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The application of innovative technologies introduces technical uncertainty affecting project outcomes. Guided by control theory, this study examines the mediating role of technical uncertainty between the four project control modes and their influence on two project outcomes. We find that outcome and clan control modes provide both internal efficiency as well as adequate psychological outcomes in uncertain innovative development environments while behavior and self-control have no effect on the project outcomes. Therefore, appropriate control modes such as outcome and clan control modes should be given priority in managing technically uncertain projects.

Abstract The practice of using deep learning methods in safety critical vision systems such as autonomous driving has come a long way. As vision systems supported by deep learning methods become ubiquitous, the possible security threats faced by these systems have come into greater focus. As it is with any artificial intelligence system, these deep neural vision networks are first trained on a data set of interest, once they start performing well, they are deployed to a real-world environment. In the training stage, deep learning systems are susceptible to data poisoning attacks. While deep neural networks have proved to be versatile in solving a host of challenges. These systems have complex data ecosystems especially in computer vision. In practice, the security threats when training these systems are often ignored while deploying these models in the real world. However, these threats pose significant risks to the overall reliability of the system. In this paper, we present the fundamentals of data poisoning attacks when training deep learning vision systems and discuss countermeasures against these types of attacks. In addition, we simulate the risk posed by a real-world data poisoning attack on a deep learning vision system and present a novel algorithm MOVCE—Model verification with Convolutional Neural Network and Word Embeddings which provides an effective countermeasure for maintaining the reliability of the system. The countermeasure described in this paper can be used on a wide variety of use cases where the risks posed by poisoning the training data are similar.

Abstract Travel patterns and mobility affect the spread of infectious diseases like COVID-19. However, we do not know to what extent local vs. visitor mobility affects the growth in the number of cases. This study evaluates the impact of state-level local vs. visitor mobility in understanding the growth with respect to the number of cases for COVID spread in the United States between March 1, 2020, and December 31, 2020. Two metrics, namely local and visitor transmission risk, were extracted from mobility data to capture the transmission potential of COVID-19 through mobility. A combination of the three factors: the current number of cases, local transmission risk, and the visitor transmission risk, are used to model the future number of cases using various machine learning models. The factors that contribute to better forecast performance are the ones that impact the number of cases. The statistical significance of the forecasts is also evaluated using the Diebold–Mariano test. Finally, the performance of models is compared for three waves across all 50 states. The results show that visitor mobility significantly impacts the case growth by improving the prediction accuracy by 33.78%. We also observe that the impact of visitor mobility is more pronounced during the first peak, i.e., March–June 2020.

Attacks launched over the Internet often degrade or disrupt the quality of online services. Various Intrusion Detection Systems (IDSs), with or without prevention capabilities, have been proposed to defend networks or hosts against such attacks. While most of these IDSs extract features from the packet headers to detect any irregularities in the network traffic, some others use payloads alongside the headers. In this study, we propose a payload-based intrusion detection scheme, PayloadEmbeddings, using byte embeddings of the payloads of network packets. We employ a shallow neural network to generate vector representations for bytes and their corresponding payloads. Our feature extraction technique is coupled with the $k$ -Nearest Neighbours ( $k$ NN) algorithm for the classification of packets as intrusive or non-intrusive. In our experiments, we evaluated 34 publicly available datasets, and used ten distinct payload-based, labeled intrusion detection datasets to train and evaluate our approach. Our empirical results show that PayloadEmbeddings reaches between 75% and 99% accuracy across all datasets. Finally, we compare our approach to other state-of-the-art and traditional intrusion detection techniques. Our findings suggest that PayloadEmbeddings demonstrates significant advantages over the other techniques on most of the datasets.

Abstract Human mobility plays an important role in the dynamics of infectious disease spread. Evidence from the initial nationwide lockdowns for COVID− 19 indicates that restricting human mobility is an effective strategy to contain the spread. While a direct correlation was observed early on, it is not known how mobility impacted COVID− 19 infection growth rates once lockdowns are lifted, primarily due to modulation by other factors such as face masks, social distancing, and the non-linear patterns of both mobility and infection growth. This paper introduces a piece-wise approach to better explore the phase-wise association between state-level COVID− 19 incidence data and anonymized mobile phone data for various states in the United States. Prior literature analyzed the linear correlation between mobility and the number of cases during the early stages of the pandemic. However, it is important to capture the non-linear dynamics of case growth and mobility to be usable for both tracking and forecasting COVID− 19 infections, which is accomplished by the piece-wise approach. The associations between mobility and case growth rate varied widely for various phases of the epidemic curve when the stay-at-home orders were lifted. The mobility growth patterns had a strong positive association of 0.7 with the growth in the number of cases, with a lag of 5 to 7 weeks, for the fast-growth phase of the pandemic, for only 20 states that had a peak between July 1st and September 30, 2020. Overall though, mobility cannot be used to predict the rise in the number of cases after initial lockdowns have been lifted. Our analysis explores the gradual diminishing value of mobility associations in the later stage of the outbreak. Our analysis indicates that the relationship between mobility and the increase in the number of cases, once lockdowns have been lifted, is tenuous at best and there is no strong relationship between these signals. But we identify the remnants of the last associations in specific phases of the growth curve.

Abstract Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states.

Development of protein 3-D structural comparison methods is important in understanding protein functions. At the same time, developing such a method is very challenging. In the last 40 years, ever since the development of the first automated structural method, ~200 papers were published using different representations of structures. The existing methods can be divided into five categories: sequence-, distance-, secondary structure-, geometry-based, and network-based structural comparisons. Each has its uniqueness, but also limitations. We have developed a novel method where the 3-D structure of a protein is modeled using the concept of Triangular Spatial Relationship (TSR), where triangles are constructed with the Cα atoms of a protein as vertices. Every triangle is represented using an integer, which we denote as “key,” A key is computed using the length, angle, and vertex labels based on a rule-based formula, which ensures assignment of the same key to identical TSRs across proteins. A structure is thereby represented by a vector of integers. Our method is able to accurately quantify similarity of structure or substructure by matching numbers of identical keys between two proteins. The uniqueness of our method includes: (i) a unique way to represent structures to avoid performing structural superimposition; (ii) use of triangles to represent substructures as it is the simplest primitive to capture shape; (iii) complex structure comparison is achieved by matching integers corresponding to multiple TSRs. Every substructure of one protein is compared to every other substructure in a different protein. The method is used in the studies of proteases and kinases because they play essential roles in cell signaling, and a majority of these constitute drug targets. The new motifs or substructures we identified specifically for proteases and kinases provide a deeper insight into their structural relations. Furthermore, the method provides a unique way to study protein conformational changes. In addition, the results from CATH and SCOP data sets clearly demonstrate that our method can distinguish alpha helices from beta pleated sheets and vice versa. Our method has the potential to be developed into a powerful tool for efficient structure-BLAST search and comparison, just as BLAST is for sequence search and alignment.

Astrocytes, the “star-shaped” glial cells, are appealing gene-delivery targets to treat neurological diseases due to their diverse roles in brain homeostasis and disease. Cationic polymers have successfully delivered genes to mammalian cells and hence present a viable, non-immunogenic alternative to widely used viral vectors. In this study, we investigated the gene delivery potential of a series of arginine- and polyethylene glycol-modified, siloxane-based polyethylenimine analogs in primary cultured human neural cells (neurons and astrocytes) and in mice. Plasmid DNAs encoding luciferase reporter were used to measure gene expression. We hypothesized that polyplexes with arginine would help in cellular transport of the DNA, including across the blood-brain barrier; polyethylene glycol will stabilize polyethylenimine and reduce its toxicity while maintaining its DNA-condensing ability. Polyplexes were non-toxic to human neural cells and red blood cells. Cellular uptake of polyplexes and sustained gene expression were seen in human astrocytes as well as in mouse brains post-intravenous-injections. The polyplexes also delivered and expressed genes driven by astrocyte-restricted glial fibrillary acidic protein promoters, which are weaker than viral promoters. To our knowledge, the presented work validates a biocompatible and effective polymer-facilitated gene-delivery system for both human brain cells and mice for the first time. Keywords: human neural cells, polyplexes, GFAP promoters, intravenous gene delivery, gene therapy, blood-brain barrier

Hypercementosis is a non-neoplastic condition in which excessive cementum is deposited in continuation with the normal radicular cementum. Although some cases of hypercementosis are idiopathic, this condition is associated with several local and systemic factors such as supra-eruption of a tooth, inflammation at the apex of a tooth, traumatic occlusion, Paget′s disease, etc. Hypercementosis may be isolated, involve multiple teeth, or appear as a generalized process. Posterior teeth are more commonly involved. The radiographic appearance of hypercementosis is an altered shape of the root with maintenance of normal relationship of the shadows of the periodontal membrane and lamina dura. The histologic study of teeth with hypercementosis shows that the cementum formed is usually osteocementum (acellular cementum). The differential diagnosis may include any radiopaque structure that is seen in the vicinity of the root, such as a dense bone island or mature cemento-osseous dysplasia. Patients with hypercementosis require no treatment. Because of a thickened root, occasional problems have been reported during the extraction of an affected tooth. Herein, an interesting case of a mammoth, dumbbell shaped hypercementosis associated with maxillary third molar is reported.

Verrucous carcinoma (VC) is a variant of squamous cell carcinoma. It most commonly affects the oral cavity with buccal mucosa being the most common site affected. Clinically it has proliferative finger-like projections or a cauliflower-like appearance which is a significant factor in its diagnosis. It is more common in tobacco user males. The histopathological diagnosis of VC is difficult and requires immense experience to report a case of VC. Though VC is described as a benign lesion with minimum aggressive potential but long-standing cases have shown transformation into squamous cell carcinoma. Therefore, early diagnosis and surgical excision of the lesion are the most appropriate treatment modality of VC.

The adamantine derivative SQ109 induces protective pro-inflammatory cytokines and promotes Th1 and Th17-immune responses that inhibit bacterial burden in a tuberculosis mouse model.