bluuebunny/crossref_metadata_2025 huggingface dataset:
Airfare Forecasting using Machine Learning to Predict Prices
Authors: Bindu Ms. A. Hima
Published: April 2024

Abstract: The issue of predicting ticket 111 costs is the focus of this essay. With the assumption that these characteristics have an impact on the cost of an airline ticket, a set of features typical of a normal flight is determined for this purpose. Eight cutting edge machine learning (ML) models using the characteristics are trained to forecast the cost of airline tickets, and the models' output is contrasted with one another. This work examines how the feature set used to represent an airline affects accuracy as well as the prediction accuracy of each model. To train each machinelearning model for the trials, a unique dataset including 1814 Aegean Airlines data flights for a particular foreign destination (from Thessaloniki to Stuttgart) was created. Key Words: Airfare price prediction, Machine learning models, Feature dependency, Regression accuracy.
Smart Chatbot to Assist Users of Employment Websites in Job Search, Skill Development, and Networking Opportunities
Authors: Jain Kala Kushal
Published: April 2024

Abstract: This paper presents the development and implementation of a smart chatbot designed to assist users of employment websites in various aspects of their job search, skill development, and networking endeavors built using bert model, spacy model and large language model. With the increasing reliance on online platforms for job hunting and career advancement, there is a growing need for intelligent tools that can streamline the process and provide personalized support to users. This article examines a chatbot that provides many functionalities to improve job search outcomes and user experience. These functionalities include personalized job recommendations, skill development suggestions, and networking opportunities tailored to the user's queries. The paper outlines the methodology used in the development of the chatbot, including the integration of natural language processing techniques and machine learning models. The paper presents findings from comprehensive evaluation, shedding light on the efficacy of the chatbot's functionalities. The implications of this research extend to both academia and industry, underscoring the potential of AI-driven chatbots to revolutionize the way individuals navigate the job market and pursue career advancement opportunities.
Environmental Monitoring in Internet of Things (IOT)
Authors: Radha Mrs. C. , Madheswaran Mr. M. , Lokesh Mr. M. , Althaf Mr. M. Mohammad
Published: April 2024

Abstract: The Internet of Things (IoT) connects physical objects to the internet, enabling autonomous data collection and analysis. In environmental monitoring, IoT plays a crucial role in gathering real-time data on parameters like air and water quality, weather, wildlife behavior, and urban heat islands. This abstract explores how IoT is used in environmental monitoring, covering standards, device types, and applications. IoT comprises interconnected devices that collect environmental data and transmit it for analysis, facilitating continuous observation and better resource management. Standards like MQTT and communication technologies like Wi-Fi govern IoT implementation. Environmental monitoring involves assessing air and water quality, weather conditions, wildlife behavior, and urban heat islands. Air quality monitoring measures pollutant levels in the atmosphere using IoT-based sensor networks. Water quality monitoring assesses chemical and biological characteristics of water bodies. Weather and climate monitoring observe atmospheric conditions using IoT-based sensor networks. Wildlife monitoring tracks animal movements and behavior using IoT-enabled devices. Urban heat island monitoring assesses temperature distribution in urban areas using IoT-based sensors. IoT revolutionizes environmental monitoring by enabling real-time, remote observation across various domains, contributing to better resource management and sustainability efforts.
Deep Learning Based Hybrid Technique for Improved Summarization of Abstractive Text
Authors: Shriya Lanka
Published: April 2024

Аbstrасt: From various source sentences, construct summary sentences by merging facts. The process of preserving information material and overall meaning while condensing it into a shorter representation is known as abstractive text summarization, or ATS. It takes a lot of effort and time for humans to manually summarise large textual publications. In this paper, we present an ATS framework (ATSDL) based on (Long Short-Term Memory-Convolutional Neural Network) LSTM- CNN that can generate new sentences by investigating semantic phrases, which are finer- grained fragments than sentences. ATSDL, in contrast to current abstraction- based methods, consists of two primary phases: the first extracts phrases from source sentences, and the second uses deep learning to produce text summaries. Experimental results on CNN and Daily Mail datasets show that our ATSDL framework outperforms the state-of-the-art models in terms of both syntactic structure and semantics, and achieves competitive results on manual linguistic quality evaluation. In this application hybrid model is giving better performance over other state of art techniques.
The Framework of Construction Workers Safety: Evaluating the Effectiveness of Safety Practices in Construction Industry
Authors: Raushan Rahul
Published: April 2024

Abstract: This study aims to investigate the multifaceted approach to safety and health management practices within the construction industry, with the ultimate goal of delineating their impact on reducing workplace incidents and enhancing overall safety culture. In order to achieve this goal, a mixed-methods research design is employed, which combines quantitative data from structured surveys across a diverse sample of construction sites with qualitative insights from in-depth interviews and case studies. The findings of the analysis reveal a significant correlation between the frequency and comprehensiveness of safety training programs and a reduction in incident rates. Furthermore, the adoption of advanced safety technologies and equipment, coupled with a strong organizational safety culture, is found to be a pivotal factor in improving safety outcomes. The study also highlights the importance of customizing safety practices to the unique needs of different construction projects to optimize effectiveness.
Enhancing the Geotechnical Properties of Black Cotton Soil using Terrazyme
Authors: Gautam Prashant
Published: April 2024

Abstract: The cost, performance, durability, and time of every project are its most crucial components. Black cotton soil, due to its strong swelling and shrinkage qualities, also known as expansive clay soil and regur soil, presents considerable difficulties in building and agriculture. Building on black cotton soil has its challenges, including foundation and road settlement, among others. Due to the shortcomings of conventional soil stabilizing technologies, new ways for enhancing the engineering properties of soil are being investigated. There is a demand for the creation of novel techniques to enhance the geotechnical properties of soil due to the inefficiency and high costs associated with conventional methods. Study examines the use of terrazyme, a state-of-the-art enzymatic soil stabilizer, to improve the geotechnical properties of black cotton soil, optimum moisture content, maximum dry density, liquid limit, plastic limit, plasticity index, and CBR test, which are used for pavement base courses, sub-base courses, and sub-grades.
The Application of Microbially Induced Calcium Carbonate Precipitation (MICP) Technology in Building Materials and its Contribution to Waste Resource Utilization
Authors: Ma Xing-yu , Xu Zan , Nie Yu-ya , Liu Nan-zhi , Cao Le
Published: April 2024

Abstract: In many countries around the world, waste generation has become an increasingly pressing issue, particularly in urban areas experiencing rapid population growth and industrialization. The volume of waste produced by human activities continues to rise, posing significant challenges to the environment, economy, and public health. Improper waste management can lead to air and water pollution, soil degradation, and the spread of diseases. Additionally, waste generation consumes substantial natural resources and energy, exacerbating resource depletion and contributing to greenhouse gas emissions.To address these challenges, sustainable waste management practices are essential to reduce waste generation and promote resource recovery and recycling. In this context, the development of innovative technologies, such as Microbially Induced Calcium Carbonate Precipitation (MICP) in building materials, offers an effective means of converting waste into valuable and sustainable applications. MICP is an environmentally friendly biogeochemical technique that employs microorganisms and chemical agents in biological processes to produce carbonate minerals, providing an energy-efficient, cost-effective, and sustainable solution to environmental and engineering challenges.Recent research indicates that waste streams can substitute for several MICP chemical components in microbial and cementitious agent growth media. MICP technology has been found not only to be effective in handling hazardous waste streams but also to be a cost-effective and sustainable solution applicable to various waste media. This comprehensive review aims to provide insights into the environmental advantages and engineering applications of MICP technology, with a particular focus on the potential contributions of waste streams. It also offers guidance for researchers on identifying and overcoming challenges associated with using waste streams for MICP technology applications.
AI Based Identification of Inappropriate Language
Authors: Ragolu Padmaja , Gollapalli Venkata Naveen Kumar , Budumuru Sai Saranya , Karagani Jayadeep , Tripirineni Vidhya Sai Vyshnavi
Published: April 2024

Abstract: In the current digital era, the widespread use of online communication has raised the demand for automated systems that can recognize and block hate speech, improper language, and objectionable information. The usefulness of three different machine learning algorithms—Decision Trees, Random Forest, Long Short-Term Memory (LSTM), and others—in determining if a given text contains objectionable language is investigated in this work. While LSTM and represent state-of-the-art deep learning algorithms capable of processing unstructured text input, Random Forest and Decision Trees are standard machine learning techniques that rely on organized feature engineering. This study compares different algorithms in an effort to shed light on their advantages and disadvantages in dealing with the changing demands of online content moderation. Our results show each model's accuracy, precision, recall, and F1-score.
Classification of Diabetic Retinopathy using Deep Learning
Authors: Reddy Mr. V. Purna Chandra , Sharma Sarabu Vignya , Reshmi M. , Reddy J. Sahithi , Priya D. Sai
Published: April 2024

Abstract: Diabetic retinopathy, a potentially fatal retinal disease, occurs in individuals with diabetes, causing vision damage and possible visual impairment traditionally, screening for diabetic retinopathy has been the norm hard by ophthalmologists manually. To facilitate this, we have DR. Our pre-trained model is trained on a large dataset of about 3662 training images, enabling it to automatically detect the DR platform in high-resolution fundus images The dataset used for this is publicly available on kaggle. The DR phases are categorized as 0, 1, 2, 3, and 4. The fundus eye images serve as the input parameters of the model. The pre-trained model then identifies the point of interest in bank eye images, then provides activation function insights By calculating weights, for example patients’ visual intensity levels, it helps classify diabetic retinopathy images into their intensity groups appropriately within the possibility of test- methods Provides variation.
Captcha Recognition Using CNN
Authors: Manga V. , Sruthi Dr. P. , Nithin A. , Sanjay N. , Srikanth G.
Published: April 2024

Abstract: Tests that are automated and intended to tell humans from machines are called CAPTCHAs. Computers find it harder to solve them, which prevents programs from abusing online services and consuming internet resources, but humans can solve them with ease. Convolutional neural nets could be used to efficiently and automatically complete the CAPTCHA tests. The highly precise CAPTCHA recognition techniques currently in use can be structurally complex. Consequently, our group investigated an alternative method for resolving CAPTCHAs: enhancing accuracy by using image processing and a Convolutional Neural Network, which is more efficient in terms of run time and structural complexity. Our networks were also evaluated using CAPTCHA datasets that included background noise and character adhesion.
Edaphic macrofauna in the establishment of an agroforestry system in the High Andean region of Southern Colombia
Authors: Muñoz-Rodriguez Martin Alonso , Santacruz-Ortiz Ana Carolina
Published: April 2024

Biological degradation of the soil due to inadequate agricultural practices that eliminate vegetative cover and, therefore, organic matter, reduces the biological communities that perform important ecological functions in the soil. The objective of the study was to evaluate some biological properties of the soil in an agroforestry system with Morella pubescens intercropped with Smallanthus sonchifolius and in a degraded pasture of Cenchrus clandestinum. The trial was conducted at the Botana Experimental Farm, Nariño, at 2,820 meters above sea level. Density, richness and Simpson and Shannon-Wiener diversity indices were determined. A Randomized Complete Block Design with three replications was used. The treatments corresponded to sowing distances of S. sonchifolius. T1: sown at 1x1m, T2: 0.8x0.8m, T3: 0.5x0.5m and the control T4 corresponded to degraded pasture of C. clandestinum. The tree component M. pubescens was established at 4x4m between seedlings and 9m between rows. Soil macrofauna sampling was carried out following the monolithic TSBF method, collecting at depths of (0-10, 10-20 and 20-30cm). A total density of 6,496i/m2 individuals was found. The highest value was obtained in T3 (3,168i/m2) represented by the taxonomic groups Diptera and Haplotaxida. Higher values of richness were observed in T1. The Shannon index was higher in T2 (0.66) while T1 (0.80) presented higher dominance in morphospecies. Density and richness were higher in the agroforestry arrangement than in the degraded Cenchrus clandestinum pasture. Treatments, depth and their interaction did not significantly affect morphospecies diversity.
New Insights in Topical Drug Delivery for Skin Disorders: A Review
Authors: Saini Madhurima , Garg Dr. Shiv , Sharma Dr. Piush , Bhatt Dr. Shailendra
Published: April 2024

Drug delivery systems are methods which are used to ensure that drugs get into the body and reach the area where they are needed. These systems must take a number of needs into account, ranging from ease of delivery to effectiveness of the drugs. The paper reviews an overview of a conventional and novel approach in the topical drug delivery system. Drug delivery via the skin is becoming progressively popular due to its convenience and affordability. The skin is the most important mechanical barrier to the penetration of many drug substances and acts as an ideal site to deliver the drug both locally and systemically. The topical route has been a favored route of drug administration over the last decades. Despite conventional topical drug delivery systems limits in poor retention and low bioavailability. To address some of the limitations posed by conventional dermatotherapy, nano-based technologies have been developed and have demonstrated a significant improvement in dermatotherapy. Their distinct physicochemical properties demonstrate their overall superior therapeutic efficacy in providing sustained and effective targeted drug release, as well as improved solubility of hydrophobic actives with optimized drug formulations. These nanocarriers are commonly classified as polymeric, lipid-based, metallic, and vesicular nanocarriers, including nanoemulsions, nanofibers, and microneedles.
O cativeiro da Terra e do Trabalho: Política neoextrativista e planejamento em São Luís, Maranhão
Authors: Burnett Frederico Lago
Published: April 2024

Após décadas de repetidos conflitos socioambientais na região portuária de São Luís, Maranhão, provocados pela apropriação de terras de comunidades tradicionais, Estado e Capital se utilizaram do planejamento para mudanças no uso e ocupação do solo, legitimando a expansão fundiária e unificando diferentes interesses empresarias. Entendendo o processo como fruto de estratégias neoextrativistas do enclave econômico local, o artigo analisa as relações entre planejamento portuário e municipal que, a partir do discurso desenvolvimentista, incorporou as demandas do capital imobiliário em um novo movimento de “acumulação por espoliação” para submissão da Terra e do Trabalho ao cativeiro da reprodução capitalista.
The sufficiency of household incomes: Estimation and economic interpretation for their financial stability
Authors: Kyrylenko Olga , Koval Svitlana , Sydor Iryna , Sydorchuk Anatoliy
Published: April 2024

The household financial activities prove that their incomes determine the levels of livelihood (Skrynkovsky & Leskiv, 2015; Israr et al., 2014) and stability (Mohr & Wagner, 2013). In this paper, we will take the view that the financial stability of households is the ability to ensure an appropriate level of livelihood by the available level of income in the face of influence factors. As a result, the study on the sufficiency of household incomes is relevant when estimating their financial stability. The purpose of the research is to develop indicators for estimating the sufficiency of household incomes and economic interpretation obtained results for their financial stability. According to the purpose, indicators for estimating the sufficiency of household incomes are proposed and made economic interpretations ones. In the work process analysis is used as a general scientific method of theoretical knowledge, and comparison and measurement are used as data research methods. The analysis was conducted to conclude the different trends of income sufficiency by Ukrainian households at the macroeconomic and microeconomic levels in 2001–2020. Moreover, future research needs to use the elements of correlation regression analysis to rank the studied factors by the power of influence.
Picosecond Laser Processing Enabled Geometrical Fill Factors Exceeding 98% for Inverted Wide Bandgap Perovskite Solar Modules
Authors: Yerci Selcuk , Uzuner Bahri Eren , Afshord Amir Zarean , Aguirre Aranzazu , Aernouts Tom , Gunbas Görkem , Kuang Yinghuan
Published: April 2024

Perovskite solar cells (PSCs) have exhibited significant advancements
over the last decade, positioning them as the most promising candidate
for the next-generation photovoltaic technology. Recently, significant
efforts have been focused on the scale-up of PSCs towards enabling their
commercialization. In this study, we performed electrical simulations to
elucidate the balance between electrical and geometric losses in PSMs
and verified our model by fabricating opaque (PSMs) and semi-transparent
wide-bandgap perovskite solar modules (ST-PSMs). We showed that a P2
width of 20-50 µm provides an optimized P2 contact resistance, resulting
in high geometric fill factors (GFF) and fill factor (FF),
simultaneously. PSMs with an aperture area of 4.2 cm
, reaching a GFF of 98.4%, an FF of 81.5%, and a
PCE of 17.78% were fabricated. To demonstrate the scalability of this
approach, 16 cm PSMs, reaching a GFF of 97.0%, an
FF of 80.1%, and a PCE of 17.58% were fabricated. ST-PSMs (4 cm
) with >92.5% GFF, 81.4% FF, and
15.68% PCE were fabricated. We believe that the proposed optoelectronic
model, along with its validation through the fabrication, exhibiting
exceptionally high GFFs and FFs, elucidates the optical-electrical
trade-off in PSMs and thus offers valuable insights for the design of
highly efficient PSMs.
The effect of nutritional and oxidative stress on the metabolome of Trypanosoma cruzi
Authors: Schenkman Sergio , Silva Michel Augusto , Izidoro Mario , Souto Mirella Aricó Vietas , Juliano Luiz
Published: April 2024

Trypanosoma cruzi, a flagellated protozoan, is the causative
agent of Chagas disease. The parasite has developed various mechanisms
to get through its intricate life cycle and adapt to different
evolutionary phases. T. cruzi proliferates in the insect vector’s
digestive tract as an epimastigote form, encountering fluctuating
nutrient availability and oxidative stress caused by the digestion of
red blood cells from the mammalian host blood meal. To unravel how the
parasite’s metabolism adapts to these changing conditions, we conducted
an analysis of the chemical species present in epimastigote forms. This
involved comparing cultured parasites with those subjected to
nutritional deficiency or oxidative stress using untargeted
metabolomics. We looked at twenty-one samples: seven biological copies
of parasites that were actively growing, seven samples that were put in
a medium without nutrients for three hours, and seven samples that were
treated with glucose oxidase for thirty minutes to make H
O continuously. Importantly, in all
conditions, parasite viability was maintained when the samples were
collected. Upon nutrient removal, we observed a substantial decrease in
amino acids and carbohydrate metabolites, accompanied by the
accumulation of fatty acids and steroids, with the predominance of
inositol and sphingolipid metabolism, along with a simultaneous decrease
in the levels of H O . In the presence
of H O , a significant rise in
components of the pentose pathway and specific amino acids such as
methionine and serine occurred, along with pathways related to an
increase in antioxidant species metabolism such as ribulose 5-phosphate
and glyceric acid. Conversely, fatty acid and steroid levels decrease.
We found no common increase in metabolites or lipids. In contrast, eight
species (succinic acid, glutamic acid, valine, 2-hydroxyisocaproic acid,
alanine, indolelactic acid, proline, and lanosterol) were consumed under
both stresses. These findings underscore the rapid and distinct
enrichment responses in amino acids, lipids, and carbohydrates required
to cope with each different environmental condition. This information
not only enhances our understanding of T. cruzi’s metabolic
flexibility but also opens avenues for studying cellular metabolism
changes in diverse environments used by the parasite.
Fluoride detection by an AIE active pyrene functionalized imidazole-based fluorescent sensor: Real-time applications and anti-counterfeiting
Authors: Iyer Sathiyanarayanan Kulathu , Kumar Selin Manoj
Published: April 2024

An AIE active imidazole-based chemoreceptor,
2-(4-(1-(benzo[d]thiazol-2-yl)-4,5-diphenyl-1H-imidazol-2-yl)benzylidene)-N-(pyren-1-ylmethylene)hydrazine-1-carbothioamide
( BIPC), has been successfully synthesized and characterized by
NMR and HRMS analyses. The aggregation-induced emission of BIPC
in a binary solvent system is fascinating. Upon aggregation,
BIPC forms nanoaggregate which is characterized by dynamic
light scattering methods (DLS). Furthermore, optical experiments
demonstrated that the sensor BIPC is highly selective and
sensitive to F with a low level of detection. A
color change with a bathochromic shift of absorbance and a ratiometric
fluorescence attributed to the induction of photoinduced electron
transfer (PET) and intramolecular charge transfer (ICT) processes upon
the addition of F . In the present study Job’s plot,
HRMS, DFT, and H NMR experiments were used to
validate BIPC’s detection mechanism and binding stoichiometry
with F . Additionally, F was
successfully detected in environmental water samples by BIPC.
Since BIPC emits a bright light, it can be used as a
counterfeit detecting tool. It was also found that BIPC was
effective for detecting F ion in live cells with a
high permeability.
The abortive SARS-CoV-2 infection of osteoclast precursors promotes their differentiation into osteoclasts
Authors: Quarleri Jorge , SVIERCZ Franco , JARMOLUK Patricio , COTO Joshua GODOY , CEVALLOS Cintia , FREIBERGER Rosa Nicole , LÓPEZ Cyntia Alicia Marcela , ENNIS Irene Lucia , Delpino M. Victoria
Published: April 2024

The COVID-19 pandemic has resulted in the loss of millions of lives,
although a majority of those infected have managed to survive.
Consequently, a set of outcomes, identified as long COVID, is now
emerging. While the primary target of SARS-CoV-2 is the respiratory
system, the impact of COVID-19 extends to various body parts, including
the bone. This study aims to investigate the effects of acute SARS-CoV-2
infection on osteoclastogenesis, utilizing both ancestral and Omicron
viral strains. Monocyte-derived macrophages (MDM), which serve as
precursors to osteoclasts, were exposed to both viral variants. However,
the infection proved abortive, even though ACE2 receptor expression
increased post-infection, with no significant impact on cellular
viability and redox balance. Both SARS-CoV-2 strains heightened
osteoclast formation in a dose-dependent manner, as well as CD51/61
expression and bone resorptive ability. Notably, SARS-CoV-2 induced
early pro-inflammatory M1 macrophage polarization, shifting towards an
M2-like profile. Osteoclastogenesis-related genes (RANK, NFATc1,
DC-STAMP, MMP9) were upregulated, and surprisingly, SARS-CoV-2 variants
promoted RANKL-independent osteoclast formation. This thorough
investigation illuminates the intricate interplay between SARS-CoV-2 and
osteoclast precursors, suggesting potential implications for bone
homeostasis and opening new avenues for therapeutic exploration in
COVID-19.
Diagnosis, Pathogenesis and Treatment of Chronic Spontaneous Urticaria
Authors: Kaplan Allen P , Ferrer Marta
Published: April 2024

Chronic urticaria, defined as having urticaria for over 6 weeks, is
divided into two categories; namely, inducible urticaria and chronic
spontaneous urticaria (CSU). Inducible urticarias have an initiating
stimulus, often “physical”, and actually occur intermittently, but the
total duration of symptoms can encompass many years. CSU is more
typically chronic, has urticarial lesions most days of the week, has no
exogenous cause, and from the patient’s point of view, does seem
spontaneous. An algorithm for the diagnosis of CSU is shown in Fig. 1.
Angioedema can accompany many inducible urticarias, but is more
typically associated with CSU affecting face, extremities, genitalia,
lips, tongue, and rarely pharynx, but not the larynx. Thus risk of
asphyxiation is nil. The disorder is strongly associated with
autoimmunity. The best studied (often referred to as type IIb
autoimmunity) involves IgG antibody to the IgE
receptor[1](#ref-0001) which cross-links unoccupied IgE receptors
of mast cells and activates the cells to cause secretion of histamine,
leucotrienes, cytokines, and chemokines. Complement is activated and
release of C5a augments the mast cell secretion[2](#ref-0002).
There is a second scenario in which patients have IgE antibody to a
large variety of autoantigens[3](#ref-0003) including
thyroperoxidase and interleukin 24, although which are pathogenic is not
yet clear. This is often designated as type I autoimmunity or
autoallergy. Clinically, other autoimmune disorders may be present. The
most prominent is Hashimoto’s thyroiditis, but also type I diabetes and
vitiligo. Antithyroid antibodies (i.e. IgG anti microsomal antigen and
IgG antithyroglobulin) are seen in 25% of patients regardless of
thyroid status. Total IgE is elevated within this population although
much less so than in asthma or atopic dermatitis. Low or very low IgE
levels may be seen and such patients are less responsive to
omalizumab[4](#ref-0004).
Clinical Characteristics and Booster Vaccine Effectiveness of the Omicron Variant
Authors: Jiang Hui , Shan Yi , Sun Lijun , Guo Yijia , Huang Yunfei , Tang Zihui , Wu Zhaojun , Li Yuwei , Zhao Liping , Sun Lijun , Xu Xiaolong
Published: April 2024

Background Although many people received the COVID-19 vaccine,
shortly after the Chinese government announced that the three-year
COVID-19 restrictions were being eased, the first large number of
Omicron infections appeared in Beijing. In this study, we describe the
epidemiological characteristics, clinical severity, and time-to-event
distribution of patients infected with SARS-CoV-2 in Beijing Omicron
outbreak, comparing those who received the booster vaccine with cases of
full/partial vaccines. Methods In this epidemiological study,
we collected epidemiological, clinical, laboratory, and clinical
management data from the hospital information system (HIS) for 1495
cases using standardized forms. We also collected the illness onset
time, diagnosis time, hospital admission time, and start and end times
of each treatment. In addition, we collected the time of vaccination,
inoculation times, and type of COVID-19 vaccination thorough the
vaccination system. We described the epidemiological characteristics
across vaccine inoculation doses, and estimated the risk of death,
mechanical ventilation, and admission to the intensive care unit for
patients admitted to hospital. We used the Kaplan–Meier method to
estimate the survival rate and plot the survival curve, and the Cox
proportional hazards model to assess the effect of covariates on
survival time. Results Of the 1495 cases, 58.1% were male and
41.9% were female. The median ages in the non-vaccinated and vaccinated
groups were 80 and 47 years, respectively. The elderly, people with
underlying medical condition, and those with a lower BMI were less
willing to be vaccinated ( p<0.05). Both the
inactivated vaccine and adenovirus vaccine could reduce clinical
severity and prolong survival time, and the protective effect of booster
vaccination was the best. The clinical severity increased linearly from
the booster vaccinated group to the full vaccinated group and
non-vaccinated groups, and the death risk of COVID-19 cases without
vaccination was the highest. Conclusion Booster vaccines of
COVID-19 can provide greater protection against severe illness and
death, and continuous monitoring and regular assessment are needed to
minimize the risk of a recurrence of the pandemic.
Analysis of stable snowpack distribution pattern and influencing factors
Authors: Zhang Zihan , Nie Lumin , Lu Heng
Published: April 2024

Under the background of climate warming, the distribution pattern of
snowpack in mountainous areas is naturally under the focus of attention,
and the changes in the snowpack pattern in mountainous areas have
important impacts on hydrological processes such as downstream river
runoff, water supply, and so on. In this study, based on the MODIS
day-by-day cloud-free snowpack area dataset, we analysed the process of
stable snowpack formation and the final pattern of snowpack in spring
and winter in two typical topographic regions, the hilly plateau and the
alpine valley region, in the eastern part of the Tibetan Plateau, and
constructed a model based on the Maximum Entropy Model (MEM) method for
predicting the influencing factors of the stable snowpack. A model was
constructed based on the maximum entropy model method to predict the
factors influencing the stable snowpack, and the dominant factors of the
stable snowpack in spring and winter in the watersheds of the two
topographic regions were analysed. The main conclusions are as follows:
(1) The snow ablation rate on the western Sichuan Plateau is different
in winter and spring, and is greater in spring than in winter; there is
also a difference in the snow ablation rate in different topographic
regions, with a greater difference in the hilly plateau than in the high
mountain valleys. (2) The distribution pattern of stable snow in the two
major terrain areas of the Western Sichuan Plateau varies in seasons,
and the spatial distribution area is relatively small, mostly showing a
rising trend with increasing altitude. (3) The influence factors of
stable snowpack in different terrain areas in different seasons are
different, and elevation is the main factor influencing the distribution
pattern of stable snowpack. The results of this paper have some
reference value for the study of snow hydrology in the climatic context
of the Western Sichuan Plateau.
Rational design of short-chain dehydrogenase DHDR for efficient synthesis of (S)-equol
Authors: qin weichuang , zhang lujia , zhou wei , yang yichen , Gao Bei
Published: April 2024

(S)-equol, the most influential metabolite of daidzein in vivo, has
aroused great attention due to the excellent biological activities.
Although existing studies have accomplished the construction of its
heterologous synthetic pathway in the context of anaerobicity and
inefficiency of natural strains, the low productivity of (S)-equol
limits its industrial application. Here, rational design strategies
based on decreasing the pocket steric hindrance and fine-tuning the
pocket microenvironment to systematically redesign the binding pocket of
enzyme were developed and processed to the rate-limiting enzyme DHDR in
(S)-equol synthesis. After iterative combinatorial mutagenesis, an
effective mutant S118G/T169A capable of significantly increasing
(S)-equol yield was obtained. Computational analyses illustrated that
the main reason of the increased activity relied on the decreased
critical distance and more stable interacting conformation. Then, the
reaction optimization was performed, and the recombinant Escherichia
coli whole-cell biocatalyst harboring S118G/T169A enabled the efficient
conversion of 2 mM daidzein to (S)-equol, achieving yields of 84.5%,
which was 2.9 times higher than that of the parental strain expressing
wide type DHDR. This study provides an effective idea and a feasible
method for enzyme modification and whole-cell catalytic synthesis of
(S)-equol, and will greatly accelerate the process of industrial
production.
Arrhythmias and Ion Channelopathies Causing Sudden Cardiac Death in Hispanic/Latino and Indigenous Populations
Authors: Boutjdir Mohamed , Zaveri Sahil , Chahine Mohamed
Published: April 2024

The limited literature and increasing interest in studies on cardiac
electrophysiology, explicitly focusing on cardiac ion channelopathies
and sudden cardiac death (SCD) in diverse populations, has prompted a
comprehensive examination of existing research. Our review specifically
targets Hispanic/Latino and Indigenous populations, which are often
underrepresented in healthcare studies. This review encompasses
investigations into genetic variants, epidemiology, etiologies, and
clinical risk factors associated with arrhythmias in these demographic
groups. The review explores the Hispanic paradox, a phenomenon linking
healthcare outcomes to socioeconomic factors within Hispanic communities
in the United States. Furthermore, it discusses studies exemplifying
this observation in the context of arrhythmias and ion channelopathies
in Hispanic populations. Current research also sheds light on
disparities in overall healthcare quality in Indigenous populations. The
available yet limited literature underscores the pressing need for more
extensive and comprehensive research on cardiac ion channelopathies in
Hispanic/Latino and Indigenous populations. Specifically, additional
studies are essential to fully characterize pathogenic genetic variants,
identify population-specific risk factors, and address health
disparities to enhance the detection, prevention, and management of
arrhythmias and SCD in these demographic groups.
A Trajectory Similarity Computation Method based on GAT-based Transformer and CNN model
Authors: Liu Dongjiang , Leixiao Li
Published: April 2024

Trajectory similarity computation is very important for trajectory data
mining. It is applied into many trajectory mining tasks, including
trajectory clustering, trajectory classification and trajectory search
etc. So efficient trajectory similarity computation method is very
useful for improving trajectory mining result. Nowadays many trajectory
similarity computation methods have been proposed. But these methods
don’t take the outline feature of long trajectory into consideration.
Thus a new trajectory similarity computation method is proposed in this
paper. This method not only takes the long-term dependence feature of
long trajectory into consideration, but also considers the outline
feature of long trajectory. The proposed method employs GAT-based
transformer to extract long-term dependence feature from long
trajectory. And it applies Convolutional Neural Network to extract
outline feature from long trajectory.
Status and future developments for downstream processing of biological products Perspectives from the Recovery XIX yield roundtable discussions
Authors: Jungbauer Alois , Ferreira Gisela , Butler Michelle , Costa Susan D’ , Brower Kevin , Rayat Andrea C.M.E , Willson Richard
Published: April 2024

Governments and biopharmaceutical organizations aggressively leveraged
expeditious communication capabilities, decision models and global
strategies to make a COVID-19 vaccine happen within a period of 12
months. This was an unusual effort and cannot be transferred to normal
times. However, this focus on a single vaccine has also led to other
treatments and drug developments being sidelined. Society expects the
pharmaceutical industry to provide an uninterrupted supply of medicines.
However, it is often overlooked how complex the manufacture of these
compounds is and what logistics are required, not to mention the time
needed to develop new drugs. The overarching theme, therefore, is
patient access and how we can help ensure access and extend it to low-
and middle-income countries. Despite unceasing efforts to make
medications available to all patient populations, this must never be
done at the expense of patient safety. A major fraction of the costs in
biopharmaceutical manufacturing are for drug discovery, process
development, and clinical studies. Infrastructure costs are very
difficult to quantify because they often depend on whether a greenfield
facility or an existing, depreciated facility is used or adapted for a
new product. To accelerate process development concepts of platform
process and prior knowledge are increasingly leveraged. While more
traditional protein therapeutics continue to dominate the field, we are
also experiencing the exciting emergence and evolution of other
therapeutic formats (bispecifics, tetravalent mAbs, antibody-drug
conjugates, enzymes, peptides, etc.) that offer unique treatment options
for patients. Protein modalities are still dominant, but new modalities
are being developed that can be learned from including advanced
therapeutics like cell and gene therapies. The industry must develop a
model-based strategy for process development and technologies such as
continuous integrated biomanufacturing must be adopted. The overall
conclusion is that the pandemic pace was unsustainable, focused on
vaccine delivery at the expense of other modalities/disease targets, and
had implications for professional and personal life (work-life balance).
Routinely reducing development time from 10 years to 1 year is nearly
impossible to achieve. Environmental aspects of sustainable downstream
processing are also described.
Phosphorylation Mechanism Switching in Histidine Kinases is a Tool for Fast Protein Evolution: Insights from AlphaFold Models
Authors: Wetzler Diana E. , Olivieri Federico , Marti Marcelo
Published: April 2024

Histidine kinases (HKs) are a central part of bacterial
environmental-sensing two component systems, providing their hosts with
the ability to respond to a wide range of physical and chemical signals,
being a paradigmatic example of protein diversity and molecular
evolution. HK are multidomain proteins consisting of at least a sensor
domain, dimerization and phosphorylation domain (DHp) and a catalytic
domain. They work as homodimers, and the existence of two different
autophosphorylation mechanisms (cis and trans), has been proposed as
relevant for pathway specificity. Although several HKs have been
intensively studied, a precise sequence-to-structure explanation of why
and how either cis or trans phosphorylation occurs, is still
unavailable, nor is there any evolutionary analysis on the subject. In
this work we show that AlphaFold can accurately determine whether a HK
dimerizes in a cis or trans structure. We then use it to explore the
molecular determinants of the phosphorylation mechanism. We conclude
that it is the difference in lengths of the helices surrounding the DHp
loop that determines the mechanism. We also show that very small changes
in these helices can cause a mechanism switch, providing these systems
with a way to diverge rapidly and adapt to new stimuli.
Exploring ligands that target von Willebrand factor selectively under oxidizing conditions through docking and molecular dynamics simulations
Authors: Interlandi Gianluca
Published: April 2024

The blood protein von Willebrand factor (VWF) is a large multimeric
protein that, when activated, binds to blood platelets tethering them to
the site of vascular injury initiating blood coagulation. This process
is critical for the normal haemostatic response, but especially under
inflammatory conditions it is thought to be a major player in
pathological thrombus formation. For this reason, VWF has been the
target for the development of anti-thrombotic therapeutics. However, it
is challenging to prevent pathological thrombus formation while still
allowing normal physiological blood coagulation as currently available
anti-thrombotic therapeutics are known to cause unwanted bleeding in
particular intracranial haemorrhage. This work explores the possibility
of inhibiting VWF selectively under the inflammatory conditions present
during pathological thrombus formation. In particular, the A2 domain of
VWF is known to inhibit the neighboring A1 domain from binding to the
platelet surface receptor GpIb α and this auto-inhibitory
mechanism has been shown to be removed by oxidizing agents released
during inflammation. Hence, finding drug molecules that bind at the
interface between A1 and A2 only under oxidizing conditions could
restore such auto-inhibitory mechanism. Here, by using a combination of
computational docking, molecular dynamics simulations and free energy
perturbation calculations, a ligand from the ZINC15 database was
identified that binds at the A1A2 interface with the interaction being
stronger under oxidizing conditions. The results provide a framework for
the discovery of drug molecules that bind to a protein selectively in
inflammatory conditions.
Prediction models for the flux decay profile and initial flux of microfiltration for therapeutic proteins
Authors: Uchiyama Susumu , Inoue Kota , Masuda Yumiko , Torisu Tetsuo , Nonaka Koichi
Published: April 2024

Microfiltration is an essential step during biopharmaceutical
manufacturing. However, unexpected flux decay can occur. Although the
flux decay profile and initial flux are important factors determining
microfiltration filterability, predicting them accurately is challenging
since the root cause of unexpected flux decay remains elusive. In this
study, the methodology for developing a prediction model of flux decay
profiles was established. First, the filtration profiles of different
monodisperse polystyrene latex and silica beads of various sizes were
evaluated. These results revealed that the size and surface
electrostatic properties of the beads affect the flux decay profile.
Taking the size and surface electrostatic properties of protein
aggregates into account, we constructed a predictive model using model
bead filtration profiles. We showed that this methodology was applicable
to two different microfiltration filters to predict the flux decay
profile of therapeutic proteins. Since this prediction model is based on
normalized flux, the initial flux must be predicted. We therefore
successfully developed a method to predict initial flux based on the
Hagen-Poiseuille equation using sample viscosity values for both
filters. These prediction models can be used for effective
microfiltration scale-up assessment and can be applied during early
stage of process development.
Who Gives, How and Why to “Undeserving Poor”?
Authors: Laštovková Jitka , Fuchsová Eva , Jánská Michaela
Published: April 2024

This study examines the role of merit in the concept of corporate social
responsibility. The text outlines the mechanisms of support for those
who are perceived by society as undeserving of the aid in question. Its
aim is to analyze the context of the decision-making process in
companies on the one hand and nonprofit organizations on the other. A
qualitative approach has been chosen to achieve the goal, which reveals
the ways in which the partakers think. The study examines the legitimacy
of the choice of the target group, the importance of the community in
the local context, and the strategy of communication support. The
results of the study identify four different mechanisms of support from
companies, including support through a foundation, the company’s own
foundation, the employee volunteer association, and a consortium of the
company and nonprofit organization. At the same time, the research has
concluded that the various methods of support intersect and together
create a comprehensive mechanism with a number of stakeholders, which
can only work under the conditions of mutual consensus.
Landscape Changes and Watershed Erosion in Prince George's County, Maryland
Authors: Stribling James
Published: April 2024

This study evaluated erosion rates and sediment production in streams,
and factors potentially influencing them throughout the Anacostia,
Patuxent, and Potomac (non-Anacostia) River watersheds within Prince
George’s County, Maryland, US. As part of the County’s watershed-scale
biological monitoring program, from approx. 1999-2008, permanent
monuments were established to allow measurement of stream channel
cross-sectional (XS) area. The intent of this study was to characterize
the intensity and spatial distribution of fluvial geomorphic instability
across the county and use the results to target and plan stormwater
management and stream restoration actions. For this study, 78 stream
locations were re-surveyed in 2020, representing a time lapse of from
12-21 years. Data collected included XS dimensions, modified Wolman
100-particle pebble counts, and reach-specific soil bulk density. Land
use/land cover data were compiled from the National Land Cover Dataset
(NLCD), precipitation from the National Weather Service Center for
Environmental Information (NCEI), and soils from the Natural Resources
Conservation Service Web Soil Survey (NRCS/WSS). We calculated percent
change in XS area, rates of erosion, sediment yield, and assigned
geomorphic classifications, and interpreted them in the context of
spatial positions relative to changes in land cover characteristics.
Sediment yields among the 78 reaches exhibited a combination of those
undergoing degradation/erosion (67.9%), aggradation/deposition
(25.6%), and the remaining 6.4% with essentially no change over the
period of record. Of the top 20 most geomorphically active reaches
surveyed in the County, 12 are in the Anacostia River basin, with the
other scattered among the Patuxent River and Potomac River basins.