Miracle-dz/UniFAR-FTUs huggingface dataset:
RAB20 Promotes Proliferation via G2/M Phase through the Chk1/cdc25c/cdc2-cyclinB1 Pathway in Penile Squamous Cell Carcinoma

Simple Summary There is no available biomarker that could be used to predict the prognosis or progression of penile squamous cell carcinoma (PSCC) currently. Here, we conducted comprehensive genomic sequencing on eight pairs of PSCC tissues and found that RAB20 was upregulated in tumors, especially in metastatic lymph nodes. We then detected the expression of RAB20 in a large cohort of 259 PSCC cases using IHC assay and analyzed the association between the RAB20 expression and clinical features. We found that high RAB20 expression predicted poor prognosis and advanced clinicopathological features. Further in vitro and in vivo tumorigenesis assays revealed that RAB20 regulates cell proliferation at the G2/M cell cycle phase through the Chk1/cdc25c/cdc2-cyclinB1 pathway. Our results indicated that RAB20 could be a promising prognostic biomarker of advanced PSCC with poor survival and could be a potential therapeutic target. Abstract RAB20, a member of the RAS GTPase oncogene family, is overexpressed in several cancers with poor outcomes, promoting tumorigenesis and inducing genomic instability. Here, we performed comprehensive genomic sequencing on eight penile squamous cell carcinoma (PSCC) and normal tissue pairs and found that RAB20 was upregulated in tumors, especially in metastatic lymph nodes. RAB20 overexpression in tumors was further verified by qPCR, Western blotting, and immunohistochemistry of our newly established PSCC cell lines and paired tissues. The clinical significance of RAB20 was validated in 259 PSCC patients, the largest cohort to date, and high RAB20 expression positively correlated with the T, N, M status, extranodal extension, and clinical stage (all p < 0.01). RAB20 was an unfavorable independent prognostic indicator in the survival analysis (p = 0.011, HR = 2.090; 95% Cl: 1.183–4.692), and PSCC patients with high RAB20 expression experienced shorter 5-year cancer-specific survival times (p < 0.001). Furthermore, tumorigenesis assays demonstrated that RAB20 knockdown inhibited cell proliferation, migration, and colony formation in vitro and tumor growth in vivo. RAB20 depletion also induced PSCC cell cycle arrest at G2/M by increasing Chk1 expression and promoting cdc25c phosphorylation to reduce cdc2-cyclinB1 complex formation. Our study revealed an oncogenic role for RAB20 in promoting PSCC cell proliferation at the G2/M phase via the Chk1/cdc25c/cdc2-cyclinB1 pathway. Thus, RAB20 could be a promising prognostic biomarker of advanced PSCC with poor patient survival outcomes and could be a potential therapeutic target.
Ghana’s Readiness to Combat Terrorism: Strategies of Security Institutions

ABSTRACT Despite Ghana’s non-experience of terrorism amid the internal and external threats, scholars are yet to push this subject into the realm of discourse. Canvasing exclusive knowledge from institutional practices, we investigated the counter-terrorism strategies employed by Ghana’s security institutions to safeguard the country. Applying thematic textual analysis via interviews from twelve security personnel availed to us, we unmasked that although the diverse institutions deployed identical strategies for the same purpose, divergencies appeared in their modus operandi and the rationale for deploying such strategies. We uncovered some institutions engaged communities for de-radicalization whereas others employed the same method for the purposes of disarmament. To garner covert intelligence, personnel disguised in civilian appearances take up temporal accommodation in the communities, surreptitiously move around in plain clothes or acted on information from civilians. At border control points, security softwares are deployed for efficient identification of suspected terrorists and for ferreting out concealed goods. To combat new and varying forms of terrorism, inter- and cross-institutional training programs are held intermittently at the domestic and international level to renew knowledge of personnel and machinated at unearthing new trends in terrorism. Institutions have also partnered media houses in an attempt to educate the public, or the media are mildly cautioned on terrorism reportage.
Game theoretic enterprise management in industrial collaborative networks with multi-agent systems

Nowadays, virtual enterprise (VE) is an important paradigm of business management in an agile environment. VE exists in several kinds of business organization through multi-layered product flows. It is obvious that a mechanism, through which these different enterprises can be integrated together, is required for the better management of VE. In this paper, we focus on the negotiation process in VE formulation as a basic research to clarify its effective management. Each enterprise in VE is defined as a software agent with multi-utilities, and three types of primitive business models are targeted, such as the vertically integrated business model, horizontally specialized business model and hybrid business model. We develop a three-layered VE model for computer simulation so as to clarify VE formulation dynamism with the proposed negotiation mechanism.
Modeling the Response of a Memcapacitor for Impulse, Step, Ramp, and Sinusoidal Inputs

Micro-Electro-Mechanical Systems, or MEMS, is a technology of very small scale devices. The dimensions of MEMS can vary from below one micron to several millimeters. MEMS have some mechanical functionalities such as the moving plate of a parallel plate capacitor (memcapacitor). MEMS researchers and developers have demonstrated an extremely large number of microsensors for almost every possible sensing modality including temperature, pressure, inertial forces, and chemical species. The equation of motion of the moving plate of a memcapacitor is governed by a non-linear differential equation with no known exact solution. Most research into determining the theoretical response of a memcapacitor to a time varying voltage, was done for the steady-state case. Non-linearity of the displacement of the plate in a memcapacitor presents a challenge in determining the plate’s position and capacitive detection. This paper presents an analytical closed form solution to the non-linear differential equation, without the steady state assumption for an Impulse input, and an approximate solution for a step, ramp, and sinusoidal inputs. Keywords— ApproximateSolution, CapacitiveDetection, Memcapacitor.
A Novel Single-Inductor Bipolar-Output DC/DC Boost Converter for OLED Microdisplays

In this paper, a novel SIBO (Single-Inductor Bipolar-Output) DC/DC Boost converter is proposed to power OLED (Organic Light-Emitting Diode) microdisplays. The proposed topology merges a conventional SISO (Single-Inductor Single-Output) DC/DC Boost converter and a switched capacitor inverter to produce a SIBO converter without both the cross-regulation effect and the unbalanced output voltages. Moreover, its control circuit and efficiency are almost the same as the conventional SISO Boost converter. Therefore, the novel converter maintains the power density, the small form factor, and the high efficiency of its conventional counterpart. The proposed converter was analyzed under continuous-conduction mode operation using the moving average operator and charge conservation principle. As a result, the authors proposed an equation set with the main averages and ripples of the circuit variables expressed as analytical functions of the circuit components, the input voltage, and the duty cycle. Both the functionality of the proposed converter and the accuracy of the developed equation set were analyzed by extensive simulations. The simulation performed using ideal components was characterized by a mean absolute percentage error of 0.774% with a standard deviation of 1.566%. These results confirm the high accuracy of the proposed equation set. Furthermore, the non-ideal model simulation confirms the functionality of the proposed converter in “real” operation conditions. Under simulation with non-ideal components, the result statistics were a mean absolute percentage error of 7.36% with a standard deviation of 6.91%. Therefore, the converter design using the proposed ideal model could be a good start point of a converter optimization process based on more complex component models and assisted by computer-aided design tools.
A Hotspot of Arid Zone Subterranean Biodiversity: The Robe Valley in Western Australia

Knowledge of subterranean fauna has mostly been derived from caves and streambeds, which are relatively easily accessed. In contrast, subterranean fauna inhabiting regional groundwater aquifers or the vadose zone (between surface soil layers and the watertable) is difficult to sample. Here we provide species lists for a globally significant subterranean fauna hotspot in the Robe Valley of the Pilbara region, Western Australia. This fauna was collected from up to 50 m below ground level using mining exploration drill holes and monitoring wells. Altogether, 123 subterranean species were collected over a distance of 17 km, comprising 65 troglofauna and 58 stygofauna species. Of these, 61 species were troglobionts and 48 stygobionts. The troglofauna occurs in small voids and fissures in mesas comprised mostly of an iron ore formation, while the stygofauna occurs in the alluvium of a river floodplain. The richness of the Robe Valley is not a localized aberration, but rather reflects the richness of the arid Pilbara region. While legislation in Western Australia has recognized the importance of subterranean fauna, mining is occurring in the Robe Valley hotspot with conditions of environmental approval that are designed to ensure species persistence.
A comparative performance of breast cancer classification using hyper-parameterized machine learning models

Breast cancer is the second most common cancer and has the second-highest mortality rate in women among all cancer types. Accurate cancer diagnosis plays a great part in breast cancer treatment. The application of machine learning methods in cancer classification has grown popular and has provided an accurate classification of malignant (cancerous) and benign (non-cancerous) breast cancer. This paper presents the application of three machine learning methods to classify malignant and benign breast cancer. The three machine learning methods used in this study are Support V ector Machine (SVM), Logistic Regression (LR), and Neural Network (NN) for breast cancer classification. For each machine learning method, multiple models had been tested with every model having a unique set of parameter values. This study used the breast cancer Wisconsin diagnostic (BCWD) dataset. The performance of the models is evaluated using the k-fold cross-validation technique and confusion matrix. The result shows that SVM outperformed both LR and NN in terms of classification accuracy, precision, recall, and specificity with k-fold cross validation technique. On the other hand, when the train-test split was used to validate the proposed model, the NN outperformed both SVM and LR achieving accuracy of 99.4%.
AIMS and resist simulation

The AIMTM45-193i is the established tool for mask performance qualification and defect printing analysis in the mask shop under scanner conditions. Vector effects are taken into account by the proprietary Zeiss vector effect emulator. In several studies an excellent correlation to wafer prints has been reported. However, a systematic offset to wafer prints in terms of mask error enhancement factor (MEEF) and exposure latitude has been observed which is attributed to well known resist effects. The AIMSTM measures the aerial image in resist whereas in a real lithography process further image blur of the latent image is caused by photo acid diffusion during wafer processing and resist development. To explain the gap between the AIMTM and wafer prints we have investigated aerial images in combination with an easy to use resist model. It does take resist effects into account with sufficient accuracy to explain printing behavior of photo masks but without the need to calibrate lots of parameters of the actually used resist which usually are not known to a mask shop. The resist effects predominantly reduce the image contrast and thus increase the MEEF and the sensitivity to mask defects. This somewhat counterintuitive behavior is labeled "contrast enhancement by contrast reduction". Additionally application of the resist model improves the agreement of e.g. the exposure latitude or MEEF measured by the AIMSTM compared to wafer prints.
Dynamic Characteristics of Bridge under Moving Constant Load

It is very important to carry out research of dynamic performance and acquire data of bridge. it can provide reference for design of bridge. Combined with a bridge in actual engineering, this paper analyzes the deformation of bridge subjected to vehicles with different velocities. Based on typical theory on vibration analysis between bridge and vehicles and simplified model, finite element model of simple-supported bridge is established by ANSYS software, and concrete process of modeling, solving, analyzing and dealing is introduced. Through the numerical simulation analysis dynamic response characteristics of the bridge body are acquired when the vehicle passes through the bridge at different speeds, variable regularity is given for dynamic response and these can provide good reference for controlling the vibration measures of bridge under moving loads.
Deformation of NaCoF3 perovskite and post-perovskite up to 30 GPa and 1013 K: implications for plastic deformation and transformation mechanism

Abstract. Texture, plastic deformation, and phase transformation mechanisms in perovskite and post-perovskite are of general interest for our understanding of the Earth's mantle. Here, the perovskite analogue NaCoF3 is deformed in a resistive-heated diamond anvil cell (DAC) up to 30 GPa and 1013 K. The in situ state of the sample, including crystal structure, stress, and texture, is monitored using X-ray diffraction. A phase transformation from a perovskite to a post-perovskite structure is observed between 20.1 and 26.1 GPa. Normalized stress drops by a factor of 3 during transformation as a result of transient weakening during the transformation. The perovskite phase initially develops a texture with a maximum at 100 and a strong 010 minimum in the inverse pole figure of the compression direction. Additionally, a secondary weaker 001 maximum is observed later during compression. Texture simulations indicate that the initial deformation of perovskite requires slip along (100) planes with significant contributions of {110} twins. Following the phase transition to post-perovskite, we observe a 010 maximum, which later evolves with compression. The transformation follows orientation relationships previously suggested where the c axis is preserved between phases and hh0 vectors in reciprocal space of post-perovskite are parallel to [010] in perovskite, which indicates a martensitic-like transition mechanism. A comparison between past experiments on bridgmanite and current results indicates that NaCoF3 is a good analogue to understand the development of microstructures within the Earth's mantle.
Visualizing the factory through meta- manufacturing processes

Abstract Milestone or deadline driven production management is predominant in most manufacturing companies. Problems and other disturbing occurrences tend to see their first daylight during phases where plans are implemented into reality, for example, when production commences, prototypes enter manufacturing and deliveries are expected. Conventional control routines fail to depict the true progress and procedures of the company on an on-line basis, which means that instead of being proactive they serve as means to react to already existing problems. This paper studies the application of advanced visualization techniques to the already existing information embedded in a comany's information infrastructure, and how it can help management to anticipate probable near future pitfalls. By studying those daily operations of a company which share a document relationship with the true manufacturing process, that is, the meta-manufacturing processes, a completely new perspective on the company's value-adding activi.
Comparative FISH mapping of 45S and 5S rDNA in the genus Gambierdiscus advances understanding of the cytogenetic diversity and mitosis of dinoflagellates

ABSTRACT Dinoflagellates are protists with exceptionally large genomes and chromosomes that lack nucleosomes. To date, information on their genome organization is scarce. Studies investigating the distribution of ribosomal RNA genes (rDNA) in plants and animals by fluorescence in situ hybridization (FISH) have provided important information to elucidate the genome organization and evolution of many species. In the present study, FISH was used to analyse the chromosomal organization of 45S and 5S rDNA and the extent of rDNA variation among five species of Gambierdiscus (G. australes, G. excentricus, G. silvae, G. carolinianus and G. caribaeus). The results, the intensity and location of the FISH signals, are summarized as follows: (1) All Gambierdiscus species have extremely high copy number of 45S rDNA repeat units arranged in a large nucleolus situated in the concave region of the typical interphasic bean-shaped nucleus. In addition, outside the large nucleolus, G. silvae and G. carolinianus have minor clusters of 45S rDNA repeats. (2) Many copies of 5S rDNA organized in one or a few clusters, depending on the species, are physically separated from the 45S rDNA but located close, peripherally, to the nucleolus. This nuclear organization is preserved during cell division. (3) Gambierdiscus australes was the species with the lowest 5S rDNA FISH signals clustered into a single site. In contrast, one major and several minor 5S rDNA clusters of FISH signals were observed in G. excentricus. We find that the species analysed can be differentiated by their species-specific FISH rDNA phenotypes demonstrating the usefulness of FISH in comparative cytogenomic studies of Gambierdiscus. In addition, using G. australes as a model, we demonstrate the usefulness of 45S and 5S rDNA as chromosomal markers to study dinomitosis, the atypical nuclear division that solely occurs in the class Dinophyceae.
Experimental evaluation of observers for tire–road forces, sideslip angle and wheel cornering stiffness

This paper proposes a new estimation process to estimate tire–road forces, sideslip angle and wheel cornering stiffness. This method uses measurements from currently–available standard sensors. The estimation process is separated into two blocks: the first block contains an observer whose principal role is to calculate tire–road forces without a descriptive force model, while in the second block an observer estimates sideslip angle and cornering stiffness with an adaptive tire-force model. The different observers are based on an Extended Kalman Filter method. Concerning the vehicle model, for observability reasons, the rear longitudinal forces are neglected relative to the front longitudinal forces. The estimation process was applied and compared to real experimental data, notably wheel force measurements. Experimental results show the accuracy and potential of the estimation process, and a limitation in the estimation of the cornering stiffness.
Corporate Failure Prediction (Bankruptcy) in Australia: From Zeta to Neural Networks

Relatively little has been written on corporate failure prediction in Australia as suitable corporate databases have not been available for research to be undertaken. The authors were given access to suitable commercial databases and have experimented with a number of different techniques of analysis including decision tree analysis, linear regression, discriminant analysis and artificial neural networks. They have then built a predictive model using a neural network approach and this has proved to be strongly predictive. Further work is being undertaken with fuzzy neural networks. Anecdotal evidence indicates that market approaches such as KMVTM and CreditMetricsTM work for the top 50 or 70 stocks in Australia but not generally. We have therefore left such analytics for further experiments. The authors welcome comment about alternative approaches that may be suitable.
A slow port scan attack detection mechanism based on fuzzy logic and a stepwise p1olicy

The slow port scan attack detection is the one of the important topics in the network security. We suggest an abnormal traffic control framework to detect slow port scan attacks using fuzzy rules. The abnormal traffic control framework acts as an intrusion prevention system to suspicious network traffic. It manages traffic with a stepwise policy: first decreasing network bandwidth and then discarding traffic. In this paper, we show that our abnormal traffic control framework effectively detects slow port scan attacks traffic using fuzzy rules and a stepwise policy.
Local Allometric Equations for Estimating Above-Ground Biomass (AGB) of Mangroves (Rhizophora spp. and Avicennia germinans) from the Komo, Mondah and Rio Mouni Estuaries in Gabon

The aboveground biomass (AGB) of Gabonese mangroves is commonly estimated from equations calibrated in other countries, and is generally adapted poorly to the local context. This paper focuses on developing local allometric equations for the AGB estimation and to evaluate their accuracy compared to other general equations. The local equations for Rhizophora spp and Avicennia germinans were performed with tree volume, bark and wood densities, and are used with the diameter as an independent variable. The heights and diameters of 408 trees (314 Rhizophora spp and 94 Avicennia germinans) were measured at 13 sites in Estuaire Province. Sixty-four aliquots were taken from the trunks of both species at the Mondah site. This site has tree diameters ranging from 2 to 127 cm for Avicennia and from 1.4 to 75.8 cm for Rhizophora. The tree height ranges from 0.9 to 24 m for Avicennia, and from 1.1 to 53 m for Rhizophora. Avicennia has an overall trunk density of 0.88 g/cm3 and Rhizophora has 1.17 g/cm3. The coefficient of determination (R2) of the equations are 0.98 for Rhizophora spp, 0.97 for Avicennia germinans, and 0.99 for the general equation. The seven equation display biases that are less than 1% and the root mean square errors vary between 0.073 and 1.68. Compared to other equations generally used, these local equations improve the accuracy of aboveground biomass estimations of Gabonese mangroves.
A Fully Integrated Neural Recording Amplifier With

This paper presents a low-power low-noise fully inte- grated bandpass operational amplifier for a variety of biomedical neural recording applications. A standard two-stage CMOS am- plifier in a closed-loop resistive feedback configuration provides a stable ac gain of 39.3 dB at 1 kHz. A subthreshold PMOS input transistor is utilized to clamp the large and random dc open circuit potentials that normally exist at the electrode-electrolyte interface. The low cutoff frequency of the amplifier is programmable up to 50 Hz, while its high cutoff frequency is measured to be 9.1 kHz. The tolerable dc input range is measured to be at least 0.25 V with a dc rejection factor of at least 29 dB. The amplifier occupies 0.107 in die area, and dissipates 115 from a 3 V power supply. The total measured input-referred noise voltage in the frequency range of 0.1-10 kHz is 7.8 . It is fabricated using AMI 1.5 double-poly double-metal n-well CMOS process. This paper presents full characterization of the dc, ac, and noise performance of this amplifier through in vitro measurements in saline using two different neural recording electrodes.