Can Science and Christianity Coexist in the Medical Profession?

I know my choice of thesis question seems strange for a biology major. Like many of my Honor College brothers and sisters, I could have chosen to stay within the lines of my degree focus. I am admittingly noting that my thesis borders dangerously on a philosophical or moralistic debate. Refreshingly, I hope to defend neither nor endorse a particular sway of opinion. I am simply using this vehicle as a measuring device that challenges and explores an unavoidable dilemma faced by any scientific professional of faith, presumably. I pose this question in terms of Christianity simply because it is relative to my own beliefs. Still, I would assume the same question could be applied to any religion-related to a believing professional regardless of their belief structure, presumably. I hope it stirs an accounting in all that read it regardless of our differences of beliefs or lack thereof. As you will see later, I believe it is our “why” that will be most challenged, and in this area, there are significantly few physical benefits that may be adequate to sustain us in this path, in light of the pandemic effects on our fields. Since we have addressed the elephant in the room, I will continue. I am sure by now you all realize I don’t always follow the crowd. This subject is close to my heart because my ambition is to become an Optometrist and use my skill and influence to help my community and the world. As a strong female and dedicated student, I hope to show you that I can deliver a professional and articulate thesis on this subject in a respectful way. iii The world has changed. It is safe to conclude that our daily lives and status quo changes have been altered irrevocably in many ways. I am sure on this point we can all agree. Our children attend school remotely. We now have to rethink how we meet and socialize. Rethink how we celebrate birthdays and anniversaries. Even our bucket list of travel has been altered or reorganized and, for some, eliminated. Our older parents and younger children need extra protection from the world outside our doors. Many of us have lost someone close or been touched by a villain that we can neither see nor feel, but we all must live with its presence and be aware and purposeful in our avoidance of it. In this time of uncertainty, many aspiring health professionals face the reality of entering fields or career paths that would place us on the vanguard of the battle with this and future pandemics. As someone aspiring to be a medical professional, possibly for the first time in history, we are faced with evident and prevalent negative cons to pursuing such a field. Gone are the days of telling friends and family that you will provide medical professionals and receive enthusiastic cheers. Now we face raised eyebrows and a mix of pity and real fear for our safety and their safety. If we were doing this for the money or because our parents predestined a career in medicine, then this path’s value may fade in its luster. We are leaving you to consider your ‘why\u27. We see the nurses exhausted and discouraged on the news daily. Now more than ever, you must have a calling, a passion, to do this work. The coronavirus pandemic has changed the dynamics of the healthcare system in countless ways. At times like these, many of us have relied on something more profound than the degrees and diplomas we hang on our walls. For some, it comes down to protecting those they love. For others, it is the pursuit of regaining a new normal. For some, it is a faith of some type that inspires them to go on and continue to rise when the world is falling apart. iv My thesis expounds that, within the context of proper application and the regulating core desire to provide beneficial medical services to all people, Christian faith and science are not in conflict and are complementary traits. My goal in this thesis is to conduct a respectful investigation of these two core elements and the effectiveness of their existence as reflected in the performance and professionalism of noted people and organizations who confidently affirm their faith as part of their profession. Using this methodology, I have supported my conclusions

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and air-conditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality and comfort. As a result, machine learning-based models and methodologies are progressively being used to forecast occupancy behaviour and routines in buildings, which may subsequently be used to aid in the design and operation of building systems. The present work reviews recent studies employing machine learning methods to predict occupancy behaviour and patterns, with a special focus on its related applications and benefits to building systems, improving energy efficiency, indoor air quality and thermal comfort. The review provides insight into the workflow of a machine learning-based occupancy prediction model, including data collection, prediction, and validation. An organised evaluation of the applicability or suitability of the different data collection methods, machine learning algorithms, and validation methods was carried out

Specific gene module pair-based target identification and drug discovery

Identification of the biological targets of a compound is of paramount importance for the exploration of the mechanism of action of drugs and for the development of novel drugs. A concept of the Connectivity Map (CMap) was previously proposed to connect genes, drugs, and disease states based on the common gene-expression signatures. For a new query compound, the CMap-based method can infer its potential targets by searching similar drugs with known targets (reference drugs) and measuring the similarities into their specific transcriptional responses between the query compound and those reference drugs. However, the available methods are often inefficient due to the requirement of the reference drugs as a medium to link the query agent and targets. Here, we developed a general procedure to extract target-induced consensus gene modules from the transcriptional profiles induced by the treatment of perturbagens of a target. A specific transcriptional gene module pair (GMP) was automatically identified for each target and could be used as a direct target signature. Based on the GMPs, we built the target network and identified some target gene clusters with similar biological mechanisms. Moreover, a gene module pair-based target identification (GMPTI) approach was proposed to predict novel compound–target interactions. Using this method, we have discovered novel inhibitors for three PI3K pathway proteins PI3Kα/β/δ, including PU-H71, alvespimycin, reversine, astemizole, raloxifene HCl, and tamoxifen

Convergent adaptation of the genomes of woody plants at the land-sea interface

Sequencing multiple species that share the same ecological niche may be a new frontier for genomic studies. While such studies should shed light on molecular convergence, genomic-level analyses have been unsuccessful, due mainly to the absence of empirical controls. Woody plant species that colonized the global tropical coasts, collectively referred to as mangroves, are ideal for convergence studies. Here, we sequenced the genomes/transcriptomes of 16 species belonging in three major mangrove clades. To detect convergence in a large phylogeny, a CCS+ model is implemented, extending the more limited CCS method (convergence at conservative sites). Using the empirical control for reference, the CCS+ model reduces the noises drastically, thus permitting the identification of 73 convergent genes with P-true (probability of true convergence) > 0.9. Products of the convergent genes tend to be on the plasma membrane associated with salinity tolerance. Importantly, convergence is more often manifested at a higher level than at amino-acid (AA) sites. Relative to >50 plant species, mangroves strongly prefer 4 AAs and avoid 5 others across the genome. AA substitutions between mangrove species strongly reflect these tendencies. In conclusion, the selection of taxa, the number of species and, in particular, the empirical control are all crucial for detecting genome-wide convergence. We believe this large study of mangroves is the first successful attempt at detecting genome-wide site convergence

A meta-analysis of the Zilongjin tablets for non-small cell lung cancer and its network pharmacology of action against NSCLC and COVID-19

ObjectiveTo objectively evaluate the efficacy of the Zilongjin tablets in non-small cell lung cancer (NSCLC) and to explore its potential mechanism of action against NSCLC and COVID-19 based on network pharmacology.MethodsThe database was searched for randomized controlled trials (RCTs) of the Zilongjin tablets for NSCLC published up to 22 August 2022. The quality of included trials was assessed using Cochrane standard guidelines, and a meta-analysis was performed using Rev Man 5.3. Gene targets for intersections of NSCLC and COVID-19 (the NC) and drugs were obtained from the TCMSP database, HERB database, GeneCards database, and the NCBI database for network pharmacology research.ResultsMeta-analysis included 14 articles with 2,430 patients. The meta-analysis showed that the Zilongjin tablets combined with conventional chemotherapy were significantly more effective than chemotherapy alone in the treatment of NSCLC. A total of 29 drug-disease intersecting targets were identified in the network pharmacology. The “ingredient-target-pathway” diagram component-target-pathway network contained 119 nodes and 429 edges, with the majority of targets associated with inflammatory responses.ConclusionThe efficacy and quality of life of the Zilongjin tablets combined conventional chemotherapy for NSCLC were significantly better than chemotherapy alone, alleviating various adverse effects. At the same time, the Zilongjin tablets may modulate the inflammatory response to alleviate NSCLC and COVID-19

The persistent impact of drought stress on the resilience of summer maize

Crop resilience refers to the adaptive ability of crops to resist drought at a certain level. Currently, most of the research focuses on the changes in root or photosynthesis traits of crops after drought and rehydration. Still, the persistence effect (drought period (T2) - rehydration period (T3) - harvest period (T4)) of drought stress on crops and quantitative estimation of resilience is still unclear. Field experiments were conducted in this study to determine the persistence effects on above-ground and below-ground growth indicators of summer maize at different levels and durations of drought. Next, an evaluation method for integrated resilience of summer maize was proposed, and a quantitative assessment of integrated resilience was made by Principal Component Analysis (PCA) and resilience index calculation. The results showed that the resilience of summer maize decreased with increasing drought levels, which persisted until harvest. Although summer maize resilience was strong after rewatering under light drought (DR1), declined after sustained rewatering. At the same time, production had decreased. However, a specific drought duration could improve the resilience of summer maize under light drought conditions. In particular, leaf biomass and root growth in the 30-50 cm layer could be enhanced under long duration light drought (LDR1), thus improving summer maize resilience and yield. Thus, under water shortage conditions, a certain level and duration drought could improve the resilience and yield of summer maize, which would persist until harvest. Clarifying the persistent effects on the growth indicators of summer maize and quantitatively evaluating the resilience of summer maize could improve agricultural food production and water use efficiency

Phosphate glass fibers facilitate proliferation and osteogenesis through Runx2 transcription in murine osteoblastic cells

Cell-material interactions and compatibility are important aspects of bioactive materials for bone tissue engineering. Phosphate glass fiber (PGF) is an attractive inorganic filler with fibrous structure and tunable composition, which has been widely investigated as a bioactive filler for bone repair applications. However, the interaction of osteoblasts with PGFs has not been widely investigated to elucidate the osteogenic mechanism of PGFs. In this study, different concentrations of short PGFs with interlaced oriented topography were co-cultured with MC3T3-E1 cells for different periods, and the synergistic effects of fiber topography and ionic product of PGFs on osteoblast responses including cell adhesion, spreading, proliferation and osteogenic differentiation were investigated. It was found that osteoblasts were more prone to adhere on PGFs through vinculin protein, leading to enhanced cell proliferation with polygonal cell shape and spreading cellular actin filaments. In addition, osteoblasts incubated on PGF meshes showed enhanced alkaline phosphatase (ALP) activity, extracellular matrix mineralization, and increased expression of osteogenesis-related marker genes, which could be attributed to the Wnt/β-catenin/Runx2 signaling pathway. This study elucidated the possible mechanism of PGF on triggering specific osteoblast behavior, which would be highly beneficial for designing PGF-based bone graft substitutes with excellent osteogenic functions

The Spectral-Spatial Joint Learning for Change Detection in Multispectral Imagery

Change detection is one of the most important applications in the remote sensing domain. More and more attention is focused on deep neural network based change detection methods. However, many deep neural networks based methods did not take both the spectral and spatial information into account. Moreover, the underlying information of fused features is not fully explored. To address the above-mentioned problems, a Spectral-Spatial Joint Learning Network (SSJLN) is proposed. SSJLN contains three parts: spectral-spatial joint representation, feature fusion, and discrimination learning. First, the spectral-spatial joint representation is extracted from the network similar to the Siamese CNN (S-CNN). Second, the above-extracted features are fused to represent the difference information that proves to be effective for the change detection task. Third, the discrimination learning is presented to explore the underlying information of obtained fused features to better represent the discrimination. Moreover, we present a new loss function that considers both the losses of the spectral-spatial joint representation procedure and the discrimination learning procedure. The effectiveness of our proposed SSJLN is verified on four real data sets. Extensive experimental results show that our proposed SSJLN can outperform the other state-of-the-art change detection methods

Cascaded attention-induced difference representation learning for multispectral change detection

Change Detection (CD) is the process of recognizing and quantitatively evaluating changes in surface objects at the exact location but at different times from remote sensing images that may be caused by extreme heat events. Attention-based CD methods have gained much traction because of their ability to concentrate on change regions. However, most current attention-based methods only calculate the attention matrix based on features extracted from a single image but fail to consider the correlation of features extracted from images at different times. The effectiveness of the correlation of learned features from change regions in bi-temporal images is an essential element influencing the improvement of CD performance. This paper proposes a Cascaded Attention-Induced Difference Representation Learning (CADRL) method for multispectral CD to explore the correlation of features extracted from bi-temporal images to obtain more discriminative features. The proposed CADRL method contains three modules: the feature extraction module, the Cascaded Cross-attention based Difference Learning Module (CCADLM) and the detection module. First, the feature extraction module extracts multi-scale features from bi-temporal images. Then, CCADLM generates more discriminative features by fusing difference features and the cross-attention matrix learned from the temporal attention unit from multiple levels to explore the correlation information of change regions in bi-temporal images. Finally, the learned discriminative features are fed to the detection module to gain the final detection map. Experimental results on three multispectral datasets demonstrate that the CADRL method outperforms other existing CD algorithms

A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery

Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researchers’ attention. Most deep-learning-based methods only explore the spatial–spectral features simultaneously. However, we assume the key spatial-change areas should be more important, and attention should be paid to the specific bands which can best reflect the changes. To achieve this goal, we propose the spatial–spectral joint attention network (SJAN). Compared with traditional methods, SJAN introduces the spatial–spectral attention mechanism to better explore the key changed areas and the key separable bands. To be more specific, a novel spatial-attention module is designed to extract the spatially key regions first. Secondly, the spectral-attention module is developed to adaptively focus on the separable bands of land-cover materials. Finally, a novel objective function is proposed to help the model to measure the similarity of learned spatial–spectral features from both spectrum amplitude and angle perspectives. The proposed SJAN is validated on three benchmark datasets. Comprehensive experiments have been conducted to demonstrate the effectiveness of the proposed SJAN