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ROLE OF THE RETROMER COMPLEX IN FERROPTOSIS
No full textFerroptosis is a recently discovered mechanism of regulated cell death that occurs due to the excessive peroxidation of plasma membrane lipids. This reaction depends on catalysis by intracellular ferrous iron (Fe2+) and is counteracted by antioxidant defense systems. Transferrin receptor 1 (TFRC) is an essential contributor to intracellular iron metabolism because it facilitates the import of extracellular iron. Unsurprisingly, the overexpression of TFRC is an accurate marker of ferroptosis. Recent findings highlight the potential involvement of TFRC recycling in ferroptosis regulation. In particular, sorting nexin 3 (SNX3), a component of the retromer complex responsible for retrograde trafficking of TFRC, has been implicated in ferroptosis-induced cardiomyopathy. This makes SNX3 a potential target for therapeutic intervention or a marker of ferroptosis sensitivity. SNX3 expression is correlated with increased iron burden and epithelial–mesenchymal transition in breast cancer patients. Therefore, we hypothesize that SNX3 could play a role in promoting cancer development by increasing the amount of intracellular iron. Moreover, SNX3 might also sensitize cancer cells to ferroptosis-inducing agents
SUSPECTSEARCH: AI-POWERED CRIMINAL IDENTIFICATION SYSTEM
No full textThe traditional approach to criminal investigation,
taken in the context of rapidly developing technological advancements,
presents itself to be rather outdated. One of the
most crucial parts of identifying a suspect is criminal sketching.
The modern process requires manual sketch craftsmanship from
assets based on the description of the suspect in order to
approximate the appearance of the culprit. This method is highly
limited and burdensome, it requires a considerable amount of
time and resources that could be used in other areas of law
enforcement.
The proposed system utilizes machine learning techniques
to approach this problem and suggests an alternative way of
identifying criminals. The system analyzes photos uploaded to
the database and evaluates pre-defined metrics to synthesize a
vector for a person that will be used to describe the appearance in
abstract terms. The system then takes prompts from the intended
users, which could be victims or witnesses, to synthesize a new
vector for each prompt and will find the most corresponding
photos from the database.
The results of testing the product indicate decent accuracy
and usability and propose a direct search from the database of
people instead of a traditional empirical search from a manual
approximated sketch. This method will remove the necessity for
the time-consuming manual sketching process and further search
for an imprecise image of a suspect. A simple and intuitive in-use
interface will not require any special training of the police staff
in order to integrate the product into the current system of law
enforcement.
The project might be limited as of today, but given enough
time and resources, several additions will drastically improve
the effectiveness of criminal investigation and contribute to the
redistribution of the police force on other important tasks
ROLE OF MITOCHONDRIA IN RESPONSE TO ATO/D-VC TREATMENT IN KRAS MUTANT CANCER CELLS
No full textKirsten rat sarcoma (KRAS) is a prevalent oncogene which is associated with pancreatic, colorectal and lung cancers. KRAS is frequently mutated in variety of tumors and is linked to poor prognosis around the world. According to growing body of literature, one of main features of KRAS mutated cancer cells is a dysregulated glucose metabolism and continuously activated signaling pathways. Therefore, it became an attractive target to investigate cancer proliferation and adaptation response to various treatment strategies. One of such targeted therapies is recently proposed combination of arsenic trioxide (ATO) and D isoform of Vitamin C (D-VC). It inflicts potent synergistic effect on cancer cells by inducing significant cytotoxic stress ultimately leading to cell death. It was found that this novel ATO and D-VC combined treatment induces apoptosis in the mouse KRAS pancreatic adenocarcinoma cells by stimulating the suicidal mitochondrial reactive oxygen species (ROS) production. One potential target site of this treatment are mitochondria. It is unknown, whether similar response can be achieved in KRAS mutant cancer cells depleted of mitochondria. In this study, we aimed to investigate the role of mitochondria in cells as a pivotal player in response to ATO/D-VC treatment
DENYING MOTHERHOOD: EXPERIENCE OF CHILDFREE WOMEN IN KAZAKHSTAN
No full textChildfree people often underrepresented in the official governmental statistics and censuses, and thus they become “invisible” members of our society. It is essential to study the experiences of childfree people to better understand the reasons for becoming childfree, what challenges they face and how they cope with negative attitued and stereotypes. Especially, it is vital to study childfree women as it is believed that motherhood is essential part of feminin identity. Consequently, this paper is comprised of qualitative research methods and secondary data research. Overall 5 semi-structured interviews were conducted with childfree women from Kazakhstan. It was identified that age, birth order and marriage status play important roles not only in developing reasons to become childfree, but also how they are perceived by other people and how they cope with the negative questions and attitudes. Additionally, challenges such as negative social attitudes and limitations of regulations regarding the sterilization were found. Moreover, interviewees believed that motherhood is too responsible and that there is choice in front of women to have a career or a family. It was investigated that, childfree women do not feel stigmatized, because their whole identity is not based solely on being childree. They do not allow others’ negative opinions and stereotypes to have an effect on their decision and self-perception. This paper is based on the theoretical frameworks such as modernization theory, gender equity theory by Peter Macdonald, a stalled gender revolution, the theory of Gary Becker, the concept of intensive motherhood, and “doing gender”
DESIGN OF OPTICAL FIBER BIOSENSORS AIMED AT VIRAL PROTEIN DETECTION
No full textThe recent pandemic situation around the globe has exposed limitations in traditional virus detection methods, characterized by sluggishness and low sensitivity. This project proposes op- tical fiber biosensors for viral pro- tein detection to overcome these chal- lenges. The design process is based on using the optic fiber sensors and test- ing their sensitivity abilities in differ- ent mediums. In theory, these biosen- sors offer high sensitivity, specificity, and real-time capabilities, combining nanotechnology, biotechnology, and optical engineering. Testing will val- idate their performance in real-world applications. This innovation promises faster, more accurate virus detection, impacting healthcare, food safety, and environmental monitoring. Therefore, the primary purpose of this paper is to propose the design of optic fiber sen- sors for the detection of viral viruses
ENHANCING EMERGENCY RESPONSE: THE ROLE OF INTEGRATED VISION-LANGUAGE MODELS IN IN-HOME HEALTHCARE AND EFFICIENT MULTIMEDIA RETRIEVAL
No full textIncidents of in-home injuries and sudden critical health conditions are relatively common
and necessitate swift medical expertise. This study introduces an innovative use of
vision-language models (VLMs) to elevate human healthcare through improved
emergency recognition and efficient multimedia search capabilities. By harnessing the
combined strengths of large language models (LLMs) and vision transformers (ViTs), this
study enhances the analysis of both visual and textual information. We propose a
framework that utilizes the PrismerZ VLM in both its Base and Large forms, along with a
key frame selection (KFS) algorithm, to pinpoint and exam- ine pertinent images within
video streams. This allows for the creation of enriched datasets, filled with images that
are paired with descriptive narratives and insights gained from visual question answering
(VQA). Through the integration of the CLIP- ViT-L-14 model and the MongoDB Atlas
cloud database, we developed a multimodal retrieval system that achieves complex query
handling and improved user experience. Additionally, this research undertakes data
collection to assess the system’s adaptabil- ity, providing proof of concept and refining
the framework. The results showcase the system’s robustness, evidenced by high
accuracy rates—86.5% in image captioning and 92.5% in VQA tasks—on the kinetics
dataset. When tested with human subject data, the PrismerZ Large model achieved 85.8%
accuracy in image captioning and 87.5% in VQA tasks. This performance was further
enhanced through fine-tuning with the GPT-4 based Chat GPT, one of the largest
language assistants, leading to a 20% improvement in semantic text similarity as
measured by the BERT model. The PrismerZ models also stand out for their speed, with
the Base and Large versions processing image captioning and VQA tasks in just seconds,
even on the NVidia Jet- son Orin NX edge device. These findings confirm the system’s
reliability in real-life scenarios. The multimodal retrieval system achieved top
performance with a mean average precision at k (MAP@k) of 93% and mean reciprocal
rank (MRR) of 94.79% on the kinetics dataset, maintaining an average search latency of
merely 0.33 seconds
for text queries. This research significantly propels the fields of human activity recognition
(HAR) and emergency detection forward, carving out new paths for anomaly
detection and enriched multimedia understanding. Our objective in integrating the VLM
with multimedia information retrieval is to establish new benchmarks for hu- man care,
improving its timeliness, comprehensiveness, and efficiency in accessing multimedia
dat
FEDERATED LEARNING FOR WEARABLE SENSOR DATA
No full textDigitalization has revolutionized healthcare, with technologies like electronic records, apps, and wearables improving patient care. The combination of the Internet of Things (IoT) and Artificial Intelligence (AI) has raised healthcare standards, while the concept of Quantified Self (QS) encourages self-tracking and personal development. Yet, IoT data gathered for QS is not fully utilized due to analysis challenges, with only 7% of data properly used and interoperability issues complicating data exchange. Federated Learning (FL) provides a solution by enabling collective model training across different sources while protecting data privacy, in contrast to Centralized Learning (CL) which centralizes data. Overcoming interoperability is essential for effective data use, but complexities in digital health frameworks make this difficult. Thus, this work aims to investigate the use of FL, leading to the development of the web application that enables to conduct a predictive analysis of interoperable data from wearable devices and sensors in healthcare. The primary objectives of the work include: (1) Achieving data interoperability via FHIR protocol; (2) Evaluating the effectiveness of FL and CL in analyzing interoperable data; (3) Creating a user-friendly application using AutoML to train ML models on interoperable data in FL and CL approaches. In this work multiple datasets were analyzed by ML algorithms using CL and FL, and the results were evaluated using relevant metrics. Subsequently, a solution that leverages data interoperability is presented. The findings reveal that FL learning is more preferable than CL for preserving interoperable data confidentiality. The implementation and evaluation demonstrate the effectiveness of the web application in conducting the predictive analysis of centralized data
IMPROVED HARDY-SOBOLEV INEQUALITIES
No full textIn this thesis work, a new version of the Hardy-Sobolev inequality is
obtained. It covers the recent inequality of Frank, Laptev, and Weidl derived in
[4] and improves the results of Persson and Samko established in [13]. It gives new
results in one dimension. We analyse radial and non-radial versions of the considered
multidimensional Hardy-Sobolev inequality and as a consequence we establish some
Heisenberg-Pauli-Weyl uncertainty principles
THE DESIGN OF SELF-CHARGING SENSOR INDUCED SIMPLIFIED INSOLE-BASED PROTOTYPES WITH PRESSURE MEASUREMENT FOR FAST SCREENING OF FLAT-FOOT
No full textFlatfoot is an orthopedic foot malformation in which the inner arch of the foot virtually or completely flattens during static or dynamic motions. This abnormal deformation can negatively affect the musculoskeletal system, leading to chronic pains and other conditions that may severely deteriorate a person’s quality of life if not treated timely. Therefore, there is a need for continuous monitoring of food conditions, and currently, available screening methods may not be sustainable in terms of objectivity, time, and money. This research aims to design and fabricate an insole-based screening device that would offer accurate and accessible screening. In order to implement our objectives, the self-powered triboelectric nanogenerators (TENG) were used as tactile pressure sensors for the insole since they propose such advantages as uncomplicated fabrication and design operations, cost-effectiveness, extensive lifetime, and high output power. TENGs’ main purpose is converting mechanical energy into electrical energy. In other words, the energy generated from the movement of the object is translated into electric output and recorded by the Arduino circuits. The collected data is analyzed using machine learning algorithms for the system to be able to immediately recognize the flatfoot conditions after undergoing the training sessions. To collect data, 82 participants were asked to march in one place and walk the same amount of time and distance to get similar numbers of outputs from each operation. The analysis showed that the middle sensors of the insoles generated much higher electricity when they were attached to people with flatfoot conditions and that they exhibited relatively uniform equal pressure distribution throughout the foot. In contrast, people with normal feet put more pressure on the front and back side of the foot. The overall accuracy of the machine learning system reached 81%, indicating that the designed insole has a high potential to be used as a flatfoot detecting device commercially
Deep Learning-Based Wind Speed Prediction for Optimized Wind Turbine Operation
No full textWind energy has been a promising source of clean energy that does not negatively
affect our environment. Because of the fluctuations in wind speed, it is crucial to
predict its values for wind turbines to have the maximum effective power output.
This project aims to develop a way for short-term wind speed prediction based
on deep learning technologies, such as CNN, LSTM, RNN, and GRU models,
alone and in combination. Through iterative experimentation and evaluation, we
develop ten final models and assess their performance based on Mean Squared
Error (MSE), score, and computational efficiency. Our findings reveal that the
GRU model achieves the highest performance with a MSE of 0.00238 m/s and R2
score of 0.8796. Additionaly, the similarly structured LSTM model demonstrates
superior computational efficiency along with high R2 value, outperforming GRU
model. By examining the performance of multiple deep learning architectures,
the project seeks to identify the most suitable approach for wind speed prediction,
thereby facilitating more efficient and sustainable utilization of wind resources
for power generation