Miracle-dz/UniFAR-FTUs huggingface dataset:
A study to understand the pattern of hyponatremia in patients using selective serotonin reuptake inhibitors and serotonin dopamine antagonists

Introduction: Hyponatremia can be a common but often overlooked side effects of psychotropics drugs. Most patients with drug-induced hyponatremia are asymptomatic and diagnosis is made incidentally following routine blood tests. Objectives: The aim of the study was to understand the pattern of hyponatremia in patients using selective serotonin reuptake inhibitors (SSRI) and serotonin dopamine antagonists (SDA). Materials and Methods: All inpatients and outpatients who were diagnosed with International Classification of Diseases-10 psychiatric disorders and undergoing treatment with SSRI, SDA, or combination of both for the same, were included in the study after simple random sampling, subject to inclusion and exclusion criteria. Statistical Analysis Used: Categorical variables were observed as numbers and percentages. Continuous variables were evaluated as mean ± standard deviation. A Chi-square test was done to find the association between categorical variables. SPSS (IBM) version 21 was used for data analysis. Results: In 150 patients, we found hyponatremia in 17 patients (11.33%). About 66–75 age group patients had maximum found cases of hyponatremia (66.66%). About 20.31% of females found hyponatremia. Among SSRIs, 16% of individuals had hyponatremia whereas among SDA it was 6%. Patients who were taking both SSRIs and SDA total prevalence of hyponatremia was 12%. Conclusions: Older age groups and females had higher chances of hyponatremia while taking SSRIs and SDAs. Among SSRIs, escitalopram had maximum percentage of hyponatremia, whereas fluvoxamine had minimum. Among SDAs, risperidone had maximum percentage, whereas quetiapine had minimum percentage of hyponatremia. Patients who were taking both fluoxetine + olanzapine or fluoxetine + risperidone had higher percentage of hyponatremia.
First-line maintenance immunotherapy in advanced urothelial cancers: Practice changing implications of the JAVELIN bladder 100 trial

SUMMARY The JAVELIN Bladder 100 trial evaluated the benefits of Avelumab, a PD-L1 inhibitor, on the overall survival (OS) and progression-free survival (PFS), as the first‑line maintenance immunotherapy in patients with advanced bladder cancer who did not progress after initial platinum-based chemotherapy. [1] This phase three multi-institutional multi-national collaborative trail recruited biopsy-proven, unresectable, locally advanced or metastatic urothelial tumors, who had responded (stable disease [SD], partial [PR], and complete response [CR]) to 4–6 cycles of gemcitabine with either cisplatin or carboplatin. Eligible patients were randomized 1:1 to receive either Avelumab 10 mg/kg + best supportive care (BSC) or BSC alone. The plan was to continue the treatment as long as possible in each arm, and the analysis was planned when 76.2% of the recruited population had died. Three hundred and fifty patients were randomized into each group, with a median duration of follow-up of 19.6 months and 19.2 months in the Avelumab + BSC and the BSC alone groups, respectively. The primary endpoint was OS. The secondary endpoints comprised of PFS, objective response rate, and adverse events. All these were evaluated among the two cohorts - the total recruited patients and the PD-L1 positive patients (51% were PD-L1 positive). The OS significantly improved with Avelumab + BSC as compared to BSC alone in the overall recruited patients (Hazard ratio [HR] 0.69; 95% confidence interval [CI] 0.56, 0.86; P = 0.0005) as well as the PD‑L1 positive patients (HR 0.56;
Apolipoprotein A1 as a novel urinary biomarker for diagnosis of bladder cancer: A systematic review and meta-analysis

Introduction: The emergence of urinary biomarkers for bladder cancer diagnosis could provide a reliable and less invasive diagnostic method. It could be also used as an adjuvant to the current gold standards of cytology and cystoscopy to improve diagnostic accuracy and decrease the percentage of false positives. Methods: We searched PubMed, SCOPUS, and Web of Science up to March 18, 2020. We selected four studies that assessed the diagnostic accuracy of urinary apolipoprotein A1 (ApoA-1) in detecting bladder cancer and met the inclusion and exclusion criteria. Two authors independently extracted the data and performed quality assessment of the studies. Results: Four studies with 771 participants were selected; 417 were bladder cancer patients and 354 were controls. Bladder cancer was either transitional cell carcinoma or squamous cell carcinoma, the stages varied between Ta to T3, and the grades varied between G1 and G3. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 90.7%, 90%, 9.478, 0.1, and 99.424, respectively. Summary receiver operating characteristic curve showed an area under the curve of 0.9544 and QFNx01 index of 0.8965. Conclusions: ApoA-1 showed high sensitivity and specificity, so it could be a useful biomarker in diagnosis of bladder cancer.
Modeling Thin-Layer Drying of Turnip Slices Under Semi-Industrial Continuous Band Dryer

This study aims at investigating the thin-layer drying behavior of turnip slices in a multistage semi-industrial continuous band dryer. Turnip slices with the thickness of 4 mm were used for the drying experiments. The experiments were conducted at three air temperatures of 45, 60, and 75C, three air velocities of 1, 1.5, and 2 m/s, and three belt linear speeds of 2.5, 6.5, and 10.5 mm/s with three replications for each treatment. To estimate the drying kinetic of turnip slices, six mathematical models were used to fit the experimental data of thin layer drying. Consequently, the Midilli et al. model was selected as the best mathematical model to describe the drying kinetics of the turnip slices. The effective moisture diffusivity varied from 8.37 × 10−10 to 4.82 × 10−9 m2/s. The energy of activation varied from 12.80 to 26.31 kJ/mol using Arrhenius type equation. After well training of the ANN models, proved that the ANN model was relatively better than the empirical models. The best neural network for the prediction of moisture ratio (MR) and drying rate was feed forward back-propagation with 4-10-10-2 structure, training algorithm of Bayesian regulation and threshold functions of tansig-purelin-logsig. The best R2 value for predication of MR and drying rate were 0.9990 and 0.9619, respectively. Practical Applications Turnip is one of the oldest cultivated vegetables that have been used for human expenditures since prehistoric times. Drying process has been used for decades in food processing industries for efficient long-term preservation of final products. It has the potential to deliver safe food products through enzyme inactivation and microbe destruction. Advantages of semi-industrial continuous band drying are including short processing time, high mass transfer coefficients, low energy consumption, and high quality. To model experimental drying data, many correlations that are available in literature may be used. However, artificial neural network (ANN) methodology has become increasingly popular recently because of its capability of giving more general and precise results as also presented in this study.
Numerical Investigation of the Inland Transport Impact on the Bed Erosion and Transport of Suspended Sediment: Propulsive System and Confinement Effect

For competitive reasons, inland ships as maritime ships are increasingly larger with powerful propulsive systems. The impact of this evolution on the environment is multiple. One of the major impacts is the erosion of the channel bed and the sediment suspension. This erosion phenomenon is essentially caused by the turbulent flow around the ship generated by its movement as well as its propulsive system. Hence, for a better prediction and understanding of this phenomenon, it is indispensable to simulate with great precision the flow around the ship hull and the induced shear stress at the bottom. Different ways were used in the past to estimate the shear stress at the waterway bottom. Some of these ways are empirical, analytical and numerical using shallow water models. In the present work to study the erosion phenomenon caused by the inland transport, a sedimentary transport model was developed and implemented in the Computational Fluid Dynamics (CFD) model (Fluent) as external code. The coupled model was firstly verified and validated using measurements. The validated model was subsequently used to assess the influence of several parameters: depth (h) to draught (T) ratio, ship advance ratio (J), ship speed and sediment size (d50). The first results show clearly that the coupled model behaves correctly and gives very satisfactory results. The impact of each parameter was compared and analyzed.
Linguistic Typology

We examine the proposal by Spronck and Nikitina (this issue) that the phenomenon known as ‘reported speech’ constitutes a dedicated syntactic domain. This domain is defined as a “dedicated syntactic relation that differs from other sentential syntactic structures”, containing elements which are “primarily conventionalized and conditioned by grammar in a narrower sense”. Their stated goal is to “identify and classify phenomena occurring in the context of reported speech, and to propose benchmarks for establishing reported speech as a cross-linguistic category”. We broadly agree with the definition of reported speech proposed as useful for investigating how specific languages constrain the inference of this particular aspect of meaning via ostensive and inferential communicative acts (LaPolla 2003). The proposed definition enables functional identification of all kinds of reported speech practices while making no assumptions about how these practices formally manifest within different language ecologies. However, we disagree with the case for syntactic exceptionalism, i.e. the category ‘reported speech’ as a dedicated syntactic domain. Our main concern is that this claim downplays important evidence regarding the unified and multimodal ‘semiotic repertoire’ (Kendon 2014; Kusters et al. 2017) available for reporting utterances, thoughts, feelings, attitudes and actions across diverse languages – including deaf signed languages – of which the highly grammaticalised and conventional encoding of reported speech utterances is just one part. We argue that acts of reported speech are primarily depictions, which may not necessarily involve conventionally symbolic form-to-meaning mappings. Indeed, they are often entirely non-conventional bodily actions that are heavily dependent on the unfolding context for interpretation: “[t]hey use material gradiently so that certain changes in form imply analogical differences in meaning.to interpret depictions, we imagine what it is like to see the thing depicted” (Dingemanse 2015: 950). These depictions may (or may not) be framed by more conventional
Influencing Parameters on Tire–Pavement Interaction Noise: Review, Experiments and Design Considerations

Tire–pavement interaction noise (TPIN) is dominant for passenger vehicles above 40 km/h and 70 km/h for trucks. In order to reduce TPIN, numerous investigations have been conducted to reveal the influencing parameters. In this work, the influencing parameters on TPIN were reviewed and divided into five categories: driver influence parameters, tire related parameters, tread pattern parameters, pavement related parameters, and environmental parameters. The experimental setup on analyzing and insights into optimizing those parameters are given. At the end, summary tables are presented to compare all the parameters discussed, including the pertinent frequency, potential noise influence, physical mechanism, etc. As such, this review article can also serve as a reference tool for new researchers on this topic. This work covers references from 1950s till present, aiming to distribute key knowledge in both classic and recent studies.
CBAM: A Contextual Model for Network Anomaly Detection

Anomaly-based intrusion detection methods aim to combat the increasing rate of zero-day attacks, however, their success is currently restricted to the detection of high-volume attacks using aggregated traffic features. Recent evaluations show that the current anomaly-based network intrusion detection methods fail to reliably detect remote access attacks. These are smaller in volume and often only stand out when compared to their surroundings. Currently, anomaly methods try to detect access attack events mainly as point anomalies and neglect the context they appear in. We present and examine a contextual bidirectional anomaly model (CBAM) based on deep LSTM-networks that is specifically designed to detect such attacks as contextual network anomalies. The model efficiently learns short-term sequential patterns in network flows as conditional event probabilities. Access attacks frequently break these patterns when exploiting vulnerabilities, and can thus be detected as contextual anomalies. We evaluated CBAM on an assembly of three datasets that provide both representative network access attacks, real-life traffic over a long timespan, and traffic from a real-world red-team attack. We contend that this assembly is closer to a potential deployment environment than current NIDS benchmark datasets. We show that, by building a deep model, we are able to reduce the false positive rate to 0.16% while effectively detecting six out of seven access attacks, which is significantly lower than the operational range of other methods. We further demonstrate that short-term flow structures remain stable over long periods of time, making the CBAM robust against concept drift.
Synthesis and characterization silver nanoparticles and coating with chitosan Gümüş nanopartikül sentezi, karakterizasyonu

Recent advancements in nanotechnology have garnered increasing attention due to the improvements in producing and utilizing particles whose sizes are extremely small. There is particular interest in metal nanoparticles due to their distinctive properties. Silver nanoparticles (Ag-NPs) have been of particular interest owing to their unique electrical, optical and physicochemical properties as well as their biomedical applications. Ag-NPs are the fastest growing class of nanoscale materials because of their antibacterial activities. They are now considered to be the next generation antimicrobials. Today, owing to their disinfectant properties, Ag-NPs are extensively used in numerous consumer products, including disinfecting medical devices and home appliances, paints, optical devices, plastics, textiles, soaps and laundry detergents, cosmetics, health-care products, as well as in food packing and food products as preservative and anti-caking agents. Despite their widespread usage, the products containing Ag-NP additives are a serious environmental and human health concern because of their toxicity. Studies on cell and bacterial cultures have shown that Ag-NPs are toxic; and the toxic effects are mainly due to the dissolved Ag ions when Ag-NPs degrade in water. In this study, considering the criteria outlined above, the purpose of the study and Ag-NPs were chemically synthesized in the laboratory environment and modified as chitosan coating. UV-Vis spectroscopy, transmission electron microscopy, scanning electron microscopy, X-ray crystallography and zeta potential analysis were used to characterize the silver nanoparticles.
Development and Optimisation of Solid-Phase Extraction of Extractable and Bound Phenolic Acids in Spelt (Triticum spelta L.) Seeds

A solid-phase extraction (SPE) technique was developed and optimised for isolation and concentration of extractable and bound phenolic acids from germinated spelt seeds, for analysis by liquid chromatography–mass spectrometry. Samples initially underwent solvent extraction under different conditions to maximise the yield of phenolic antioxidants. Optimal extraction conditions for extractable phenolics were absolute methanol as solvent, sample-to-methanol ratio 1:9, and reconstitution in non-acidified water. The bound phenolics were extracted from sample pellets using hydrolysis with 2 M NaOH, acidification of the hydrolysate with formic acid, and simultaneous isolation and purification using Strata X polymeric RP tubes. Compared to liquid-liquid extraction, this direct SPE protocol has significant advantages in terms of higher extraction efficiencies of total and individual phenolics and their antioxidant activities. These data suggest that direct SPE represents a rapid and reliable method for quantitative analysis of both the extractable and the commonly overlooked bound phenolics in Triticum spelta seeds.
The Impacts of Emerging Technologies on Accountants’ Role and Skills: Connecting to Open Innovation—A Systematic Literature Review

Purpose: This paper reviews the recent accounting literature focusing on emerging technologies’ impacts on accountants’ role and skills. Specifically, it determines what emerging technologies are most studied concerning their impacts on accountants’ role and skills, which research strategies are used in the studies that focus on this theme, and the impacts of the identified emerging technologies on accountants’ skills. It also investigates whether open innovation is an influencing factor in this connection. Methodology: Through a systematic literature review following the five-step approach described by Denyer and Tranfield, the Web of Science and Scopus databases are used as a source of article collection. Thus, our analysis starts with a total of 157 articles. Findings: The main analytical results of the study identify the skills that today’s accountants must have and what role is assigned to them. Practical implications: Professional bodies and regulators may take the results into account in informing the revision of standards, rules, and laws for the new environment. Educational institutions can use the results to adjust their programs to prepare students with the skills employers expect them to have. Contribution: This study provides an integrated understanding of the implications of recent technological developments on the accountant’s role and skills that have been hitherto discussed in the existing literature in a fragmented way. Future research suggestions are also provided, based on identified gaps in the literature, assisting other researchers in finding a way to augment knowledge in this area.
Static Malware Detection using Recurrent Neural Networks

An ever-growing number of malicious attacks on our IT infrastructure calls for new and better methods of protection. In this thesis, we focus on the use of recurrent neural networks as an agile and accurate way of detecting malware. We only used features extracted from files in the PE file format to represent the suspicious programs which we used to train various types of recurrent neural networks. In this work, we present unique neural network architecture combining dense and stacked LSTM layers to classify PE files. We worked with our dataset of 30,154 files collected from available resources with which we achieved an accuracy of 98.41%, while only 0.5% of benign samples were misclassified as malware on our balanced dataset. All this was accomplished with only 250 epochs of training. These results prove that machine-learning algorithms, especially LSTM networks, can be used as a quick and reliable tool for malware detection.
primary structure

The NMDA subtype of ionotroplc glutamate receptors has been implicated m the activity-dependent modification of synaptic efficacy in the mammalian brain Here we describe a cDNA Isolated from Drosophila melunogaster which encodes a putative Invertebrate NMDA receptor protein (DNMDAR-I). The deduced amino acid sequence of DNMDAR-I displays 46% ammo acid ldentlty to the rat NMDARl polypeptide and shows significant homology (16-23%) to other vertebrate and invertebrate glutamate receptor proteins. The DNMDAR-I gene maps to position 83AB of chromosome 3R and is highly expressed in the head of adult flies Our data indicate that the NMDA subtype of glutamate receptors evolved early during phylogeny and suggest the existence of activity-dependent synaptic plasticity in the insect brain.
Surface Roughness and Electrochemical Performance Properties of Biosynthesized α-MnO2/NiO-Based Polyaniline Ternary Composites as Efficient Catalysts in Microbial Fuel Cells

In this study, biosynthesized α-MnO2/NiO NPs and chemically oxidative polyaniline (PANI) were synthesized to form ternary composite anode material for MFC. The synthesized materials were characterized with different materials (UV-Vis, FTIR, XRD, TGA-DTA-DSC, SEM-EDX-Gwyddion, CV, and EIS) to deeply examine their optical, structural, morphological, thermal, roughness, and electrocatalytic properties. The degree of surface roughness for α-MnO2/NiO/PANI was 23:65 ± 5:652 nm. This value was higher than the pure α-MnO2, pure PANI, and even α-MnO2/PANI nanocomposite due to surface modification. The total charge storing performance for bare PGE, α-MnO2/PGE, PANI/PGE, α-MnO2/PANI/PGE, and α-MnO2/NiO/PANI/PGE were 5.291, 17.267, 20.659, 23.258, and 24.456mC. From this, the charge storing performance formed by α-MnO2/NiO/PANImodified PGE was highest, indicating that this electrode is best in cycle stability and increases its life cycle during energy conversion time in MFC. This is also supported by its effective surface area, having a value of 0.00984 cm. From this, it is evidenced that the ternary composite catalyst-modified anode facilitates the fast electrocatalytic activity as observed from its high peak current and lower peak-to-peak potential separation (ΔEp = 0:216V) than other electrodes. Such surface modification helps to store more electrical charge by increasing electrical conductivity during its charge/discharge processing time. In addition, the lower charge transfer resistance property with a value of 788.9 Ω and the fast heterogeneous electron transfer rate of ~2.92 s enable to facilitate glucose oxidation, and this enhances to produce high power output and increase wastewater treatment efficiency. As a result, the bioelectrical activity of α-MnO2/NiO/PANI composite-modified PGE was very effective in producing a maximum power density of 506.96mWm with COD of 81.92%. The above observations justified that αMnO2/NiO/PANI/PGE serves as an effective anode material in double-chambered MFC application.