Supplementary Material for: Longitudinal Decrease in Left Ventricular Size with Age: Impact on Mortality and Cardiovascular Hospitalization by figshare admin karger (2628495)

“…Participants were categorized by LVEDD change from baseline: No Change (<5 mm), Decreased (≥5 mm), and Increased (≥5 mm). Results: A decrease in LVEDD was observed in 24% of participants (mean change -9±3 mm) and was significantly associated with older age, female sex, decreased volumes, concentric remodeling and diastolic dysfunction. …”

<b>The loss of insulin-positive cell clusters precedes the decrease of islet frequency and beta cell area in type 1 diabetes</b> by Denise M. Drotar (21679539)

“…Moreover, changes in endocrine composition also occurred in mAAb+ donors, including a significant decrease in the INS+ islet fraction. These data suggest preferential loss of INS+ small endocrine objects early in type 1 diabetes development.…”

Innovation and night light in large and small cities. by Saul Estrin (8629173)

<b>Nest mass in forest tits </b><b><i>Paridae</i></b><b> </b><b>increases with elevation and decreasing body mass, promoting reproductive success</b> by Clara Wild (19246606)

“…Nest boxes were installed along an elevational gradient of approximately 1000 m a.sl., either in forest gaps with fluctuating microclimatic conditions or in closed forests with buffered microclimates. We found that nest mass increased by ~ 60% along the elevational gradient, but the effect of canopy openness on nest mass was not significant, while nest mass decreased along the ranked species from the smallest <i>Periparus ater</i> to the medium-sized <i>Cyanistes caeruleus</i> and the largest <i>Parus major</i>. …”

RNA targets of Mod identified by RiP-Seq. by Amalia S. Parra (4173004)

“…Mod is expressed in larval brains and its loss leads to a significant decrease in the number of central brain NBs. …”

Study-related adverse events. by Benjamin R. Lewis (22279166)

“…Study limitations that affect the generalizability of results include a small sample size, homogenous study population, and significant differences in intervention intensity.…”

Study flow chart. by Benjamin R. Lewis (22279166)

“…Study limitations that affect the generalizability of results include a small sample size, homogenous study population, and significant differences in intervention intensity.…”

Study CONSORT diagram. by Benjamin R. Lewis (22279166)

“…Study limitations that affect the generalizability of results include a small sample size, homogenous study population, and significant differences in intervention intensity.…”

Patient enrollment and inclusion information. by Ruitian Song (11045127)

“…During chemotherapy, PVS decreased significantly from 3M to 12M. Subsequently, from 12M to FollowUp, PVS increased again. …”

Flow chart of participants selection. by Linjia Duan (13276989)

“…Moreover, the risk of mortality significantly decreased with a greater number of cognitive activities. …”

Passive sensing data. by Thierry Jean (20691795)

“…Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. …”

Surveys. by Thierry Jean (20691795)

“…Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. …”

Summary of previous work. by Junyan Wang (4738518)

“…Specifically, to tackle the difficulties associated with small object detection, we replace conventional convolutions in the Backbone with SPDConv modules to enhance feature capture capabilities for small targets and low-resolution images while reducing computational overhead. …”

Comparison of MAP@0.5 results from experiments. by Junyan Wang (4738518)

“…Specifically, to tackle the difficulties associated with small object detection, we replace conventional convolutions in the Backbone with SPDConv modules to enhance feature capture capabilities for small targets and low-resolution images while reducing computational overhead. …”

YOLO11. by Junyan Wang (4738518)

“…Specifically, to tackle the difficulties associated with small object detection, we replace conventional convolutions in the Backbone with SPDConv modules to enhance feature capture capabilities for small targets and low-resolution images while reducing computational overhead. …”

Structure of the SCI-YOLO11 network. by Junyan Wang (4738518)

“…Specifically, to tackle the difficulties associated with small object detection, we replace conventional convolutions in the Backbone with SPDConv modules to enhance feature capture capabilities for small targets and low-resolution images while reducing computational overhead. …”

Comparative experimental results. by Junyan Wang (4738518)

“…Specifically, to tackle the difficulties associated with small object detection, we replace conventional convolutions in the Backbone with SPDConv modules to enhance feature capture capabilities for small targets and low-resolution images while reducing computational overhead. …”

Algorithm operation steps. by Junyan Wang (4738518)

“…Specifically, to tackle the difficulties associated with small object detection, we replace conventional convolutions in the Backbone with SPDConv modules to enhance feature capture capabilities for small targets and low-resolution images while reducing computational overhead. …”

SCI-YOLO11. by Junyan Wang (4738518)

“…Specifically, to tackle the difficulties associated with small object detection, we replace conventional convolutions in the Backbone with SPDConv modules to enhance feature capture capabilities for small targets and low-resolution images while reducing computational overhead. …”

Dataset for insulator defect detection. by Junyan Wang (4738518)

“…Specifically, to tackle the difficulties associated with small object detection, we replace conventional convolutions in the Backbone with SPDConv modules to enhance feature capture capabilities for small targets and low-resolution images while reducing computational overhead. …”