Miracle-dz/UniFAR-FTUs huggingface dataset:
Telomerase activity and expression of the telomerase catalytic subunit, hTERT, in meningioma progression.

OBJECT In recent reports, 6 to 19% of meningiomas have been classified as atypical or anaplastic/malignant. Some atypical and anaplastic meningiomas appear to arise from benign tumors by progression. Telomerase activation has recently been associated with malignant progression of human tumors. The authors have investigated a series of benign, atypical, and anaplastic/malignant meningiomas for telomerase activity and expression of the telomerase catalytic subunit human telomerase reverse transcriptase (hTERT). METHODS A quantitative telomeric repeat amplification protocol was used to detect telomerase enzyme activity in seven (21%) of 34 benign, but in nine (75%) of 12 atypical and in seven (100%) of seven anaplastic/malignant meningiomas. Very high levels of telomerase activity were observed only in highly aggressive tumors. Messenger (m)RNA expression of the catalytic subunit hTERT was found in 11 (33%) of 33 benign, 12 (92%) of 13 atypical, and all seven anaplastic/malignant tumors. All telomerase-positive lesions were also positive for hTERT mRNA, whereas no telomerase activity was detected in six (21%) of 29 hTERT-positive tumors. This indicates that upregulation of hTERT is the rate-limiting step for telomerase activation in the majority of meningiomas. Expression of telomerase and hTERT was seen in all four tumors with gross brain invasion. All recurrent tumors or meningiomas recurring during follow up expressed hTERT. CONCLUSIONS The results are consistent with a role for telomerase activation during the development of malignancy in meningiomas. Hence, expression of telomerase activity and hTERT might prove to be potentially useful markers for the evaluation of these tumors.
Development of a Rehabilitation Monitoring System on Web-Based Application for Patients with Knee Pain

Internet-based technology significantly enhanced quality of health service delivery processes. Web-based intervention delivered via the computer or eHealth has been used intensively to provide medical information for supporting behavior changes, and to manage chronic diseases and health issues such as diabetes, obesity, pain, and mental health. Exercise therapy or rehabilitation also needs an approach to delivery rehabilitation programs for home-based exercises, and to monitor patient's recovery progresses. Knee pain is a common complaint of musculoskeletal disorders which was recommended for managing with exercise therapy. This research aimed to design and develop the system that can provide two-way communication for rehabilitation monitoring between physical therapists and patients with knee pain. Functions for delivery exercise regimens and track patient's recovery data were also included. Requirement from stakeholders (physical therapists and patients) were analyzed. System contents and relevant data were gathered. Data flow diagrams and system flows were designed. Rehabilitation monitoring system on web-based application for patients with knee pain was developed. The system was evaluated by stakeholder for usability study. A total of 5 physical therapists and 6 patients were recruited and participated in this study. The usability questionnaire was used to collect quantitative information on subjective user preferences and recommendation. Positive feedback revealed the utility of this rehabilitation monitoring system. This system provided an approach to delivery exercise regimens, alternative approach for memory-aid exercise instructed, support rehabilitation monitoring data tracking, and facilitate communication between health care provider and patients.
Classification of EEG Learning and Resting States using 1D-Convolutional Neural Network for Cognitive Load Assesment

Classification of resting and cognitive states has its importance in brain neuroscience for understating the underlying behaviors of cognition. The human brain is considered as a complex system having different mental states such as resting, active or cognitive states. It is a well-understood fact that the brain activity increases with the increased demand of cognition. The differences among different mental states can be explored by using classification techniques. The deep learning algorithm based on 1D convolutional neural network (CNN) model has been proposed for the classification of rest and cognitive states and also the cognitive load has been assessed using brain waves particularly alpha wave. EEG data were collected from 34 human participants at resting and during learning state. After preprocessing, EEG data has been segmented into equal number of segments to investigate the deep temporal information for cognitive load assessment. The brain waves were extracted using discrete wavelet transform (DWT) for each segment and fed these segments to proposed model for classification and assessment of cognitive load. The results show that alpha brain wave produced consistent behavior using 1D-CNN technique for cognitive load measurement and provides efficient solution in EEG data for learning and resting state task.
Desieve the Attacker: Thwarting IP Theft in Sieve-Valve-based Biochips

Researchers develop bioassays following rigorous experimentation in the lab that involves considerable fiscal and highly-skilled-person-hour investment. Previous work shows that a bioassay implementation can be reverse engineered by using images or video and control signals of the biochip. Hence, techniques must be devised to protect the intellectual property (IP) rights of the bioassay developer. This study is the first step in this direction and it makes the following contributions: (1) it introduces use of a sieve-valve as a security primitive to obfuscate bioassay implementations; (2) it shows how sieve-valves can be used to obscure biochip building blocks such as multiplexers and mixers; (3) it presents design rules and security metrics to design and measure obfuscated biochips. We assess the cost-security trade-offs associated with this solution and demonstrate practical sieve-valve based obfuscation on real-life biochips.
From the Goldbach Conjecture Cipher to the Han Ge CODE

At present, there is an unprecedented crisis in the world of cryptography and the cryptographers around the world are worried: There is no possibility of avoiding code breaking. Is there any cipher that can be secure, efficient and not cracked? The first thing one might think of is the prime number cipher, but since the big prime numbers are too difficult to obtain, this road is difficult to get through. So, what’s the best cipher? The answer is: the combination of Chinese Coding and Goldbach Conjecture Cipher (CCG), called Han Ge Code in Chinese. The CCG code has the advantages of simple materials collection, easy compilation, simple decoding, changeability, wide application, and so on. One of the greatest advantages is that it is the safest code in the world. Moreover, an encrypted CCG code of more than 100,000 digits is hard to decipher for 100 years for even all the scientists in the world who use the most advanced computers. Given this the CCG code must be recognized as the best code in the world.
Identification of Influential Factors for Productivity and Sustainability of Crops Using Data Mining Techniques

India is a country in which agriculture plays a wide role with around 70% of its population is dependent on agriculture good in direct or indirect way. The agricultural sector contributes about 14% to GDP of India. As the population is increasing, demand for crops is also increasing day by day. About all suicides in India farmer suicide accounts for 11.2%. There are many reasons given for farmer suicides like inadequate monsoon rainfall, interest rate is high, certain government policies are not in welfare, mental health issues, personal and family problems. It is also noticed that even the states are responsible for manipulating certain statistics. this research work uses data mining clustering technique to group attributes that results in low crop production. Case studies are conducted to map and co-relate the low crop production, monsoon factors along with farmer suicidal data. The states are affected most is identified and further analysed.
Impedance Matching of Layered Structures with Metamaterials

Impedance matching of layered structures involving metamaterial is investigated. The base isotropic material is selected from commonly used microwave materials with dielectric permittivity ranging from 2.1 to 10.2, such as Teflon and Alumina. The method adapted is immune to polarization of the incident wave regardless if it is TE or TM wave. The metamaterial permittivity and permeability parameters are obtained for the minimum reflectivity by varying the thickness of the metamaterial for each isotropic material for broad range of incident angles and operational frequency. The results presented in this paper can be used for applications where antireflection (AR) is a requirement.
A Game Theoretic Formulation of Path Selection in Urban Transportation Networks

Vehicular transportation networks are formed by interconnection of multiple networks leading to a particular class of complex networks called multi-layer networks. These networks are formed due to interaction between vehicles, vehicles and infrastructure (like a cloud), and data exchange at the infrastructure level. The interaction between these networks, and particularly the dependency of the networks formed between the infrastructure and on-road (possibly connected) vehicles at different geographical areas, affect the behaviour of all the elements in these networks. An integrated architecture of vehicular transportation networks for a multi-commodity heterogeneous traffic in an urban scenario is proposed in this paper. The heterogeneity exists due to different detection abilities of vehicles, owing to connectivity facilities and sensing capabilities, and the mode of driving (autonomous or manual) in the vehicles. Considering the multi-layer network architecture, the interconnection between the infrastructure network and the vehicle mobility network is studied. Using game theoretic tools, the impact of path prediction via infrastructure network on decision-making in vehicles and vice-versa is analyzed. The problem is formulated and modelled as a repeated Stackelberg game with incomplete information.
Suppressing E-Plane Scan Resonance for UWB Millimeter-Wave Differential Phased Array

Recent advancements in differential Radio Frequency (RF) front-ends at millimeter-waves (mm-wave) create a need for high gain differentially fed arrays for 5G applications. In this paper, we present an ultra-wideband (UWB) mm-wave dual polarized Tightly Coupled Dipole Array (TCDA) with a novel differential feeding network. Notably, we introduce a new H-Wall structure used to eliminate common-mode coupling that causes E-Plane scan resonances with high radiation efficiency and 45° scanning in both planes. This differential TCDA (D-TCDA) is developed for operation across K-Ka and 5G bands (viz. 22–80 GHz) in a low profile of 1.38 mm.
Identification of Output-Error (OE) Models using Generalized Spectral Decomposition

This article proposes a novel approach for the identification of output-error (OE) models. Classical approaches like prediction error minimization (PEM) and Steiglitz-McBride deliver consistent estimates of model parameters for given information of delay, input-output orders of the underlying process. In the absence of such critical information, user is forced to adapt trial and error approach. In this work, we propose a novel two-step non-iterative framework to estimate the delay and order in automated manner using the generalized spectral decomposition. The first step of the proposed algorithm computes the order by analyzing generalized eigenvalues of the covariance matrix for the lagged input-output measurements. The second step involves the parameter estimation by utilizing the same approach of spectral decomposition on lagged input-output measurements with correct stacking order. Simulation studies are presented to show the efficacy of proposed method.
Novel Approach for Test Case Prioritization Using ACO Algorithm

Regression testing is used to retest the component of a system that verifies that after modifications defects are removed from the in effected software. Automation tools are required for these types of testing. This work is based on manual slicing and automated slicing for test case prioritization to detect maximum number of faults from the project in which some changes are done for the new version release. The slicing is the technique which will divide the whole project function wise and detect associated functions. To test the performance of proposed and existing algorithm MATLAB is being used by considering the dataset of ten projects. Each project has seven functions and four numbers of changes are defined for the regression testing. In the simulation it is being analyzed that fault detection rate is increased and execution time is reduced with the implementation of automated test case prioritization as compared to manual test case prioritization in regression testing.
Cognitive Machine to Machine Communication with Energy Harvesting in IoT networks

Machine to machine (M2M) communication is the enabling technology for Internet of Things (IoT) networks. This paper investigates the throughput performance of a Cognitive IoT network where a cluster of energy harvesting (EH) machine type communication (MTC) devices communicates with an eNodeB base station, while sharing spectrum of a primary network in an underlay mode. Successful decoding probability of MTC devices at eNodeB using RACH (Random Access Channel) and retransmission is evaluated in presence of primary interference while MTC devices harvest energy from RF signal of power transfer units (PTUs). The impact of energy harvesting, number of MTC devices, power transmitted by PTUs, primary interference and interference constraint at primary receiver due to underlay operation on throughput and successful decoding probability at eNodeB is evaluated. MATLAB based simulations are carried out following our analytical formulations. Analytical expressions of throughput and successful decoding probability are derived capturing various network parameters related to energy harvesting and underlay cognitive radio network.
Game State Prediction with Ensemble of Machine Learning Techniques

In the game industry, applying machine learning techniques has become more and more important and it will be an essential part in near future. However, most of researches have focused on churn prediction and purchase prediction. In this paper, we try to predict the future state of games by observing the game events. In a game, multiple events are open at the same time, so it is very hard to predict the effect of events. We define game state that set of variables which reflects user behaviors in the game, such as the number of playing users and the amount of money spent by users. We make a game state prediction models by applying an ensemble of machine learning techniques.
A review on the structure, properties and characterization of 2D Molybdenum Disulfide

2D Molybdenum disulfide (MoS2) is one among the most significant transition metal dichalcogenides which is attracting a wide range of research interests due to its potential applications in a broad spectrum. Monolayer of MoS2 shows a direct band gap in the range of 1.8 eV and its layer dependence related to the band structure addresses the perennial problems related to graphene. Its applicability in such a versatile spectrum makes it an interesting candidate of study and this review attempts to explain the structure, properties and different characterization methods of 2D MoS2.
An Integrated, Broadband Circularly-Polarized Millimeter-Wave Metasurface-Based Transmit-Array

In this paper, we present the design of an integrated circularly-polarized high-gain antenna with a broad bandwidth at the millimeter-wave Ka-band. The proposed compact antenna is enabled by the integration of a metasurfaces-based transmit-array and a broadband circularly-polarized feed radiator. The feed radiator is a substrate integrated waveguide fed patch with a finite-sized metasurfaces superstrate, while the transmit-array utilizes the rotation phase for collimating the waves emitted from the feeding source. The full-wave simulation results show that the integrated transmit-array antenna achieves an overall bandwidth of about 18.3% within which the S11 is below −10 dB, the axial ratio is smaller than 3 dB, and the gain variation is less than 3 dB with a peak gain of 23.5 dBi.
Elastic fibres in normal and sun‐damaged skin: an immunohistochemical study

Sun‐exposed and sun‐protected skin obtained at post mortem from the nape of the neck in 14 subjects was immunostained using antisera to elastic, lysozyme, amyloid P component, and the plasma protease inhibitors alpha‐I antitrypsin, alpha‐I amichymotrypsin and alpha‐2 macro‐globulin. Both the normal clastic fibres in sun‐protected skin, and clastosis in sun‐exposed skin were positively immunostained for elastin, lysozyme and amyloid P component. Collagen fibres were unstained. No immunostaining of normal elastic fibres or elastosis in the skin was obtained with antisera to alpha‐I antitrypsin, alpha‐I antichymotrypsin or alpha‐2 macroglobulin. It was concluded that the clastosis in sun‐exposed skin does contain elastic fibres. The absence of immunostaining for plasma protease inhibitors probably indicates that the elastic material is mature, and not newly‐formed.
Induction Heating of Thermoplastic Composites in the Presence of a Susceptor

The issue of inductive heating and welding of two pieces of thermoplastic composites in the presence of a susceptor alloy consisting of 67% nickel and 27% copper is studied using Maxwell. Simulation results clearly show solid losses in the material which can be used to heat and weld the composite panels. Location based temperature increase due to I2R losses are calculated which show significant heating and welding potentials.
Pathogenesis and prevention of necrotizing enterocolitis

Purpose of review Necrotizing enterocolitis (NEC) remains the most common serious acquired gastrointestinal disorder affecting preterm infants. Here we review recent advances in our understanding of the pathogenesis of this multifactorial condition and consider the implications for practice and research. Recent findings NEC is an important cause of mortality and serious morbidity in preterm infants. The risk is inversely proportional to gestational age and weight at birth. Fetal growth restriction and compromise may be additional specific risk factors. NEC, particularly severe NEC requiring surgical intervention and NEC with invasive infection, is associated with acute morbidity and mortality and adverse neurodevelopmental outcomes. The principal modifiable postnatal risk factors for NEC in preterm infants relate to enteral feeding practices including formula milk feeding, early and rapid advancement of enteral feed volumes, and exposure to H2-receptor antagonists. Summary Our understanding of the pathogenesis of this condition remains incomplete. With the exception of feeding with human milk, only limited evidence is currently available to support interventions to prevent NEC. Promising strategies that merit further evaluation in randomized controlled trials include the use of prebiotics and probiotics and the avoidance of exposure to H2-receptor antagonists.
Flight mode affects allometry of migration range in birds.

Billions of birds migrate to exploit seasonally available resources. The ranges of migration vary greatly among species, but the underlying mechanisms are poorly understood. I hypothesise that flight mode (flapping or soaring) and body mass affect migration range through their influence on flight energetics. Here, I compiled the tracks of migratory birds (196 species, weighing 12-10 350 g) recorded by electronic tags in the last few decades. In flapping birds, migration ranges decreased with body mass, as predicted from rapidly increasing flight cost with increasing body mass. The species with higher aspect ratio and lower wing loading had larger migration ranges. In soaring birds, migration ranges were mass-independent and larger than those of flapping birds, reflecting their low flight costs irrespective of body mass. This study demonstrates that many animal-tracking studies are now available to explore the general patterns and the underlying mechanisms of animal migration.
Application of Output Embedding on Word2Vec

The word vector of distributed representation that embeds the semantic relationship of words into a vector using Word2Vec has attracted attention in recent years. Furthermore, this word vector has become widely to be used in the field of Natural Language Processing such as parsing and document classification, and its effectiveness has been reported. Generally, it is the input embedding on Word2Vec that is used as the elements of a word vector, and the output embedding generated at the same time is not used. On the other hand, the authors focus on the usefulness of the paired output embedding. In this paper, we propose word vectors using the input and output embeddings together. In addition, we experimentally investigate the performance of the proposed word vectors and carry out document classification experiments using the proposed word vectors. The result shows that the classification performance was improved by the proposed word vectors.
Duodenal and gallbladder metastasis of regressive melanoma: a case report and review of the literature.

BACKGROUND Malignant melanoma involving the gastrointestinal (GI) tract may be primary or metastatic. Small bowel is the commonest site of GI metastases from cutaneous malignant melanoma, metastatic lesion in the gallbladder is extremely rare. CASE PRESENTATION This case report describes the presentation of metastatic melanoma in duodenum and gallbladder. A 45-year-old man has presented melena with intermittent abdominal pain. On physical examination we found a small lesion between the fourth and fifth toes, associated with inguinal lymph node. An Abdominal ultrasound revealed diffuse duodenal thickening. Upper endoscopy was performed and discovered an ulcerative lesion in the second part of the duodenum. The biopsy with immunohistochemical stains was in favor of a duodenal location of melanoma. Computed tomography (CT) revealed many circumferential thickening of ileal loops associated with a nodular lesion in the anterior wall of the gallbladder. The patient was treated by palliative chemotherapy. DISCUSSION Malignant melanoma of the GI tract may be primary or secondary. The small bowel is the most affected, but it's rare in the gallbladder. The clinical presentation can mimic the other intestinal tumors, and the diagnosis is based on imaging; CT scan and GI endoscopy have a key role on the diagnosis, and the treatment depends on the location and the number of lesions. CONCLUSIONS Metastases of melanoma in the GI tract are uncommon, the diagnosis must be suspected in any patient with a history of melanoma with digestive signs.
“It Felt Good but Weird at the Same Time”

Although sexual behavior is multidimensional, little research has focused on the experience of nonintercourse behaviors for adolescents and emerging adults. This article uses open-ended coded data from a longitudinal study of college students (N = 346; M age = 18.5, 52% female, 27% Hispanic/Latino [HL], 25% non-HL European American, 23% non-HL Asian American, 16% non-HL African American, 9% non-HL multiracial) to examine what emotional responses emerging adults report about their first experiences of six sexual behaviors. The four most common emotional reactions were happy, excited, fearful, and indifferent. Descriptions were largely positive, although mixed reactions were relatively common and emotional reactions varied by behavior. Results suggest the importance of including multiple types of sexual behaviors, as well as their possible positive and negative outcomes, in sexuality education programs.