University of Missouri- St. Louis Comprehensive School Safety Initiative (UMSL CSSI)

View the website for this project here: https://www.umsl.edu/ccj/research/cssi.htm

Properties of star-forming galaxies in a cluster and its surrounding structure at z=1.46

We conduct a narrow-band imaging survey of [OII] emitters over a 32'x23' area in and around the XMMXCS J2215.9-1738 cluster at z=1.46 with Subaru/Suprime-Cam, and select 380 [OII] emitting galaxies down to 1.4E-17 erg/s/cm2. Among them, 16 [OII] emitters in the cluster central region are confirmed by NIR spectroscopy with Subaru/MOIRCS. We find that [OII] emitters are distributed along filamentary large-scale structures around the cluster. The z'-K vs K colour-magnitude diagram shows that a significantly higher fraction of [OII] emitters is seen on the red sequence in the cluster core than in other environments we define in this paper. It is likely that these red galaxies are nearly passively evolving galaxies which host [OII] emitting AGNs, rather than dust-reddened star-forming galaxies. We argue therefore that AGN feedback may be one of the critical processes to quench star formation in massive galaxies in high density regions. We also find that the cluster has experienced high star formation activities at rates comparable to that in the field at z=1.46. In addition, a mass-metallicity relation exists in the cluster at z=1.46, which is similar to that of star-forming galaxies in the field at z~2. These results all suggest that at z~1.5 star formation activity in the cluster core becomes as high as those in low density environments and there is apparently not yet a strong environmental dependence, except for the red emitters.Comment: 19 pages, 17 figures, 4 tables, accepted for publication in MNRA

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.Comment: 19 pages, 8 tables, 18 figure

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient’s quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization

Mutations in 3 genes (MKS3, CC2D2A and RPGRIP1L) cause COACH syndrome (Joubert syndrome with congenital hepatic fibrosis)

OBJECTIVE: To identify genetic causes of COACH syndrome BACKGROUND: COACH syndrome is a rare autosomal recessive disorder characterised by Cerebellar vermis hypoplasia, Oligophrenia (developmental delay/mental retardation), Ataxia, Coloboma, and Hepatic fibrosis. The vermis hypoplasia falls in a spectrum of mid-hindbrain malformation called the molar tooth sign (MTS), making COACH a Joubert syndrome related disorder (JSRD). METHODS: In a cohort of 251 families with JSRD, 26 subjects in 23 families met criteria for COACH syndrome, defined as JSRD plus clinically apparent liver disease. Diagnostic criteria for JSRD were clinical findings (intellectual impairment, hypotonia, ataxia) plus supportive brain imaging findings (MTS or cerebellar vermis hypoplasia). MKS3/TMEM67 was sequenced in all subjects for whom DNA was available. In COACH subjects without MKS3 mutations, CC2D2A, RPGRIP1L and CEP290 were also sequenced. RESUlTS: 19/23 families (83%) with COACH syndrome carried MKS3 mutations, compared to 2/209 (1%) with JSRD but no liver disease. Two other families with COACH carried CC2D2A mutations, one family carried RPGRIP1L mutations, and one lacked mutations in MKS3, CC2D2A, RPGRIP1L and CEP290. Liver biopsies from three subjects, each with mutations in one of the three genes, revealed changes within the congenital hepatic fibrosis/ductal plate malformation spectrum. In JSRD with and without liver disease, MKS3 mutations account for 21/232 families (9%). CONCLUSIONS: Mutations in MKS3 are responsible for the majority of COACH syndrome, with minor contributions from CC2D2A and RPGRIP1L; therefore, MKS3 should be the first gene tested in patients with JSRD plus liver disease and/or coloboma, followed by CC2D2A and RPGRIP1L

Salve Regina University Act on Climate: Strategic Plan for the University to Reach State Carbon Neutrality Goals

In order to become more sustainable and meet the mandate set by the 2021 Rhode Island Act on Climate law (RI General Law §42-6.2), Salve Regina University must work to reach net-zero greenhouse gas emissions by the year 2050. Action to meet these standards begins now and must be continually built upon to ensure that Salve Regina University, as leader in Rhode Island, is always working for a more sustainable future. Throughout the Spring 2022 semester, students of the BIO-140: Humans and Their Environment course instructed by Dr. Jameson Chace have researched ways in which Salve Regina can begin on the path to zero greenhouse gas emissions today. By focusing on change in the areas of energy, transportation, food, financial investments, and sequestration, Salve Regina can reduce the greenhouse gas emissions of today for a more sustainable tomorrow. Recommendations are broken into three time periods. Action for today to achieve by 2030 include improving energy efficiency, installing the first electric vehicle (EV) parking/charging stations, increasing carbon sequestration, reducing beef in the campus diet, and assessing the carbon impact of university financial holdings. Actions to be initiated soon and to be achieved by 2040 include shifting away from natural gas heating when system renewals take place, increasing EV parking to meet rising demand, during turnover replace current university vehicles with electric or hybrid, continuing with sequestration efforts on campus, begin phasing out high carbon diet items, and by 2040 the university investment portfolio should be carbon neutral. If carbon neutrality can be reached by 2050 the most challenging aspects of campus life that need to change will require planning now and thoughtful implementation. The class in 2022 envisions a campus in 2050 where solar lights illuminate campus and buildings through the night, all university vehicles and most faculty and staff vehicles are electric and are found charging during the day at solar powered charging stations, dining services in Miley supports community agriculture and includes incentives for meatless and low carbon meal plans, the university has become a leader in low carbon/green market investing demonstrating how careful planning can reap high returns, and carbon sequestration on campus grounds has maximized such that off campus carbon offsets are established with local land trusts to complete the carbon neutrality goals. In doing so no only will the university be recognized as a state-wide leader in climate action, but will also be a global leader in working towards a world that is more harmonious, just, and merciful.https://digitalcommons.salve.edu/bio140_arboretum/1033/thumbnail.jp

the impact of uterine immaturity on obstetrical syndromes during adolescence

Pregnant nulliparous adolescents are at increased risk, inversely proportional to their age, of major obstetric syndromes, including preeclampsia, fetal growth restriction, and preterm birth. Emerging evidence indicates that biological immaturity of the uterus accounts for the increased incidence of obstetrical disorders in very young mothers, possibly compounded by sociodemographic factors associated with teenage pregnancy. The endometrium in most newborns is intrinsically resistant to progesterone signaling, and the rate of transition to a fully responsive tissue likely determines pregnancy outcome during adolescence. In addition to ontogenetic progesterone resistance, other factors appear important for the transition of the immature uterus to a functional organ, including estrogen-dependent growth and tissue-specific conditioning of uterine natural killer cells, which plays a critical role in vascular adaptation during pregnancy. The perivascular space around the spiral arteries is rich in endometrial mesenchymal stem-like cells, and dynamic changes in this niche are essential to accommodate endovascular trophoblast invasion and deep placentation. Here we evaluate the intrinsic (uterine-specific) mechanisms that predispose adolescent mothers to the great obstetrical syndromes and discuss the convergence of extrinsic risk factors that may be amenable to intervention

CNS targets of adipokines

This is the author accepted manuscript. The final version is available from American Physiological Society via the DOI in this record.Our understanding of adipose tissue as an endocrine organ has been transformed over the last twenty years. During this time a number of adipocyte-derived factors or adipokines have been identified. This paper will review evidence for how adipokines acting via the central nervous system (CNS) regulate normal physiology and disease pathology. The reported CNS-mediated effects of adipokines are varied and include the regulation of energy homeostasis, autonomic nervous system activity, the reproductive axis, neurodevelopment, cardiovascular function, and cognition. Due to the wealth of information available and the diversity of their known functions, the archetypal adipokines leptin and adiponectin will be the focused on extensively. Other adipokines with established CNS actions will also be discussed. Due to the difficulties associated with studying CNS function on a molecular level in humans, the majority of our knowledge, and as such the studies described in this paper, comes from work in experimental animal models; however, where possible the relevant data from human studies are also highlighted