The Golem in the Machine: FERPA, Dirty Data, and Digital Distortion in the Education Record

Like its counterpart in the criminal justice system, dirty data—data that is inaccurate, incomplete, or misleading—in K-12 education records creates and catalyzes catastrophic life events. The presence of this data in any record suggests a lack of data integrity. The systemic problem of dirty data in education records means the data stewards of those records have failed to meet the data integrity requirements embedded in the Family Educational Rights and Privacy Act (FERPA). FERPA was designed to protect students and their education records from the negative impact of erroneous information rendered from the “private scribblings” of educators. The legislative history of FERPA indicates that legislators were concerned about the harm to students’ education and the structure of opportunities based on misinformation in secret files created and kept in schools. Dirty data created, collected, and processed as accurate and reliable, notwithstanding the disproportionate impact of school discipline, on marginalized students in general, and Black children specifically, is exactly the kind of harm that FERPA was intended to prevent. This Article demonstrates (1) how educational inequities linked to dirty data implicate student privacy interests understood at the time FERPA was created; and (2) how FERPA should be enhanced to prevent dirty data harms at the point of collection and creation. Additionally, this Article outlines the concept of dirty data and data integrity requirements embedded in FERPA and proceeds to examine the phenomenon of dirty data and student harm in historically marginalized students’ education records, starting at the point of creation and collection. While several Articles have examined the failure of FERPA, none of the prior scholarship has analyzed FERPA’s connection to dirty data in the education record related to racial discrimination. This Article introduces a two-step process that would require input validation in the educational record context through (1) substantive content and input validation; and (2) a reasonable inference review. Finally, this Article introduces a requirement of accounting of disclosures to law enforcement

The Soft-Shoe and Shuffle of Law School Hiring Committee Practices

It is in the spirit of Ida B. Wells that we seek to turn the light upon the systemic racism of hiring practices. We believe these practices are indicators of the systemic failures on campuses and in workplaces that prevent them from being antiracist. We seek to use this Essay as a “tool for exposing, analyzing, and challenging the majoritarian stories of racial privilege.” Our specifc intention is to recognize the largely performative nature of claiming to be committed to an idea while substantively and concretely ensuring the opposite. This Essay is written with specific experiences, patterns, and practices in mind that are directly connected to broader contexts and phenomena. The data and trends on law school faculty hiring, and on the performance of students of color in law school and on bar exams show that acts of discrimination are often obscured by the outcomes of systemic oppression misconstrued as academic achievement. We wrote this not in the often fraught and silencing tradition of typical legal scholarship; but instead drew from diverse traditions that center narrative, storytelling, and satire (both classical and modern). We wrote this to speak truth to who we are, the roles played, and compromises made. Most of all, we wrote this with students in mind. Students are very involved on their campuses and contribute an immense amount of time and effort listening to law faculty candidates. Students speak up when faculty use their teaching platforms to espouse harmful rhetoric and when decisions disproportionately and negatively harm marginalized people. Marginalized students o#en lead the service and contributions on these issues, while also dealing with the everyday challenges law school presents to the average law student. We want students to know that we hear them and see them. We also want students to know that the burden of these structures is not theirs to carry. We hope that by illuminating faculty recruiting and hiring practices we can empower students to refocus and conserve their energy. Ill-conceived meetings and discussions called by administrators and faculty are distractions that devour precious time. All of our students’ time and energy matter because their lives matter. We recognize but do not accept or assume the risk in writing this Essay. We have thought about and have been reminded of how we may face retaliation and other insidious responses. We do not know any Black or otherwise marginalized person in the legal academy whose silence or complicity has allowed them to escape these kinds of harms. Could writing this create more or intensify those barriers? Certainly. But that is why we have said we acknowledge but do not accept or assume the risk

A Hierarchical Method Based on Active Shape Models and Directed Hough Transform for Segmentation of Noisy Biomedical Images; Application in Segmentation of Pelvic X-ray Images

Background Traumatic pelvic injuries are often associated with severe, life-threatening hemorrhage, and immediate medical treatment is therefore vital. However, patient prognosis depends heavily on the type, location and severity of the bone fracture, and the complexity of the pelvic structure presents diagnostic challenges. Automated fracture detection from initial patient X-ray images can assist physicians in rapid diagnosis and treatment, and a first and crucial step of such a method is to segment key bone structures within the pelvis; these structures can then be analyzed for specific fracture characteristics. Active Shape Model has been applied for this task in other bone structures but requires manual initialization by the user. This paper describes a algorithm for automatic initialization and segmentation of key pelvic structures - the iliac crests, pelvic ring, left and right pubis and femurs - using a hierarchical approach that combines directed Hough transform and Active Shape Models. Results Performance of the automated algorithm is compared with results obtained via manual initialization. An error measures is calculated based on the shapes detected with each method and the gold standard shapes. ANOVA results on these error measures show that the automated algorithm performs at least as well as the manual method. Visual inspection by two radiologists and one trauma surgeon also indicates generally accurate performance. Conclusion The hierarchical algorithm described in this paper automatically detects and segments key structures from pelvic X-rays. Unlike various other x-ray segmentation methods, it does not require manual initialization or input. Moreover, it handles the inconsistencies between x-ray images in a clinical environment and performs successfully in the presence of fracture. This method and the segmentation results provide a valuable base for future work in fracture detection

Brain mapping and detection of functional patterns in fMRI using wavelet transform; application in detection of dyslexia

Background Functional Magnetic Resonance Imaging (fMRI) has been proven to be useful for studying brain functions. However, due to the existence of noise and distortion, mapping between the fMRI signal and the actual neural activity is difficult. Because of the difficulty, differential pattern analysis of fMRI brain images for healthy and diseased cases is regarded as an important research topic. From fMRI scans, increased blood ows can be identified as activated brain regions. Also, based on the multi-sliced images of the volume data, fMRI provides the functional information for detecting and analyzing different parts of the brain. Methods In this paper, the capability of a hierarchical method that performed an optimization algorithm based on modified maximum model (MCM) in our previous study is evaluated. The optimization algorithm is designed by adopting modified maximum correlation model (MCM) to detect active regions that contain significant responses. Specifically, in the study, the optimization algorithm is examined based on two groups of datasets, dyslexia and healthy subjects to verify the ability of the algorithm that enhances the quality of signal activities in the interested regions of the brain. After verifying the algorithm, discrete wavelet transform (DWT) is applied to identify the difference between healthy and dyslexia subjects. Results We successfully showed that our optimization algorithm improves the fMRI signal activity for both healthy and dyslexia subjects. In addition, we found that DWT based features can identify the difference between healthy and dyslexia subjects. Conclusion The results of this study provide insights of associations of functional abnormalities in dyslexic subjects that may be helpful for neurobiological identification from healthy subject

Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes

Objective The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR), rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA) patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals. Materials and Methods Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF) was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI) model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA) technique. Results 358 defibrillations were evaluated (218 unsuccessful and 140 successful). Non-linear properties (Lyapunov exponent \u3e 0) of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity) outperformed AMSA (53.6% sensitivity). At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity. Conclusion At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations. Addition of partial end-tidal carbon dioxide (PetCO2) signal improves accuracy and sensitivity of the MDI prediction model

A narrative review of heavy metals in cosmetics; health risks

Cosmetics products since the dawn of civilization are considered a part of routine body care. The last few decades these products have had increasing and applied to the human body for beautification. Xenobiotics and heavy metals including chromium, copper, iron, mercury, cadmium, arsenic and nickel, classified as a light metal, are determinate in various types of cosmetics such as color cosmetics, face and body care products, hair cosmetics, herbal cosmetics. In cosmetic products was harmful when they occur in excessive amounts. Evidence studies determinate that in commercially available cosmetics toxic metals might present in amounts creating a danger to human health. The aim of this review is to assess identification of elimination, sources and control of sources, and monitoring countries marketed exposures and hazards can be used to prevent heavy metals toxicity. © 2019, Advanced Scientific Research. All rights reserved

Long-term results in pancreatic transplantation with special emphasis on the use of prolamine

Our pancreatic transplantation programme was initiated in 1979. Since then a total of 102 pancreas transplantations have been performed, blocking exocrine secretion using the duct occlusion technique with prolamine. Early non-immunological complications are frequent. The long-term results (9 years) in combined pancreas and kidney transplanted patients are satisfying: the survival rate for pancreas is 38% and 54% for kidney. Patient survival rate in this period is 85%. Beyond the first year post-transplant the exocrine activity disappears whereas the endocrine function remains well preserved

Types of poisoning in a tertiary care hospital in center of Iran (2014 to 2017)

The global problem of acute poisoning has steadily increased over the past decade. It is an importantcause of morbidity and mortality in developing countries. Better preventive and management strategiescan be developed if the incidence and pattern of acute poisoning is known. The study aims at analyzingthe pattern, cause and mortality rate of poisoning.The study was conducted in aurban and rural area in the center of Iran. This retrospective study was conducted fromJanuary 2014-March 2017. The data was analysed using descriptive and analytical statistics.:Out of the 1329 cases 754 were males and 575 females. Poisoning was common in the age group of 21-30 years. The poisons consumed were as follows:63.8 were suicides, 17.8 accidental and 18.4 had a variety of different reasons. Mortality rate was 6.5.The results of the study showed that the highest rate of poisoning in the young age group was due to suicidal ideation. Accurate training for youth and counseling is of particular importance.Establishment of strict policies against the sale and availability of pesticides and over the counter drugs is an effective way to control drug poisoning. © 2019, Advanced Scientific Research. All rights reserved

Fracture Detection in Traumatic Pelvic CT Images

Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately