Exploring the tradeoff between data privacy and utility with a clinical data analysis use case.

BACKGROUND: Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset's utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility. METHODS: Predictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two. RESULTS: All 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores. CONCLUSIONS: As the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data's intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.

A Comparative Analysis of ARX and ANFIS Models for Tumor Growth Prediction Under Single and Multi-agent Chemotherapy.

BACKGROUND/AIM: Despite the advances in oncology and cancer treatment over the past decades, cancer remains one of the deadliest diseases. This study focuses on further understanding the complex nature of cancer by using mathematical tumor modeling to understand, capture as best as possible, and describe its complex dynamics under chemotherapy treatment. MATERIALS AND METHODS: Focusing on autoregressive with exogenous inputs, i.e., ARX, and adaptive neuro-fuzzy inference system, i.e., ANFIS, models, this work investigates tumor growth dynamics under both single and combination anticancer agent chemotherapy treatments using chemotherapy treatment data on xenografted mice. RESULTS: Four ARX and ANFIS models for tumor growth inhibition were developed, estimated, and evaluated, demonstrating a strong correlation with tumor weight data, with ANFIS models showing superior performance in handling the multi-agent tumor growth complexities. These findings suggest potential clinical applications of the ANFIS models through further testing. Both types of models were also tested for their prediction capabilities across different chemotherapy schedules, with accurate forecasting of tumor growth up to five days in advance. The use of adaptive prediction and sliding (moving) data window techniques allowed for continuous model updating, ensuring more robust predictive capabilities. However, long-term forecasting remains a challenge, with accuracy declining over longer prediction horizons. CONCLUSION: While ANFIS models showed greater reliability in predictions, the simplicity and rapid deployment of ARX models offer advantages in situations requiring immediate approximations. Future research with larger, more diverse datasets and by exploring varying model complexities is recommended to improve the models' reliability and applicability in clinical decision-making, thereby aiding the development of personalized chemotherapy regimens.

Deep learning based model predictive controller on a magnetic levitation ball system.

The magnetic levitation (maglev) ball system is a prototypical Single-Input-Single-Output (SISO) system, characterized by its pronounced nonlinearity, rapid response, and open-loop instability. It serves as the basis for many industrial devices. For describing the dynamics of the maglev ball system precisely in the pseudo linear model, the long short-term memory (LSTM) based auto-regressive model with exogenous input variables (LSTM-ARX) is proposed. Firstly, the LSTM network is modified by incorporating the auto-regressive structure with respect to sequence input, allowing it to deduce a locally linearized model without the need for Taylor expansion. Then, the LSTM-ARX model is transformed into a linear parameter varying (LPV) state space model, and upon this foundation, a model predictive controller (MPC) is proposed. Specifically, when deducing the MPC, the deep learning-based model is linearized by fixing its state input at the current state, so that the nonlinear, non-convex optimization problem can be converted to a finite-horizon quadratic programming problem, thereby deriving the explicit form of MPC. To further enhance the efficiency of the controller in real-time control tasks, a predictive functional controller (PFC) is proposed. It employs multiple nonlinear functions to fit the control sequence, thereby reducing the number of decision variables of the on-line optimization problem in MPC. The proposed controller was successfully applied to the real-time control of the maglev ball system. Simulation and real-time control experiments have validated the improvement in transient performance and efficiency of the LSTM-ARX model-based PFC (LSTM-ARX-PFC).

Type 1 early infantile epileptic encephalopathy: A case report and literature review.

BACKGROUND: Variants in the Aristaless-related homeobox (ARX) gene lead to a variety of phenotypes, with intellectual disability being a steady feature. Other features can include severe epilepsy, spasticity, movement disorders, hydranencephaly, and ambiguous genitalia in males. X-linked Ohtahara syndrome or Type 1 early infantile epileptic encephalopathy (EIEE1) is a severe early-onset epileptic encephalopathy with arrested psychomotor development caused by hemizygous mutations in the ARX gene, which encodes a transcription factor in fundamental brain developmental processes. METHODS: We presented a case report of a 2-year-old boy who exhibited symptoms such as microcephaly, seizures, and severe multifocal epileptic abnormalities, and genetic techniques such as autozygosity mapping, Sanger sequencing, and whole-exome sequencing. RESULTS: We confirmed that the patient had the NM_139058.3:c.84C>A; p.(Cys28Ter) mutation in the ARX gene. CONCLUSION: The patient with EIEE1 had physical symptoms and hypsarrhythmia on electroencephalogram. Genetic testing identified a causative mutation in the ARX gene, emphasizing the role of genetic testing in EIEE diagnosis.

Predicting recurrence in pancreatic neuroendocrine tumours: role of ARX and alternative lengthening of telomeres (ALT).

BACKGROUND: While many pancreatic neuroendocrine tumours (PanNET) show indolent behaviour, predicting the biological behaviour of small nonfunctional PanNETs remains a challenge. Nonfunctional PanNETs with an epigenome and transcriptome that resemble islet alpha cells (ARX-positive) are more aggressive than neoplasms that resemble islet beta cells (PDX1-positive). In this study, we explore the ability of immunohistochemistry for ARX and PDX1 and telomere-specific fluorescence in situ hybridisation (FISH) for alternative lengthening of telomeres (ALT) to predict recurrence. METHODS: Two hundred fifty-six patients with PanNETs were identified, and immunohistochemistry for ARX and PDX1 was performed. Positive staining was defined as strong nuclear staining in >5% of tumour cells. FISH for ALT was performed in a subset of cases. RESULTS: ARX reactivity correlated with worse disease-free survival (DFS) (P = 0.011), while there was no correlation between PDX1 reactivity and DFS (P = 0.52). ALT-positive tumours (n = 63, 31.8%) showed a significantly lower DFS (P < 0.0001) than ALT-negative tumours (n = 135, 68.2%). ARX reactivity correlated with ALT positivity (P < 0.0001). Among nonfunctional tumours, recurrence was noted in 18.5% (30/162) of ARX-positive tumours and 7.5% (5/67) of ARX-negative tumours. Among WHO grade 1 and 2 PanNETs with ≤2 cm tumour size, 14% (6/43) of ARX-positive tumours recurred compared to 0 of 33 ARX-negative tumours and 33.3% (3/9) ALT-positive tumours showed recurrence versus 4.4% (2/45) ALT-negative tumours. CONCLUSION: Immunohistochemistry for ARX and ALT FISH status may aid in distinguishing biologically indolent cases from aggressive small low-grade PanNETs, and help to identify patients who may preferentially benefit from surgical intervention.

Proud Syndrome: A Rare Cause of Corpus Callosum Agenesis.

Agenesis of the corpus callosum (ACC) is one of the most common congenital brain anomalies with variable associations and outcomes. The incidence of ACC varies from 1.8 per 10,000 live births in normal children to as high as 600 per 10,000 in children with neurodevelopmental problems. Here, we report the case of a female neonate delivered in our institute at term gestation to a gravida 4 mother with partial ACC. The neonate was antenatally diagnosed with ACC. The mother had a previous fetus with a supratentorial cyst that was medically terminated. The neonate had a normal clinical examination, but the ultrasound of the cranium suggested ACC. Given the significant family history, a clinical exome sequencing test revealed a pathogenic frameshift mutation in the ARX gene that causes Proud syndrome. We discuss the relevant points in the diagnosis, workup, and prognosis of ACC through this case. This case highlights the importance of antenatal assessment for timely amniocentesis and a genetic diagnosis to guide the parental decision for continuation of the pregnancy, level 2 scans to detect associated anomalies, and postnatal assessment to determine the cause and prognosis of a neonate with ACC.

A quantitative model of the cerebral windkessel and its relevance to disorders of intracranial dynamics.

OBJECTIVE: Traditional models of intracranial dynamics fail to capture several important features of the intracranial pressure (ICP) pulse. Experiments show that, at a local amplitude minimum, the ICP pulse normally precedes the arterial blood pressure (ABP) pulse, and the cranium is a band-stop filter centered at the heart rate for the ICP pulse with respect to the ABP pulse, which is the cerebral windkessel mechanism. These observations are inconsistent with existing pressure-volume models. METHODS: To explore these issues, the authors modeled the ABP and ICP pulses by using a simple electrical tank circuit, and they compared the dynamics of the circuit to physiological data from dogs by using autoregressive with exogenous inputs (ARX) modeling. RESULTS: The authors' ARX analysis showed close agreement between the circuit and pulse suppression in the canine cranium, and they used the analogy between the circuit and the cranium to examine the dynamics that underlie this pulse suppression. CONCLUSIONS: This correspondence between physiological data and circuit dynamics suggests that the cerebral windkessel consists of the rhythmic motion of the brain parenchyma and CSF that continuously opposes systolic and diastolic blood flow. Such motion has been documented with flow-sensitive MRI. In thermodynamic terms, the direct current (DC) power of cerebral arterial perfusion drives smooth capillary flow and alternating current (AC) power shunts pulsatile energy through the CSF to the veins. This suggests that hydrocephalus and related disorders are disorders of CSF path impedance. Obstructive hydrocephalus is the consequence of high CSF path impedance due to high resistance. Normal pressure hydrocephalus (NPH) is the consequence of high CSF path impedance due to low inertance and high compliance. Low-pressure hydrocephalus is the consequence of high CSF path impedance due to high resistance and high compliance. Ventriculomegaly is an adaptive physiological response that increases CSF path volume and thereby reduces CSF path resistance and impedance. Pseudotumor cerebri is the consequence of high DC power with normal CSF path impedance. CSF diversion by shunting is an accessory windkessel-it drains energy (and thereby lowers ICP) and lowers CSF path resistance and impedance. Cushing's reflex is an accessory windkessel in extremis-it maintains DC power (arterial hypertension) and reduces AC power (bradycardia). The windkessel theory is a thermodynamic approach to the study of energy flow through the cranium, and it points to a new understanding of hydrocephalus and related disorders.

A Mobile Sensing Framework for Bridge Modal Identification through an Inverse Problem Solution Procedure and Moving-Window Time Series Models.

With the rise and development of smart infrastructures, there has been a great demand for installing automatic monitoring systems on bridges, which are key members of transportation networks. In this regard, utilizing the data collected by the sensors mounted on the vehicles passing over the bridge can reduce the costs of the monitoring systems, compared with the traditional systems where fixed sensors are mounted on the bridge. This paper presents an innovative framework for determining the response and for identifying modal characteristics of the bridge, utilizing only the accelerometer sensors on the moving vehicle passing over it. In the proposed approach, the acceleration and displacement response of some virtual fixed nodes on the bridge is first determined using the acceleration response of the vehicle axles as the input. An inverse problem solution approach based on a linear and a novel cubic spline shape function provides the preliminary estimations of the bridge's displacement and acceleration responses, respectively. Since the inverse solution approach is only capable of determining the response signal of the nodes with high accuracy in the vicinity of the vehicle axles, a new moving-window signal prediction method based on auto-regressive with exogenous time series models (ARX) is proposed to complete the responses in the regions with large errors (invalid regions). The mode shapes and natural frequencies of the bridge are identified using a novel approach that integrates the results of singular value decomposition (SVD) on the predicted displacement responses and frequency domain decomposition (FDD) on the predicted acceleration responses. To evaluate the proposed framework, various numerical but realistic models for a single-span bridge under the effect of a moving mass are considered; the effects of different levels of ambient noise, the number of axles of the passing vehicle, and the effect of its speed on the accuracy of the method are investigated. The results show that the proposed method can identify the characteristics of the three main modes of the bridge with high accuracy.

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model.

In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.

EpiCRISPR targeted methylation of Arx gene initiates transient switch of mouse pancreatic alpha to insulin-producing cells.

Introduction: Beta cell dysfunction by loss of beta cell identity, dedifferentiation, and the presence of polyhormonal cells are main characteristics of diabetes. The straightforward strategy for curing diabetes implies reestablishment of pancreatic beta cell function by beta cell replacement therapy. Aristaless-related homeobox (Arx) gene encodes protein which plays an important role in the development of pancreatic alpha cells and is a main target for changing alpha cell identity. Results: In this study we used CRISPR/dCas9-based epigenetic tools for targeted hypermethylation of Arx gene promoter and its subsequent suppression in mouse pancreatic αTC1-6 cell line. Bisulfite sequencing and methylation profiling revealed that the dCas9-Dnmt3a3L-KRAB single chain fusion constructs (EpiCRISPR) was the most efficient. Epigenetic silencing of Arx expression was accompanied by an increase in transcription of the insulin gene (Ins2) mRNA on 5th and 7th post-transfection day, quantified by both RT-qPCR and RNA-seq. Insulin production and secretion was determined by immunocytochemistry and ELISA assay, respectively. Eventually, we were able to induce switch of approximately 1% of transiently transfected cells which were able to produce 35% more insulin than Mock transfected alpha cells. Conclusion: In conclusion, we successfully triggered a direct, transient switch of pancreatic alpha to insulin-producing cells opening a future research on promising therapeutic avenue for diabetes management.

Phenotypes of a female patient with novel de novo frameshift ARX variant identified by whole-exome sequencing: a case report.

Variants in the aristaless-related homeobox (ARX) gene cause a diverse spectrum of phenotypes of neurodevelopmental disorders (NDD) in male patients. This article describes the role of genetic testing using whole-exome sequencing (WES) in detecting a novel de novo frameshift variant in the ARX gene in a female patient with autism, seizure, and global developmental delay. Case presentation: A 2-year-old girl with frequent seizures, global developmental delay, and autistic features was referred to our hospital. She was the second child of consanguineous non-affected parents. She had a high forehead, mildly prominent ears, and prominent nasal root. A generalized epileptiform discharge was noted in her electroencephalography. Brain MRI revealed corpus callosum agenesis, cerebral atrophy, and a left parafalcine cyst. The WES result showed a likely pathogenic variant identified as a novel de novo deletion in exon 4 of the ARX gene, which creates a frameshift variant. The patient is on dual therapy of antiepilepsy drugs, physiotherapy, speech therapy, occupational therapy, and oral motor exercises. Clinical discussion: Variants in the ARX gene can result in various phenotypes in males transmitted from asymptomatic carrier females. However, several reports showed that the ARX variants might cause phenotypes in females with milder symptoms than affected males. Conclusion: We report a novel de novo ARX variant in an affected female with a NDD. Our study confirms that the ARX variant might cause remarkable pleiotropy phenotypes in females. Moreover, WES could help to identify the pathogenic variant in NDD patients with diverse phenotypes.

Aggressive versus indolent insulinomas: new clinicopathological insights.

Insulinomas are rare functional pancreatic neuroendocrine tumors. While most insulinomas are indolent and cured after surgery, 10-15% of cases show aggressive or malignant tumor behavior and metastasize locally or to distant organs. Patients with metastatic insulinoma survive significantly shorter. Recognizing aggressive insulinomas can help to predict prognosis, guide therapy and determine follow-up intensity after surgery. This review offers a summary of the literature on the significant clinical, pathological, genetic and epigenetic differences between indolent and aggressive insulinomas. Aggressive insulinomas are characterized by rapid onset of symptoms, larger size, expression of ARX and alpha-1-antitrypsin and decreased or absent immunohistochemical expression of insulin, PDX1 and GLP-1R. Moreover, aggressive insulinomas often harbor ATRX or DAXX mutations, the alternative lengthening of telomeres phenotype and chromosomal instability. Tumor grade and MEN1 and YY1 mutations are less useful for predicting behavior. Aggressive insulinomas have similarities to normal alpha-cells and non-functional pancreatic neuroendocrine tumors, while indolent insulinomas remain closely related to normal beta-cells. In conclusion, indolent and aggressive insulinoma are different entities, and distinguishing these will have future clinical value in determining prognosis and treatment.

Cortical interneuron development is affected in 4H leukodystrophy.

4H leukodystrophy is a rare genetic disorder classically characterized by hypomyelination, hypodontia and hypogonadotropic hypogonadism. With the discovery that 4H is caused by mutations that affect RNA polymerase III, mainly involved in the transcription of small non-coding RNAs, patients with atypical presentations with mainly a neuronal phenotype were also identified. Pathomechanisms of 4H brain abnormalities are still unknown and research is hampered by a lack of preclinical models. We aimed to identify cells and pathways that are affected by 4H mutations using induced pluripotent stem cell models. RNA sequencing analysis on induced pluripotent stem cell-derived cerebellar cells revealed several differentially expressed genes between 4H patients and control samples, including reduced ARX expression. As ARX is involved in early brain and interneuron development, we studied and confirmed interneuron changes in primary tissue of 4H patients. Subsequently, we studied interneuron changes in more depth and analysed induced pluripotent stem cell-derived cortical neuron cultures for changes in neuronal morphology, synaptic balance, network activity and myelination. We showed a decreased percentage of GABAergic synapses in 4H, which correlated to increased neuronal network activity. Treatment of cultures with GABA antagonists led to a significant increase in neuronal network activity in control cells but not in 4H cells, also pointing to lack of inhibitory activity in 4H. Myelination and oligodendrocyte maturation in cultures with 4H neurons was normal, and treatment with sonic hedgehog agonist SAG did not improve 4H related neuronal phenotypes. Quantitative PCR analysis revealed increased expression of parvalbumin interneuron marker ERBB4, suggesting that the development rather than generation of interneurons may be affected in 4H. Together, these results indicate that interneurons are involved, possibly parvalbumin interneurons, in disease mechanisms of 4H leukodystrophy.

Activity of Oral Tebipenem-Avibactam in a Mouse Model of Mycobacterium abscessus Lung Infection.

The combination of the ß-lactam tebipenem and the ß-lactamase inhibitor avibactam shows potent bactericidal activity against Mycobacterium abscessus in vitro. Here, we report that the combination of the respective oral prodrugs tebipenem-pivoxil and avibactam ARX-1796 showed efficacy in a mouse model of M. abscessus lung infection. The results suggest that tebipenem-avibactam presents an attractive oral drug candidate pair for the treatment of M. abscessus pulmonary disease and could inform the design of clinical trials.

Bypassing Mendel's First Law: Transmission Ratio Distortion in Mammals.

Mendel's law of segregation states that the two alleles at a diploid locus should be transmitted equally to the progeny. A genetic segregation distortion, also referred to as transmission ratio distortion (TRD), is a statistically significant deviation from this rule. TRD has been observed in several mammal species and may be due to different biological mechanisms occurring at diverse time points ranging from gamete formation to lethality at post-natal stages. In this review, we describe examples of TRD and their possible mechanisms in mammals based on current knowledge. We first focus on the differences between TRD in male and female gametogenesis in the house mouse, in which some of the most well studied TRD systems have been characterized. We then describe known TRD in other mammals, with a special focus on the farmed species and in the peculiar common shrew species. Finally, we discuss TRD in human diseases. Thus far, to our knowledge, this is the first time that such description is proposed. This review will help better comprehend the processes involved in TRD. A better understanding of these molecular mechanisms will imply a better comprehension of their impact on fertility and on genome evolution. In turn, this should allow for better genetic counseling and lead to better care for human families.

Phase 1 multicenter, dose-expansion study of ARX788 as monotherapy in HER2-positive advanced gastric and gastroesophageal junction adenocarcinoma.

ARX788 is an anti-human epidermal growth factor receptor 2 (HER2) antibody-drug conjugate with AS269 as cytotoxic payload. In this phase 1 multicenter dose-expansion clinical trial, patients with HER2-positive advanced gastric/gastroesophageal junction adenocarcinoma failing to respond to prior trastuzumab-based standard treatment were enrolled. Between July 15th, 2019, and March 14th, 2022, 30 participants were enrolled. Twenty-eight (93.3%) patients experienced at least one drug-related adverse event (AE) and 13.3% experienced grade 3 ARX788-related AEs. The confirmed objective response rate is 37.9% (95% confidence interval [CI]: 20.7%-57.7%) and the disease control rate is 55.2% (95% CI: 35.7%-73.6%). With a median follow up of 10 months, the median progression-free survival and overall survival are 4.1 (95% CI: 1.4-6.4) and 10.7 months (95% CI: 4.8-not reached), respectively. The median duration of response is 8.4 (95% CI: 2.1-18.9) months. ARX788 is well tolerated and has promising anti-tumor activity in patients with HER2-positive advanced gastric adenocarcinoma (ChinaDrugTrials.org.cn: CTR20190639).

Insights into Epigenetic Changes Related to Genetic Variants and Cells-of-Origin of Pancreatic Neuroendocrine Tumors: An Algorithm for Practical Workup.

Current knowledge on the molecular landscape of pancreatic neuroendocrine tumors (PanNETs) has advanced significantly. Still, the cellular origin of PanNETs is uncertain and the associated mechanisms remain largely unknown. DAXX/ATRX and MEN1 are the three most frequently altered genes that drive PanNETs. They are recognized as a link between genetics and epigenetics. Moreover, the acknowledged impact on DNA methylation by somatic mutations in MEN1 is a valid hallmark of epigenetic mechanism. DAXX/ATRX and MEN1 can be studied at the immunohistochemical level as a reliable surrogate for sequencing. DAXX/ATRX mutations promote alternative lengthening of telomeres (ALT) activation, determined by specific fluorescence in situ hybridization (FISH) analysis. ALT phenotype is considered a significant predictor of worse prognosis and a marker of pancreatic origin. Additionally, ARX/PDX1 expression is linked to important epigenomic alterations and can be used as lineage associated immunohistochemical marker. Herein, ARX/PDX1 association with DAXX/ATRX/MEN1 and ALT can be studied through pathological assessment, as these biomarkers may provide important clues to the mechanism underlying disease pathogenesis. In this review, we present an overview of a new approach to tumor stratification based on genetic and epigenetic characteristics as well as cellular origin, with prognostic consequences.

Prognostic and Predictive Biomarkers for Pancreatic Neuroendocrine Tumors.

Pancreatic neuroendocrine tumors (PanNETs) represent a clinically challenging disease because these tumors vary in clinical presentation, natural history, and prognosis. Novel prognostic biomarkers are needed to improve patient stratification and treatment options. Several putative prognostic and/or predictive biomarkers (eg, alternative lengthening of telomeres, alpha-thalassemia/mental retardation, X-linked (ATRX)/Death Domain Associated Protein (DAXX) loss) have been independently validated. Additionally, recent transcriptomic and epigenetic studies focusing on endocrine differentiation have identified PanNET subtypes that display similarities to either α-cells or ß-cells and differ in clinical outcomes. Thus, future prospective studies that incorporate genomic and epigenetic biomarkers are warranted and have translational potential for individualized therapeutic and surveillance strategies.

Testing for Serial Correlation in Autoregressive Exogenous Models with Possible GARCH Errors.

Autoregressive exogenous, hereafter ARX, models are widely adopted in time series-related domains as they can be regarded as the combination of an autoregressive process and a predictive regression. Within a more complex structure, extant diagnostic checking methods face difficulties in remaining validity in many conditions existing in real applications, such as heteroscedasticity and error correlations exhibited between the ARX model itself and its exogenous processes. For these reasons, we propose a new serial correlation test method based on the profile empirical likelihood. Simulation results, as well as two real data examples, show that our method has a good performance in all mentioned conditions.

A bit-vector differential model for the modular addition by a constant and its applications to differential and impossible-differential cryptanalysis.

ARX algorithms are a class of symmetric-key algorithms constructed by Addition, Rotation, and XOR. To evaluate the resistance of an ARX cipher against differential and impossible-differential cryptanalysis, the recent automated methods employ constraint satisfaction solvers to search for optimal characteristics or impossible differentials. The main difficulty in formulating this search is finding the differential models of the non-linear operations. While an efficient bit-vector differential model was obtained for the modular addition with two variable inputs, no differential model for the modular addition by a constant has been proposed so far, preventing ARX ciphers including this operation from being evaluated with automated methods. In this paper, we present the first bit-vector differential model for the n-bit modular addition by a constant input. Our model contains O ( log 2 ( n ) ) basic bit-vector constraints and describes the binary logarithm of the differential probability. We describe an SMT-based automated method that includes our model to search for differential characteristics of ARX ciphers including constant additions. We also introduce a new automated method for obtaining impossible differentials where we do not search over a small pre-defined set of differences, such as low-weight differences, but let the SMT solver search through the space of differences. Moreover, we implement both methods in our open-source tool ArxPy to find characteristics and impossible differentials of ARX ciphers with constant additions in a fully automated way. As some examples, we provide related-key impossible differentials and differential characteristics of TEA, XTEA, HIGHT, LEA, SHACAL-1, and SHACAL-2, which achieve better results compared to previous works.