Prioritizing Regional Wildlife Conservation by Rejuvenating the Western Hemisphere Convention on Nature Protection

Last year, parties to the Convention on Biological Diversity (“CBD”), representing nearly every nation, signed a milestone agreement committing, among other things, to conserve thirty percent of Earth’s lands and oceans to stave off the rapid diminution of the planet’s biodiversity. Implementing these global commitments will require not only strong domestic measures, but also enhanced regional cooperation targeting the conservation of the region’s migratory wildlife and shared resources. Although the United States is the sole major holdout from the CBD, it can still reassert its leadership in regional wildlife conservation by rejuvenating the Convention on Nature Protection and Wildlife Preservation in the Western Hemisphere (“Western Hemisphere Convention”)

Prioritizing Regional Wildlife Conservation by Rejuvenating the Western Hemisphere Convention on Nature Protection

Last year, parties to the Convention on Biological Diversity (“CBD”), representing nearly every nation, signed a milestone agreement committing, among other things, to conserve thirty percent of Earth’s lands and oceans to stave off the rapid diminution of the planet’s biodiversity. Implementing these global commitments will require not only strong domestic measures, but also enhanced regional cooperation targeting the conservation of the region’s migratory wildlife and shared resources. Although the United States is the sole major holdout from the CBD, it can still reassert its leadership in regional wildlife conservation by rejuvenating the Convention on Nature Protection and Wildlife Preservation in the Western Hemisphere (“Western Hemisphere Convention”)

Experimental Verification of PCH-EM Algorithm for Characterizing DSERN Image Sensors

The Photon Counting Histogram Expectation Maximization (PCH-EM) algorithm has recently been reported as a candidate method for the characterization of Deep Sub-Electron Read Noise (DSERN) image sensors. This work describes a comprehensive demonstration of the PCH-EM algorithm applied to a DSERN capable quanta image sensor. The results show that PCH-EM is able to characterize DSERN pixels for a large span of quanta exposure and read noise values. The per-pixel characterization results of the sensor are combined with the proposed Photon Counting Distribution (PCD) model to demonstrate the ability of PCH-EM to predict the ensemble distribution of the device. The agreement between experimental observations and model predictions demonstrates both the applicability of the PCD model in the DSERN regime as well as the ability of the PCH-EM algorithm to accurately estimate the underlying model parameters.Comment: 8 pages, 9 figure

In mother's lap : microcomputers, mother's teaching behavior and young children's classification skills

Forty-one preschool children enrolled in a southeastern university enrichment program, 21 2-year-olds and 20 3-year-olds, were randomly assigned to two treatment groups: a microworld computer experience designed to teach the concept inside/outside and an ABC computer experience designed to drill the alphabet. Mothers assisted children in gaining computer competency. Mother/child dyads were videotaped during each 15-minute session for a total of one hour of treatment. Videotapes were coded and scored using the Wood and Middleton (1975) Assisted Problem-Solving Scale with interrater reliabilities consistently over .80. Children were administered a classification task at the end of treatment

PCH-EM: A solution to information loss in the photon transfer method

Working from a Poisson-Gaussian noise model, a multi-sample extension of the Photon Counting Histogram Expectation Maximization (PCH-EM) algorithm is derived as a general-purpose alternative to the Photon Transfer (PT) method. This algorithm is derived from the same model, requires the same experimental data, and estimates the same sensor performance parameters as the time-tested PT method, all while obtaining lower uncertainty estimates. It is shown that as read noise becomes large, multiple data samples are necessary to capture enough information about the parameters of a device under test, justifying the need for a multi-sample extension. An estimation procedure is devised consisting of initial PT characterization followed by repeated iteration of PCH-EM to demonstrate the improvement in estimate uncertainty achievable with PCH-EM; particularly in the regime of Deep Sub-Electron Read Noise (DSERN). A statistical argument based on the information theoretic concept of sufficiency is formulated to explain how PT data reduction procedures discard information contained in raw sensor data, thus explaining why the proposed algorithm is able to obtain lower uncertainty estimates of key sensor performance parameters such as read noise and conversion gain. Experimental data captured from a CMOS quanta image sensor with DSERN is then used to demonstrate the algorithm's usage and validate the underlying theory and statistical model. In support of the reproducible research effort, the code associated with this work can be obtained on the MathWorks File Exchange (Hendrickson et al., 2024).Comment: 14 pages, 8 figure

Uncovering the effects of heterogeneity and parameter sensitivity on within‑host dynamics of disease : malaria as a case study

CITATION: Horn, S., Snoep, J. L. & Van Niekerk, D. D. 2021. Uncovering the effects of heterogeneity and parameter sensitivity on within‑host dynamics of disease: malaria as a case study. BMC Bioinformatics, 22:384, doi:10.1186/s12859-021-04289-z.The original publication is available at https://bmcbioinformatics.biomedcentral.comPublication of this article was funded by the Stellenbosch University Open Access FundBackground: The fidelity and reliability of disease model predictions depend on accurate and precise descriptions of processes and determination of parameters. Various models exist to describe within-host dynamics during malaria infection but there is a shortage of clinical data that can be used to quantitatively validate them and establish confidence in their predictions. In addition, model parameters often contain a degree of uncertainty and show variations between individuals, potentially undermining the reliability of model predictions. In this study models were reproduced and analysed by means of robustness, uncertainty, local sensitivity and local sensitivity robustness analysis to establish confidence in their predictions. Results: Components of the immune system are responsible for the most uncertainty in model outputs, while disease associated variables showed the greatest sensitivity for these components. All models showed a comparable degree of robustness but displayed different ranges in their predictions. In these different ranges, sensitivities were well-preserved in three of the four models. Conclusion: Analyses of the effects of parameter variations in models can provide a comparative tool for the evaluation of model predictions. In addition, it can assist in uncovering model weak points and, in the case of disease models, be used to identify possible points for therapeutic intervention.https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04289-zPublisher's versio

Multiple Gene Variants Linked to Alzheimer\u27s-Type Clinical Dementia via GWAS are Also Associated with Non-Alzheimer\u27s Neuropathologic Entities

The classic pathologic hallmarks of Alzheimer’s disease (AD) are amyloid plaques and neurofibrillary tangles (AD neuropathologic changes, or ADNC). However, brains from individuals clinically diagnosed with “AD-type” (amnestic) dementia usually harbor heterogeneous neuropathologies in addition to, or other than, ADNC. We hypothesized that some AD-type dementia associated genetic single nucleotide variants (SNVs) identified from large genomewide association studies (GWAS) were associated with non-ADNC neuropathologies. To test this hypothesis, we analyzed data from multiple studies with available genotype and neuropathologic phenotype information. Clinical AD/dementia risk alleles of interest were derived from the very large GWAS by Bellenguez et al. (2022) who reported 83 clinical AD/dementia-linked SNVs in addition to the APOE risk alleles. To query the pathologic phenotypes associated with variation of those SNVs, National Alzheimer’s disease Coordinating Center (NACC) neuropathologic data were linked to AD Sequencing Project (ADSP) and AD Genomics Consortium (ADGC) data. Separate data were obtained from the harmonized Religious Orders Study and the Rush Memory and Aging Project (ROSMAP). A total of 4811 European participants had at least ADNC neuropathology data and also genotype data available; data were meta-analyzed across cohorts. As expected, a subset of dementia-associated SNVs were associated with ADNC risk in Europeans—e.g., BIN1, PICALM, CR1, MME, and COX7C. Other gene variants linked to (clinical) AD dementia were associated with non-ADNC pathologies. For example, the associations of GRN and TMEM106B SNVs with limbic-predominant age-related TDP-43 neuropathologic changes (LATE-NC) were replicated. In addition, SNVs in TNIP1 and WNT3 previously reported as ADrelated were instead associated with hippocampal sclerosis pathology. Some genotype/neuropathology association trends were not statistically significant at P \u3c 0.05 after correcting for multiple testing, but were intriguing. For example, variants in SORL1 and TPCN1 showed trends for association with LATE-NC whereas Lewy body pathology trended toward association with USP6NL and BIN1 gene variants. A smaller cohort of non-European subjects (n = 273, approximately one-half of whom were African-Americans) provided the basis for additional exploratory analyses. Overall, these findings were consistent with the hypothesis that some genetic variants linked to AD dementia risk exert their affect by influencing non-ADNC neuropathologies

Analysis of Genes (\u3ci\u3eTMEM106B\u3c/i\u3e, \u3ci\u3eGRN\u3c/i\u3e, \u3ci\u3eABCC9\u3c/i\u3e, \u3ci\u3eKCNMB2\u3c/i\u3e, and \u3ci\u3eAPOE\u3c/i\u3e) Implicated in Risk for LATE-NC and Hippocampal Sclerosis Provides Pathogenetic Insights: A Retrospective Genetic Association Study

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is the most prevalent subtype of TDP-43 proteinopathy, affecting up to 1/3rd of aged persons. LATE-NC often co-occurs with hippocampal sclerosis (HS) pathology. It is currently unknown why some individuals with LATE-NC develop HS while others do not, but genetics may play a role. Previous studies found associations between LATE-NC phenotypes and specific genes: TMEM106B, GRN, ABCC9, KCNMB2, and APOE. Data from research participants with genomic and autopsy measures from the National Alzheimer’s Coordinating Center (NACC; n = 631 subjects included) and the Religious Orders Study and Memory and the Rush Aging Project (ROSMAP; n = 780 included) were analyzed in the current study. Our goals were to reevaluate disease-associated genetic variants using newly collected data and to query whether the specific genotype/phenotype associations could provide new insights into disease-driving pathways. Research subjects included in prior LATE/HS genome-wide association studies (GWAS) were excluded. Single nucleotide variants (SNVs) within 10 kb of TMEM106B, GRN, ABCC9, KCNMB2, and APOE were tested for association with HS and LATE-NC, and separately for Alzheimer’s pathologies, i.e. amyloid plaques and neurofibrillary tangles. Significantly associated SNVs were identified. When results were meta-analyzed, TMEM106B, GRN, and APOE had significant gene-based associations with both LATE and HS, whereas ABCC9 had significant associations with HS only. In a sensitivity analysis limited to LATE-NC + cases, ABCC9 variants were again associated with HS. By contrast, the associations of TMEM106B, GRN, and APOE with HS were attenuated when adjusting for TDP-43 proteinopathy, indicating that these genes may be associated primarily with TDP-43 proteinopathy. None of these genes except APOE appeared to be associated with Alzheimer’s-type pathology. In summary, using data not included in prior studies of LATE or HS genomics, we replicated several previously reported gene-based associations and found novel evidence that specific risk alleles can differentially affect LATE-NC and HS

A conceptual framework for invasion in microbial communities

There is a growing interest in controlling-promoting or avoiding-the invasion of microbial communities by new community members. Resource availability and community structure have been reported as determinants of invasion success. However, most invasion studies do not adhere to a coherent and consistent terminology nor always include rigorous interpretations of the processes behind invasion. Therefore, we suggest that a consistent set of definitions and a rigorous conceptual framework are needed. We define invasion in a microbial community as the establishment of an alien microbial type in a resident community and argue how simple criteria to define aliens, residents, and alien establishment can be applied for a wide variety of communities. In addition, we suggest an adoption of the community ecology framework advanced by Vellend (2010) to clarify potential determinants of invasion. This framework identifies four fundamental processes that control community dynamics: dispersal, selection, drift and diversification. While selection has received ample attention in microbial community invasion research, the three other processes are often overlooked. Here, we elaborate on the relevance of all four processes and conclude that invasion experiments should be designed to elucidate the role of dispersal, drift and diversification, in order to obtain a complete picture of invasion as a community process

Brain arteriolosclerosis

Brain arteriolosclerosis (B-ASC), characterized by pathologic arteriolar wall thickening, is a common finding at autopsy in aged persons and is associated with cognitive impairment. Hypertension and diabetes are widely recognized as risk factors for B-ASC. Recent research indicates other and more complex risk factors and pathogenetic mechanisms. Here we describe aspects of the unique architecture of brain arterioles, histomorphologic features of B-ASC, relevant neuroimaging findings, epidemiology and association with aging, established genetic risk factors, and the co-occurrence of B-ASC with other neuropathologic conditions such as Alzheimer’s disease and limbic-predominant age-related TDP-43 encephalopathy (LATE). There may also be complex physiologic interactions between metabolic syndrome (e.g. hypertension and inflammation) and brain arteriolar pathology. Although there is no universally applied diagnostic methodology, several classification schemes and neuroimaging techniques are used to diagnose and categorize cerebral small vessel disease pathologies that include B-ASC, microinfarcts, microbleeds, lacunar infarcts, and cerebral amyloid angiopathy (CAA). In clinical-pathologic studies that include consideration of comorbid diseases, B-ASC is independently associated with impairments in global cognition, episodic memory, working memory, and perceptual speed, and has been linked to autonomic dysfunction and motor symptoms including parkinsonism. We conclude by discussing critical knowledge gaps related to B-ASC and suggest that there are probably subcategories of B-ASC that differ in pathogenesis. Observed in over 80% of autopsied individuals beyond 80 years of age, B-ASC is a complex and under-studied contributor to neurologic disability