Miracle-dz/UniFAR-FTUs huggingface dataset:
Anxiety and post-traumatic stress disorders in patients with chronic respiratory diseases

Purpose of review This brief critical review evaluates recent research on the impact of anxiety disorders and post-traumatic stress disorders (PTSD) on patients with chronic obstructive pulmonary disease (COPD) and asthma. In these patients, untreated anxiety and PTSD exacerbate acute symptoms, increase disability and impair quality of life. Therefore, effective interventions are also reviewed. Recent findings Anxiety disorders and PTSD are prevalent in COPD and asthma, worsen symptoms in acute exacerbations, and increase morbidity and healthcare utilization. Pulmonary rehabilitation (PR), cognitive behavioural therapy and pharmacological therapy are each effective in COPD patients with anxiety or PTSD. However, very little work examined therapy for combined anxiety and PTSD in patients with either COPD or asthma. Summary Despite the high prevalence of anxiety disorder or PTSD in patients with chronic respiratory diseases, a paucity of literature remains demonstrating the efficacy of pharmacological therapies for these conditions. This review highlights the promising benefits of PR on anxiety, but prospective trials are needed to demonstrate the efficacy of interventions with PTSD alone, or with concomitant anxiety.
Methods for detection and identification of beer-spoilage microbes

It is critical that breweries of all sizes routinely monitor the microbiome of their process to limit financial losses due to microbial contamination. Contamination by beer-spoiling microbes (BSMs) at any point during the brewing process may lead to significant losses for breweries if gone undetected and allowed to spread. Testing and detection of BSMs must be routine and rapid, and because even small breweries need the capability of BSM detection and identification, the method also needs to be affordable. Lactic acid bacteria (LAB) are responsible for most spoilage incidents, many of which have been shown to enter the viable but nonculturable (VBNC) state under conditions present in beer such as cold or oxidative stress. These bacteria are invisible to traditional methods of detection using selective media. This article describes several methods of BSM detection and identification that may be useful in the majority of craft breweries. While there are several genomic methods that meet some or many qualifications of being useful in craft breweries, real-time quantitative polymerase chain reaction (qPCR) currently best meets the desired method characteristics and holds the most utility in this industry, specifically SYBR Green qPCR. qPCR is a targeted method of detection and identification of microbes that is affordable, rapid, specific, sensitive, quantitative, and reliable, and when paired with valid DNA extraction techniques can be used to detect BSMs, including those in the VBNC state.
Cocaine and Ischemic or Hemorrhagic Stroke: A Systematic Review and Meta-Analysis of Clinical Evidence

Cocaine consumption has increased over the last decade. The potent sympathomimetic effects of the drug can lead to serious neurovascular complications in the form of ischemic stroke (IS), intracerebral hemorrhage (ICH), or subarachnoid hemorrhage (SAH). This systematic review and meta-analysis were designed to describe the clinical features and outcomes of patients suffering from IS, ICH, or SAH occurring in the context of cocaine use. The PubMed, Embase, Cochrane, and Web of Science libraries were queried in December 2022. Studies were included if they provided information regarding the epidemiology, clinical presentation, or outcomes in cocaine-associated strokes. Odds ratios (OR) were pooled using a random-effects model. A total of 36 papers were included. Strokes associated with cocaine use were more prevalent in younger populations and those of African American descent. Cocaine use increased the odds of IS, ICH, or SAH (OR = 5.05, p < 0.001). The odds of mortality (OR = 1.77, p = 0.0021), vasospasm (OR = 2.25, p = 0.0037), and seizures (OR = 1.61, p < 0.001) were also worse when strokes were associated with cocaine use. In addition to counseling patients on the benefits of drug cessation, clinicians should remain vigilant of the potential complications in patients who are hospitalized with cocaine-associated strokes.
Pressure derivative approach to transient test analysis: A highpermeability North Sea reservoir example

The recently developed method of transient test analysis, utilising the derivative of pressure data vs. time rather than the pressure/time data alone, is discussed in general terms and with regard to its application to very high permeability reservoirs. The methods extension to the various well/reservoir system models considered by several authors is summarized. The procedure involves the combined use of existing type curves, in both the conventional dimensionless pressure form (P /SUB D/ ) and converted to the new dimensionless pressure derivative grouping (p' /SUB D/ x /SUP t/ D/C /SUB D/ ). The procedure proved both theoretically, and through examination of data from the Oseberg field, to be a powerful tool in transient test analysis. The identification and interpretation of reservoir heterogeneites, both linear and dispersed, as well as earth tidal effects, which are often not easily discernable through existing methods, is greatly facilitated by the new method.
Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective

This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services.
Effect of mirror therapy on upper extremity functional and motor skills among patients with chronic stroke

Background: Mirror therapy is one of the cognitive-induced interventions based on mirror neurons on neurologic grounds. Aim: to evaluate the effect of mirror therapy on upper extremity functional and motor skills among chronic stroke patients. Design: a quasi-experimental design (pre/post-test). Setting: The study was conducted at Egypt of Health care Authority, Port Said hospitals ICU departments. Subjects: All patients (50) with chronic stroke who were admitted to the previously mentioned settings at the time of data collection, which was chosen using a convenient sampling technique. Tools: Three tools were used for data collection: Tool 1: Structured interview questionnaire; Tool 2: Fugl-Meyer assessment, lower limb function (FMA); Tool 3: Brunnstrom stages, Tool 4: Functional Independence Measure (FIM). Tool 5: Modified Ashworth Scale. Results: The results of the study illustrated that the mirror therapy group showed significant differences in muscle strength, range of motion of wrist extension, and muscle tone of wrist flexors in the post-test compared to the pre-test as demonstrated by the Dualer IQ Inclinometer and Modified Ashworth Scale. (p<0.05).
Earthquake Sources and Hazard in northern Central America

Northern Central America is a tectonically complex zone defined by its borders with Cocos and North America plates. The Middle America subduction zone and the strike-slip motion along the North America-Caribbean plate boundary, in that order, control most of its deformation. The interaction between the different elements of the studied area is evident from the high seismicity in the region, especially along plate boundaries. Also in the interior of the region, seismicity shows that deformation takes place, though in lesser degree. In a time window of 30 years, three earthquakes with moment magnitude larger than 7 struck northern Central America evincing the need to estimate the seismic hazard for the zone. To tackle the problem, we compiled a catalogue of hypocenters commencing in 1964, defined seismogenic sources and described the evolution of earthquake activity through a Poisson model. Probabilistic seismic hazard (PSH) calculations for the next 50 years were performed. The highest estimate of seismic hazard was obtained for the zone adjacent to the subduction zone. Because of the fundamental importance of demarcating seismogenic sources in the PSH analysis, i.e. defining the seismotectonic model, we extended the catalogue to cover 102 years for the whole northern Central America. We have studied the North America-Caribbean plate boundary in order to refine the fault representation. Different techniques were used, like that of body-waveform modeling, allowing us to limit the extent of depth of faulting to 20 km. The seismic moment tensor was used to estimate the deformation velocities on known tectonic structures, including those of the Honduras depression and borderland faults. Finally, we made use of the Coulomb stress criterion to determine the relation between earthquake occurrence and static stress changes following major earthquakes.
Artificial intelligence-based machine learning data classification for the analysis of underwater noise of ship traffic and bottlenose dolphin sounds

In this paper, we present a machine learning technique based on artificial intelligence (AI) Neural Network (NN) and its application to the classification of underwater sounds, underwater noise, and underwater sounds of marine mammals (dolphins, cetaceans) recorded by hydrophones. The data collection process generates a large amount of underwater sound recordings, which are very time consuming to process by humans and, due to the monotonous nature of the work, there is a high potential for human error and human inattention. Considering these aspects, the possibility of applying artificial intelligence to post-process the sound recordings, classifying the sounds, up to the level of species recognition in the case of cetaceans and dolphins, and up to the level of vessel type recognition in the case of vessel traffic noise, has been raised. Artificial intelligence and deep learning tools greatly assist in the machine analysis of vast amounts of data and automated categorization. With the help of the trained model, it can be estimated which of the previously categorized types is characteristic of a new audio clip. We tested the accuracy of this method and concluded that our machine-learning sound classification has the best accuracy more than 93% when processing and classifying underwater hydrophone sound recordings with high background shipping and tourist boats’ underwater noise, recorded during our fieldwork on the Adriatic Sea.
Segmentation of tooth enamel microstructure images using classical image processing and U-Net approaches

Tooth enamel is the hardest tissue in human organism, formed by prism layers in regularly alternating directions. These prisms form the Hunter–Schreger Band (HSB) pattern when under side illumination, which is composed of light and dark stripes resembling fingerprints. We have shown in previous works that HSB pattern is highly variable, seems to be unique for each tooth and can be used as a biometric method for human identification. Since this pattern cannot be acquired with sensors, the HSB region in the digital photograph must be identified and correctly segmented from the rest of the tooth and background. Although these areas can be manually removed, this process is not reliable as excluded areas can vary according to the individual‘s subjective impression. Therefore, the aim of this work was to develop an algorithm that automatically selects the region of interest (ROI), thus, making the entire biometric process straightforward.We used two different approaches: a classical image processing method which we called anisotropy-based segmentation (ABS) and a machine learning method known as U-Net, a fully convolutional neural network. Both approaches were applied to a set of extracted tooth images.U-Net with some post processing outperformed ABS in the segmentation task with an Intersection Over Union (IOU) of 0.837 against 0.766. Even with a small dataset, U-Net proved to be a potential candidate for fully automated in-mouth application. However, the ABS technique has several parameters which allow a more flexible segmentation with interactive adjustments specific to image properties.
ACETYLCHOLINESTERASE INHIBITORY ACTIVITY OF A CYCLITOL ISOLATED FROM THE LEAVES OF

INTRODUCTION Rational drug discovery from plants started at the beginning of the 19th century, when the German apothecary assistant Friedrich Sertürner succeeded in isolating the analgesic and sleep-inducing agent from opium which he named morphium (morphine) (Atanasov 2015). This triggered the examination of other medicinal herbs, and during the following decades of the 19th century, many bioactive natural products, primarily alkaloids, such as quinine, caffeine, nicotine, codeine, atropine, colchicine, cocaine, capsaicin wer from their natural sources (Ata 2015). Research on plants which are used for the treatment of memory dysfunction in folk medicine led to the isolation of acetylcholinesterase inhibitor, conypododiol from methanol extract of Asparagus adscendens (Khan et al., 2010). Bauhinia shrub in the family Fabaceae with a medicinal application in the treatment of mycosis, diarrhea and malaria etc. The plant is reported to contain p-coumaric acid, ferulic acid, hyperoside from its Leaves (Compaore 2011). In this work, Leaves of B. rufescens investigated for a potent anti-alzheimer’s drug using acetylcholinesterase inhibitory activity. ABSTRACT Bauhinia rufescens is a medicinal plant used in the treatment of mycosis, diarrhea malaria. Leaves of B. rufescens were petroleum ether, ethyl acetate and methanol. Silica gel column purification of methanol extract afforded the isolation of a cyclitol, identified as 3 extracts and the isolated compound were evaluated for their anticholinesterase inhibitory activity using bioautographic and microplate techniques. The cyclitol demonstrated hi activity in the microplate assay relative to the crude extract with IC The Inhibitory activity exhibited by the cyclitol against acetylcholinesterase enzyme indicates its potential as a drug agent for the treatment of
Utilization and Outcomes of Primary Prevention Implantable Cardioverter‐Defibrillators in Patients With Hypertrophic Cardiomyopathy

Background There is uncertainty about the appropriate use of primary prevention implantable cardioverter‐defibrillators (ICDs) among older patients with hypertrophic cardiomyopathy. Methods and Results Patients with hypertrophic cardiomyopathy who received a primary prevention ICD between 2010 and 2016 were identified using the National Cardiovascular Data Registry ICD Registry. Trends in ICD utilization and patient characteristics were assessed over time. Using linked Centers for Medicare and Medicaid Service claims data, Cox proportional hazard models assessed factors associated with mortality and postdischarge hospitalization for cardiac arrest/ventricular arrhythmia. Of 5571 patients with hypertrophic cardiomyopathy, 1511 (27.1%) were ≥65 years old. ICD utilization increased over time in all age groups. There were no changes in the prevalence of risk factors for sudden cardiac death over time. The variables most strongly associated with postdischarge mortality were older age (adjusted hazard ratio (aHR) 1.80 [95% CI, 1.47–2.21]), New York Heart Association class (III/IV versus I/II aHR 2.17 [95% CI, 1.57–2.98]), and left ventricular ejection fraction (left ventricular ejection fraction ≤35% versus >50% aHR 2.34 [95% CI, 1.58–3.48]; left ventricular ejection fraction 36%–50% versus >50% aHR 2.98 [95% CI, 2.02–4.40]), while history of nonsustained ventricular tachycardia (aHR 2.38 [95% CI, 1.62–3.51]) and New York Heart Association class (III/IV versus I/II aHR 1.84 [95% CI, 1.22–2.78]) were strongly associated with hospitalization for ventricular arrhythmia/cardiac arrest. Conclusions Primary prevention ICD utilization in patients with hypertrophic cardiomyopathy increased over time, including among those ≥65 years old. Among older patients, the strongest risk factors for hospitalization for ventricular arrhythmia/cardiac arrest following ICD implantation were history of nonsustained ventricular tachycardia and New York Heart Association class.
Towards Smart Manufacturing using Reinforcement Learning in a Sparse-Rewarded Environment for Through-Hole Technology

Advance automation, digitization, and interconnectivity are driving rapid evolution in the manufacturing industry. As a result, smart manufacturing has become increasingly popular, driven by the integration of deep reinforcement learning (DRL) and digital twins in agent training. DRL has proven to be particularly effective in smart manufacturing, allowing agents to respond dynamically to an ever-changing environment with a wide range of manufacturing components which leads to a slight variation in the objectives. This study proposes an efficient DRL approach to smart manufacturing tasks by breaking them into three simpler tasks: reach, push, and pick-and-place. The Truncated Quantile Critics (TQC) algorithm is employed as the learning algorithm for the agent, which is trained using Hindsight Experience Replay (HER) to formulate virtual goals in a sparserewarded environment. Our experiments on these environments demonstrate that the proposed method of TQC+HER achieves the best results on all three of the smart manufacturing tasks.
A Comparison of OpenNMT Sequence Model for Indonesian Automatic Question Generation

Evaluation of learners is a crucial aspect of the educational system. However, creating evaluation instruments is a process that demands teachers' time and energy. The researcher developed the Indonesia Automatic Question Generator in this study using an architecture modified from past studies. The primary goals of this project are (1) to construct an AQG tool utilizing the OpenNMT series and (2) to analyze and compare the model's performance. As a data source, this study employs the SQuAD 2.0 dataset and numerous sequence techniques, including BiGRU, BiLSTM, and Transformer. The researcher trained the models using OpenNMT-py and Google Collaboratory. This approach generates questions that are relevant to the context of the source. This study found that the model was acceptable.
On Interrupt Location Design for Vehicular Platoon via Eigenvalue Constraint

The development of automatic driving functions is currently active. Vehicle platoon control is expected to improve fuel efficiency and reduce traffic congestion by enabling vehicles to travel via V2V communication between vehicles. When a platoon of vehicles is running in formation, interruptions by vehicles outside the platoon may occur. When an interruption occurs, the vehicles in the platoon are affected by speed fluctuations and other effects, which vary depending on the location of the interruption. In this study, the eigenvalue constraint is used to design the location of the interrupter for the platoon of vehicles.
Simulating Our Way to Safer Software: A Tale of Integrating Microarchitecture Simulation and Leakage Estimation Modeling

An important step to protect software against side-channel vulnerability is to rigorously evaluate it on the target hardware using standard leakage tests. Recently, leakage estimation tools have received a lot of attention to improve this time-consuming process. Despite their advancements, existing tools often neglect the impact of microarchitecture and its underlying events in their leakage model which leads to inaccuracies. This paper takes the first step in addressing these issues by integrating a physical side-channel leakage estimation tool into a microarchitectural simulator. To achieve this, we first systematically explore the impact of various architecture and microarchitecture activities and their underlying interactions on the produced physical side-channel signals and integrate that into the microarchitecture model. Second, to create a comprehensive leakage estimation report, we leverage taint tracking and symbolic execution to accurately analyze different paths and inputs. The final outcome of this work is a tool that takes a binary and generates a leakage report that covers architecture and microarchitecture-related leakages for both data-dependent and path-dependent information leakage scenarios.
0 Th World Congress on Structural and Multidisciplinary Optimization Room Acoustic Optimization with Variable Thickness Columns

In this work, a rigorous and practical design method for room acoustics is proposed. The ability to control acoustic characteristics has a wide range of applications, such as a car interior with controlled engine noise, barrier structures to suppress industrial and traffic noise, and design of concert halls and recording studios. The approach used in this work is based on installing rectangular columns with various thicknesses at some parts of room boundaries. This is a practical approach as the manufacturing and the installation are simple, compared to designs obtained using a topology optimization approach [1], and is utilized in some of the advanced acoustic rooms. A crucial task in the design process is to determine the effective layouts (i.e. thicknesses) of columns, and an optimization method can be used to achieve this potentially very challenging task in an efficient manner. For instance, in [2], rooms with evenly distributed frequency response are designed by finding optimal layouts of columns installed at one of the walls. In their work, the deviation between the obtained and ideal frequency responses is computed and a metamodel of the deviation is minimized to design the room. The work proposed here uses a rigorous optimization method based on the sensitivity analysis. The Helmholtz equation is solved using a finite element method to find the acoustic pressure field distribution. The performance of the room is expressed in terms of the acoustic pressure field, and its sensitivity with respect to the thicknesses of the columns is computed by solving an adjoint problem in a similar manner as [1]. One difference is that the gradients of the stiffness matrix with respect to the column thicknesses need to be computed in our work, instead of the effective density of the material. Our current investigation focuses on the implementation of the method in 2D problems, where the objective is to minimization of the acoustic pressure level within a prescribed region of a room. We have conducted studies on the effects of various properties of the problem such as the room shape, frequency of the noise, location of the observation region, and materials used for the columns, which have resulted in several different designs of optimal acoustic rooms.