION COL15A1, ICAM2, SLC15A2 and PIP5K1B are potential diagnostic biomarkers of periodontitis.
Deoxyelephantopin and Its Isomer Isodeoxyelephantopin: Anti-Cancer Natural Products with Multiple Modes of Action

Cancer is a leading cause of morbidity and mortality worldwide. The development of cancer involves aberrations in multiple pathways, representing promising targets for anti-cancer drug discovery. Natural products are regarded as a rich source for developing anti-cancer therapies due to their unique structures and favorable pharmacology and toxicology profiles. Deoxyelephantopin and isodeoxyelephantopin, sesquiterpene lactone compounds, are major components of Elephantopus scaber and Elephantopus carolinianus, which have long been used as traditional medicines to treat multiple ailments, including liver diseases, diabetes, bronchitis, fever, diarrhea, dysentery, cancer, renal disorders, and inflammation-associated diseases. Recently, deoxyelephantopin and isodeoxyelephantopin have been extensively explored for their anti-cancer activities. This review summarizes and discusses the anti-cancer activities of deoxyelephantopin and isodeoxyelephantopin, with an emphasis on their modes of action and molecular targets. Both compounds disrupt several processes involved in cancer progression by targeting multiple signaling pathways deregulated in cancers, including cell cycle and proliferation, cell survival, autophagy, and invasion pathways. Future directions of research on these two compounds towards anti-cancer drug development are discussed.
Lecture 13: Learning with Data Types 4: Graphs and relations

Figure 1: Simple graph The vertices of this graph are V = 1, 2, 3, 4 and edges E = (1, 2), (2, 3), (2, 4), (3, 4). We do not add pairs like (4, 2) since we assume this graph is undirected. The graph can also be a directed graph if we draw directed edges. We can use an adjacency matrix as Figure 2 below to represent the graph. The adjacency matrix is a square matrix whose elements indicate whether pairs of vertices are connected. Aij is 1 if there is a connection from vertex i to j
Activation of the VQ Motif-Containing Protein Gene VQ28 Compromised Nonhost Resistance of Arabidopsis thaliana to Phytophthora Pathogens

Nonhost resistance refers to resistance of a plant species to all genetic variants of a non-adapted pathogen. Such resistance has the potential to become broad-spectrum and durable crop disease resistance. We previously employed Arabidopsis thaliana and a forward genetics approach to identify plant mutants susceptible to the nonhost pathogen Phytophthora sojae, which resulted in identification of the T-DNA insertion mutant esp1 (enhanced susceptibility to Phytophthora). In this study, we report the identification of VQ motif-containing protein 28 (VQ28), whose expression was highly up-regulated in the mutant esp1. Stable transgenic A. thaliana plants constitutively overexpressing VQ28 compromised nonhost resistance (NHR) against P. sojae and P. infestans, and supported increased infection of P. parasitica. Transcriptomic analysis showed that overexpression of VQ28 resulted in six differentially expressed genes (DEGs) that are involved in the response to abscisic acid (ABA). High performance liquid chromatography-mass spectrometry (HPLC-MS) detection showed that the contents of endogenous ABA, salicylic acid (SA), and jasmonate (JA) were enriched in VQ28 overexpression lines. These findings suggest that overexpression of VQ28 may lead to an imbalance in plant hormone homeostasis. Furthermore, transient overexpression of VQ28 in Nicotiana benthamiana rendered plants more susceptible to Phytophthora pathogens. Deletion mutant analysis showed that the C-terminus and VQ-motif were essential for plant susceptibility. Taken together, our results suggest that VQ28 negatively regulates plant NHR to Phytophthora pathogens.
AINTINGS OF USIC

Paintings of music are a significant presence in modern art. They are crossmodal representations, aimed at representing music, say, musical works or forms, using colours, lines and shapes in the visual modality. This paper aims to provide a conceptual framework for understanding paintings of music. Using examples from modern art, the paper addresses the question of what a painting of music is. Implications for the aesthetic appreciation of paintings of music are also drawn.
Modelling of spatial and spatio-temporal non-Gaussian data with FRK

Non-Gaussian spatial and spatial-temporal data are becoming increasingly prevalent, arising from studies as diverse as small area demographics (counts) and global remote sensing (radiant energies). FRK is an R package for spatial/spatio-temporal modelling and prediction with large data sets. In this vignette, we provide examples where we model non-Gaussian data using FRK version 2.x and above. All of the functionality that FRK oﬀers in a Gaussian setting extends to a non-Gaussian setting: See also the vignette “FRK intro”, which describes how the BAUs and basis functions work; inference over diﬀerent spatial manifolds (such as the sphere); inference in a spatio-temporal setting; and spatial change of support.
بررسی اثر کود شیمیایی و زیستی فسفر بر فعالیت آنزیمهای آنتیاکسیدان و برخی صفات بیوشیمیایی گلرنگ بهاره (Carthamus tinctorius L.) تحت تنش کمبود آب

ب ه سررب روظنم ی ک رثا ـ اهدو ي ز ی تس ی ش و ی م ی ا یی لاعف رب رفسف ی ـ زنآ ت ی م اه ي تنآ ی سکا ی کـشخ شنـت رد گنرلگ ناد ی امزآ ی ـ ش ی ـ ب ه تروـص لپسا ی ت روتکاف ی كولب حرط بلاق رد ل اه ي فداصت لماک ی اب هـس ـ عارز لاـس رد رارـکت ی 91 1390 ـ عرزم رد ، ۀ ـ شهوژپ ی هدکـشناد زرواشک ي ش هاگشناد درگ ارجا دها ی د . لصا لماع ی کشخ شنت لماش ی رد هس حطس : بآ ی را ي لماک ی شنت نودب ا ) بآ ی را ي لخت ساـسارب ـ ی ه 50 فرظ تبوطر دصرد ی عارز ت ی ( کشخ شنت ، ی ور دشر لحارم رد ی ش ی از و ی ش ی ) بآ ی را ي لخت ساسارب ی ه 75 فرظ تبوطر دصرد ی عارز ت ی ( و عرف لماع ی ب ه روتکاف تروص ی لماش ل 6 ت ی هک رام هس ش دوک نآ حطس ی م ی ا یی داقم اب رفسف ی ر ) ،رفص 50 و 100 ک ی رت تافسف رپوس مرگول ی لـپ راتکه رد ( ز دوک و ی تس ی فسف ر روراب 2 رد ، ود حطس ) قلت ی قلت نودب و ح ی روراب اب ح 2 ( دوب . اتن ساسارب ی ج لاعف ی زنآ ت ی م اه ي ـ تنآ ی ـ سکا ی ناد ت تحت أ ث ی لااب فرصم ر ي ت رد رفسف دوک ی ب مدع رام ه گراک ی ر ي وک د ز ی تس ی ارش رد ی ور شنت ط ی ش ی از و ی ش ی ازفا ی ش ی تفا . اـتن ی داد ناـشن ج قلت ی ز دوک ح ی تس ی روراب رفسف 2 ازفا ثعاب رفسف دوک زا هدافتسا نودب ی لاعف ش ی زنآ ت ی م و زلااتاک م ی ئتورپ ناز ی ن اه ي ارش رد گرب لولحم ی ط شنت لحارم رد ور ی ش ی از و ی ش ی درگ ی د . مه رد ی ارش ن ی ،ط س رد رفسف دوک فرصم ز دوک حوط ی تس ی ب ی رتش ی ـ ت ن أ ث ی ازـفا رد ار ر ی لاعف ش ـ ی ت زنآ ی م اه ي لپ ی سکا لنف ی سکارپوس و زاد ی د د ی تشاد زاتومس . اقم ی م هس ی گنا ی ن اه قلت هک داد ناشن ی ز دوک ح ی تس ی روراب رفسف 2 ارش رد ی شنـت ط از ی ش ی نعم شهاک ببس رفسف دوک زا هدافتسا نودب ی راد م ی ناز آد نولام ل هد ی دش د . ا رد ی ررب ن س ی م ی ب ناوت ی قلت هک تشاد نا ی هراهب گنرلگ ح ز دوک اب ی تس ی روراب رفسف 2 کشخ شنت حوطس رد رفسف دوک اب هارمه ی م ی شان ءوس تارثا دناوت ی دهد شهاک ار شنت زا .
QubitE: Qubit Embedding for Knowledge Graph Completion

Knowledge graph embeddings (KGEs) learn 001 low-dimensional representations of entities 002 and relations to predict missing facts based 003 on existing ones. Quantum-based KGEs uti-004 lize variational quantum circuits for link pre-005 diction and score triples via the probability dis-006 tribution of measuring the qubit states. But 007 current quantum-based KGEs either lose quan-008 tum advantages during optimizing, or require 009 a large number of parameters to store quan-010 tum states, thus leading to overﬁtting and 011 low performance. Besides, they ignore the-012 oretical analysis which are essential for un-013 derstanding the model performance. To ad-014 dress performance issue and bridge theory 015 gap, we propose QubitE which is lightweight 016 and suitable for link prediction task. In ad-017 dition, our model preserves quantum advan-018 tages which enable quantum logical comput-019 ing based on semantics. Furthermore, we 020 prove that (1) QubitE is full-expressive; (2) 021 QubitE can infer various relation patterns 022 including symmetry/antisymmetry, inversion, 023 and commutative/non-commutative composi-024 tion; (3) QubitE subsumes several existing 025 approaches, e.g. DistMult, pRotatE, RotatE, 026 TransE and ComplEx; (4) QubitE owns lin-027 ear space complexity and linear time com-028 plexity. Experiments on multiple benchmark 029 knowledge graphs demonstrate that QubitE 030 can achieve comparable results to the state-of-031 the-art classical models. 032
SketchGen: Generating Constrained CAD Sketches Supplementary Material

In this document we provide additional details and experiments. We give a description of the primitives and constraints used, deﬁne the full grammar of our sequences and the key notation used in the paper. A description of the complete experimental setup and the metrics used to measure the quality of different sampling strategies is also provided. Additionally, we provide more quantitative and qualitative results for our sampled sketches and provide a few examples of failure cases. We also include additional experiments with conditional versions of our model, that are conditioned on sketch images
Closed-Form Analytical Expression for the Electric Field Distribution in an Ideal Stripline

– In this article, we propose a closed-form explicit solution for the electric ﬁeld distribution on an ideal stripline. The solution is obtained by applying an exponential conformal transformation to the stripline geometry. The results obtained are in close agreement with numerical simulations. The exact expression presented here can be used to estimate power ratings, voltage breakdown thresholds, characteristic impedance, per-unit-length capacitance, background ﬁeld values, and ﬁeld enhancement factors in stripline geometries.
Attention Guided Deep Supervision Model for Prostate Segmentation in Multisite Heterogeneous MRI Data

Prostate cancer and benign prostatic hyperplasia are common diseases in men and require early and accurate diagnosis for optimal treatment. Standard diagnostic tests such as the prostate-specific antigen test and digital rectal examination are inconvenient. Thus, non-invasive methods such as magnetic resonance imaging (MRI) and automated image analysis are increasingly utilised to facilitate and improve prostate diagnostics. Segmentation is a vital part of the prostate image analysis pipeline, and deep neural networks are now the tool of choice to automate this task. In this work, we benchmark various deep neural networks for 3D prostate segmentation using four different publicly available datasets and one private dataset. We show that popular networks such as U-Net trained on one dataset typically generalise poorly when tested on others due to data heterogeneity. Aiming to address this issue, we propose a novel deep-learning architecture for prostate whole-gland segmentation in T2-weighted MRI images that exploits various techniques such as pyramid pooling, concurrent spatial and channel squeeze and excitation, and deep supervision. Our extensive experiments demonstrate that it performs superiorly without requiring special adaptation to any specific dataset.
Disturbance Compensation-Based Nonlinear Control for Pneumatic Artificial Muscle Systems With Hardware Experiments

Owing to variable stiffness, natural compliance, and similarities with skeletal muscles, pneumatic artificial muscles (PAMs) have been widely utilized in the fields of bionic robots, medical rehabilitation, and industrial manufacturing. However, in addition to high nonlinearities, time variation, and uncertainties, PAMs are extremely sensitive to external disturbances in practical applications, most of which are unknown, complicated, and constantly changing. Regarding these challenging issues, a disturbance compensation-based robust control method is proposed in this paper, which realizes satisfactory tracking control of PAM systems without a prior knowledge of exact models. Particularly, without assuming that disturbances or their first-order derivatives are invariable, a high-order disturbance observer is proposed to estimate lumped disturbances and their nth-order derivatives. On this basis, a nonlinear feedback controller is designed to simultaneously ensure that the observation errors converge to zero in finite time and the tracking error converges to zero asymptotically. Further, based on Lyapunov techniques, the stability analysis for the equilibrium point of the closed-loop system is provided in detail. Finally, the practicability and the robustness of the proposed method are validated by hardware experiments on a self-built horizontal PAM platform.
Global Nonsingular Fast Terminal Sliding Mode Control of Aerodynamic Heat Simulation Heating System Based on Linear Extended State Observer

In this paper, a novel global nonsingular fast terminal sliding mode control (GNFTSMC) method based on linear extended state observer (LESO) is proposed for Aerodynamic Heat Simulation Heating System (AHSHS). The global nonsingular fast terminal sliding mode surface (GNFTSMS), comprising constant reaching law and LESO, establishes a closed-loop control for the whole control process. Firstly, the designed GNFTSMS eliminates the reaching stage, so that the whole control process is confined on the sliding mode surface. The fast term and nonsingular term ensure convergence speed in the sliding phase, and eliminate the singularity and suppress the chattering phenomenon. Then, the constant reaching law can effectively solve the problem of convergence stagnation in the control process and further accelerate the convergence speed. Then, LESO can feed back the observation of input disturbances and external disturbances to the control system. Furthermore, Lyapunov stability theorem is utilized to prove the stability of the closed-loop system. Finally, the simulation results verify that the proposed GNFTSMC method based on LESO is superior.
Robust Decentralized Fuzzy Controller Design for Nonlinear Large-Scale Interconnected Descriptor Systems

This paper dealt with the robust control problem for fuzzy large-scale descriptor systems (FLSDS) with uncertainties. First, each nonlinear subsystem of the large-scale descriptor system was described as a FLSDS with interconnectivity and uncertainty. Based on the proportional-plus-derivative state feedback (PD) method, the decentralized fuzzy controller in each subsystem was designed to stabilize the whole system. In order to solve the uncertainties problem in the control system simply, a benefits robust approach has been proposed. The final stability conditions can be recast into the linear matrix inequality (LMI) problem by the Schur complement. At last, a numerical example was given to illustrate the control design procedure and its effectiveness.