Miracle-dz/UniFAR-FTUs huggingface dataset:
To Evaluate The Effect Of Pre-Eclampsia On Platelet Count In The Pregnant Women : Case-Control Study

Aim: To evaluate the effect of Pre-eclampsia on platelet count in the pregnant Women. Material and methods: This case control study was carried out in the Department of Pathology, Patna Medical College and Hospital Patna, Bihar, India from December 2018 to November 2019. Total 200 patients were divided in two groups as Cases and Control. Cases study group includes 100 females with the thrombocytopenia and Control includes 100 females without thrombocytopenia. Results: The Most affected age group in which thrombocytopenia seen was between 20 to 25 years. In women with thrombocytopenia with mild Pre-eclampsia Platelet Count (x10 9 /L) was 65-288 , thrombocytopenia with Severe Pre eclampsia Platelet Count (x10 9 /L) was 27259 and women without thrombocytopenia had normal Platelet Count. Conclusion: Clinically platelet indices can be a useful screening test for early identification of preeclampsia and eclampsia. Also platelet indices can assess the prognosis of this disease in pregnant women and can be used as an effective prognostic marker because it correlates with severity of the disease. Platelet count is a simple, low cost, and rapid routine screening test.
Design and Characterization of an In Vitro CRISPR/Cas9 Model System

CRISPR/Cas9 is a technique in molecular biology yielding effective DNA cleavage. For effective DNA cutting, CRISPR/Cas9 system requires appropriate single guide RNA (sgRNA) and a functional sgRNA binding site on Cas9 enzyme. The goal of this project is to design a functional CRISPR/Cas9 test tube model system for further research in the lab. The costs and inaccuracy associated with RNA synthesis constitute a limitation for CRISPR technology use. To this end, a truncated functional sgRNA is desired. In this study, we will also design a shortened sgRNA that is missing the trans-activating CRISPR RNA (tracrRNA) portion. The cleaving potential of sgRNA system composed entirely of shortened sgRNA is evaluated thermodynamically using isothermal titration calorimetry (ITC) and in vitro gel cleavage assays. The findings indicate cleavage ineffectiveness of the designed RNA, while full sgRNA is functioning normally. The ITC data suggests no noticeable difference in the thermodynamic interactions between the modified sgRNA cas9 system and the full sgRNA cas9 system.
IOT security framework for IOT healthcare devices

—Over the years, advancement of technology has led to an increase in research and development of IoT devices. The healthcare sector is one of the most affected by IoT devices. IoT healthcare devices presents a new option to treat patients. IoT healthcare devices range from pacemakers to remote patient monitoring and wearable devices. However, IoT devices are connected to the Internet. This means that IoT devices may be in risk of cyber-attacks that can steal sensitive patient data and medical records. In this paper, potential technologies and techniques that can improve the overall security posture of IoT devices will be reviewed. Previous security frameworks by past researchers will also be highlighted for their effectiveness in improving IoT security.
Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System

Tether-net launched from a chaser spacecraft provides a promising method to capture and dispose of large space debris in orbit. This tether-net system is subject to several sources of uncertainty in sensing and actuation that affect the performance of its net launch and closing control. Earlier reliability-based optimization approaches to design control actions however remain challenging and computationally prohibitive to generalize over varying launch scenarios and target (debris) state relative to the chaser. To search for a general and reliable control policy, this paper presents a reinforcement learning framework that integrates a proximal policy optimization (PPO2) approach with net dynamics simulations. The latter allows evaluating the episodes of net-based target capture, and estimate the capture quality index that serves as the reward feedback to PPO2. Here, the learned policy is designed to model the timing of the net closing action based on the state of the moving net and the target, under any given launch scenario. A stochastic state transition model is considered in order to incorporate synthetic uncertainties in state estimation and launch actuation. Along with notable reward improvement during training, the trained policy demonstrates capture performance (over a wide range of launch/target scenarios) that is close to that obtained with reliability-based optimization run over an individual scenario.
Evolving short- and long-term goals of management of IBD: getting it right, making it last.

Short- and long-term treatment targets in inflammatory bowel diseases (IBD) evolved during the last decade, shifting from symptom control to endoscopic healing and patient-centered parameters. STRIDE-II consensus placed these targets on a timeline from initiating treatment, and introduced additional targets, normalization of serum and fecal biomarkers, absence of quality of life and disability as well as restoration of growth in children. Transmural healing in Crohn's disease and histological healing in ulcerative colitis currently serve as adjunct measures to gauge remission depth. However, whether early treatment according to a treat-to-target paradigm impacts the natural course of IBD remains unclear, leading to the need for prospective disease-modification trials. The SPIRIT consensus defines the targets for these trials to assess the long-term impact of early treatment on quality of life, disability, disease complications, risk of neoplastic lesions and mortality. As further data emerge about the risk-benefit balance of aiming toward deeper healing, the targets in treating IBD may continue to shift.
Infrastructure Allocation for Improving Sensing Accuracy and Connectivity Probability Based on Combination Strategy in Vehicular Networks

Sensing and communication are the two major concerns in vehicle-road collaboration system, whose function realization needs the support of RSE (roadside equipment). A misleading deployment incurs in waste of valuable resources and degradation of the network performance. Based on the combination strategy, this study proposed a hybrid allocation method to deploy RSE on a highway by considering the sensing capability of traditional ITS sensors and the communication feature of popular networking devices. Two sorts of RSE, the one integrated ITS sensor and networking device, named sensing & communication-RSE (scRSE), and the other belongs to networking devices, named communication-RSE (cRSE), were involved. A hybrid RSE allocation framework was proposed, in which an optimal method was adopted to deploy scRSE on key points for data acquisition and a connectivity probability-based uniform method was used to deploy cRSE for communication. The effectiveness of these methods was verified by case analysis. The results indicated that the proposed hybrid method was very effective and outperformed the conventional uniform deployment method. The key parameters, including the coverage range, RSE spacing, and vehicle density, had an important impact on network connectivity probability. To obtain high connectivity probability, a lower density of connected vehicles (CVs) needs the support of wide coverage of RSE, and vice versa. When the density of CVs reaches to a certain value, vehicular networks could be realized without the help of RSE. Benefitting from the mobility of CVs, increasing their transmission range was better than increasing RSE coverage radius in improving connectivity probability.
Effects of climate change on the distribution of threatened invertebrates in a Mediterranean hotspot

The effects of climate change may constitute a major threat factor for endemic and threatened species of invertebrates. A particularly dramatic case can be found in the Iberian Peninsula, because of its high rate of species diversity and endemism. We aim to evaluate the effects of climate change on the distribution of 36 endangered and endemic species of invertebrates within the Iberian Peninsula using species distribution models. We used an ensemble species distribution modelling framework to estimate the species potential distributions under current and future (2050 and 2070) climatic conditions for two different areas: the whole Iberian Peninsula and the current species ranges. We assessed species vulnerability to climate change by calculating three complementary indexes: change in suitable area, persistence in suitable area and turnover in suitable area. Annual mean precipitation was the variable that contributed most to the climatic models. We categorised 25 species as ‘losers’ because they will experience a reduction in their total suitable area under future climatic conditions, six species were categorised as ‘winners’ and six showed contradictory results. Climate change will have several effects on species by changing their suitable distributions and may affect their persistence. Species with narrow distributions associated with mountain ranges will experience the worst future projected conditions, while arid‐adapted species are expected to expand their distributions. Factors assessing the vulnerability of endemic and endangered species can be taken into account to develop strategies that mitigate the negative effects of climate change.
A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification

Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are a class of Recurrent Neural Networks (RNN) suitable for sequential data processing. Bidirectional LSTM (BLSTM) enables a better understanding of context by learning the future time steps in a bidirectional manner. Moreover, GRU deploys reset and update gates in the hidden layer, which is computationally more efficient than a conventional LSTM. This paper proposes an efficient network model based on hybrid BLSTM-GRU for ciphertext classification aiming to mark the category to which the ciphertext belongs. The proposed model performance was evaluated using well-known evaluation metrics on two publicly available datasets encrypted with various classical cipher methods and performance was compared against one-dimensional convolutional neural network (1D-CNN) and various other deep learning-based approaches. The experimental results showed that the BLSTM-GRU cell unit network model achieved a high classification accuracy of up to 95.8%. To the best of our knowledge, this is the first time an RNN-based model has been applied for the ciphertext classification.
Position in global value chains and wages in Central and Eastern European countries

This paper examines the relationship between the relative position of industries in Global Value Chains (GVC) and wages in 10 Central and Eastern European countries. We combine GVC measures of global import intensity of production, upstreamness and the length of the value chain with micro-data on workers. We find that the wages of Central and Eastern European countries workers are higher when their industry is at the beginning of the chain or at the end than in the middle. Secondly, wage changes depend on the interplay between upstreamness and GVC intensity. In sectors close to final demand, greater production fragmentation is associated with lower wages.
Fluorescence-Aided Identification Technique (FIT) Improves Tooth Surface Clean-Up after Debonding of Buccal and Lingual Orthodontic Appliances

The aim of this study was to evaluate the use of fluorescence inducing light to aid the clean-up of tooth surfaces after bracket removal when using buccal or lingual orthodontic appliances. Two full sets of dental arches using extracted human teeth were assembled, with 14 teeth per arch. All teeth were bonded on their buccal and lingual surfaces. After debonding, a single blinded operator performed the tooth surface clean-up, as commonly performed in clinical practice; without the use of fluorescent light (non-FIT) and with two methods using fluorescent light to identify composite remnants on the tooth surfaces (FIT; OPAL and BRACE). Tooth surfaces were scanned before bonding and after clean-up, and the two scans were superimposed using the best-fit method. The results showed that the debonding method, type of tooth and type of tooth surface had a significant effect on the presence of composite remnants, enamel defects, and on debonding time. Contrary to the non-FIT method, there were no composite remnants after clean-up with the use of fluorescence inducing light. Clean-up time was significantly reduced on the buccal surfaces when using the FIT methods. On the lingual surfaces, the FIT methods resulted in larger enamel defects.
A Sociocultural Appraisal of Yorùbá Kegites’ Songs

This is a study of how the oral aesthetics of textual production by a group, the Kegites, are used in the construction of an identity rooted in a collective context that reflects a socio-political engagement with the complex social histories of the Yorùbá society and Nigeria at large. The study identifies common practices of the Kegites and themes of the texts of their songs. It shows the practice of engagement by the Kegites through which identity can be textually constructed in ways that politicize self-representation and challenge discourses grounded in the colonial and postcolonial histories of the Yorùbá people. Through the discussion of themes such as ancestral presence, the aesthetics of orature, and the political significance of group or society (ẹgbé) ̣ among the Yorùbá, the paper seeks to showcase the presence of a characteristic of Yorùbá oral literature in which personal, cultural, economic, social and political issues become inseparable. It demonstrates how oral text reflects societal elements and performers use various societal elements to create an expression of an identity within the multiple currents and traditions by taking the rules, creativity, and artistry of self-representation and shaping them with cultural content, as well as with the rhythms and speech patterns of the Yorùbá people.
Detection and Mitigation of MITM Attack in Software Defined Networks

Software Defined Network (SDN) is the networking architecture that segregates the activities of the control plane from the data plane. Man In The Middle (MITM) is a type of digital attack in a network where the attacker utilizes duplicated ARP messages by spoofing the attacker’s MAC address with the authorized user’s IP address. This paper sorts out the ARP spoofing, which is the suite of MITM attacks using IP-MAC address bindings. SDN is emulated using Mininet and the MITM attack over this network is done using arpspoof, which is the segment of the tool named Dsniff. For the evaluation of the proposed algorithm, various network parameters are compared and analyzed in both RYU and POX controllers. As a result, the proposed algorithm mitigates the MITM attack successfully by dropping the attacked packets.
Risk Stratification and Treatment in Smoldering Multiple Myeloma

Smoldering multiple myeloma is a heterogeneous asymptomatic precursor to multiple myeloma. Since its identification in 1980, risk stratification models have been developed using two main stratification methods: clinical measurement-based and genetics-based. Clinical measurement models can be subdivided in three types: baseline measurements (performed at diagnosis), evolving measurements (performed over time during follow-up appointments), and imaging (for example, magnetic resonance imaging). Genetic approaches include gene expression profiling, DNA/RNA sequencing, and cytogenetics. It is important to accurately distinguish patients with indolent disease from those with aggressive disease, as clinical trials have shown that patients designated as “high-risk of progression” have improved outcomes when treated early. The risk stratification models, and clinical trials are discussed in this review.
Change Management Process: An Integrated Analysis of the Importance of Effective Communication in an Organization

Effective communication can be regarded as the bedrock of all successful businesses. The study investigated the role of effective communication in change management in an organization. The target population was top organizations in Nigeria. The study employed convenient sampling technique in which 140 respondents who were both employees and employers of the various organizations were determined as the sample size. The core principles of change management formed the theoretical basis for the study. The study employed the ex-post factor research design based on qualitative and quantitative research approach in the research process. Descriptive statistics based on graphical presentation of findings and inferential statistics using the Binary Logit Model (BLM) were employed as analytical techniques. The findings of the study revealed that the respondents who were specialists in their respective fields and having requisite years of working experience supported the view that effective communication has a highly significant role in change management in an organization. Similarly, the result of the Binary Logit Model revealed that effective communication causes approximately 34% increase in the variation of change management in an organization. Also, the Wald test ratio revealed that effective communication was statistically significant to change management at the 10% level of significance. Overall, the study concludes that effective communication is a sine qua non for organizational change management. Therefore, it is recommended that for change management processes to yield the require outcome, organizational leaders should inculcate effective communication mechanisms into all organizational operations.
Statistical approach to structural monitoring using scalable wireless sensor networks

A wireless sensor network (WSN) for structural monitoring applications was designed, developed, and deployed to investigate scalability of the network and the effect of dense spatial and temporal sampling on statistical characterization of structural vibration modes. A sensor board based on micro-electro-mechanical-systems (MEMS) technology with dual accelerometers in two directions was developed to provide a wide range of sensitivity with acceptable resolution. The sensor board integrates a MICAz mote micro-controller for control and communication to form a node in a WSN. In the software architecture, the requirement of high-frequency sampling for structural monitoring is the critical challenge for temporal scalability of the network. TinyOS, the widely used operating system for MEMS-based devices, was modified significantly, in coordinated research, to impose an acceptable threshold on the time-synchronization error (jitter). Using a recently developed communication protocol for reliable data collection and command dissemination, a new data pipelining technique was implemented to reuse the spatial bandwidth of a multi-hop wireless network. For initial analysis of the WSN, the integrated hardware and software system was deployed and evaluated on a pedestrian bridge. ARMA models with ambient data were used to identify modal properties of the bridge and calibrate a finite element model of the structure. Large-scale deployment of the WSN on the Golden Gate Bridge consisted of sixty-four nodes on the main-span and south tower. The three-month deployment, the largest of its kind to date, provided ambient vibration data that was used to identify modal properties of the bridge. The superior performance of the network was demonstrated by maintaining a bandwidth of 550 bytes/sec throughout forty-five wireless hops. Using the spatially dense network, sixty-seven vibration modes of the bridge were identified. Multiple data sets were used to estimate statistical properties of the identified modes as histograms and confidence intervals. Comparisons are made between modes identified by alternative system realization methods (spectral methods), ambient data from a previous instrumentation of the bridge, and finite elements models. The advantage of dense sensor networks is further demonstrated through a novel algorithm for local damage detection, which monitors the influence coefficients between signals collected by the sensors around critical points of a structure, and identifies damage using a Bayesian statistic with the desired confidence level.
Assessment of contamination level of a Tanzanian river system with respect to trace metallic elements and their fate in the environment

The quality of water and sediments from a marginally-studied river was investigated with respect to As, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn including their fractionation behavior and environmental risk. Samples were collected along the Kou River that flows across two districts in the Manyara region of Tanzania. The leaching behavior of Fe was studied using sequential extraction fractionation and kinetics approach. The Kou water failed to meet the irrigation, aquatic, and biological life standards with respect to one of more trace metallic elements (TMEs). Fe concentration in the river water ranged from 4.1 to 5.38 mg/L exceeding all the three standards. Six pollution indices were applied to assess the contamination and ecological risks of the nine trace metallic elements in the sediments. Overall, the metals were found to moderately contaminate the sediments. Cr, Fe, and Mn fell under the ‘severely polluted’ sediment quality class. Fe was the only metal that was found to significantly pollute both the river water and sediments. The Fe fractions in the sediments were in the order of residuals>Fe-Mn bound>organic bound>carbonate bound>water soluble>ion exchangeable. 7.8% of the total Fe content was bioavailable with a low potential to leach from the sediments. Under natural conditions, the sharpest release of the non-residual mobile fractions of Fe were identified to occur within the first 24 hours with the maximum Fe leached being 0.14% on the 12th day. None of the metals in the sediments were found with a potential to pose ecological risk.
Temporal perspective and mental functioning of socially maladjusted youth held in conditions of institutional isolation

Introduction: The aim of the research presented was to determine whether the temporal perspective of socially maladjusted adolescents, held against their will in conditions of institutional isolation, can be linked to the level of their mental functioning. Method: The researchers applied standardized psychological tests in a survey of a group of 311 adolescents (38 females, 273 males), aged 13–18, who were held in a youth education center (YEC) following family court rulings. Results: After a psychological characterization of three groups of adolescents with different temporal perspectives, distinguished on the basis of cluster analysis, it was proven that the temporal perspective of adolescents held in institutional isolation is crucial to the quality of their mental functioning. The participants who scored significantly lower in all dimensions of temporal perspective seemed to be experiencing lower-intensity loneliness, depression and stress. Those, on the other hand, whose perspective was of the present-hedonistic or past-negative types felt lonelier, lived under more stress and suffered from more severe forms of depression. The teenagers whose temporal perspective was past-positive and future-oriented turned out to be most balanced in terms of the investigated psychological variables, which may confirm the importance of a balanced temporal perspective for good quality of life. Conclusions: The strongest effect was found in relation to peer and family loneliness, which indicates the importance of family and peers in the teenagers’ psychosocial development. These findings promote further reflection on the legitimacy of institutional measures used in the rehabilitation of socially maladjusted youth to date, and the search for new, effective kinds of social rehabilitation intervention.