Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing Incremental Burden of Surgical Bleeding Associated with Multiple Comorbidities as Measured by the Elixhauser Comorbidity Index: A Retrospective Database Analysis

Purpose Disruptive bleeding can complicate surgical procedures, increasing resource use, and impacting patients’ well-being. This study aims to elucidate the impact of comorbidity on the risk of disruptive surgical-related bleeding and selected transfusion-associated complications, as well as the incremental cost of such bleeding. Patients and Methods This retrospective analysis of the Premier Healthcare Database included patients who were age ≥18 years and who had a procedure of interest between 1-Jan-2019—31-Dec-2019: cholecystectomy, coronary artery bypass grafting, cystectomy, hepatectomy, hysterectomy, pancreatectomy, peripheral vascular, thoracic, and valve procedures (first=index). The Elixhauser comorbidity index was assessed on index date and patients were grouped by cumulative comorbidity score (0, 1, 2, 3, 4, 5, ≥6). Outcomes, all measured as in-hospital during index, included bleeding (diagnosis and/or intervention for bleeding), transfusion-associated complications (diagnosis of infection, acute renal failure, or vascular events), and incremental total hospital costs associated with bleeding. Multivariable generalized linear models were used to examine the association of comorbidity/bleeding with outcomes. Results Of the 304,074 patients included, 7% experienced bleeding. The Elixhauser scores were distributed as follows: 0=29%, 1=23%, 2=18%, 3=12%, 4=8%, 5=5%, ≥6=5%. Odds of bleeding significantly increased with Elixhauser score: 1 comorbidity vs 0 (odds ratio [OR] =1.30, 95% confidence interval [95% CI] =1.19–1.43), and this trend continued to surge (≥6 comorbidities [OR=3.22, 95% CI=2.94–3.53]). Similarly, the odds of transfusion-associated complications significantly increased with comorbidities score: 1 comorbidity vs 0 (OR=2.14, 95% CI=1.88–2.34), ≥6 comorbidities vs 0 (OR=12.37, 95% CI=10.80–14.16). The incremental cost of bleeding also increased with comorbidities score; per-patient costs with and without bleeding were $18,132 vs $13,190, p < 0.001 among patients with 0 comorbidities and $28,952 vs $19,623, p < 0.001 among patients with ≥6 comorbidities. Conclusion Higher comorbidity burden was associated with significant increases in the risk of surgical bleeding, subsequent transfusion-related complications, and incremental cost burden of bleeding.
Pediatric Pulmonology

Funding information New Zealand Tertiary Eduction Commission (TEC) MedTech CoRE (Center of Research Excellence), Grant/Award Number: 3705718; New Zealand CureKids Foundation, Grant/ Award Number: 3904 Abstract Objectives: A pilot study to compare pulmonary mechanics in a neonatal intensive care unit (NICU) cohort, specifically, comparing lung elastance between male and female infants in the NICU. Hypothesis: Anecdotally, male infants are harder to ventilate than females. We hypothesize that males have higher model‐based elastance (converse: lower specific compliance) compared to females, reflecting underlying stiffer lungs. Study Design: A clinically validated, single‐compartment model is used to identify specific elastance (inverse of specific compliance) and resistance for each breath. Specific elastance accounts for weight differences when comparing male and female infants. Relative percent breath‐to‐breath variability (%ΔE) in specific elastance is also compared. Level of asynchrony was also determined. Patient‐subject Selection: Ten invasively mechanically ventilated patients from Christchurch Women’s Hospital. Methodology: Airway pressure and flow data from 10 invasive mechanical ventilation (MV) infants from Christchurch Women’s Hospital Neonatal Intensive Care Unit, New Zealand was prospectively recorded under standard MV care. Model‐based specific elastance and resistance are identified for each breath, as well as relative percent breath‐to‐breath variability (%ΔE) in specific elastance. Results: Male infants overall had higher specific elastance compared to females infants (P ≤ .01), with median (interquartile range) for males of 1.91 (1.33‐2.48) cmH2O kg/mL compared to 1.31 (0.86‐2.02) cmH2Okg/mL in females. Male infants had lower variability with %ΔE of −0.03 (−7.56 to 8.01)% vs female infants of −0.59 (12.56‐12.86)%. Males had 14.75% asynchronous breaths whereas females had 17.54%. Conclusion: Overall, males had higher specific elastance and correspondingly lower breath‐to‐breath variability. These results indicate male and female infants may require different MV settings, mode, and monitoring.
Sexual, reproductive and mental health among young men (10–24) in low-and-middle income countries: a scoping review

Background The relationship between SRH and mental health among men is not well documented, especially in the 10–24 age group. This scoping review aimed to investigate what is known about the association between SRH and mental health among young men (10–24) in LMICs. Methods Embase, APA PsycInfo, MEDLINE(R) ALL, ASSIA and the Cochrane Library of Database of Systematic Reviews were searched from the year of establishment up to August 2022. The review was reported using the PRISMA-ScR checklist. Results A total of (n = 2636) studies were identified from the five databases. After the completion of screening, only ten studies (n = 8 cross-sectional, n = 1 mixed methods and n = 1 qualitative) met the eligibility criteria and were included in the review. The findings suggest that there is a reciprocal relationship between mental health and SRH. Sperm concentration and total sperm count were found to be lower in depressed men. Poor mental health was associated with early sexual debut, higher rates of sexual activity and an increased number of sexual partners. Poor mental health was also found among men who had sex with men (MSM). In addition, we found a relationship between sexual abuse, sexual coercion and poor mental health. Conclusion The findings of this unique study indicate that poor mental health is associated with poor SRH outcomes and vice versa among young men (10–24) living in LMICs. However, further research will be needed to establish the temporal relationship between SRH and mental health outcomes.
Load Balancing Algorithms for Software-Defined Networks

This paper proposes two different algorithms for load balancing in the data plane of the Software Defined Networking (SDN) framework. The first algorithm emphasizes the Flexible Connection Handoff (FCH) that considers the maximum load and the difference of loads between the Access Points. The load represents the number of connected stations to a particular Access Point. The proposed algorithm optimizes the load between Access Points by considering the fairness index. The second algorithm describes a Quality of Service Load Balancing approach (QLB) for a High-Density software-defined Wi-Fi network by designing a module at the Access Points side and another module at the controller side. The Access Points are the OpenFlow Access Points (OFAPs) that push the capacity and load information of the OFAPs toward the controller side. The OFAPs module controls the OFAPs connection and reconnection with minimum controller intervention. The OFAPs module dynamically sets the load level based on the network's current state. The destination Access Point for reconnection is selected based on three metrics, for instance, Throughput, Received Signal Strength Indicator, and Packet Loss Rate. After receiving the OFAPs reports, the controller module makes decisions about load balancing by getting a complete view of the network condition. The integration of SDN into Wireless networks allows the development of load-balancing algorithms for control and data planes.
Extreme Weather Events over India in the last 100 years

India being mainly an agricultural country the economy and further its growth purely depends on the vagaries of the weather and in particular the extreme weather events. The information on extreme weather events lie scattered in the scientific and technical papers and in the research work of many authors and if put together will help the research community for further analysis. The authors in this paper present a factual and a brief review of the extreme weather events that occurred in India during the last 100 years (1991-2004). The socio-economic impacts of the extreme weather events such as floods, droughts, cyclones, hail storm, thunderstorm, heat and cold waves have been increasing due to large growth of population and its migration towards urban areas which has led to greater vulnerability. In recent years as per WMO review global losses from such extreme weather events is about US $ 50-100 billion annually with loss of life of about 2,50,000. Thus, greater efforts are needed to improve the forecast skill and use these better forecasts in disaster management.
Deep Learning-Based Location Spoofing Attack Detection and Time-of-Arrival Estimation through Power Received in IoT Networks

In the context of the Internet of Things (IoT), location-based applications have introduced new challenges in terms of location spoofing. With an open and shared wireless medium, a malicious spoofer can impersonate active devices, gain access to the wireless channel, as well as emit or inject signals to mislead IoT nodes and compromise the detection of their location. To address the threat posed by malicious location spoofing attacks, we develop a neural network-based model with single access point (AP) detection capability. In this study, we propose a method for spoofing signal detection and localization by leveraging a feature extraction technique based on a single AP. A neural network model is used to detect the presence of a spoofed unmanned aerial vehicle (UAV) and estimate its time of arrival (ToA). We also introduce a centralized approach to data collection and localization. To evaluate the effectiveness of detection and ToA prediction, multi-layer perceptron (MLP) and long short-term memory (LSTM) neural network models are compared.
Control of a Mobile Robot Through Brain Computer Interface

En este articulo se presenta una interfaz de control que permite comandar el movimiento de un robot movil en funcion de la captura de senales provenientes del cerebro del usuario. Dichas senales son adquiridas e interpretadas por medio del dispositivo Emotiv Epoc, el cual cuenta con 14 sensores tipo electrodo que captan senales electroencefalograficas (EEG) de alta resolucion, que despues son enviadas a un equipo de computo para ser procesadas. Se desarrolla una interfaz cerebro-computador (BCI) basada en el software y SDK del desarrollador del Emotiv mediante la cual se comanda de forma remota el robot movil. Se realizan pruebas de funcionalidad con el sensor para discriminar una intencion de desplazamiento por parte de un grupo de usuarios y un controlador difuso para sostener la direccion en casos de perdida de la concentracion. Como conclusion, se logra obtener un sistema eficiente para la manipulacion del robot
Pirfenidone-Loaded Polymeric Micelles as an Effective Mechanotherapeutic to Potentiate Immunotherapy in Mouse Tumor Models

Ongoing research is actively exploring the use of immune checkpoint inhibitors to treat solid tumors by inhibiting the PD-1/PD-L1 axis and reactivating the function of cytotoxic T effector cells. Many types of solid tumors, however, are characterized by a dense and stiff stroma and are difficult to treat. Mechanotherapeutics have formed a recent class of drugs that aim to restore biomechanical abnormalities of the tumor microenvironment, related to increased stiffness and hypo-perfusion. Here, we have developed a polymeric formulation containing pirfenidone, which has been successful in restoring the tumor microenvironment in breast tumors and sarcomas. We found that the micellar formulation can induce similar mechanotherapeutic effects to mouse models of 4T1 and E0771 triple negative breast tumors and MCA205 fibrosarcoma tumors but with a dose 100-fold lower than that of the free pirfenidone. Importantly, a combination of pirfenidone-loaded micelles with immune checkpoint inhibition significantly delayed primary tumor growth, leading to a significant improvement in overall survival and in a complete cure for the E0771 tumor model. Furthermore, the combination treatment increased CD4+ and CD8+ T cell infiltration and suppressed myeloid-derived suppressor cells, creating favorable immunostimulatory conditions, which led to immunological memory. Ultrasound shear wave elastography (SWE) was able to monitor changes in tumor stiffness during treatment, suggesting optimal treatment conditions. Micellar encapsulation is a promising strategy for mechanotherapeutics, and imaging methods, such as SWE, can assist their clinical translation.
Wireless Monitoring of Gearbox Vibrations with Embedded Sensors Using LabVIEW

The development of sensor monitoring systems for rotating machinery is important in automating industrial machine monitoring. Traditionally, the primary method for monitoring rotating machinery involved wired sensors such as accelerometers that were attached to the housing of the component under operation. However, this approach has a limitation since it cannot capture vibrations from the rotating part underneath the housing. Vibration measurements are crucial for machine health monitoring and analysis. Wireless vibration monitoring offers many benefits over wired monitoring. Thus, we implemented a wireless monitoring system using an embedded system design as a reliable option. A wireless monitoring system based on MEMS accelerometers with 3-axis digital outputs was used to measure vibrations ranging from -10 g to 10 g and temperature in a range from 0 to +70 °C. The system used the IEEE 802.11 wireless communication protocol and ESP modules. All the sensors were connected to a microcontroller and integrated into a printed circuit board, and the signals, data logging, and alarms were integrated into a PC platform using National Instruments LabVIEW software. The proposed monitoring system was embedded in a planetary gearbox to test its capabilities. The experimental results showed that the proposed wireless monitoring system could monitor gearbox vibrations in real-time, rendering data in the time domain, frequency domain, and root mean square. The data log files were used for vibration analysis. The research result verified the reliability and effectiveness of the wireless monitoring system by analyzing and diagnosing the vibration measurement signals.
MOKSLAS – LIETUVOS ATEITIS SCIENCE – FUTURE OF LITHUANIA

In the research process the landscape space of Latvian rehabilitation centers was inspected and analyzed within the social context. The centers were singled out not only by their aesthetical quality, but also by their functional landscape values contained. Rehabilitation gardens and parks are spaces, where people do more than receive medical treatment, they can relax surrounded by nature, engage in physical activities and rest without taking into account ones social status, age, gender, nationality, political views and religion. The goal is to summarize how Latvian rehabilitation gardens and parks promote patients’ physical activities in open space and analyze the functional quality of landscape of Latvian rehabilitation gardens and parks. Considering the quality of rehabilitation center environment, it is important to evaluated their accessibility and usability by possibly greater user spectrum that is characterized as a universal design. In Latvia a conceptual direction of design like this is relatively new, but already positively accepted and applied by specialists. Open space designs of rehabilitation center landscaping directly influence how a person feels and lives in the landscape. They sculpt not only the material quality of the environment, but also improve people’s communication facility and attitude towards one another. It is important for the environment of Latvian rehabilitation institutions to be friendly, because they are created for the use by all social groups and individuals by applying universal standard principles for open space improvement.
Cookie-less Predictive Targeting in Programmatic Media using Topic Modelling

Advertising be it traditional or digital deals with reaching out to the right set of audience. In Programmatic Advertising this is made easy with the help of cookies facilitating 1:1 targeting. These cookies could be used by data vendors to form audience groups that helped categorize the users based on the interests shown on the internet. This made life easier when we needed to reach out to potential new customers or optimize ad campaigns on audience segments showing better performance. With the advent of cookie-less policies from Google and privacy restrictions on identifiers set by Apple, this luxury is set to go away, we would not be able to use these cookie identifiers directly anymore. To accommodate this change, the industry has shifted its focus mainly to Key Performance Indicator (KPI) driven optimization, targeting geos, domains with higher performance; possibly losing out on the key essence of advertising – “Intent driven targeting”. Intent based targeting helps advertisers reach the right cohort of audience based on their online behavior, also helping them create relevant ads which could potentially help drive more engagement and KPI results. In this paper, we looked at the journey of a set of users across the internet and leveraged NLP and Unsupervised Learning to extract the topics they could associate to thus capturing the user’s intent. We then extend this intent onto geo, device and temporal combinations that tend to perform well for a given KPI.
Prospective clinical validation of the Empatica EmbracePlus wristband as a reflective pulse oximeter

Introduction Respiratory diseases such as chronic obstructive pulmonary disease, obstructive sleep apnea syndrome, and COVID-19 may cause a decrease in arterial oxygen saturation (SaO2). The continuous monitoring of oxygen levels may be beneficial for the early detection of hypoxemia and timely intervention. Wearable non-invasive pulse oximetry devices measuring peripheral oxygen saturation (SpO2) have been garnering increasing popularity. However, there is still a strong need for extended and robust clinical validation of such devices, especially to address topical concerns about disparities in performances across racial groups. This prospective clinical validation aimed to assess the accuracy of the reflective pulse oximeter function of the EmbracePlus wristband during a controlled hypoxia study in accordance with the ISO 80601-2-61:2017 standard and the Food & Drug Administration (FDA) guidance. Methods Healthy adult participants were recruited in a controlled desaturation protocol to reproduce mild, moderate, and severe hypoxic conditions with SaO2 ranging from 100% to 70% (ClinicalTrials.gov registration #NCT04964609). The SpO2 level was estimated with an EmbracePlus device placed on the participant's wrist and the reference SaO2 was obtained from blood samples analyzed with a multiwavelength co-oximeter. Results The controlled hypoxia study yielded 373 conclusive measurements on 15 subjects, including 30% of participants with dark skin pigmentation (V–VI on the Fitzpatrick scale). The accuracy root mean square (Arms) error was found to be 2.4%, within the 3.5% limit recommended by the FDA. A strong positive correlation between the wristband SpO2 and the reference SaO2 was observed (r = 0.96, P < 0.001), and a good concordance was found with Bland–Altman analysis (bias, 0.05%; standard deviation, 1.66; lower limit, −4.7%; and upper limit, 4.8%). Moreover, acceptable accuracy was observed when stratifying data points by skin pigmentation (Arms 2.2% in Fitzpatrick V–VI, 2.5% in Fitzpatrick I-IV), and sex (Arms 1.9% in females, and 2.9% in males). Discussion This study demonstrates that the EmbracePlus wristband could be used to assess SpO2 with clinically acceptable accuracy under no-motion and high perfusion conditions for individuals of different ethnicities across the claimed range. This study paves the way for further accuracy evaluations on unhealthy subjects and during prolonged use in ambulatory settings.
Water-Filling for Friendly Wide-Band Jamming

We are deriving “Water-Filling” in the realm of jamming with a power constraint. In contrast to the conventional Water-Filling, not the signal power is distributed given a power constraint, but a jamming power is distributed with a corresponding constraint. Instead of the channel capacity, the secrecy capacity is maximized. Additionally, we also apply a minimum mean squared criterion for jamming power distribution as a counterpart to applying it to equalization and precoding. The jamming power is allocated such that it outperforms a simple flat jamming spectrum. As an example, we discuss eavesdropping with a near-field probe. Friendly jamming of this form can reduce leakage to an eavesdropper. To illustrate the procedure, we use USB 2.0 signal spectra, despite of the fact that the independence assumption is violated that one makes use of in Water-Filling. Furthermore, we use the loop of shield and ground to radiate our jamming signal. Later applications that we envisage are, however, on-chip or on-board eavesdropping with dedicated RF designs, also ensuring that interference to other systems will be negligible.
USDA FOREST SERVICE, PACIFIC SOUTHWEST RESEARCH STATION

The team examined how altered water flows caused by hydroelectric dams impact the life cycle of the foothill yellow-legged frog (Rana boylii). The frog, which lives in foothill regions from southern California to southern Oregon, completes its life cycle exclusively in riverine environments. The species is well-adapted to predictable flow patterns that are high during the spring run-off period and low during the summer. Changes to these patterns affect the survival of eggs and tadpoles and consequently are likely to be a primary factor in limiting populations of this declining species, scientists say.
Facial Palsy Detection Through Changes in Facial Muscle Functionality Using CNN Algorithm

Facial palsy is a common condition in the medical field that results from facial nerve disorders, leading to weakened facial motor functions. Detecting facial palsy typically involves observing the functional ability of facial muscles, a process currently performed manually by healthcare professionals relying on visual cues. However, manual detection is prone to errors, this needs for early and automated facial palsy detection. Overall, our research aims to improve the detection of facial palsy by leveraging deep learning techniques. By automating the detection process, we hope to provide a more accurate and efficient approach for early identification of this condition. In this research, we aim to develop a facial palsy detection system using the Convolutional Neural Network (CNN) algorithm. We utilize three datasets, namely CK+, JAFFE, and YouTube Facial Palsy, which are organized into normal and dysfunctional classes. The dataset is divided into training and testing data in an 80:20 ratio, and preprocessing techniques such as image resizing and grayscale conversion are applied. To find the optimal parameters for our model, we employ k-fold cross validation during the training process. The evaluation results show that the best model achieved optimal performance with the following parameters: 3 hidden layers, a fully connected layer (fc)-1 with a hidden size of 128, fc-2 with a hidden size of 16, and fc-3 with a hidden size of 32. The optimizer used was Adam, with a learning rate of 0.001, a batch size of 64, and trained for 10 epochs. This best model attained an average training accuracy of 0.958, testing accuracy of 1, recall of 1, and precision of 0.99.