Miracle-dz/UniFAR-FTUs huggingface dataset:
β-Catenin Expression and Its Association with Prognostic Factors in Hepatocellular Carcinoma: A Study on Alpha-fetoprotein, Histologic Grade, and Microvascular Invasion

Background. Hepatocellular carcinoma (HCC), the most common primary liver cancer. In addition to its high incidence, the disease burden is high due to its poor prognosis and high recurrence rate. Some of the currently known clinicopathologic prognostic factors include alpha-fetoprotein (AFP) level, histologic grade, and microvascular invasion. At the molecular level, β-catenin is one of the most common driver mutation found in HCC. The Wnt/β-catenin pathway regulates cellular processes related to initiation, growth, survival, migration, differentiation, and apoptosis. Although the underlying pathogenesis of hepatocarcinogenesis is known, clinical application warrants a greater understanding of the molecular characteristics and tumor phenotype, especially for determining the prognosis. This study aims to analyze the expression of β-catenin and its association with AFP, histologic grade, and microvascular invasion. Materials and methods. Thirty-five samples of surgically resected HCCs at Cipto Mangunkusumo National Referral Hospital were examined. Diagnoses were made based on histopathological and immunohistochemical findings followed by β-catenin staining. β-catenin expression was analyzed to determine difference between variables. Results and conclusions. Here we show that β-catenin expression is significantly associated with low serum alpha-fetoprotein and well to moderate differentiation Implications. Strong nuclear β-catenin expression implies better prognosis in HCC.
A Comprehensive Survey on Nanophotonics Neural Networks

: In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides, and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of Neural Networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization has been realized along with the study of special issues such as optical activation functions and cost functions.
Screening for genotypic variation in potato for phosphorus efficiency

Potato has higher phosphorus requirement for optimum growth and yield. One option to sustain productivity on soil poor in P availability is to use P-efficient genotypes/cultivars. Thus, a pot experiment was conducted in growth chamber to screen genotypes for P efficiency. To meet the objective, 20 potato genotypes were grown in soil under two P levels, 100 mg kg-1 soil (low P) and 700 mg P kg-1 soil (high P). Treatments were arranged in RCB design with 6 replications. Almost for all genotypes, high P level increased shoot dry matter yield, relative growth rate of shoot, shoot P concentration and total P uptake. The shoot dry matter yield ranged from 924.8 mg plant-1 for genotype 1 to 21.3 mg plant-1 for genotype 20 while RGR varied from 1.26 g g-1 day-1 for genotype 12 to 0.08 g g-1 day-1 for genotype 20 at low P level. Shoot P concentration was weakly correlated with shoot dry matter yield (R2 = 0.33) while total P uptake showed significant positive correlation with shoot dry matter yield (R2 = 0.94). Results revealed that genotypes 12, 11, 10, 3 16, 13, 1 and 4 were P-efficient whereas genotypes 9, 2,7,19,17,18, 8,14,15 and 20 were Pinefficient in terms of both shoot dry matter yield and relative growth rate at low P level.
Construction and Verification of a Hypoxia-Stemness-Based Gene Signature for Risk Stratification in Esophageal Cancer

Background Numerous studies have shown that esophageal cancer (ESCA) contains areas of intertumoral hypoxia. It is widely accepted that the association of hypoxia with cancer stemness in the tumor microenvironment of ESCA is of profound clinical significance. However, reliable prognostic signatures based on hypoxia and cancer stemness are still lacking in ESCA. Material/Methods The t-SNE algorithm was used to estimate the hypoxia status based on the transcriptome profiles of the discovery cohort in the TCGA database. Median values of the stemness index were used to group and identify stemness-associated differentially expressed genes (DEGs). The LASSO method and Cox regression model were combined to screen for prognostic genes and to establish a genetic signature based on hypoxia-stemness. The robustness of the prognostic model was then tested in an external independent validation cohort of the GEO database. Results A total of 8 genes – FBLN2, IL17RB, CYP2W1, AMTN, FABP1, FOXA2, GAS1, and CTSF – were identified to construct a gene signature for ESCA risk stratification. Overall survival was significantly lower in the high-risk group than in the low-risk group in both the internal discovery set and the external validation set. The risk score was found to be an independent prognostic factor for ESCA patients. In addition, a higher risk score was significantly associated with the sensitivity of ESCA patients to gefitinib, bexarotene, dasatinib, and imatinib. Conclusions The hypoxia-stemness-based genetic signature established for the first time in our study could be a promising tool for ESCA cancer risk stratification.
Current progress in biosensors for organophosphorus pesticides based on enzyme functionalized nanostructures: a review.

Organophosphorus pesticides analysis has become an increasingly significant research area due to their widespread application and contamination of the environment. With the worsening of environmental contamination, the control of pesticides is important to both human health and environment protection. Distinct from traditional methods of pesticides detection, application of electrochemical biosensors based on enzyme-functionalized nanostructures in the field of pesticides analysis is promising. This review mainly introduces the recent achievements and problems in biosensors based on enzyme-functionalized nanostructures for organophosphorus pesticides detection, such as efficiency and high sensitivity, and meanwhile the problem of practicability is also discussed.
How do feedback seekers think? Disparate cognitive pathways towards incremental and radical creativity

ABSTRACT We explored the effects of feedback-seeking behaviour (FSB) on two types of creativity, radical and incremental, and directed our attention to the cognitive processes that mediate the relationship between FSB and creativity. On the basis of the dual pathway to creativity model, we propose that feedback-seeking (FS) frequency and breadth are associated with cognitive flexibility and persistence, which are related to the radical and incremental creativity of feedback seekers. In Study 1, our analysis of the field data comprising 239 employees from 95 teams revealed that FS frequency is positively related to cognitive flexibility and persistence while FS breadth is positively related to cognitive flexibility. Only cognitive flexibility mediates the relationship between FSB and radical creativity. In Study 2, our online vignette studies revealed the indirect effects of FS breadth on radical and incremental creativity through cognitive flexibility and the indirect effect of FS frequency on radical creativity through cognitive persistence. This study advances the literature on FSB and creativity by developing and validating a theoretical model that considers the intervening cognitive mechanisms and the various forms of creativity.
Graphite and Graphene-Oxide based PGM-free model catalysts for the Oxygen Reduction Reaction

The world currently relies heavily on fossil fuels such as coal, oil, and natural gas for its energy. Fossil fuels are non-renewable, that is, they draw on finite resources that will eventually dwindle, becoming too expensive or too environmentally damaging to retrieve. One alternative source of energy are fuel cells, electrochemical devices that convert chemical energy to cleanly and efficiently produce electricity. They can be used in a wide range of applications, including transportation, stationary, portable and emergency power sources. Their development has been slowed by the high cost of PGM electrocatalysts needed at both electrodes as well as sluggish oxygen reduction reaction (ORR) occurring at the cathode. To replace the costly PGM-based materials, a new generation of PGM-free ORR catalysts has emerged, composed by earth abundant elements such as carbon, nitrogen and transition metals. Current heterogeneous PGM-free
Visible-light active and magnetically recyclable Ag-coated Fe3O4/TiO2 nanocomposites for efficient photocatalytic oxidation of 2,4-dichlorophenol

Fe3O4/TiO2/Ag nanocomposites with different amounts of silver were prepared using the sol–gel method. X-ray diffraction, energy-dispersive X-ray spectroscopy, field-emission scanning electron microscopy, transmission electron microscopy, diffuse reflectance spectroscopy, Brunauer–Emmett– Teller surface areas, and the vibration sample magnetometry were used to characterize these nanocomposites. Photocatalytic activity of the nanocomposites was examined via decomposing the solution of 2,4-dichlorophenol (2,4-DCP) exposed to UV/Vis irradiation. Exposing the solution to pure TiO2 and ternary Fe3O4/TiO2/Ag nanocomposite, we obtained 30% and almost 60% degradation of 2,4-DCP after 180 min irradiation, respectively. The excellent photocatalytic activity of Fe3O4/TiO2/Ag samples can be related to the surface plasmon resonance effect of Ag nanoparticles deposited on Fe3O4/TiO2 nanocomposite. The Fe3O4/TiO2/Ag nanocomposites were retrieved from the solution of reaction through a constant magnetic bar, followed by assessing their photocatalytic activity after three repetitive recycling cycles. Afterward, this nanocomposite might be applied as an efficient and reusable photocatalyst.
Resiliency against malicious agents in maximum-based consensus

In this paper, we develop distributed algorithms for achieving resilient consensus via the maximum value-based approach when adversarial agents may be present in the network. The adversaries intend to prevent the nonfaulty, normal agents from reaching consensus. We extend the class of resilient methods known as the mean subsequence reduced (MSR) algorithms, where the agents make selections on their neighbours' information at the time of their updates so as to reduce the influence of the malicious agents. In particular, the normal agents try to arrive at the maximum value of their initial states. Due to the malicious agents, the exact maximum may not be reached, the advantage of the approach is the finite-time convergence. We present both synchronous and asynchronous versions of the update schemes and characterize graph theoretic conditions for achieving resilient consensus. A numerical example is provided to illustrate our results.
Ultrasound Cyclo Plasty for Treatment of Surgery-Naïve Open-Angle Glaucoma Patients: A Prospective, Multicenter, 2-Year Follow-Up Trial

Background: The purpose of this prospective study was to evaluate the efficacy and safety of the Ultrasound Cyclo Plasty (UCP) procedure using high-intensity focused ultrasound in surgery-naïve open-angle glaucoma patients. Methods: prospective, non-randomized, single-arm, multicenter clinical trial. Sixty-six eyes with primary open-angle glaucoma, intraocular pressure (IOP) ≥21 mmHg and with no history of filtering surgery were enrolled. Patients were treated by UCP with a therapy probe comprising six piezoelectric transducers, consecutively activated for 8 s each. Complete ophthalmic examination was performed before the procedure, 1 day after the procedure, and 1, 3, 6, 12, 18 and 24 months after the procedure. Primary outcomes were complete success (defined as IOP lowering from baseline ≥20% without additional glaucoma medications) and vision-threatening complications. Secondary outcomes were the presence of complications and the reduction of the number of medications used. Results: IOP was significantly reduced after one procedure (p < 0.05), from a mean pre-operative value of 24.3 ± 2.9 mmHg (n = 2.3 hypotensive medications) to a mean value of 15.9 ± 3.6 mmHg (n = 2.2 hypotensive medications) at 2 years (mean IOP lowering of 33%). Surgical success was achieved in 74% of eyes. Notwithstanding side effects such as transient anterior chamber inflammation, refractive error changes, transient hypotony and macular edema, no major intra or post-operative complications such as phthisis, induced cataract, neovascularization or significant vision loss were observed. Conclusions: Ultrasound Cyclo Plasty is a valuable, effective and well-tolerated procedure to lower IOP in patients with open-angle glaucoma without previous filtering surgery.
Middle Miocene marine flooding: New 40Ar/39Ar age constraints with integrated biostratigraphy on tuffs from the North Croatian Basin

In the North Croatian Basin which is located in the southwestern part of the Pannonian Basin System, Miocene tuff deposits have been observed at several localities in the area of Banovina, Medvednica Mt. and Slavonia. Here we present new 40Ar/39Ar age results obtained from volcanic glass from the Laz tuff (15.42 ± 0.15 Ma) intercalated with lacustrine freshwater/brackish deposits, the Jovac tuff (15.10 ± 0.06 Ma) intercalated with lacustrine freshwater deposits, the Čučerje tuff (14.81 ± 0.08 Ma) and the Nježić tuff (14.40 ± 0.03 Ma) both deposited in a marine environment. Fossil data (calcareous nannofossils/foraminifera) from the underlying and overlying beds of the tuffs from Čučerje and Nježić match the geochronological data i.e. NN5 zone and M6 zone were determined. Integration of biostratigraphic and geochronological data enable a better understanding of the NCB sedimentary evolution and constrain the Middle Miocene marine flooding event in the marginal areas of the western part of the NCB at ~15 Ma i.e. early/middle Badenian boundary. These results together with the existence of lower Badenian marine sediments in the Sava depression (in the southern part of NCB) suggest it is possible to conclude that during the early Badenian in the NCB, freshwater lacustrine and marine environments coexisted.
Bakterie kwasu octowego jako potencjalne probiotyki nowej generacji

Bakterie kwasu octowego (ang. AAB – Acetic Acid Bacteria) występują powszechnie w środowisku. Biorą udział w procesach fermentacji żywności, na której występują naturalnie lub są dodawane celowo dla uzyskania produktu o pożądanych właściwościach. Bakterie te są izolowane m.in. z roślin i produktów z nich wytwarzanych, z fermentowanej żywności i napojów. Mikroorganizmy te w procesie fermentacji wytwarzają wiele związków, wśród których można wyróżnić kwasy glukuronowy i glukonowy, a także inne kwasy organiczne oraz witaminy. Z przedstawionego przeglądu literatury naukowej wynika, że bakterie kwasu octowego mogą mieć zastosowanie terapeutyczne oraz biotechnologiczne i technologiczne. AAB mają duże znaczenie dla przemysłu spożywczego, zarówno jako bakterie korzystne, jak i szkodliwe w wielu procesach produkcyjnych, w łańcuchu dostaw i podczas przechowywania produktów. Pomimo dużego potencjału i szerokich możliwości aplikacyjnych wykorzystywanie bakterii kwasu octowego na skalę masową jest jednak wciąż ograniczone. W ostatnich latach duże zainteresowanie wzbudzają bakterie wykazujące właściwości prozdrowotne i probiotyczne, jednak, aby dany szczep został uznany za probiotyczny, musi spełnić określone wymagania. Bakterie kwasu octowego nie kolonizują jelita grubego, ale wykazują wiele właściwości prozdrowotnych i terapeutycznych, co uzasadnia możliwość ich zaliczenia do grupy probiotyków nowej generacji.
Shadows Don't Lie: n-Sequence Trajectory Inspection for Misbehaviour Detection and Classification in VANETs

This paper presents a machine learning approach to detect and classify misbehaviour in Vehicular Ad- hoc Networks. We describe three novel features obtained from analysis of n consecutive locations of a vehicle to form a judgement about its behaviour. These features are used in two machine learning algorithms (K-Nearest Neighbour and Support Vector Machine) for detecting attacks in the VeReMi dataset. We show that the overall precision rates can be as high as 99.7%, whilst the recall rates are consistently higher than 99%. The features we propose also help to reduce the overall confusion rate to less than 4.7% when classifying different types of attacks. We also show that our models can be used for effective classification after as few as 3 observations, suggesting the potential for application of the method in near real-time situations thereby improving safety and security.
A High-accuracy and Semi-dense Feature-based VSLAM System

SLAM (Simultaneous Localization and Mapping) technology is the basis of a mobile robot, furthermore the most important condition for robot intelligence. VSLAM (Visual Simultaneous Localization and Mapping) [1] technology uses visual sensors to build incremental environment maps and simultaneously realize the self-positioning of a moving robot. Currently, feature-based VSLAM systems tend to build sparse maps, but sparse maps are not accurate enough for navigation and obstacle avoidance applications. Therefore, this paper proposed a high-accurate and semi-dense VSLAM system with a monocular camera. The proposed work used polar line searching and block matching modules to help feature-based VSLAM systems accurately measure depth information. Experimental results show that the proposed semi-dense VSLAM system can reconstruct more dense point cloud maps. The synchronization tracking trajectory accuracy of the system is 9.13% higher than that of the ORB-SLAM2 system.
Decision-Making Framework for Implementing Safer Human–Robot Collaboration Workstations: System Dynamics Modeling

Human–Robot Collaboration (HRC) systems are often implemented seeking for reducing risk of Work-related Musculoskeletal Disorders (WMSD) development and increasing productivity. The challenge is to successfully implement an industrial HRC to manage those factors, considering that non-linear behaviors of complex systems can produce counterintuitive effects. Therefore, the aim of this study was to design a decision-making framework considering the key ergonomic methods and using a computational model for simulations. It considered the main systemic influences when implementing a collaborative robot (cobot) into a production system and simulated scenarios of productivity and WMSD risk. In order to verify whether the computational model for simulating scenarios would be useful in the framework, a case study in a manual assembly workstation was conducted. The results show that both cycle time and WMSD risk depend on the Level of Collaboration (LoC). The proposed framework helps deciding which cobot to implement in a context of industrial assembly process. System dynamics were used to understand the actual behavior of all factors and to predict scenarios. Finally, the framework presented a clear roadmap for the future development of an industrial HRC system, drastically reducing risk management in decision-making.
Supporting Risk-Aware Use of Online Translation Tools in Delivering Mental Healthcare Services among Spanish-Speaking Populations

Neural machine translation technologies are having increasing applications in clinical and healthcare settings. In multicultural countries, automatic translation tools provide critical support to medical and health professionals in their interaction and exchange of health messages with migrant patients with limited or non-English proficiency. While research has mainly explored the usability and limitations of state-of-the-art machine translation tools in the detection and diagnosis of physical diseases and conditions, there is a persistent lack of evidence-based studies on the applicability of machine translation tools in the delivery of mental healthcare services for vulnerable populations. Our study developed Bayesian machine learning algorithms using relevance vector machine to support frontline health workers and medical professionals to make better informed decisions between risks and convenience of using online translation tools when delivering mental healthcare services to Spanish-speaking minority populations living in English-speaking countries. Major strengths of the machine learning classifier that we developed include scalability, interpretability, and adaptability of the classifier for diverse mental healthcare settings. In this paper, we report on the process of the Bayesian machine learning classifier development through automatic feature optimisation and the interpretation of the classifier-enabled assessment of the suitability of original English mental health information for automatic online translation. We elaborate on the interpretation of the assessment results in clinical settings using statistical tools such as positive likelihood ratios and negative likelihood ratios.
An exploration of government administrative processes in supporting agro-smallholders’ access to municipal markets in eThekwini Municipality, KwaZulu-Natal Province

Background: The government’s inability to ensure that both the local and the national markets adequately accommodate smallholder producers is inhibiting the sector’s ability to grow and expand.Aim: This article explored the influence of the governmental administrative processes on agro-smallholders’ ability to access municipal markets.Setting: The study is based in KwaZulu-Natal (KZN), specifically in the eThekwini Municipality. The target population is agricultural (agro-) smallholder producers who are located within the traditional leadership and government officials (KZN Department of Agriculture and Rural Development and eThekwini Municipality). Methods: A qualitative exploratory research methodology using semi-structured interviews was adopted for this study.Results: The results showed that the government structures and municipal markets do not provide substantial support to agro-smallholder producers who are seeking access to markets. The results further showed that there is no integration or relationship between the government institutions on the issues related to the promotion and development of agro-smallholder producers.Conclusion: Through the application of administrative theory, the article posited the recommendations that the government institutions should incorporate stakeholders’ insights, lay a policy foundation for a whole-of-government approach to planning and set the direction for agro-smallholder’s planned future. It is also recommended that the government departments create a single planning and coordination structure or forum that will be beneficial to the smallholder sub-sector and avoid duplication and resource wastage directed to this sub-sector.