Paper Session V: Forensic Software Tools for Cell Phone Subscriber Identity Modules

Cell phones and other handheld devices incorporating cell phone capabilities (e.g., smart phones) are ubiquitous. Besides placing calls, cell phones allow users to perform other tasks such as text messaging and phonebook entry management. When cell phones and cellular devices are involved in a crime or other incident, forensic specialists require tools that allow the proper retrieval and speedy examination of data present on the device. For devices conforming to the Global System for Mobile Communications (GSM) standards, certain data such as dialed numbers, text messages, and phonebook entries are maintained on a Subscriber Identity Module (SIM). This paper gives a snapshot of the state of the art of forensic software tools for SIMs. Keywords: Cell Phone, Forensic Tool, Subscriber Identity Modul

Forensic Tools for Mobile Phone Subscriber Identity Modules

Mobile phones and other handheld devices incorporating cellular capabilities, such as Personal Digital Assistants, are ubiquitous. Besides placing calls, these devices allow users to perform other useful tasks, including text messaging and phonebook entry management. When cell phones and cellular devices are involved in a crime or other incident, forensic specialists require tools that allow the proper retrieval and speedy examination of data present on the device. For devices conforming to the Global System for Mobile Communications (GSM) standards, certain data such as dialed numbers, text messages, and phonebook entries are maintained on a Subscriber Identity Module (SIM). This paper gives a snapshot of the state of the art of forensic software tools for SIMs and an explanation of the types of digital evidence they can recover

The association of North Dakota skilled nursing facility characteristics with COVID-19 outbreak severity

Context: COVID-19 exerted severe challenges on skilled nursing facility (SNF) residents and staff. A combination of internal and external factors predisposed SNFs to an increased propensity of COVID-19 spread. Objective: The purpose of this paper is to examine which facility characteristics may have contributed to COVID-19 outbreaks within urban and rural North Dakota skilled nursing facilities. Methods: A 23-question survey regarding facility characteristics was developed and distributed to all 78 North Dakota skilled nursing facilities (SNF). Findings: Of the North Dakota SNF, 40 out of 78 total facilities (51.2%) participated in the survey. Of those participating, 38 of 40 (95%) were in counties with populations under 50,000, with the smallest county population being 1,876. A Spearman’s rank test suggested a relationship between the community spread of COVID-19 and the COVID-19 positivity of SNF residents. Spearman’s rank also suggested a positive association between the SNF resident COVID-19 positivity in relation to staff positivity (p-value 0.042) and county rates (p-value 0.045). Limitations: While this is a comprehensive survey with a very good response rate, two key limitations are identified. First, the survey relies on self-reported data from SNF staff. Second, it is not clear what data would have been received from non-responding SNFs. Implications: Substantial lessons have been learned, which may not only aid future pandemic preparedness but improve the quality of care for nursing home residents during a pandemic or other respiratory disease outbreaks. Proactively knowing susceptibilities and vulnerabilities ahead of time will allow local and state leaders to plan and allocate resources. Future state and local pandemic emergency plans need to be reviewed with the prioritization of skilled nursing facilities as front line facilities during a pandemic, rather than placing their “traditional” emphasis of emergency preparedness on hospitals

WeFaceNano:a user-friendly pipeline for complete ONT sequence assembly and detection of antibiotic resistance in multi-plasmid bacterial isolates

Background: Bacterial plasmids often carry antibiotic resistance genes and are a significant factor in the spread of antibiotic resistance. The ability to completely assemble plasmid sequences would facilitate the localization of antibiotic resistance genes, the identification of genes that promote plasmid transmission and the accurate tracking of plasmid mobility. However, the complete assembly of plasmid sequences using the currently most widely used sequencing platform (Illumina-based sequencing) is restricted due to the generation of short sequence lengths. The long-read Oxford Nanopore Technologies (ONT) sequencing platform overcomes this limitation. Still, the assembly of plasmid sequence data remains challenging due to software incompatibility with long-reads and the error rate generated using ONT sequencing. Bioinformatics pipelines have been developed for ONT-generated sequencing but require computational skills that frequently are beyond the abilities of scientific researchers. To overcome this challenge, the authors developed ‘WeFaceNano’, a user-friendly Web interFace for rapid assembly and analysis of plasmid DNA sequences generated using the ONT platform. WeFaceNano includes: a read statistics report; two assemblers (Miniasm and Flye); BLAST searching; the detection of antibiotic resistance- and replicon genes and several plasmid visualizations. A user-friendly interface displays the main features of WeFaceNano and gives access to the analysis tools. Results: Publicly available ONT sequence data of 21 plasmids were used to validate WeFaceNano, with plasmid assemblages and anti-microbial resistance gene detection being concordant with the published results. Interestingly, the “Flye” assembler with “meta” settings generated the most complete plasmids. Conclusions: WeFaceNano is a user-friendly open-source software pipeline suitable for accurate plasmid assembly and the detection of anti-microbial resistance genes in (clinical) samples where multiple plasmids can be present.</p

Metabolomics signatures of depression:the role of symptom profiles

Depression shows a metabolomic signature overlapping with that of cardiometabolic conditions. Whether this signature is linked to specific depression profiles remains undetermined. Previous research suggested that metabolic alterations cluster more consistently with depressive symptoms of the atypical spectrum related to energy alterations, such as hyperphagia, weight gain, hypersomnia, fatigue and leaden paralysis. We characterized the metabolomic signature of an "atypical/energy-related" symptom (AES) profile and evaluated its specificity and consistency. Fifty-one metabolites measured using the Nightingale platform in 2876 participants from the Netherlands Study of Depression and Anxiety were analyzed. An 'AES profile' score was based on five items of the Inventory of Depressive Symptomatology (IDS) questionnaire. The AES profile was significantly associated with 31 metabolites including higher glycoprotein acetyls (β = 0.13, p = 1.35*10 -12), isoleucine (β = 0.13, p = 1.45*10 -10), very-low-density lipoproteins cholesterol (β = 0.11, p = 6.19*10 -9) and saturated fatty acid levels (β = 0.09, p = 3.68*10 -10), and lower high-density lipoproteins cholesterol (β = -0.07, p = 1.14*10 -4). The metabolites were not significantly associated with a summary score of all other IDS items not included in the AES profile. Twenty-five AES-metabolites associations were internally replicated using data from the same subjects (N = 2015) collected at 6-year follow-up. We identified a specific metabolomic signature-commonly linked to cardiometabolic disorders-associated with a depression profile characterized by atypical, energy-related symptoms. The specific clustering of a metabolomic signature with a clinical profile identifies a more homogenous subgroup of depressed patients at higher cardiometabolic risk, and may represent a valuable target for interventions aiming at reducing depression's detrimental impact on health. </p

Metabolomic profiles discriminating anxiety from depression

Objective: Depression has been associated with metabolomic alterations. Depressive and anxiety disorders are often comorbid diagnoses and are suggested to share etiology. We investigated whether differential metabolomic alterations are present between anxiety and depressive disorders and which clinical characteristics of these disorders are related to metabolomic alterations. Methods: Data were from the Netherlands Study of Depression and Anxiety (NESDA), including individuals with current comorbid anxiety and depressive disorders (N = 531), only a current depression (N = 304), only a current anxiety disorder (N = 548), remitted depressive and/or anxiety disorders (N = 897), and healthy controls (N = 634). Forty metabolites from a proton nuclear magnetic resonance lipid-based metabolomics panel were analyzed. First, we examined differences in metabolites between disorder groups and healthy controls. Next, we assessed whether depression or anxiety clinical characteristics (severity and symptom duration) were associated with metabolites. Results: As compared to healthy controls, seven metabolomic alterations were found in the group with only depression, reflecting an inflammatory (glycoprotein acetyls; Cohen's d = 0.12, p = 0.002) and atherogenic-lipoprotein-related (e.g., apolipoprotein B: Cohen's d = 0.08, p = 0.03, and VLDL cholesterol: Cohen's d = 0.08, p = 0.04) profile. The comorbid group showed an attenuated but similar pattern of deviations. No metabolomic alterations were found in the group with only anxiety disorders. The majority of metabolites associated with depression diagnosis were also associated with depression severity; no associations were found with anxiety severity or disease duration. Conclusion: While substantial clinical overlap exists between depressive and anxiety disorders, this study suggests that altered inflammatory and atherogenic-lipoprotein-related metabolomic profiles are uniquely associated with depression rather than anxiety disorders